A novel matching method for simultaneous multi-target recognition is proposed by jointly considering target's prior scattering knowledge and the polarization parameters of radar echoes. The matching coefficients a...A novel matching method for simultaneous multi-target recognition is proposed by jointly considering target's prior scattering knowledge and the polarization parameters of radar echoes. The matching coefficients are calculated for the judgment. MATLAB simulations show that several targets can be accurately recognized simultaneously, and a high recognition probability can be achieved in Monte Carlo simulations. The total execution time can be remarkably reduced in the Field Programmable Gate Array (FPGA) implementation of the matching procedure.展开更多
Flying plots detection has been the focus of relay protection in power system for a long time. With the promotion of Smart substation in our country, the number of SV devices is greatly increased. Abnormal data (flyin...Flying plots detection has been the focus of relay protection in power system for a long time. With the promotion of Smart substation in our country, the number of SV devices is greatly increased. Abnormal data (flying plot) caused by sampling device itself has brought tremendous pressure to the power system. The traditional flying plot detection algorithm has plenty of defects, such as low pertinence, low sensitivity and long sampling period. This paper proposes a new algorithm to identify flying plot by analyzing the wave form characteristics of sampling data. The traditional waveform recognition methods are combined in this algorithm. It has the concept of standard wave window and can distinguish flying plot in a short time. In addition, sine recovery algorithm is used to recover the flying plot. This paper uses PSCAD software to verify the validity of this algorithm. Simulation results show that the proposed method has high reliability.展开更多
Gesture recognition plays an increasingly important role as the requirements of intelligent systems for human-computer interaction methods increase.To improve the accuracy of the millimeter-wave radar gesture detectio...Gesture recognition plays an increasingly important role as the requirements of intelligent systems for human-computer interaction methods increase.To improve the accuracy of the millimeter-wave radar gesture detection algorithm with limited computational resources,this study improves the detection performance in terms of optimized features and interference filtering.The accuracy of the algorithm is improved by refining the combination of gesture features using a self-constructed dataset,and biometric filtering is introduced to reduce the interference of inanimate object motion.Finally,experiments demonstrate the effectiveness of the proposed algorithm in both mitigating interference from inanimate objects and accurately recognizing gestures.Results show a notable 93.29%average reduction in false detections achieved through the integration of biometric filtering into the algorithm’s interpretation of target movements.Additionally,the algorithm adeptly identifies the six gestures with an average accuracy of 96.84%on embedded systems.展开更多
With technology advances and human requirements increasing, human-computer interaction plays an important role in our daily lives. Among these interactions, gesture-based recognition offers a natural and intuitive use...With technology advances and human requirements increasing, human-computer interaction plays an important role in our daily lives. Among these interactions, gesture-based recognition offers a natural and intuitive user experience that does not require physical contact and is becoming increasingly prevalent across various fields. Gesture recognition systems based on Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar are receiving widespread attention due to their ability to operate without wearable sensors, their robustness to environmental factors, and the excellent penetrative ability of radar signals. This paper first reviews the current main gesture recognition applications. Subsequently, we introduce the system of gesture recognition based on FMCW radar and provide a general framework for gesture recognition, including gesture data acquisition, data preprocessing, and classification methods. We then discuss typical applications of gesture recognition systems and summarize the performance of these systems in terms of experimental environment, signal acquisition, signal processing, and classification methods. Specifically, we focus our study on four typical gesture recognition systems, including air-writing recognition, gesture command recognition, sign language recognition, and text input recognition. Finally, this paper addresses the challenges and unresolved problems in FMCW radar-based gesture recognition and provides insights into potential future research directions.展开更多
This paper introduces the principle for recognition of engine work wave signal with neural network. A diagnosis method for recognizing engine trouble by its work wave is proposed. The designing process is illustrated ...This paper introduces the principle for recognition of engine work wave signal with neural network. A diagnosis method for recognizing engine trouble by its work wave is proposed. The designing process is illustrated by diagnosing the voltage trouble of the fuel injector of an electronic control (EC) engine.展开更多
With the development of wireless technology, Frequency-Modulated Continuous Wave (FMCW) radar has increased sensing capability and can be used to recognize human activity. These applications have gained wide-spread at...With the development of wireless technology, Frequency-Modulated Continuous Wave (FMCW) radar has increased sensing capability and can be used to recognize human activity. These applications have gained wide-spread attention and become a hot research area. FMCW signals reflected by target activity can be collected, and human activity can be recognized based on the measurements. This paper focused on human activity recognition based on FMCW and DenseNet. We collected point clouds from FMCW and analyzed them to recognize human activity because different activities could lead to unique point cloud features. We built and trained the neural network to implement human activities using a FMCW signal. Firstly, this paper presented recent reviews about human activity recognition using wireless signals. Then, it introduced the basic concepts of FMCW radar and described the fundamental principles of the system using FMCW radar. We also provided the system framework, experiment scenario, and DenseNet neural network structure. Finally, we presented the experimental results and analyzed the accuracy of different neural network models. The system achieved recognition accuracy of 100 percent for five activities using the DenseNet. We concluded the paper by discussing the current issues and future research directions.展开更多
During the test on transient pressure signal in explosion field,false trigger caused by field interference can lead to test failure.To improve the stability of test system,a signal detection and recognition technology...During the test on transient pressure signal in explosion field,false trigger caused by field interference can lead to test failure.To improve the stability of test system,a signal detection and recognition technology is proposed for transient pressure test system.In the process of signal acquisition,firstly,electrical levels are monitored in real time to find effective abrupt changes and mark them;then the effective data segments are detecdted totected;thus the effective signals can be acquired in turn finally.The experimental results show that the shock wave signal can be collected effectively and the reliability of the test system can be improved after removal of interferences.展开更多
Accurately detecting the arrival time of a channel wave in a coal seam is very important for in-seam seismic data processing. The arrival time greatly affects the accuracy of the channel wave inversion and the compute...Accurately detecting the arrival time of a channel wave in a coal seam is very important for in-seam seismic data processing. The arrival time greatly affects the accuracy of the channel wave inversion and the computed tomography （CT） result. However, because the signal-to-noise ratio of in-seam seismic data is reduced by the long wavelength and strong frequency dispersion, accurately timing the arrival of channel waves is extremely difficult. For this purpose, we propose a method that automatically picks up the arrival time of channel waves based on multi-channel constraints. We first estimate the Jaccard similarity coefficient of two ray paths, then apply it as a weight coefficient for stacking the multi- channel dispersion spectra. The reasonableness and effectiveness of the proposed method is verified in an actual data application. Most importantly, the method increases the degree of automation and the pickup precision of the channel-wave arrival time.展开更多
Firstly,the concepts of the traveling wave entropy and the feature function of traveling wave entropy were defined.Then the statistic characters of the traveling wave entropy feature function,mean value and variance w...Firstly,the concepts of the traveling wave entropy and the feature function of traveling wave entropy were defined.Then the statistic characters of the traveling wave entropy feature function,mean value and variance were analyzed after the zero-order component of the traveling wave of online cable was selected to serve as the observed object.Finally,the new recognition algorithm of minimum risk neural network was pre- sented.The simulation experiments show that the recognitions of the early fault states can be completed correctly by using the proposed recognition algorithm.The classes of cable faults include in 1-phase ground faults,and the 2-phase short circuit faults or ground faults and the 3-phase short circuit faults or ground faults,open circuit.The fault resistance range is 1×10^(-1)～1×10~9Ω.展开更多
We present a novel model for recognizing long-term complex activities involving multiple persons. The proposed model, named ‘decomposed hidden Markov model’ (DHMM), combines spatial decomposition and hierarchical ab...We present a novel model for recognizing long-term complex activities involving multiple persons. The proposed model, named ‘decomposed hidden Markov model’ (DHMM), combines spatial decomposition and hierarchical abstraction to capture multi-modal, long-term dependent and multi-scale characteristics of activities. Decomposition in space and time offers conceptual advantages of compaction and clarity, and greatly reduces the size of state space as well as the number of parameters. DHMMs are efficient even when the number of persons is variable. We also introduce an efficient approximation algorithm for inference and parameter estimation. Experiments on multi-person activities and multi-modal individual activities demonstrate that DHMMs are more efficient and reliable than familiar models, such as coupled HMMs, hierarchical HMMs, and multi-observation HMMs.展开更多
In this paper,a new multiclass classification algorithm is proposed based on the idea of Locally Linear Embedding(LLE),to avoid the defect of traditional manifold learning algorithms,which can not deal with new sample...In this paper,a new multiclass classification algorithm is proposed based on the idea of Locally Linear Embedding(LLE),to avoid the defect of traditional manifold learning algorithms,which can not deal with new sample points.The algorithm defines an error as a criterion by computing a sample's reconstruction weight using LLE.Furthermore,the existence and characteristics of low dimensional manifold in range-profile time-frequency information are explored using manifold learning algorithm,aiming at the problem of target recognition about high range resolution MilliMeter-Wave(MMW) radar.The new algorithm is applied to radar target recognition.The experiment results show the algorithm is efficient.Compared with other classification algorithms,our method improves the recognition precision and the result is not sensitive to input parameters.展开更多
Precision control over ligand-receptor recognition is critical in biochemistry and pharmacology.Traditional meth-ods that alter reaction environments have limitations in fine-tuning the thermodynamic and kinetic aspec...Precision control over ligand-receptor recognition is critical in biochemistry and pharmacology.Traditional meth-ods that alter reaction environments have limitations in fine-tuning the thermodynamic and kinetic aspects of biochemical reactions within biological systems.The advent of terahertz wave technology represents a significant breakthrough,providing a refined approach to modulating ligand-receptor interactions.This perspective explores the cutting-edge potential of terahertz waves in refining ligand-receptor recognition,featuring their innovative application in modulating neuronal functions.The capabilities of terahertz technology to selectively influence molecular interactions are discussed,highlighting its transformative potential for advancing therapeutic strategies and deepening our understanding of biological mechanisms.展开更多
Shallow surface wave methods are mostly used for investigation of the surface velocity structure in environmental and engineering geophysics in non-desert areas. For the special geological features of the Takelamagan ...Shallow surface wave methods are mostly used for investigation of the surface velocity structure in environmental and engineering geophysics in non-desert areas. For the special geological features of the Takelamagan Desert area, we use the multi-channel analysis of surface wave （MASW） method to process multi-channel shallow surface wave records to determine the near surface velocity structure in the desert area. We also process, analyze, and compare the surface waves in many-trace records extracted from the oil exploration shot gathers in the area. We show that the MASW method can determine detailed shallow velocity structure in desert areas and the many-trace records can be used to get detailed deep geological structure. The combination of the two different datasets can obtain the exact velocity structure upper 60 m depth in the survey area.展开更多
Most of existing metasurfaces usually have limited channel behavior,which seriouslyhinders their development and application.In this paper,we propose a multi-channel terahertz focused beam generator based on shared-ap...Most of existing metasurfaces usually have limited channel behavior,which seriouslyhinders their development and application.In this paper,we propose a multi-channel terahertz focused beam generator based on shared-aperture metasurface,and the generator consists of a top square metal strip,a middle layer of silica and a metal bottom plate.By changing the position and size of the shared-aperture array,the designed metasurface can generate any number of multi-channel focusing beams at different predicted positions.In addition,the energy intensity of focusing beams can be controlled.The full-wave simulation results show that the metasurface achieves four-channel vortex focused beam generation with different topological charges,and five-,six-,eight-channel focused beam generation with different energy intensities at a frequency of 1 THz,which are in good agreement with the theoretically calculated predictions.This work can provide a new idea for designing the terahertz multichannel devices.展开更多
文摘A novel matching method for simultaneous multi-target recognition is proposed by jointly considering target's prior scattering knowledge and the polarization parameters of radar echoes. The matching coefficients are calculated for the judgment. MATLAB simulations show that several targets can be accurately recognized simultaneously, and a high recognition probability can be achieved in Monte Carlo simulations. The total execution time can be remarkably reduced in the Field Programmable Gate Array (FPGA) implementation of the matching procedure.
文摘Flying plots detection has been the focus of relay protection in power system for a long time. With the promotion of Smart substation in our country, the number of SV devices is greatly increased. Abnormal data (flying plot) caused by sampling device itself has brought tremendous pressure to the power system. The traditional flying plot detection algorithm has plenty of defects, such as low pertinence, low sensitivity and long sampling period. This paper proposes a new algorithm to identify flying plot by analyzing the wave form characteristics of sampling data. The traditional waveform recognition methods are combined in this algorithm. It has the concept of standard wave window and can distinguish flying plot in a short time. In addition, sine recovery algorithm is used to recover the flying plot. This paper uses PSCAD software to verify the validity of this algorithm. Simulation results show that the proposed method has high reliability.
基金supported by the National Natural Science Foundation of China(No.12172076)。
文摘Gesture recognition plays an increasingly important role as the requirements of intelligent systems for human-computer interaction methods increase.To improve the accuracy of the millimeter-wave radar gesture detection algorithm with limited computational resources,this study improves the detection performance in terms of optimized features and interference filtering.The accuracy of the algorithm is improved by refining the combination of gesture features using a self-constructed dataset,and biometric filtering is introduced to reduce the interference of inanimate object motion.Finally,experiments demonstrate the effectiveness of the proposed algorithm in both mitigating interference from inanimate objects and accurately recognizing gestures.Results show a notable 93.29%average reduction in false detections achieved through the integration of biometric filtering into the algorithm’s interpretation of target movements.Additionally,the algorithm adeptly identifies the six gestures with an average accuracy of 96.84%on embedded systems.
文摘With technology advances and human requirements increasing, human-computer interaction plays an important role in our daily lives. Among these interactions, gesture-based recognition offers a natural and intuitive user experience that does not require physical contact and is becoming increasingly prevalent across various fields. Gesture recognition systems based on Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar are receiving widespread attention due to their ability to operate without wearable sensors, their robustness to environmental factors, and the excellent penetrative ability of radar signals. This paper first reviews the current main gesture recognition applications. Subsequently, we introduce the system of gesture recognition based on FMCW radar and provide a general framework for gesture recognition, including gesture data acquisition, data preprocessing, and classification methods. We then discuss typical applications of gesture recognition systems and summarize the performance of these systems in terms of experimental environment, signal acquisition, signal processing, and classification methods. Specifically, we focus our study on four typical gesture recognition systems, including air-writing recognition, gesture command recognition, sign language recognition, and text input recognition. Finally, this paper addresses the challenges and unresolved problems in FMCW radar-based gesture recognition and provides insights into potential future research directions.
文摘This paper introduces the principle for recognition of engine work wave signal with neural network. A diagnosis method for recognizing engine trouble by its work wave is proposed. The designing process is illustrated by diagnosing the voltage trouble of the fuel injector of an electronic control (EC) engine.
文摘With the development of wireless technology, Frequency-Modulated Continuous Wave (FMCW) radar has increased sensing capability and can be used to recognize human activity. These applications have gained wide-spread attention and become a hot research area. FMCW signals reflected by target activity can be collected, and human activity can be recognized based on the measurements. This paper focused on human activity recognition based on FMCW and DenseNet. We collected point clouds from FMCW and analyzed them to recognize human activity because different activities could lead to unique point cloud features. We built and trained the neural network to implement human activities using a FMCW signal. Firstly, this paper presented recent reviews about human activity recognition using wireless signals. Then, it introduced the basic concepts of FMCW radar and described the fundamental principles of the system using FMCW radar. We also provided the system framework, experiment scenario, and DenseNet neural network structure. Finally, we presented the experimental results and analyzed the accuracy of different neural network models. The system achieved recognition accuracy of 100 percent for five activities using the DenseNet. We concluded the paper by discussing the current issues and future research directions.
基金The 11th Postgraduate Technology Innovation Project of North University of China(No.20141142)
文摘During the test on transient pressure signal in explosion field,false trigger caused by field interference can lead to test failure.To improve the stability of test system,a signal detection and recognition technology is proposed for transient pressure test system.In the process of signal acquisition,firstly,electrical levels are monitored in real time to find effective abrupt changes and mark them;then the effective data segments are detecdted totected;thus the effective signals can be acquired in turn finally.The experimental results show that the shock wave signal can be collected effectively and the reliability of the test system can be improved after removal of interferences.
基金supported by the National Major Scientific and Technological Special Project during the 13th Five-year Plan Period(No.2016ZX05045003-005)
文摘Accurately detecting the arrival time of a channel wave in a coal seam is very important for in-seam seismic data processing. The arrival time greatly affects the accuracy of the channel wave inversion and the computed tomography （CT） result. However, because the signal-to-noise ratio of in-seam seismic data is reduced by the long wavelength and strong frequency dispersion, accurately timing the arrival of channel waves is extremely difficult. For this purpose, we propose a method that automatically picks up the arrival time of channel waves based on multi-channel constraints. We first estimate the Jaccard similarity coefficient of two ray paths, then apply it as a weight coefficient for stacking the multi- channel dispersion spectra. The reasonableness and effectiveness of the proposed method is verified in an actual data application. Most importantly, the method increases the degree of automation and the pickup precision of the channel-wave arrival time.
基金the Science and Technology Foundation of Shaanxi Province in China(2003K06G19)
文摘Firstly,the concepts of the traveling wave entropy and the feature function of traveling wave entropy were defined.Then the statistic characters of the traveling wave entropy feature function,mean value and variance were analyzed after the zero-order component of the traveling wave of online cable was selected to serve as the observed object.Finally,the new recognition algorithm of minimum risk neural network was pre- sented.The simulation experiments show that the recognitions of the early fault states can be completed correctly by using the proposed recognition algorithm.The classes of cable faults include in 1-phase ground faults,and the 2-phase short circuit faults or ground faults and the 3-phase short circuit faults or ground faults,open circuit.The fault resistance range is 1×10^(-1)～1×10~9Ω.
基金Project (No. 60772050) supported by the National Natural Science Foundation of China
文摘We present a novel model for recognizing long-term complex activities involving multiple persons. The proposed model, named ‘decomposed hidden Markov model’ (DHMM), combines spatial decomposition and hierarchical abstraction to capture multi-modal, long-term dependent and multi-scale characteristics of activities. Decomposition in space and time offers conceptual advantages of compaction and clarity, and greatly reduces the size of state space as well as the number of parameters. DHMMs are efficient even when the number of persons is variable. We also introduce an efficient approximation algorithm for inference and parameter estimation. Experiments on multi-person activities and multi-modal individual activities demonstrate that DHMMs are more efficient and reliable than familiar models, such as coupled HMMs, hierarchical HMMs, and multi-observation HMMs.
基金Supported by the National Defense Pre-Research Foundation of China (Grant No.9140A05070107BQ0204)
文摘In this paper,a new multiclass classification algorithm is proposed based on the idea of Locally Linear Embedding(LLE),to avoid the defect of traditional manifold learning algorithms,which can not deal with new sample points.The algorithm defines an error as a criterion by computing a sample's reconstruction weight using LLE.Furthermore,the existence and characteristics of low dimensional manifold in range-profile time-frequency information are explored using manifold learning algorithm,aiming at the problem of target recognition about high range resolution MilliMeter-Wave(MMW) radar.The new algorithm is applied to radar target recognition.The experiment results show the algorithm is efficient.Compared with other classification algorithms,our method improves the recognition precision and the result is not sensitive to input parameters.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0450102)the National Natural Science Foundation of China(22277118)+2 种基金the STI2030-Major Projects[2021ZD0203000(2021ZD0203003)]the Science and Technology Development Plan Project of Jilin Province(20220402045GH)the Chinese Academy of Sciences(CAS)Pioneer Hundred Talents Program.
文摘Precision control over ligand-receptor recognition is critical in biochemistry and pharmacology.Traditional meth-ods that alter reaction environments have limitations in fine-tuning the thermodynamic and kinetic aspects of biochemical reactions within biological systems.The advent of terahertz wave technology represents a significant breakthrough,providing a refined approach to modulating ligand-receptor interactions.This perspective explores the cutting-edge potential of terahertz waves in refining ligand-receptor recognition,featuring their innovative application in modulating neuronal functions.The capabilities of terahertz technology to selectively influence molecular interactions are discussed,highlighting its transformative potential for advancing therapeutic strategies and deepening our understanding of biological mechanisms.
文摘Shallow surface wave methods are mostly used for investigation of the surface velocity structure in environmental and engineering geophysics in non-desert areas. For the special geological features of the Takelamagan Desert area, we use the multi-channel analysis of surface wave （MASW） method to process multi-channel shallow surface wave records to determine the near surface velocity structure in the desert area. We also process, analyze, and compare the surface waves in many-trace records extracted from the oil exploration shot gathers in the area. We show that the MASW method can determine detailed shallow velocity structure in desert areas and the many-trace records can be used to get detailed deep geological structure. The combination of the two different datasets can obtain the exact velocity structure upper 60 m depth in the survey area.
基金Project supported by the National Natural Science Foundation of China (Grant No.62271460)the Zhejiang Key Research and Development Project,China (Grant Nos.2021C03153 and 2022C03166)。
文摘Most of existing metasurfaces usually have limited channel behavior,which seriouslyhinders their development and application.In this paper,we propose a multi-channel terahertz focused beam generator based on shared-aperture metasurface,and the generator consists of a top square metal strip,a middle layer of silica and a metal bottom plate.By changing the position and size of the shared-aperture array,the designed metasurface can generate any number of multi-channel focusing beams at different predicted positions.In addition,the energy intensity of focusing beams can be controlled.The full-wave simulation results show that the metasurface achieves four-channel vortex focused beam generation with different topological charges,and five-,six-,eight-channel focused beam generation with different energy intensities at a frequency of 1 THz,which are in good agreement with the theoretically calculated predictions.This work can provide a new idea for designing the terahertz multichannel devices.
