• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.31
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• pp.177-186
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• 1994

Most causes of accidents are due to physical unsafety conditions and human unsafety actions. The design of safety work by ergonomics method is one of the methodes which effectively reduce these unsafety conditions and unsafety actions. This paper presents considerations in design of safety work. And when we try to analyze the accident event by means of probability, there exist some problems because of fuzziness in physical unsafety conditions' components and human unsafety actions' components which are the causes of basic event. For this reason, it is impossible for input probability of basic event to define a crisp value. In consideration of the uncertain probability of components, this paper deals with the Fuzzy set theory by membership value and suggests calculation procedure and analysis of disaster event.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.67
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• pp.77-82
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• 2001

A study was conducted to develop a better classification method of Consumer Attributes that can enhance user-centered product design process. A modified QFD(Quality Function Deployment) survey form based upon Fuzzy set theory was proposed which contains 9 steps of importance level, and Certainty and Necessity function to improve the reliability of extracted consumer attributes. To verify the betterment and advantage of proposed classification method, a series of questionnaire survey was performed. Thirty male and 30 female university students were participated in the survey using a VCR as a target product. The result of the study showed that 80% of subjects were preferred the proposed classification over existing method. A cluster analysis was performed to further verify the betterment of the proposed method. The result also supported that the proposed classification method is more reliable and enhanced method in extracting consumer attributes and can be applied in the product design.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.93-104
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• 1997

The major economic health problem of dairy cattle is mastitis which can affect 10 to 50% of cow-quarters. This health problem is difficult for many dairy farmers and health advisors to understand, diagnose and control. Without special laboratory testing, most mastitis is overlooked. Estimates of annual mastitis cast per cow vary from $50 to $200. For the nearly 9 million cows in the United States, annual loss to the dairy industry amounts to over one billion. A knowledge-based decision aid has been developed to evaluate mastitis data retrieved electronically from two of nine U. S. regional dairy records processing centers. Heuristic rules to diagnose herd mastitis problems were collected and incorporated into the system from various domain experts. This system information. It allows users to select mastitis control schemes with various degrees of aggressiveness and teaches commonly accepted mastitis control practices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2055-2062
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• 2013

Digital evidence which is the new form of evidence to crime makes little difference in value and function with existing evidences. As time goes on, digital evidence will be the important part of the collection and the admissibility of evidence. Usually a digital forensic investigator has to spend a lot of time in order to find clues related to the investigation among the huge amount of data extracted from one or more potential containers of evidence such as computer systems, storage media and devices. Therefore, these evidences need to be ranked and prioritized based on the importance of potential relevant evidence to decrease the investigate time. In this paper we propose a methodology which prioritizes order in which evidences are to be examined in order to help in selecting the right evidence for investigation. The proposed scheme is based on Fuzzy Multi-Criteria Decision Making, in which uncertain parameters such as evidence investigation duration, value of evidence and relation between evidence, and relation between the case and time are used in the decision process using the aggregation function in fuzzy set theory.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.88-96
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• 2010

This paper presents a two-phase quantitative framework to aid the decision making process for effective selection of an efficient freight logistics hub from 8 alternatives in Thailand on the North-South economic corridor. Phase 1 employs both multiple regression and Pearson Feature selection to find the important criteria, as defined by logistics hub score, and to reduce number of criteria by eliminating the less important criteria. The result of Pearson Feature selection indicated that only 5 of 15 criteria affected the logistics hub score. Moreover, Genetic Algorithm (GA) was constructed from original 15 criteria data set to find the relationship between logistics criteria and freight logistics hub score. As a result, the statistical tools are provided the same 5 important criteria, affecting logistics hub score from GA, and data mining tool. Phase 2 performs the fuzzy stochastic AHP analysis with the five important criteria. This approach could help to gain insight into how the imprecision in judgment ratios may affect their alternatives toward the best solution and how the best alternative may be identified with certain confidence. The main objective of the paper is to find the best alternative for selecting freight logistics hub under proper criteria. The experimental results show that by using this approach, Chiang Mai province is the best place with the confidence interval 95%.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1302-1306
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• 2004

This paper proposes a model for information filtering (IF) on the Web. The user information need is described into two levels in this model: profiles on category level, and Boolean queries on document level. To efficiently estimate the relevance between the user information need and documents by fuzzy, the user information need is treated as a rough set on the space of documents. The rough set decision theory is used to classify the new documents according to the user information need. In return for this, the new documents are divided into three parts: positive region, boundary region, and negative region. We modified user profile by the user's relevance feedback and discerning words in the documents. In experimental we compared the results of three methods, firstly is to search documents that are not passed the filtering system. Second, search documents that passed the filtering system. Lastly, search documents after modified user profile. The result from using these techniques can obtain higher precision.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.217-222
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• 2011

Genetic algorithms are one of the most important methods used to solve the Traveling Salesman Problem. Therefore, many researchers have tried to improve the Genetic Algorithm by using different methods and operations in order to find the optimal solution within reasonable time. This paper intends to find a new approach that adopts an improved genetic algorithm to solve the Traveling Salesman Problem, and compare with the well known heuristic method, namely, Kohonen Self-Organizing Map by using different data sets of symmetric TSP from TSPLIB. In order to improve the search process for the optimal solution, the proposed approach consists of three strategies: two separate tour segments sets, the improved crossover operator, and the improved mutation operator. The two separate tour segments sets are construction heuristic which produces tour of the first generation with low cost. The improved crossover operator finds the candidate fine tour segments in parents and preserves them for descendants. The mutation operator is an operator which can optimize a chromosome with mutation successfully by altering the mutation probability dynamically. The two improved operators can be used to avoid the premature convergence. Simulation experiments are executed to investigate the quality of the solution and convergence speed by using a representative set of test problems taken from TSPLIB. The results of a comparison between the new approach using the improved genetic algorithm and the Kohonen Self-Organizing Map show that the new approach yields better results for problems up to 200 cities.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.238-246
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• 2011

This paper proposes a new semantic representation and its associated similarity measure. The representation expresses textual context observed in a context of a certain term as a network where nodes are terms and edges are the number of cooccurrences between connected terms. To compare terms represented in networks, a graph kernel is adopted as a similarity measure. The proposed representation has two notable merits compared with previous semantic representations. First, it can process polysemous words in a better way than a vector representation. A network of a polysemous term is regarded as a combination of sub-networks that represent senses and the appropriate sub-network is identified by context before compared by the kernel. Second, the representation permits not only words but also senses or contexts to be represented directly from corresponding set of terms. The validity of the representation and its similarity measure is evaluated with two tasks: synonym test and unsupervised word sense disambiguation. The method performed well and could compete with the state-of-the-art unsupervised methods.

• Journal of the Korean Society for information Management
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• v.12 no.1
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• pp.87-97
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• 1995

Boolean retrieval systems have been most widely used in the area of information retrieval due to easy implementation and efficient retrieval. Conventional Boolean retrieval systems. however, cannot rank retrieved documents in decreasing order of query-document similarities because they cannot compute similarity coefficients between queries and documents. Extended Boolean models such as fuzzy set. Waller-Kraft, Paice, P-Norm and Infinite-One have been developed to provide the document ranking facility. In extended Boolean models, the formulas evaluating Boolean operators AND and OR are an important component to affect the quality of document ranking. In this paper we present mathematical properties of the formulas, and analyse their effect on retrieval effectiveness. Our analyses show that P-Norm is the most suitable for achieving high retrieval effectiveness.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.52-58
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• 2005

We have developed a Group Method of Data Handling (GMDH) type algorithm for designing multi-layered neural networks. The algorithm is general enough that it will accept any number of inputs and any sized training set. Each neuron of the resulting network is a function of two of the inputs to the layer. The equation for each of the neurons is a quadratic polynomial. Several forms of the equation are tested for each neuron to make sure that only the best equation of two inputs is kept. All possible combinations of two inputs to each layer are also tested. By carefully testing each resulting neuron, we have developed an algorithm to keep only the best neurons at each level. The algorithm's goal is to create as accurate a network as possible while minimizing the size of the network. Software was developed to train and simulate networks using our algorithm. Several applications were modeled using our software, and the result was that our algorithm succeeded in developing small, accurate, multi-layer networks.