• Science of Emotion and Sensibility
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• v.5 no.1
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• pp.17-24
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• 2002

This research was the first step to develop Expert System for Evaluation of Human Sensibility, where human sensibility can be inferred from objective physiological signals. The study aim was to develop an algorithm in which human arousal level can be judged using measured physiological signals. Fuzzy theory was applied for mathematical handling of the ambiguity related to evaluation of human sensibility, and the degree of belonging to a certain sensibility dimension was quantified by membership function through which the sensibility evaluation was able to be done. Determining membership function was achieved using results from a physiological signal database of arousal/relaxation that was generated from imagination. To induce one final result (arousal level) based on measuring the results of more than 2 physiological signals and the membership function of each physiological signal, Dempster-Shafer's Rule of Combination in Evidence was applied, through which the final arousal level was inferred.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.308-317
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• 2016

Multiple evidences based decision making is an important functionality for computers and robots. To combine multiple evidences, mathematical theory of evidence has been developed, and it involves the most vital part called Dempster's rule of combination. The rule is used for combining multiple evidences. However, the combined result gives a counterintuitive conclusion when highly conflicting evidences exist. In particular, when we obtain two different sources of evidence for a single hypothesis, only one of the sources may contain evidence. In this paper, we introduce a modified combination rule based on the partial conflict measurement by using an absolute difference between two evidences' basic probability numbers. The basic probability number is described in details in Section 2 "Mathematical Theory of Evidence". As a result, the proposed combination rule outperforms Dempster's rule of combination. More precisely, the modified combination rule provides a reasonable conclusion when combining highly conflicting evidences and shows similar results with Dempster's rule of combination in the case of the both sources of evidence are not conflicting. In addition, when obtained evidences contain multiple hypotheses, our proposed combination rule shows more logically acceptable results in compared with the results of Dempster's rule.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1479-1485
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• 1999

This paper proposes a fuzzy decision making method for power transformer protection to identify an internal fault from other transient states such as inrush, over-excitation and an external fault with current transformer (CT) saturation. In this paper, analyzing over 300 EMTP simulations of disturbances, four input variables are selected and fuzzified. At every sampling interval from half to one cycle after a disturbance, from the EMPT simulations, different fuzzy rule base is composed of twelve if-then fuzzy rules associated with their basic probability assignments for singleton- or compound-support hypotheses. Dempster's rule of combination is used to process the fuzzy rules and get the final decision. A series of test results clearly indicate that the method can identify not only an internal fault but also the other transients. The average of relay operation times is about 12(ms). The proposed method is implemented into a Digital Signal Processor (TMS320C31) and tested.

• Journal of the Ergonomics Society of Korea
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• v.23 no.1
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• pp.1-12
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• 2004

The purpose of this study was to develop an automatic expert system for the evaluation of human sensibility, where human sensibility can be inferred from objective physiological signals. The study aim was also to develop an algorithm in which human arousal and pleasant level can be judged by using measured physiological signals. Fuzzy theory was applied for mathematical handling of the ambiguity related to evaluation of human sensibility. and the degree of belonging to a certain sensibility dimension was quantified by membership function through which the sensibility evaluation was able to be done. Determining membership function was achieved using results from a physiological signal database of arousal/relaxation and pleasant/unpleasant that was generated from imagination. To induce one final result (arousal and pleasant level) based on measuring the results of more than 2 physiological signals and the membership function of each physiological signal. Dempster-Shafer's rule of combination in evidence was applied, through which the final arousal and pleasant level was inferred.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.11
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• pp.1401-1409
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• 1999

In this paper, a conceptual framework of a new concept of protectability is proposed, which indicates the protection level of the system. Evaluation attributes have been identified and a hierarchical evaluation model has been established. Dempster-Shafer Theory of Evidence is applied in combining multiple uncertain judgements to produce an aggregated evaluation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3965-3982
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• 2015

Event detection is one of the key issues in many wireless sensor network (WSN) applications. The uncertainties that are derived from the instability of sensor node, measurement noise and incomplete sampling would influence the performance of event detection to a large degree. Many of the present researches described the sensor readings with crisp values, which cannot adequately handle the uncertainties inhered in the imprecise sensor readings. In this paper, a fault-tolerant event detection algorithm is proposed based on Dempster-Shafer (D-S) theory (also called evidence theory). Instead of crisp values, all possible states of the event are represented by the Basic Probability Assignment (BPA) functions, with which the output of each sensor node are characterized as weighted evidences. The combination rule was subsequently applied on each sensor node to fuse the evidences gathered from the neighboring nodes to make the final decision on whether the event occurs. Simulation results show that even 20% nodes are faulty, the accuracy of the proposed algorithm is around 80% for event region detection. Moreover, 97% of the error readings have been corrected, and an improved detection capability at the boundary of the event region is gained by 75%. The proposed algorithm can enhance the detection accuracy of the event region even in high error-rate environment, which reflects good reliability and robustness. The proposed algorithm is also applicable to boundary detection as it performs well at the boundary of the event.