In this paper, we focus on the power allocation of Integrated Sensing and Communication(ISAC) with orthogonal frequency division multiplexing(OFDM) waveform. In order to improve the spectrum utilization efficiency in ...In this paper, we focus on the power allocation of Integrated Sensing and Communication(ISAC) with orthogonal frequency division multiplexing(OFDM) waveform. In order to improve the spectrum utilization efficiency in ISAC, we propose a design scheme based on spectrum sharing, that is,to maximize the mutual information(MI) of radar sensing while ensuring certain communication rate and transmission power constraints. In the proposed scheme, three cases are considered for the scattering off the target due to the communication signals,as negligible signal, beneficial signal, and interference signal to radar sensing, respectively, thus requiring three power allocation schemes. However,the corresponding power allocation schemes are nonconvex and their closed-form solutions are unavailable as a consequence. Motivated by this, alternating optimization(AO), sequence convex programming(SCP) and Lagrange multiplier are individually combined for three suboptimal solutions corresponding with three power allocation schemes. By combining the three algorithms, we transform the non-convex problem which is difficult to deal with into a convex problem which is easy to solve and obtain the suboptimal solution of the corresponding optimization problem. Numerical results show that, compared with the allocation results of the existing algorithms, the proposed joint design algorithm significantly improves the radar performance.展开更多
In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar codes.Based on the optimal quantizer of binary-input discrete mem...In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar codes.Based on the optimal quantizer of binary-input discrete memoryless channels(BDMCs),the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information(MMI)between source bits and quantized symbols.The nested structure of polar codes ensures that the MMI quantization can be implemented stage by stage.Simulation results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error(MMSE)with 4 quantization bits,and yield even better performance than uniform MMI quantized decoders with 5 quantization bits.Furthermore,the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss.展开更多
The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attr...The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.展开更多
Fuzzy entropy was designed for non convex fuzzy membership function using well known Hamming distance measure.The proposed fuzzy entropy had the same structure as that of convex fuzzy membership case.Design procedure ...Fuzzy entropy was designed for non convex fuzzy membership function using well known Hamming distance measure.The proposed fuzzy entropy had the same structure as that of convex fuzzy membership case.Design procedure of fuzzy entropy was proposed by considering fuzzy membership through distance measure,and the obtained results contained more flexibility than the general fuzzy membership function.Furthermore,characteristic analyses for non convex function were also illustrated.Analyses on the mutual information were carried out through the proposed fuzzy entropy and similarity measure,which was also dual structure of fuzzy entropy.By the illustrative example,mutual information was discussed.展开更多
Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still...Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.展开更多
In this paper,we introduce and investigate the mutual information and relative entropy on the sequentialeffect algebra,we also give a comparison of these mutual information and relative entropy with the classical ones...In this paper,we introduce and investigate the mutual information and relative entropy on the sequentialeffect algebra,we also give a comparison of these mutual information and relative entropy with the classical ones by thevenn diagrams.Finally,a nice example shows that the entropies of sequential effect algebra depend extremely on theorder of its sequential product.展开更多
The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and e...The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.展开更多
The quality of the radiation dose depends upon the gamma count rate of the radionuclide used. Any reduction in error in the count rate is reflected in the reduction in error in the activity and consequently on the qua...The quality of the radiation dose depends upon the gamma count rate of the radionuclide used. Any reduction in error in the count rate is reflected in the reduction in error in the activity and consequently on the quality of dose. All the efforts so far have been directed only to minimize the random errors in count rate by repetition. In the absence of probability distribution for the systematic errors, we propose to minimize these errors by estimating the upper and lower limits by the technique of determinant in equalities developed by us. Using the algorithm we have developed based on the tech- nique of determinant inequalities and the concept of maximization of mutual information (MI), we show how to process element by element of the covariance matrix to minimize the correlated systematic errors in the count rate of 113 mIn. The element wise processing of covariance matrix is so unique by our technique that it gives experimentalists enough maneuverability to mitigate different factors causing systematic errors in the count rate and consequently the activity of 113 mIn.展开更多
A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively rem...A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper.From the whole process point of view,the method makes use of the characteristic of mutual information to select the optimal variable subset.It extracts the correlation among variables in the whitening process without limiting to only linear correlations.Further,PCA(Principal Component Analysis)dimension reduction is used to extract feature subset before fault diagnosis.The application results of the TE(Tennessee Eastman)simulation process show that the dynamic modeling process of MIFE(Mutual Information Feature Engineering)can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.展开更多
This paper presents a method for detecting the small infrared target under complex background. An algorithm, named local mutation weighted information entropy (LMWIE), is proposed to suppress background. Then, the g...This paper presents a method for detecting the small infrared target under complex background. An algorithm, named local mutation weighted information entropy (LMWIE), is proposed to suppress background. Then, the grey value of targets is enhanced by calculating the local energy. Image segmentation based on the adaptive threshold is used to solve the problems that the grey value of noise is enhanced with the grey value improvement of targets. Experimental results show that compared with the adaptive Butterworth high-pass filter method, the proposed algorithm is more effective and faster for the infrared small target detection.展开更多
Structural redundancy elimination in case resource pools (CRP) is critical for avoiding performance bottlenecks and maintaining robust decision capabilities in cloud computing services. For these purposes, this pape...Structural redundancy elimination in case resource pools (CRP) is critical for avoiding performance bottlenecks and maintaining robust decision capabilities in cloud computing services. For these purposes, this paper proposes a novel approach to ensure redundancy elimination of a reasoning system in CRP. By using α entropy and mutual information, functional measures to eliminate redundancy of a system are developed with respect to a set of outputs. These measures help to distinguish both the optimal feature and the relations among the nodes in reasoning networks from the redundant ones with the elimination criterion. Based on the optimal feature and its harmonic weight, a model for knowledge reasoning in CRP (CRPKR) is built to complete the task of query matching, and the missing values are estimated with Bayesian networks. Moreover, the robustness of decisions is verified through parameter analyses. This approach is validated by the simulation with benchmark data sets using cloud SQL. Compared with several state-of-the-art techniques, the results show that the proposed approach has a good performance and boosts the robustness of decisions.展开更多
In this paper, the mutual information between clock-controlled input and output sequences is discussed. It is proved that the mutual information is a strictly monotone increasing function of the length of output seque...In this paper, the mutual information between clock-controlled input and output sequences is discussed. It is proved that the mutual information is a strictly monotone increasing function of the length of output sequence, and its divergent rate is gaven.展开更多
in this paper, a new approach to relativistic information entropy is used to assess some relative uncertainties in structural reliability assessment. This approach is composed of the information theory and the relativ...in this paper, a new approach to relativistic information entropy is used to assess some relative uncertainties in structural reliability assessment. This approach is composed of the information theory and the relativistic theory, and can be used to measure the relativity of parameter uncertainty and system uncertainty in structural reliability theory based on the same generalized relativistic reference system. Therefore, the structural reliability assessment can be assessed reasonably by the approach.展开更多
The frame of text classification system was presented. The high dimensionality in feature space for text classification was studied. The mutual information is a widely used information theoretic measure, in a descript...The frame of text classification system was presented. The high dimensionality in feature space for text classification was studied. The mutual information is a widely used information theoretic measure, in a descriptive way, to measure the stochastic dependency of discrete random variables. The measure method was used as a criterion to reduce high dimensionality of feature vectors in text classification on Web. Feature selections or conversions were performed by using maximum mutual information including linear and non-linear feature conversions. Entropy was used and extended to find right features commendably in pattern recognition systems. Favorable foundation would be established for text classification mining.展开更多
For a single-structure deep learning fault diagnosis model,its disadvantages are an insufficient feature extraction and weak fault classification capability.This paper proposes a multi-scale deep feature fusion intell...For a single-structure deep learning fault diagnosis model,its disadvantages are an insufficient feature extraction and weak fault classification capability.This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy.First,a normal autoencoder,denoising autoencoder,sparse autoencoder,and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure.A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features.Finally,the advantage of the deep belief network probability model is used as the fault classifier to identify the faults.The effectiveness of the proposed method was verified by a gearbox test-bed.Experimental results show that,compared with traditional and existing intelligent fault diagnosis methods,the proposed method can obtain representative information and features from the raw data with higher classification accuracy.展开更多
Configurational information entropy(CIE)theory was employed to determine the neutron-skin thickness of neutron-rich calcium isotopes.The nuclear density distributions and fragment cross sections in 350 MeV/u ^(40-60)C...Configurational information entropy(CIE)theory was employed to determine the neutron-skin thickness of neutron-rich calcium isotopes.The nuclear density distributions and fragment cross sections in 350 MeV/u ^(40-60)Ca+^(9)Be projectile fragmentation reactions were calculated using a modified statistical abrasion-ablation model.CIE quantities were determined from the nuclear density,isotopic,mass,and charge distributions.The linear correlations between the CIE determined using the isotopic,mass,and charge distributions and the neutron-skin thickness of the projectile nucleus show that CIE provides new methods to extract the neutron-skin thickness of neutron-rich nuclei.展开更多
Configurational information entropy(CIE)analysis has been shown to be applicable for determining the neutron skin thickness(δnp)of neutron-rich nuclei from fragment production in projectile fragmentation reactions.Th...Configurational information entropy(CIE)analysis has been shown to be applicable for determining the neutron skin thickness(δnp)of neutron-rich nuclei from fragment production in projectile fragmentation reactions.The BNN+FRACS machine learning model was adopted to predict the fragment mass cross-sections(σ_(A))of the projectile fragmentation reactions induced by calcium isotopes from ^(36)Ca to ^(56)Ca on a ^(9)Be target at 140MeV/u.The fast Fourier transform was adopted to decompose the possible information compositions inσA distributions and determine the quantity of CIE(S_(A)[f]).It was found that the range of fragments significantly influences the quantity of S_(A)[f],which results in different trends of S_(A)[f]~δnp correlation.The linear S_(A)[f]~δnp correlation in a previous study[Nucl.Sci.Tech.33,6(2022)]could be reproduced using fragments with relatively large mass fragments,which verifies that S_(A)[f]determined from fragmentσAis sensitive to the neutron skin thickness of neutron-rich isotopes.展开更多
This paper studied on the clustering problem for intrusion detection with the theory of information entropy, it was put forward that the clustering problem for exact intrusion detection based on information entropy is...This paper studied on the clustering problem for intrusion detection with the theory of information entropy, it was put forward that the clustering problem for exact intrusion detection based on information entropy is NP complete, therefore, the heuristic algorithm to solve the clustering problem for intrusion detection was designed, this algorithm has the characteristic of incremental development, it can deal with the database with large connection records from the internet.展开更多
As a convenient approach to the characterization of cerebral cortex electrical information, electroencephalograph (EEG) has potential clinical application in monitoring the acupuncture effects. In this paper, a meth...As a convenient approach to the characterization of cerebral cortex electrical information, electroencephalograph (EEG) has potential clinical application in monitoring the acupuncture effects. In this paper, a method composed of the mutual information method and Lempel-Ziv complexity method (MILZC) is proposed to investigate the effects of acupuncture on the complexity of information exchanges between different brain regions based on EEGs. In the experiments, eight subjects are manually acupunctured at 'Zusanli' acupuncture point (ST-36) with different frequencies (i.e., 50, 100, 150, and 200 times/min) and the EEGs are recorded simultaneously. First, MILZC values are compared in general. Then average brain connections are used to quantify the effectiveness of acupuncture under the above four frequencies. Finally, significance index P values are used to study the spatiality of the acupuncture effect on the brain. Three main findings are obtained: (i) MILZC values increase during the acupuncture; (ii) manual acupunctures (MAs) with 100 times/rain and 150 times/min are more effective than with 50 times/min and 200 times/rain; (iii) contralateral hemisphere activation is more prominent than ipsilateral hemisphere's. All these findings suggest that acupuncture contributes to the increase of brain information exchange complexity and the MILZC method can successfully describe these changes.展开更多
文摘In this paper, we focus on the power allocation of Integrated Sensing and Communication(ISAC) with orthogonal frequency division multiplexing(OFDM) waveform. In order to improve the spectrum utilization efficiency in ISAC, we propose a design scheme based on spectrum sharing, that is,to maximize the mutual information(MI) of radar sensing while ensuring certain communication rate and transmission power constraints. In the proposed scheme, three cases are considered for the scattering off the target due to the communication signals,as negligible signal, beneficial signal, and interference signal to radar sensing, respectively, thus requiring three power allocation schemes. However,the corresponding power allocation schemes are nonconvex and their closed-form solutions are unavailable as a consequence. Motivated by this, alternating optimization(AO), sequence convex programming(SCP) and Lagrange multiplier are individually combined for three suboptimal solutions corresponding with three power allocation schemes. By combining the three algorithms, we transform the non-convex problem which is difficult to deal with into a convex problem which is easy to solve and obtain the suboptimal solution of the corresponding optimization problem. Numerical results show that, compared with the allocation results of the existing algorithms, the proposed joint design algorithm significantly improves the radar performance.
基金financially supported in part by National Key R&D Program of China(No.2018YFB1801402)in part by Huawei Technologies Co.,Ltd.
文摘In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar codes.Based on the optimal quantizer of binary-input discrete memoryless channels(BDMCs),the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information(MMI)between source bits and quantized symbols.The nested structure of polar codes ensures that the MMI quantization can be implemented stage by stage.Simulation results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error(MMSE)with 4 quantization bits,and yield even better performance than uniform MMI quantized decoders with 5 quantization bits.Furthermore,the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss.
基金Anhui Province Natural Science Research Project of Colleges and Universities(2023AH040321)Excellent Scientific Research and Innovation Team of Anhui Colleges(2022AH010098).
文摘The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.
基金Work supported by the Second Stage of Brain Korea 21 Projects Work(2010-0020163) supported by the Priority Research Centers Program through the National Research Foundation (NRF) funded by the Ministry of Education,Science and Technology of Korea
文摘Fuzzy entropy was designed for non convex fuzzy membership function using well known Hamming distance measure.The proposed fuzzy entropy had the same structure as that of convex fuzzy membership case.Design procedure of fuzzy entropy was proposed by considering fuzzy membership through distance measure,and the obtained results contained more flexibility than the general fuzzy membership function.Furthermore,characteristic analyses for non convex function were also illustrated.Analyses on the mutual information were carried out through the proposed fuzzy entropy and similarity measure,which was also dual structure of fuzzy entropy.By the illustrative example,mutual information was discussed.
基金supported by National Basic Research Project of China(2013CB329006)National Natural Science Foundation of China(No.61622110,No.61471220,No.91538107)
文摘Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.
基金Supported by Research Foundation of Kumoh National Institute of Technology
文摘In this paper,we introduce and investigate the mutual information and relative entropy on the sequentialeffect algebra,we also give a comparison of these mutual information and relative entropy with the classical ones by thevenn diagrams.Finally,a nice example shows that the entropies of sequential effect algebra depend extremely on theorder of its sequential product.
基金supported by the Natural Science Foundation Research Plan of Shanxi Province (2023JCQN0728)。
文摘The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.
文摘The quality of the radiation dose depends upon the gamma count rate of the radionuclide used. Any reduction in error in the count rate is reflected in the reduction in error in the activity and consequently on the quality of dose. All the efforts so far have been directed only to minimize the random errors in count rate by repetition. In the absence of probability distribution for the systematic errors, we propose to minimize these errors by estimating the upper and lower limits by the technique of determinant in equalities developed by us. Using the algorithm we have developed based on the tech- nique of determinant inequalities and the concept of maximization of mutual information (MI), we show how to process element by element of the covariance matrix to minimize the correlated systematic errors in the count rate of 113 mIn. The element wise processing of covariance matrix is so unique by our technique that it gives experimentalists enough maneuverability to mitigate different factors causing systematic errors in the count rate and consequently the activity of 113 mIn.
基金Supported by the National Natural Science Foundation of China(21576143).
文摘A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper.From the whole process point of view,the method makes use of the characteristic of mutual information to select the optimal variable subset.It extracts the correlation among variables in the whitening process without limiting to only linear correlations.Further,PCA(Principal Component Analysis)dimension reduction is used to extract feature subset before fault diagnosis.The application results of the TE(Tennessee Eastman)simulation process show that the dynamic modeling process of MIFE(Mutual Information Feature Engineering)can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.
基金supported by the National Natural Science Foundation of China (61171194)
文摘This paper presents a method for detecting the small infrared target under complex background. An algorithm, named local mutation weighted information entropy (LMWIE), is proposed to suppress background. Then, the grey value of targets is enhanced by calculating the local energy. Image segmentation based on the adaptive threshold is used to solve the problems that the grey value of noise is enhanced with the grey value improvement of targets. Experimental results show that compared with the adaptive Butterworth high-pass filter method, the proposed algorithm is more effective and faster for the infrared small target detection.
基金supported by the National Natural Science Foundation of China (7117114371201087)+1 种基金the Tianjin Municipal Research Program of Application Foundation and Advanced Technology of China (10JCY-BJC07300)the Science and Technology Program of FOXCONN Group (120024001156)
文摘Structural redundancy elimination in case resource pools (CRP) is critical for avoiding performance bottlenecks and maintaining robust decision capabilities in cloud computing services. For these purposes, this paper proposes a novel approach to ensure redundancy elimination of a reasoning system in CRP. By using α entropy and mutual information, functional measures to eliminate redundancy of a system are developed with respect to a set of outputs. These measures help to distinguish both the optimal feature and the relations among the nodes in reasoning networks from the redundant ones with the elimination criterion. Based on the optimal feature and its harmonic weight, a model for knowledge reasoning in CRP (CRPKR) is built to complete the task of query matching, and the missing values are estimated with Bayesian networks. Moreover, the robustness of decisions is verified through parameter analyses. This approach is validated by the simulation with benchmark data sets using cloud SQL. Compared with several state-of-the-art techniques, the results show that the proposed approach has a good performance and boosts the robustness of decisions.
文摘In this paper, the mutual information between clock-controlled input and output sequences is discussed. It is proved that the mutual information is a strictly monotone increasing function of the length of output sequence, and its divergent rate is gaven.
文摘in this paper, a new approach to relativistic information entropy is used to assess some relative uncertainties in structural reliability assessment. This approach is composed of the information theory and the relativistic theory, and can be used to measure the relativity of parameter uncertainty and system uncertainty in structural reliability theory based on the same generalized relativistic reference system. Therefore, the structural reliability assessment can be assessed reasonably by the approach.
文摘The frame of text classification system was presented. The high dimensionality in feature space for text classification was studied. The mutual information is a widely used information theoretic measure, in a descriptive way, to measure the stochastic dependency of discrete random variables. The measure method was used as a criterion to reduce high dimensionality of feature vectors in text classification on Web. Feature selections or conversions were performed by using maximum mutual information including linear and non-linear feature conversions. Entropy was used and extended to find right features commendably in pattern recognition systems. Favorable foundation would be established for text classification mining.
基金Supported by National Natural Science Foundation of China and Civil Aviation Administration of China Joint Funded Project(Grant No.U1733108)Key Project of Tianjin Science and Technology Support Program(Grant No.16YFZCSY00860).
文摘For a single-structure deep learning fault diagnosis model,its disadvantages are an insufficient feature extraction and weak fault classification capability.This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy.First,a normal autoencoder,denoising autoencoder,sparse autoencoder,and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure.A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features.Finally,the advantage of the deep belief network probability model is used as the fault classifier to identify the faults.The effectiveness of the proposed method was verified by a gearbox test-bed.Experimental results show that,compared with traditional and existing intelligent fault diagnosis methods,the proposed method can obtain representative information and features from the raw data with higher classification accuracy.
基金supported by the National Natural Science Foundation of China(Nos.11975091 and U1732135)the Program for Innovative Research Team(in Science and Technology)in University of Henan Province,China(No.21IRTSTHN011)。
文摘Configurational information entropy(CIE)theory was employed to determine the neutron-skin thickness of neutron-rich calcium isotopes.The nuclear density distributions and fragment cross sections in 350 MeV/u ^(40-60)Ca+^(9)Be projectile fragmentation reactions were calculated using a modified statistical abrasion-ablation model.CIE quantities were determined from the nuclear density,isotopic,mass,and charge distributions.The linear correlations between the CIE determined using the isotopic,mass,and charge distributions and the neutron-skin thickness of the projectile nucleus show that CIE provides new methods to extract the neutron-skin thickness of neutron-rich nuclei.
基金the National Natural Science Foundation of China(No.11975091)the Program for Innovative Research Team(in Science and Technology)in the University of Henan Province,China(No.21IRTSTHN011).
文摘Configurational information entropy(CIE)analysis has been shown to be applicable for determining the neutron skin thickness(δnp)of neutron-rich nuclei from fragment production in projectile fragmentation reactions.The BNN+FRACS machine learning model was adopted to predict the fragment mass cross-sections(σ_(A))of the projectile fragmentation reactions induced by calcium isotopes from ^(36)Ca to ^(56)Ca on a ^(9)Be target at 140MeV/u.The fast Fourier transform was adopted to decompose the possible information compositions inσA distributions and determine the quantity of CIE(S_(A)[f]).It was found that the range of fragments significantly influences the quantity of S_(A)[f],which results in different trends of S_(A)[f]~δnp correlation.The linear S_(A)[f]~δnp correlation in a previous study[Nucl.Sci.Tech.33,6(2022)]could be reproduced using fragments with relatively large mass fragments,which verifies that S_(A)[f]determined from fragmentσAis sensitive to the neutron skin thickness of neutron-rich isotopes.
文摘This paper studied on the clustering problem for intrusion detection with the theory of information entropy, it was put forward that the clustering problem for exact intrusion detection based on information entropy is NP complete, therefore, the heuristic algorithm to solve the clustering problem for intrusion detection was designed, this algorithm has the characteristic of incremental development, it can deal with the database with large connection records from the internet.
基金supported by the Key Program of the National Natural Science Foundation of China (Grant No.50537030)the National Natural Science Foundation of China (Grant No.61072012)the Young Scientists Fund of the National Natural Science Foundation of China (Grant Nos.50907044,61104032,and 60901035)
文摘As a convenient approach to the characterization of cerebral cortex electrical information, electroencephalograph (EEG) has potential clinical application in monitoring the acupuncture effects. In this paper, a method composed of the mutual information method and Lempel-Ziv complexity method (MILZC) is proposed to investigate the effects of acupuncture on the complexity of information exchanges between different brain regions based on EEGs. In the experiments, eight subjects are manually acupunctured at 'Zusanli' acupuncture point (ST-36) with different frequencies (i.e., 50, 100, 150, and 200 times/min) and the EEGs are recorded simultaneously. First, MILZC values are compared in general. Then average brain connections are used to quantify the effectiveness of acupuncture under the above four frequencies. Finally, significance index P values are used to study the spatiality of the acupuncture effect on the brain. Three main findings are obtained: (i) MILZC values increase during the acupuncture; (ii) manual acupunctures (MAs) with 100 times/rain and 150 times/min are more effective than with 50 times/min and 200 times/rain; (iii) contralateral hemisphere activation is more prominent than ipsilateral hemisphere's. All these findings suggest that acupuncture contributes to the increase of brain information exchange complexity and the MILZC method can successfully describe these changes.