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电子技术（上海） 2024年第3期53卷 94-95页

作者：韩得强临洮县辛店初级中学甘肃730500

阐述信息技术在计算机基础教学中的应用策略,包括利用资讯科技创造课堂情境,利用信息技术进行任务驱动、开展游戏活动、加强知识讲解,以及通过信息技术强化实践操作。

关键词：信息技术计算机技术基础教学

De-combination of belief function based on optimization

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Chinese Journal of Aeronautics 2022年第5期35卷 179-193页

作者： Xiaojing FAN deqiang han Yi YANG Jean DEZERT School of Automation Science and Engineering Xi’an Jiaotong UniversityXi’an 710049China SKLSVMS School of AerospaceXi’an Jiaotong UniversityXi’an 710049China ONERA The French Aerospace LabPalaiseau 91761France

In the theory of belief functions,the evidence combination is a kind of decision-level information *** two or more Basic Belief Assignments(BBAs)originated from different information sources,the combination rule is used to combine them to expect a better decision *** only a combined BBA is given and original BBAs are discarded,if one wants to analyze the difference between the information sources,evidence de-combination is needed to determine the original *** de-combination can be considered as the inverse process of the information *** paper focuses on such a defusion of information in the theory of belief *** is an under-determined problem if only the combined BBA is *** this paper,two optimization-based approaches are proposed to de-combine a given BBA according to the criteria of divergence maximization and information maximization,*** new proposed approaches can be used for two or more information *** numerical examples and an example of application are provided to illustrate and validate our approaches.

关键词： Belief functions De-combination Divergence maximization Information fusion Information maximization

Novel moderate transformation of fuzzy membership function into basic belief assignment

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Chinese Journal of Aeronautics 2023年第1期36卷 369-385页

作者： Xiaojing FAN deqiang han Jean DEZERT Yi YANG School of Automation Science and Engineering Xi’an Jiaotong UniversityXi’an 710049China ONERA The French Aerospace LabPalaiseau 91761France SKLSVMS School of AerospaceXi’an Jiaotong UniversityXi’an 710049China

In information fusion,the uncertain information from different sources might be modeled with different theoretical *** one needs to fuse the uncertain information represented by different uncertainty theories,constructing the transformation between different frameworks is *** transformations of a Fuzzy Membership Function(FMF)into a Basic Belief Assignment(BBA)have been proposed,where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal ***,these two transformations that based on uncertainty optimization emphasize the extreme cases of *** avoid extreme attitudinal bias,a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more *** this paper,two moderate transformations of an FMF into a trade-off BBA are *** is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism,where the weighting factor can be user-specified or determined with some prior *** rationality and effectiveness of our transformations are verified through numerical examples and classification examples.

关键词： Basic belief assignment Belief functions Fuzzy membership function Information fusion Moderate transformation

Active learning based on belief functions

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Science China(Information Sciences) 2020年第11期63卷 69-83页

作者： Shixing ZhanG deqiang han Yi YANG Institute of Integrated Automation Xi'an Jiaotong University School of Aerospace Xi'an Jiaotong University

Active learning involves selecting a few critical unlabeled samples for manual and credible labeling to improve the performance of the current classifier. The critical step of active learning is the sample selection strategy. Uncertainty sampling is a well-known sample selection strategy, which involves selecting the samples for which the current classifier is uncertain. For the generalized linear model, these samples are usually distributed around the current classification hyperplane. However, uncertain samples include samples near the current classification hyperplane, and samples far from the current classification hyperplane and the labeled samples. Traditional uncertainty sampling fails to describe the latter, and traditional methods are easily affected by outliers. In this paper, belief functions are used to describe the uncertainty that exists in various samples. Furthermore, we propose a sample selection strategy based on belief *** results based on benchmark datasets show that the proposed approach outperforms several classical methods. Through this approach, higher classification accuracy can be achieved using the same number of new labeled samples.

关键词： active learning uncertainty sampling belief functions generalized linear model

A novel approximation of basic probability assignment based on rank-level fusion

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Chinese Journal of Aeronautics 2013年第4期26卷 993-999页

作者： Yang Yi han deqiang han Chongzhao Cao Feng State Key Laboratory for Strength and Vibration of Mechanical Structures School of Aerospace Xi'an Jiaotong University Institute of Integrated Automation MOE KLINNS Lab School of Electronic and Information Engineering Xi'an Jiaotong University School of Energy and Power Engineering Xi'an Jiaotong University

Belief functions theory is an important tool in the field of information fusion. However, when the cardinality of the frame of discernment becomes large, the high computational cost of evidence combination will become the bottleneck of belief functions theory in real applications. The basic probability assignment （BPA） approximations, which can reduce the complexity of the BPAs, are always used to reduce the computational cost of evidence combination. In this paper, both the cardinalities and the mass assignment values of focal elements are used as the criteria of reduction. The two criteria are jointly used by using rank-level fusion. Some experiments and related analyses are provided to illustrate and justify the proposed new BPA approximation approach.

关键词： Belief approximation Distance of evidence Evidence combination Information fusion Rank-level fusion

Basic belief assignment approximations using degree of non-redundancy for focal element

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Chinese Journal of Aeronautics 2019年第11期32卷 2503-2515页

作者： Yi YANG deqiang han Jean DEZERT SKLSVMS School of AerospaceXi'an Jiaotong UniversityXi'an 710049China Center for Information Engineering Science Research XVan Jiaotong UniversityXi'an 710049China ONERA The French Aerospace LabChemin de la HunierePalaiseau F-91761France

Dempster-Shafer evidence theory, also called the theory of belief function, is widely used for uncertainty modeling and reasoning. However, when the size and number of focal elements are large, the evidence combination will bring a high computational complexity. To address this issue,various methods have been proposed including the implementation of more efficient combination rules and the simplifications or approximations of Basic Belief Assignments(BBAs). In this paper,a novel principle for approximating a BBA into a simpler one is proposed, which is based on the degree of non-redundancy for focal elements. More non-redundant focal elements are kept in the approximation while more redundant focal elements in the original BBA are removed first. Three types of degree of non-redundancy are defined based on three different definitions of focal element distance, respectively. Two different implementations of this principle for BBA approximations are proposed including a batch and an iterative type. Examples, experiments, comparisons and related analyses are provided to validate proposed approximation approaches.

关键词： BBA approximation Belief functions Evidence theory Focal element Non-redundancy

An improved α-cut approach to transforming fuzzy membership function into basic belief assignment

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Chinese Journal of Aeronautics 2016年第4期29卷 1042-1051页

作者： Yang Yi X.Rong Li han deqiang SKLSVMS School of Aerospace Xi'an Jiaotong University Department of Electrical Engineering University of New Orleans Center for Information Engineering Science Research Xi'an Jiaotong University

In practical applications, pieces of evidence originated from different sources might be modeled by different uncertainty theories. To implement the evidence combination under the Dempster–Shafer evidence theory (DST) framework, transformations from the other type of uncertainty representation into the basic belief assignment are needed. α-Cut is an important approach to transforming a fuzzy membership function into a basic belief assignment, which provides a bridge between the fuzzy set theory and the DST. Some drawbacks of the traditional α-cut approach caused by its normalization step are pointed out in this paper. An improved α-cut approach is proposed, which can counteract the drawbacks of the traditional α-cut approach and has good properties. Illustrative examples, experiments and related analyses are provided to show the rationality of the improved α-cut approach. © 2016 Chinese Society of Aeronautics and Astronautics

关键词： Fuzzy set theory Fuzzy sets Uncertainty analysis

Measuring Conflict Functions in Generalized Power Space

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Chinese Journal of Aeronautics 2011年第1期24卷 65-73页

作者： HU Lifang GUAN Xin DENG Yong han deqiang HE You Research Institute of Information Fusion Naval Aeronautical and Astronautical University Yantai 264001 China Sche Institute of Electronic Science and Engineering National University of Defense Technology Changsha 410073 China School of Electronics and Information Technology Shanghai Jiaotong University Shanghai 200240 China Institute of Integrated Automation Xi 'an Jiaotong University Xi 'an 710049 China

One of the most important open issues is that the classical conflict coefficient in D-S evidence theory （DST） cannot correctly determine the conflict degree between two pieces of evidence. This drawback greatly limits the use of DST in real application systems. Early researches mainly focused on the improvement of Dempster’s rule of combination （DRC）. However, the current research shows it is very important to define new conflict coefficients to determine the conflict degree between two or more pieces of evidence. The evidential sources of information are considered in this work and the definition of a conflict measure function （CMF） is proposed for selecting some useful CMFs in the next fusion work when sources are available at each instant. Firstly, the definition and theorems of CMF are put forward. Secondly, some typical CMFs are extended and then new CMFs are put forward. Finally, experiments illustrate that the CMF based on Jousselme and its similar ones are the best suited ones.

关键词： D-S evidence theory conflict evidence generalized power space information fusion uncertainty

New probabilistic transformation of imprecise belief structure

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Journal of Systems Engineering and Electronics 2011年第5期22卷 721-729页

作者： Lifang Hu You He Xin Guan deqiang han Yong Deng Research Institute of Information Fusion Naval Aeronautical and Astronautical University Yantai 264001 E R. China Navy Armament Academy Beijing 102249 E R. China College of Electronic Science and Engineering National University of Defense Technology Changsha 410073 E R. China Institute of Integrated Automation Xi＇an Jiaotong University Xi＇an 710049 E R. China School of Electronics and Information Technology Shanghai Jiaotong University Shanghai 200240 E R. China

The case when the source of information provides precise belief function/mass, within the generalized power space, has been studied by many people. However, in many decision situations, the precise belief structure is not always available. In this case, an interval-valued belief degree rather than a precise one may be provided. So, the probabilistic transformation of imprecise belief function/mass in the generalized power space including Dezert-Smarandache （DSm） model from scalar transformation to sub-unitary interval transformation and, more generally, to any set of sub-unitary interval transformation is provided. Different from the existing probabilistic transformation algorithms that redistribute an ignorance mass to the singletons involved in that ignorance pro- portionally with respect to the precise belief function or probability function of singleton, the new algorithm provides an optimization idea to transform any type of imprecise belief assignment which may be represented by the union of several sub-unitary （half-） open intervals, （half-） closed intervals and/or sets of points belonging to [0,1]. Numerical examples are provided to illustrate the detailed implementation process of the new probabilistic transformation approach as well as its validity and wide applicability.

关键词： pignistic probability transformation generalized power space interval value information fusion uncertainty.

A New Probabilistic Transformation in Generalized Power Space

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Chinese Journal of Aeronautics 2011年第4期24卷 449-460页

作者： HU Lifang HE You GUAN Xin DENG Yong han deqiang Research Institute of Information Fusion Naval Aeronautical and Astronautical University Yantai 264001 China Sche Institute of Electronic Science and Engineering National University of Defense Technology Changsha 410073 China School of Electronics and Information Technology Shanghai Jiao Tong University Shanghai 200240 China Institute of Integrated Automation Xi 'an Jiaotong University Xi'an 710049 China

The mapping from the belief to the probability domain is a controversial issue, whose original purpose is to make （hard） decision, but for contrariwise to erroneous widespread idea/claim, this is not the only interest for using such mappings nowadays. Actually the probabilistic transformations of belief mass assignments are very useful in modern multitarget multisensor tracking systems where one deals with soft decisions, especially when precise belief structures are not always available due to the existence of uncertainty in human being’s subjective judgments. Therefore, a new probabilistic transformation of interval-valued belief structure is put forward in the generalized power space, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the new transformation works and we compare it to the main existing transformations proposed in the literature so far. Results are provided to illustrate the rationality and efficiency of this new proposed method making the decision problem simpler.

关键词： Dempster-Shafer theory generalized power space information fusion interval value uncertainty