In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too...In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information (TSCI) should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA (joint probabilistic data association). The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.展开更多
Support vector machine (SVM) is an important classi- fication tool in the pattern recognition and machine learning community, but its training is a time-consuming process. To deal with this problem, we propose a nov...Support vector machine (SVM) is an important classi- fication tool in the pattern recognition and machine learning community, but its training is a time-consuming process. To deal with this problem, we propose a novel method to mine the useful information about classification hidden in the training sample for improving the training algorithm, and every training point is as- signed to a value that represents the classification information, respectively, where training points with the higher values are cho- sen as candidate support vectors for SVM training. The classifica- tion information value for a training point is computed based on the classification accuracy of an appropriate hyperplane for the training sample, where the hyperplane goes through the mapped target of the training point in feature space defined by a kernel fimction. Experimental results on various benchmark datasets show the effectiveness of our algorithm.展开更多
Hyper-connectivity in Industry 4.0 has resulted in not only a rapid increase in the amount of information,but also the expansion of areas and assets to be protected.In terms of information security,it has led to an en...Hyper-connectivity in Industry 4.0 has resulted in not only a rapid increase in the amount of information,but also the expansion of areas and assets to be protected.In terms of information security,it has led to an enormous economic cost due to the various and numerous security solutions used in protecting the increased assets.Also,it has caused difficulties in managing those issues due to reasons such as mutual interference,countless security events and logs’data,etc.Within this security environment,an organization should identify and classify assets based on the value of data and their security perspective,and then apply appropriate protection measures according to the assets’security classification for effective security management.But there are still difficulties stemming from the need to manage numerous security solutions in order to protect the classified assets.In this paper,we propose an information classification management service based on blockchain,which presents and uses a model of the value of data and the security perspective.It records transactions of classifying assets and managing assets by each class in a distributed ledger of blockchain.The proposed service reduces assets to be protected and security solutions to be applied,and provides security measures at the platform level rather than individual security solutions,by using blockchain.In the rapidly changing security environment of Industry 4.0,this proposed service enables economic security,provides a new integrated security platform,and demonstrates service value.展开更多
Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are...Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.展开更多
In this paper, decision mechanism of credit-risk for banks is studied when the loan interest rate is fixed with asymmetry information in credit market. We give out the designs of rationing and non-rationing on credit ...In this paper, decision mechanism of credit-risk for banks is studied when the loan interest rate is fixed with asymmetry information in credit market. We give out the designs of rationing and non-rationing on credit risky decision mechanism when collateral value provided by an entrepreneur is not less than the minimum demands of the bank. It shows that under the action of the mechanism, banks could efficiently identify the risk size of the project. Finally, the condition of the project investigation of bank is given over again.展开更多
Consider the efficiency of p-norm multiple kernel learning (MKL), which is extended to a semi-supervised learning (SSL) scenario by applying the manifold regularization technique. A manifold regularized p-norm multipl...Consider the efficiency of p-norm multiple kernel learning (MKL), which is extended to a semi-supervised learning (SSL) scenario by applying the manifold regularization technique. A manifold regularized p-norm multiple kernels model is constructed and applied to a semi-supervised classification task. Solutions are proposed for the case of p = 1, p > 1 and p = ∞, with an analysis of theorems and their proofs. In addition, experiments are conducted on several datasets using state-of-the-art methods to verify the efficiency of the proposed manifold regularized p-norm multiple kernels model in semi-supervised classification. ? 2016 Beijing Institute of Aerospace Information.展开更多
A new hyperspectral image compression method of spectral feature classification vector quantization (SFCVQ) and embedded zero-tree of wavelet (EZW) based on Karhunen-Loeve transformation (KLT) and integer wavele...A new hyperspectral image compression method of spectral feature classification vector quantization (SFCVQ) and embedded zero-tree of wavelet (EZW) based on Karhunen-Loeve transformation (KLT) and integer wavelet transformation is represented. In comparison with the other methods, this method not only keeps the characteristics of high compression ratio and easy real-time transmission, but also has the advantage of high computation speed. After lifting based integer wavelet and SFCVQ coding are intro- duced, a system of nearly lossless compression of hyperspectral images is designed. KLT is used to remove the correlation of spectral redundancy as one-dimensional (1D) linear transform, and SFCVQ coding is applied to enhance compression ratio. The two-dimensional (2D) integer wavelet transformation is adopted for the decorrelation of 2D spatial redundancy. EZW coding method is applied to compress data in wavelet domain. Experimental results show that in comparison with the method of wavelet SFCVQ (WSFCVQ), the method of improved BiBlock zero tree coding (IBBZTC) and the method of feature spectral vector quantization (FSVQ), the peak signal-to-noise ratio (PSNR) of this method can enhance over 9 dB, and the total compression performance is improved greatly.展开更多
基金the Youth Science and Technology Foundection of University of Electronic Science andTechnology of China (JX0622).
文摘In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information (TSCI) should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA (joint probabilistic data association). The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.
基金Supported by the National Natural Science Foundation of China (61070137,60933009)the Science and Technology Research Development Program in Shaanxi Province of China (2009K01-56)
文摘Support vector machine (SVM) is an important classi- fication tool in the pattern recognition and machine learning community, but its training is a time-consuming process. To deal with this problem, we propose a novel method to mine the useful information about classification hidden in the training sample for improving the training algorithm, and every training point is as- signed to a value that represents the classification information, respectively, where training points with the higher values are cho- sen as candidate support vectors for SVM training. The classifica- tion information value for a training point is computed based on the classification accuracy of an appropriate hyperplane for the training sample, where the hyperplane goes through the mapped target of the training point in feature space defined by a kernel fimction. Experimental results on various benchmark datasets show the effectiveness of our algorithm.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2020-2018-0-01799)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation).
文摘Hyper-connectivity in Industry 4.0 has resulted in not only a rapid increase in the amount of information,but also the expansion of areas and assets to be protected.In terms of information security,it has led to an enormous economic cost due to the various and numerous security solutions used in protecting the increased assets.Also,it has caused difficulties in managing those issues due to reasons such as mutual interference,countless security events and logs’data,etc.Within this security environment,an organization should identify and classify assets based on the value of data and their security perspective,and then apply appropriate protection measures according to the assets’security classification for effective security management.But there are still difficulties stemming from the need to manage numerous security solutions in order to protect the classified assets.In this paper,we propose an information classification management service based on blockchain,which presents and uses a model of the value of data and the security perspective.It records transactions of classifying assets and managing assets by each class in a distributed ledger of blockchain.The proposed service reduces assets to be protected and security solutions to be applied,and provides security measures at the platform level rather than individual security solutions,by using blockchain.In the rapidly changing security environment of Industry 4.0,this proposed service enables economic security,provides a new integrated security platform,and demonstrates service value.
基金Defense Advanced Research Project "the Techniques of Information Integrated Processing and Fusion" in the Eleventh Five-Year Plan (513060302).
文摘Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.
基金This project was supported by Fubangs Science & Technology Company Ltd.
文摘In this paper, decision mechanism of credit-risk for banks is studied when the loan interest rate is fixed with asymmetry information in credit market. We give out the designs of rationing and non-rationing on credit risky decision mechanism when collateral value provided by an entrepreneur is not less than the minimum demands of the bank. It shows that under the action of the mechanism, banks could efficiently identify the risk size of the project. Finally, the condition of the project investigation of bank is given over again.
基金supported by the National Natural Science Foundation of China(61272358)
文摘Consider the efficiency of p-norm multiple kernel learning (MKL), which is extended to a semi-supervised learning (SSL) scenario by applying the manifold regularization technique. A manifold regularized p-norm multiple kernels model is constructed and applied to a semi-supervised classification task. Solutions are proposed for the case of p = 1, p > 1 and p = ∞, with an analysis of theorems and their proofs. In addition, experiments are conducted on several datasets using state-of-the-art methods to verify the efficiency of the proposed manifold regularized p-norm multiple kernels model in semi-supervised classification. ? 2016 Beijing Institute of Aerospace Information.
基金This work was supported by the National Natural Science Foundation of China (No. 60472081) the Avigation Science Foundation (No. 05F07001)the 985 Innovation Project on Information Technique of Xiamen University (2004-2007).
文摘A new hyperspectral image compression method of spectral feature classification vector quantization (SFCVQ) and embedded zero-tree of wavelet (EZW) based on Karhunen-Loeve transformation (KLT) and integer wavelet transformation is represented. In comparison with the other methods, this method not only keeps the characteristics of high compression ratio and easy real-time transmission, but also has the advantage of high computation speed. After lifting based integer wavelet and SFCVQ coding are intro- duced, a system of nearly lossless compression of hyperspectral images is designed. KLT is used to remove the correlation of spectral redundancy as one-dimensional (1D) linear transform, and SFCVQ coding is applied to enhance compression ratio. The two-dimensional (2D) integer wavelet transformation is adopted for the decorrelation of 2D spatial redundancy. EZW coding method is applied to compress data in wavelet domain. Experimental results show that in comparison with the method of wavelet SFCVQ (WSFCVQ), the method of improved BiBlock zero tree coding (IBBZTC) and the method of feature spectral vector quantization (FSVQ), the peak signal-to-noise ratio (PSNR) of this method can enhance over 9 dB, and the total compression performance is improved greatly.