Complex evidence theory is a generalized Dempster-Shafer evidence theory,which has the ability to express uncertain information.One of the key issues is the uncertainty measure of Complex Basic Belief Assignment(CBBA)...Complex evidence theory is a generalized Dempster-Shafer evidence theory,which has the ability to express uncertain information.One of the key issues is the uncertainty measure of Complex Basic Belief Assignment(CBBA).However,the research on the uncertainty measure of complex evidence theory is still an open issue.Therefore,in this paper,first,the Fractal-based Complex Belief(FCB)entropy as a generalization of Fractal-based Belief(FB)entropy,which has superiority in uncertainty measurement of CBBA,is proposed.Second,on the basis of FCB entropy,we propose Fractal-based Supremum Complex Belief(FSCB)entropy and Fractal-based Infimum Complex Belief(FICB)entropy,with FSCB entropy as the upper bound and FICB entropy as the lower bound.They are collectively called the proposed FCB entropy.Furthermore,we analyze the properties,physical interpretation and numerical examples to prove the rationality of the proposed method.Finally,a practical information fusion application is proposed to prove that the proposed FCB entropy can reasonably measure the uncertainty of CBBA.The results show that,the proposed FCB entropy can handle the uncertainty measure of CBBA,which can be a reasonable way for uncertainty measure in complex evidence theory.展开更多
Objective:To study on Prof. GAO Zhong-ying’s drug selection law for treatment of chronic gastritis with data mining technique. Methods: The 407 medical records of chronic gastritis treated by Prof. GAO Zhong-ying wer...Objective:To study on Prof. GAO Zhong-ying’s drug selection law for treatment of chronic gastritis with data mining technique. Methods: The 407 medical records of chronic gastritis treated by Prof. GAO Zhong-ying were collected and the study on these drugs in the recipes was carried out with data mining method. Among them, the recipe composed of one drug was studied with frequency statistical method, correlativity between drug pairs with improved mutual information, correlativity among multi-drugs with complex system entropy cluster technique. Results: In treatment of chronic gastritis by Prof. GAO Zhong-ying there were 30 drugs with a higher use frequency of over 38 times, 94 commonly-used drug pairs with correlation coefficient of over 0.05, 11 commonly-used drug core combinations. Conclusion: The results attained with data mining technique for studying experience of famous and old TCM physicians conform to the clinical practice and the method is of an important significance for summarization of famous and old TCM physicians’ experiences.展开更多
We extend the complexity entropy causality plane（CECP） to propose a multi-scale complexity entropy causality plane（MS-CECP） and further use the proposed method to discriminate the deterministic characteristics of ...We extend the complexity entropy causality plane（CECP） to propose a multi-scale complexity entropy causality plane（MS-CECP） and further use the proposed method to discriminate the deterministic characteristics of different oil-in-water flows. We first take several typical time series for example to investigate the characteristic of the MS-CECP and find that the MS-CECP not only describes the continuous loss of dynamical structure with the increase of scale, but also reflects the determinacy of the system. Then we calculate the MS-CECP for the conductance fluctuating signals measured from oil–water two-phase flow loop test facility. The results indicate that the MS-CECP could be an intrinsic measure for indicating oil-in-water two-phase flow structures.展开更多
Objective:To explore Yan Zhenghua’s drug selection rule for treating digestive system diseases using data mining.Methods:The 609 medical records of digestive system diseases treated by Yan Zhenghua were collected and...Objective:To explore Yan Zhenghua’s drug selection rule for treating digestive system diseases using data mining.Methods:The 609 medical records of digestive system diseases treated by Yan Zhenghua were collected and the herbs in these recipes were examined using a data mining technique.The correlativity between herb pairs and association rules was studied using an Apriori algorithm and the correlativity among multi-herbs was studied using a complex system entropy cluster technique.Results:Yan Zhenghua’s treatment of digestive system diseases featured 15 herbs prescribed at least 159 times each,22 herb pairs prescribed at least 155 times each,and eight frequently used herb core combinations.A confidence greater than 0.91 and a support level greater than 20%were achieved using the modified mutual information method.Conclusion:The data mining results conformed to findings from clinical practice.The data mining method is a valuable technique with which to study the experience of famous,elderly traditional Chinese medicine physicians.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
Using a model anharmonic oscillator with asymptotically decreasing effective mass to study the effect of compositional grading on the quantum mechanical properties of a semiconductor heterostructure, we determine the ...Using a model anharmonic oscillator with asymptotically decreasing effective mass to study the effect of compositional grading on the quantum mechanical properties of a semiconductor heterostructure, we determine the exact bound states and spectral values of the system. Furthermore, we show that ordering ambiguity only brings about a spectral shift on the quantum anharmonic oscillator with spatially varying effective mass. A study of thermodynamic properties of the system reveals a resonance condition dependent on the magnitude of the anharmonicity parameter. This resonance condition is seen to set a critical value on the said parameter beyond which a complex valued entropy which is discussed, emerges.展开更多
Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains.One of the major challenges is how to differentiate relevant signals to operational conditions of be...Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains.One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment.In this work,we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum（MPS） through a multi-scale morphology analysis procedure.The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves.Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.展开更多
Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between moni...Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study,we present a straightforward and fast computational method, the multivariable linkage coarse graining(MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.展开更多
基金supported by the National Natural Science Foundation of China (No. 62473067)Chongqing Talents: Exceptional Young Talents Project, China (No. cstc2022ycjh-bgzxm0070)Chongqing Overseas Scholars Innovation Program, China (No. cx2022024)
文摘Complex evidence theory is a generalized Dempster-Shafer evidence theory,which has the ability to express uncertain information.One of the key issues is the uncertainty measure of Complex Basic Belief Assignment(CBBA).However,the research on the uncertainty measure of complex evidence theory is still an open issue.Therefore,in this paper,first,the Fractal-based Complex Belief(FCB)entropy as a generalization of Fractal-based Belief(FB)entropy,which has superiority in uncertainty measurement of CBBA,is proposed.Second,on the basis of FCB entropy,we propose Fractal-based Supremum Complex Belief(FSCB)entropy and Fractal-based Infimum Complex Belief(FICB)entropy,with FSCB entropy as the upper bound and FICB entropy as the lower bound.They are collectively called the proposed FCB entropy.Furthermore,we analyze the properties,physical interpretation and numerical examples to prove the rationality of the proposed method.Finally,a practical information fusion application is proposed to prove that the proposed FCB entropy can reasonably measure the uncertainty of CBBA.The results show that,the proposed FCB entropy can handle the uncertainty measure of CBBA,which can be a reasonable way for uncertainty measure in complex evidence theory.
文摘Objective:To study on Prof. GAO Zhong-ying’s drug selection law for treatment of chronic gastritis with data mining technique. Methods: The 407 medical records of chronic gastritis treated by Prof. GAO Zhong-ying were collected and the study on these drugs in the recipes was carried out with data mining method. Among them, the recipe composed of one drug was studied with frequency statistical method, correlativity between drug pairs with improved mutual information, correlativity among multi-drugs with complex system entropy cluster technique. Results: In treatment of chronic gastritis by Prof. GAO Zhong-ying there were 30 drugs with a higher use frequency of over 38 times, 94 commonly-used drug pairs with correlation coefficient of over 0.05, 11 commonly-used drug core combinations. Conclusion: The results attained with data mining technique for studying experience of famous and old TCM physicians conform to the clinical practice and the method is of an important significance for summarization of famous and old TCM physicians’ experiences.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.41174109 and 61104148)the National Science and Technology Major Project of China(Grant No.2011ZX05020-006)the Zhejiang Key Discipline of Instrument Science and Technology,China(Grant No.JL130106)
文摘We extend the complexity entropy causality plane（CECP） to propose a multi-scale complexity entropy causality plane（MS-CECP） and further use the proposed method to discriminate the deterministic characteristics of different oil-in-water flows. We first take several typical time series for example to investigate the characteristic of the MS-CECP and find that the MS-CECP not only describes the continuous loss of dynamical structure with the increase of scale, but also reflects the determinacy of the system. Then we calculate the MS-CECP for the conductance fluctuating signals measured from oil–water two-phase flow loop test facility. The results indicate that the MS-CECP could be an intrinsic measure for indicating oil-in-water two-phase flow structures.
基金the National Science and Technology Support Program of China(No.2007BAI10B01)the Science and Technology Development Project of TCM of Beijing(No.JJ-2010-70)+1 种基金the Scientific Research Innovation Team Project of Beijing University of Chinese Medicine(No.2011-CXTD-14)the open project of key disciplines of Beijing University of Chinese Medicine(No.2013-ZDXKKF-19).
文摘Objective:To explore Yan Zhenghua’s drug selection rule for treating digestive system diseases using data mining.Methods:The 609 medical records of digestive system diseases treated by Yan Zhenghua were collected and the herbs in these recipes were examined using a data mining technique.The correlativity between herb pairs and association rules was studied using an Apriori algorithm and the correlativity among multi-herbs was studied using a complex system entropy cluster technique.Results:Yan Zhenghua’s treatment of digestive system diseases featured 15 herbs prescribed at least 159 times each,22 herb pairs prescribed at least 155 times each,and eight frequently used herb core combinations.A confidence greater than 0.91 and a support level greater than 20%were achieved using the modified mutual information method.Conclusion:The data mining results conformed to findings from clinical practice.The data mining method is a valuable technique with which to study the experience of famous,elderly traditional Chinese medicine physicians.
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.
文摘Using a model anharmonic oscillator with asymptotically decreasing effective mass to study the effect of compositional grading on the quantum mechanical properties of a semiconductor heterostructure, we determine the exact bound states and spectral values of the system. Furthermore, we show that ordering ambiguity only brings about a spectral shift on the quantum anharmonic oscillator with spatially varying effective mass. A study of thermodynamic properties of the system reveals a resonance condition dependent on the magnitude of the anharmonicity parameter. This resonance condition is seen to set a critical value on the said parameter beyond which a complex valued entropy which is discussed, emerges.
基金supported by the National Natural Science Foundation of China (Grant 51205017)the National Science and Technology Support Program (Grant 2015BAG12B01)the National Basic Research Program of China (Grant 2015CB654805)
文摘Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains.One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment.In this work,we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum（MPS） through a multi-scale morphology analysis procedure.The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves.Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.
基金supported by the National Natural Science Foundation of China(Grant No.51375375)
文摘Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study,we present a straightforward and fast computational method, the multivariable linkage coarse graining(MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.
