- Current Issue
- Online First
- Adopt
- Most Downloaded Archive
-
Liang Jingyuan, Pang Mingzhi, Ke Xizheng
2024,38(11):1-14,
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.
-
Li Wenyue, He Yigang, Xing Zhikai, Zhou Yazhong, Lei Leixiao
2024,38(11):15-24,
Abstract:
The operation of power transformers involves significantly fewer fault data compared to normal data, resulting in a severe data imbalance issue. Additionally, the complex coupling relationships among the monitored variables make the modeling of condition assessment tasks challenging and lead to low evaluation accuracy. Aiming at the related problems, a power transformer health condition evaluation method based on double input residual graph convolutional network is proposed. First, SMOTE Tomek mixed sampling algorithm was used to pre-process the unbalanced data of the training data, which solved the problem of insufficient fault data and difficult classification. Then, a multi-metric fusion graph construction method is proposed to learn the correlation between variables from multiple variables and construct the graph structure data. Finally, a double input residual graph convolutional network(DI-ResGCN) based on the ChebyNet is proposed, feature extraction is carried out on the constructed graph structure data, and feature fusion is carried out through the self attention mechanism to obtain the transformer health evaluation results. Experiments were carried out on a dataset of dissolved gases in oil and oil test collected by a real power transformer, and experimental results show that the proposed method has a state assessment accuracy of 94.7% and F1 score of 0.942, outperforming other deep learning methods and exhibiting the best evaluation performance.
-
Zhou Yun, Zhou Lingke, Li Sheng, Wu Yonghao
2024,38(11):25-32,
Abstract:
To improve the reliability of the integrated navigation system, an improved fault detection and information fusion method is proposed for the fault-tolerant mechanism of the integrated navigation system. Designed an improved sequential probability ratio test method, introduced a fading factor to improve the tracking speed of residual information at the current time, combined with Mahalanobis distance to determine the end time of the fault, and fully reset the judgment information at the appropriate time based on the judgment result; A self-adaptive normalization fusion algorithm based on federated filtering was designed to construct normalized detection values of fault detection statistics, which were used as weight coefficients for the measurement noise variance matrix. The corresponding sub filters were weighted and updated to change the weight allocation in the global fusion process. The results of the in vehicle experiment show that the improved fully reset sequential probability ratio test algorithm has improved the positive detection rate of soft fault detection by 96.43%, 25.00%, and 19.57% respectively compared to the traditional residual chi square test, fading sequential probability ratio, and fast reset sequential probability ratio methods. The adaptive normalization fusion algorithm used also improved the positioning accuracy by 44.70% and 35.60% compared to the traditional federated filtering method. Therefore, the two improved methods can greatly enhance the fault tolerance performance of the integrated navigation system and have high practical value.
-
Nan Jingchang, Chen Xin, Yan Jie
2024,38(11):33-39,
Abstract:
To improve the accuracy of fault detection in array antenna, an enhanced differential evolution-genetic algorithm (DE-GA) is proposed. This algorithm combines the advantages of genetic algorithm (GA) and differential evolution (DE) by employing a dual crossover strategy to help individuals escape local optima. An adaptive weighting mechanism further optimizes offspring selection, enhancing the algorithm’s sensitivity and adaptability to fault conditions. Applied to array antenna fault detection, the DE-GA algorithm models the array and optimizes its radiation pattern to match the known faulty pattern, allowing the faulty array’s amplitude to be estimated. Experiments show that compared with DE and GA, DE-GA reduces the fitness function value by 11.15% and 12.90%, the mean absolute error by 19.36% and 23.85%, the mean square error by 12.90% and 11.15%, and the maximum error by 12.30% and 13.18%. This demonstrates higher accuracy and improved approximation capabilities. Additionally, the algorithm maintains excellent stability with larger arrays, making it suitable for large-scale fault detection.
-
Gas turbine rotor fault diagnosis based on improved DenseNet-ViT joint network and transfer learning
Qiao Qi, Wang Hongjun, Ma Kang, Wang Zheng, Yu Chenglong
2024,38(11):40-47,
Abstract:
In the actual industrial environment, the collection of gas turbine rotor fault data is challenging, leading to a scarcity of fault samples and an inability to meet the massive training requirements of fault models. Leveraging the advantages of DenseNet in image feature extraction and the Transformer structure in the visual field, an improved gas turbine rotor fault diagnosis method based on the DenseNet-ViT joint network was proposed. Firstly, the classification layer of DenseNet was abandoned, and only the feature extraction layer of DenseNet was utilized. Subsequently, the output layer of the modified DenseNet was connected to the input layer of the ViT model to form the joint network. Additionally, in response to the issue of lengthy training time for the fault model, transfer learning was employed to transfer the trained model’s weight parameters, which could expedite the training process and conserve computing resources. Simulated data of gas turbine rotor faults could be acquired through the gas turbine rotor simulation experimental platform constructed in the laboratory, and real fault data of different types of rotors in the actual environment were obtained on a certain type of gas turbine test bed. Utilizing both the simulated and real data for model testing could better verify the reliability of the proposed method. The experimental results indicate that the fault recognition accuracy rates reached 96.8% and 97.3% in the tests of two distinct rotor fault datasets, respectively, demonstrating that this method possesses a relatively high rotor fault recognition accuracy. In the subsequent comparative verification experiments, by comparing with CNN and VGG-16, etc, the fault classification accuracy of this model was also higher than those networks, thereby further validating the superiority and reliability of this model.
-
Zhao Yunji, Wei Sicheng, Xu Xiaozhuo
2024,38(11):48-57,
Abstract:
To address the inconsistent bearing fault data distribution that leads to the difficulty of feature offset and distinctive feature extraction, a bearing fault diagnosis method based on time-frequency filter and offset attention neural network is proposed, which processes the fault signal from offline and online parts. In the offline part, a time-frequency filter is proposed to extract the distinctive features from time domain and frequency domain; A spatial sampling method considering both global and local features is proposed. In the online part, an offset attention neural network is proposed. Compared with self attention, offset attention is more conducive to the extraction of offset features, so as to reduce the impact caused by inconsistent data distribution. Experiments on the bearing datasets of Xi′an Jiaotong University (XJTU) and Case Western Reserve University (CWRU) have achieved 100% accuracy, which proves that the proposed method can efficiently extract the distinctive features of fault signals, and effectively suppress the influence of feature offset. The comparative experiment on the bearing dataset of CWRU proves the superiority of the proposed method. In addition, experiments are also carried out on the dataset of gas turbine main bearing collected in the industrial field, and the results show that the proposed method has practical significance.
-
2024,38(11):58-69,
Abstract:
Aiming at the problem that the convolutional neural network extracts the features from the input signal through the local receptive field, and cannot effectively capture the global context information under variable load and noise environments, resulting in the low recognition accuracy of rolling bearing fault diagnosis, a rolling bearing fault diagnosis method based on multiscale adaptive depthwise separable convolution (MADSC) and spatial interaction double-stream Swin Transformer (SIDSwinT) is proposed. Firstly, one-dimensional vibration signals are converted into two-dimensional time-frequency maps using wavelet transform to retain the complete information. Next, MADSC is constructed to extract local feature information and capture the characteristic changes of rolling bearing vibration signals at different scales. After that, SIDSwinT is designed to extract the global feature information, and the proposed spatial interaction module (SIM) is utilized to adaptively adjust the feature weights, while the sampled information is weighted by the deformable attention to eliminate the distributional differences caused by fluctuations in working conditions. Finally, bidirectional long short-term memory (BiLSTM) is utilized to better understand the contextual information and to improve the diagnostic accuracy and stability. Two different datasets are used to verify the fault diagnosis performance of the proposed method, and the experimental results show that the accuracy of the proposed method is higher than 93.00% when the signal-to-noise ratio is -4, and the accuracy is higher than 92.00% under the condition of variable load, which verifies that the proposed method has a stronger anti-noise performance and generalization ability than the comparison methods.
-
2024,38(11):70-78,
Abstract:
In the railway system, the switch machine is a critical device to ensure the safe and smooth operation of trains. Fault diagnosis of the S700K switch machine is crucial for accident prevention and the maintenance of railway operations. To address the shortcomings of traditional diagnostic methods in terms of speed and accuracy, a diagnostic model integrating a deep residual shrinking network with a bidirectional long short-term memory network is proposed. First, the power curve of the switch machine is preprocessed. Next, DRSN is used to automatically learn features from the preprocessed data and compress the data length, improving the speed of diagnosis. Its attention mechanism and soft thresholding reduce the influence of noise features, and the DRSN structure helps to overcome network degradation and overfitting issues. Following that, the bidirectional structure of BiLSTM is utilized to capture complex relationships in the time-series data. Finally, a Softmax classifier is employed for fault classification. Simulation results show that the accuracy, precision, and recall rates of the DRSN-BiLSTM model all exceed 98.3%. Compared with models such as DRSN, deep neural network, and convolutional neural network, the diagnostic accuracy of this model is improved by at least 1.47%. Even when Gaussian white noise in the range of 15~40 dB is added, the accuracy remains above 92.7%, an improvement of at least 2% over other models. This model not only ensures the efficiency of the training process but also improves the accuracy of point machine fault diagnosis and demonstrates excellent robustness in noisy environments.
-
Zhang Bide, Chen Guang, Liao Qilong, Qiu Jie, Ma Junmei, He Hengzhi, Yan Tiesheng
2024,38(11):79-89,
Abstract:
To enhance the accuracy of photovoltaic (PV) array fault diagnosis, this study proposes a novel method that utilizes an improved golden jackal optimization (IGJO) algorithm to optimize a deep hybrid kernel extreme learning machine (DHKELM) for PV array fault diagnosis. Initially, a range of PV array faults are simulated using the MATLAB/Simulink platform. Based on a comprehensive analysis of fault characteristics, a 12-dimensional feature set is proposed for fault diagnosis. Subsequently, the golden jackal algorithm is improved by introducing lens imaging reverse learning strategy, cosine and sine algorithm strategy, and adaptive T-distribution perturbation strategy to enhance its convergence speed and global optimization capability. Additionally, IGJO is compared with other optimization algorithms using test functions. Furthermore, radial basis kernel functions and polynomial kernel functions are incorporated into the extreme learning machine and combined with an autoencoder to form the DHKELM model. Finally, IGJO is employed to optimize the initial parameters of the DHKELM model, resulting in the establishment of the IGJO-DHKELM PV array fault diagnosis model. Analysis of the results indicates that the proposed 12-dimensional feature set provides higher diagnostic accuracy compared to traditional 4-dimensional and 5-dimensional feature sets. Moreover, the IGJO-DHKELM-based fault diagnosis method demonstrates superior diagnostic accuracy compared to other fault diagnosis models.
-
Zheng Puzhen, Lin Jun, Liang Shangqing, Zhang Dong
2024,38(11):90-98,
Abstract:
Hull corrosion is primarily caused by the deterioration of protective coatings. Given that inspections of hulls are both time-consuming and labor-intensive, thereby conducted infrequently, this study aims to propose a rapid detection and localization method for hull coating damage. Using the changes about underwater electric potential caused by the impressed current cathodic protection system and coating damage, the location of the damage can be identified by measuring the potential difference underwater at symmetrical positions on the hull. Underwater electric field transmission experiments have confirmed the feasibility of methods for ranging and locating fault positions based on electric field characteristic signals. Additionally, COMSOL Multiphysics simulation software was utilized to model the corrosion electric field generated by the cathodic protection current and the electrochemical corrosion processof metallic surfaces on the hull.By analysing the potential variation along the measurement segments on both sides of the hull, it is observed that the underwater potential difference is notably largest around the damaged coating area.Based on the transmission characteristics of the underwater electric field, longitudinal and transverse positioning of the damage points have been achieved, with average deviations of 0.2 m and 0.21 m. Moreover, a linear relationship between potential magnitude and damaged area was observed. This method offers high accuracy and is suitable for longitudinal positioning of multiple points with intervals greater than 3 m. It mitigates environmental factors’ interference with detection, enabling early and rapid detection of hull coating damage and improving corrosion management for vessels.
-
Yu Hongwu, Tang Zhanjun, Ma Jinxiong
2024,38(11):99-108,
Abstract:
In order to achieve accurate monitoring of abnormal wind turbine blades, a method combining complementary ensemble empirical mode decomposition with the sound features of wind turbine blades was proposed. Firstly, the voiceprint data of four kinds of fan blades under abnormal working conditions and normal operating conditions are collected and pre-processed for noise reduction, frame division and window addition. Through experimental comparison, the complementary ensemble empirical mode decomposition algorithm is selected for secondary noise reduction of voiceprint data. Secondly, the modal decomposition of frame signals after secondary noise reduction is carried out to extract modal components. The effective modal components were selected by calculating the Pearson correlation coefficient of the modal components, and the characteristics of mel frequency cepstrum coefficient, linear prediction cepstrum coefficient, gammatone cepstrum coefficient, short-time energy and short-time mean zero crossing rate were extracted for each layer of modal components. Finally, based on these feature combinations, support vector machine, naive Bayes and neural network are used as fault classification models to identify voicing data. The research results show that the neural network model based on the combination of the above five vowels features and the parameter optimization can achieve the accurate recognition of blade anomalies, with the recognition accuracy of 97.5%. The model has a good recognition effect on early abnormal fan blades, and has good generalization performance.
-
Li Li, Ning Muyi, Li Zhibin, Zeng Changjian, Zhang Zhiyan, Yao Lina, Kong Han
2024,38(11):109-117,
Abstract:
In order to improve the accuracy and classification speed of fault diagnosis of valve cooling equipment in converter station, a fusion feature algorithm based on Fisher ratio criterion and a fault classification model based on particle swarm optimization least squares support vector machine are proposed. Firstly, the static parameters and dynamic first-order difference parameters of Mel cepstrum coefficient and inverse Mel cepstrum coefficient are extracted as fault feature quantities respectively, and all the high and low frequency information of valve cooling equipment fault is obtained. Then, Fisher ratio criterion is used to fuse the fault features of valve cooling equipment twice, so as to reduce the repeated data and interference signal caused by direct superposition signal. The 1×13 dimensional Fisher ratio data is selected as the fusion feature of the noise signal of the valve cooling equipment. Secondly, in order to improve the accuracy and classification speed of LSSVM algorithm fault identification, the PSO algorithm is used to optimize the kernel function bandwidth and penalty factor of LSSVM algorithm, and the optimal solution of the two parameters is obtained, and the LSSVM valve cooling equipment fault classification model is established. Finally, the main pump between the valve cooling equipment is taken as an example, and different feature fusion algorithms and fault identification methods are used for comparative analysis. The results of the example verify that the proposed method can quickly and accurately identify the fault signals of the valve cooling equipment at different frequencies, and the accuracy of fault identification can reach 96.67%.
-
Zhang Junning, Zhao Lihao, Chen Ningbo, Yang Liwei, Liu Gang, Lyu Shusheng
2024,38(11):118-125,
Abstract:
When using a Doppler microwave sensor to measure the flow of granular fertilizer, the vibration generated by the operation of the fertilizer applicator and various external disturbances can cause the collected signal to be distorted. This article first explores the optimal parameters for wavelet analysis and Kalman filtering algorithms. By comparing the denoising effects of the two algorithms, a denoising algorithm based on the combination of empirical mode decomposition and sample entropy combined with wavelet is proposed. Taking Stanley 15-15-15 granular fertilizer as the experimental object, the detection system such as Doppler microwave sensor is deployed on the fertilizer applicator to collect the mass flow signal of granular fertilizer for algorithm effect experimental verification.The results indicate that, compared to the original signal, the average signal-to-noise ratio of the Kalman filtering algorithm improved by 3.548 dB after optimizing the gain coefficient. After optimizing the wavelet denoising parameters, the average SNR of the wavelet analysis algorithm increased by 7.184 dB. When combining the optimized wavelet analysis with the denoising algorithm of integrated empirical mode decomposition and sample entropy, the average SNR of the denoised signal increased by 7.899 dB, while the average root mean square error decreased by 0.184, this algorithm demonstrates significant advantages in denoising the mass flow rate signals of granular fertilizers.
-
Bai Xue, Li Chenxi, Zhai Jia, Zou Yingxue, Liu Rong, Chen Wenliang
2024,38(11):126-131,
Abstract:
Pneumonia, a common respiratory infection worldwide, often leads to various complications, making its precise classification a critical issue in clinical diagnosis and treatment. This study addresses the need for accurate classification of respiratory infections and pneumonia by developing an effective diagnostic method based on the Raman spectroscopy of respiratory mucus. Initially, respiratory mucus samples from normal individuals, patients with common pneumonia, and those with concomitant plastic bronchitis were collected. Through Raman spectroscopic analysis, molecular features and chemical changes related to mucin glycosylation and fibrosis in each group were accurately identified, detailing the components and molecular bond alterations associated with the disease. Subsequently, combining principal component analysis and partial least squares discriminant analysis, a classification model capable of distinguishing between different types of pneumonia was constructed. Experimental results demonstrated high accuracy of the model in classifying pneumonia, with an overall classification accuracy reaching 99.08%, and specifically, 100% and 97.4% accuracy in distinguishing common pneumonia and plastic bronchitis, respectively. The study not only confirms the potential of Raman spectroscopy in the diagnosis of infectious diseases but also provides a reference for the broader application of molecular spectroscopic techniques in infectious disease diagnostics.
-
Li Xiangfei, Yi Zhixuan, Liu Junqin, Zhao Kaihui, Zou Lihua
2024,38(11):132-145,
Abstract:
For flux weakening control of the internal permanent magnet synchronous motors, when the degree of flux weakening is deeper, the motor parameter perturbation and external disturbances will cause the voltage loop output, torque and current pulsation to increase, and the speed convergence is too slow. A speed-voltage loop feedback super-twisting non-singular fast terminal sliding mode controller (FST-NFTSMC) is proposed for deep flux weakening control. To reduce the dependence of flux weakening control on the system model, the voltage-loop hyperlocal model is constructed according to the mathematical model of the built-in permanent magnet synchronous motor during parameter perturbation. And it is combined with the speed loop hyperlocal model to establish the speed-voltage loop hyperlocal model. Based on this hyperlocal model, the speed-voltage loop FST-NFTSMC is designed by combining the feedback super-twisting algorithm and the non-singular fast terminal switching function. At the same time, an improved sliding mode disturbance observer is built to estimate the unknown part of the system and compensate for the estimated value feedforward to FST-NFTSMC, which further improves the robustness and control accuracy of the system. Simulation and experiment show that compared with the traditional PI control, the convergence speed of the proposed method in the no flux weakening region, shallow flux weakening region, and deep flux weakening region is improved by 66%, 40.6%, and 28.6% respectively. It has better stability and fewer pulsations of the torque and current, proving that the method in the flux weakening control is effective in suppressing the output jitter after the voltage loop is perturbed as well as improving the speed response.
-
Peng Duo, Zha Haiyin, Cao Jian, Zhang Yanbo, Zhang Minghu
2024,38(11):146-157,
Abstract:
This study addresses the long delay issue in underwater wireless sensor networks (UWSNs) caused by the spatio-temporal complexity and dynamics of the underwater environment, which significantly impacts the information propagation between mobile sensor nodes and consequently leads to large node localization errors. To this end, a novel underwater mobile node localization algorithm based on CNN-LSTM sound speed prediction is proposed. Initially, the sound speed dataset is partitioned using the K-fold cross-validation method. Subsequently, a hybrid CNN-LSTM model is constructed and trained, leveraging the feature extraction capability of CNN and the sequence modeling strength of LSTM. This model efficiently captures both spatial and temporal information from the sound speed dataset, thereby enhancing the prediction accuracy. During the localization process, the predicted sound speed values from the CNN-LSTM model are employed for time difference of arrival (TDOA) ranging, and the ranging values are refined accordingly. Finally, the refined ranging values are utilized to adaptively select the optimal ranging and localization method for unknown nodes under varying node densities, based on the number of reference nodes, thereby achieving precise localization of underwater mobile nodes. Experimental results demonstrate that, compared to existing localization algorithms such as SLMP, DMP, NDSMP, and BLSM, the proposed MCLS localization algorithm reduces the mean localization error by 46.96%, 39.93%, 27.64%, and 15.24%, respectively, under the same beacon node conditions, significantly improving the localization accuracy and stability of underwater mobile nodes.
-
2024,38(11):158-167,
Abstract:
The photometric loss has been playing an important role in the training of video-based unsupervised monocular depth estimation models. However, it generally has large errors in special regions such as weak texture regions and edge regions, which leads to strong instability in the supervision signal of the training network. To solve the problem, a more robust unsupervised monocular depth estimation method is proposed. The method first combines the dual-branch encoder and the channel attention module to improve the extraction ability of the single-frame depth network for depth features. Then, the single-frame depth network results are used to guide the multi-frame depth estimation to improve the accuracy of depth estimation. On the basis, a new photometric loss function is designed. By calculating the photometric loss on the image gradient, the unreasonable supervision caused by local brightness changes is eliminated. At the same time, the difference between successive pixels is used to define the blurry pixels. Finally, the false supervision caused by the blurred pixels on the target frame and the reconstructed target frame is excluded based on the binary mask. In the test results of the KITTI dataset, multiple indicators such as the average relative error, the square relative error and the root mean square error have improved. The average relative error and the squared relative error are reduced to 0.075 and 0.548 respectively. The experimental result shows that the proposed method further improves the performance of existing models compared with other advanced methods.
-
Hui Yongyong, Sun Wenkai, Tuo Benben, Chen Peng, Zhao Xiaoqiang
2024,38(11):168-181,
Abstract:
For the difficulty in extracting deep features of batch process variables, as well as low quality prediction accuracy caused by the temporal, nonlinear, and dynamic characteristics of variables, this article proposes a quality prediction model for batch processes based on convolutional neural networks spatial and temporal attention with double long short term memory networks (CNN-STA-DLSTM). Firstly, the three-dimensional data of the batch process are expanded into a two-dimensional matrix along the direction of the variables, and the two-dimensional data are normalized by the Max-Min method. Then, the partial least squares (PLS) method is used to reduce the dimension of the original data, and the variables with strong correlation with the quality variables are retained. The convolutional neural network (CNN) is used to mine the potential features of the process data and improve the attention of the quality-related feature information. Secondly, the temporal attention mechanism and the spatial attention mechanism are introduced to construct the encoder-decoder structure network of the double-layer LSTM, and the attention mechanism is used to adaptively learn the relevant historical information of the time step, so as to improve the long-term memory ability of the model and strengthen the spatio-temporal correlation between the process variables and the quality variables. Then, the random-grid search method is used to optimize the hyperparameters of the prediction model, and the prediction model is constructed. Finally, the penicillin fermentation simulation platform and the hot strip rolling production process data are used for experimental verification. The results show that the proposed model has more accurate prediction effect.
-
Xu Zhizhao, Du Qinjun, Zhao Jinyang, Wu Yutong, Ma Bingtu
2024,38(11):182-192,
Abstract:
Aiming at the problems of large torque ripple in Switched Reluctance Motor and traditional optimization design that only starts from the motor without considering the drive control strategy, a system-level multi-objective optimization design strategy for SRM considering model predictive torque control is proposed by simultaneously considering the motor structure parameters and control parameters. Firstly, the structural parameters of SRM were designed according to the design requirements and MPTC was adopted as the control method to determine the initial values and variation ranges of the motor structure and control parameters; Secondly, an SRM design model considering MPTC was established, and the relationship between structural parameters and prediction models was determined through magnetic circuit analysis. The optimization process of the motor was determined with torque ripple, average torque current ratio, and copper loss as optimization objectives. Sensitivity analysis of structural and control parameters was conducted through orthogonal experiments, and decision variables were selected based on the analysis results. Taguchi algorithm was used for multi-objective optimization of decision variables; Finally, in order to verify the effectiveness of the method, simulation verification was conducted, and a prototype was trial produced based on the optimization results. The experimental results showed that compared with the conventional design, the optimization results reduced the peak motor phase current by 33%, increased the average torque ampere ratio by 33.3%, and reduced torque ripple by 26.3%. The rationality and effectiveness of the optimization method were verified through experiments.
-
2024,38(11):193-199,
Abstract:
Aiming at the problems of small effective area, fixed position and cumbersome arrangement of the traditional velocimetry device in the field of area-intercept velocimetry, based on the principle of electromagnetic induction, a new type of electromagnetic induction sensing unit is proposed and verified for the accurate measurement of the initial velocity of the projectile. Compared with the traditional magnetic induction coil, this structure adopts an induction coil wrapped with a permanent magnet, so that the projectile does not need to be magnetised to generate an induced electromotive force, which improves the sensitivity and measurement accuracy of the velocimetry target. In addition, the sensing unit is independently arranged coaxially with the trajectory, which effectively solves the problem of the relative position between the direction of the ballistic trajectory and the stable position of the test device, increases the effective area of the magnetic induction, and strengthens the portability of the measurement device, which can make it Flexible use in a variety of projectile velocity measurement occasions. The solution uses COMSOL software to model the sensing unit, and conducts detailed simulation analysis of the permanent magnet model and the dynamic process of the projectile passing through the magnetic field under different conditions. Based on the simulation data to create a coil sensing unit, and the simulation results of a number of experimental verification, test results show that the sensing unit sensing voltage increases with the speed of the projectile, and the two are linear within a certain range, consistent with the results obtained from the simulation. This study not only provides theoretical basis and data support for the optimisation of electromagnetic induction velocity target, but also outlines an effective solution for the measurement of in-bore and out-of-bore ballistic muzzle velocity of electromagnetic artillery and other high-speed launch systems.
-
Chen Bo, Sun Hui, Chu Zhaobi, Li Yuling, Wei Jiale
2024,38(11):200-209,
Abstract:
Leveraging extensive ECG data, intelligent ECG recognition represents a pivotal research focus aimed at supporting physicians in conducting thorough data analysis and diagnosis, thereby enhancing efficiency and mitigating medical resource consumption. In order to solve the problem of feature loss and limited performance of single image and single deep learning algorithm in ECG intelligent recognition, a two-input improved VIT recognition method for ECG rainbow code is proposed. Firstly, a mathematical model is proposed to predict the standard period of ECG, and the potential features of ECG are mined by pumping method to generate ECG rainbow code. Then, a dual input feature extraction module is constructed with convolutional neural network to extract local features of multiple ECG images for fusion to achieve multi-dimensional ECG feature representation and fusion. A VIT coding module is used to pay global attention to fusion features to realize ECG recognition based on multi-feature images as input. The ECG recognition method in MIT-BIH database is used for experiments, and the average accuracy of the proposed ECG recognition method is 99.41%, and the accuracy of the N-type ECG collected in the field is 100%. The experimental results show that the proposed image transformation method can effectively visualize ECG features, and the effect is better than the traditional method. The proposed recognition method can realize ECG recognition effectively and has better performance than other similar methods.
-
2024,38(11):210-218,
Abstract:
In the transformation and upgrade of special steel enterprises into “lighthouse factories”, real-time tracking of steel pipe materials is a core component. Due to the diversity of materials and the complexity of the production line, proximity sensors fail to meet the reliability requirements of material detection. Therefore, according to the existing environment and requirements of the workshop, a material tracking camera system is built, and the image data set composed of some characteristics of materials and production lines is collected. Based on video analysis, a steel pipe target detection algorithm for real-time material tracking in special steel workshops is introduced. The algorithm is based on the PPYOLOE network. Firstly, the CSPRepResNet backbone in PPYOLOE is replaced with the lightweight HGNetV2 backbone, which enhances feature extraction capabilities while reducing the number of parameters. Secondly, HG-Block and SPPELAN are integrated into the Neck, further reducing the parameters and improving speed. Finally, in the upsampling stage, the Dysample dynamic upsampling operator is employed to enhance the fusion of multi-scale features, thus improving detection accuracy. Experimental results show that compared with the original PPYOLOE algorithm, the improved algorithm enhances detection accuracy by 1.6%, reaching 80.5%, and increases detection speed by 16%, reaching 56.4 FPS, while GFlops and parameters are reduced by 35% and 33%, respectively. The improved algorithm effectively boosts both detection accuracy and speed,and through on-site deployment, it meets the real-time tracking requirements of steel pipe materials.
-
Yu Zhejun, Liu Lu, Gao Zibo, Kong Ming
2024,38(11):219-227,
Abstract:
Current research on sensor array layout in magnetic positioning systems primarily focuses on quantity and spacing. In related research, the sensor array layout is typically evenly distributed, with limited investigation into the impact of spatial design on system positioning accuracy. Addressing the non-uniform distribution of sensor arrays in magnetic localization systems, this paper proposes an optimization method combining genetic algorithms with finite element simulations. This method determines the optimal sensor layout based on specific trajectories of magnetic targets. Firstly, a simulation model was established for numerical simulation of the magnetic positioning process, and the sensor array layout corresponding to the motion trajectory of each target was optimized by genetic algorithms. Secondly, based on the simulation optimization, an experimental platform for magnetic positioning systems with adjustable sensor installation positions was designed and constructed. Finally, comparative experiments were conducted on five specific magnetic target trajectories using both uniformly distributed and optimized non-uniformly distributed sensor layouts. For example, under trajectory five, the average positioning error of the optimized layout is reduced by 14.3% compared to the pre-optimization layout, and the average orientation error is reduced by 16.3%. The results indicate that uniformly distributed sensor array is not the optimal layout, and optimizing sensor array layout can effectively improve system localization and orientation accuracy.
-
Li Han, Hu Shaobing, Cheng Weibin
2024,38(11):228-234,
Abstract:
Aiming at the problem of low measurement accuracy due to the existence of random error in micro electromechanical system (MEMS) gyroscope, a compensation method based on chaotic particle swarm algorithm (CPSO) optimized back propagation (BP) neural network is proposed to deal with the random error. Firstly, The MEMS gyroscope data are collected, the reconstruction parameters are determined and the phase space is reconstructed using the C-C method, and the chaotic properties are analyzed and verified based on the Lyapunov exponent. Then, the reconstructed data are used as the training samples for the BP neural network model. The BP neural network model is trained, and the weights and thresholds of BP neural network are optimized by using the CPSO algorithm, then the optimized model for error compensation is obtained. Finally, ADXRS624 is used to validate the compensation effect of the optimized model in static experiment, and the compensation results are compared with BP model and particle swarm optimization (PSO) model. Experimental analysis results show that the mean and standard deviation of the gyroscope output errors are -5.76×10-4（°）/s and 5.19×10-4（°）/s, which are decreased by 68.6% and 98.4% compared with the BP model, and 52.1% and 93.5% compared with the particle swarm optimization model, respectively. By comparing the error coefficients after compensation for each method using Allan variance identification, the quantization noise, angle random walk and zero bias instability after being compensated by CPSO-BP method are reduced to 0.000 59 μrad, 0.001 51 （(°)·h-1/2） and 2.82 （(°)·h-1）, respectively. The new method has obvious effect in suppressing the random error and can improve the measurement accuracy of MEMS gyroscope.
-
Zhu Yongkun, Shang Xin, Feng Zhenhua, Wang Pai, Huang Yuchen
2024,38(11):235-241,
Abstract:
Aiming at the problems that the current inspection and monitoring methods cannot accurately identify the fire area below 10 m2 square meters in the high-concentration smoke environment, and it cannot accurately identify the mountain fire in the low-lying area, the fire location technology of the transmission line in the high-concentration smoke environment was studied based on the integrated inertial navigation multi-data fusion. This technology combines and fuses multi-source monitoring data such as satellite remote sensing and inertial navigation data of transmission lines in mountainous areas by using federated filter. An adaptive threshold detection algorithm was designed for ignition points based on context judgment and absolute threshold method, and implement the extraction of ignition point information for transmission lines in mountainous areas. A multi band optoelectronic composite detection target recognition method was designed combining PSO algorithm and BP neural network to achieve the recognition of fire point targets on transmission lines in mountainous areas under high concentration smoke environment. Location of transmission lines in mountainous areas fire point location method based on laser ranging, fire point location of transmission lines in mountainous areas in high smog environment is realized. The experimental test results indicate that the design technology is effective for fire points (10 m2 above the fire area), fire points in plain areas (10 m2 The fire area below), and the fire point in low-lying areas (10 m2 above the fire area). The positioning accuracy of the above three situations is higher than 99.5%.
Volume 38,2024 Issue 11
-
Lightweight PCB Defect Detection Algorithm Based on Improved YOLOv8s
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 (PCBs), 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. Firstly, we introduce a lightweight Cross-Scale Feature Fusion Module (CCFM) and remove the final convolutional layer and C2f layer from the backbone network, reducing model depth and improving the detection accuracy of small objects while achieving model lightweighting. Secondly, we introduce Distribution Shifting Convolution (DSConv), combining C2f and DSConv to create the C2f_DSConv module, which is then integrated with the lightweight attention mechanism CBAM (Convolutional Block Attention Module) 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, improving the regression contribution 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.
- 1
-
Knowledge Distillation Based Spatial Channel Dual Autoencoders for Unsupervised Anomaly Detection
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.
- 1
-
The SOC and SOH of the battery are estimated based on the EKF-HInformer model
Abstract:
To address the issues of low accuracy and poor robustness in traditional SOC and 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 State of Charge (SOC) and State of Health (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.
- 1
-
Configuration reconstruction and strategy of multi-section chain tiltrotor aircraft
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 simulation. 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.
- 1
-
Multi Scale Reverse Correction Enhancement and Lossless Downsampling for Millimeter Wave Image Object Detection Method
Abstract:
A detection method based on multi-scale inverse correction enhancement and lossless downsampling is proposed to improve the detection of hidden targets in millimeter wave images with low local signal-to-noise ratio. Firstly, a multi-scale reverse correction feature enhancement module was designed, which integrates the reverse correction operation on the Res2Net multi convolution kernel. This achieves the reverse correction of convolution calculation between large receptive field regions and related small receptive field regions, enabling finer-grained features across multiple scales; Secondly, utilizing non-step convolutional layers of SPD-Conv to achieve lossless downsampling and preserve more information; Finally, the K-means++ clustering algorithm generates new anchor boxes suitable for hidden object detection tasks. The experiment selected YOLOv5s, which has moderate performance in all aspects, as the basic framework, targeting two existing millimeter wave image datasets (array image dataset and line scan image dataset). mAP@.5 Reaching 96.21% and 97.97% respectively. Compared to the original YOLOv5s and other YOLO series, the performance has significantly improved. The experimental results show that this method can effectively improve the detection performance of deep models without significantly increasing the number of parameters and inference time.
- 1
-
Optical current sensor error prediction based on radial basis Koopman-Kalman
Abstract:
Optical Current Sensor (OCS) is very sensitive to temperature changes, and temperature changes lead to errors in its measurement, which makes it difficult to meet the requirements of power system metering. Therefore, accurate prediction of OCS measurement errors caused by temperature changes is of great significance for monitoring its operational stability and ensuring the safe operation of the power system. Since the OCS output current is strongly nonlinear due to the influence of temperature, this paper proposes a radial basis Koopman-Kalman prediction algorithm for nonlinear power systems, which solves the problem that the OCS output current is difficult to predict under the influence of temperature due to strong nonlinearity. Firstly, the nonlinear OCS output current state quantities are mapped into the high-dimensional space to form an extended state by the Radial Basis Function (RBF), and the extended state is decomposed by the Extended Dynamic Mode Decomposition (EDMD) algorithm to calculate the approximate Koopman-Kalman algorithm in the high-dimensional space. Koopman operator approximation matrix. Secondly, the approximated Koopman operator is used for batch prediction in the high-dimensional linear space. Finally, Kalman filtering is used to update the correction to the last prediction of the batch prediction to follow the state change of the system. The OCS temperature-current data obtained from experimental measurements are used for experiments, and the results show that the mean square error MSE of the prediction algorithm proposed in this paper is reduced by more than 90% in comparison with both the standard Koopman prediction and the LSTM prediction for different temperature variations, which proves the effectiveness of the proposed algorithm.
- 1
-
Prediction Model for Dissolved Gases in Oil Considering Spatiotemporal Characteristics
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.
- 1
-
Unsupervised Person Re-Identification Based on Deep Clustering Learning
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 (GEM) 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 them. 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.
- 1
-
Global-local feature fusion for lightweight sample-level classification of thyroid fine-needle aspiration biopsy whole-slide images
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 6.1M, 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.
- 1
-
Review of digital twin technology and its application in aerospace
宋海龙, 潘建立, 姜震, 朱玉娉, 刘燕, 袭超, 罗震, 王鹏
Abstract:
With the rapid development of intelligent sensing and new generation information technology, digital twin technology is leading the transformation of obstetrics. This article provides an overview of the development history, concepts, characteristics and related technologies of digital twin. At present, there is no universal understanding of the concept of digital twin among various institutions and scholars, and extensive discussions and research are needed in the future. It summarizes the application of digital twins in aerospace in the United States, and elaborates on the current application status of digital twin technology in the design, production, and operation stages of domestic aerospace. The current main problem is that there is still no effective connection between virtual and real entities, and so the direct guidance and optimization of physical entities by virtual models have not been truly achieved. It also summarizes the technical problems that still need to be continuously solved in the application of digital twins in aerospace, including sensing and data processing, high fidelity models, software platform construction, integration with new generation technologies such as artificial intelligence, etc. The current application of digital twin technology in aerospace is still in its early stages, and in the future, it is necessary to actively expand its application scope and scenarios in order to maximize the value and role of digital twin technology.
- 1
-
Research on inclination angle detection of MEMS accelerometer on shearer rocker arm
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 (Micro-Electro-Mechanical Systems) 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 contrib-uting to safer and more efficient mining operations.
- 1
-
Detection and Quantification of Pipeline Magnetic Flux Leakage Defects based on Random Forest
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 PSO-RF algorithm combining particle swarm optimization and random forest 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 obtained by combining two algorithms, and 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.
- 1
-
Research on Cardiovascular Disease Risk Assessment Method Based on Whole Blood Spectral Information Fusion
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.
- 1
-
Multi-Sensor Attitude Estimation Algorithm Based on Mahony Filter and Adaptive CKF
Abstract:
To address the issues of low attitude estimation accuracy and magnetic interference affecting low-cost inertial navigation devices, this paper proposes a multi-sensor fusion algorithm based on Mahony Complementary Filter and Adaptive Cubature Kalman Filter (MACKF). First, the Mahony filter is used to fuse magnetometer and accelerometer data to correct gyroscope output in real-time and actively compensate for magnetically disturbed data through a keyframe mechanism. The corrected attitude quaternion is then used in the Cubature Kalman Filter, with adaptive adjustment of the measurement noise covariance matrix to reduce magnetic interference. Vehicle-mounted experimental results show that this algorithm significantly improves attitude estimation accuracy, with roll, pitch, and yaw angle precision improved by 45.3%, 50.2%, and 32.8%, respectively, compared to traditional methods. Therefore, the proposed algorithm demonstrates excellent performance in mitigating gyroscope drift and resisting magnetic interference.
- 1
-
Multi-Modal Fusion Model for Industry Environment Gas Leakage Detection
Abstract:
Industrial gas leak detection remains a critical challenge, with existing methods predominantly relying on single-modality data. This reliance neglects the complementary nature of different modalities and limits the ability to accurately and robustly detect leaks in complex environments. To address these limitations, this study proposes a novel gas leak detection model, the Multimodal Fusion Transformer (MFT), which integrates data from multiple industrial modalities. The MFT model employs two distinct feature encoders to effectively extract features from each modality, tailored to their unique characteristics. To fully leverage the potential of multimodal data, a multi-head attention mechanism is utilized to fuse the latent representations of different modalities. This approach ensures that the complementary information from each modality is effectively combined. Experimental results demonstrate that the proposed method significantly improves the accuracy and robustness of gas leak detection. The MFT model achieves an impressive 98.05% accuracy on the publicly available MultimodalGasData dataset, highlighting its efficacy in utilizing the complementary information across various modalities. This advancement marks a substantial step forward in enhancing the reliability and performance of industrial gas leak detection systems.
- 1
-
Fault detection algorithm based on evidential reasoning with dependent evidence under complex interference environment
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.
- 1
-
Dynamic obstacle avoidance path planning algorithm for AGVs based on improved HLO and dynamic windows
Abstract:
Aiming at the problems that the human learning optimization algorithm is not efficient in searching, easy to fall into local optimum, and unable to realize dynamic obstacle avoidance, a path planning algorithm integrating improved HLO, and dynamic window is proposed. Firstly, the nonlinear increasing and decreasing probability parameters are used to improve the convergence rate of HLO, and the particle swarm algorithm is introduced to update IKD and SKD and adaptively adjust the inertia weight coefficients, to avoid falling into the local optimum. Secondly, an angular evaluation function is added to the evaluation function of the DWA algorithm to avoid the small angle with the obstacle, and the weights of the speed evaluation function and angular evaluation function are dynamically changed to adjust the speed and angle. Finally, the experiments show that the planning path length of the fusion algorithm is 4% less than the ACO algorithm and 15% less than the HLOPSO algorithm, and the other two algorithms contact with the obstacles 5 times more than the algorithm in this paper, which verifies the feasibility of the algorithm.
- 1
-
Research on electromagnetic conductivity detection method with wide range and high precision
Abstract:
Aiming at solving the problems existing in the traditional water quality detection process with the limited measurement range and the instability of the measurement system, a wide-range and high-precision electromagnetic conductivity measuring device was designed, and the range switching control method was optimized to achieve wide-range, high-precision and high-stability detection of solution concentration. The large-range conductivity measurement system is rather sensitive to the threshold setting during range switching. Firstly, through experiments, the optimal threshold inflection point of the solution conductivity is optimized and selected. For the signals that frequently switch ranges near the threshold, the method of locking the range by comparing point by point is adopted to lock them in a certain range interval, so as to improve the stability and reliability of the system. Secondly, considering the low accuracy of signals in the locked area, the data fusion algorithm based on the fuzzy membership function is used. After conducting the least root mean square error experiments on ten kinds of conductive solutions, the optimal fuzzy interval is selected to complete the fusion processing of the locked signals. Finally, multiple sets of measured data were substituted into the proposed method to verify its effectiveness. The experimental results show that the fusion algorithm after parameter optimization can achieve accurate measurement of the conductivity at the threshold edge, with the maximum measurement error being 0.85%, which is significantly better than the traditional single-range measurement method with a maximum relative error of 2.86%. In addition, the detection range of the designed conductivity sensor is from 0.1 μS/cm to 2000 mS/cm, and the relative errors in the full-range tests are all less than 1%. It indicates that the method proposed in this paper can achieve high-precision and wide-range detection of solution conductivity.
- 1
-
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.
- 1
-
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.
- 1
-
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.
-
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.
-
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
-
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.
