• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.4
• /
• pp.9-17
• /
• 1997

This paper describes a monitoring system that examines water quality by analyzing behavioral patterns of fishes. The water quality inspection system (WQIS) captures color images of fishes with a CCD camera, extracts out fish regions from the images, and determines motion characteristics of fishes by computing consecutive frames. We define five types of measures that reflect behavioral patterns of fishes : floatness, fledness, clustemess, diffusiveness, and mobility. These measures are utilized when the system performs fuzzy inference to induce the conclusion about water quality. We believe that the proposed system can be a solution for securing clean water.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.11
• /
• pp.843-851
• /
• 2020

This study focuses on identifying and prioritizing the broader performance measures for the service supply chains by taking the case of Majan Electricity Company, Sohar, in the Sultanate of Oman. For an examination of the uniformity of ultimate objectives and the priorities therein, two strata of respondents with a total of fourteen respondents were approached for their valuable insights. Suitable structured instruments were personally administered to elicit the insightful and worthy responses. The two multi-criteria decision-making techniques, namely, the Fuzzy Analytical Hierarchy Process and the Best-Worst Method were used to reach a meaningful prioritization of the identified and refined broader performance measurement dimensions. The results show that there exists a minor gap between the two respondents' groups in terms of their prioritizations. The major finding points to the difference in terms of the topmost priorities as revealed by the two set of respondents. For one group of respondents, the customer satisfaction matters the most, whereas for the other group, it is the overall profitability that matters the most. This gap against the utopian state assists in concluding that there is a requirement to reorient the employees so as to have a shared and common understanding of the organizational priorities.

• Journal of Korean Society of Transportation
• /
• v.22 no.4 s.75
• /
• pp.159-173
• /
• 2004

This study utilizes the fuzzy logic and genetic algorithm to improve the existing incident detection models by addressing the problems associated with "crisp" thresholds and model transferability (applicability). The model's major components were designed to be a set of the fuzzy inference engines, and for the self-adaptation capability the genetic algorithm was introduced in optimization(or training) of the fuzzy membership functions. This approach is often called "the hybrid of fuzzy-genetic algorithm" The model performance was tested and found to be compatible with that of the existing well-recognized models in terms of performance measures such as detection rate, false alarm rate, and detection time. This study was not an effort for simple improvement of the model performance, but an experimental attempt to incorporate new characteristics essential for the incident detection model to be universally applicable for various roadway and traffic conditions. The study results prove that the initial objective of the study was satisfied, and suggest a direction that the future research work in this area must follow.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.39 no.1
• /
• pp.14-22
• /
• 2002

We propose the fuzzy BCMP queueing network model for the performance evaluation of distributed processing system with the ambiguous arrival rates of job, service requirements, and service rates of server by the network environments. This model is classified as the open and closed type whether or not the network accepts jobs from the system outside. We derived the measures for system performances such as the job average spending time, average job number in the system and server utilizations using fuzzy mean value analysis which can process the fuzzy factors for both types. Computer simulation was performed for verifying the effectiveness of derived equations of performance evaluation. The fuzzy BCMP queueing network model was evaluated according to the fuzzy arrival rates of job, the number of clients, and the fuzzy service requirements of job for each the open and closed type. The results were agreed with the predicted performance evaluations of the system.

• Journal of KIISE:Software and Applications
• /
• v.31 no.12
• /
• pp.1591-1601
• /
• 2004

Clustering, a technique for the analysis of the genes, organizes the patterns into groups by the similarity of the dataset and has been used for identifying the functions of the genes in the cluster or analyzing the functions of unknown gones. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods which assign a sample to a group. In this paper, a Bayesian validation method is proposed to evaluate the fuzzy partitions effectively. Bayesian validation method is a probability-based approach, selecting a fuzzy partition with the largest posterior probability given the dataset. At first, the proposed Bayesian validation method is compared to the 4 representative conventional fuzzy cluster validity measures in 4 well-known datasets where foray c-means algorithm is used. Then, we have analyzed the results of Saccharomyces cell cycle expression data evaluated by the proposed method.

• The KIPS Transactions:PartA
• /
• v.9A no.1
• /
• pp.45-52
• /
• 2002

This paper proposes the fuzzy closed RCMP queueing network model using fuzzy set theory for the performance evaluation of centralized distributed processing system with ambiguous system factors in the network environments. This model can derive the measures for system performances such as the job spending time, the system throughput, average job number and server utilizations using fuzzy mean value analysis which can process the fuzzy factors. Computer simulation has been performed centralized distributed system with fuzzy service requirement time for verifying the effectiveness of derived equations of performance evaluation according to the numbers of clients, and the results were analyzed. The proposed model provides more and flexible realistic than performance evaluation of conventional method when we evaluated system performance with ambiguous factors.

• Journal of the Korean Society of Safety
• /
• v.37 no.1
• /
• pp.78-87
• /
• 2022

In the risk assessment of urban railway systems, a hazard log is created by identifying hazards from accident and failure data. Then, based on a risk matrix, evaluators analyze the frequency and severity of the occurrence of the hazards, conduct the risk assessment, and then establish safety measures for the risk factors prior to risk control. However, because subjective judgments based on the evaluators' experiences affect the risk assessment results, a more objective and automated risk assessment system must be established. In this study, we propose a risk assessment model in which an adaptive neuro-fuzzy inference system (ANFIS), which is combined in artificial neural networks (ANN) and fuzzy inference system (FIS), is applied to the risk assessment of urban railway systems. The newly proposed model is more objective and automated, alleviating the limitations of risk assessments that use a risk matrix. In addition, the reliability of the model was verified by comparing the risk assessment results and risk control priorities between the newly proposed ANFIS-based risk assessment model and the risk assessment using a risk matrix. Results of the comparison indicate that a high level of accuracy was demonstrated in the risk assessment results of the proposed model, and uncertainty and subjectivity were mitigated in the risk control priority.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.27 no.2
• /
• pp.113-118
• /
• 2017

Various studies on the method of ship collision risk assessment for alarm have been reported constantly, and the result of the studies is applied to navigation devices. However, it is known that navigators ignore or turn off frequent alarms from the devices of predicting collision risk, because they may avoid collisions in the most of situations. In oder to make the prediction of ship collision risk more useful, it is necessary to consider the customary actions of ship collision avoidance. This paper proposes a system of analyzing collision avoidance measures of ships according to the types of encounter and managing the avoidance history of each ship. The core module of the system is designed as a neuro-fuzzy based inference system, and the test of the module validates the proposed system.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.38 no.4
• /
• pp.1-9
• /
• 2001

In this paper, we propose Fuzzy M/M/1/K queueing network model which has derived by appling the fuzzy set theory to M/M/l/K queueing network model in which has single server and system capacity K. When the arriving rate of input job and the servicing rate of a server arc represented as the linguistic attributes, the system analysis can be performed by using this model. The major evaluation measures of system such as the average number of jobs existing in the system, the average number of jobs into system, and the average spending time of job in system etc. are derived for the evaluation of system. Computer simulation was performed for verifying the effectiveness of these result equations. In which the various fuzzy arriving rates and fuzzy servicing rates according to varying the system capacity K were given for the system evaluation. We verified that the results of simulation are in accord with the expected evaluations in the proposed fuzzy model.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.2
• /
• pp.147-157
• /
• 2017

In recent years, Super-pixels have become very popular for use in computer vision applications. Super-pixel algorithm transforms pixels into perceptually feasible regions to reduce stiff features of grid pixel. In particular, super-pixels are useful to depth estimation, skeleton works, body labeling, and feature localization, etc. But, it is not easy to generate a good super-pixel partition for doing these tasks. Especially, super-pixels do not satisfy more meaningful features in view of the gestalt aspects such as non-sum, continuation, closure, perceptual constancy. In this paper, we suggest an advanced algorithm which combines simple linear iterative clustering with fuzzy clustering concepts. Simple linear iterative clustering technique has high adherence to image boundaries, speed, memory efficient than conventional methods. But, it does not suggest good compact and regular property to the super-pixel shapes in context of gestalt aspects. Fuzzy similarity measures provide a reasonable graph in view of bounded size and few neighbors. Thus, more compact and regular pixels are obtained, and can extract locally relevant features. Simulation shows that fuzzy similarity based super-pixel building represents natural features as the manner in which humans decompose images.