- Current Issue
- Online First
- Most Downloaded Archive
-
Li Zihao, He Yigang, Zhou Yazhong, Lei Leixiao
2025,39(3):1-12,
Abstract:
In complex operating environments of power transformers, the dissolved gases in transformers have non-stationary and nonlinear characteristics. The prediction models of the neural network are difficult to meet high accuracy and reliability requirements which only consider the temporal features. During the data collection process, it is inevitable to exist outliers, which leads to a decrease in data quality and subsequently affects the accuracy of the prediction model. Firstly, density-based spatial clustering of applications with noise (DBSCAN) is proposed to clean the time-series data of dissolved gases in oil in this paper. Then, the adaptive nonlinear weight and Levy flight strategy are proposed to improve the whale optimization algorithm, enhancing its local and global optimization capabilities. The improved whale optimization algorithm is used to optimize hyperparameters in DBSCAN which improves the efficiency of data cleaning. Finally, the complex correlation between gases is analyzed, and a spatiotemporal coupled convolutional neural network model is constructed to mine the spatiotemporal characteristics of gases and achieve gas prediction. Verified by the dissolved gases in the oil of the power station, the results show that the R-squared increased by 0.727 after data cleaning. The R-squared is above 0.9 in all six characteristic gas predictions. Compared with other models, this prediction model proposed in this paper has achieved the best prediction results in feature gas prediction, which demonstrates the effectiveness of the prediction models.
-
Zheng Wenpei, Zhou Shaojie, Wang Yingjun, Zhou Taotao
2025,39(3):13-20,
Abstract:
Electric submersible pump oil recovery is currently one of the most important oil recovery methods in offshore oil fields. Its failure can affect the normal production and operation of oil wells and cause economic losses. Therefore, it is particularly important to predict the remaining service life of electric submersible pumps and prevent failures. To ensure the high-quality operation of electric submersible pumps, a remaining service life prediction method based on ensemble learning model is proposed according to the data characteristics of electric submersible pumps. Firstly, the remaining service life at each time point is calculated as the label function, and the random forest algorithm is used to screen the high contribution feature parameters input model. An ensemble model consisting of SSA-CNN and SSA-LSTM base models weighted by absolute error is constructed. On site data verification shows that the two base model algorithms have their own advantages and disadvantages in different situations. SSA-CNN has more advantages during data fluctuation periods, while SSA-LSTM has more accurate overall predictions. When the same data is input into the ensemble model, it is found that the prediction error of the ensemble model is significantly smaller than that of the two base models, combining the advantages of both. There is a significant improvement in overall accuracy and evaluation accuracy during the change stage. The actual calculation verification shows that the prediction accuracy of the integrated model is 6.41% higher than that of the base model, which is significantly improved compared to existing methods and has strong robustness and stability.
-
Peng Ziran, Yang Xiaoyang, Xiao Shenping
2025,39(3):21-33,
Abstract:
To address the issues of low accuracy and poor robustness in traditional state of charge (SOC) and state of health (SOH) estimation models, an improved model, EKF-HInformer, is proposed based on the extended Kalman filter (EKF) and the deep learning model Informer. This model enables real-time and accurate estimation of the SOC and SOH of electric vehicle batteries. First, the EKF algorithm is used to normalize the real-time battery data, and the adaptive gain factor is adjusted to reduce noise fluctuations, enhancing the performance of EKF data filtering. Then, the Informer network model is used to intelligently estimate the normalized battery data. To reduce the bias in attention weights caused by outliers or abnormal values, the Hampel algorithm is applied to optimize the Informer model, improving the feature learning ability of the multi-head probabilistic sparse self-attention mechanism. Finally, the filtered data is fed into the HInformer network to estimate real-time SOC and SOH. Experiments are conducted using battery datasets from the University of Oxford and the University of Maryland. The results show that the estimation accuracy for SOC and SOH exceeds 99.5%, with RMSE less than 1% and MAXE less than 0.5%. Compared to traditional Informer, Transformer, and LSTM models, this model is faster and more accurate in estimating SOC and SOH, demonstrating superior robustness and generalization ability.
-
Yan Yue, Xu Shihao, He Haixingyue, Zhou Xue
2025,39(3):34-43,
Abstract:
A novel wavelength selection algorithm, based on wave cluster interval, for infrared spectroscopy in the detection of volatile organic gases is presented. The algorithm employs a series selection mode, utilizing characteristic wavelength point cluster classification and absorption peak interval screening. To begin with, cluster analysis is conducted to retain prominent absorption peak features while minimizing the potential for algorithmic over splitting or random uncertainty in wavelength intervals. Subsequently, an improved moving window method is devised, and a greedy algorithm is employed to re-screen wavelength points within the same cluster class. This process ensures the retention of the optimal wavelength range, crucial for representing spectral characteristics and facilitating subsequent model predictions. Experimental validation was conducted using infrared spectral data of styrene, para-xylene, and o-xylene, employing four models: partial least squares, ridge regression, support vector machine. The results demonstrate that, while maintaining model accuracy, the dataset can be reduced to 43.71%~36.35% of its original size. Additionally, utilizing a dataset comprising three gases (two concentrations each), as well as fully arranged and combined mixed gases, we conducted comparative experiments on three different CNN structures. The effectiveness of the proposed algorithm in reducing machine learning model complexity while ensuring prediction accuracy was validated through experimental comparisons before and after spectral waveform selection, with the CNN prediction models demonstrating a 90% increase in operational efficiency post-wavelength selection.
-
Wang Tianyang, Liu Lu, Wang Taiyong, Sha Zongtai, Jiang Hao
2025,39(3):44-52,
Abstract:
There is a trade-off between the lightweight nature of YOLO algorithm models and maintaining detection accuracy. To address the task of detecting small defects in printed circuit boards, we propose a lightweight object detection algorithm based on an improved YOLOv8s. This approach significantly reduces the number of parameters and model size while enhancing detection accuracy. First, remove the last convolutional layer and the C2f layer from the backbone network. Then, introduce the lightweight cross-scale feature fusion module to achieve model lightweighting while enhancing the detection accuracy of small objects. Secondly, we introduce distribution shifting convolution, combining C2f and DSConv to create the C2f_DSConv module, which is then integrated with the lightweight attention mechanism CBAM to design the C2f_DSConv_CBAM module. This module replaces the C2f components in both the backbone and neck networks, further reducing the number of model parameters and enhancing feature extraction capability. Finally, by combining the auxiliary bounding box loss functions Inner-IoU, the bounding box focal loss function Focal IoU Loss, and the original bounding box loss function CIoU, we design the Focal Inner-CIoU. This introduces a controllable auxiliary bounding box to calculate localization loss, increasing the proportion of high IoU bounding boxes and ultimately enhancing detection accuracy. Experimental results show that compared to the original YOLOv8s model, the improved model reduces the number of parameters by 81.5%, computation by 21.3%, and model size by 72.3%, while increasing mAP by 3.0%. This effectively lowers the computational cost of the algorithm, making it more suitable for practical applications and deployment.
-
Zhang Shuaibo, He Fei, Li Baofeng
2025,39(3):53-64,
Abstract:
With the extensive promotion of new energy vehicles, the state assessment of their core component power batteries and the accurate prediction of rechargeable capacity (RC) are of considerable significance for evaluating the reliability, driving range and residual value of new energy vehicles. This paper presents a prediction method for the rechargeable capacity of new energy vehicle power batteries based on the ITPA-Informer model. Firstly, the rechargeable capacity is estimated by the ampere-hour integration method in combination with the Kalman filter, and two-stage feature engineering (recursive feature elimination and kernel principal component analysis) is employed to select features and reduce dimensions to alleviate the curse of dimensionality in actual working conditions. regarding model, an improved time pattern attention (ITPA) mechanism is introduced in the decoder of the Informer model to extract features at different time scales apart from the sampling time interval. The contribution of each time step to the current prediction is adjusted by an exponential decay factor to enhance the temporal dependency of the rechargeable capacity gradually decreasing with the increase of driving mileage. The experimental results indicate that the proposed model outperforms traditional CNN, LSTM and GRU models in multiple evaluation metrics, and the operation data in different months verify that the model possesses good generalization ability.
-
Wu Shixun, Wang Xiao, Lan Zhangli, Xu Kai, Zhang Miao, Jin Shuang
2025,39(3):65-76,
Abstract:
Ultra-wide band (UWB) technology has garnered significant attention in the field of indoor positioning due to its high temporal resolution and strong penetration capability. However, traditional UWB positioning methods for non-line-of-sight (NLOS) identification and compensation often fail to accurately characterize channel states in complex environments, leading to insufficient positioning accuracy and precision. This study proposes an autonomous classification approach, termed SimCLR-CIR-SC, which leverages the SimCLR framework for feature extraction from channel impulse response (CIR) data, and combined with the principles of spectral clustering (SC). Based on the autonomous classification results, we designed a time convolutional neural network with attention mechanisms (TCN-A) model to determine channel state categories. For each identified channel state category, a customized TCN-A model is then employed to predict ranging errors. These errors are used to compensate measuring distances and calibrate ranging weights, integrating with the weighted least squares (WLS) algorithm to locate unknown nodes. Experimental results demonstrate that the proposed SimCLR-CIR-SC method effectively and autonomously classifies and labels channel states, outperforming three existing clustering methods. The TCN-A classification model achieved an accuracy of 98.16%, surpassing five existing classification models. Furthermore, the proposed positioning method achieved an average error of 0.57 meters with three anchors, enhancing the positioning accuracy by at least 31.3% compared to four existing methods, and the positioning accuracy improves substantially as the number of anchors increases.
-
Yan Xiaoheng, Ding Yifan, Chen Weihua, Zhang Xue
2025,39(3):77-91,
Abstract:
In this paper, a multi-attention residual spiking neural network (MAR-SNN)-based grounding grid fault diagnosis method is proposed to deal with the existing single and unintelligent problems in the diagnosis of grounding grid. Firstly, creating the grounding grid dataset for training, using the electrical impedance tomography (EIT) after re-meshing to improve imaging speed and enhancing image features between different fault levels by using the local adaptive contrast enhancement method; Secondly, the MAR-SNN model is built by a new multi-attention spiking residual block is proposed to realize the intelligent fault diagnosis of grounding grid. The residual block performs identity mapping after two spiking neurons, adopts PLIF spiking neurons and BN layer, and introduces multi-attention mechanism to improve the accuracy of model recognition separately; Finally, using EIT and the trained MAR-SNN model to construct the intelligent fault diagnosis model of grounding grid. The comparative analysis of the models shows that the performance of MAR-SNN is superior to the existing advanced models, and in the test set the accuracy is 96.31%. Among them, the accuracy of mild and medium corrosion degree can reach 100% and 97.20% respectively. At the same time, the experimental results show that the proposed method can realize the intelligent fault diagnosis of grounding grid including fault detection and level identification, so verify the effectiveness and feasibility of the method.
-
Duan Tao, Liu Meirong, Gao Xiong, He Yigang
2025,39(3):92-101,
Abstract:
Analog circuits are the core components of modern electronic systems, and as electronic devices become increasingly complex, traditional fault diagnosis methods are no longer able to meet the demand for fault detection in modern analog circuits, especially in soft fault diagnosis, where similar signal responses make fault localization difficult. To solve this problem, a pure data-driven fault detection method based on the Koopman operator is proposed. First, the Hankel matrix is constructed through the delay embedding method, which maps the circuit output signal to a high-dimensional space and achieves system global linearization. Then, the Koopman operator is solved using dynamic mode decomposition, and the modal distribution and signal modal energy ratio are analyzed in the Koopman operator’s eigenfunction space. By extracting the Van der Monde matrix that stores the changes in the eigenvalue, the critical modes are obtained to construct a feature vector with good discriminability. Finally, it is input into a convolutional neural network to complete the fault identification. To verify the effectiveness of the method, a joint simulation model of a four-op-amp dual second-order high-pass filter circuit based on Pspice and Simulink was established, and the circuit state parameters were automatically collected using the SLPS module combined with the circuit netlist. The experimental results show that the proposed method has a high accuracy, with an average accuracy of 99.86%, which is higher than other methods.
-
Zhu Zhongjun, Hu Dinghua, Li Qiang, Zhou Kaihang
2025,39(3):102-114,
Abstract:
The ring oscillator (RO), as an FPGA-based temperature sensor, has been widely applied in the field of temperature detection due to its advantages of simple structure, low cost, and ease of integration. However, the temperature measurement accuracy of ring oscillators is susceptible to multiple factors, including the number of inverters, inverter layout, oscillation frequency, sampling duration, sampling interval, and cooling time, which are critical design and operational parameters. Therefore, optimizing these parameters to enhance measurement accuracy holds significant research importance. This paper systematically analyzes the impact of these key parameters on the temperature measurement performance of the ring oscillator using the control variable method. Firstly, experimental studies on the influence of different inverter counts on oscillation frequency and temperature error reveal that increasing the number of inverters decreases the oscillation frequency. Further experiments demonstrate that optimizing the inverter count to 40~48 achieves the best measurement accuracy and resolution. Additionally, this paper performs an in-depth analysis of the inverter layout, finding that the delay caused by interconnections between left and right slices within the same configurable logic block (CLB) is significantly greater than that of interconnections across CLBs. Through layout optimization and the selection of specific configurations, the delay can be effectively increased, thus improving measurement accuracy. By comparing various parameter combinations, such as sampling duration, sampling interval, and cooling time, the optimal system parameter configuration is proposed. The experimental validation under the optimal parameter combination shows that the temperature error can be reduced by at least 0.5 ℃.In the environment of 25 ℃~85 ℃, compared with the comparative parameter combination, the average temperature error has decreased from 2.0 ℃ to 1.2 ℃, which is a reduction of 0.7 ℃.Furthermore, at temperatures above 65℃, the temperature error remains consistently controlled within ±1℃.The final results demonstrate that the parameter optimization method proposed in this paper significantly enhances the temperature measurement accuracy of the ring oscillator, providing strong support for the design and application of FPGA-based temperature sensors.
-
Han Yan, Wu Di, Huang Qingqing, Zhang Yan
2025,39(3):115-124,
Abstract:
Aiming at the problem that the convolutional neural network （CNN） is insufficiently mined on the information of vibration data structure, which leads to the low accuracy of fault diagnosis, a CNN-GraphSAGE dual-branch feature fusion method for gearbox fault diagnosis is proposed. Firstly, the vibration data of the gearbox is subjected to wavelet packet decomposition, and the wavelet packet coefficients are constructed into graph structured data containing nodes and edges. Then a dual branch feature extraction network is established, with the CNN branch using a dilated convolutional network to extract global features of the data, and the GraphSAGE branch using a multi-layer feature fusion strategy to mine the implicit correlation information in the data structure. Finally, an attention fusion module based on the SKNet attention mechanism is constructed to fuse the dual-branch extracted features, and then the fused features are input into the fully connected layer to realize the fault diagnosis of gearbox. In order to verify the excellent performance of the proposed method in gearbox fault diagnosis, the ablation experiments were conducted first, and then comparative experiments were carried out under the condition of no added noise and adding 1 dB noise. The experimental results show that even under the condition of 1 dB noise, the average diagnostic accuracy of the proposed method is 92.07%, which is higher than the comparison models. The proposed method can effectively recognize various types of faults in gearboxes.
-
2025,39(3):125-135,
Abstract:
The multi-section chain tiltrotor aircraft possesses diverse body configurations, rich combination transformation sequences, and non-unique configuration solution sets. To enable the aircraft to achieve optimal flight performance and mission accomplishment schemes under varying mission conditions, thereby enhancing its variant execution efficiency and mission adaptability, this study investigates the configuration reconstruction and strategy of multi-link tiltrotor aircraft. Initially, the attributes of the multi-section chain tiltrotor aircraft were analyzed, identifying three key factors: passability, stability, and energy consumption, as evaluation indexes for the reconstruction strategy. Subsequently, the weights of each index were determined using analytic hierarchy process (AHP) analysis, and a reconstruction decision-making method was established based on the weighted average approach. Finally, the effectiveness and scientific validity of the reconstruction strategy were verified through experiment. The results indicated that the reconstruction strategy increased the comprehensive score by an average of 26.87%, effectively enhancing the aircraft’s performance, particularly in terms of passability and stability. These findings suggest that the proposed reconstruction strategy not only improves the adaptability of the aircraft in complex environments but also provides a significant theoretical foundation and practical guidance for the advancement of drone technology in various applications.
-
Qiao Meiying, Du Heng, Han Haotian, Qiu Yunqiang
2025,39(3):136-145,
Abstract:
In the application of MEMS inertial measurement devices, the study of effective multi-sensor data fusion algorithms is one of the key technologies for improving attitude estimation accuracy and enhancing anti-interference capability. To address the challenges of low attitude estimation accuracy and the susceptibility of magnetometers to magnetic interference, this paper proposes an attitude estimation algorithm that combines Mahony filtering with the cubature Kalman filter. First, the magnetometer and accelerometer data are used to construct an error correction term to compensate for gyroscope data. Additionally, keyframe techniques are employed to actively compensate for data affected by magnetic interference. The corrected preliminary attitude quaternion is then used as the state information for constructing the Cubature Kalman Filter. Next, the attitude estimates from the magnetometer and accelerometer are used as the observation data, and an adaptive measurement noise covariance matrix is established based on the residual information from the magnetometer data, in order to mitigate the influence of magnetic interference on the attitude estimation. Vehicle-mounted experiments demonstrate that the proposed algorithm significantly improves the accuracy of attitude estimation. Compared to conventional methods, the accuracy of roll, pitch, and yaw angles is enhanced by 45.3%, 50.2%, and 32.8%, respectively. Therefore, the proposed algorithm exhibits excellent performance in suppressing gyroscope drift and resisting magnetic interference.
-
2025,39(3):146-158,
Abstract:
In industrial detection scenario, according to whether anomalies that do not exist in normal samples are introduced, anomaly detection problems can be divided into two categories: structural anomaly detection and logical anomaly detection. Logical anomaly detection places higher demands on the global understanding ability of the network. Faced with the problem that the existing unsupervised anomaly detection model has a good detection accuracy on structural anomalies, but cannot meet the requirements of logical anomaly detection, a dual autoencoder structure consisting of spatial reunion module and channel reunion module is proposed. Our method consists of three components: Initially, the parallel space channel dual autoencoder architecture is introduced, by obtaining feature vectors containing global information from spatial and channel directions, the long-range dependencies of the network is improved. Secondly a selective fusion module is designed to fuse the information of the dual autoencoder and amplify features containing important information to further improve the ability to express logical anomalies. Lastly cosine loss is proposed to the loss function between autoencoder and student network to avoid the network being sensitive to individual pixel differences, so as to focus on global differences. We conducted experiments on MVTec LOCO AD dataset, and achieved 89.4% in logical anomaly detection accuracy, 94.9% in structural anomaly detection accuracy, and 92.1% in average detection accuracy, surpassing the baseline method and other unsupervised defect detection methods, verifying the effectiveness and superiority of the method.
-
Gao Juntao, Zhang Jing, Sun Meng, Zhuo Li
2025,39(3):159-168,
Abstract:
Cytologic examination of thyroid fine needle aspiration biopsy whole slide image (FNAB-WSI) is crucial for the diagnosis of papillary thyroid carcinoma or benign nodular hyperplasia. Due to the ultra-high resolution in sample-level FNAB-WSI, sample-level classification using deep networks consumes computational resources of considerable scale. Given that the sample-level FNAB-WSI has both global and cell cluster local detail features, a lightweight sample-level FNAB-WSI classification method with global-local feature fusion is proposed. Firstly, the global features are extracted using lightweight GhostNet, the feature map size is controlled by setting the convolutional stride, and the local features are obtained by feature slicing and fusion. Then, the global and local features are fused into global-local features after max-pooling and dimensionality reduction, respectively. Finally, the global-local features are fully connected to classify the benign-malignant FNAB-WSI by the softmax classifier. On the self-build FNAB-WSI sample-level dataset, our method surpasses other lightweight methods in all performance indicators, with 89.9% precision, 91.2% recall, 91.7% Acc, and 92.5% AUC, respectively, while the number of parameters is comparable to 6.1×106, demonstrating a tradeoff result. The proposed method not only improves the accuracy of sample-level classification, but also optimizes the computational efficiency of the model by reducing the number of parameters, providing an effective auxiliary tool for clinical diagnosis of thyroid cancer.
-
Tang Ao, Xu Sixiang, Song Yuchen, Ren Jiaqi
2025,39(3):169-176,
Abstract:
Aiming at the problems of long feature point matching time, high false matching rate and low binocular vision measurement accuracy of traditional algorithms, a binocular vision measurement method based on two-dimensional entropy and low-dimensional descriptor is proposed. Firstly, the two-dimensional entropy of the image is used to screen the feature points, filter some useless feature points, and ensure the stability of the feature points. Then, a low-dimensional SIFT feature descriptor with multiple gradient directions is constructed to improve the discriminative ability of feature points and to obtain more gradient information of seed points, while using the mahalanobis distance as a similarity metric for feature point matching and eliminating false matches with the random sampling consistency RANSAC algorithm to optimize the matching accuracy and to reduce the complexity of the algorithm. Finally the sub-pixel coordinates of the feature points are obtained by binary quadratic surface fitting, and the spatial 3D coordinates are obtained by triangulation to calculate the relevant dimensions of the measured object. Taking the continuous casting slab model as the measurement object, the experimental results show that the average relative error of the measurement is 0.41%, which is 1.45% and 0.72% lower than that of the SIFT algorithm and ORB algorithm, respectively, and meets the requirements of the measurement accuracy. The correct rate of the feature point matching improves by 20.94%, 18.19% and 11.38% compared with that of the SIFT, BRISK, and ORB algorithms, and the time taken for feature point matching reduces by 57.48% compared with SIFT, which verifies the accuracy and efficiency of the algorithm.
-
Zhao Yongzhuang, Sun Chuanmeng, Pei Dongxing, Shi Haowei, Wang Yu
2025,39(3):177-189,
Abstract:
Obtaining accurate projectile acceleration signals is essential for evaluating the performance of electromagnetic guns. However, the projectile is affected by different environmental factors in the chamber and out of the muzzle, which makes the acceleration signal have different modal characteristics in the bore and the muzzle stage, which leads to the failure of the conventional nonlinear non-stationary signal global processing method. Therefore, an adaptive analysis and reconstruction method of acceleration signal fusing maximum likelihood-wavelet and improved fully adaptive noise ensemble empirical mode decomposition is proposed in order to obtain accurate acceleration signals. Secondly, the partition signal was decomposed by ICEEMDAN to reduce the interference of harmful signals on signal parsing. Finally, the effective modal components were extracted based on the t-test for signal reconstruction to achieve accurate extraction of the effective acceleration signal. Correlation experiments show that the improvement rate of root mean square error is greater than 0, the correlation coefficient is increased to 0.673 1, and the signal-to-noise ratio is increased to 3.861 4, which avoids the problem of over-decomposition or incomplete decomposition of some regions compared with the conventional global processing methods, and realizes the accurate extraction of acceleration signals.
-
He Yang, Li Zhigang, Yang Ruige, Wang Ruixin, Yang Zilong
2025,39(3):190-198,
Abstract:
Cardiovascular disease is one of the leading causes of morbidity and mortality worldwide. Timely and reliable risk assessment is crucial for reducing disease risk and ensuring safety. The aim of this research is to propose an efficient and convenient risk assessment method for cardiovascular disease. In this research, Fourier transform infrared attenuated total reflectance spectra and Raman spectra of 108 whole blood samples were collected for the construction and evaluation of risk assessment models. To address the issue of low efficiency in risk assessment models based on traditional PLS, siPLS, and other feature extraction algorithms, a chemical bond-driven synergy interval partial least squares algorithm (CBDsiPLS) is proposed for feature extraction, and combined with machine learning to construct a risk assessment model using single data sets. The test results show that the proposed method outperforms traditional feature extraction algorithms. In addition, by utilizing the complementary information from mid-infrared and Raman spectroscopy, a risk assessment model for fused data was established through feature-level information fusion combined with machine learning methods. The final fused data risk assessment model achieves an accuracy of more than 90%, a sensitivity of more than 80%, and a specificity of 95%. The experimental results show that the proposed method can effectively assess the risk of cardiovascular disease.
-
Shi Qing, Zhang Guoshan, Gang Bei, Li Zhihua, Hu Jiacheng, Liu Sijiao
2025,39(3):199-207,
Abstract:
The quantification of defect size in oil and gas pipelines is a key issue and ultimate goal of pipeline inspection. Traditional defect detection methods often remain in the stage of defect classification, and the lack of detailed data increases the difficulty of subsequent processing; Intelligent recognition methods have higher requirements for the quality of magnetic leakage data however. Therefore, a combining particle swarm optimization and random forest （PSO-RF） is proposed to quantify the length, width, and depth of pipeline defects. Firstly, multi-dimensional feature extraction is performed on a set of defect magnetic leakage data, and then the random forest algorithm is used for regression prediction; In view of the difficulty of obtaining the best parameters of random forest algorithm, particle swarm optimization algorithm is used to optimize the hyperparameters, and finally more accurate prediction data of defect length, width and depth are obtained. The PSO-RF algorithm was compared with classical CNN and PSO-SVR training algorithms. The quantization accuracy of length, width and depth was improved by 28%, 32% and 68% respectively, verifying the effectiveness and superiority of the PSO-RF algorithm. Finally, a set of labeled pipeline defect data was used to validate the algorithm, and the data with quantization errors of length, width and depth within 20% achieved 80.3%, 88.5% and 95.9% respectively.
-
2025,39(3):208-216,
Abstract:
Unsupervised person re-identification is a computer vision method that identifies and matches pedestrians without any labeled data, utilizing feature extraction and clustering algorithms. To address common issues in current unsupervised person re identification methods, such as insufficient feature extraction, inaccurate clustering, high computational complexity and lack of model robustness, this paper proposes a deep clustering learning-based approach for unsupervised person re-identification. First, we investigate the use of IBN-Net combined with generalized mean pooling as the feature extraction network, which enhances the discriminative power of the extracted features. Second, to mitigate the sensitivity of clustering algorithms to hyperparameters, we introduce the OPTICS algorithm to assist DBSCAN in selecting hyperparameters, thus reducing DBSCAN’s dependency on those hyperparameters. Additionally, to fully utilize all the data in the training set, outliers are treated as separate clusters and included in the initialization and updating process of the memory dictionary. Finally, to address the inconsistency in update rates among clusters in the memory dictionary, we propose a cluster-level memory dictionary that eliminates this issue. Experimental results validate the effectiveness of our approach, demonstrating significant improvements in both precision and accuracy in unsupervised person re-identification tasks.
-
Wang Hongsen, Wang Jianguo, Yang Jiandong, Feng Yong
2025,39(3):217-225,
Abstract:
In heavy industries such as non-ferrous metal metallurgy, the detection of hazardous gas leaks is crucial for ensuring employee safety and maintaining stable production. Traditional single-modal detection methods often struggle with reduced accuracy in complex industrial environments due to their limited ability to handle disturbances, particularly in noisy conditions. To address these challenges, this paper introduces a multimodal gas leak detection model designed for industrial environments. This model integrates smoke sensor data and infrared image data, leveraging the complementary strengths of each data source to enhance detection accuracy and robustness. Initially, the gMLP architecture is utilized to capture complex patterns in sensor data; concurrently, the Swin-Transformer is employed to extract local and global features from infrared images. Subsequently, a fusion strategy based on multi-head attention effectively combines the latent representations of different modal data to achieve hazardous gas detection. Experiments conducted on multimodal gas datasets in both normal and noisy environments demonstrate that the model achieves a detection accuracy of 97.92%. The results indicate that, compared to single-modal methods, the multimodal approach significantly improves detection accuracy and robustness, enhancing performance in complex industrial gas leak detection scenarios.
-
2025,39(3):226-234,
Abstract:
Aiming at the demand for unmanned and less manned airports, in-depth research is carried out on the operation control of autonomous specialized vehicles at the intersection of apron lanes. First, a non-conventional intersection model is established for the apron lane intersection. On this basis, the problem of possible conflicts between specialized vehicles entering the apron through the lanes and other specialized vehicles is the first time to combine decision and gap theory, improve the gap theory, and propose a specialized vehicle control strategy LSGO based on the gap theory. RSU calculates the optimal steering lanes for the specialized vehicles and the optimal gaps between the specialized vehicles′ queues through the proposed control strategy and provides execution commands in combination with the vehicle kinematics model. With the LSGO strategy, inter-vehicle conflicts can be effectively avoided and the time for specialized vehicles to pass through intersections can be reduced. Finally, simulations are conducted in a typical apron and lane scenario to verify the functionality and performance of the strategy. The simulation results show that with the proposed LSGO strategy, the time for specialized vehicle queues to pass through intersections can be reduced by up to 29.2% and the energy consumption can be reduced by up to 11.6% compared with the traditional control strategy. The time for a single vehicle to pass through the adjustment optimization zone and the intersection zone is reduced by up to 20.5% compared with the traditional control strategy.
-
Liu Yanglong, Chen Xiaolei, Ni Jun, Liang Nan
2025,39(3):235-245,
Abstract:
Existing fault detection algorithms based on evidence theory typically assume that the evidence is independent. However, this assumption is often difficult to satisfy in practical engineering, especially when data sources are affected by complex interference environment, leading to significant discrepancies between theoretical analysis and actual results. In response to the above problems, a fault detection algorithm based on evidential reasoning with dependent evidence under complex interference environment is proposed. Initially, the evidence reliability is used to determine the evidence fusion sequence within a weighted model, reducing the uncertainty of fusion results caused by complex disturbances. Subsequently, considering the correlation of non-independent evidence in the evidence fusion stage, the maximum information coefficient is calculated to evaluate the degree of correlation between evidence. Furthermore, the dependence discounting factor is calculated based on the dependence coefficient of the evidence and incorporated into evidential reasoning rule. Lastly, considering the complex interference characteristics of data sources, a two-layer evidence decision-making mechanism inspired by boosting methods in statistical learning is designed to compute the final fault detection result. The feasibility and efficacy of the proposed algorithm are demonstrated through a fault detection experiment of aviation electromagnetic relays. Compared with existing methods, the advantage of the proposed algorithm is that it relaxes the requirement for independence of evidence, which is especially suitable for engineering environments that are subject to greater noise interference.
-
Gao Wenchao, Chen Yifan, Chen Shiyu, Zhou Sijie, Huang Jun
2025,39(3):246-255,
Abstract:
Planetary gearboxes are widely used as essential transmission devices in industrial applications, yet they are prone to failures under complex operating conditions and prolonged loads. Traditional fault diagnosis methods heavily rely on expert knowledge and expensive equipment, facing challenges such as data scarcity and low diagnostic efficiency. To address these limitations, the development of generative adversarial networks (GANs) has provided innovative solutions for image generation and data augmentation in recent years. However, existing GAN models often encounter issues such as semantic misalignment and artifacts when processing small-sample datasets, limiting their potential in intelligent fault diagnosis. In this context, this paper proposes a multi-scale attention and progressive feature fusion GAN (MSA-PF-GAN) model, which integrates a progressive decoder structure with multi-scale attention mechanisms to significantly improve image generation quality and fault diagnosis accuracy under small-sample conditions. Experiments conducted on two independent planetary gearbox fault datasets validate the proposed method, showing that it substantially reduces the FID score and enhances diagnostic accuracy (by 35% and 20%, respectively). Across multiple evaluation metrics, the MSA-PF-GAN outperforms other state-of-the-art methods. Further analysis demonstrates that the model, through progressive feature fusion and multi-scale attention mechanisms, excels in generating diverse and realistic images while effectively capturing complex fault features. Therefore, this technique shows promising potential and practical value in the field of planetary gearbox fault diagnosis.
-
2025,39(3):256-265,
Abstract:
To address the challenge of maintaining accurate and reliable height control of shearers’ rocker arms under underground mining conditions, this study proposes an improved method for detecting inclination angles using a MEMS accelerometer. Traditional detection methods, such as the cylinder stroke displacement and coded potentiometer rotation ranging techniques, are prone to decreased accuracy and reliability due to long-term wear on the rocker hinge shafts and difficult maintenance. In this work, we introduce filtering strategies designed to mitigate high-frequency and high-amplitude vibration noise encountered in harsh vibration environments, thereby enhancing measurement accuracy. Specifically, the critical damping method and combined integration approach are employed to process the raw triaxial data from the accelerometer, effectively isolating and extracting useful gravitational acceleration data to determine the angle. A simulation experiment platform was constructed to replicate the vibration conditions experienced by the rocker arm. Through this platform, dynamic inclination angle identification within a vibrating environment is achieved, significantly improving angle measurement accuracy. The experimental results indicate that in a 5g vibration environment, both filter designs exhibit faster response speeds and can rapidly track changes in the input signal. After applying the combined integral filter, the angle error is less than 0.3°, and after the critical damping filter application, the angle error is reduced to less than 0.1°. This level of precision satisfies the actual demand for controlling the mining height of the rocker arm. The proposed method provides a feasible solution for detecting the inclination angle of the shearer’s rocker arm, offering enhanced accuracy and reliability without being affected by mechanical wear or maintenance challenges, thus contributing to safer and more efficient mining operations.
-
Zhao Jiali, Zhao Yan, Wang Zihan, Li Feiyu, Li Qiaolin, Wu Dan
2025,39(3):266-274,
Abstract:
Roundness is an important index for evaluating the manufacturing accuracy and interchangeability of tiny cylindrical parts. In order to address the inaccurate positioning of the part in the roundness measurement process, which leads to the existence of tilting errors in the measurement data, a segmentation-interception roundness measurement method is proposed, in which the cross-section circle of the part is segmented into eight, ten and twelve equal parts, and the surface of the part is scanned linearly with a profilometer after the equal parts are divided, and a series of coordinate data obtained by scanning is used to characterize the arc contour of each segment, and then the center part of each segment is reconstructed to obtain the radius and roundness of the measured part. The center part of each arc is intercepted to reconstruct the cross-sectional circular profile of the part, and the radius and roundness of the measured part are fitted to realize the high-precision roundness measurement of tiny cylindrical parts. Taking a needle with a diameter of 3 mm as an example for roundness measurement experiments, the measurement results of different segmentation-interception cases show that the radius and roundness of the needle obtained by ten equal interceptions of 75° arc fitting are 1.500 892 9 mm and 0.092 μm, respectively, with a deviation within ±0.1 μm. The uncertainty components affecting the measurement results were analyzed by the GUM method, and the integrated standard uncertainty affecting the radius of cylindrical parts was calculated to be 0.049 96 μm, which demonstrated the reliability and consistency of the proposed splitting-intercepting circularity measurement method.
Volume 39,2025 Issue 3
-
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%.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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．
-
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.
-
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
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
