Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition me...Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.展开更多
Trajectory clustering and behavior pattern extraction are the foundations of research into activity perception of objects in motion. In this paper, a new framework is proposed to extract behavior patterns through traj...Trajectory clustering and behavior pattern extraction are the foundations of research into activity perception of objects in motion. In this paper, a new framework is proposed to extract behavior patterns through trajectory analysis. Firstly, we introduce directional trimmed mean distance （DTMD）, a novel method used to measure similarity between trajectories. DTMD has the attributes of anti-noise, self-adaptation and the capability to determine the direction for each trajectory. Secondly, we use a hierarchical clustering algorithm to cluster trajectories. We design a length-weighted linkage rule to enhance the accuracy of trajectory clustering and reduce problems associated with incomplete trajectories. Thirdly, the motion model parameters are estimated for each trajectory＇s classification, and behavior patterns for trajectories are extracted. Finally, the difference between normal and abnormal behaviors can be distinguished.展开更多
Traditional pattern representation in information extraction lack in the ability of representing domain-specific concepts and are therefore devoid of flexibility. To overcome these restrictions, an enhanced pattern re...Traditional pattern representation in information extraction lack in the ability of representing domain-specific concepts and are therefore devoid of flexibility. To overcome these restrictions, an enhanced pattern representation is designed which includes ontological concepts, neighboring-tree structures and soft constraints. An information-(extraction) inference engine based on hypothesis-generation and conflict-resolution is implemented. The proposed technique is successfully applied to an information extraction system for Chinese-language query front-end of a job-recruitment search engine.展开更多
In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extractin...In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extracting algorithms, i.e. the statistical algorithm and the neural network （NN） algorithm are presented, then uses the RBF NN as the classitier in the processing model. Finally the two algorithms are validated and compared through some simulations.展开更多
An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-v...An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-varying characteristics.Therefore, in several previous studies, various machine-learning methods have been applied. A DBN is a fast, greedy learning algorithm that can find a fairly good set of weights rapidly, even in deep networks with a large number of parameters and many hidden layers. To evaluate this model, we acquired EMG signals, extracted their features, and then compared the model with the DBN and other conventional classifiers. The accuracy of the DBN is higher than that of the other algorithms. The classification performance of the DBN model designed is approximately 88.60%. It is 7.55%(p=9.82×10-12) higher than linear discriminant analysis(LDA) and 2.89%(p=1.94×10-5) higher than support vector machine(SVM). Further, the DBN is better than shallow learning algorithms or back propagation(BP), and this model is effective for an EMG-based user-interfaced system.展开更多
Five-electrode configurations were designed to simulate the distribution inhomogeneity of electric field intensities in the air-insulating medium, and the characteristic data waveforms of partial discharge generated b...Five-electrode configurations were designed to simulate the distribution inhomogeneity of electric field intensities in the air-insulating medium, and the characteristic data waveforms of partial discharge generated by different electrode configurations under the excitation of power frequency AC voltage were carefully collected in this paper. Furthermore, the feature vectors of the corresponding fingerprint, contained in partial discharge data, were extracted by rigorous mathematical algorithms, and the artificial neural network was employed to realize the pattern recognition of partial discharge caused by the inhomogeneity of electric field intensity with different electrode configurations. The results indicate that the J<sub>4</sub> value in the space of 7 feature quantities is 1905.6, and the recognition rate is 100% when the hidden layer neuron of the network is 19. However, the J<sub>5</sub> value of 9 feature quantities is 1589.9, and the purpose of recognition has been achieved when the number of hidden layer neurons of the network is 6. Increasing the number of hidden layer neurons will only waste computing resources. Of course, PD information collection mode, feature quantity selection, optimal feature space composition, network structure and classification algorithm are the key to realizing PD fault intelligence identification.展开更多
Fabric pattern contains many types of the available pattern elements, which not only can be used for the researchers, but also as the material for the designer. But existing method focus on the complete image retrieva...Fabric pattern contains many types of the available pattern elements, which not only can be used for the researchers, but also as the material for the designer. But existing method focus on the complete image retrieval, therefore lack methods of retrieving pattern elements. This article proposes a pattern elements retrieval algorithm based on cosine transform. Firstly, automatically segment the patterns according to size and location and filter the similar primary patterns, then, through cosine transform, analyze elements features in DCT domain, extract amplitude frequency and phase frequency. We employ 2-norm to measure the similarity, search 10 similar pattern elements in the sample library and save them in the design resources library. Experiment results indicate that this algorithm performs well while used in palace costume and carpet patterns, and got more than 75% of the average recall in 100 times experiments展开更多
基金supported by the National Natural Science Foundation of China (Project No.72301293)。
文摘Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.
文摘Trajectory clustering and behavior pattern extraction are the foundations of research into activity perception of objects in motion. In this paper, a new framework is proposed to extract behavior patterns through trajectory analysis. Firstly, we introduce directional trimmed mean distance （DTMD）, a novel method used to measure similarity between trajectories. DTMD has the attributes of anti-noise, self-adaptation and the capability to determine the direction for each trajectory. Secondly, we use a hierarchical clustering algorithm to cluster trajectories. We design a length-weighted linkage rule to enhance the accuracy of trajectory clustering and reduce problems associated with incomplete trajectories. Thirdly, the motion model parameters are estimated for each trajectory＇s classification, and behavior patterns for trajectories are extracted. Finally, the difference between normal and abnormal behaviors can be distinguished.
文摘Traditional pattern representation in information extraction lack in the ability of representing domain-specific concepts and are therefore devoid of flexibility. To overcome these restrictions, an enhanced pattern representation is designed which includes ontological concepts, neighboring-tree structures and soft constraints. An information-(extraction) inference engine based on hypothesis-generation and conflict-resolution is implemented. The proposed technique is successfully applied to an information extraction system for Chinese-language query front-end of a job-recruitment search engine.
文摘In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extracting algorithms, i.e. the statistical algorithm and the neural network （NN） algorithm are presented, then uses the RBF NN as the classitier in the processing model. Finally the two algorithms are validated and compared through some simulations.
基金supported by Inha University Research Grant,Korea
文摘An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-varying characteristics.Therefore, in several previous studies, various machine-learning methods have been applied. A DBN is a fast, greedy learning algorithm that can find a fairly good set of weights rapidly, even in deep networks with a large number of parameters and many hidden layers. To evaluate this model, we acquired EMG signals, extracted their features, and then compared the model with the DBN and other conventional classifiers. The accuracy of the DBN is higher than that of the other algorithms. The classification performance of the DBN model designed is approximately 88.60%. It is 7.55%(p=9.82×10-12) higher than linear discriminant analysis(LDA) and 2.89%(p=1.94×10-5) higher than support vector machine(SVM). Further, the DBN is better than shallow learning algorithms or back propagation(BP), and this model is effective for an EMG-based user-interfaced system.
文摘Five-electrode configurations were designed to simulate the distribution inhomogeneity of electric field intensities in the air-insulating medium, and the characteristic data waveforms of partial discharge generated by different electrode configurations under the excitation of power frequency AC voltage were carefully collected in this paper. Furthermore, the feature vectors of the corresponding fingerprint, contained in partial discharge data, were extracted by rigorous mathematical algorithms, and the artificial neural network was employed to realize the pattern recognition of partial discharge caused by the inhomogeneity of electric field intensity with different electrode configurations. The results indicate that the J<sub>4</sub> value in the space of 7 feature quantities is 1905.6, and the recognition rate is 100% when the hidden layer neuron of the network is 19. However, the J<sub>5</sub> value of 9 feature quantities is 1589.9, and the purpose of recognition has been achieved when the number of hidden layer neurons of the network is 6. Increasing the number of hidden layer neurons will only waste computing resources. Of course, PD information collection mode, feature quantity selection, optimal feature space composition, network structure and classification algorithm are the key to realizing PD fault intelligence identification.
基金Supported by National Natural Science Foundation of China(61163044)Philosophy and Social Key Fund Project(12AZD120)+1 种基金Project ofBeijing Scientific Committee(Z141110004414074Z141100001914035)
文摘Fabric pattern contains many types of the available pattern elements, which not only can be used for the researchers, but also as the material for the designer. But existing method focus on the complete image retrieval, therefore lack methods of retrieving pattern elements. This article proposes a pattern elements retrieval algorithm based on cosine transform. Firstly, automatically segment the patterns according to size and location and filter the similar primary patterns, then, through cosine transform, analyze elements features in DCT domain, extract amplitude frequency and phase frequency. We employ 2-norm to measure the similarity, search 10 similar pattern elements in the sample library and save them in the design resources library. Experiment results indicate that this algorithm performs well while used in palace costume and carpet patterns, and got more than 75% of the average recall in 100 times experiments
