Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundan...Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.展开更多
Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram o...Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram of the signal is calculated based on a multi-window T-F analysis,and a speech test statistic is constructed based on the characteristic difference between the signal and background noise.Second,the dynamic double-threshold processing is used for preliminary detection,and then the global double-threshold value is obtained using K-means clustering.Finally,the detection results are obtained by sequential decision.The experimental results show that the overall performance of the method is better than that of traditional methods under various SNR conditions and background noises.This method also has the advantages of low complexity,strong robustness,and adaptability to multi-national languages.展开更多
The optimization of condition-based maintenance (CBM) poses challenges due to the rapid advancement of monitoring technologies. Traditional CBM research has mainly relied on theory-driven approaches, which lead to the...The optimization of condition-based maintenance (CBM) poses challenges due to the rapid advancement of monitoring technologies. Traditional CBM research has mainly relied on theory-driven approaches, which lead to the development of several effective maintenance models characterized by their wide applicability and attractiveness. However, when the system reliability model becomes complex, such methods may run into intractable cost models. The Markov decision process (MDP), a classic framework for sequential decision making, has drawn increasing attention for optimization of CBM optimization due to its appealing tractability and pragmatic applicability across different problems. This paper presents a review of research that optimizes CBM policies using MDP, with a focus on mathematical modeling and optimization methods to enable action. We have organized the review around several key components that are subject to similar mathematical modeling constraints, including system complexity, the availability of system conditions, and diverse criteria of decision-makers. An increase in interest has led to the optimization of CBM for systems possessing increasing numbers of components and sensors. Then, the review focuses on joint optimization problems with CBM. Finally, as an important extension to traditional MDPs, reinforcement learning (RL) based methods are also reviewed as ways to optimize CBM policies. This paper provides significant background research for researchers and practitioners working in reliability and maintenance management, and gives discussions on possible future research directions.展开更多
Dynamic fault tree analysis is widely used for the reliability analysis of the complex system with dynamic failure characteristics. In many circumstances, the exact value of system reliability is difficult to obtain d...Dynamic fault tree analysis is widely used for the reliability analysis of the complex system with dynamic failure characteristics. In many circumstances, the exact value of system reliability is difficult to obtain due to absent or insufficient data for failure probabilities or failure rates of components. The traditional fuzzy operation arithmetic based on extension principle or interval theory may lead to fuzzy accumulations. Moreover, the existing fuzzy dynamic fault tree analysis methods are restricted to the case that all system components follow exponential time-to-failure distributions. To overcome these problems, a new fuzzy dynamic fault tree analysis approach based on the weakest n-dimensional t-norm arithmetic and developed sequential binary decision diagrams method is proposed to evaluate system fuzzy reliability. Compared with the existing approach,the proposed method can effectively reduce fuzzy cumulative and be applicable to any time-tofailure distribution type for system components. Finally, a case study is presented to illustrate the application and advantages of the proposed approach.展开更多
文摘Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.
基金The National Natural Science Foundation of China(No.12174053,91938203,11674057,11874109)the Fundamental Research Funds for the Central Universities(No.2242021k30019).
文摘Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram of the signal is calculated based on a multi-window T-F analysis,and a speech test statistic is constructed based on the characteristic difference between the signal and background noise.Second,the dynamic double-threshold processing is used for preliminary detection,and then the global double-threshold value is obtained using K-means clustering.Finally,the detection results are obtained by sequential decision.The experimental results show that the overall performance of the method is better than that of traditional methods under various SNR conditions and background noises.This method also has the advantages of low complexity,strong robustness,and adaptability to multi-national languages.
基金supported by the National Natural Science Foundation of China(Grant Nos.72401253,72371182,72002149,and 72271154)and the National Social Science Fund of China(23CGL018)+1 种基金the State Key Laboratory of Biobased Transportation Fuel Technology,China(Grant No.512302-X02301)a start-up grant from the ZJU-UIUC Institute at Zhejiang University(Grant No.130200-171207711).
文摘The optimization of condition-based maintenance (CBM) poses challenges due to the rapid advancement of monitoring technologies. Traditional CBM research has mainly relied on theory-driven approaches, which lead to the development of several effective maintenance models characterized by their wide applicability and attractiveness. However, when the system reliability model becomes complex, such methods may run into intractable cost models. The Markov decision process (MDP), a classic framework for sequential decision making, has drawn increasing attention for optimization of CBM optimization due to its appealing tractability and pragmatic applicability across different problems. This paper presents a review of research that optimizes CBM policies using MDP, with a focus on mathematical modeling and optimization methods to enable action. We have organized the review around several key components that are subject to similar mathematical modeling constraints, including system complexity, the availability of system conditions, and diverse criteria of decision-makers. An increase in interest has led to the optimization of CBM for systems possessing increasing numbers of components and sensors. Then, the review focuses on joint optimization problems with CBM. Finally, as an important extension to traditional MDPs, reinforcement learning (RL) based methods are also reviewed as ways to optimize CBM policies. This paper provides significant background research for researchers and practitioners working in reliability and maintenance management, and gives discussions on possible future research directions.
基金supported by the National Defense Basic Scientific Research program of China (No.61325102)
文摘Dynamic fault tree analysis is widely used for the reliability analysis of the complex system with dynamic failure characteristics. In many circumstances, the exact value of system reliability is difficult to obtain due to absent or insufficient data for failure probabilities or failure rates of components. The traditional fuzzy operation arithmetic based on extension principle or interval theory may lead to fuzzy accumulations. Moreover, the existing fuzzy dynamic fault tree analysis methods are restricted to the case that all system components follow exponential time-to-failure distributions. To overcome these problems, a new fuzzy dynamic fault tree analysis approach based on the weakest n-dimensional t-norm arithmetic and developed sequential binary decision diagrams method is proposed to evaluate system fuzzy reliability. Compared with the existing approach,the proposed method can effectively reduce fuzzy cumulative and be applicable to any time-tofailure distribution type for system components. Finally, a case study is presented to illustrate the application and advantages of the proposed approach.
