• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.86-89
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• 2002

본 연구는 객관적인 생리 신호로부터 인간의 감성을 추론할 수 있는 감성 평가 전문가 시스템을 개발하기 위한 첫 번째 단계로, 측정된 생리 신호를 이용하여 인간의 긴장도를 판단하는 알고리즘의 개발이 목표이다. 감성 평가에 관련된 애매함을 수리적으로 취급하기 위해 퍼지 이론을 적용하여 임의의 감성 영역에 속하는 정도를 소속 함수로 정량화 함으로써 감성 평가를 가능하게 하고자 하였다 소속 함수의 결정은 상상을 통해 유발된 긴장/이완의 생리 신호 데이터 베이스 결과를 사용하였다. 그리고 두 가지 이상의 생리 신호 측정 결과와 각 생리 신호의 소속 함수로부터 하나의 최종 결과 (긴장도)를 유추하기 위해서 Dempster-Shafer 증거합 법칙을 적용하였고, 이를 통해 최종적인 긴장도를 도출할 수 있도록 하였다.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.133-140
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• 2019

In this paper, we present an algorithm for adjusting degree of belief for consistency on the evidence dependency network where various sets of evidence support different sets of hypotheses. It is common for experts to assign higher degree of belief to a hypothesis when there is more evidence over the hypothesis. Human expert without knowledge of uncertainty handling may not be able to cope with how evidence is combined to produce the anticipated belief value. Belief in a hypothesis changes as a series of evidence is known to be true. In non-monotonic reasoning environments, the belief retraction method is needed to clearly deal with uncertain situations. We create evidence dependency network from rules and apply the evidence retraction algorithm to refine belief values on the hypothesis set. We also introduce negative belief values to reflect the reverse effect of evidence combination.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.891-893
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• 1997

In this paper an intelligent power transformer protective relaying algorithm based on Fuzzy Decision-Making is presented. The introduced protection algorithm contains several internal fuzzy rule-bases including bpa(Basic Probability Assignment: m) which are subject to off-line pre-installation by the analysis of the transformer transient characteristics for detecting the internal fault. Dempster-Shafer's rule of combination is used for the inference method with rules to decide the situation of a transformer, The proposed algorithm immunes to the saturation of transformer, inrush conditions, over excitation, and external fault. The included results of testing show practically sufficient sensitivity and selectivity of the proposed algorithm.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.248-250
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• 2003

An automatic approach and strategy for extracting building information from aerial images using combined image analysis and interpretation techniques is described in this paper. A dense DSM is obtained by stereo image matching. Multi-band classification, DSM, texture segmentation and Normalised Difference Vegetation Index (NDVI) are used to reveal building interest areas. Then, based on the derived approximate building areas, a shape modelling algorithm based on the level set formulation of curve and surface motion has been used to precisely delineate the building boundaries. Data fusion, based on the Dempster-Shafer technique, is used to interpret simultaneously knowledge from several data sources of the same region, to find the intersection of propositions on extracted information derived from several datasets, together with their associated probabilities. A number of test areas, which include buildings with different sizes, shape and roof colour have been investigated. The tests are encouraging and demonstrate that the system is effective for building extraction, and the determination of more accurate elevations of the terrain surface.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.389-390
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• 2002

It is important that acquire information about if customer has some habit in electronic commerce application of internet base that led in recommendation service for customer in dynamic web contents supply. Collaborative filtering that has been used as a standard approach to Web personalization can not get rapidly user's preference change due to static user profiles and has shortcomings such as reliance on user ratings, lack of scalability, and poor performance in the high-dimensional data. In order to overcome this drawbacks, Web usage mining has been prevalent. Web usage mining is a technique that discovers patterns from We usage data logged to server. Specially. a technique that discovers Web usage patterns and clusters patterns is used. However, the discovery of patterns using Afriori algorithm creates many useless patterns. In this paper, the enhanced method for the construction of dynamic user profiles using validated Web usage patterns is proposed. First, to discover patterns Apriori is used and in order to create clusters for user profiles, ARHP algorithm is chosen. Before creating clusters using discovered patterns, validation that removes useless patterns by Dempster-Shafer theory is performed. And user profiles are created dynamically based on current user sessions for Web personalization.

• Journal of Korean Port Research
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• v.12 no.1
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• pp.9-17
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• 1998

The evaluation structure of complex problems is composed of multi-attributes and hierarchy. A many studies were existed on this problems, but that based on the assumption that the evaluation elements were independent. The actual evaluation problems have the complexity, ambiguity and interlinkage among the elements. In this situation, the fuzzy evaluation process is very effective in settling the complex problems. For evaluation of large scale hierarchical MADM problem, the fuzzy evaluation algorithm is developed in this paper, and that is centering on the identification of fuzzy measures. In this study, we newly identified the weight and interaction among the evaluation attributes. The results of this study are as follows: we can identified the hierarchical structure of the evaluation problem which is composed of the evaluation structure, function and hierarchy; we improved the existed weighting method which could be accomplished by normalizing process, considering the uncertainty and new weight integrating method which come from Dempster-Shafer theory. And we take into account the interaction properties among more than 3 evaluation attributes, which can be compared with the existed studies in which only 2 evaluation attributes taked into account.

• 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.

• Smart Structures and Systems
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• v.15 no.1
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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.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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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.