• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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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.

• 한국국방경영분석학회지
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• 제23권2호
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• pp.45-71
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• 1997

There has been considerable research recently on uncertainty handling in the fields of artificial intelligence and knowledge-based system. Various numerical and non-numerical methods have been proposed for representing and propagating uncertainty in knowledge-based system. The Bayesian method, the Dempster-Shafer's Evidence Theory, the Certainty Factor model and the Fuzzy Set Theory are most frequently appeared in the knowledge-based system. Each of these four methods views uncertainty from a different perspective and propagates it differently. There is no single method which can handle uncertainty properly in all kinds of knowledge-based systems' domain. Therefore a knowledge-based system will work more effectively when the uncertainty handling method in the system fits to the system's environment. This paper proposed a framework for selecting proper uncertainty handling methods in knowledge-based system with respect to characteristics of problem domain and cognitive styles of experts. A schema with strategic/operational and unstructured/structured classification is employed to differenciate domain. And a schema with systematic/intuitive and preceptive/receptive classification is employed to differenciate experts' cognitive style. The characteristics of uncertainty handling methods are compared with characteristics of problem domains and cognitive styles respectively. Then a proper uncertainty handling method is proposed for each category.

• 대한원격탐사학회지
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• 제28권6호
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• pp.611-622
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• 2012

This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

• 정보관리학회지
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• 제8권1호
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• pp.100-116
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• 1991

본 연구에서는 지식베이스의 용어간 관계와 증거추론이론을 이용한 정보검색 시스템을 설계하였다. 실험을 위한 문헌파일은 한국전자통신연구소의 통신분야 기술문서를 대상으로 작성하였으며, 지식베이스는 INSPEC 시소러스의 통신분야 용어 및 용어간 관계들을 발췌하여 구성하였다. 그리고 용어간의 관련성은 용어간 관계의 종류에 따라 수치로 표현하였으며, 이들 수치를 이용하여 뎀스터-셰이퍼 이론에 따라 질문과 문헌간의 관련성을 추론, 산출하였다. 실험결과 질문의 탐색어외에도 관련된 용어의 확장검색을 통하여 포괄적인 검색을 할 수 있었으며, 용어간 관계를 반영하여 질문과 문헌간의 관련성을 산출하고, 관련성 순위에 따라 검색결과를 제시할 수 있었다.

• Smart Structures and Systems
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• 제15권1호
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• pp.151-167
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• 2015

Instrumentation on structural health monitoring system imposes critical issues for applying the structural monitoring system to real world structures, for which not only on the configuration and geometry, but also aesthetics on the system to be monitored should be considered. To illustrate this point, two real world structural health monitoring systems, the structural health monitoring system of Shenzhen Vanke Center and the structural health monitoring system of Shenzhen Bay Stadium in China, are presented in the paper. The instrumentation on structural health monitoring systems of real world structures is addressed by providing the description of the structure, the purpose of the structural health monitoring system implementation, as well as details of the system integration including the installations on the sensors and acquisition equipment and so on. In addition, an intelligent algorithm on stress identification using measurements from multi-region is presented in the paper. The stress identification method is deployed using the fuzzy pattern recognition and Dempster-Shafer evidence theory, where the measurements of limited strain sensors arranged on structure are the input data of the method. As results, at the critical parts of the structure, the stress distribution evaluated from the measurements has shown close correlation to the numerical simulation results on the steel roof of the Beijing National Aquatics Center in China. The research work in this paper can provide a reference for the design and implementation of both real world structural health monitoring systems and intelligent algorithm to identify stress distribution effectively.

• Genomics & Informatics
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• 제20권4호
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• pp.45.1-45.7
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• 2022

Food security will be affected by climate change worldwide, particularly in the developing world, where the most important food products originate from plants. Plants are often exposed to environmental stresses that may affect their growth, development, yield, and food quality. Auxin is a hormone that plays a critical role in improving plants' tolerance of environmental conditions. Auxin controls the expression of many stress-responsive genes in plants by interacting with specific cis-regulatory elements called auxin-responsive elements (AuxREs). In this work, we performed an in silico prediction of AuxREs in promoters of five auxin-responsive genes in Zea mays. We applied a data fusion approach based on the combined use of Dempster-Shafer evidence theory and fuzzy sets. Auxin has a direct impact on cell membrane proteins. The short-term auxin response may be represented by the regulation of transmembrane gene expression. The detection of an AuxRE in the promoter of prolyl oligopeptidase (POP) in Z. mays and the 3-fold overexpression of this gene under auxin treatment for 30 min indicated the role of POP in maize auxin response. POP is regulated by auxin to perform stress adaptation. In addition, the detection of two AuxRE TGTCTC motifs in the upstream sequence of the bx1 gene suggests that bx1 can be regulated by auxin. Auxin may also be involved in the regulation of dehydration-responsive element-binding and some members of the protein kinase superfamily.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2012년도 제42회 동계 정기 학술대회 초록집
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• pp.241-241
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• 2012

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

• 산학경영연구
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• 제14권
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• pp.31-44
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• 2001

현대의 회계감사는 피 감사회사의 재무제표에 대하여 합리적인 확신을 요구하기 때문에 피 감사 회사의 재무제표에 대하여 정밀감사를 실시하여 감사의견을 표명하는 것이 아니라 중요성의 원칙에 따라 피 감사회사의 재무제표에 대하여 표본감사를 실시하는 것을 원칙으로 하고 있기 때문에 감사위험이 필연적으로 발생한다. 감사위험이란 피 감사회사의 재무제표가 중요하게 잘못 표시되어 있음에도 불구하고 적정하게 표시되어 있는 것으로 잘못 판단하는 경우와 반대로 재무제표가 적정하게 표시되어 있음에도 이를 적정하게 표시되어 있지 아니한 것으로 잘못 판단하여 감사범위를 확대하는 경우가 있다. 지금까지의 감사위험은 주로 AICPA의 SAS NO. 47에서 발표한 감사위험모형을 사용하여 왔어나 이 모형은 감사위험수준의 결정문제와 감사인의 판단과정을 적정하게 절명하지 못하는 등의 부정적인 견해가 대두되고 있다. 한편 Dempster-Shafer의 신념함수(belief functions)모형은 수학적 증거이론(Mathematical Theory of Evidence)에 기초하여 주어진 자료가 불충분하고 부정확한 상황하에서 보다 정확한 정보를 얻기 위한 문제들을 다루기 위한 수학적 기법들을 제공하여 불확실한 상황에서 판단의 정확성을 높일 수 있게된다. 이러한 신념함수모형을 사용하여 감사위험모형 보다 판단과정을 더 정확히 묘사하는 것으로 나타나고 있다. 따라서 본 연구는 재무제표의 신뢰성을 제고하고 감사의 유효성과 효율성을 높이기 위하여 감사 위험모형의 문제점을 알아보고 신념함수모형에 대한 이론과 기존연구를 고찰하여 신념함수모형을 제시하고자 한다.