• Proceedings of KOSOMES biannual meeting
• /
• 2004.11a
• /
• pp.83-88
• /
• 2004

This paper suggests on evaluation of risk level for damage of marine accidents in SRRs. This paper intoduces a concept of fuzzy logic with the plenty of related literature riview, fuzzy measure t-seminormed fuzzy integral and in the Korean. SRRs of RCC and RSC. The methodology of this paper is max$\cdot$min composition of fuzzy extensive principle, defuzzifiation is centroid of gravity methods. And final evaluation value using t-seminormed fuzzy integral. At the result, the evaluation of risk level is especially over Serious for marine accident of Mokpo, Tongyoung, Busan SRRs. This paper recommends tint many Rescue Vessels and Equipments need to the reduction of risk level about those.

• Journal of KIISE:Software and Applications
• /
• v.34 no.6
• /
• pp.519-529
• /
• 2007

In this paper, we investigate the use of fuzzy rules for efficient intrusion detection. We use evolutionary algorithm to optimize the set of fuzzy rules for intrusion detection by constructing fuzzy decision trees. For efficient execution of evolutionary algorithm we use supervised clustering to generate an initial set of membership functions for fuzzy rules. In our method both performance and complexity of fuzzy rules (or fuzzy decision trees) are taken into account in fitness evaluation. We also use evaluation with data partition, membership degree caching and zero-pruning to reduce time for construction and evaluation of fuzzy decision trees. For performance evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that our method outperformed the existing methods. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• Journal of Korean Institute of Industrial Engineers
• /
• v.27 no.3
• /
• pp.305-314
• /
• 2001

In this paper, we construct an automatic evaluation system of psychological feeling for corporate identity (CI) symbol mark based on a fuzzy neural network technique. The system is modelled by trainable fuzzy inference rules with several input variables (qualitative and quantitative design components of CI symbol mark) and a single output variable (consumer's feeling). The back propagation learning algorithm, which is a conventional learning method of multilayer feedforward neural networks, is used for parameter identification of the fuzzy inference system. The learning ability to train data and the generalization ability to test data are evaluated for the proposed evaluation system by computer simulations.

• Journal of Advanced Navigation Technology
• /
• v.15 no.6
• /
• pp.1098-1103
• /
• 2011

In spite of many advantages of cloud computing, security concerns are a barrier in users' adopting the cloud service. In this paper, we evaluate relative priorities between security attributes of ISO 7498-2 standards affecting overall security quality in cloud computing. For an objective evaluation, the fuzzy AHP(Analytic hierarchical process) is applied. The evaluation results represented the relative priority with concrete number can be an effective management method to choose and develop the cloud computing service.

• Korean Management Science Review
• /
• v.14 no.1
• /
• pp.11-29
• /
• 1997

This paper presents the better efficient evaluation of the Small and Medium Enterprises by use of fuzzy multi-criteria decision making methodology under intersectional dependence relations. The five Small and Medium Enterprises alternative will be evaluated by Fuzzy Analytic Hierarchy Process(FAHP) based on entropy weight in this study. A case study is presented to illustrate the use of entropy weight measurement with intersectional dependence problems. These problems are evaluated seven criteria : market criteria, thchnology criteria, management ability criteria, planning criteria, propulsion ability criteria, project propulsion basis criteria, propulsion result criteria.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.25 no.4
• /
• pp.61-68
• /
• 2002

This paper provides a method of consciousness structure analysis for research and development project evaluation using fuzzy structure modeling(FSM). Fuzzy structure modeling, which is a modeling method for consciousness structure, has a large number of pairwise comparison by human subjective judgement and is difficult to check the consistency index of denoting the precision for human judgement. Thus, in this paper, we analyzed the structure of consciousness by fuzzy structural modeling method, introducing the concept of pairwise comparison matrix in Analytic Hierarchy Process.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.5
• /
• pp.451-459
• /
• 2013

In this paper we analyze the Korean foods and sensory adjectives that can be used for the taste expression of corresponding food based on the fuzzy relation. In order to construct fuzzy relation we gathered and chose 87 related Korean adjectives for expressing not only taste but also smell from foods. After then we performed a sensory evaluation for 51 Korean foods with 20 subjects to check the proper adjectives when they take a food. Based on the data collected by the evaluation a fuzzy relation is constructed and used for the analysis of the properties of food and adjectives. In addition the composition of the fuzzy relation provides the fuzzy tolerance(compatibility) relation among foods as well as that among adjectives. From the fuzzy complete ${\alpha}$-cover of the relations we could explore the taxonomy of food or adjectives. We expect that the fuzzy relation-based scheme in the paper can be utilized for analysis of the sensory adjectives like smelling and tactile sensation.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.59 no.1
• /
• pp.44-54
• /
• 2023

In this study, the second-order Nomoto's nonlinear expansion model was implemented as a Tagaki-Sugeno fuzzy model based on the heading angular velocity to design the automatic steering system of a ship considering nonlinear elements. A Tagaki-Sugeno fuzzy PID controller was designed using the applied fuzzy membership functions from the Tagaki-Sugeno fuzzy model. The linear models and fuzzy membership functions of each operating point of a given nonlinear expansion model were simultaneously tuned using a genetic algorithm. It was confirmed that the implemented Tagaki-Sugeno fuzzy model could accurately describe the given nonlinear expansion model through the Zig-Zag experiment. The optimal parameters of the sub-PID controller for each operating point of the Tagaki-Sugeno fuzzy model were searched using a genetic algorithm. The evaluation function for searching the optimal parameters considered the route extension due to course deviation and the resistance component of the ship by steering. By adding a penalty function to the evaluation function, the performance of the automatic steering system of the ship could be evaluated to track the set course without overshooting when changing the course. It was confirmed that the sub-PID controller for each operating point followed the set course to minimize the evaluation function without overshoot when changing the course. The outputs of the tuned sub-PID controllers were combined in a weighted average method using the membership functions of the Tagaki-Sugeno fuzzy model. The proposed Tagaki-Sugeno fuzzy PID controller was applied to the second-order Nomoto's nonlinear expansion model. As a result of examining the transient response characteristics for the set course change, it was confirmed that the set course tracking was satisfactorily performed.

• 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.

• Proceedings of the Korea Association of Information Systems Conference
• /
• 1996.11a
• /
• pp.211-216
• /
• 1996

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input-output pair. First, an architecture o fuzzy neural networks with fuzzy weights and fuzzy biases is shown. Next, a cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value. A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so 솜 t the level set of the fuzzy output includes the target output. Last, the proposed method is applied to the quality evaluation problem of injection molding