• Journal of Information Processing Systems
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• v.7 no.3
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• pp.397-412
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• 2011

Granular Computing has emerged as a unified and coherent framework of designing, processing, and interpretation of information granules. Information granules are formalized within various frameworks such as sets (interval mathematics), fuzzy sets, rough sets, shadowed sets, probabilities (probability density functions), to name several the most visible approaches. In spite of the apparent diversity of the existing formalisms, there are some underlying commonalities articulated in terms of the fundamentals, algorithmic developments and ensuing application domains. In this study, we introduce two pivotal concepts: a principle of justifiable granularity and a method of an optimal information allocation where information granularity is regarded as an important design asset. We show that these two concepts are relevant to various formal setups of information granularity and offer constructs supporting the design of information granules and their processing. A suite of applied studies is focused on knowledge management in which case we identify several key categories of schemes present there.

• Proceedings of the KSR Conference
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• 2004.06a
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• pp.1155-1162
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• 2004

This research proposes a new risk analysis method in order to guarantee successful performance of construction projects. The proposed risk analysis methods consists of four phases. First step, AHP model can help contractors decide whether or not they bid for a project by analysing risks involved in the project. Second step, the influence diagraming, decision tree and Monte Carlo simulation are used as tools to analyze and evaluate project risks quantitatively. Third step, Monte Carlo simulation is used to assess risk for groups of activities with probabilistic branching and calendars. Finally, Fuzzy theory suggests a risk management method for construction projects, which is using subjective knowledge of an expert and linguistic value, to analyze and quantify risk. The result of study is expected to improve the accuracy of risk analysis because three factors, such as probability, impact and exposure, for estimating membership function are introduced to quantify each risk factor. Consequently, it will help contractors identify risk elements in their projects and quantify the impact of risk on project time and cost.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.15-19
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• 2012

A Domain Dictionary generation algorithm based on pseudo feedback model is presented in this paper. This algorithm can increase the precision of domain dictionary generation algorithm. The generation of Domain Dictionary is regarded as a domain term retrieval process: Assume that top N strings in the original retrieval result set are relevant to C, append these strings into the dictionary, retrieval again. Iterate the process until a predefined number of domain terms have been generated. Experiments upon corpus show that the precision of pseudo feedback model based algorithm is much higher than existing algorithms.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.387-390
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• 2009

Wireless Sensor Network (WSN) can easily compromised from attackers because it has the limited resource and deployed in exposed environments. When the sensitive packets are occurred such as enemy's movement or fire alarm, attackers can selectively drop them using a compromised node. It brings the isolation between the basestation and the sensor fields. To detect selective forwarding attack, Xiao, Yu and Gao proposed checkpoint-based multi-hop acknowledgement scheme (CHEMAS). The check-point nodes are used to detect the area which generating selective forwarding attacks. However, CHEMAS has static probability of selecting check-point nodes. It cannot achieve the flexibility to coordinate between the detection ability and the energy consumption. In this paper, we propose the control method for the number fo check-point nodes. Through the control method, we can achieve the flexibility which can provide the sufficient detection ability while conserving the energy consumption.

• Structural Engineering and Mechanics
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• v.45 no.5
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• pp.693-702
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• 2013

Recently, artificial intelligence tools are most used for structural engineering and mechanics. In order to predict reserve prices and prices of awards, this study proposed a novel regression prediction model by the intelligent Kalman filtering method. An artificial intelligent multiple regression model was established using categorized data and then a prediction model using intelligent Kalman filtering. The rather precise construction bid price model was selected for the purpose of increasing the probability to win bids in the simulation example.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.170-177
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• 2010

Poker is a game of imperfect information where competing players must deal with multiple risk factors stemming from unknown information while making the best decision to win, and this makes it an interesting test-bed for artificial intelligence research. This paper introduces a new learning AI algorithm with embedded opponent modeling that can be used for these types of situations and we use this AI and apply it to a poker program. The new AI will be based on several graphs with each of its nodes representing inputs, and the algorithm will learn the optimal decision to make by updating the weight of the edges connecting these nodes and returning a probability for each action the graphs represent.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.310-315
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• 2005

This paper proposes a fast optimization method by combining queen-bee evolution and derivative evaluation in genetic algorithms. These two operations make it possible for genetic algorithms to focus on highly fitted individuals and rapidly evolved individuals, respectively. Even though the two operations can also increase the probability that genetic algorithms fall into premature convergence phenomenon, that can be controlled by strong mutation rates. That is, the two operations and the strong mutation strengthen exploitation and exploration of the genetic algorithms, respectively. As a result, the genetic algorithm employing queen-bee evolution and derivative evaluation finds optimum solutions more quickly than those employing one of them. This was proved by experiments with one pattern matching problem and two function optimization problems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.313-321
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• 2014

In the current era of data-intensive services, the handling of big data is a crucial issue that affects almost every discipline and industry. In this study, we propose a classification method for large volumes of numeric data, which is implemented in a distributed programming framework, i.e., MapReduce. The proposed method partitions the data space into a grid structure and it then models the probability distributions of classes for grid cells by collecting sufficient statistics using distributed MapReduce tasks. The class labeling of new data is achieved by k-nearest neighbor classification based on Bayesian inference.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1997.04a
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• pp.27-34
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• 1997

In this paper, systematic and comprehensive approaches are suggested for the application of quantitative PRA techniques especially for those risk events that cannot be easily evaluated quantitatively In addition, dominant risk events are identified based on their occurrence frequency assessed by both actual survey of construction site conditions and the statistical data related with the probable accidents. Practical FTA(Fault Tree Analysis) and ETA(Event Tree Analysis) models are used for the assessment of the identified risks. When the risk events are lack of statistical data, appropriate Bayesian models incorporating engineering judgement and test results are also introduced in this paper. Moreover, a fuzzy probability technique is used for the quantitative risk assessment of those risk components which are difficult to evaluate quantitatively.

• Journal of the Korean Society of Marine Environment & Safety
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• v.16 no.1
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• pp.81-91
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• 2010

IMO(International Maritime Organization) and the Shipping World rave complied with various kinds of international regulations for maritime safety and marine environmental protection, but the main reason of maritime accidents is that developing countries cannot implement maritime safety related regulations. Although Korea has been a member of the "A group" council of IMO, maritime technology transfer records of Korea are not good. To promote the project of official development assistance in Korea, it is required to select the technology which has a high degree of importance in the fields of maritime safety and has a high degree of demand on the transfer to developing countries, and to concentrate on the selected technology. So, it is necessary to draw valuation factors for maritime safety technology and to decide the priority in order among maritime safety technologies on the basis of valuation factors. Because the weights which show the degree of importance among valuation factors are different from factor to factor, interdependent relationship between factors should be considered on evaluation. In this study, the valuation factors were divided into three groups as the maturity of maritime safety technology, the promotion probability of projects and the degree of importance of technology, and the detailed factors of each group were drawn. A model which used Fuzzy AHP and limiting probability to consider the weights of importance and correlation among valuation factors was developed. To adopt this model, nine types of maritime safety technology in the field of maritime safety information were selected and points were scored for each technology through evaluation. In conclusion, first, ENC related technology was scored to be the highest as 0.0139. Second, the point of ship monitoring technology was scored as 0.0133. Last, oil spill response technology was scored as 0.0132.