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Wang Min, Wang Ying, Chen Kai, Cheng Yuhua, Qiu Gen
2024,38(8):1-14,
Abstract:
Insulate gate bipolar transistor (IGBT) is extensively utilized in aerospace, weapon systems, modern industry, transportation, and power systems. Due to the complex environment, IGBTs are highly susceptible anomalies, leading to system failures and significant economic losses as well as casualties. Consequently, IGBT fault detection has garnered widespread attention and significant focus from researchers. However, systematic reviews of fault detection techniques for IGBTs are scarce, hindering practical engineers' deep understanding and knowledge of this field. Therefore, this review provides a systematic overview of research achievements in IGBT fault detection from a methodological perspective. Firstly, an overview of the basic structure, operation principles, and common failure mechanisms of IGBTs is presented. Secondly, IGBT fault detection technologies are categorized into three major classes based on detection methods, with a summary of the advantages and limitations of each class. Finally, considering the current technological advancements, a deep analysis of challenges and prospects of the field of IGBT fault detection is provided.
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Zou Kaixin, Zhang Zijia, Sun Wei, Fu Jinyi
2024,38(8):15-25,
Abstract:
In view of the low contrast and complex topological structure of pavement defect images, most of the currently proposed segmentation algorithms still have great shortcomings in capturing the receptive fields and extracting pavement defect features. Therefore, this article proposes an improved U-Net road defect image segmentation algorithm. First, the SN-Disout residual block is proposed in the classic U-Net convolution block to enhance the model’s robustness against overfitting. Secondly, a criss-cross module is introduced between the encoder and the decoder to enhance the model’s ability to capture features between different positions in the feature map and more accurately model the boundaries of defects. Finally, the spatial channel squeeze and excitation module is introduced in the decoder, which enables the network to focus more on important features while reducing the dependence on irrelevant or noisy features; position-aware multi-head attention is added to the neck of the model to further helps the model better understanding and utilizing the internal relationship of the input data, thereby improving the performance and performance capabilities of the model, and using the hybrid loss function Dice+BCE to replace a single loss function. The intersection ratio and F1 of this algorithm on the Crack500 image data set reached 60.13% and 75.22% respectively, both exceeding mainstream semantic segmentation networks such as U-Net, DeepLabV3+, PSPNet, TransU-Net, and UNet++. Experimental results show that this algorithm can effectively improve the prediction accuracy of the network and the segmentation results of small targets, and it also meets the real-time requirements while ensuring segmentation accuracy.
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Zeng Yongjie, Fan Bishuang, Yang Yawen, Jiang Chong
2024,38(8):26-35,
Abstract:
Wind turbine blades, being the core component of wind power generation systems, have their health status directly impacting the overall power generation efficiency and operational safety. Addressing the challenges of blade defect detection, researchers delved into the YOLOv8n network and innovatively proposed the Efficient Multi-Scale Convolutional module (EMSConv). This module effectively replaces the convolutional layers in traditional residual blocks, significantly reducing the interference of redundant features on detection results through grouped convolution techniques, thereby enhancing detection accuracy. Furthermore, in the detection head, a diverse set of attention mechanisms from Dynamic Head are incorporated. These self-attention mechanisms work in concert, spanning across different feature layers, to achieve precise perception of target scales, spatial locations, and detection tasks, vastly strengthening the comprehensive capabilities of the target detection module. Moreover, researchers innovatively integrated Inner-IoU, Wise-IoU, and MPDIoU, creatively proposing Inner-Wise-MPDIoU to replace the traditional CIoU loss function. This move not only improved the network’s detection precision but also accelerated the convergence process. During testing on a self-made dataset of wind turbine blade defects, YOLOv8-EDI exhibited remarkable performance, achieving an mAP50 value of 81.0%, a 2.3% increase compared to the original YOLOv8n. The recall rate also reached 76.8%, marking a 3.7% improvement. While enhancing detection performance, this model managed to reduce computational requirements by 5.5%, fully meeting the need for efficient, accurate, and large-scale blade defect detection in industrial settings.
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Yang Yulong, Zhang Yinsheng, Duan Xiuxian, Chen Xin, Ji Ru, Shan Huilin
2024,38(8):36-45,
Abstract:
The power generation efficiency and service life of wind turbines are related to their surface integrity. This study aims to address the issue of inaccurate detection results and long detection time in traditional surface defect detection methods for wind turbines. A surface defect detection model for wind turbines is designed. Firstly, lightweight convolution technology is integrated into the model, effectively enhancing the ability of information exchange between channels and improving the detection effect of small-sized defects through richer feature information. Secondly, the visual attention network module has been introduced into the backbone network, enriching the contextual information and improving the feature extraction ability of convolutional neural networks. Then, a coordinated attention mechanism is introduced into the neck network to capture the location information of defects through spatial orientation. Finally, modify the loss function of bounding box regression to WIoU to develop an appropriate gradient gain allocation strategy. The experimental results show that the improved detection model improves the detection accuracy by 4.14% compared to the original model, significantly enhancing the detection ability of small defects. At the same time, the parameter count of the improved model was reduced by 2.29 M, while the parameter count was reduced by 6.2 G. The detection speed was significantly improved, meeting the real-time detection requirements of the model.
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Zhao Yunliang, Tang Donglin, He Yuanyuan, Ding Chao, Yang Zhou
2024,38(8):46-55,
Abstract:
To address the challenges in detecting dispersed and fine defects on metal surfaces, convolutional neural network (CNN) often fall short due to their limited ability to capture global features, leading to missed detections and loss of detail in identifying defects such as oxidation particles, cracks, and scratches. Although Transformers can capture comprehensive global information, the extensive computation required can be costly. In pursuit of an efficient and accurate method for metal surface defect detection, this study introduces a novel network architecture, the DPG-Transformer, which synergistically combines the local feature extraction capabilities of CNNs with the global modeling strengths of Transformer. This integration is facilitated through the use of depthwise separable convolutions (DW-Conv) and pooling grid window attention mechanisms (PGW-Attention). The effectiveness of the DPG-Transformer was validated on both a proprietary metal defect dataset (ST-DET) and a public dataset (NEU-CLS), achieving defect detection accuracies of 99.3% and 99.6%, respectively, and outperforming several classic networks in terms of accuracy, computational efficiency, and floating-point operations. Additionally, visualization experiments demonstrated that the DPG-Transformer more comprehensively extracts defect features associated with corrosion and scaling compared to CNN models, and more precisely focuses on the global features of elongated cracks and scratches than Transformer models. The results indicate that the DPG-Transformer not only reduces computational load and complexity but also enhances the comprehensive and precise detection of metal surface defects, making it a highly suitable approach for practical applications in metal surface defect detection.
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Chen Xiaolei, Wen Runyu, Yang Fulong, Li Zhengcheng, Shen Xingyang
2024,38(8):56-67,
Abstract:
The classification of wafer defect patterns plays a crucial role in the wafer manufacturing process. Accurate identification of wafer defects enables the determination of the root causes of defects, thereby pinpointing issues in the production process.However, existing deep learning-based wafer defect classification methods are designed solely from the spatial or frequency domain, failing to achieve mutual supplementation and integration of spatial and frequency information. This limitation constrains the improvement of wafer defect classification accuracy. To address this issue, a dual-stream wafer defect classification network based on the fusion of spatial and frequency domain features, named SFWD-Net, is proposed.The network utilizes the proposed multi-scale feature extraction convolution module and multi-view attention module to form the spatial stream branch, which extracts spatial information from wafer images. The frequency stream branch, utilizing discrete wavelet transform, extracts frequency information from wafer images. After integrating spatial and frequency information, defect classification is performed. Experiments on the large-scale semiconductor wafer image dataset WM-811K demonstrate that SFWD-Net, by simultaneously designing the network from both spatial and frequency domains, achieves a classification accuracy of 99.299 2%, outperforming five other state-of-the-art methods and significantly improving the accuracy of wafer defect classification.
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Lyu Li, Yao Zhenjian, Yang Mengran, Zhao Yuxing
2024,38(8):68-78,
Abstract:
In order to solve the problem that the defect echo overlaps with the surface echo when measuring the defect depth near the surface of metal components under sub-Nyquist sampling condition which leads to inaccurate depth measurement, a high-precision measurement method for the near-surface defect depth of metal components based on ultrasonic echo resampling and time-frequency transformation is proposed. The ultrasonic echo signal is decomposed into a series of narrowband mode components by variational mode decomposition, and the useful mode components are clustered based on the correlation coefficient to obtain the preprocessed signal. The preprocessed signal is then processed by cubic spline interpolation to realize the data expansion of the sub-Nyquist sampling echo signal and obtain the reconstructed signal. Based on the synchro extracting transform, the time frequency spectrum and amplitude distribution curve of the reconstructed signal are obtained. According to the principle of ultrasonic single transceiver, the high-precision depth measurement of the near-surface defect of metal components is finally realized. The experimental results show that the proposed method can accurately measure the near-surface defect depth of the metal component under the condition of sub-Nyquist sampling condition, and the measurement relative error is 2.161%, which is obviously less than that of the envelope method and the time-frequency transform method, whose relative error are 3.570% and 13.182%, respectively. Furthermore, the effects of the sub-Nyquist sampling rate, the signal noise and the defect location on the measurement accuracy of the near-surface defect depth are investigated. The results consistently demonstrate that the proposed method has better accuracy and robustness than the traditional methods for the near-surface defect depth measurement under different sampling rates, different noise and different defect depths.
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Xing Hongyan, Zheng Jincheng, Xu Wei, Wang Xinyi
2024,38(8):79-86,
Abstract:
In order to solve the problems of poor accuracy of the charge inversion method for thunderstorm clouds and the influence of the existing charge inversion model by the environmental error caused by multi-station network observation, a nonlinear equation system is derived to establish a charge inversion model based on the three-dimensional atmospheric electric field. On the basis of the assumption of equal thickness model of thunderstorm clouds, a set of nonlinear equations required for the charge inversion of thunderstorm clouds is derived, and a three-dimensional atmospheric electric field-based charge inversion model of thunderstorm clouds is established. The population initialisation of the sparrow search algorithm (SSA) is optimised by the sinusoidal chaotic mapping function to improve the nonlinearity of the distribution of the populations, and the Levy function and the inverse learning strategy are used to optimise the position updating of the algorithm’s discoverer way, a thunderstorm cloud charge inversion method based on improved sparrow search algorithm (ISSA) is proposed. A 3D atmospheric electric field instrument is used to observe the ground electric field data and analyse the electric field characteristics, and the improved SSA algorithm using the hybrid strategy is used to invert the thunderstorm cloud charging model parameters. The experimental results show that the inversion of the data obtained from the three-dimensional atmospheric electric field instrument (3DAEF) can effectively eliminate the errors caused by the multi-station network observation. Compared with the SSA, the deviation rate of the two-second neighboring charges of the thunderstorm cloud obtained by the improved ISSA algorithm is around 1%, and the fitness value reaches as low as 5.38, which is able to accurately invert the charging parameters of the thunderstorm cloud, and provide a certain reference to the study of its charging and discharging process.
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Hao Shunyi, Li Jianwen, Lu Hang, Huang Guorong
2024,38(8):87-94,
Abstract:
To consider increased difficulties of the GNSS signal acquisition due to a wider range of the frequency bandwidth in high dynamic environment, the transmission characteristics of the numeric intermediate frequency signal in the GNSS receiver was analyzed, as well as the correlation peak detection of the complex baseband signal processed by the FFT module, a fast detection method for high dynamic GNSS signal acquisition based on ML estimation is proposed. Firstly, by the construction of Binary hypothesis testing conditions based on the statistical theory of random signals, an acquisition threshold model was presented using the Neyman Pearson Criterion; Secondly, the variance of equivalent White Gaussian Noise based on ML is estimated by judgment statistical characteristics and an acquisition threshold is calculated from variance estimated value，in the meantime the estimation error caused by increased judgment samples was resolved by means of false alarm rate quantized amplification. Finally, the acquisition detection simulation experiment of Beidou B3I signal was conducted in different high dynamic conditions. The result showed the proposed method have a wider scope of high dynamic adaptive capacity, and the precision accuracy of the acquired doppler frequency shift was on a par with the SINS information aiding method, also increased by more than 28% compared to the sequential detection method, as well as a faster detection speed in the same acquisition condition.
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Huang Yunzhi, Wang Lei, Han Liang
2024,38(8):95-102,
Abstract:
Partial discharge of electrical equipment is not only the main factor of insulation deterioration, but also an important parameter to effectively characterize insulation defects. The accurate detection of partial discharge can detect the potential faults that endanger the safety of equipment in time. UHF detection has the advantages of good real-time performance and strong anti-interference, and is widely used in discharge detection. However, the existing microstrip antenna sensor is limited by the structure size, and the working bandwidth is difficult to increase. In this paper, a new flexible microstrip antenna sensor based on polyimide is developed. The partial flooring technology combined with the beveled meandering technology is used to improve the structure. The nonlinear relationship between antenna size and working bandwidth is considered to optimize the size. The working bandwidth is expanded while keeping the antenna area unchanged. In order to solve the problem that the antenna performance is unstable due to the adjustment of single size parameters in the process of size optimization, a relationship model between multi-size and working bandwidth is proposed by using radial basis function (RBF) neural network, and an improved beluga whale optimization (IBWO) algorithm is used to optimize antenna size. The simulation results show that the size of the new flexible microstrip antenna is reduced by 59.59%. The operating bandwidth is increased from 0.598~0.6 GHz to 0.3~3 GHz, which fully covers the design requirements of partial discharge detection. By simulating partial discharge test and comparing with Archimedean spiral antenna and three-dimensional spiral antenna, the results show that the new flexible microstrip antenna has more reliable detection performance.
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Xu Haodong, Nian Fushun, Deng Jianqin, Zhang Shengzhou
2024,38(8):103-112,
Abstract:
To address the issue of weak out-of-band suppression in traditional artificial surface plasmon (SSPPs) microwave filters, a miniaturized SSPPs wide-stopband microstrip low pass filter featuring a novel butterfly structure is devised and fabricated. Firstly, by integrating the fan structure in the conventional microstrip filter with the SSPPs theory, the butterfly structure of SSPPs is designed to obtain a broader stopband. To enhance the out-of-band suppression level of the filter, an anti-directional arrowhead resonant patch is introduced to suppress the parasitic passband at 25 GHz within the stopband. The optimal design of the SSPPs unit is accomplished by simulating the dispersion characteristics of the unit structure. A complete SSPPs filter structure is designed, and the impact of key parameters on the performance of the filter is simulated. The metal-medium-metal (MIM) structure of the filter is fabricated using F4B substrate, and its performance is measured. The results indicate that the 3-dB cut-off frequency of the filter is 10.08 GHz,with the highest stopband frequency reaching 33 GHz. Return loss within the passband consistently exceeds 15 dB, while maximum insertion loss within this range is measured at 2.6 dB.Notably, minimum attenuation within the stopband measured at 22.98 dB between 11~35 GHz, with return loss generally exceeding 3 dB across the range of 10~30 GHz and peaking at 4 dB from 10~34 GHz. The test results of this design meet the design index, the out-of-band suppression effect is ideal, and it can be integrated with mixers, signal generators, and other devices to exert a superior circuit performance.
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Qiao Wan, Liu Xiuli, Wu Guoxin, Huang Jinpeng
2024,38(8):113-123,
Abstract:
In the context of time-varying operating conditions, fault diagnosis often exhibits high dynamism, while the limited model learning under small samples makes the issue more challenging. For the above situation, a fault diagnosis method based on contrastive deep convolutional networks is proposed. Firstly, considering the characteristic of small data samples, take advantage of differences in vibration data distribution caused by speed changes, and naturally realize data enhancement without manual operation. Subsequently, in the process of data processing, the vibration data of the same healthy state at different rotational speeds are used as positive samples, while the vibration data from different health states are used as negative samples. The key features are extracted by comparing the similarity between the samples so as to reduce the distance between the positive samples while increasing the distance between the negative samples. Finally, the feature extractor is trained and optimized by comparative training method, where a weighted combination of contrastive loss and cross-entropy loss is used as the composite loss function, enabling the model to effectively perform classification tasks while learning feature representations. The method is applied to two different bearing failure datasets at different time-varying rotational speeds for case studies respectively. The experimental results show that the proposed model not only performs well in the feature extraction and classification tasks, but also realizes high accuracy fault diagnosis under both data scarcity and time-varying speed conditions. It is verified that the proposed model shows high feasibility and effectiveness in dealing with time-varying small-sample data, and outperforms other advanced diagnostic methods.
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Li Xin, Liu Zhiyong, Yang Zhen, Li Hao, Zhou Jing, Bu Jingran, Wang Yiru
2024,38(8):124-136,
Abstract:
The microseismic signals generated during the deformation and rupture of loaded composite coal rock contain information about the rupture of the internal structure of the coal rock, and the microseismic signals collected by traditional equipment cannot be analyzed directly because of the presence of a large amount of environmental noise. In order to effectively extract the change characteristics of the microseismic signals during the deformation and rupture of loaded coal rock, a new CEEMD-IDWT joint denoising algorithm is proposed by integrating the complementary ensemble empirical modal decomposition algorithm (CEEMD) with the improved dmey wavelet (IDWT) algorithm. The algorithm firstly utilizes the CEEMD algorithm to decompose the original signal, then applies the IDWT algorithm to denoise the IMF components obtained from the decomposition, and finally reconstructs the processed components to obtain the denoised signal. The effectiveness of the algorithm is verified using simulation analysis and uniaxial compression experiments, and the results show that: the CEEMD-IDWT joint algorithm improves the signal-to-noise ratio by a maximum of 204.5% compared with the traditional algorithm in simulation analysis, and increases the signal-to-noise ratio of other improved denoising algorithms by a minimum of 11.8%, which is an obvious advantage in denoising ability; the microseismic voltage obtained by embedding the algorithm into the self-researched microseismic voltage acquisition equipment is significantly higher than that obtained by the conventional algorithm in the uniaxial compression experiments on the composite coal rock. The noise-to-noise ratio of the microseismic voltage signal obtained in the compression experiment is only 0.089 75, and the actual denoising effect is obvious; the microseismic voltage after denoising by the joint CEEMD-IDWT algorithm has obvious change characteristics, which significantly improves the signal denoising effect and effectively avoids the distortion of the microseismic voltage signal, and can be used as an ideal algorithm for the denoising of deformation and rupture of the microseismic voltage signal of the loaded coal rock and provides an ideal algorithm to accurately predict the coal rock dynamics and disaster. It can be used as an ideal algorithm for de-noising the microseismic voltage signal of loaded coal rock deformation and rupture, which provides a reliable and advanced technical reference for the accurate prediction of coal-rock power disasters.
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Wu Wenjin, Wu Jing, Guo Haiting, Zha Shenlong, Su Jianhui
2024,38(8):137-144,
Abstract:
During the long-term use of lithium battery packs, there is a problem of inconsistent voltages among the series-connected individual batteries. To solve this problem, a cascade bidirectional Cuk equalization circuit system based on variable domain fuzzy PID control is proposed in this paper. In this system, a cascade bidirectional Cuk equalization circuit is employed to achieve equalization between non-adjacent cells, and the variable domain fuzzy PID algorithm is exploited to enhance the voltage equalization speed. To verify the feasibility and superiority of the system, simulation models of the traditional bidirectional Cuk equalization circuit and the cascaded bidirectional Cuk equalization circuit were designed in MATLAB/Simulink. The comparison of simulation results under the control of the fuzzy PID algorithm and the variable domain fuzzy PID algorithm reveals that the balancing time of the cascade bidirectional Cuk balancing topology based on the variable domain fuzzy PID algorithm is decreased by 64.29% compared with the traditional bidirectional Cuk balancing topology without a control algorithm. The equalization time of the cascade bidirectional Cuk equalization topology without a control algorithm is reduced by 50.82%. The balancing time of the cascade bidirectional Cuk balancing topology based on the fuzzy PID algorithm is reduced by 14.29%. According to the analysis of the simulation results, it is demonstrated that the cascade bidirectional Cuk equalization circuit system based on fuzzy PID control in the variable theory domain can augment the voltage equalization rate of the battery.
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Ye Zhiyu, Jia Xiaofen, Wang Tianqi
2024,38(8):145-152,
Abstract:
Coal and gangue have the characteristics of dense targets and small feature differences, and the recognition methods based on image processing generally have the problems of slow detection speed and low accuracy. To further improve the speed and accuracy of coal gangue detection, a GE-YOLOv5s coal gangue detection model is proposed. Firstly, Ghost Conv is introduced based on YOLOv5s instead of convolution operation, and a new module GhostCSP is designed to improve the detection speed of the model while realizing the lightweight of the network; secondly, the GC self-attention mechanism is added in the prediction layer, which integrates the lightweight of SENet and the advantage of global capture of long-distance information of NLNet, to enable the network to memorize and magnify the Then in the Neck part, a bidirectional feature pyramid network (BiFPN) structure is adopted, and BiFPN is used to fuse the features of three different dimensions to improve the computational efficiency of the model through the weighted feature fusion mechanism to further enhance the speed of coal gangue detection; finally, a new type of activation function is designed to replace the activation function of SiLU, which can improve the utilization rate and accelerate the convergence of the model. Finally, a new activation function Eswish is designed to replace the SiLU activation function, which improves the parameter utilization rate, accelerates the convergence speed of the model and improves the robustness. The experimental data show that compared with the YOLOv5s model, the number of parameters is reduced by 34.1% the amount of floating-point operations is reduced by 38.6%, and the mAP 0.5:0.95 index is improved by 1.9%. Comparison experiments show that the mAP 0.5:0.95 metric is improved by 16.6%, 4.8%, 13.6% and 3.8% compared to YOLOv3, SSD, FasterR-CNN, and YOLOv5-scSE, respectively. Applying the GE-YOLOv5s model to the gangue target detection process has better recognition performance, robustness, and network generalization ability, and can effectively avoid the phenomena of leakage, misdetection and overlapping.
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Zhang Longci, Zhou Guofang, Lan Zhenli
2024,38(8):153-159,
Abstract:
In order to meet the high-precision measurement requirements of titanium alloy nanofilm pressure sensors, based on the influences of thin film thickness, convex islands, resistance shape and arrangement position to the nonlinearity and sensitivity of sensors, two range pressure sensors are designed and optimized using titanium alloy thin films as sensitive elements. The results indicate that whether there are convex islands or not, the maximum stress of the sensor occurs at the edge of the diaphragm. The maximum stress decreases and the position remains basically unchanged after adding convex islands. Adding convex islands or as the diameter of the convex island increases, the nonlinearity decreases and the sensitivity increases accordingly. To ensure that the sensitivity is not less than 2.5 mV/V in theory, 2 MPa sensor increasing convex islands with Φ2 mm, the nonlinearity decreases to 0.05% and the sensitivity is about 2.67 mV/V. 4 MPa sensor increasing convex islands with Φ3 mm, the nonlinearity decreases to 0.02% and the sensitivity is about 2.89 mV/V. Two ranges of sensors are prepared and tested, the results show that at a range of 2 MPa, the maximum deviations of the sensitivity and nonlinearity between actual values and theoretical values are 0.01 mV/V and 0.01%, respectively. At a range of 4 MPa, the maximum deviations of the sensitivity and nonlinearity between actual values and theoretical values are 0.16 mV/V and 0.02%, respectively. This study provides an important basis for the design of titanium alloy thin film pressure sensors.
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Xian Richang, Zheng Xiaogang, Li Jiayang, Zhang Haiqiang, Zhao Rujie, Hu Yuyao
2024,38(8):160-168,
Abstract:
Power transformers are critical components of the electrical grid, and the insulation status of their windings is directly linked to the operational safety and reliability of power supply. Short circuits at the low-voltage side outlets can easily damage the inter-turn insulation, leading to inter-turn short circuit faults in the windings. To further study the transient processes of inter-turn short circuits and low-voltage side outlet short circuits in transformer windings, this research investigates the weak links in heat generation and mechanical stresses, and elucidates the mechanisms of insulation failure development. An electromagnetic-force coupled model, congruent with the actual structural dimensions of the transformer, was developed. Utilizing finite element simulation software, the electromagnetic transient processes of the windings under various operational conditions were examined. A comparative analysis of the electromagnetic force distribution characteristics was conducted to explore the influence of different fault conditions on winding insulation. The results indicate that during inter-turn short circuit faults in transformer windings, the current in the short-circuited turn significantly exceeds that in three-phase outlet short circuits. Compared to normal load conditions, the peak currents through the short-circuited turns and overall windings increased by 5 318% and 3 314%, respectively; the maximum magnetic field intensity rose by 1 511% and 2 111%, and the peak electromagnetic force density on the winding turns surged by 5 210% and 11 489%. Inter-turn short circuit faults in transformer windings are likely to severely damage the insulation, whereas three-phase outlet short circuits can lead to unstable deformations and degradation of insulation.
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Zhang Jin, Li Jie, Wei Zixuan, Wang Xiaolu, Zhang Li
2024,38(8):169-177,
Abstract:
Aiming at the problem of poor effect of X-ray and ultrasonic technologies in non-destructive detection of glass fiber reinforced plastic (GFRP), characteristics of high resolution of X ray images and high contrast of ultrasound images are used for complementary imaging fusion, and by integrating the detail information of the defect edge of X-ray images and the high contrast outline information of ultrasonic images, new images are formed to improve the defect display effect. The frequency domain algorithm based on cross sector filter is used to remove the horizontal and vertical fringe noise of X-ray images, the morphological filtering algorithm is used to remove the salt and pepper noise of ultrasonic images, and the image fusion algorithm based on region segmentation and static wavelet transform is proposed to fuse X-ray and ultrasonic image traits. The test results show that the standard deviation SD of the fused images is increased by 154.1% on average, the entropy H is decreased by 92.2% on average, and the contrast of defect detection images is higher and the edge details are clear. The algorithm can effectively remove the fringe noise and pepper and salt noise in the two kinds of images, and can effectively improve the weakness of low contrast of X-ray images and poor resolution of ultrasonic images, and provide a new idea for the defect damage detection of composite materials.
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Wang Sen, Chang Ying, Cui Yaoyao, Liu Bin
2024,38(8):178-187,
Abstract:
The two-dimensional angular displacement table often consists of two two dimensional angular displacement table. It is difficult to avoid installation errors when mounting a biaxial tilt sensor on a two-dimensional angular displacement table, which can lead to inaccuracies in the measurement of two-dimensional tilt angles. Meanwhile, there is a two-dimensional angular coupling between the rotating axes of the off-axis angular displacement table. And the tilt of the bottom angular displacement table will cause the angle measurement deviation of the upper angular displacement table. Based on the existing angle accuracy of the angular displacement table and the tilt sensor, we hope that a method can be proposed to correct the above linear and nonlinear angle errors through the system model establishment and error analysis. Our work consists of the following parts. First, the systematic angle errors of the two-dimensional off-axis tilting system are analyzed and modeled in this paper, which are categorized into quasi-linear errors and nonlinear errors. The quasi-linear errors include vertical and horizontal tilt sensor installation errors. The nonlinear error is only caused by the off-axis layout of the angular displacement table. Second, a correction method combining the linear homography matrix and the nonlinear off-axis inclination model is proposed for compensating the two types of errors. Furthermore, the automatic correction process is designed. Finally, the calibration accuracy of the proposed method was verified by experiments. In order to verify the calibration accuracy of the proposed method, the off-axis angle control experiment was carried out in the range of ±12° to verify the calibration accuracy of the proposed method. The experimental results proved that the measurement accuracy of the two-dimensional angular displacement table tilt angle is improved from 0.559° and -0.216° before correction to 0.025° and 0.013°, which reduces the error by an order of magnitude. The method can meet the requirements of precise measurement and control of off-axis tilt angles. Furthermore, in order to verify the effectiveness of the proposed method, a comparative experiment was conducted with two existing methods. The experimental results show that the proposed method is superior to the other two methods in terms of accuracy and ease of operation. The errors of the biaxial tilt sensor mounted on a two-dimensional angular displacement table are analyzed and modeled, which are classified into quasi-linear errors and nonlinear errors. The corresponding correction method has been established by combining linear homography matrix and nonlinear off-axis inclination model. The experimental results proved that the proposed method effectively improve the measurement accuracy of the two-dimensional tilt angles. Compared with two existing methods, the accuracy of proposed method is higher, while the operation is much simpler.
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He Lifang, Xiong Qing, Liu Wenhao
2024,38(8):188-200,
Abstract:
To address the issue of output saturation caused by higher-order term constraints, we propose a novel unsaturated tri-stable second-order stochastic resonance (UTSOSR) system that leverages the excellent anti-saturation properties of piecewise potential function. First, simulation experiments verified that this system can significantly mitigate the output saturation problem of classical tri-stable second-order stochastic resonance system. Next, based on the adiabatic approximation theory, we derived the steady-state probability density, mean first-passage time, and spectral amplification factor (SA) of the UTSOSR system. By analyzing the influence of various system parameters on these performance metrics, we can further explore the system’s dynamic behavior in greater depth. Subsequently, using the SA and the signal-to-noise ratio gain (Gsnr) as evaluation metrics, numerical simulations were conducted to verify the superior signal enhancement and noise robustness performance of the UTSOSR system. Additionally, to achieve superior output performance, we combined maximum correlated kurtosis deconvolution (MCKD) with the UTSOSR system, proposing the MCKD-UTSOSR method for extracting target signal features. Finally, a combined approach using genetic algorithm and variable step-size grid optimization algorithm is employed to identify the optimal parameters for the MCKD-UTSOSR method in bearing fault diagnosis. The data analysis results indicate that compared to other methods, the MCKD-UTSOSR method improved the signal-to-noise ratio by 1.128 9~23.585 4 dB and the spectral peak value by 88.423~7 488.118 133. This provides an innovative and reliable solution for efficient signal processing and fault detection in practical engineering applications.
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Hu Jiusong, Liu Zhangchi, Yu Qian, Gu Zhiru, Zhong Hao
2024,38(8):201-207,
Abstract:
In the crucible of public safety, the imperative to guard against the scourge of fire is non-negotiable, yet conventional detection methodologies often falter when confronted with the complexities of specific environments. Herein lies the promise of computer vision technology, which offers the capability to monitor expansive territories in real-time and to identify the telltale signs of impending fires, most notably smoke. However, the intricate morphologies, textural variations, and chromatic subtleties of smoke present a significant challenge to the precision of its detection through machine vision.Addressing this exigency, we have conceived and developed an innovative smoke classification algorithm, seamlessly integrating a lightweight neural network and the convolutional block attention module (CBAM) within the YOLOv8 framework. This approach is designed to augment the accuracy and efficiency of smoke classification. Our algorithm leverages the GhostNet architecture, ingeniously replacing standard convolutional layers with a more efficient alternative, thereby maintaining high performance while drastically reducing the computational load on the model.Furthermore, the integration of CBAM imbues the algorithm with the ability to dynamically adjust its focus across different regions of the image, ensuring that salient smoke features are prioritized for detailed analysis. This feature enhances the model’s robustness and adaptability to diverse scenarios.To validate the efficacy of our algorithm, we conducted extensive experiments using both a publicly available smoke dataset and a custom dataset augmented with challenging samples. Empirical results have demonstrated that our algorithm achieves a smoke recognition accuracy of 99.9% on the public dataset and 99.2% on the custom dataset, outperforming existing methods. On our experimental machine, the algorithm exhibited a frame rate of 833 fps under GPU-accelerated conditions and 28 fps under CPU-only operation, affirming its potential for rapid and accurate early fire detection.
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2024,38(8):208-217,
Abstract:
Statically deployed marine ranching WSN is prone to the energy hole in the region close to the sink node during multi-hop data transmission. A reliable communication path planning method for the mobile sink node based on improved ant colony algorithm is proposed. First, for the traditional ant colony algorithm, non-uniform distribution of initial pheromone concentration is used to solve the problem of blind search at the initial stage of the algorithm. The heuristic function value in the transition probability function is modified and the crowding influence factor is added to avoid deadlock and speed up the convergence. In order to ensure the convergence ability of the algorithm at a later stage, an improved update rule for the pheromone is used. Secondly, LEACH protocol is used to cluster the network. According to the location of the cluster head node and the communication coverage area, the ergodic point set of the mobile sink node is constructed. Finally,the path planning problem of the mobile sink node is regarded as the traveling salesman problem, and the optimal path of the mobile sink node is obtained by the improving ant colony algorithm and the constructing backbone node set. The simulation results show that, at the scale of 275 network nodes, compared with other algorithms, the path length of PMRM is reduced by 41.9%, 20.3% and 30.4%, the data transmission delay is reduced by 42%, 38.5% and 46.7%, and the network throughput is increased by 10%, 10.6% and 16.4%. The superiority of the method is verified. The proposed method can effectively optimize the reliability of marine ranching WSN data reception, the energy consumption characteristics of the network and the life cycle of the network by introducing the mobile sink node and exploiting the rationality of their mobile paths.
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Chen Yinjie, Lin Tianqi, Dong Shuangshuang, Yang Shihan, Lin Ziyi, Yang Wei, Li Guozhan, Zhang Hongjun
2024,38(8):218-226,
Abstract:
To meet the requirements of the multi-hole balance orifice plate structure parameters design, a general idea for the design and calculation of structural parameters was proposed, and take the plate containing a central hole and one circle of function holes as an example to give the specific design method and calculation example. Firstly, determine the thickness and chamfer angle of the orifice plate. Secondly, according to the relationship between the permanent pressure loss ratio and the equivalent hole opening diameter ratio (Δω/ΔP-β), and the relationship between the pressure loss coefficient and the equivalent hole opening diameter ratio (ζ-β), the β value is determined by iterative calculation. Then, determine the diameter of the hole opening circle Db and the hole number N, according to the pipe diameter. After that, according to the equivalent hole opening diameter ratio definition and supplementary geometric relationship, establish an equation group about the central hole diameter d0 and the equilibrium hole diameter db, then solving the equation group to obtain values of them. Finally, calculate the outflow coefficient. The general idea, process of the method and a design calculation example are given in the paper. In this paper, the structure parameter calculation process of a DN250 balance orifice plate is taken as an example to show this method. The results show that deviations between the calculated and the calibrated data of the discharge coefficient and full range differential pressure are within 5% and 10%, respectively, which meet the needs of engineering design. The example proves the practicability of this method.
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Wang Xueer, Ye Ming, Liu Fei, Bai Yongjiang, Yang Fang, Xie Yongjun
2024,38(8):227-236,
Abstract:
Crack detection is of great practical significance for steel structures and concrete buildings, nuclear power, aviation and other fields. In this paper, a resonant coaxial probe is used to construct a microwave microscope to carry out the study of non-destructive testing methods for metal and non-metallic cracks. First of all, the influence of key parameters such as coupling gap, lifting height and probe material on the detection performance is studied through electromagnetic simulation. Then, the experimental verification was carried out with machined brass, aluminum alloy samples and 3D printed non-metallic samples. The results show that the S11 amplitude of the coaxial probe at the resonant frequency can be used for non-destructive detection of cracks on the surface of the material. The coupling gap and lifting height are the main design parameters that affect the detection sensitivity. Compared with the traditional reflective coaxial probe, the detection sensitivity of the resonant probe (S11 at the observation frequency point when there is a crack or not) The amplitude difference) has been increased by about 20~30 times; the simulation of the 3.9 mm crack in the width of the metal sample is in good agreement with the measured results. The relative error of the crack width detection is < 7.7%, and for 3D printed non-metallic crack samples with a crack width of 1.7 mm, the measured relative error of the crack width is Within 6%. This research has certain reference significance for the non-destructive detection of metal and non-metallic cracks.
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Hu Xuenan, Jiang Xuesong, Cheng Yuanjie, Liu Jingxin, Li Tian
2024,38(8):237-244,
Abstract:
It is needed to achieve very high multipath delay accuracy to better approximate the real communication scenario in the process of channel simulation, which puts higher requirements on the simulation ability of the channel emulator. Channel models is processed and loaded usually within the digital baseband, and so the clock resolution is limited. It is necessary to make use of Farrow structured fractional filters to achieve higher delay accuracy. In according to the characteristics of channel simulation algorithms, the Farrow structure fractional filter was designed and optimized by mixing DSP and distributed multiplication to achieve ultra-high precision delay simulation. The design scheme was validated and tested on the TRANSCOM Pathrot X80 channel emulator. The results show that,the improved Farrow filter′s structural design significantly reduces the consumption of FPGA computing resources, enabling the fractional delay algorithm to achieve a balance between high delay accuracy and low resource overhead; the multipath delay accuracy is consistent with theoretical calculations in the low frequency range, meeting the expected 0.1 ns requirement for channel simulation; the delay accuracy differs significantly from theoretical calculations in the high-frequency range. In order to achieve better performance or reduce signal distortion, it can be considered to increase the order of the filter or find a better coefficient calculation algorithm.
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Shen Jiacheng, Qiu Guohua, Yuan Qinwen
2024,38(8):245-253,
Abstract:
The vector network analyzer is an essential instrument in RF and microwave testing. The traditional methods of detection necessitate the presence of on site personnel for operation and control, which is inefficient and inflexible, failing to meet the contemporary requirements for intelligent and convenient electronic measurement. To address these challenges, an online software system based on a cloud server was designed for vector network analyzer. The system connects the vector network analyzer to a local computer via the SCPI protocol. The local computer uploads the S parameters, comprising frequency points, real and imaginary components, to the cloud server’s MySQL database via SSH. The Django developed website allows users to access real time visual data of S parameters, including frequency domain, time domain, and standing wave ratio graphs, from any device. The system was tested on the E5071C vector network analyzer for dual port S parameter detection of rectangular stainless steel tubes from 10~12 GHz. Results showed no data loss during multiple transmissions between the instrument and the local computer. The error between S parameter data and the instrument’s detection results was less than 001%. Users could access real time S parameter waveforms simultaneously from multiple devices, proving the system’s practicality and providing a reliable solution for vector network analyzer detection.
Volume 38,2024 Issue 8
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Research on fault diagnosis method of rotor system combined digital and analog
Abstract:
Aiming To address the problems of limited sample size, uneven sample distribution, and cross-operating condition fault diagnosis for gas turbine rotor systems, a fault diagnosis method based on the linkage of numerical model and physical model is proposed. A classical central mass method is used to establish the dynamic model of the rotor system, and fault dynamics differential equations are established in the model by introducing misalignment fault and unbalance fault. Finally, the differential equations of rotor system fault dynamics are solved by the Runge-Kutta method (ode-4/5), and the simulated signal of fault displacement is obtained, which is prepared for subsequent data augmentation and model linkage methods. A conditional deep convolutional generative adversarial network (GAN) model is established by combining the orthogonal gradient penalty algorithm, and the model is used to generate signals by inputting the simulated signals obtained from the physical model as the generator input, and inputting the real experimental signals as the discriminator input to obtain a generated signal that integrates the intrinsic characteristics of the physical model and the actual mechanical characteristics. Secondly, a cross-operating condition domain adaptation fault diagnosis model is established based on the theory of transfer learning, and the data is converted into a two-dimensional temporal-frequency image sample using a combined short-time fractional Fourier transform and inverse convolution algorithm, which provides more obvious resolution and features. The data that integrates the intrinsic characteristics of the physical model and the mechanical characteristics is used as the source domain and the other target domain data to be measured at other operating conditions is used to train the fault diagnosis model. The experimental verification shows that the diagnostic accuracy of the five different fault categories at different speeds and fault levels in different operating conditions is all above 91%. The results are explained and analyzed through confusion matrix, and the method can effectively improve the model's generalization ability and realize cross-operating condition fault diagnosis of rotor systems, which is superior to other mainstream diagnosis methods under the same conditions.
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Online compensation of acceleration on error while drilling based on MICOA
Abstract:
（研究的问题）To improve the measurement accuracy of the downhole accelerometer, a method for online compensation of accelerometer errors based on a magnetic-inertial coati optimization algorithm is designed. （研究的过程和方法）Firstly, an error compensation model is established based on the sources of error; the constraint conditions of the gravity angle and the magnetic-gravity angle are established using a gyroscope and a magnetometer; the difference between the true value of the acceleration and the modulus of the theoretical value is set as the objective function. Secondly, based on the coati optimization algorithm, the initial search boundary for error parameters is determined according to the recursive gravity acceleration, and the boundary is narrowed based on the relative distance among the current error parameters, the optimal error parameters, and the boundary values; a boundary point selection is designed to screen the initial error parameters, enabling the algorithm to initially search in the direction of high-quality solutions while retaining some inferior solutions to increase the diversity of error parameters; in the global exploration stage of the algorithm, parameters are designed to adjust the search range of accelerometer error parameters based on the error between the current error parameters and the average error parameters. Finally, the ratio of the modulus of gravity is set as the threshold for deep development, and a Gaussian mutation vector is constructed to enable the accelerometer error parameters to break out of local optima. （研究结果）Experimental results show that after MICOA compensation, the accelerometer error decreases, and the range of inclination angle decreases by approximately 62.5%; at different drilling angles, the root mean square error and standard deviation of the inclination angle can be maintained below 1°.
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Evaluation Method of Measurement Uncertainty of TransducerBased on Convolution
Abstract:
As the first part of the whole testing system, the measurement uncertainty of transducer influences greatly on the uncertainty of measurement results. For this reason, the main sources of transducer uncertainty have been analyzed, and the evaluation methods have been discussed about their properties; proposes a new method to evaluate the measurement uncertainty of a transducer has been proposed based on convolution of probability density function of sources of measurement uncertainty; the method has been realized via MATLAB .Finally, the method has been successfully applied to evaluate the measurement uncertainty of a load cell, which reveals the effectiveness of the method.
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On-line fault detection method of hydraulic turbine combining PCA and adaptive K-Means clustering
Abstract:
During the operation of the bulb tubular hydropower unit, due to hydraulic factors, machinery, working conditions and other factors, it is easy to cause the runner blades and runner chamber to malfunction, which seriously affects the safe operation of the hydropower unit. Based on the analysis of the fault signal characteristics of the runner blades and runner chamber of the bulb tubular hydropower unit, an online fault detection method for hydropower units based on K-Means and Wright"s criterion is proposed. This method uses principal component analysis (PCA) to reduce the dimensionality of the vibration and noise signal characteristics of the hydropower unit, and integrates the Wright criterion to improve the traditional K-means algorithm to realize the adaptive selection of the K value, and perform online clustering of the features, which can quickly and accurately identify .The variable load state of the turbine and the failure of the metal sweeping chamber. The method proposed in this paper is applied to the fault detection of the bulb tubular unit of Wuling Electric Power’s Jinweizhou Hydropower Station. The experimental results show that the accuracy of the online fault detection using this method is 100% and the accuracy of the variable load online detection is 96.7. %, there has been no fault false positives and false negatives in the past 10 months of operation, indicating the effectiveness of the method.
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Research on positioning of mobile robot based on Laser Information
Abstract:
Aiming at the problems of slower particle convergence and poor positioning accuracy when using traditional Monte Carlo positioning algorithms in the navigation and positioning process of mobile robots, as well as low relocation efficiency after artificial kidnapping, this article gives an improved Particle filter positioning method to improve the navigation and positioning efficiency of mobile robots. First of all, it is improved on the basis of the Monte Carlo positioning algorithm and integrated into the method of adaptive region division to ensure that the region contains more effective information, reduce the convergence time of particles, and complete the preliminary coarse positioning of the robot. Then, in the particle sampling and resampling stage, the normal distribution probability model is used to update the particle weights to achieve faster and more efficient global positioning. Through experimental comparison and analysis, compared with the Monte Carlo positioning algorithm, the given method has shortened the time consumption by 4s, and the adaptive Monte Carlo positioning method in this paper can keep the positioning error at about 6cm, thus verifying the given method Effectiveness and stability.
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Gaussian process enhanced robust cubature Kalman filter and application in integrated navigation
Abstract:
The observable degree of navigation state has a significant effect on the state estimation of GNSS/INS. In order to improve the accuracy of heading of land vehicle, an improved robust cubature Kalman filter (RCKF) method is proposed. First, the resampling-free sigma-point update framework is employed to separate the cubature point update from the Gaussian information limitation, and thus improving the propagation efficiency of the information contained in instantiated points in the iteratively filtering period. Secondly, in order to improve the heading of land vehicle when it travels along a straight-line, the Gaussian process (GP) is introduced into the uncertainty calibration of moment approximation of system model based on state observability analysis. Simulation results indicate that GP-RCKF improves the heading angle obviously when the state observability is weak, and compared with RCKF the heading is improved by 28.9%.
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Research on Traffic Sign Recognition Technology Based on YOLOv5 Algorithm
Abstract:
Aiming at the low detection accuracy of traditional traffic sign recognition algorithms,a traffic sign recognition method with improved YOLOv5 algorithm is proposed.First,improve the loss function of the YOLOv5 algorithm,use the EIOU loss function instead of the GIOU loss function used by the YOLOv5 algorithm to optimize the training model,improve the accuracy of the algorithm, and achieve faster identification of the target,then use the weighted Cluster NMS to improve the YOLOv5 itself.The weighted NMS algorithm improves the accuracy of generating the detection frame.The experimental results show that the mAP value of the model trained on the CCTSDB traffic sign dataset produced by Changsha University of Science and Technology by the improved YOLOv5 algorithm reaches 84.35%,which is 6.23% higher than the original YOLOv5 algorithm.Therefore,the improved YOLOv5 algorithm has higher accuracy in traffic sign recognition and can be better applied to practice.
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Study On the Concept of Space Time Keeping System
Abstract:
To solve the problem that there is no rule to unify time for wider space such as solar system, a new rule for time uniformity is proposed in this paper. Space Time-keeping system (STKS) evolves from the concept of Space Metrology which is based on the theory of General Relativity, and meets the convention of time unit and the beginning of time. It uses the cesium atomic clock to measure proper time and pulsars to measure coordinate time, unifying time through the coordinate time on the origin of solar system barycentre coordinate. The viewpoint of relative time denies the uniqueness of standard time. For different local area or coordinates, when looking from each other, one’s measurement of the other’s time interval would be uneven, showing a curved coordinate axis. While on the viewpoint of absolute time, the standard time is unique and different timing devices could be synchronized by dissemination technology. The concept of STKS will revolutionize the traditional viewpoint of absolute time. With a customized feedback mechanism designed, it would improve time keeping technology to a more stable scale.
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Research on pressure magnetic measurement system based on j-a model of force-magnetic coupling
Abstract:
A pressure measurement method for in-service vessel based on magneto-mechanical effect is proposed. Non-intrusive and non-contact pressure measurement is realized by using the stable correspondence between magnetic signals outside the vessel and the stress on the vessel wall. The relationship between magnetic permeability and stress of steel in weak geomagnetic field is analyzed by using J-A coupling model, and the feasibility of using external magnetic field of vessel to measure internal pressure is theoretically proved. A multi-channel synchronous magnetic signal acquisition system is designed, which can simultaneously acquire the magnetic signals of three components of the multi-sensor. Experiments are carried out to verify the performance and advantages of this method. The results show that in the range of 0-3 MPa, there is a good linear relationship between the magnetic field near the surface and the internal pressure of the vessel. The sensitivity to the pressure change of the magnetic field at different parts of the vessel surface is different, so multi-point deployment and calibration optimization are required. For low carbon steel pressure vessel with an outer diameter of 275 mm and a wall thickness of 7.5 mm, the sensitivity of magnetic measurement can reach up to 131.4 mGs/MPa. Via axis-symmetrically arranging a pair of sensors and adding their output up, the influence of rotating vessel on measurement accuracy can be significantly weakened.
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The design research of 625MS/s 12bit two-channel time interleaved ADC
Abstract:
A 625MS/s 12bit two-channel time interleaved ADC is designed in 40nm CMOS process. The single channel is pipeline ADC with no sample-and-hold-amplifier (SHA) front-end for low-power consumption. A wideband and high-linearity foreground input buffer and a high speed and high precision bootstrapped switch are used for ensuring the effective input bandwidth of the interleaved system。A background calibration algorithm based on reference channel is applied for sampling time mismatch calibration between channels. This background calibration method is appropriate for completely random input signals. The core area of the system is 0.69mm2。The post-simulation results show that the 625MS/s 12bit time interleaved ADC achieves 67dB of SFDR and 58.5dB of SNDR with the Nyquist sampling at full sampling speed, while its power consumption is 295mW, which meets the design targets and confirms the effectiveness of the design.
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Cell image segmentation method combined with anti-background difference and Otsu
Abstract:
Aiming at the problems of low contrast, uneven background and halo artifacts in the images of mesenchymal stem cells collected by the phase contrast microscope, this paper proposes a cell image segmentation method combined with anti-background difference and Otsu. The method constructs anti-background difference to enhance the difference between the cell body and the non-cellular area and reduce the influence of uneven background, combines the Otsu threshold segmentation method to roughly distinguish the cells and the background, and further corrects the segmentation results by a combination of algorithms including binary morphology operations, image filtering, and local gradient iteration. The four evaluation indexes of pixel accuracy, intersection over union, dice similarity coefficient, and confluency error achieved values of 0.9338, 0.7296, 0.8524, and 0.07, respectively, by segmentation validation on the actually acquired cell images. The results indicate that the algorithm has high segmentation performance, can objectively, accurately and automatically analyze the confluency of cells, and can process images of cells in different culture periods, which has high application value.
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Research on crack defect detection of solar cell based on PSO_SVM
Abstract:
Aiming at the problem of cracks in solar cells during the production process and under the condition of limited database of solar cell defects, the particle swarm optimization (PSO) is applied to optimize the support vector machines (SVM) to detect the surface cracks of solar cells. Firstly, in order to reduce the influence of uneven light distribution caused by electroluminescence (EL) detection in the image acquisition process, Retinex enhancement processing is performed on the image of the solar cell assembly. Secondly, in the frequency domain, the Gabor transform is used to extract the texture features of the image to obtain the crack feature. Finally, the texture features of each solar cell component are reduced by principal component analysis (PCA) and then they are input into the PSO_SVM system for classification and recognition. Using this method to experiment with 600 EL images of solar cells, only one image was detected by mistake, and the classification accuracy is 99.33%. Comparing this algorithm with decision tree classification, Extreme learning machine (ELM), Convolutional Neural Network (CNN) and SVM algorithm, PSO_SVM achieves the highest recognition rate.
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Energy Hole Repair Algorithm of Terahertz Nano Sensor Network Based on Chain Clustering Game Mechanism
Abstract:
In order to improve the energy consumption of the link nodes in the deployment process of terahertz nano sensor network, the difficulty of data exchange between the inner and outer layers and the low transmission performance, an energy hole repair algorithm based on the chain clustering game mechanism is proposed. First of all, based on the equidistant division model, a kind of equidistant ring structure is designed to improve the link performance of nodes rapidly. By exchanging and transmitting data between the inner and outer nodes of the equidistant ring structure, the interaction quality between the inner and outer layers of the data is optimized. By polling and balancing the energy consumption of the inner nodes, a new chain clustering game mechanism based on the prediction scheme of energy hole formation is designed to share the energy. Then, based on the idea of flow balance, an optimization method of balanced consumption based on energy controllable is designed to improve the energy consumption level of nodes at different levels, enhance the adaptability of nodes to energy constraints, and avoid the risk of node constraints. The simulation results show that compared with the current energy-saving scheme based on dynamic clustering mechanism and the scheme based on affinity propagation considering the residual energy of nodes and reducing the burden of cluster head, the algorithm in this paper has a higher network life cycle, a lower number of data transmission rounds and a lower degree of node restriction.
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Improved BP neural network with ADAM optimizer and the Application of Dynamic Weighing
Abstract:
To improve the operational efficiency and measurement accuracy of the dynamic check weigher, the interference of mechanical vibration to the measurement and the generating mechanism of the sensor's nonlinear characteristics are deeply analyzed. A multi-layer BP neural network based on ADAM optimizer is proposed to realize the nonlinear correction of weighing sensor and estimates the dynamic weighing results accurately. The classical gradient descent algorithm, gradient descent algorithm with momentum and root-mean-square propagation algorithm are compared with the ADAM algorithm through experiment. According to the results, the ADAM algorithm had faster convergence speed and more accurate prediction results as it comprehensively considered the first and second sample moment of parameter's gradient. The high speed dynamic check weigher with full range of 400 g and maximum running speed of 2 m/s is manufactured, The type test results showed that all of its indicators are meet the requirements of national standard GB/T 27739-2011 Automatic Divider for XIII check weigher.
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Research on Simulation Method of Signal Acquisition and Tracking of UAV Aerial Survey High Precision RTK Receiver
Abstract:
In recent years, the real-time kinematic (RTK) high-precision satellite positioning technology with carrier phase difference technology as the core has developed rapidly in the field of surveying and mapping. This article is based on the RTK positioning principle, aiming at light and miniaturization, high precision, stable and fast satellite positioning receivers, the focus is on multi-system and multi-band Global Navigation Satellite System (GNSS) signal processing technology, including the GNSS multi-frequency RF front-end processing and baseband signal processing key technologies of the UAV RTK receiver. Through professional simulation methods, it can be concluded that the designed RF front-end receiving sensitivity is higher than -130dBm. The execution time for searching and capturing 6758 sampling points is only 0.68s, and the capture frequency shift error is about 0.932% of the Doppler frequency shift, and the frequency error after the GNSS signal carrier tracking stabilizes is basically concentrated below 0.75KHz. The simulation results show that the designed GNSS signal processing module meets the requirements of the actual multi-frequency RTK positioning receiver.
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Research on bolt looseness detection method based on multi domain feature
Abstract:
Bolts are the most commonly used connectors for mechanical equipment. The stability of bolt connection plays an important role in ensuring the safe operation of mechanical equipment. It is of great significance to detect the state of bolt looseness.Aiming at the four different states of bolt loosening, a bolt looseness detection method based on variational mode decomposition (VMD) and time-frequency sensitive feature combined with least square support vector machine (LSSVM) is proposed in this paper.In order to identify the four different states of Bolt looseness, a simulation experimental platform for bolt loosening detection is built, and the vibration response data of four different states of bolt looseness are obtained by accelerometer.The time-frequency sensitive features are extracted, and the IMF component energy entropy decomposed by VMD is combined to form the sensitive multi-feature vector. The extracted multi-feature vectors are combined with least square support vector machine to detect different looseness states of bolts. The recognition results are compared with the results of based on empirical mode decomposition(EMD)-LSSVM and EMD multi-feature-LSSVM recognition.?The recognition rate of bolt looseness detection method based on proposed VMD multi-feature in this paper is better than that of EMD-LSSVM detection method .
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Optimization method of diagnosis strategy based on elite ant system under unreliable test conditions
Abstract:
The optimization design of diagnosis strategy is an important part in the process of testability design. Unreliable test factors seriously affect the optimization design process. This paper summarizes previous research results. Aiming at the problem that heuristic search algorithm is difficult to solve the problem of diagnosis strategy optimization under unreliable testing conditions, this paper proposes a diagnosis strategy optimization algorithm based on the essence ant system. This paper establishes a mathematical model for the optimization of the diagnosis strategy under unreliable conditions, and then constructs the optimized target with the cost of testing and the cost of error. Then, it uses the improved ant ant system algorithm to solve the problem. Finally, the algorithm is applied to an equipment for instance analysis. Compared with greedy algorithm and common ant colony algorithm, it shows the advantages of the algorithm in precision and convergence speed, and verifies the feasibility and effectiveness of the algorithm.
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An narrow-band imaging method for compound micro-motion space target
Abstract:
A narrow-band imaging method for compound micro-motion space target is proposed. Due to the advantages of narrow-band radar in target detection and tracking, narrow-band radar is widely used in space target detection. For micro-motion space target, there is time-varying Doppler modulation, which contains the important structural information of the target. By applying the inverse Radon transform (IRT) method on the time-frequency image, the position of scattering centers of the target can be obtained and the narrow-band imaging can be realized. Narrow-band imaging reduces the requirement of radar bandwidth and has advantages in space target detection. In the real detection scene, the target motion is a composition of micro-motion and translation, which makes the narrow-band imaging method invalid. In this paper, based on the radar echo model of compound micro-motion space target, the time-varying Doppler modulation characteristics of the target are analyzed, and a compound micro motion narrow-band imaging method is proposed. Firstly, the micro-motion period is estimated based on the time-frequency correlation coefficient, and then the translational influence is removed by the Doppler cancellation method to realize the target translational parameter estimation, and then the translational compensation is achieved. Finally, the IRT is used to obtain the narrow-band image. The proposed method is not affected by the target translation, and can effectively achieve the narrow-band imaging of the compound micro-motion space target.
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Application and research of improving polynomial fitting algorithms for images de-noising
Abstract:
In order to further improve the de-noising ability of the conventional polynomial matching algorithm, an improved polynomial matching filtering algorithm based on edge protection is proposed for the deficiency of the conventional polynomial matching algorithm and the image characteristics and noise problems. Based on the conventional polynomial matching algorithm, this method improves the selection method of the filter window, extracts the adaptive sliding filter window along the direction of the image texture, and selects the window with the smallest matching error for matched filtering and uses it as the final output result. Then add Gaussian white noise and pepper and salt noise to the gray image and CT image respectively for testing. The data verification shows that the method can maintain edge / texture information under the premise of effectively suppressing noise, and the peak signal-to-noise ratio is increased by more than 80% on average, mean square The root error is reduced by more than 80%. It is concluded that compared with the conventional polynomial filtering method, median filtering method, bilateral filtering method and edge preserving filtering method, the improved method can effectively improve the image visual effect, meet the image application requirements, and has good application prospects.
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Landing site recommendation for unmanned aerial vehicles based on Lidar data
Abstract:
The autonomous path planning and landing of unmanned aerial vehicles in open scenes has been the focus of research in related fields. We propose a method based on the point cloud data collected by the lidar to recommend the optimal landing address for the UAV. This method corrects and expands the original point cloud data by using the UAV pose information and maintaining the site selection window, then we propose to improve the RANSAC algorithm to evaluate and select the generated candidate planes, and finally output the coordinate information of the optimal landing site. The experiments in the simulation environment show that the results of this method are stable and accurate, and the computational speed satifies the requirements of the UAV to work in real time. At the same time, the space and time cost of this method is light, which meets the standard of the operation in real scenes.
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2017,31(1):45-50, DOI: 10.13382/j.jemi.2017.01.007
Abstract:
The concentration of nitrogen oxides (NO2, NO, N2O, etc.) in power plant is an important index of environmental protection. Aiming at the problem that the detection accuracy of nitrogen oxides concentration based on spectral analysis could be interfered by all kinds of factors, such as temperature, moisture content, tar, naphthalene, noise of electric devices, optical lens aging, interference at spectral absorption characteristics of polluting gases etc, it is difficult to improve in a single way. At first, the hardware modification is favorable for gas purification and filter. And then, the self learning and self training ability of RBF neural network can save the traditional model for the study of interference factors, and make the data processing more efficient. On the basis of a large thermal power plant’s real data in 2015, the computer simulation and analysis show that this method can improve the accuracy effectively. The overall average deviation is 0.841%.
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Wang Wen, Zhang Min, Zhu Yewen, Tang Chaofeng
2017,31(1):1-8, DOI: 10.13382/j.jemi.2017.01.001
Abstract:
Spherical joint is a commonly multi degree of freedom mechanical hinge which has many advantages such as compact structure, good flexibility, and high carrying capacity. Realization of its multi dimensional angular displacement measurement is of great significance in the prediction, feedback, and control of the system motion error. Firstly, the application of spherical joint and its structural characteristics were presented in the paper. Then, the motion description of the spherical joint and needed angles for measurement were analyzed. A review of multi dimensional angular displacement measurement method, including structural decoupling detection method, optical based detection method and magnetic field based detection method, at home and abroad was provided, Finally, the development of research on multi dimensional angular displacement measurement method for spherical joint was summarized. The focus and the difficulty of the research were pointed out, and the challenges and the breakthroughs in the key technologies were also stated.
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Liu Kun, Zhao Shuaishuai, Qu Erqing, Zhou Ying
2017,31(1):9-14, DOI: 10.13382/j.jemi.2017.01.002
Abstract:
The complex and various defects of the steel surface bring great difficulty to the feature extraction and selection. Therefore, this paper proposes a new R AdaBoost future selection method with a fusion of feature selection and sample weights updated. The proposed algorithm selects features and reduces the dimension of features via Relief feature selection according to updated samples in each cyle of AdaBoost algorithm, and uses reduced features to remove noise samples by intra class difference among samples, and then update sample library according to dynamic weight of AdaBoost. The weak classifiers are trained by the resulting optimal features, and combined to generate the final AdaBoost strong classifier, and detect and locate strip surface defects by AdaBoost two classifiers. Aiming at a variety of defects such as scratch, wrinkle, mountain, stain, etc. in the actual strip production line, the experimental results show that the proposed R AdaBoost algorithm can effectively extract features with high distinction and independence and reduce the feature dimension, and simultaneously improve the accuracy of defect detection.
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Sun Wei, Wen Jian, Zhang Yuan, Geng Shihan
2017,31(1):15-20, DOI: 10.13382/j.jemi.2017.01.003
Abstract:
Aiming at the random error of MEMS gyroscope is the main factor that restricts its precision and application range, the Kalman filter estimation method based on regression moving average (ARMA) model is proposed in this paper. Firstly, based on the results of Allan variance analysis, the quantization noise, angle random walk and zero bias instability are the main parts of the MEMS gyroscope random noise. Then, the stability of MEMS gyroscope random noise is tested by using time series analysis. Finally, based on the random drift of the auto regressive moving average (ARMA) model, a discrete Kalman filter equation is built to actualize its error estimation and compensation. The results of static vehicle and dynamic environment of digital noise reduction and Kalman filtering compensation experiments show that the Kalman filter estimation method based on the ARMA model has more obvious advantages in MEMS Gyroscope random error compensation.
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Luo Ting, Wang Xiaodong, Ma Jun, Yang Chuangyan
2021,35(12):116-125, DOI:
Abstract:
In view of the nonlinear dynamic characteristics of rolling bearing vibration signal and the low accuracy of reliability evaluation, a rolling bearing health condition assessment method based on improved cross fuzzy entropy (ICFE) and Weibull proportional hazards model (WPHM) was proposed. Firstly, the original vibration signal is decomposed by improved DLMD (Crt- DLMD), and the effective component with the most fault information is selected for reconstruction. Then, the ICFE of the reconstructed signal is calculated by using the sliding mean instead of the original coarse-grained process. Finally, the ICFE is used as the covariate of WPHM for health status assessment. The life cycle data and experiments of rolling bearing from national aeronautics and space administration (NASA) and Xi′an Jiaotong University Changxing Shengyang technology (XJTU-SY) show that the proposed method can accurately and effectively evaluate the health status of rolling bearings.
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He Lifang, Cao Li, Zhang Tianqi
2017,31(1):21-28, DOI: 10.13382/j.jemi.2017.01.004
Abstract:
Empirical mode decomposition（EMD）method attenuates the signals’ energy and generates false signals in decomposing signal noise, which leads to incorrect detection results. In order to solve this problem, a stochastic resonance method under Levy noise after denoised by EMD decomposition is presented in this paper. After decomposed by EMD, the noisy signals are handled by overlaying, averaging and resampling to meet the condition of stochastic resonance. An adaptive algorithm is used to optimize system parameters, and then the processed signal can generate stochastic resonance in bistable system to achieve precise detection. The theoretical analysis and experimental results prove that the method can detect single frequency signal and multi frequency signal under the same characteristic exponent with the Levy noise. The experimental results demonstrate that the SNR of single frequency signal can increase 14 dB in the case of SNR of -28 dB. The spectral amplitude of the 5 Hz spectrum is increased from 311.8 to 724 and 10 Hz spectrum amplitude is increased from 138.9 to 143.2. This method that reduces the residual noise energy and false signal can improve the signal energy in a complex noisy condition. Compared to EMD decomposition which cannot determine the signal components, this method can achieve the detection effect better.
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Yan Fan, Zhang Ying, Gao Ying, Tu Yongtao, Zhang Dongbo
2017,31(1):36-44, DOI: 10.13382/j.jemi.2017.01.006
Abstract:
To solve the time consuming problem of image stitching algorithm based on KAZE, a simple and effective image stitching algorithm based on AKAZE is proposed. Firstly, AKAZE feature points are extracted. Secondly, feature vectors are constructed using the M LDB descriptor and matched by computing the Hamming distance. Thirdly, wrong matches are eliminated by RANSAC and the global homography transform, and then a local projection transform is estimated using moving direct linear transformation in the overlapping regions. The image registration is achieved by combining the two transforms. Finally, the weighted fusion method fuses the images. A performance comparison test can be conducted aiming at KAZE, SIFT, SURF, ORB, BRISK. The experimental results show that the proposed algorithm has better robustness for the various transform, and the processing time is greatly reduced.
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Pan Yuehao, Song Zhihuan, Du Wangze, Wu Legang
2017,31(1):29-35, DOI: 10.13382/j.jemi.2017.01.005
Abstract:
To help nursing staff in senile apartment find the elderly fall and other actions timely, an action recognition method based on video surveillance is proposed. Firstly, the foreground images are extracted by the GMM background modeling method in HS color space. Feature extraction is performed by combining the motion features and morphological features. And action recognition can be achieved by HMM with Gaussian output. The method proposed in this paper can adapt to the changes of illumination. The method also has good robustness to the change of motion direction and motion range, and the recognition accuracy rate reaches 90%. The result shows that the method can meet the basic requirements of action recognition and the method has certain practical value.
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Yin Min, Shen Ye, Jiang Lei, Feng Jing
2017,31(1):76-82, DOI: 10.13382/j.jemi.2017.01.011
Abstract:
In disaster rescue and emergency situations, node energy in sensor network is especially limited. In order to reduce unnecessary forwarding consumption, this paper presents a MANET multicast routing tree algorithm with least forwarding nodes, which is based on shortest routing tree and sub tree deletion. The algorithm is proved and analyzed in detail. Its practical distributed version is also presented. The simulation comparison shows that this distributed algorithm reduces the forwarding transmission in improved ODMRP, especially there are much more receivers in MANET. Minimum forwarding routing tree has the minimum network overhead. It is an effective way to extend the network lifetime.
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Chen Shuo, Luo Tengbin, Liu Feng, Tang Xusheng
2017,31(1):144-149, DOI: 10.13382/j.jemi.2017.01.021
Abstract:
In order to solve the low efficiency and the influence of manual factors and many other problems existed in current water meter verification, the water meter verification system using machine vision technology is proposed. And the research keynote is how to realize the template matching algorithm for rapid location of plum blossom needle and the image morphological algorithm for eliminating the bubble of wet water meter dial. Harris algorithm is used to extract the corner points of the plum blossom needle template beforehand, and the corner points of the on site image are extracted in real time. Then, the fast localization of the plum blossom needle is realized by the partial Hausdorff distance method. Finally, the effect of bubbles is eliminated by using the image morphological algorithm, and the count value of the rotating teeth of the plum blossom needle is completed. The experimental results show that the proposed system can shorten the verification time and improve the verification efficiency while ensuring the verification accuracy. The system solves the adverse effect of the bubble on the dial of the wet water meter, and it’s suitable for the verification of various types of water meters.
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Cao Xinrong, Xue Lanyan, Lin Jiawen, Yu Lun
2017,31(1):51-57, DOI: 10.13382/j.jemi.2017.01.008
Abstract:
A simple, rapid and efficient retinal vessels segmentation method is proposed. After a general analysis on gray value distribution and contrast changes of fundus images, the standardizing fundus images are obtained by using the matched filtering technique to overcome the interference of background and noise. Then, a threshold can be automatically selected to achieve the effective segmentation of blood vessels in the fundus images by estimating the proportion of the background pixels. A lot of tests show that the good performance is achieved in the public fundus images database. The experiment shows that the proposed method based on matched filtering and automatic threshold has strong practicability and high accuracy. It is useful for computer aided diagnosis of ocular diseases．
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Sun Li, Zhang Xiaofeng, Zhang Lifeng, Zhou Wenju
2017,31(1):106-111, DOI: 10.13382/j.jemi.2017.01.015
Abstract:
Velocity smoothing is one problem which is proposed in high speed machining and coal mine safety production, the aim of which is to improve machining accuracy and equipment life. Aiming at this problem, this paper proposes a stage wise model and deduces the closed form expression solution for each stage based on the relationship of acceleration and velocity, and then deduces the general solutions of cubic equation in detail for the model. Finally, the solutions are applied to the velocity smoothing. The proposed schema shows the advantages of easy to program and smoothing in transition curve when being applied for velocity smoothing in coalmine. The result demonstrates that the proposed method adapts the high speed scenarios well and has used in other several projects.
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2017,31(1):83-91, DOI: 10.13382/j.jemi.2017.01.012
Abstract:
A fuzzy perception model is proposed to the directional sensor nodes based on the sensing characteristics of the nodes, and also the fuzzy data fusion rule is built to reduce the network uncertain region. Aiming at the problem of directional sensor network strong barrier coverage, a directional sensor network strong barrier coverage enhancement algorithm based on particle swarm optimization is proposed. The convergence rate of the algorithm is improved through the n dimensional problem be transformed into one dimensional problem. The simulation results show that, under random deployment, the perception direction of sensor nodes can be adjusted continuously. Compared with the existing algorithms, the proposed algorithm can effectively form strong barrier coverage to the target area, has a faster convergence rate, and prolongs the network lifetime.
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Zhang Gang, Bi Lujie, Jiang Zhongjun
2023,37(1):177-190, DOI: 10.13382/j.issn.1000-7105.2023.01.020
Abstract:
For the difficulties of classical bi-stable stochastic resonance (CBSR) system in amplification and detection of weak signals, an underdamped exponential tri-stable stochastic resonance (UETSR) system in a Levy noise background is proposed. The UETSR system is constructed by combining the bi-stable potential and exponential potential function, and using the property that non-Gaussian noise can effectively improve the signal-to-noise ratio. Firstly, the steady-state probability density function of the system is derived. The mean signal-to-noise ratio improvement (MSNRI) is adopted as an index to measure the stochastic resonance performance. The quantum particle swarm algorithm is used on parameters optimization. The effect of each parameter of the system on the output variation pattern of the UETSR system with different parameters α and β of Levy noise is investigated. Finally, the UETSR, CBSR and classical tri-stable stochastic resonance system (CTSR) are applied to the bearing fault diagnosis, and the amplitudes at the inner and outer ring fault frequencies after the system output increased by 197. 58, 1. 153, 18. 81 and 238. 87, 26. 63, 39. 72, respectively, compared to the input signal. The spectral level ratios of the highest peak to the second highest peak were 5. 44, 4. 03, 3. 85 and 5. 10, 3. 79, 5. 05. The experimental results show that SR phenomena can be induced by different system parameters, and the UETSR system outperformed the CBSR system and the CTSR system. The above conclusions prove that the system has excellent performance and strong practical significance
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Wan Yong, Zhang Xiaobin, Ni Weining, Zhang Wei, Sun Weifeng, Dai Yongshou
2017,31(1):99-105, DOI: DOI: 10.13382/j.jemi.2017.01.014
Abstract:
The key point of azimuthal propagation resistivity logging while drilling focuses on the structural design of the coil system. And the detection performance of azimuthal propagation resistivity LWD is mainly affected by the transmission frequency of electromagnetic wave signal, the transmitter receiver spacing, the receiver interval, the coil’s angle and the formation resistivity. The testing method of measurements is determined with different inspection requirements of azimuthal propagation resistivity LWD. According to the various constraints of the coil system under the condition of different testing method, the structure of the coil system for azimuthal propagation resistivity LWD is designed by experimental simulation method. The results provide reference for the structural design of the coil system for azimuthal propagation resistivity LWD.
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Zhou Na, Lu Changhua, Xu Tingjia, Jiang Weiwei, Du Yun
2017,31(1):139-143, DOI: 10.13382/j.jemi.2017.01.020
Abstract:
In order to improve the multi target tracking robustness and enhance the difference between the targets, this paper uses an energy minimization method for multi target tracking. Different to the existing algorithm, the algorithm focuses on the representation of the complex problem in multi target tracking as energy function model, which includes a better target segmentation strategy (similarity model). By assigns every possible solutions a cost (the “energy”), the algorithm transforms the multiple target tracking problem into an energy minimization problem. In the energy minimization optimization method, the algorithm uses the conjugate gradient algorithm and a series of jump moves to find the minimum energy value. The experimental results of open data demonstrate the effectiveness. And the quantitative analysis results show that this algorithm can improve the difference between targets or between target and background so as to obtain better robust performance compared with other algorithms.
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Xia Fei, Luo Zhijiang, Zhang Hao, Peng Daogang, Zhang Qian, Tang Yiwen
2017,31(1):118-124, DOI: 10.13382/j.jemi.2017.01.017
Abstract:
Aiming at the shortcoming of the low accuracy of transformer fault diagnosis, the PSO SOM LVQ（particle swarm optimization,self organizing maps,learning vector quantization） mixed neural network algorithm is presented in this paper. Firstly, the weight of SOM neural network is optimized by the method of PSO algorithm to obtain the more effective topology. Based on that, LVQ neural network is combined to cover the shortage of unsupervised learning SOM neural network. The mixed neural network algorithm combined with PSO, SOM and LVQ can improve the accuracy and reduce the error of transformer fault diagnosis. Through simulation, the three algorithms of SOM, PSO SOM and PSO SOM LVQ are compared. The comparison result show that the PSO SOM LVQ mixed neural network algorithm has the highest accuracy, and the fault diagnosis accuracy rate is 100%. Thus it can be seen, the PSO SOM LVQ mixed neural network algorithm can enhance the performance of transformer fault diagnosis effectively.
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Chen Zhenhai, Yu Zongguang, Wei Jinghe, Su Xiaobo, Wan Shuqin
2017,31(1):132-138, DOI: 10.13382/j.jemi.2017.01.019
Abstract:
A low power, small die size 14 bit 125 MSPS pipelined ADC is presented. Switched capacitor pipelined ADC architecture is chosen for the 14 bit ADC. In order to achieve low power and compact die size, the sample and hold amplifier is removed, the 4.5 bit sub stage circuit is used in the first pipelined stage. The capacitor down scaling technique is introduced, and the current mode serial transmitter is used. A modified miller compensation technique is used in the operation amplifiers in the pipelined sub stage circuits, which offers a large bandwidth without additional current consumption. A 1.75 Gbps transmitter is introduced to drive the digital output code, which only needs 2 output pins. The ADC is fabricated in 0.18 μm 1.8 V 1P5M CMOS technology. The test results show that the 14 bit 125 MSPS ADC achieves the SNR of 72.5 dBFS and SFDR of 83.1 dB, with 10.1 MHz input at full sampling speed, while consumes the power consumption of 241 mW and occupies an area of 1.3 mm×4 mm.
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Cao Shasha, Wu Yongzhong, Cheng Wenjuan
2017,31(1):125-131, DOI: 10.13382/j.jemi.2017.01.018
Abstract:
Musical simulation based on spectrum model is the use of acoustic theory that can achieve musical instrument’s sounds by sum of products of a series of basic functions and time varying amplitude. A new digital piano sound simulation technique is proposed by analyzing piano string vibration and damping characteristics and investigating the resonance effect of resonance box. The simulation model consists of two parts: the excitation system and the resonance system. Based on the vibration equation of the strings, the envelope modification of time domain is carried out to simulate the natural attenuation of the strings, which can make music harmonious between the notes. Then, the filter group is modeled by spectrum envelope in frequency domain to achieve the simulation of resonance system. This new method can more effectively carving voice, has better performance timbre at the same time, therefore, it makes the sound more harmonious.
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Identification method of Dongba pictograph based on topological characteristic and projection method
Xu Xiaoli, Jiang Zhanglei, Wu Guoxin, Wang Hongjun, Wang Ning
2017,31(1):150-154, DOI: 10.13382/j.jemi.2017.01.022
Abstract:
Dongba pictograph has been known as "the only living pictograph in the world".In the aspects of image recognition, content interpretation,the current English and Chinese character recognition system often can not be applied to Dongba pictograph.Concerning the difficulties in the identification of Dongba pictograph, a new character recognition is proposed. Topological features processing and projection methodcompose thefeature extraction method,then, the character recognition method based on template matching is adopted.It is showed that the feature extraction method based on the intrinsic characteristic of the pictograph,and the Dongba character recognition method based on template matching,has high accuracy through the experiment.
