Based on the information theory,the performance of maneuvering target tracking can be improved by increasing the input information( observation vector).In this paper,the estimations of radial acceleration and radial v...Based on the information theory,the performance of maneuvering target tracking can be improved by increasing the input information( observation vector).In this paper,the estimations of radial acceleration and radial velocity obtained in the signal processing are introduced into the measurement vector by coordinate transformation.In order to solve the problem of high nonlinearity of the radial acceleration,radial velocity and the state vector,a new algorithm of multi-parameter sequential extended Kalman filter( MSEKF) is proposed.The tracking performance of this algorithm is tested and compared with the other tracking algorithms.It is shown that the proposed algorithm outperforms these algorithms in strong and weak maneuvering environments.展开更多
In order to meet the strict requirements for information in engineering management, the positive interval （0, 1 ] in Shannon information entropy is extended to the real number interval [ - 1, 1 ]. The information the...In order to meet the strict requirements for information in engineering management, the positive interval （0, 1 ] in Shannon information entropy is extended to the real number interval [ - 1, 1 ]. The information theory and the decision theory are combined effectively, and the deficiencies that the traditional Bayes decision-making methods only consider a single factor are made up for. The multi-factors engineering decision-making methods are proposed, and some critical problems are solved in the practical engineering management decision-making process.展开更多
An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main f...An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main factors influencing the selection of mining method were taken into account,and the comprehensive evaluation index system of mining method selection was constructed.The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively.New measurement standards were constructed.Then,the unascertained measurement function of each evaluation index was established.The index weights of the factors were calculated by entropy theory,and credible degree recognition criteria were established according to the unascertained measurement theory.The results of mining method evaluation were obtained using the credible degree criteria,thus the best underground mining method was determined.Furthermore,this model was employed for the comprehensive evaluation and selection of the chosen standard mining methods in Xinli Gold Mine in Sanshandao of China.The results show that the relative superiority degrees of mining methods can be calculated using the unascertained measurement optimization model,so the optimal method can be easily determined.Meanwhile,the proposed method can take into account large amount of uncertain information in mining method selection,which can provide an effective way for selecting the optimal underground mining method.展开更多
We extend basic entropies in the classical information theory to matrix ones in the quantum information theory. Then we show that relations between matrix entropies similar to the classical ones hold.
Guided by relevant learning theories and based on the analysis of the attributes of captioned video, the Chinese ESL learners＇ characteristics and the characteristics of ESL learning itself, the paper tries to propos...Guided by relevant learning theories and based on the analysis of the attributes of captioned video, the Chinese ESL learners＇ characteristics and the characteristics of ESL learning itself, the paper tries to propose a model of integrating captioned video in ESL acquisition process. The model focuses on the feasibility of using captioned video to facilitate or support ESL learning.展开更多
By exposing deficiency of the usual superoperators that have no explicit operator-expression in quantuminformation theory we introduce thermo entangled state representation to endow each of these superoperators a defi...By exposing deficiency of the usual superoperators that have no explicit operator-expression in quantuminformation theory we introduce thermo entangled state representation to endow each of these superoperators a definiteoperator-expression in an enlarged space in which one mode is a fictitious.This helps us to directly derive the role ofexponential of superoperators and the solutions of some master equations.展开更多
Using the concepts from information theory, it is possible to improve the traditional methodologies of asset allocation. In this paper, it was studied and extended the two existent approaches： the first is based on t...Using the concepts from information theory, it is possible to improve the traditional methodologies of asset allocation. In this paper, it was studied and extended the two existent approaches： the first is based on the Shannon entropy concept and the second on the Kullback-Leibler distance. In modem portfolio theory, the investor has two basic procedures： the choice of a portfolio that maximizes its risk-adjusted excess return or the mixed allocation between the maximum Sharpe portfolio and the risk-free asset. The first procedure was already addressed in the related literature. One important contribution of this paper is the consideration of the second procedure in the information theory context. The performance of these approaches was compared with the three traditional asset allocation methodologies： the Markowitz＇s mean-variance, the resampled mean-variance and the equally weighted portfolio. It was used simulated and real data from Brazilian stocks. The information theory-based methodologies were verified to be more robust when dealing with the estimation errors.展开更多
In nonlinear error growth dynamics,the initial error cannot be accurately determined,and the forecast error,which is also uncertain,can be considered to be a random variable.Entropy in information theory is a natural ...In nonlinear error growth dynamics,the initial error cannot be accurately determined,and the forecast error,which is also uncertain,can be considered to be a random variable.Entropy in information theory is a natural measure of the uncertainty of a random variable associated with a probability distribution.This paper effectively combines statistical information theory and nonlinear error growth dynamics,and introduces some fundamental concepts of entropy in information theory for nonlinear error growth dynamics.Entropy based on nonlinear error can be divided into time entropy and space entropy,which are used to estimate the predictabilities of the whole dynamical system and each of its variables.This is not only applicable for investigating the dependence between any two variables of a multivariable system,but also for measuring the influence of each variable on the predictability of the whole system.Taking the Lorenz system as an example,the entropy of nonlinear error is applied to estimate predictability.The time and space entropies are used to investigate the spatial distribution of predictability of the whole Lorenz system.The results show that when moving around two chaotic attractors or near the edge of system space,a Lorenz system with lower sensitivity to the initial field behaves with higher predictability and a longer predictability limit.The example analysis of predictability of the Lorenz system demonstrates that the predictability estimated by the entropy of nonlinear error is feasible and effective,especially for estimation of predictability of the whole system.This provides a theoretical foundation for further work in estimating real atmospheric multivariable joint predictability.展开更多
Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribut...Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribute reduction is a key issue in analysis of interval-valued data. Existing attribute reduction methods for single-valued data are unsuitable for interval-valued data. So far, there have been few studies on attribute reduction methods for interval-valued data. In this paper, we propose a framework for attribute reduction in interval-valued data from the viewpoint of information theory. Some information theory concepts, including entropy, conditional entropy, and joint entropy, are given in interval-valued information systems. Based on these concepts, we provide an information theory view for attribute reduction in interval-valued information systems. Consequently, attribute reduction algorithms are proposed. Experiments show that the proposed framework is effective for attribute reduction in interval-valued information systems.展开更多
基金National Natural Science Foundations of China(Nos.61531020,61471383)
文摘Based on the information theory,the performance of maneuvering target tracking can be improved by increasing the input information( observation vector).In this paper,the estimations of radial acceleration and radial velocity obtained in the signal processing are introduced into the measurement vector by coordinate transformation.In order to solve the problem of high nonlinearity of the radial acceleration,radial velocity and the state vector,a new algorithm of multi-parameter sequential extended Kalman filter( MSEKF) is proposed.The tracking performance of this algorithm is tested and compared with the other tracking algorithms.It is shown that the proposed algorithm outperforms these algorithms in strong and weak maneuvering environments.
文摘In order to meet the strict requirements for information in engineering management, the positive interval （0, 1 ] in Shannon information entropy is extended to the real number interval [ - 1, 1 ]. The information theory and the decision theory are combined effectively, and the deficiencies that the traditional Bayes decision-making methods only consider a single factor are made up for. The multi-factors engineering decision-making methods are proposed, and some critical problems are solved in the practical engineering management decision-making process.
基金Project(2007CB209402) supported by the National Basic Research Program of China Project(SKLGDUEK0906) supported by the Research Fund of State Key Laboratory for Geomechanics and Deep Underground Engineering of China
文摘An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main factors influencing the selection of mining method were taken into account,and the comprehensive evaluation index system of mining method selection was constructed.The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively.New measurement standards were constructed.Then,the unascertained measurement function of each evaluation index was established.The index weights of the factors were calculated by entropy theory,and credible degree recognition criteria were established according to the unascertained measurement theory.The results of mining method evaluation were obtained using the credible degree criteria,thus the best underground mining method was determined.Furthermore,this model was employed for the comprehensive evaluation and selection of the chosen standard mining methods in Xinli Gold Mine in Sanshandao of China.The results show that the relative superiority degrees of mining methods can be calculated using the unascertained measurement optimization model,so the optimal method can be easily determined.Meanwhile,the proposed method can take into account large amount of uncertain information in mining method selection,which can provide an effective way for selecting the optimal underground mining method.
文摘We extend basic entropies in the classical information theory to matrix ones in the quantum information theory. Then we show that relations between matrix entropies similar to the classical ones hold.
文摘Guided by relevant learning theories and based on the analysis of the attributes of captioned video, the Chinese ESL learners＇ characteristics and the characteristics of ESL learning itself, the paper tries to propose a model of integrating captioned video in ESL acquisition process. The model focuses on the feasibility of using captioned video to facilitate or support ESL learning.
基金Supported by the President Foundation of Chinese Academy of Science
文摘By exposing deficiency of the usual superoperators that have no explicit operator-expression in quantuminformation theory we introduce thermo entangled state representation to endow each of these superoperators a definiteoperator-expression in an enlarged space in which one mode is a fictitious.This helps us to directly derive the role ofexponential of superoperators and the solutions of some master equations.
文摘Using the concepts from information theory, it is possible to improve the traditional methodologies of asset allocation. In this paper, it was studied and extended the two existent approaches： the first is based on the Shannon entropy concept and the second on the Kullback-Leibler distance. In modem portfolio theory, the investor has two basic procedures： the choice of a portfolio that maximizes its risk-adjusted excess return or the mixed allocation between the maximum Sharpe portfolio and the risk-free asset. The first procedure was already addressed in the related literature. One important contribution of this paper is the consideration of the second procedure in the information theory context. The performance of these approaches was compared with the three traditional asset allocation methodologies： the Markowitz＇s mean-variance, the resampled mean-variance and the equally weighted portfolio. It was used simulated and real data from Brazilian stocks. The information theory-based methodologies were verified to be more robust when dealing with the estimation errors.
基金supported by National Natural Science Foundation of China (Grant No. 40975031)
文摘In nonlinear error growth dynamics,the initial error cannot be accurately determined,and the forecast error,which is also uncertain,can be considered to be a random variable.Entropy in information theory is a natural measure of the uncertainty of a random variable associated with a probability distribution.This paper effectively combines statistical information theory and nonlinear error growth dynamics,and introduces some fundamental concepts of entropy in information theory for nonlinear error growth dynamics.Entropy based on nonlinear error can be divided into time entropy and space entropy,which are used to estimate the predictabilities of the whole dynamical system and each of its variables.This is not only applicable for investigating the dependence between any two variables of a multivariable system,but also for measuring the influence of each variable on the predictability of the whole system.Taking the Lorenz system as an example,the entropy of nonlinear error is applied to estimate predictability.The time and space entropies are used to investigate the spatial distribution of predictability of the whole Lorenz system.The results show that when moving around two chaotic attractors or near the edge of system space,a Lorenz system with lower sensitivity to the initial field behaves with higher predictability and a longer predictability limit.The example analysis of predictability of the Lorenz system demonstrates that the predictability estimated by the entropy of nonlinear error is feasible and effective,especially for estimation of predictability of the whole system.This provides a theoretical foundation for further work in estimating real atmospheric multivariable joint predictability.
基金Project supported by the National Natural Science Foundation of China(Nos.61473259,61502335,61070074,and60703038)the Zhejiang Provincial Natural Science Foundation(No.Y14F020118)the PEIYANG Young Scholars Program of Tianjin University,China(No.2016XRX-0001)
文摘Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribute reduction is a key issue in analysis of interval-valued data. Existing attribute reduction methods for single-valued data are unsuitable for interval-valued data. So far, there have been few studies on attribute reduction methods for interval-valued data. In this paper, we propose a framework for attribute reduction in interval-valued data from the viewpoint of information theory. Some information theory concepts, including entropy, conditional entropy, and joint entropy, are given in interval-valued information systems. Based on these concepts, we provide an information theory view for attribute reduction in interval-valued information systems. Consequently, attribute reduction algorithms are proposed. Experiments show that the proposed framework is effective for attribute reduction in interval-valued information systems.
