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Liu Yameng, Zhao Youquan, Sun Zhentao, Chen Chen
2024,38(5):1-9,
Abstract:
Circular ripples are surface defects in the manufacturing process of contact lenses, resulting from uneven distribution of hydrogel materials, causing a concentric contraction along the edge of the lens. These defects are challenging to detect in projection inspections, leading to poor product quality. Detecting circular ripple defects poses a technical challenge in the production of contact lenses. In this study, a circular ring illumination imaging system was constructed based on the characteristics of this defect. An image model database of circular ripple defects was collected, and a lightweight detection algorithm for contact lens circular ripple defects based on an improved RT-DETR was introduced. Initially, the original ResNet18 backbone of RT-DETR was modified by replacing the BasicBlock with a lightweight FasterNetBlock. Subsequently, the SimAM three-channel attention mechanism was integrated into the Neck part of RT-DETR to enhance the model’s accuracy. Finally, the GIoU loss function was replaced with the MPDIoU loss function to accelerate convergence and improve detection accuracy. Experimental results demonstrate that the improved RT-DETR algorithm achieved a mAP@0.5 of 94% on the contact lens circular ripple database, a 3.1% improvement over the original RT-DETR algorithm. Params and FLOPs were reduced by 15.6% and 13%, respectively, compared to the original algorithm. This algorithm effectively reduces computational complexity, enhancing the mean average precision of contact lens circular ripple defect detection. It holds promise for overcoming technical challenges in online detection of circular ripple defects in contact lenses.
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Tang Zhi, Bo Lin, Bai Hao, Wu Guo, Wang Zhangxu
2024,38(5):10-18,
Abstract:
The aerospace engine test bench is a key equipment for verifying engine reliability, and its health status assessment is of great significance for ensuring the safe operation of the engine. The gas circuit system of the engine test bench has the characteristics of complex and variable fault modes, strong correlation between multi-point and multimodal sensing information, etc. Moreover, there are issues such as uneven distribution of collected health status samples, high signal noise, human resource waste caused by manual monitoring of the operating status of the gas pipeline system, and high false alarm rates. To this end, a health assessment model for test benches based on adaptive reconstruction of phase space and support for high-order tensor machines is proposed. This method first involves designing stability criterion for E1(m) to achieve adaptive phase space reconstruction of the gas path system. Secondly, tensors are used to characterize the multi-point and multimodal data of the pneumatic system. Then, a high-order tensor machine is used to mine the multi-source sensor correlation information and fault modes in tensor samples, achieving a health status assessment of the test bench pneumatic system. Finally, the proposed method is compared with the support vector machine, decision tree and plain Bayesian algorithms based on the actual test data from the engine test bench of a China National Aviation Corporation (CNAC). The results show that the proposed method has a good evaluation capability in a weak data environment, with an overall evaluation accuracy of 89.7%, and the accuracy drop is kept within 8% in an extremely weak data environment.
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Li Peifeng, Liu Li, Wang Yu, Liu Xin, Bai Qing, Jin Baoquan
2024,38(5):19-28,
Abstract:
Phase-sensitive optical time domain reflectometer (Φ-OTDR) usually uses coherent detection to achieve long-distance, distributed and high-sensitivity vibration detection. To accurately obtain the position and phase information of the vibration signal, the quadrature demodulation algorithm is an important technology widely used at present, but the algorithm has the limitation of time-consuming. In order to solve this problem, a fast demodulation scheme of Rayleigh scattering signal based on field programmable gate array (FPGA) is proposed. The pipeline structure is used to realize the synchronization of sensor data acquisition and data demodulation. Two orthogonal signals are obtained by digital quadrature mixing technology. The finite impulse response low-pass filter is used to remove the high-frequency component, and the coordinate rotation digital algorithm (CORDIC) vector mode is used to realize the hardware demodulation of the vibration phase, which can improve the overall real-time performance of the coherent detection Φ-OTDR system. The experimental results show that the scheme can successfully realize the positioning and phase reduction of the vibration signal under the condition of a detection distance of 40 kilometer. When the detection distance unchanged and the pieces of data acquisition increased to 4 000, the FPGA demodulation scheme only takes 1.60 seconds, which is 145.61 seconds shorter than the traditional host computer CPU demodulation scheme, thus providing a reference for the real-time demodulation of Φ-OTDR vibration sensing data.
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Ma Chao, Zheng Xinhui, Wang Shaohong, Xu Xiaoli
2024,38(5):29-37,
Abstract:
The operating conditions of planetary transmission are mostly non-stationary operating conditions. During the operation, the gear meshing vibration signals are coupled with each other, which leads to the aliasing of test signals, and the difficulty of hidden fault diagnosis increases. At the same time, when applying complex neural network models for fault diagnosis and prediction, most of them will be limited by the hardware of industrial field edge computing equipment. Aiming at the related problems, an intelligent recognition model based on smooth and pseudo Wigner-Vile distribution (SPWVD) and knowledge distillation is proposed to reduce the parameters of the network model while ensuring the accuracy of planetary transmission fault diagnosis. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the multi-component vibration signal, then the single-component signal is selected for SPWVD calculation and linearly superimposed to obtain a two-dimensional time-frequency diagram as input. The ResNet101 is used as the teacher model to guide the student model MobileNet for training. The complex teacher model imparts the knowledge in the data to the student model, which improves the accuracy of the student model.The method is compared with similar methods. The results show that the storage cost of the model is reduced to 24.55% of the teacher model at the expense of 2.43% accuracy, which is 9.61% higher than that of MobileNet without knowledge distillation. This research method provides an effective and feasible solution to improve the practical application of deep learning model in engineering and reduce the deployment cost of edge computing equipment.
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Wang Nan, Wei Yujie, Zhang Nan, Wang Dandan, Wang Mingwu, Zhang Changming
2024,38(5):38-46,
Abstract:
Existing test water lubrication bearing characteristics of the important characterisation parameter water film pressure of the many methods, because the sensor distance from the real measurement point is far away, or trauma to the shaft system is large and other reasons, the accurate water film pressure empirical data is difficult to obtain, restricting the bearings of further research and development. In response to these challenges, a new monitoring method is proposed in which thin film sensors are embedded in bearing shingles, while pressure data are transmitted via wireless sensing. Firstly, the physical model of axial tile grooved bearing is established, and the location, structure and number of grooves are determined by finite element analysis of axial tile deformation near the grooves; A physical model of the bearing fluid domain and solid domain is established to simulate and analyse the water film pressure distribution; Then, a thin film sensor calibration method is proposed to calibrate it accurately; Finally, a multi-operating condition bearing water film pressure test experiment is carried out, it is compared and analysed with the simulation results and existing methods. The results of the study show that it is feasible to embed a thin film sensor in the groove of the bearing shaft tile and transmit the data wirelessly, and the measured data of water film pressure deviates less than 10% from the simulation results, which is more accurate than the measured data of existing methods. The water film pressure decreases along the axial direction, and there exists a part of lubrication film inside the bearing in a mixed lubrication state.
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Wei Xinyuan, Zhou Jinghuan, Qian Muyun, Li Dan, Huang San′ao
2024,38(5):47-55,
Abstract:
Ultrasonic detection is a common method of steel defect detection. The classification model established by machine learning algorithm can realize effective defect identification. Neural network is the most commonly used algorithm at present, but it has the problem of complex model structure and large amount of training data. In this paper, an ultrasonic defect recognition method based on random forest is proposed, which can realize intelligent and accurate identification of defect types to solve the problems of complex model structure and large training data requirements. Firstly, ultrasonic detection experiments were carried out for defects of different shapes, sizes and depths in the specimen. Based on the experimental data, an ultrasonic defect recognition model was established using random forest algorithm. Then, the defect recognition effect of the model is analyzed, and compared with support vector machine, K-nearest neighbor classification algorithm, AdaBoosting algorithm and convolutional neural network. Then the defect identification verification experiment is carried out with the verification specimen to further verify the validity of the established defect identification model. The results show that the proposed method has the highest accuracy compared with other algorithms, and the accuracy of defect classification reaches 94.6% in the verification experiment.
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Shan Zebiao, Guo Jinghao, Liu Xiaosong, Sun Yuqi, Bai Yu
2024,38(5):56-63,
Abstract:
Addressing the challenges of low measurement accuracy and restricted applicability range in existing passive sound source localization algorithms, this paper proposes a passive sound source localization estimation method based on an optimal quadruple-array. This method constructs an optimal quadruple-array structure to enable multi-element point sharing, aiming to achieve fusion localization estimation of the target sound source with a reduced total number of elements, thereby enhancing localization accuracy. Spatial target localization equations are derived from the array model, transforming the problem of solving position coordinates into that of determining time delay differences between array elements. Subsequently, a second-order fractional low-order covariance algorithm is employed to resolve the corresponding time delay differences between array elements in an impulse noise environment. After obtaining the self-fractional low-order covariance of array signals and the mutual-fractional low-order covariance between two array elements, the mutual-fractional low-order covariance of both is recalculated to further mitigate the impact of impulse noise and improve time delay estimation accuracy. Finally, the obtained time delay estimation information is incorporated back into the localization equation set to achieve localization estimation of spatial sound sources. The feasibility of the proposed method and the superiority of the array structure are validated through numerical simulations and field experiments. In the field experiments, the estimation error of sound source localization is only 0.085 1 meters, demonstrating the method’s capability to achieve high accuracy in sound source localization under impulse noise environments. This work extends the application scenarios of passive sound source localization algorithms and holds practical application value.
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Zhao Kaihui, Qiao Mengjie, Lyu Yuying, You Xin, Zhang Changfan, Zheng Jian
2024,38(5):64-74,
Abstract:
To address the decline in drive system efficacy due to uncertainties in the model, variations in parameters, and interruptions from external sources affecting permanent magnet synchronous motors (PMSMs), a novel control strategy is proposed. First, to reduce the reliance on the system’s mathematical model, a new super-twisting algorithm is formulated for the speed loop of the PMSM. Secondly, based on the new super-twisting model of the speed loop, a novel model-free super-twisting fast integral terminal sliding mode controller (MFSTFITSMC) is designed by integrating a new type of integral terminal sliding surface and an improved super-twisting control law, achieving precise control of the motor speed. Furthermore, a non-singular fast terminal sliding surface and a dual-power approaching law are used to devise an improved extended nonsingular terminal sliding mode disturbance observer (IENTSMDO). This observer accurately detects and provides feedforward compensation for unknown disturbances, effectively suppressing parameter perturbations and external disturbances, thus enhancing the system’s robustness and improving both dynamic and steady-state performances. Finally, through simulation and experimental comparison with traditional control methods, the proposed algorithm has been verified to improve speed overshoot resistance by 0.412 5% and enhance the torque response speed by 0.013 s. The results indicate that the proposed method possesses strong robustness and good interference rejection capabilities in the presence of unknown disturbances.
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Zhou Xianchun, Shi Zhenting, Wang Ziwei, Li Ting, Zhang Ying
2024,38(5):75-89,
Abstract:
Currently, most image denoising models based on convolutional neural networks cannot fully utilize the redundancy of image data, which limits the expressive power of the models. Moreover, edge information is often used as a priori knowledge for effective denoising, while texture information is usually ignored. To address these issues, a new image denoising network is proposed, which firstly uses the attentional similarity module to extract global similarity features of the image, and smooths and suppresses the noise in the attentional similarity module through average pooling to further improve the network performance; secondly, the dilated residual module is used to extract both local and global features of the image; finally, a global residual learning is utilized to enhance the denoising performance from shallow to deep layers. In addition, a texture extraction network is designed to extract local binary patterns from noisy images to obtain texture information, which can be utilized as a priori knowledge to preserve the details in the evolved images during the denoising process. The experimental results show that compared with some advanced denoising networks, the newly proposed denoising network has a great improvement in image vision, higher efficiency and peak signal-to-noise ratio by about 2 dB, and structural similarity by about 3%, which is more conducive to practical applications.
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2024,38(5):90-97,
Abstract:
In the existing algorithm for fault diagnosis in analog circuits, artificial intelligence-based fault diagnosis algorithms require a large amount of training data and long training time, making it difficult to achieve parameter identification. Traditional circuit analysis methods require multiple test points and involve complex calculations. Based on this, a fault diagnosis algorithm for analog circuits based on optimized matrix perturbation analysis is proposed. Firstly, the Laplace operator is used to convolve the output response matrix of the tested circuit, thereby enhancing the perturbation pattern between matrix elements and circuit component parameters. Secondly, the trace and spectral radius of the matrix are selected as fault characteristics, and a matrix model is established using this perturbation pattern. Then, an improved diagnostic algorithm is used to verify examples in Sallen_Key bandpass filter circuits and leapfrog low-pass filter circuits. The results show that with only one test point, the proposed method can achieve parameter identification of faulty components. The fault diagnosis rate reaches 100%, with parameter identification error controlled within 1%, and computation time controlled at millisecond level. Therefore, this method is easy to implement for online testing and suitable for situations requiring high accuracy in fault localization and precise parameter identification.
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He Lifang, Xu Jiaqi, Huang Xiaoxiao
2024,38(5):98-111,
Abstract:
In order to solve the problems of output saturation and signal amplification difference of the traditional two-dimensional tri-stable stochastic resonance system driven by dual input signals (DTDTSR), a novel system, coupled piecewise symmetric tri-stable stochastic resonance system (coupled piecewise symmetric tri-stable stochastic resonance system) driven by dual-input signals, is ingeniously proposed. A novel system is proposed: coupled piecewise symmetric tri-stable stochastic resonance system driven by dual-input signals (DCPSTSR). Firstly, the problem of output saturation of the system is studied in depth, which provides a key theoretical foundation for the optimization of the system performance. Secondly, the output spectral amplification (SA) function of the system is derived within the framework of the adiabatic approximation theory. The influence of system parameters on it is analyzed in detail, which provides theoretical support for deeper understanding. Further, a comprehensive comparison of the DCPSTSR, coupled piecewise symmetric tri-stable stochastic resonance system (CPSTSR) and DTDTSR systems is carried out through numerical simulations, and the results clearly indicate that the DCPSTSR system is significantly superior to the other systems in terms of output spectral amplification function. Finally, the system parameters are precisely optimized by genetic algorithm and successfully applied to bearing fault detection. The experimental results verify the excellent performance of the DCPSTSR system and provide strong theoretical support and feasibility verification for future theoretical research and engineering applications. This design and its successful application in bearing fault detection provide a new direction and example for further research and practical application in the field of resonance systems, which has important scientific and engineering value.
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2024,38(5):112-118,
Abstract:
The current pedestrian detection algorithm is a research hotspot in the field of driverless driving, but the pedestrian occlusion problem has not been well solved due to factors such as relatively small sample size, diverse occlusion situations, and reduced visual features. Aiming at the problem of missed detection caused by pedestrians blocking each other or pedestrians being blocked by other objects, a pedestrian detection method based on inter-frame directional gradient histogram feature correlation is proposed. First, a tracking method is added based on the YOLOv7 baseline network model to discover missed pedestrians and estimate their location information; the nearest local image containing missed pedestrians is used as the new information, using directional gradient histogram features and support vectors, a machine-based method is used to detect pedestrians at the estimated position of the missed target to improve the missed detection phenomenon caused by partial occlusion. Experimental results compared with the baseline network, the precision (P) value of this method increased by 6.25%, and the average precision (AP) of occluded pedestrians increased from 26.67% to 53.42%. Experiments show that the pedestrian detection method based on inter-frame directional gradient histogram feature correlation can improve pedestrian detection accuracy, has low computational complexity, does not significantly increase the computational overhead of the original method, and has certain application value.
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Wang Zhen, Ye Wenhua, Chen Yuhao, Liang Ruijun
2024,38(5):119-129,
Abstract:
In response to the complex imaging environment, irregular deformation of batteries, and uneven diffuse reflection of metal surfaces encountered during the automated disassembly process of retired cylindrical power lithium batteries, existing visual recognition methods are unable to accurately extract contour and pose information. We propose an accurate contour extraction method based on the Fr-chet distance similarity function and a pose detection method based on rectangles and edge morphology features. By establishing a Lambert diffuse reflection model for cylindrical lithium batteries and using morphological operation methods to obtain the rough localization contour of lithium batteries, as well as based on the similarity function defined by the Fr-chet distance, the contour is accurately extracted by classifying each pixel band in the rough localization image; Subsequently, utilizing the positive and negative terminal features of cylindrical lithium batteries, feature contours of the positive and negative terminal ROI regions are extracted employing an adaptive threshold segmentation algorithm. Finally, by comparing the rectangular values of the two end regions, the pose information of the lithium battery can be calculated. The experimental results show that in the Self-built retired cylindrical lithium battery image dataset that includes deformation, corrosion rust spots, and uneven lighting conditions, the proposed method has high accuracy in identifying lithium batteries of different models and poses. The diameter length detection error is less than 3%, and the pose detection accuracy is higher than 94%, which can meet the actual needs of automated disassembly and detection.
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Chen Yuanmei, Wang Fengsui, Wang Luyao
2024,38(5):130-138,
Abstract:
Unsupervised person re-identification aims to identify the same person from non-overlapping cameras under unsupervised settings. Aiming at the problem that the existing unsupervised person re-identification network cannot fully extract pedestrian features and the difference between cameras leads to pedestrian retrieval errors, we propose an unsupervised person re-identification of adversarial disentangling learning guided by refined features. A feature refinement information fusion module is designed and embedded into different layers of ResNet50 network to enhance the ability of the network to extract key information. A disentangled feature learning method is designed to minimize the mutual information between pedestrian features and camera features, and reduce the negative impact of camera differences on the network. At the same time, the adversarial disentangling loss function is designed for unsupervised joint learning. Using the Market-1501 and DukeMTMC-reID public datasets, we tested the proposed method. The mean average precision increased by 4.6% and 3.1% respectively. Compared with the baseline algorithm, it has strong robustness and meets the needs of pedestrian recognition in unsupervised background.
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Jiang Bing, Li Xiang, Chao Yifan, Yu Ziyu, Tao Kai
2024,38(5):139-147,
Abstract:
To address the problems posed by redundant features in transformer fault recognition and the low accuracy of traditional methods, a transformer fault recognition method leveraging kernel principal component analysis (KPCA) in conjunction with chaotic sparrow search algorithm (CGSSA) is introduced. Initially, KPCA is employed to preprocess the transformer fault data, aiming to mitigate the correlations among features. Subsequently, CGSSA is improved by incorporating the improved Tent map and Gaussian mutation to increase the search accuracy and convergence speed of the algorithm. Comparing the results involving CGSSA, SSA, GWO and WOA. Utilizing the data extracted through the KPCA as the model input, CGSSA is then used to select the kernel function parameters and regularization coefficient of KELM, thereby establishing the KPCA-CGSSA-KELM transformer fault recognition model. The experimental results demonstrate that, with the identical input data, CGSSA has the best results in terms of convergence speed and optimization accuracy. In addition, the proposed method shows the fault recognition accuracy of 95.7%, which is 18.6%, 10%, and 15.7% higher than WOA-KELM, GWO-KELM, and SSA-KELM, respectively. These findings suggest that the proposed method effectively manages the impact of redundant features and enhances the precision of transformer fault recognition, thus verifying the validity and feasibility of the proposed method for transformer fault recognition under the feature redundancy.
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Li Guoyan, Tian Mingda, Dong Chunhua, Hao Zhipeng
2024,38(5):148-157,
Abstract:
To address the limitation of standard attention mechanisms that can only generate coarse-grained attention regions, failing to capture the geographical relationships between remote sensing objects and underutilize the semantic content of remote sensing images, a structured image description network named GRSRC (geo-object relational segmentation for remote sensing image captioning) is proposed. Firstly, considering the highly structured nature of remote sensing image features, a feature extraction method based on structured semantic segmentation of remote sensing images is introduced, enhancing the encoder’s feature extraction capability for more accurate representation. Simultaneously, an attention mechanism is incorporated to weight the segmented regions, enabling the model to focus more on crucial semantic information. Secondly, taking advantage of the well-defined spatial relationships among objects in remote sensing images, geographical spatial relations are integrated into the attention mechanism, ensuring more accurate and spatially consistent descriptions. Finally, experimental evaluations are conducted on three publicly available remote sensing datasets, RSICD, UCM, and Sydney. On the UCM dataset, BLEU-1 achieved 84.06, METEOR reached 44.35, and ROUGE_L attained 77.01, demonstrating improvements of 2.32%, 1.15%, and 1.88%, respectively, compared to classical models. The experimental results indicate that the model can better leverage the semantic content of remote sensing images, demonstrating its superior performance in remote sensing image captioning tasks.
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2024,38(5):158-168,
Abstract:
A mathematical method is proposed to evaluate the delay performance of RF-Mesh-Networks that can realize data classification transmission and ensure real-time data requirements, for deficiency in evaluating and analyzing the delay performance of different types of transmission data when the large-scale wireless advanced measurement instruments are applied in smart distribution grid. Based on the analysis of the WMNs architecture of the smart distribution grid, the functional relationship between the initiation stages of the two consecutive time slots is established using Markov chain modulation techniques. In order to avoid the difficulty of solving high-order differential equations during the process of obtaining steady-state solutions, the solution method based on error iteration to obtain steady-state operating points is proposed, and the detailed solution process is also provided. On the basis of obtaining the steady-state working point, the analytical formula to evaluate the average delay performance of real-time data and non-real-time data for uplink and downlink transmission. To verify the effectiveness of the proposed delay performance evaluation method for WMNs applied in smart distribution grid, the delay performance of real-time and non-real-time data is simulated and tested，where they are set up three different transmission rates. The experimental simulation and test results show that the proposed method can achieve performance evaluation and analysis of transmission delay for different types of communication data in intelligent distribution networks, and can improve the transmission performance of WMNs.
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Chen Jian, Jiang Tao, Chen Pin
2024,38(5):169-177,
Abstract:
When the preferred measurement signal for equipment condition monitoring in industrial field is acoustic signal for various reasons, it is especially necessary to propose an equipment condition monitoring method based on acoustic signal. In this paper, a certain type of centrifugal pump is taken as the basis object, and the Mel-scale frequency cepstral coefficients（MFCC）are extracted from the acoustic signals collected in the field as the initial features of the signals, then the dispersion entropy（DE）values of these MFCC initial features are calculated, and the matrix is downscaled by principal component analysis（PCA）, so as to construct the feature matrix. The penalty coefficients and kernel function parameters of the support vector machine（SVM）are optimized by using the bat algorithm（BA）to carry out diagnosis of various fault conditions of centrifugal pumps and compared with various diagnostic methods. The experimental results show that the model optimized by BA improves the diagnostic accuracy by 21.7%; on the basis of this model, the deep mining of the signals extracted by MFCC using DE improves the diagnostic accuracy of the model by 2.05%.
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Xu Hao, Bao Jun, Huang Guoyong, Deng Weiquan, Zhao Chengjun
2024,38(5):178-187,
Abstract:
With the increase of aircraft service time and the extreme service environment, fatigue cracks and other defects may occur in the multi-layer metal riveting structure of aircraft. It is of great significance for damage assessment and maintenance to find defects in time and obtain information such as defect depth and direction. However, due to the concealment of defects caused by multi-layer structure, the detection signal characteristics of conventional eddy current probes are indistinct, and conventional eddy current probes are not sensitive to defects in certain directions, making it difficult to determine the direction of fatigue cracks. To address these problems, a cross-runway-type differential eddy current probe is designed, which is mainly composed of a cross-runway-type excitation coil and two sets of differential detection coils. The feasibility of the new eddy current probe is investigated by establishing a three-dimensional finite element model for defect detection of aircraft multi-layer metal riveting structures, including the optimization of the structure of the probe, and simulations are conducted to analyze the different directions of defects, the buried depths and the lift-off heights, respectively. The results indicate that the new probe can effectively detect deep defects with a buried depth of 6 mm and dimensions of 10 mm×1 mm×1 mm, and it can obtain the direction information of the defects. Compared to traditional probes, designed probes have advantages such as no missing defects in all directions, resistance to lift off effects, and high resolution. The research results can provide some reference for the design of eddy current probes for aircraft multi-layer metal riveting structure detection.
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Niu Jing, Shen Chuanyan, Zhang Lipeng, Li Qijun, Liu Shifeng
2024,38(5):188-200,
Abstract:
Large scale plant protection machinery in non-standard orchards in mountainous areas has poor accessibility, and small wheeled plant protection robots have broad application prospects. A path planning algorithm for wheeled plant protection robots based on improved ACO-DWA algorithm is proposed to solve the problems of visual information misjudgment caused by closed orchard branches and leaves, as well as delayed obstacle avoidance caused by complex working terrain. Firstly, the orchard environment information is obtained through LiDAR, and the voxel grid method is applied to simplify the point cloud density. The grid method is used to segment the ground point cloud, and the K-means algorithm is used to extract the robot’s inter row passage area. Combined with the kinematic model and job specification constraints of the plant protection robot, a series of candidate trajectory sets are generated using the model based prediction algorithm (SBMPO). Then, using the improved ACO-DWA algorithm, the robot’s travel cost is integrated into the objective function of the search node, and path planning is carried out online based on the environmental map. Finally, simulation validation and real-world deployment experiments were conducted using MATLAB R2021 simulation platform and robot ROS operating system, respectively. The experimental results show that this method can significantly improve the traffic capacity of robots in complex orchard scenes, and the path planning effect and operational efficiency are significantly improved.
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Gao Gang, Wei Lisheng, Zhu Shengbo
2024,38(5):201-209,
Abstract:
Aiming at the problem of irregular and random distribution of defects on the surface of texture images, such as scratches and cracks, which leads to low accuracy of defect detection, a self-supervised defect detection method based on the bi-radial fusion of positive and negative sample difference features is proposed. Firstly, Otsu threshold segmentation is used to extract image foreground information, and Perlin noise is superimposed on the data-enhanced positive samples or the texture images, from the DTD dataset, to simulate defects on the positive sample images and synthesize the negative samples. Then, the mean-square error is calculated for feature matching using the intermediate features output from the encoder, while the coordinate attention (CA) and path aggregation network (PANet) are combined to enhance the information fusion of the matched features. Finally, the fused features are input into the decoder together with the low-level and high-level features output from the encoder, and the weights of Focal, L1, and Dice loss functions are optimized and adjusted to realize the prediction of the defective masks more accurately. Experiments show that the average image level and pixel-level AUROC of the proposed model on the texture category of the MVTec AD dataset reaches 0.995 and 0.968, respectively, which improves the classification and segmentation accuracies compared with the other defect detection models, demonstrating the effectiveness of the proposed method in texture defect detection.
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Li Zhongbing, Liu Yajie, Liang Haibo, Ni Pengbo, Yan Bi
2024,38(5):210-218,
Abstract:
The effective monitoring of hydrocarbon gas content is an important aspect of safety assurance in oil and gas exploration and production processes. Infrared spectroscopy, as a safe and efficient detection method, has attracted the attention of on-site engineers. However, it mainly uses offline models for measurement, which cannot cope with the complex working conditions and various nonlinear influencing factors on site, making it difficult for this non updated model to maintain high prediction accuracy. A weighted kernel partial least squares method based on fusion of similarity measurement criteria in just-in-time learning for quantitative analysis of alkane gases is proposed in this paper. Firstly, a similarity criterion based on fusion of multiple similarity measurement criteria is designed to effectively select historical samples for online modeling. Secondly, nonlinear kernel functions are introduced into local PLS models to effectively extract nonlinear features and compensate for the nonlinear processing ability of linear partial least squares models. The experimental results on the multi-component mixed gas infrared spectral data have verified the effectiveness of this method, with a goodness of R2 of 0.994 1. Compared with that of the PLS model, the RMSE and MRE of the proposed model have improved by 43.6% and 85.8%, respectively. It can be effectively used for online updating and high-precision prediction of infrared spectral quantitative analysis models for hydrocarbon gas.
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Liu Xiaoqian, Cui Huanyong, Liu Haining, Fu Yu, Zeng Wensheng, Li Fajia
2024,38(5):219-228,
Abstract:
The process of proton exchange membrane fuel cells (PEMFC) involves strong coupling of multiple physical fields, components, and factors, inevitably leading to prolonged performance degradation and local performance fluctuations during operation. However, effectively identifying key features from the multitude of parameters under the multiple couplings and capturing the overall performance degradation trend becomes exceptionally challenging. In response to these issues, a PEMFC degradation prediction model based on XGBoost and Self-Atten-LSTM is developed. First, a wavelet threshold denoising method is employed to remove noise interference from the original PEMFC data. Then, the XGBoost algorithm is used to select the main features significantly affecting PEMFC performance from the numerous parameters, achieving precise feature selection. Finally, the introduction of the self-attention mechanism in LSTM addresses its limitations in global modeling and complex interaction among multi-dimensional vectors when dealing with long sequences. Through adaptive weighting, it more effectively utilizes PEMFC degradation information. Compared to traditional LSTM, Bi-LSTM, and GRU models, the developed model can more accurately predict fuel cell degradation under both steady-state and dynamic conditions. The model exhibits a reduction in the average mean absolute error by 56.34% to 77.04%, with a predictive accuracy of up to 99.09%. This approach can find broad applications in developing vehicle operation and maintenance strategies and enhancing system reliability.
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2024,38(5):229-237,
Abstract:
The non-contact voltage measurement method is not in direct contact with the metal conductor of the line and can adapt to the voltage monitoring in a variety of application scenarios. This paper designs a system which uses the improved non-contact voltage measurement technology to measure the line voltage and applies the measured voltage waveform to the line fault voltage diagnosis. Based on the topology analysis of the traditional non-contact voltage measurement technology and the improvement of the measurement circuit topology, the voltage on the line can be measured accurately without being affected by the coupling capacitance. Because of the limitation of the current single fault feature extraction method, in order to accurately identify and diagnose the line fault voltage by using the voltage waveform measured by the non-contact voltage measurement technology, in this paper, a fault voltage state identification system based on integrated learning is proposed, and a variety of feature extraction methods are used to extract the voltage waveform features obtained from non-contact voltage measurement. The identification results are used for early warning and processing of line faults. In this paper, aiming at the voltage monitoring system, the measurement accuracy and fault identification test are designed, and the steady-state average error is 0.9%, and the highest fault identification accuracy is 93%, which shows that the voltage monitoring system has high accuracy and fault identification accuracy.
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2024,38(5):238-248,
Abstract:
The improved Harris Hawks optimization algorithm (IHHO) is proposed to solve the problem that analog circuit fault diagnosis is difficult due to multiple fault types, unstable fault states and redundant fault data. IHHO optimized back propagation (BP) neural network to realize fault feature selection and diagnosis of analog circuits. Firstly, the nonlinear adaptive factor, Cauchy variation and stochastic difference perturbation are introduced into the Harris Hawks optimization algorithm to improve the convergence speed and accuracy. Secondly, IHHO is used to select the characteristics of the single fault and the combined fault simulation data of the analog circuit to complete the data preprocessing. Finally, IHHO-BP algorithm is used to train and test the preprocessed fault data to realize the fault diagnosis of analog circuits. The diagnostic results show that the proposed method improves the diagnostic accuracy by 5.5% compared with other algorithms.
Volume 38,2024 Issue 5
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A Gearbox Oil Status Recognition Method Based on PCA Feature Optimization and AdaBoost Ensemble Learning
Abstract:
A gearbox oil state recognition method based on PCA feature optimization and AdaBoost ensemble learning is proposed to address the problems of low accuracy and limited generalization ability in traditional gearbox oil analysis methods. Firstly, the multi parameter oil data is cleaned using box plots and SMOTE interpolation to improve the quality of the oil data; Secondly, PCA is used for oil product feature optimization to obtain a subset of oil product feature optimization that is helpful for identification. While effectively integrating multi parameter information of oil, it can significantly reduce the time complexity of model operation; Then, a basic model for oil state recognition is established using BP neural network, and the GWO wolf pack optimization algorithm is introduced to optimize the model. A weak classifier GWO-BP with optimal initial weights and thresholds is constructed, and an adaptive boosting Adaboost algorithm is adopted to combine multiple weak classifiers GWO-BP, integrating them into a strong classifier with strong robustness. Finally, the experimental data was applied for verification and analysis. The experimental results showed that the proposed method had the best performance, with an average recognition rate of 99.30 ± 0.16% and an average time of 32.77 ± 1.27 seconds. It could quickly, efficiently, and accurately identify the oil state of the gearbox lubricating oil, laying a good foundation for realizing online oil state recognition of gearboxes and having important engineering application value.
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Research on Wi-Fi Gesture Recognition System Based on DSC-SGRU Model
Abstract:
Wi-Fi wireless sensing technology has become a research hotspot in the field of perception, which can realize the intelligent perception of human activities and the surrounding environment. Aiming at the problem that the existing wireless sensing models have a large number of parameters and are difficult to sense in real time in the scenarios with limited computing power such as mobile devices, a lightweight feature extraction module based on Depthwise Separable Convolution (DSC) mixed with Stacked Gate Recurrent Unit (SGRU) hybrid classification and recognition model. The model first captures the spatial features of human gestures using DSC and keeps the temporal nature of the features unchanged, and then learns the spatio-temporal features of the gestures using the SGRU network. The performance of the model is validated using the open source dataset Widar. The results show that the proposed DSC-SGRU model has only 236.891 K parameters with an accuracy of 77.6%. Compared with existing gesture recognition models, DSC-SGRU greatly reduces the number of parameters of the model with approximate performance。
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Dynamic performance compensation of six dimensional acceleration sensor based on NDE-FLNN with zero-pole configuration method
Abstract:
Six dimensional acceleration sensor can be widely used for dynamic holographic detection in the field of humanoid robotics, so as to guarantee the flexibility and stability of robot motion. The existing six dimensional acceleration sensor have the problems of slow response speed, narrow response range and other poor dynamic characteristics, which limit the sensor's sharp and wide-range response to the real-time dynamic position information of the measurement carrier. To address this problem, a study on the compensation of the dynamic performance of the six dimensional acceleration sensor in the time-frequency domain is carried out. The dynamic model of the sensor is established using differential equations, a high-precision dynamic model parameter identification algorithm based on NDE-FLNN is proposed, and the dynamic compensation model is further derived to compensate for the response speed of the sensor and to enhance the dynamic performance of the sensor in the time domain. After that, the dynamic compensator of each channel of the sensor is designed based on the zero-polarity configuration method, which eliminates the original poles and introduces new poles to expand the response range of the sensor and improve the dynamic performance of the sensor in the frequency domain. The experimental results show that compared with the DE-FLNN algorithm, the improved NDE-FLNN algorithm is able to identify the dynamic model parameters of the sensor with higher accuracy, and the adjustment time of each component of the sensor compensation is reduced to about half of the original one, which is within 150ms, and the operating bandwidth is expanded from 22Hz to 84Hz, so the dynamic performance of the sensor in the time-frequency domain has been significantly improved.
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Temperature control method of high and low temperature test chamber based on DBO optimization fuzzy PID
Abstract:
The temperature control system of high and low temperature test chamber has nonlinear and time-delay. The traditional PID control has high overshoot and long adjustment time, but the effect of fuzzy PID control is affected by the formulation of quantization factor and scale factor. In order to improve the response speed and stability of the temperature control system of the test chamber, a method of temperature control of the high and low temperature test chamber based on DBO algorithm was proposed to optimize the fuzzy PID quantization factor and scale factor. Firstly, the transfer function of the heating model of the high and low temperature test chamber was established, and the traditional PID, fuzzy PID, PSO optimized fuzzy PID and DBO optimized fuzzy PID models were built in MATLAB/Simulink for simulation. In addition, the PLC, touch screen and temperature control box were used to build experimental devices to carry out actual temperature control experiments. The simulation results show that the overshoot of DBO optimized fuzzy PID is reduced by 1.02% and the adjustment time is reduced by 106s compared with that of PSO optimized fuzzy PID. The experimental results show that the overshoot of the fuzzy PID optimized by DBO is reduced by 1.1% and the adjustment time is reduced by 120s compared with that of the fuzzy PID optimized by PSO, which verifies that the DBO algorithm has a better effect than PSO in optimizing the quantization factor and scale factor of fuzzy PID. The temperature control effect of the optimal quantization factor and scale factor optimized by DBO at different temperatures is tested, which indicates the feasibility of optimizing the fuzzy PID control scheme by DBO algorithm.
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A Human Drop Action Recognition Method Based on 2D-SPWVD and PCA-SSA-RF for Ultra-wideband Radar*
Abstract:
Aiming at the deficiency of similar motion recognition in the current UWB radar attitude recognition research domain, a motion recognition model integrating time-frequency analysis and random forest (RF) is proposed. A time-frequency analysis method of two-dimensional smoothed pseudo Wigner-Ville distribution (2D-SPWVD) based on smoothed pseudo Wigner-Ville distribution (SPWVD) is proposed to extract the time-frequency features of the preprocessed human motion echo signals. Principal component analysis (PCA) was employed to reduce the dimension of the feature vectors, and the top 30 principal components with a high cumulative contribution rate were selected as new feature vectors to be input into the RF classification model optimized by sparrow search algorithm (SSA) for the identification of five distinct human similar drop actions in the presence of obstacles. The experimental outcomes demonstrate that the pretreatment algorithm can effectively enhance the SNR of the action echo signal, and the PCA-SSA-RF classification model can effectively distinguish five different human fall movements, overcome the particularity of data and the interference of obstacles, with an accuracy rate as high as 96.6%. In the fall detection task within the real-time data stream, the average classification accuracy of the model reaches 93%, and it is profoundly compared with RF, PSO-RF and other diverse classical classification models, featuring high accuracy and short overall time, and possessing both accuracy and classification efficiency. The superiority and effectiveness of the proposed method are verified.
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Photovoltaic Cell Parameter Estimation through Collaborative Optimization of the Bézier Function and the Improved Squirrel Search Algorithm
Abstract:
To address the issues of low accuracy, slow convergence, and difficult data acquisition in intelligent search algorithms for solar cell parameter estimation, we propose a method that combines second-order Bézier curves with an enhanced Squirrel Search Algorithm. First, the optimum Bézier control point is found on the line that passes through the maximum power point and is parallel to the line of the open circuit voltage point and the short circuit current point. This approach leverages the relationship between control point positions and battery fill factor to achieve precise modeling of the I-V characteristic curve without the need for experiments. This method not only accurately describes the output characteristics of HIT cells but also effectively reduces the impact of measurement noise on parameter identification.Secondly, we introduce Sobol sequences, reverse learning, and chaos theory to improve the standard Squirrel Algorithm. Sobol sequences are integrated into the initialization process as quasi-random samples, and a reverse learning strategy enhances popula-tion diversity and search space coverage. Additionally, a tent chaotic mapping perturbs the optimal solution, enhancing the algo-rithm's capability to escape local optima. The improved Squirrel Optimization Algorithm is applied to heterogeneous junction solar cell parameter estimation and compared with other intelligent optimization algorithms. The results showed that the improved algorithm achieved root mean square errors of 0.02825, 0.017458, and 0.02361, respectively, indicating the highest accuracy. This demonstrates the effectiveness and accuracy of the algorithm in the parameter identification of heterojunction solar cells, providing a reliable and precise new method for solar cell parameter identification.
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Atmospheric turbulence suppression method in optical wireless communication
Abstract:
Research questions: In optical wireless communication systems, atmospheric turbulence can cause the transmission beam to expand, drift and light intensity fluctuation, which will seriously reduce the signal quality of the receiving end and reduce the performance of the communication system. Therefore, the study of methods to suppress atmospheric turbulence is the key to improve the performance of optical wireless communication systems. Method and process: Large-aperture receiving technology, diversity technology, partially coherent beam technology and adaptive optics can effectively suppress the atmospheric turbulence effect, which is an important means to improve the performance of optical wireless communication systems. Detailed detail the principle of suppress atmospheric turbulence and its means. These key technologies can improve the quality of the received signals and enhance the reliability of the communication system by changing the transmission or reception strategy, regulating the structure of the optical field, enlarging the receiving aperture, and compensating for wavefront distortion. Meanwhile, the effects of different parameter indicators on the system performance are also analyzed. The current status of domestic and international research on the relevant suppression techniques is discussed, and the improvement of different performance indexes of the system under the influence of atmospheric turbulence by the relevant techniques is showed. Conclusions: Finally, the challenges and problems in atmospheric turbulence suppression in the field of optical wireless communication are summarized, and the future development trend of the technology is outlooked, which can provide a reference for the future development in this field.
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Establishment and precision analysis of long-straight track measurement control network for rocket sled test
Abstract:
Rocket sled tests hold significant experimental value in the development of aerospace, weaponry, electronics, and nuclear weapons. To establish a precise measurement control network for the track and accurately obtain the spatiotemporal parameters during rocket sled tests, a combined measurement method based on a distance–angle mixed intersection adjustment model is proposed. Firstly, a mixed intersection adjustment model was constructed utilizing angle measurements from total stations and distance measurements from laser trackers. Principles for constructing the measurement error matrix were defined, and the global coordinates were optimally estimated using a nonlinear least squares method. Secondly, the Monte Carlo method was employed to simulate and analyze the measurement equipment layout and the accuracy of the mixed intersection adjustment model. Simulation results indicated that positioning the measurement equipment centrally within the measurement range minimized the overall coordinate measurement errors of position markers, thereby reducing initial value errors in the adjustment model and enhancing the model's solution accuracy. Finally, experimental verification was conducted at a rocket sled test site. Within a measurement range of 669 meters, the standard deviation of the position marker distances in the track measurement control network was found to be 0.19 mm, validating the feasibility of the mixed intersection adjustment model for long straight track measurements. This method offers significant reference value for full-range measurement tasks.
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Optical remote sensing small ship detection algorithm based on improved YOLOv8
Abstract:
Aiming at complex scenes such as sea and land boundaries and nearshore rocky reefs, where the imaging features of optical remote sensing ship detection are not obvious and the target occupies a small proportion, this paper proposes an improved small ship detection method for YOLOv8. First, the model structure is improved by modifying the prediction layer based on the introduction of shallow feature maps in the neck layer, balancing the weights of shallow positional information and deep semantic information, and enhancing the model"s attention to small targets; second, the C2f-FE module, which integrates the FasterNet Block and the efficient multi-scale attention mechanism, is designed to utilize the channel grouping and the cross-channel information interactions, to enhance the tiny ships" feature extraction to reduce the model parameters; finally, the dynamic detection head module is used to improve the detection capability of the model for different spatial scales and mission targets at different levels. The experimental results show that on the MASATI dataset, the improved model reduces the amount of parameters by 42.3% compared with the original YOLOv8s, and the detection accuracy mAP50 and mAP50:95 values are improved by 4.2% and 2.2%, respectively, which effectively realizes the detection of small ships with lightweight and high accuracy. On the DOTA-Ship and DOTA-Small Vehicle datasets, the improved model improves the detection accuracy mAP50:95 values by 1.7% and 1.4%, respectively, over the original YOLOv8s.
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Study on Improved Sand Cat Swarm Optimized SLAM Algorithm for Gas Pipeline Inspection Quadruped Robot
Abstract:
To solve the map construction problem of the quadruped robot for natural gas pipeline inspection, an ISCSO-FastSLAM algorithm optimized by the improved sand cat swarm algorithm is proposed. Firstly, the Cauchy variation strategy is introduced to improve the ability of the sand cat swarm algorithm to jump out of the local optimum and accelerate the convergence speed, and the adaptive genetic parameters are added to improve the stability of the sand cat swarm algorithm. Then, the predicted particle set of the FastSLAM algorithm is updated by improving the optimal solution of the position prediction output of the sand cat swarm algorithm to improve the estimation accuracy. Meanwhile, the low weight particle optimization strategy is used to replace the original resampling step in particle filtering to ensure the diversity of particles. Then, different simulation environments are constructed to compare the different algorithms, and the simulation results show that the ISCSO-FastSLAM algorithm constructs the map more accurately than the WOA-FastSLAM algorithm, and the estimation errors of the robot position and the environmental signposts are reduced by 17.1% and 23.3%, respectively, under the simulation environment of 20m×20m. Finally, the quadruped robot is used to conduct map construction experiments in a residential area of 60m×100m, and the experimental results show that, compared with the FastSLAM algorithm and the WOA-FastSLAM algorithm, the ISCSO-FastSLAM algorithm is able to construct a more accurate map of the residential area inspection, and the estimation errors of the key inspection locations such as valve wells and regulator tanks are reduced by 16.2% and 6.0%, respectively.
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Ultrasonic water immersion phased array detection technology in R angle area of cast stainless steel casing
Abstract:
The stainless steel casing structure of large gas turbine engine contains a large number of R angle regions, which are prone to micro-cracks, pores, inclusions and other defects in the manufacturing process because of its large wall thickness and curved surface structure. （问题）Due to the shape limitation of the curved surface of the R angle, the radiographic film cannot be arranged, and the R angle area with large thickness is difficult to be penetrated by the ray, so that the sensitivity of the radiographic testing is reduced, and defects are missed. Therefore, the ultrasonic immersion phased array testing for the internal defects in the R corner region of the large stainless steel case is proposed.（方法）The phased array probe is arranged on the inner ring curved surface of the casing, and the array element is controlled to transmit ultrasonic waves to form a focused sound field in the R angle area by modifying the curved surface focusing rule. The numerical simulation model is established to analyze the focusing performance of the sound field before and after the modification of the focusing law, and the influence of the water distance on the focusing sound field is also analyzed. Based on the modified focusing rule and the optimized water distance, the ultrasonic phased array testing of the R focal region of the casing is carried out. （结果）The results show that a focused sound field can be formed in the R angle region by modifying the curved surface focusing rule and optimizing the water distance, and the image quality of the sector scanning in this region is significantly improved; the transverse hole defect with the equivalent size of Φ 1. 5 mm can be displayed with good resolution, and the quantitative relative error is as low as 6. 7% by use of -6dB quantitative method.
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A quantum-optimized noise reduction model for gas-containing coal rupture signals
Abstract:
In order to eliminate the disturbance noise in the rupture signal of gas-bearing coal, a quantum-optimized noise reduction model for the rupture signal of gas-containing coal is proposed. The improved quantum particle swarm algorithm (IQPSO) is used to optimize variational mode decomposition (VMD) parameters, and the decision weight coefficient and dynamic factor are introduced into the QPSO algorithm to improve the decision adaptability and parameter search ability of the algorithm. The parameter-optimized VMD algorithm is used to decompose the gas-bearing coal rupture signal, calculate the effective correlation coefficient of each signal component to identify the noise critical point, and use wavelet transform to process the high-frequency noise and reconstruct the remaining components to obtain the gas-containing coal rupture signal after noise reduction. Through simulation experiments and field tests, the noise reduction model is compared with EMD, VMD, PSO-VMD, SSA-VMD, GWO-VMD models, and the signal-to-noise ratio is increased by more than 20%, the rms error is reduced to less than 0.03, and the energy proportion is more than 90%. The model is proposed with strong adaptability and decomposition efficiency, which can effectively retain the local characteristics of the signal and have better noise reduction effect for complex signals on site.
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Signal detection method for magnetic flux leakage small defects based on composite backbone network
Abstract:
Magnetic flux leakage (MFL) internal detection is the core technology of pipeline internal detection, which is crucial to ensuring the safe transportation of pipelines. Due to the long-term underground or deep sea environment of pipelines, there are many small defects on the surface of pipelines. Due to the limited information available on small defects, traditional deep learning defect detection methods have difficulty achieving satisfactory detection results for small defects. A composite backbone network-based signal detection method for small magnetic leakage defects is proposed. First, a data enhancement method called background compression is proposed to compress background signals and thus enhance key features of small defects. Secondly, an adaptive positive and negative sample allocation strategy is designed to address the issue of uneven positive and negative sample allocation for small defects in the region proposal network. Finally, a multi-branch high-resolution feature extraction network for small defects is proposed, which uses a multi-branch composite structure to obtain high-resolution features for feature fusion, thereby improving the network's utilization of small defect texture information. The proposed method is validated using pipeline data from a test site, and the experimental results show that the proposed method is effective, achieving a detection accuracy of 90.3%, with an 8.4% mAP improvement compared to the best results.
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Intelligent detection of transmission line construction machinery based on DAMF-NET
Abstract:
The stability of transmission lines is a crucial guarantee for the normal operation of the power grid. To prevent accidents caused by accidental contact with conductors during line construction, this paper proposes a feature extraction network based on a multi-branch dual attention mechanism, DAMF-NET, addressing the low accuracy and poor reliability of existing detection methods. This algorithm enhances the network's focus on local features of target information by constructing a multi-branch dual attention mechanism, optimizing the feature extraction process. A multi-branch lightweight feature fusion network is proposed to reinforce the global multi-scale semantic information and feature significance under dense tasks, thereby improving the completeness of image features. A small object detection network is introduced to mitigate network scale variance and enhance the sensitivity of small object detection. By employing focal loss and EIoU optimized loss functions, the method reduces noise generated by positive and negative sample imbalance, accelerating the convergence speed of model training. Finally, a state recognition algorithm based on risk area localization is designed and deployed in the intelligent detection system of construction machinery. Experiments show that this method has better average precision compared to most current detection models, indicating its research significance in the detection of construction machinery and intelligent inspection.
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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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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.
