Special issue
2022, 37(2):463-470.
DOI: 10.16337/j.1004-9037.2022.02.019
Abstract:
Aiming at the problems such as low estimation accuracy and poor robustness of traditional static soft measurement model in wind tunnel flow measurement， an Attention-LSTM-Kalman measurement model combing attention mechanism （Attention）， long short-term memory （LSTM） and Kalman filtering （Kalman） is proposed： a static soft-measuring model is established through LSTM network. On this basis， an improved scheme based on attention mechanism is proposed. Considering the dynamic characteristics of the system， Kalman filter is used to dynamically adjust the output sequence of the soft-measuring model. Experimental results show that LSTM is better than recurrent neural network （RNN） and gated recurrent unit （GRU） models. The comparison of the prediction results of the three models based on LSTM， Attention-LSTM and Attention-LSTM-Kalman shows that the attention mechanism could effectively improve the accuracy of the model， and the introduction of Kalman filter improves the dynamic measurement characteristics of the model. The feasibility and effectiveness of the proposed model are verified by the flow measurement in the wind tunnel system.
Aiming at the problems such as low estimation accuracy and poor robustness of traditional static soft measurement model in wind tunnel flow measurement， an Attention-LSTM-Kalman measurement model combing attention mechanism （Attention）， long short-term memory （LSTM） and Kalman filtering （Kalman） is proposed： a static soft-measuring model is established through LSTM network. On this basis， an improved scheme based on attention mechanism is proposed. Considering the dynamic characteristics of the system， Kalman filter is used to dynamically adjust the output sequence of the soft-measuring model. Experimental results show that LSTM is better than recurrent neural network （RNN） and gated recurrent unit （GRU） models. The comparison of the prediction results of the three models based on LSTM， Attention-LSTM and Attention-LSTM-Kalman shows that the attention mechanism could effectively improve the accuracy of the model， and the introduction of Kalman filter improves the dynamic measurement characteristics of the model. The feasibility and effectiveness of the proposed model are verified by the flow measurement in the wind tunnel system.
2022, 37(2):359-370.
DOI: 10.16337/j.1004-9037.2022.02.009
Abstract:
Magnetic acoustic emission （MAE） is an acoustic emission signal generated in the magnetization process of ferromagnetic materials， which has a wide range of applications in component stress detection and micro damage detection. Aiming at the characteristics of MAE signal instability， complexity， and attenuation， a denoising method based on seagull optimization algorithm combined with variational mode decomposition （SOA-VMD） is proposed. In order to overcome the problem of getting into the local optimal solution in the solving process of the seagull algorithm， we use the Cauchy variation operator to generate random iterations， making Cauchy variation seagull optimization algorithm （CVSOA） to jump out of premature convergence. The amplitude spectrum entropy is used as the fitness function， and the SOA is used to optimize the number of decomposed modes K and secondary penalty termα ![]()
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Magnetic acoustic emission （MAE） is an acoustic emission signal generated in the magnetization process of ferromagnetic materials， which has a wide range of applications in component stress detection and micro damage detection. Aiming at the characteristics of MAE signal instability， complexity， and attenuation， a denoising method based on seagull optimization algorithm combined with variational mode decomposition （SOA-VMD） is proposed. In order to overcome the problem of getting into the local optimal solution in the solving process of the seagull algorithm， we use the Cauchy variation operator to generate random iterations， making Cauchy variation seagull optimization algorithm （CVSOA） to jump out of premature convergence. The amplitude spectrum entropy is used as the fitness function， and the SOA is used to optimize the number of decomposed modes K and secondary penalty term
2022, 37(2):405-414.
DOI: 10.16337/j.1004-9037.2022.02.013
Abstract:
Aiming at the problem that the existing dam disease detection methods can only roughly locate the area where the crack is located， a dam crack extraction method based on a universal target detector is proposed. Firstly， a two-target detector is designed to detect the crack area and the water stain area as two independent targets on the image at the same time. Secondly， the geometric position relationship between the crack area and the water stain area associated with the same crack is established. Finally， the upper boundary of the water stain frame contained in the crack frame is uniformly sampled， and the curve fitting is performed on the sampling points to obtain the crack curve. The experimental results show that the proposed algorithm can not only accurately detect the crack frame and water stain frame， but also fit the crack curve completely， and it has been effectively verified in the detection of dam diseases with millimeter-level width.
Aiming at the problem that the existing dam disease detection methods can only roughly locate the area where the crack is located， a dam crack extraction method based on a universal target detector is proposed. Firstly， a two-target detector is designed to detect the crack area and the water stain area as two independent targets on the image at the same time. Secondly， the geometric position relationship between the crack area and the water stain area associated with the same crack is established. Finally， the upper boundary of the water stain frame contained in the crack frame is uniformly sampled， and the curve fitting is performed on the sampling points to obtain the crack curve. The experimental results show that the proposed algorithm can not only accurately detect the crack frame and water stain frame， but also fit the crack curve completely， and it has been effectively verified in the detection of dam diseases with millimeter-level width.
2022, 37(5):1157-1168.
DOI: 10.16337/j.1004-9037.2022.05.020
Abstract:
In order to improve the joint estimation accuracy of time of arrival （TOA） and direction of arrival （DOA） in existing impulse radio ultra wideband （IR-UWB） systems， a joint estimation method of TOA and DOA based on discrete Fourier transform （DFT） is proposed. First， the received signal is modeled in the frequency domain. Second， the rough TOA estimation results of the two antennas can be obtained via processing DFT on the covariance matrix of the received signal in the frequency domain. Based on the rough estimation results， we search the compensation value with a small interval， thereby the accurate estimation of TOA can be obtained. Third， according to the relationship between the time difference of arrival of the two antennas and DOA， we obtain the DOA estimation results， thus realize the joint estimation of TOA and DOA. The simulation results show that the performance of the proposed algorithm is better than that of the matrix pencil algorithm and the traditional propagation method， and the advantage becomes more obvious with the increase of SNR. Moreover， the proposed algorithm is easier to be implemented in engineering because it does not need complex eigenvalue decomposition and generalized inverse solution steps.
In order to improve the joint estimation accuracy of time of arrival （TOA） and direction of arrival （DOA） in existing impulse radio ultra wideband （IR-UWB） systems， a joint estimation method of TOA and DOA based on discrete Fourier transform （DFT） is proposed. First， the received signal is modeled in the frequency domain. Second， the rough TOA estimation results of the two antennas can be obtained via processing DFT on the covariance matrix of the received signal in the frequency domain. Based on the rough estimation results， we search the compensation value with a small interval， thereby the accurate estimation of TOA can be obtained. Third， according to the relationship between the time difference of arrival of the two antennas and DOA， we obtain the DOA estimation results， thus realize the joint estimation of TOA and DOA. The simulation results show that the performance of the proposed algorithm is better than that of the matrix pencil algorithm and the traditional propagation method， and the advantage becomes more obvious with the increase of SNR. Moreover， the proposed algorithm is easier to be implemented in engineering because it does not need complex eigenvalue decomposition and generalized inverse solution steps.
2022, 37(6):1288-1296.
DOI: 10.16337/j.1004-9037.2022.06.010
Abstract:
The optimization of resource allocation in wireless communication networks can be described as a mixed integer nonlinear programming （MINLP） problem. It is essentially a non-convex NP hard problem. In order to reduce the computational complexity and ensure the optimal performance of the allocation scheme， a binary whale optimization algorithm （WOA） is proposed to allocate wireless resources. Based on the original WOA position update is carried out based on the switch between values 1 and 0. The current position changes are determined by the probability calculated by the humpback spiral movement. Meanwhile， different transfer functions are used to map the continuous search space to discrete actions， and the penalty method and the optimization constraint processing are introduced. Two cases of resource allocation in wireless networks are analyzed in the experiment： The power allocation problem with maximum confidentiality and the mobile edge computing migration. The results show that the proposed method has excellent system performance and obtains similar effects to other methods， but its convergence speed is faster and its complexity is lower.
The optimization of resource allocation in wireless communication networks can be described as a mixed integer nonlinear programming （MINLP） problem. It is essentially a non-convex NP hard problem. In order to reduce the computational complexity and ensure the optimal performance of the allocation scheme， a binary whale optimization algorithm （WOA） is proposed to allocate wireless resources. Based on the original WOA position update is carried out based on the switch between values 1 and 0. The current position changes are determined by the probability calculated by the humpback spiral movement. Meanwhile， different transfer functions are used to map the continuous search space to discrete actions， and the penalty method and the optimization constraint processing are introduced. Two cases of resource allocation in wireless networks are analyzed in the experiment： The power allocation problem with maximum confidentiality and the mobile edge computing migration. The results show that the proposed method has excellent system performance and obtains similar effects to other methods， but its convergence speed is faster and its complexity is lower.
2022, 37(1):207-216.
DOI: 10.16337/j.1004-9037.2022.01.018
Abstract:
To improve the intelligent design of the rural power distribution network， this paper proposes to identify the typical power targets that affect the design of the distribution network in the aerial images using deep neural networks. Firstly， we use UAV to obtain high spatial resolution aerial images of the distribution network planning area， and construct a data set containing 11 categories and 32 118 typical power targets. Then， through the practical comparison of Faster-RCNN， YOLO and single shot multibox detector （SSD） methods， SSD is selected to detect and identify typical power targets. Finally， feasible areas of distribution network pole planning are obtained. Experimental results show that compared with Faster-RCNN and YOLO， SSD can effectively detect and identify typical power targets such as the substation， distribution room and box transformer， and the recognition accuracy reaches 68.5%， which meets the practical requirements. The proposed method provides the technical support for the power design， reduces the labor cost and improves the efficiency of distribution network design.
To improve the intelligent design of the rural power distribution network， this paper proposes to identify the typical power targets that affect the design of the distribution network in the aerial images using deep neural networks. Firstly， we use UAV to obtain high spatial resolution aerial images of the distribution network planning area， and construct a data set containing 11 categories and 32 118 typical power targets. Then， through the practical comparison of Faster-RCNN， YOLO and single shot multibox detector （SSD） methods， SSD is selected to detect and identify typical power targets. Finally， feasible areas of distribution network pole planning are obtained. Experimental results show that compared with Faster-RCNN and YOLO， SSD can effectively detect and identify typical power targets such as the substation， distribution room and box transformer， and the recognition accuracy reaches 68.5%， which meets the practical requirements. The proposed method provides the technical support for the power design， reduces the labor cost and improves the efficiency of distribution network design.
2022, 37(5):1169-1178.
DOI: 10.16337/j.1004-9037.2022.05.021
Abstract:
The equipment monitoring of the merging unit relies on on-site staff records， practical experience and preset alarm threshold， and the lack of analysis and mining of the system monitoring data makes it impossible to realize the device state prediction. In view of this， according to the timing characteristics of the level data of the monitoring merge unit， a combined model of the traditional timing model of autoregressive integrated moving average （ARIMA） and long short-term memory（LSTM） is constructed and optimized by using shuffled frog leaping algorithm（SFLA）. The optimized model is applied to the level data prediction analysis of the combined unit laser monitoring. The comparison of the ARIMA-LSTM optimized combination model with the single model verifies that the former has higher accuracy than the latter. Further comparison experimental results show that the combined model is superior to the other combined models after the SFLA algorithm optimization， which can better mine the hidden information and trend in the data， improving the accuracy of time series data prediction and the efficiency of fault troubleshooting. By comparing the combined ARIMA-SVM model and the proposed ARIMA-LSTM model， experimental results show that the proposed ARIMA-LSTM model is superior to the ARIMA-SVM model， and it can better analyze and grasp the device state information， and realize the level data prediction of the merging unit equipment.
The equipment monitoring of the merging unit relies on on-site staff records， practical experience and preset alarm threshold， and the lack of analysis and mining of the system monitoring data makes it impossible to realize the device state prediction. In view of this， according to the timing characteristics of the level data of the monitoring merge unit， a combined model of the traditional timing model of autoregressive integrated moving average （ARIMA） and long short-term memory（LSTM） is constructed and optimized by using shuffled frog leaping algorithm（SFLA）. The optimized model is applied to the level data prediction analysis of the combined unit laser monitoring. The comparison of the ARIMA-LSTM optimized combination model with the single model verifies that the former has higher accuracy than the latter. Further comparison experimental results show that the combined model is superior to the other combined models after the SFLA algorithm optimization， which can better mine the hidden information and trend in the data， improving the accuracy of time series data prediction and the efficiency of fault troubleshooting. By comparing the combined ARIMA-SVM model and the proposed ARIMA-LSTM model， experimental results show that the proposed ARIMA-LSTM model is superior to the ARIMA-SVM model， and it can better analyze and grasp the device state information， and realize the level data prediction of the merging unit equipment.
2023, 38(3):727-740.
DOI: 10.16337/j.1004-9037.2023.03.020
Abstract:
With the advent of the data era， the classification of unbalanced data is receiving more and more attention. In the classification of unbalanced data， classification results are often incorrect due to an imbalance in the ratio of minority class samples to majority class ones. Therefore， we propose an oversampling algorithm based on the Bootstrap method under the maximum entropy principle. Firstly， the probability distribution of the data sample is obtaited through self-help method and optimized using the principle of maximum entropy. Secondly， a probability enhancement algorithm based on minority class sample distribution is proposed based on different abilities of minority classes to generate new minority classes. The algorithm allows the randomness of the data to be fully represented and ensures that the probability density of the minority class remains consistent before and after the data set is balanced， thus improving the effectiveness of the classification algorithm. Finally， experiments are conducted by selecting eight data sets from the UCI and KEEL databases， whose results show that the proposed algorithm is more effective than other algorithms.
With the advent of the data era， the classification of unbalanced data is receiving more and more attention. In the classification of unbalanced data， classification results are often incorrect due to an imbalance in the ratio of minority class samples to majority class ones. Therefore， we propose an oversampling algorithm based on the Bootstrap method under the maximum entropy principle. Firstly， the probability distribution of the data sample is obtaited through self-help method and optimized using the principle of maximum entropy. Secondly， a probability enhancement algorithm based on minority class sample distribution is proposed based on different abilities of minority classes to generate new minority classes. The algorithm allows the randomness of the data to be fully represented and ensures that the probability density of the minority class remains consistent before and after the data set is balanced， thus improving the effectiveness of the classification algorithm. Finally， experiments are conducted by selecting eight data sets from the UCI and KEEL databases， whose results show that the proposed algorithm is more effective than other algorithms.
2023, 38(1):150-161.
DOI: 10.16337/j.1004-9037.2023.01.013
Abstract:
Vortex plays a crucial role in the formation and maintenance of various flow structures in fluid motion. The identification and detection of vortices are helpful to understand the flow laws. Traditional vortex detection methods have many shortcomings， such as inaccurate definition， heavy dependence on empirical threshold and poor generalization performance， which make vortex detection challenging. In this paper， a vortex detection model based on object detection algorithm is proposed from the perspective of computer vision. Aiming at the problem that the original object detection model has unsatisfactory detection accuracy on slender vortices with extreme aspect ratio， this paper analyzes the data characteristics of two different types of vortices. A feature adaptive module based on deformable convolutional network （DCN） and a slender sample mining method based on improved loss function are proposed. The cylindrical wake vortex and submarine tail vortex data sets are used to verify the proposed model. Experimental results show that the improved model improves the detection accuracy significantly， and the detection accuracy of slender vortex is especially significantly improved， which effectively balances the performance of various types of vortex detection.
Vortex plays a crucial role in the formation and maintenance of various flow structures in fluid motion. The identification and detection of vortices are helpful to understand the flow laws. Traditional vortex detection methods have many shortcomings， such as inaccurate definition， heavy dependence on empirical threshold and poor generalization performance， which make vortex detection challenging. In this paper， a vortex detection model based on object detection algorithm is proposed from the perspective of computer vision. Aiming at the problem that the original object detection model has unsatisfactory detection accuracy on slender vortices with extreme aspect ratio， this paper analyzes the data characteristics of two different types of vortices. A feature adaptive module based on deformable convolutional network （DCN） and a slender sample mining method based on improved loss function are proposed. The cylindrical wake vortex and submarine tail vortex data sets are used to verify the proposed model. Experimental results show that the improved model improves the detection accuracy significantly， and the detection accuracy of slender vortex is especially significantly improved， which effectively balances the performance of various types of vortex detection.
2023, 38(5):1180-1190.
DOI: 10.16337/j.1004-9037.2023.05.016
Abstract:
Aiming at the path planning problem of unmanned aerial vehicle （UAV） with limited view ability in unknown environment， a particle swarm optimization （PSO） algorithm based on convex optimization is proposed to select path points. In the iterative optimization process， the fitness function of particle swarm is designed based on the trajectory， obstacle avoidance and the distance to the end point solved by the convex optimization. The trajectory between the path points is displayed after the optimal path point is obtained. The obtained trajectory is used as a part of simultaneous localization and mapping （SLAM） to build a more reliable environment map. Theoretical analysis and experimental simulation results show that compared with other intelligent algorithms and sample-based path planning algorithms， the proposed PSO based on convex optimization can effectively improve the efficiency of path planning and reduce the length of the planned path.
Aiming at the path planning problem of unmanned aerial vehicle （UAV） with limited view ability in unknown environment， a particle swarm optimization （PSO） algorithm based on convex optimization is proposed to select path points. In the iterative optimization process， the fitness function of particle swarm is designed based on the trajectory， obstacle avoidance and the distance to the end point solved by the convex optimization. The trajectory between the path points is displayed after the optimal path point is obtained. The obtained trajectory is used as a part of simultaneous localization and mapping （SLAM） to build a more reliable environment map. Theoretical analysis and experimental simulation results show that compared with other intelligent algorithms and sample-based path planning algorithms， the proposed PSO based on convex optimization can effectively improve the efficiency of path planning and reduce the length of the planned path.
2023, 38(2):451-467.
DOI: 10.16337/j.1004-9037.2023.02.019
Abstract:
To solve the problems of the traditional A* algorithm and the traditional dynamic window approach （DWA） in mobile robot path planning， a dynamic path planning method combining the improved A* algorithm and the improved DWA is proposed. First， the 16-neighborhood and 16-direction path search method is adopted to expand the path search field and reduce the number of the nodes accessed and the turning angles. Second， the heuristic function is optimized to enhance the purpose of the path search. Next， the redundant point deletion strategy is adopted to reduce the number of the turning points and further improve the smoothness of the path. Third， the path corner is processed by the B-spline curve， and the path is relatively smooth. Then， the sensitivity of obstacle avoidance can be improved by classifying and treating obstacles differently and adding the speed adaptive factor in the evaluation function of DWA. Finally， through three parts of the simulation experiments with the other algorithms， and the priority strategy simulation experiments， the effectiveness of the improved A* algorithm and the superiority of the fusion method in obstacle avoidance are verified.
To solve the problems of the traditional A* algorithm and the traditional dynamic window approach （DWA） in mobile robot path planning， a dynamic path planning method combining the improved A* algorithm and the improved DWA is proposed. First， the 16-neighborhood and 16-direction path search method is adopted to expand the path search field and reduce the number of the nodes accessed and the turning angles. Second， the heuristic function is optimized to enhance the purpose of the path search. Next， the redundant point deletion strategy is adopted to reduce the number of the turning points and further improve the smoothness of the path. Third， the path corner is processed by the B-spline curve， and the path is relatively smooth. Then， the sensitivity of obstacle avoidance can be improved by classifying and treating obstacles differently and adding the speed adaptive factor in the evaluation function of DWA. Finally， through three parts of the simulation experiments with the other algorithms， and the priority strategy simulation experiments， the effectiveness of the improved A* algorithm and the superiority of the fusion method in obstacle avoidance are verified.
2023, 38(3):616-628.
DOI: 10.16337/j.1004-9037.2023.03.011
Abstract:
Kernel method transforms the linear non-separable problem in low-dimensional space into the linear separable problem in high-dimensional space. It is widely used in a variety of learning models. However， the existing kernel selection methods have low computational efficiency and high time cost in large-scale data. Aiming at above problems， this paper introduces the random Fourier feature to transform the original kernel feature space into another relatively low dimensional explicit random feature space. The theoretical analysis of the upper bound of the kernel approximation error and the upper bound of the error of training the learning model in the kernel approximation random feature space are given. The convergence consistency of kernel approximation and the relationship between error upper bound and kernel approximation parameters are obtained. Moreover， the optimal model parameters are selected based on random Fourier feature space， which can avoid the large-scale search for the optimal original Gaussian kernel model parameters， so as to greatly reduce the time cost required for the selection of the original Gaussian kernel model. Experiments show that the error upper bound proved in this paper is controlled by the kernel approximation parameters. The optimal model selected by the kernel approximation has good performance compared with the original Gaussian kernel function model， and the model selection time is greatly reduced compared with the grid search method.
Kernel method transforms the linear non-separable problem in low-dimensional space into the linear separable problem in high-dimensional space. It is widely used in a variety of learning models. However， the existing kernel selection methods have low computational efficiency and high time cost in large-scale data. Aiming at above problems， this paper introduces the random Fourier feature to transform the original kernel feature space into another relatively low dimensional explicit random feature space. The theoretical analysis of the upper bound of the kernel approximation error and the upper bound of the error of training the learning model in the kernel approximation random feature space are given. The convergence consistency of kernel approximation and the relationship between error upper bound and kernel approximation parameters are obtained. Moreover， the optimal model parameters are selected based on random Fourier feature space， which can avoid the large-scale search for the optimal original Gaussian kernel model parameters， so as to greatly reduce the time cost required for the selection of the original Gaussian kernel model. Experiments show that the error upper bound proved in this paper is controlled by the kernel approximation parameters. The optimal model selected by the kernel approximation has good performance compared with the original Gaussian kernel function model， and the model selection time is greatly reduced compared with the grid search method.
2023, 38(3):608-615.
DOI: 10.16337/j.1004-9037.2023.03.010
Abstract:
Long-term detection and evaluation of electrocardiogram （ECG） signals is crucial for the diagnosis and prevention of cardiovascular disease. However， the detection of ECG signals usually needs to install electrodes on the patient， which can easily cause discomfort to the subject， and the scope of application is thus limited. In contrast， pulse wave signals detected by photoplethysmography （PPG） not only contains rich cardiovascular physiological and pathological information， but also is easy to be measured. Considering the inherent mapping relationship between PPG and ECG signals， a model of transferring PPG to ECG signals based on generative adversarial network （GAN） is proposed. The generator network is composed of the Unet model， the structure of Unet++ is referenced in the feature map fusion， and the discriminator network is composed of a convolutional neural network. During the training process， gradient penalty is utilized to increase the stability of the model. The experiment is conducted based on public datasets. By comparing the processing results of a sample of 53 subjects， the root mean square error （RMSE）， Pearson correlation coefficient （ρ） and Fréchet distance （FD） of the ECG signal generated by the new model are improved by 3.4%， 5.5% and 0.4%， respectively， proving that the new model has better PPG-ECG transfer effect.
Long-term detection and evaluation of electrocardiogram （ECG） signals is crucial for the diagnosis and prevention of cardiovascular disease. However， the detection of ECG signals usually needs to install electrodes on the patient， which can easily cause discomfort to the subject， and the scope of application is thus limited. In contrast， pulse wave signals detected by photoplethysmography （PPG） not only contains rich cardiovascular physiological and pathological information， but also is easy to be measured. Considering the inherent mapping relationship between PPG and ECG signals， a model of transferring PPG to ECG signals based on generative adversarial network （GAN） is proposed. The generator network is composed of the Unet model， the structure of Unet++ is referenced in the feature map fusion， and the discriminator network is composed of a convolutional neural network. During the training process， gradient penalty is utilized to increase the stability of the model. The experiment is conducted based on public datasets. By comparing the processing results of a sample of 53 subjects， the root mean square error （RMSE）， Pearson correlation coefficient （ρ） and Fréchet distance （FD） of the ECG signal generated by the new model are improved by 3.4%， 5.5% and 0.4%， respectively， proving that the new model has better PPG-ECG transfer effect.
2023, 38(6):1235-1256.
DOI: 10.16337/j.1004-9037.2023.06.001
Abstract:
The hot research topic of adversarial sample attacks on modulation recognition is reviewed. Firstly， we introduce the concepts and terms related to modulation recognition adversarial samples. Then we review and sort out the related research results on adversarial sample attacks and defense methods， and classify the existing adversarial attack methods and explain their generation mechanisms. Finally， based on the existing research， potential opportunities and challenges， and the advantages of artificial intelligence algorithms， the technical directions and development prospects of adversarial attacks in next-generation intelligent wireless communications are presented.
The hot research topic of adversarial sample attacks on modulation recognition is reviewed. Firstly， we introduce the concepts and terms related to modulation recognition adversarial samples. Then we review and sort out the related research results on adversarial sample attacks and defense methods， and classify the existing adversarial attack methods and explain their generation mechanisms. Finally， based on the existing research， potential opportunities and challenges， and the advantages of artificial intelligence algorithms， the technical directions and development prospects of adversarial attacks in next-generation intelligent wireless communications are presented.
2023, 38(1):63-73.
DOI: 10.16337/j.1004-9037.2023.01.004
Abstract:
A shortwave wideband specific signal detection algorithm based on frequency-sensitive attention is proposed to improve the accuracy of specific signal detection and recognition in a shortwave complex electromagnetic environment. A frequency-sensitive attention mechanism with a narrow and long shape receptive field is designed based on the correlation in the time direction and the locality in the frequency direction of shortwave specific signals in the spectrogram， and an end-to-end shortwave specific signal detector frequency sensitive signal detector （FSSDet） is constructed on this basis by segmenting the feature map into strip block along the time-axis direction and calculating the self-attention in the strip block， capturing the long-distance dependence in time-axis direction and limiting the sensing range in frequency-axis direction. FSSDet can directly output the modulation type of several specific signals， as well as important parameter information such as start and end time， center frequency， and bandwidth when a spectrogram of a shortwave wideband signal is given as input. Experiments are carried out on a simulation dataset of 47 880 samples from eight classes， and the proposed method has mean average precision （mAP） as high as 98.5 above 0 dB and remains above 72.5 when the signal noise ratio （SNR） is as low as -10 dB. The results show that the proposed method detects and recognizes short wave specific signals with high accuracy and robustness under low SNR.
A shortwave wideband specific signal detection algorithm based on frequency-sensitive attention is proposed to improve the accuracy of specific signal detection and recognition in a shortwave complex electromagnetic environment. A frequency-sensitive attention mechanism with a narrow and long shape receptive field is designed based on the correlation in the time direction and the locality in the frequency direction of shortwave specific signals in the spectrogram， and an end-to-end shortwave specific signal detector frequency sensitive signal detector （FSSDet） is constructed on this basis by segmenting the feature map into strip block along the time-axis direction and calculating the self-attention in the strip block， capturing the long-distance dependence in time-axis direction and limiting the sensing range in frequency-axis direction. FSSDet can directly output the modulation type of several specific signals， as well as important parameter information such as start and end time， center frequency， and bandwidth when a spectrogram of a shortwave wideband signal is given as input. Experiments are carried out on a simulation dataset of 47 880 samples from eight classes， and the proposed method has mean average precision （mAP） as high as 98.5 above 0 dB and remains above 72.5 when the signal noise ratio （SNR） is as low as -10 dB. The results show that the proposed method detects and recognizes short wave specific signals with high accuracy and robustness under low SNR.
2023, 38(6):1331-1341.
DOI: 10.16337/j.1004-9037.2023.06.009
Abstract:
In order to increase the bits of binary data transmitted per second in direct sequence spread spectrum（DSSS） systems and enhance the security of information transmission， a mapping transmission mechanism is established， and a two-dimensional data transmission method with constellation rotation mapping is proposed. As the one-dimensional data is transmitted， the two-dimensional data is added， and the relationship model is established by using the M-ary conversion and constellation rotation. The constellation is selected according to the ratio between one-dimensional data rate and two-dimensional data rate， and then the two-dimensional data is converted into mapping data by constellation rotation mapping， so as to obtain the corresponding pseudo-code channel and achieve the transmission of one-dimensional data and the mapping transmission of two-dimensional data. The simulation results show that compared with the traditional DSSS system， the two-dimensional data transmission method with constellation rotation mapping can obtain higher data transmission rate and better error code rate performance， as well as meet the requirements of better confidentiality performance.
In order to increase the bits of binary data transmitted per second in direct sequence spread spectrum（DSSS） systems and enhance the security of information transmission， a mapping transmission mechanism is established， and a two-dimensional data transmission method with constellation rotation mapping is proposed. As the one-dimensional data is transmitted， the two-dimensional data is added， and the relationship model is established by using the M-ary conversion and constellation rotation. The constellation is selected according to the ratio between one-dimensional data rate and two-dimensional data rate， and then the two-dimensional data is converted into mapping data by constellation rotation mapping， so as to obtain the corresponding pseudo-code channel and achieve the transmission of one-dimensional data and the mapping transmission of two-dimensional data. The simulation results show that compared with the traditional DSSS system， the two-dimensional data transmission method with constellation rotation mapping can obtain higher data transmission rate and better error code rate performance， as well as meet the requirements of better confidentiality performance.
2023, 38(3):717-726.
DOI: 10.16337/j.1004-9037.2023.03.019
Abstract:
With the development of communication technology， increasingly higher communication frequency bands are adopted. However， the electromagnetic wave diffraction capability decreases with increasing frequency. New generation communication systems become more dependent on line-of-sight propagation. Frequent beam switching is required in complex mobile scenarios， which increases excessive system overhead and delay. To address this problem， a position information-assisted grid-based beam switching method is proposed. The feature that the optimal beam pair remains constant in the presence of line of sight（LOS） path is utilized. The grid beam one-to-one correspondence and coverage distribution structure are divided. A position-beam mapping table is establised. All the next switching points and switching information are predicted based on position information and motion speed. The simulation and analysis results show that the proposed method significantly improves the spectral efficiency of the system compared with the non-grid switching method， and the proposed square hexagonal grid switching performance is better than the square grid， and the beam switching probability is reduced by 50%.It guarantees the communication quality and verifies the rationality of the grid-based beam switching method assisted by the position information.
With the development of communication technology， increasingly higher communication frequency bands are adopted. However， the electromagnetic wave diffraction capability decreases with increasing frequency. New generation communication systems become more dependent on line-of-sight propagation. Frequent beam switching is required in complex mobile scenarios， which increases excessive system overhead and delay. To address this problem， a position information-assisted grid-based beam switching method is proposed. The feature that the optimal beam pair remains constant in the presence of line of sight（LOS） path is utilized. The grid beam one-to-one correspondence and coverage distribution structure are divided. A position-beam mapping table is establised. All the next switching points and switching information are predicted based on position information and motion speed. The simulation and analysis results show that the proposed method significantly improves the spectral efficiency of the system compared with the non-grid switching method， and the proposed square hexagonal grid switching performance is better than the square grid， and the beam switching probability is reduced by 50%.It guarantees the communication quality and verifies the rationality of the grid-based beam switching method assisted by the position information.
2023, 38(6):1445-1457.
DOI: 10.16337/j.1004-9037.2023.06.018
Abstract:
The CS-Flow method based on normalizing flows has achieved good results in the field of defect detection， but its way of repeatedly stacking single coupling blocks increases the complexity of the network. Therefore， we propose a network ValidFlow composed of two coupling blocks stacking： Feature advection flow （FA flow） and feature blending flow （FB flow）. In the subnetwork of FA flow， the short-cut branch of up and down sampling is removed and depth-separable convolution is introduced. The subnetworks within FB flow are fused across scales at three scales. This allows ValidFlow to reduce the number of parameters while keeping the information well mixed. Compared with the existing methods on MVTec AD，MTD and DAGM datasets， it can be seen that on MVTec AD datasets， the average AUROC of ValidFlow in 15 categories is 99.2%， and the AUROC of ValidFlow in four categories is 100%. On the MTD dataset， AUROC achieves 99.6%. At the same time， compared with CS-Flow， ValidFlow has 207.61M fewer parameters and 22 higher reasoning speed FPS. On the DAGM dataset， the average AUROC of the 10 categories is 99.0%， which is very close to the monitored method in terms of performance.
The CS-Flow method based on normalizing flows has achieved good results in the field of defect detection， but its way of repeatedly stacking single coupling blocks increases the complexity of the network. Therefore， we propose a network ValidFlow composed of two coupling blocks stacking： Feature advection flow （FA flow） and feature blending flow （FB flow）. In the subnetwork of FA flow， the short-cut branch of up and down sampling is removed and depth-separable convolution is introduced. The subnetworks within FB flow are fused across scales at three scales. This allows ValidFlow to reduce the number of parameters while keeping the information well mixed. Compared with the existing methods on MVTec AD，MTD and DAGM datasets， it can be seen that on MVTec AD datasets， the average AUROC of ValidFlow in 15 categories is 99.2%， and the AUROC of ValidFlow in four categories is 100%. On the MTD dataset， AUROC achieves 99.6%. At the same time， compared with CS-Flow， ValidFlow has 207.61M fewer parameters and 22 higher reasoning speed FPS. On the DAGM dataset， the average AUROC of the 10 categories is 99.0%， which is very close to the monitored method in terms of performance.
19 Signal Acquisition and Processing Technology of Flexible Sensor Intelligent Pulse Diagnosis System
2024, 39(1):236-246.
DOI: 10.16337/j.1004-9037.2024.01.021
Abstract:
The development and application of pulse diagnostic instruments provide an objective basis for the intelligent diagnosis of traditional Chinese medicine. However， the existing pulse diagnostic instruments do not consider the influence of the collection region （Cun， Guan， Chi） and pressure （Fu， Zhong， Chen） on the diagnostic results， and there is still room for the improvement of the diagnostic accuracy. In order to recognize pulse condition more accurately， this paper presents an intelligent pulse diagnosis system based on flexible sensors and the corresponding pulse signal processing method. By installing three array flexible sensors at the collection region of Cun， Guan and Chi and setting different pressure thresholds of Fu， Zhong and Chen， multiple pulse signals are obtained. Signal features are then extracted， and multi-channel features are integrated based on multi-set canonical correlations analysis （MCCA） to get more pulse information. Experimental results show that the proposed method can further improve the accuracy of pulse condition classification in four typical pulse types. The multi-point pulse condition induction designed in this paper based on two aspects of region and pressure can simulate and restore the real Chinese medicine diagnosis process and help to extract real pulse signals， providing a theoretical basis and reference value for the subsequent research and development of intelligent pulse diagnosis instruments based on flexible sensors.
The development and application of pulse diagnostic instruments provide an objective basis for the intelligent diagnosis of traditional Chinese medicine. However， the existing pulse diagnostic instruments do not consider the influence of the collection region （Cun， Guan， Chi） and pressure （Fu， Zhong， Chen） on the diagnostic results， and there is still room for the improvement of the diagnostic accuracy. In order to recognize pulse condition more accurately， this paper presents an intelligent pulse diagnosis system based on flexible sensors and the corresponding pulse signal processing method. By installing three array flexible sensors at the collection region of Cun， Guan and Chi and setting different pressure thresholds of Fu， Zhong and Chen， multiple pulse signals are obtained. Signal features are then extracted， and multi-channel features are integrated based on multi-set canonical correlations analysis （MCCA） to get more pulse information. Experimental results show that the proposed method can further improve the accuracy of pulse condition classification in four typical pulse types. The multi-point pulse condition induction designed in this paper based on two aspects of region and pressure can simulate and restore the real Chinese medicine diagnosis process and help to extract real pulse signals， providing a theoretical basis and reference value for the subsequent research and development of intelligent pulse diagnosis instruments based on flexible sensors.
2024, 39(2):471-480.
DOI: 10.16337/j.1004-9037.2024.02.019
Abstract:
Frequency estimation of sine wave signals is a common problem in the radar field. When the true frequency approaches the quantization frequency points， the calculation of the frequency shift factor in the I-Rife algorithm can introduce significant errors. In order to improve the accuracy of frequency estimation， this paper analyzes the performance and error sources of the Rife and I-Rife algorithms. By utilizing a spectral refinement method， a modified I-Rife algorithm is proposed. It replaces the amplitude of the spectral peak point with the amplitudes at 0.5 points to the left and right of the peak point， and interpolates the amplitude using the second highest frequency point. This approach allows for a more accurate estimation of the frequency offset. The proposed algorithm effectively enhances the estimation accuracy of frequency while maintaining a similar computational complexity to the original I-Rife algorithm. Simulation results demonstrate that the improved I-Rife algorithm outperforms the original I-Rife algorithm in overall performance and achieves an estimated root mean square error closer to the Cramér-Rao lower bound.
Frequency estimation of sine wave signals is a common problem in the radar field. When the true frequency approaches the quantization frequency points， the calculation of the frequency shift factor in the I-Rife algorithm can introduce significant errors. In order to improve the accuracy of frequency estimation， this paper analyzes the performance and error sources of the Rife and I-Rife algorithms. By utilizing a spectral refinement method， a modified I-Rife algorithm is proposed. It replaces the amplitude of the spectral peak point with the amplitudes at 0.5 points to the left and right of the peak point， and interpolates the amplitude using the second highest frequency point. This approach allows for a more accurate estimation of the frequency offset. The proposed algorithm effectively enhances the estimation accuracy of frequency while maintaining a similar computational complexity to the original I-Rife algorithm. Simulation results demonstrate that the improved I-Rife algorithm outperforms the original I-Rife algorithm in overall performance and achieves an estimated root mean square error closer to the Cramér-Rao lower bound.
2024, 39(2):481-489.
DOI: 10.16337/j.1004-9037.2024.02.020
Abstract:
Since variable fractional delay （VFD） filter contains a large number of coefficients to be solved， this paper presents a study on sparse-constrained Farrow structure variable fractional delay filter. We add a L1 regularization constraint to further enhance the sparsity based on coefficient symmetry and optimize its frequency response to approximate a desired frequency response in the minimax error sense. In addition， the alternating direction method of multipliers （ADMM） algorithm is used to iteratively obtain the filter coefficients. Simulation experiments demonstrate that the proposed sparse-constrained VFD filter not only ensures high delay accuracy but also reduces the use of multipliers and adders by 47.69% and 58.60% respectively， thus lowering system computation and complexity greatly.
Since variable fractional delay （VFD） filter contains a large number of coefficients to be solved， this paper presents a study on sparse-constrained Farrow structure variable fractional delay filter. We add a L1 regularization constraint to further enhance the sparsity based on coefficient symmetry and optimize its frequency response to approximate a desired frequency response in the minimax error sense. In addition， the alternating direction method of multipliers （ADMM） algorithm is used to iteratively obtain the filter coefficients. Simulation experiments demonstrate that the proposed sparse-constrained VFD filter not only ensures high delay accuracy but also reduces the use of multipliers and adders by 47.69% and 58.60% respectively， thus lowering system computation and complexity greatly.
2024, 39(3):649-658.
DOI: 10.16337/j.1004-9037.2024.03.012
Abstract:
Under the criterion of maximum output signal-to-noise ratio （SNR）， the problem of difficult control of complex-valued coefficients in generalized eigenvalue （GEV） beamforming is encountered， and severe distortion of the output signal can be caused in complex acoustic environments. To address the issue of complex-valued coefficient estimation， a complex-valued coefficient estimation method based on minimum mean square error （MMSE） is proposed in this paper. By introducing a speech distortion weight factor （SDW）， the weight relationship between noise reduction and speech distortion is adjusted， thereby proposing a method for generalized eigenvalue robust beamforming based on SDW-MMSE. The power spectra of the target and noise signals are estimated using maximum likelihood method， and the main generalized eigenvectors are then determined. Furthermore， the complex-valued coefficients are estimated ， and the complex coefficients are combined with the principal generalized eigenvector to obtain the generalized eigenvalue robust beamforming filter vector based on SDW-MMSE. Through simulation experiments， it is demonstrated that the proposed beamforming method effectively eliminates coherent and incoherent noise， and exhibits robust performance with high output SNR and low speech distortion.
Under the criterion of maximum output signal-to-noise ratio （SNR）， the problem of difficult control of complex-valued coefficients in generalized eigenvalue （GEV） beamforming is encountered， and severe distortion of the output signal can be caused in complex acoustic environments. To address the issue of complex-valued coefficient estimation， a complex-valued coefficient estimation method based on minimum mean square error （MMSE） is proposed in this paper. By introducing a speech distortion weight factor （SDW）， the weight relationship between noise reduction and speech distortion is adjusted， thereby proposing a method for generalized eigenvalue robust beamforming based on SDW-MMSE. The power spectra of the target and noise signals are estimated using maximum likelihood method， and the main generalized eigenvectors are then determined. Furthermore， the complex-valued coefficients are estimated ， and the complex coefficients are combined with the principal generalized eigenvector to obtain the generalized eigenvalue robust beamforming filter vector based on SDW-MMSE. Through simulation experiments， it is demonstrated that the proposed beamforming method effectively eliminates coherent and incoherent noise， and exhibits robust performance with high output SNR and low speech distortion.
2024, 39(4):1033-1042.
DOI: 10.16337/j.1004-9037.2024.04.021
Abstract:
Bearing fault types are complex， and it is difficult to obtain enough training samples for each fault type under different working conditions. Convolutional neural network with training interference （TICNN）with wide convolutional kernel is introduced as the subnetwork of the Siamese network used to extract features， reducing the impact of industrial environment noise. Siamese network is a structure commonly used for few-shot learning. By inputting the same or different categories of samples for training， the mapping relationship between different attribute samples and features is learned， and the similarity between samples is used as measure index. The test sample is classified by finding the class of the nearest neighbor. Experimental results on the standard Case Western Reserve University （CWRU） bearing fault diagnosis benchmark dataset show that， in the case of limited data， the proposed model shows better results in fault diagnosis. The performance of the proposed few shot learning model exceeds the baseline model with a reasonable noise level when testing with the least training data in different noise environments， and the accuracy of fault diagnosis reaches 94.41%. When evaluating on test sets with new fault types or new working conditions， the proposed model also performs well.
Bearing fault types are complex， and it is difficult to obtain enough training samples for each fault type under different working conditions. Convolutional neural network with training interference （TICNN）with wide convolutional kernel is introduced as the subnetwork of the Siamese network used to extract features， reducing the impact of industrial environment noise. Siamese network is a structure commonly used for few-shot learning. By inputting the same or different categories of samples for training， the mapping relationship between different attribute samples and features is learned， and the similarity between samples is used as measure index. The test sample is classified by finding the class of the nearest neighbor. Experimental results on the standard Case Western Reserve University （CWRU） bearing fault diagnosis benchmark dataset show that， in the case of limited data， the proposed model shows better results in fault diagnosis. The performance of the proposed few shot learning model exceeds the baseline model with a reasonable noise level when testing with the least training data in different noise environments， and the accuracy of fault diagnosis reaches 94.41%. When evaluating on test sets with new fault types or new working conditions， the proposed model also performs well.
2024, 39(5):1228-1239.
DOI: 10.16337/j.1004-9037.2024.05.014
Abstract:
In the steel industry， carbide is a vital constituent， whose distribution in steel materials holds significant reference value for evaluating steel quality. However， the current detection methods for carbide in steel bars primarily rely on manual inspection， which is costly and lacks stability. This study introduces advanced deep learning techniques from the domain of artificial intelligence， which collects and annotates 3 192 high quality images of banded carbides on steel bars， alongside 11 complete samples to create a banded carbide dataset on object detection for steel bars （BCDOD）. Common deep learning methods for object detection are applied to the dataset through experimental analysis. With a focus on the specific characteristics of the application scenario and data， the cascade R-CNN model is enhanced with rotation data augmentation， improvement to the Focal Loss function and negative sample fine-tuning， resulting in performance improvement. The achieved average precision reaches 96%， with 100% recognition accuracy on complete sample data， showcasing promising outcomes that address the existing gap in artificial intelligence technology within the field of carbide metallographic detection.
In the steel industry， carbide is a vital constituent， whose distribution in steel materials holds significant reference value for evaluating steel quality. However， the current detection methods for carbide in steel bars primarily rely on manual inspection， which is costly and lacks stability. This study introduces advanced deep learning techniques from the domain of artificial intelligence， which collects and annotates 3 192 high quality images of banded carbides on steel bars， alongside 11 complete samples to create a banded carbide dataset on object detection for steel bars （BCDOD）. Common deep learning methods for object detection are applied to the dataset through experimental analysis. With a focus on the specific characteristics of the application scenario and data， the cascade R-CNN model is enhanced with rotation data augmentation， improvement to the Focal Loss function and negative sample fine-tuning， resulting in performance improvement. The achieved average precision reaches 96%， with 100% recognition accuracy on complete sample data， showcasing promising outcomes that address the existing gap in artificial intelligence technology within the field of carbide metallographic detection.
