Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher acc...Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open.Owing to spectral analysis in feature extraction,an adaptive bands filter bank (ABFB) is presented.The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm (GA) to optimize the design parameters.The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop.The deployment of ABFB together with zero-crossing peak amplitude (ZCPA) feature as a front process for radial basis function (RBF) system shows significant improvement in robustness compared with the Bark-scale filter bank.In ABFB,several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria.For the ease of optimization,only symmetrical bands are considered here,which still provide satisfactory results.展开更多
In order to improve the resource allocation mechanism of artificial immune recognition system(AIRS) and decrease the memory cells,a fuzzy logic resource allocation and memory cell pruning based AIRS(FPAIRS) is propose...In order to improve the resource allocation mechanism of artificial immune recognition system(AIRS) and decrease the memory cells,a fuzzy logic resource allocation and memory cell pruning based AIRS(FPAIRS) is proposed.In FPAIRS,the fuzzy logic is determined by a parameter,thus,the optimal fuzzy logics for different problems can be located through changing the parameter value.At the same time,the memory cells of low fitness scores are pruned to improve the classifier.This classifier was compared with other classifiers on six UCI datasets classification performance.The results show that the accuracies reached by FPAIRS are higher than or comparable to the accuracies of other classifiers,and the memory cells decrease when compared with the memory cells of AIRS.The results show that the algorithm is a high-performance classifier.展开更多
The hypersonic target detection and recognition system is studied,on the basis of overall planning and design,a multi-agent system(MAS)structure and intelligent+information processing mechanism based on target detecti...The hypersonic target detection and recognition system is studied,on the basis of overall planning and design,a multi-agent system(MAS)structure and intelligent+information processing mechanism based on target detection and recognition are proposed,and the multi-agent operation process is analyzed and designed in detail.In the specific agents construction,the information fusion technology is introduced to defining the embedded agents and their interrelations in the system structure,and the intelligent processing ability of complex and uncertain problems is emphatically analyzed from the aspects of autonomy and collaboration.The aim is to optimize the information processing strategy of the hypersonic target detection and recognition system and improve the robustness and rapidity of the system.展开更多
Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image f...Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image for the person identification. In this work, the fuzzy least brain storm optimization and Euclidean distance(EED) are proposed for the vein based recognition system. Initially, the input image is fed into the region of interest(ROI) extraction which obtains the appropriate image for the subsequent step. Then, features or vein pattern is extracted by the image enlightening, circular averaging filter and holoentropy based thresholding. After the features are obtained, the entropy based Euclidean distance is proposed to fuse the features by the score level fusion with the weight score value. Finally, the optimal matching score is computed iteratively by the newly developed fuzzy least brain storm optimization(FLBSO) algorithm. The novel algorithm is developed by the least mean square(LMS) algorithm and fuzzy brain storm optimization(FBSO). Thus, the experimental results are evaluated and the performance is compared with the existing systems using false acceptance rate(FAR), false rejection rate(FRR) and accuracy. The performance outcome of the proposed algorithm attains the higher accuracy of 89.9% which ensures the better recognition rate.展开更多
Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the rea...Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.展开更多
Taoren and Xingren are commonly used herbs in East Asian medicine with different medication functions but huge economic differences,and there are cases of adulterated sales in market transactions.An effective adultera...Taoren and Xingren are commonly used herbs in East Asian medicine with different medication functions but huge economic differences,and there are cases of adulterated sales in market transactions.An effective adulteration recognition based on hyperspectral technology and machine learning was designed as a non-destructive testing method in this paper.A hyperspectral dataset comprising 500 Taoren and 500 Xingren samples was established;six feature selection methods were considered in the modeling of radial basis function-support vector machine(RBF-SVM),whose interaction between the two optimization methods was further researched.Two mixed metaheuristics modeling methods,Mixed-PSO and Mixed-SA,were designed,which fused both band selection and hyperparameter optimization from two-stage into one with detailed process analysis.The metrics of this mixed model were improved by comparing with traditional two-stage method.The accuracy of Mixed-PSO was 89.2%in five-floods crossvalidation that increased 4.818%than vanilla RBF-SVM;the accuracy of Mixed-SA was 88.7%which could reach the same as the traditional two-stage method,but it only relied on 48 crux bands in full 100 bands in RBF-SVM model fitting.展开更多
Ground penetrating radar(GPR),as a fast,efficient,and non-destructive detection device,holds great potential for the detection of shallow subsurface environments,such as urban road subsurface monitoring.However,the in...Ground penetrating radar(GPR),as a fast,efficient,and non-destructive detection device,holds great potential for the detection of shallow subsurface environments,such as urban road subsurface monitoring.However,the interpretation of GPR echo images often relies on manual recognition by experienced engineers.In order to address the automatic interpretation of cavity targets in GPR echo images,a recognition-algorithm based on Gaussian mixed model-hidden Markov model(GMM-HMM)is proposed,which can recognize three dimensional(3D)underground voids automatically.First,energy detection on the echo images is performed,whereby the data is preprocessed and pre-filtered.Then,edge histogram descriptor(EHD),histogram of oriented gradient(HOG),and Log-Gabor filters are used to extract features from the images.The traditional method can only be applied to 2D images and pre-processing is required for C-scan images.Finally,the aggregated features are fed into the GMM-HMM for classification and compared with two other methods,long short-term memory(LSTM)and gate recurrent unit(GRU).By testing on a simulated dataset,an accuracy rate of 90%is obtained,demonstrating the effectiveness and efficiency of our proposed method.展开更多
Using function one direction S-rough sets （function one direction singular rough sets）, f-law and F- law and the concept of law distance and the concept of system law collided by F-law are given. Using these concept...Using function one direction S-rough sets （function one direction singular rough sets）, f-law and F- law and the concept of law distance and the concept of system law collided by F-law are given. Using these concepts, state characteristic presented by system law collided by F-law and recognition of these states characteristic and recognition criterion and applications are given. Function one direction S-rough sets is one of basic forms of function S-rough sets （function singular rough sets）. Function one direction S-rough sets is importance theory and is a method in studying system law collision.展开更多
If a system is not disturbed （or invaded） by some law, there is no doubt that each system will move according to the expected law and keep stable. Although such a fact often appears, some unknown law breaks into the...If a system is not disturbed （or invaded） by some law, there is no doubt that each system will move according to the expected law and keep stable. Although such a fact often appears, some unknown law breaks into the system and leads it into turbulence. Using function one direction S-rough sets, this article gives the concept of the F-generation law in the system, the generation model of the F-generation law and the recognition method of the system law. Function one direction singular rough sets is a new theory and method in recognizing the disturbance law existing in the system and recognizing the system law.展开更多
Using dual function one direction S-rough sets, this article gives the f-law, the F-law, law distance and the concept of system law collided by the F-law. The characteristics presented by the system law collided by th...Using dual function one direction S-rough sets, this article gives the f-law, the F-law, law distance and the concept of system law collided by the F-law. The characteristics presented by the system law collided by the F-law, the recognition of these characteristics and recognition criterion are also proposed. The dual function one direction S-rough sets is one of the basic forms of function S-rough sets. Its basic theory and application in the study of system law collision are reviewed.展开更多
To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion,but the expert knowledge is not fully utilized,a heterogeneous informa-tion fusion recognition method based on be...To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion,but the expert knowledge is not fully utilized,a heterogeneous informa-tion fusion recognition method based on belief rule structure is proposed.By defining the continuous probabilistic hesitation fuzzy linguistic term sets(CPHFLTS)and establishing CPHFLTS distance measure,the belief rule base of the relationship between feature space and category space is constructed through information integration,and the evidence reasoning of the input samples is carried out.The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition.Compared with the other methods,the proposed method has a higher correct recognition rate under different noise levels.展开更多
The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-n...The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.展开更多
Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the ...Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.展开更多
RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy protection.Existing RFID-based HAR methods use modeling,CNN,or LSTM to extract features effectively.Still...RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy protection.Existing RFID-based HAR methods use modeling,CNN,or LSTM to extract features effectively.Still,they have shortcomings:1)requiring complex hand-crafted data cleaning processes and 2)only addressing single-person activity recognition based on specific RF signals.To solve these problems,this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called TransTM.This model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without pre-processing.Concretely,we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes singlehuman activities and human-to-human interactions.Compared with existing CNN-and LSTM-based methods,the Transformer-based method has more data fitting power,generalization,and scalability.Furthermore,using RF signals,our method achieves an excellent classification effect on human behaviorbased classification tasks.Experimental results on the actual RFID datasets show that this model achieves a high average recognition accuracy(99.1%).The dataset we collected for detecting RFID-based indoor human activities will be published.展开更多
There are many motors in operation or on standby in nuclear power plants,and the startup of group motors will have a great impact on the voltage of the emergency bus.At present,there is no special or inexpensive softw...There are many motors in operation or on standby in nuclear power plants,and the startup of group motors will have a great impact on the voltage of the emergency bus.At present,there is no special or inexpensive software to solve this problem,and the experience of engineers is not accurate enough.Therefore,this paper developed a method and system for the startup calculation of group motors in nuclear power plants and proposed an automatic generation method of circuit topology in nuclear power plants.Each component in the topology was given its unique number,and the component class could be constructed according to its type and upper and lower connections.The subordination and topology relationship of switches,buses,and motors could be quickly generated by the program according to the component class,and the simplified direct power flow algorithm was used to calculate the power flow for the startup of group motors according to the above relationship.Then,whether the bus voltage is in the safe range and whether the voltage exceeds the limit during the startup of the group motor could be judged.The practical example was used to verify the effectiveness of the method.Compared with other professional software,the method has high efficiency and low cost.展开更多
In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise p...In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise parameter information,particularly in low signal-to-noise ratio(SNR)situations.In this paper,an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue.Firstly,a joint algorithm based on YOLOv5 convolutional neural networks(CNNs)is proposed,which is used to achieve the jamming signal classification and preliminary parameter estimation.Furthermore,an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test,feature region search,position regression,spectrum interpolation,etc.,which realizes the accurate estimation of jamming carrier frequency,relative delay,Doppler frequency shift,and other parameters.Finally,the approach has improved performance for complex jamming recognition and parameter estimation under low SNR,and the recognition rate can reach 98%under−15 dB SNR,according to simulation and real data verification results.展开更多
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) ba...In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.展开更多
In this paper, the problem of reliable automatic target recognition from incoherent radar returns is discussed and a new method for ship target recognition is proposed. Based on this method, an experimental system for...In this paper, the problem of reliable automatic target recognition from incoherent radar returns is discussed and a new method for ship target recognition is proposed. Based on this method, an experimental system for ship target recognition is implemented. The results obtained from the theoretical and experimental study indicate that a high reliability of recognition can be achieved by using the designed recognition system. An average success rate of more than 90% is reached for 8 classes of ships.展开更多
This paper presents a mode-switching collaborative defense strategy for spacecraft pursuit-evasiondefense scenarios.In these scenarios,the pursuer tries to avoid the defender while capturing the evader,while the evade...This paper presents a mode-switching collaborative defense strategy for spacecraft pursuit-evasiondefense scenarios.In these scenarios,the pursuer tries to avoid the defender while capturing the evader,while the evader and defender form an alliance to prevent the pursuer from achieving its goal.First,the behavioral modes of the pursuer,including attack and avoidance modes,were established using differential game theory.These modes are then recognized by an interactive multiple model-matching algorithm(IMM),that uses several smooth variable structure filters to match the modes of the pursuer and update their probabilities in real time.Based on the linear-quadratic optimization theory,combined with the results of strategy identification,a two-way cooperative optimal strategy for the defender and evader is proposed,where the evader aids the defender to intercept the pursuer by performing luring maneuvers.Simulation results show that the interactive multi-model algorithm based on several smooth variable structure filters perform well in the strategy identification of the pursuer,and the cooperative defense strategy based on strategy identification has good interception performance when facing pursuers,who are able to flexibly adjust their game objectives.展开更多
The spin-exchange relaxation-free atomic gyroscope,with its exceptionally high theoretical precision,demonstrates immense potential to become the next-generation strategic-grade gyroscope.However,due to technological ...The spin-exchange relaxation-free atomic gyroscope,with its exceptionally high theoretical precision,demonstrates immense potential to become the next-generation strategic-grade gyroscope.However,due to technological noise,there is still a significant gap between its actual precision and theoretical precision.This study identifies the key factor limiting the precision of the SERF gyroscope as coupling noise.By optimizing the detection loop structure,a distinction between the dual-axis signals'response to optical and magnetic fields was achieved-where the optical errors responded similarly,while the response to magnetic noise was opposite.Based on the differences in the optical-magnetic response of the dual-axis signals,empirical mode decomposition was used to decompose the dual-axis gyroscope signals into multiple intrinsic mode functions,and Allan deviation analysis was applied to analyze the noise characteristics of the intrinsic mode functions over various periods.This study successfully reveals that optical errors caused by thermal-optical coupling and long-period magnetic noise induced by thermal-magnetic coupling are the dominant factors limiting the long-term stability of the SERF gyroscope.Based on these analyses,the study concludes that to achieve strategic-grade precision for the SERF gyroscope,it is essential to effectively address the noise issues caused by multi-physical field couplings.展开更多
基金Project(61072087) supported by the National Natural Science Foundation of ChinaProject(20093048) supported by Shanxi ProvincialGraduate Innovation Fund of China
文摘Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open.Owing to spectral analysis in feature extraction,an adaptive bands filter bank (ABFB) is presented.The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm (GA) to optimize the design parameters.The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop.The deployment of ABFB together with zero-crossing peak amplitude (ZCPA) feature as a front process for radial basis function (RBF) system shows significant improvement in robustness compared with the Bark-scale filter bank.In ABFB,several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria.For the ease of optimization,only symmetrical bands are considered here,which still provide satisfactory results.
基金Project(61170199)supported by the National Natural Science Foundation of ChinaProject(11A004)support by the Scientific Research Fund of Education Department of Hunan Province,China
文摘In order to improve the resource allocation mechanism of artificial immune recognition system(AIRS) and decrease the memory cells,a fuzzy logic resource allocation and memory cell pruning based AIRS(FPAIRS) is proposed.In FPAIRS,the fuzzy logic is determined by a parameter,thus,the optimal fuzzy logics for different problems can be located through changing the parameter value.At the same time,the memory cells of low fitness scores are pruned to improve the classifier.This classifier was compared with other classifiers on six UCI datasets classification performance.The results show that the accuracies reached by FPAIRS are higher than or comparable to the accuracies of other classifiers,and the memory cells decrease when compared with the memory cells of AIRS.The results show that the algorithm is a high-performance classifier.
基金This work was supported by the National Natural Science Foundation of China(61471391).
文摘The hypersonic target detection and recognition system is studied,on the basis of overall planning and design,a multi-agent system(MAS)structure and intelligent+information processing mechanism based on target detection and recognition are proposed,and the multi-agent operation process is analyzed and designed in detail.In the specific agents construction,the information fusion technology is introduced to defining the embedded agents and their interrelations in the system structure,and the intelligent processing ability of complex and uncertain problems is emphatically analyzed from the aspects of autonomy and collaboration.The aim is to optimize the information processing strategy of the hypersonic target detection and recognition system and improve the robustness and rapidity of the system.
文摘Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image for the person identification. In this work, the fuzzy least brain storm optimization and Euclidean distance(EED) are proposed for the vein based recognition system. Initially, the input image is fed into the region of interest(ROI) extraction which obtains the appropriate image for the subsequent step. Then, features or vein pattern is extracted by the image enlightening, circular averaging filter and holoentropy based thresholding. After the features are obtained, the entropy based Euclidean distance is proposed to fuse the features by the score level fusion with the weight score value. Finally, the optimal matching score is computed iteratively by the newly developed fuzzy least brain storm optimization(FLBSO) algorithm. The novel algorithm is developed by the least mean square(LMS) algorithm and fuzzy brain storm optimization(FBSO). Thus, the experimental results are evaluated and the performance is compared with the existing systems using false acceptance rate(FAR), false rejection rate(FRR) and accuracy. The performance outcome of the proposed algorithm attains the higher accuracy of 89.9% which ensures the better recognition rate.
基金supported by the National Natural Science Foundation of China(62101575)the Research Project of NUDT(ZK22-57)the Self-directed Project of State Key Laboratory of High Performance Computing(202101-16).
文摘Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.
基金Supported by the Natural Science Foundation of Heilongjiang Province(LH2020C003)。
文摘Taoren and Xingren are commonly used herbs in East Asian medicine with different medication functions but huge economic differences,and there are cases of adulterated sales in market transactions.An effective adulteration recognition based on hyperspectral technology and machine learning was designed as a non-destructive testing method in this paper.A hyperspectral dataset comprising 500 Taoren and 500 Xingren samples was established;six feature selection methods were considered in the modeling of radial basis function-support vector machine(RBF-SVM),whose interaction between the two optimization methods was further researched.Two mixed metaheuristics modeling methods,Mixed-PSO and Mixed-SA,were designed,which fused both band selection and hyperparameter optimization from two-stage into one with detailed process analysis.The metrics of this mixed model were improved by comparing with traditional two-stage method.The accuracy of Mixed-PSO was 89.2%in five-floods crossvalidation that increased 4.818%than vanilla RBF-SVM;the accuracy of Mixed-SA was 88.7%which could reach the same as the traditional two-stage method,but it only relied on 48 crux bands in full 100 bands in RBF-SVM model fitting.
基金National Natural Science Foundation of China(62071147)。
文摘Ground penetrating radar(GPR),as a fast,efficient,and non-destructive detection device,holds great potential for the detection of shallow subsurface environments,such as urban road subsurface monitoring.However,the interpretation of GPR echo images often relies on manual recognition by experienced engineers.In order to address the automatic interpretation of cavity targets in GPR echo images,a recognition-algorithm based on Gaussian mixed model-hidden Markov model(GMM-HMM)is proposed,which can recognize three dimensional(3D)underground voids automatically.First,energy detection on the echo images is performed,whereby the data is preprocessed and pre-filtered.Then,edge histogram descriptor(EHD),histogram of oriented gradient(HOG),and Log-Gabor filters are used to extract features from the images.The traditional method can only be applied to 2D images and pre-processing is required for C-scan images.Finally,the aggregated features are fed into the GMM-HMM for classification and compared with two other methods,long short-term memory(LSTM)and gate recurrent unit(GRU).By testing on a simulated dataset,an accuracy rate of 90%is obtained,demonstrating the effectiveness and efficiency of our proposed method.
基金the Natural Science Foundation of Shandong Province of China (Y2004A04)the Natural Science Foundation of Fujian Province of China (Z0511049).
文摘Using function one direction S-rough sets （function one direction singular rough sets）, f-law and F- law and the concept of law distance and the concept of system law collided by F-law are given. Using these concepts, state characteristic presented by system law collided by F-law and recognition of these states characteristic and recognition criterion and applications are given. Function one direction S-rough sets is one of basic forms of function S-rough sets （function singular rough sets）. Function one direction S-rough sets is importance theory and is a method in studying system law collision.
基金This project was supported by the Ministry of Education of China (206089)Shangdong Provincial Natural Science Foundation of China (Y2004A04)Fujian Provincial Natural Science Foundation of China (Z051049).
文摘If a system is not disturbed （or invaded） by some law, there is no doubt that each system will move according to the expected law and keep stable. Although such a fact often appears, some unknown law breaks into the system and leads it into turbulence. Using function one direction S-rough sets, this article gives the concept of the F-generation law in the system, the generation model of the F-generation law and the recognition method of the system law. Function one direction singular rough sets is a new theory and method in recognizing the disturbance law existing in the system and recognizing the system law.
基金Natural Science Foundation of Shandong Province of China (Y2004A04)Education Hall Foundation of Fujian Education Official of China(JA04268).
文摘Using dual function one direction S-rough sets, this article gives the f-law, the F-law, law distance and the concept of system law collided by the F-law. The characteristics presented by the system law collided by the F-law, the recognition of these characteristics and recognition criterion are also proposed. The dual function one direction S-rough sets is one of the basic forms of function S-rough sets. Its basic theory and application in the study of system law collision are reviewed.
基金This work was supported by the Youth Foundation of National Science Foundation of China(62001503)the Special Fund for Taishan Scholar Project(ts 201712072).
文摘To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion,but the expert knowledge is not fully utilized,a heterogeneous informa-tion fusion recognition method based on belief rule structure is proposed.By defining the continuous probabilistic hesitation fuzzy linguistic term sets(CPHFLTS)and establishing CPHFLTS distance measure,the belief rule base of the relationship between feature space and category space is constructed through information integration,and the evidence reasoning of the input samples is carried out.The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition.Compared with the other methods,the proposed method has a higher correct recognition rate under different noise levels.
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation 2022M720419 to provide fund for conducting experiments。
文摘The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation under Grant No.2022M720419。
文摘Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.
基金the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDC02040300)for this study.
文摘RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy protection.Existing RFID-based HAR methods use modeling,CNN,or LSTM to extract features effectively.Still,they have shortcomings:1)requiring complex hand-crafted data cleaning processes and 2)only addressing single-person activity recognition based on specific RF signals.To solve these problems,this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called TransTM.This model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without pre-processing.Concretely,we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes singlehuman activities and human-to-human interactions.Compared with existing CNN-and LSTM-based methods,the Transformer-based method has more data fitting power,generalization,and scalability.Furthermore,using RF signals,our method achieves an excellent classification effect on human behaviorbased classification tasks.Experimental results on the actual RFID datasets show that this model achieves a high average recognition accuracy(99.1%).The dataset we collected for detecting RFID-based indoor human activities will be published.
基金Key Project of National Natural Science Foundation of China(52237008)Beijing Municipal Education Commission Research Program Funding Project(KM202111232022)。
文摘There are many motors in operation or on standby in nuclear power plants,and the startup of group motors will have a great impact on the voltage of the emergency bus.At present,there is no special or inexpensive software to solve this problem,and the experience of engineers is not accurate enough.Therefore,this paper developed a method and system for the startup calculation of group motors in nuclear power plants and proposed an automatic generation method of circuit topology in nuclear power plants.Each component in the topology was given its unique number,and the component class could be constructed according to its type and upper and lower connections.The subordination and topology relationship of switches,buses,and motors could be quickly generated by the program according to the component class,and the simplified direct power flow algorithm was used to calculate the power flow for the startup of group motors according to the above relationship.Then,whether the bus voltage is in the safe range and whether the voltage exceeds the limit during the startup of the group motor could be judged.The practical example was used to verify the effectiveness of the method.Compared with other professional software,the method has high efficiency and low cost.
基金supported by Shandong Provincial Natural Science Foundation(ZR2020MF015)Aerospace Technology Group Stability Support Project(ZY0110020009).
文摘In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise parameter information,particularly in low signal-to-noise ratio(SNR)situations.In this paper,an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue.Firstly,a joint algorithm based on YOLOv5 convolutional neural networks(CNNs)is proposed,which is used to achieve the jamming signal classification and preliminary parameter estimation.Furthermore,an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test,feature region search,position regression,spectrum interpolation,etc.,which realizes the accurate estimation of jamming carrier frequency,relative delay,Doppler frequency shift,and other parameters.Finally,the approach has improved performance for complex jamming recognition and parameter estimation under low SNR,and the recognition rate can reach 98%under−15 dB SNR,according to simulation and real data verification results.
基金supported by the National Natural Science Foundation of China (62271255,61871218)the Fundamental Research Funds for the Central University (3082019NC2019002)+1 种基金the Aeronautical Science Foundation (ASFC-201920007002)the Program of Remote Sensing Intelligent Monitoring and Emergency Services for Regional Security Elements。
文摘In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
文摘In this paper, the problem of reliable automatic target recognition from incoherent radar returns is discussed and a new method for ship target recognition is proposed. Based on this method, an experimental system for ship target recognition is implemented. The results obtained from the theoretical and experimental study indicate that a high reliability of recognition can be achieved by using the designed recognition system. An average success rate of more than 90% is reached for 8 classes of ships.
基金the Science and Technology Department,Heilongjiang Province under Grant Agreement No JJ2022LH0315。
文摘This paper presents a mode-switching collaborative defense strategy for spacecraft pursuit-evasiondefense scenarios.In these scenarios,the pursuer tries to avoid the defender while capturing the evader,while the evader and defender form an alliance to prevent the pursuer from achieving its goal.First,the behavioral modes of the pursuer,including attack and avoidance modes,were established using differential game theory.These modes are then recognized by an interactive multiple model-matching algorithm(IMM),that uses several smooth variable structure filters to match the modes of the pursuer and update their probabilities in real time.Based on the linear-quadratic optimization theory,combined with the results of strategy identification,a two-way cooperative optimal strategy for the defender and evader is proposed,where the evader aids the defender to intercept the pursuer by performing luring maneuvers.Simulation results show that the interactive multi-model algorithm based on several smooth variable structure filters perform well in the strategy identification of the pursuer,and the cooperative defense strategy based on strategy identification has good interception performance when facing pursuers,who are able to flexibly adjust their game objectives.
基金supported by Hefei National Laboratory,Innovation Program for Quantum Science and Technology(2021ZD0300400/2021ZD0300402)the Beijing Natural Science Foundation(3252013)the China Postdoctoral Science Foundation(2024T171116).
文摘The spin-exchange relaxation-free atomic gyroscope,with its exceptionally high theoretical precision,demonstrates immense potential to become the next-generation strategic-grade gyroscope.However,due to technological noise,there is still a significant gap between its actual precision and theoretical precision.This study identifies the key factor limiting the precision of the SERF gyroscope as coupling noise.By optimizing the detection loop structure,a distinction between the dual-axis signals'response to optical and magnetic fields was achieved-where the optical errors responded similarly,while the response to magnetic noise was opposite.Based on the differences in the optical-magnetic response of the dual-axis signals,empirical mode decomposition was used to decompose the dual-axis gyroscope signals into multiple intrinsic mode functions,and Allan deviation analysis was applied to analyze the noise characteristics of the intrinsic mode functions over various periods.This study successfully reveals that optical errors caused by thermal-optical coupling and long-period magnetic noise induced by thermal-magnetic coupling are the dominant factors limiting the long-term stability of the SERF gyroscope.Based on these analyses,the study concludes that to achieve strategic-grade precision for the SERF gyroscope,it is essential to effectively address the noise issues caused by multi-physical field couplings.
