• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.142-149
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• 2004

It is important to retrieve exact information coinciding with user's need from lots of Usenet news and filter desired information quickly. Differently from email system, we must previously register our interesting news group if we want to get the news information. However, it is not easy for a novice to decide which news group is relevant to his or her interests. In this work, we present a service classifying user preferred news groups among various news groups by the use of Kohonen network. We first extract candidate terms from example documents and then choose a number of representative keywords to be used in Kohonen network from them through fuzzy inference. From the observation of training patterns, we could find the sparsity problem that lots of keywords in training patterns are empty. Thus, a new method to train neural network through reduction of unnecessary dimensions by the statistical coefficient of determination is proposed in this paper. Experimental results show that the proposed method is superior to the method using every dimension in terms of cluster overlap defined by using within cluster distance and between cluster distance.

• Korean Journal of Construction Engineering and Management
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• v.13 no.2
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• pp.70-79
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• 2012

The Korean Government recently has been focused on strengthening competitiveness of order and stimulating construction market in the international construction industry. It has planned to extend the ODPs (overseas development projects) in order to diversifying the international construction market of which is domestic construction companies, placing too much emphasis on plant projects of the Middle East. However, literature review of risk analysis in the ODPs shows that the number of case study is several. Therefore, Authors asserted the necessity of risk analysis in the ODPs. The purpose of this study is to suggest a methodology that find KRFs (key risk factors) in the ODPs and analyze them, using AHP and Fuzzy theory. As a result, the 37 KRFs are selected and explained characteristics of them. A future direction of this study is to suggest a risk management model in the ODPs and prove feasibility of it.

• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.11-21
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• 2015

If the cloud computing environment is not sufficiently provide the required resources due to the number of virtual server to process the request, may cause a problem that the load applied to the specific server. Migration administrator receive the resources of each physical server for improving the efficiency of the virtual server that exists in the physical servers, and determines the migration destination based on the simulation results. But, there is more overhead predicting the future resource consumption of all the physical server to decide the migration destination through the simulation process in large and complex cloud computing environments. To solve this problem, we propose an improved prediction method with the simulation-based approach. The proposed method is a fuzzy-logic based priority model for VM migration. We design a proposed model with the DEVS formalism. And we also measure and compare a performance and migration count with existing simulation-based migration method. FPLM shows high utilization.

• Smart Structures and Systems
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• v.23 no.5
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• pp.449-466
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• 2019

Cable-stayed bridges are attractive due to their beauty, reducing material consumption, less harm to the environment and so on, in comparison with other kinds of bridges. As a massive structure with long period and low damping (0.3 to 2%) under many dynamic loads, these bridges are susceptible to fatigue, serviceability disorder, damage or even collapse. Tuned Mass Damper (TMD) is a suitable controlling system to reduce the vibrations and prevent the threats in such bridges. In this paper, Multi Tuned Mass Damper (MTMD) system is added to the Ahvaz cable stayed Bridge in Iran, to reduce its seismic vibrations. First, the bridge is modeled in SAP2000 followed with result verification. Dead and live loads and the moving loads have been assigned to the bridge. Then the finite element model is developed in OpenSees, with the goal of running a nonlinear time-history analysis. Three far-field and three near-field earthquake records are imposed to the model after scaling to the PGA of 0.25 g, 0.4 g, 0.55 g and 0.7 g. Two MTMD systems, passive and active, with the number of TMDs from 1 to 8, are placed in specific points of the main span of bridge, adding a total mass ratio of 1 to 10% to the bridge. The parameters of the TMDs are optimized using Genetic Algorithm (GA). Also, the optimum force for active control is achieved by Fuzzy Logic Control (FLC). The results showed that the maximum displacement of the center of the bridge main span reduced 33% and 48% respectively by adding passive and active MTMD systems. The RMS of displacement reduced 37% and 47%, the velocity 36% and 42% and also the base shear in pylons, 27% and 47%, respectively by adding passive and active systems, in the best cases.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.127-135
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• 2023

Nowadays, the number of patients committing suicide due to depression and stress is rapidly increasing. In addition, if stress and depression last for a long time, they are dangerous factors that can cause heart disease, brain disease, and high blood pressure. However, no matter how modern medicine has developed, it is a very difficult situation for patients with depression and heart disease without special drugs or treatments. Therefore, in many countries around the world, studies are being actively conducted to determine patients at risk of depression and patients at risk of suicide at an early stage using electrocardiogram, oxygen saturation, and brain wave analysis functions. In this paper, in order to analyze these problems, a computer simulation was performed to determine heart disease risk patients by establishing heart disease hypothesis data. In particular, in order to improve the predictive rate of heart disease by more than 10%, a simulation using fuzzy inference was performed.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.168-174
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• 2000

All the visiting customers makes a queue and then can be served at an unoccupied server, in the queueing system of multi-server. The customer and server, however, have a conflictive feeling of satisfaction at all times. To solve the problem, in this paper, it is proposed that new design method of server number to maximize the satisfaction level of customer and server by the hzzy concept which is applied to the queuing system.

• International Journal of Safety
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• v.11 no.1
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• pp.6-10
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• 2012

This paper presents a study on the analysis for reducing the number of false alarms in fire detection system. In order to intelligent algorithm fuzzy logic is adopted in developing fire detection system to reduce false alarm. The intelligent fire detection algorithm compared and analyzed the fire and non-fire signatures measured in circuits simulating flame fire and smoldering fire. The algorithm has input variables obtained by fire experiment with K-type thermocouple and optical smoke sensor. Also triangular membership function is used for inference rules. And the antecedent part of inference rules consists of temperature and smoke density, and the consequent part consists of fire probability. A fire-experiment is conducted with paper, plastic, and n-heptane to simulate actual fire situation. The results show that the intelligent fire detection algorithm suggested in this study can more effectively discriminate signatures between fire and similar fire.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.2
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• pp.173-178
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• 2007

This paper is to establish an appropriate model for predicting the temperature decreases in the batch transferred from the refining process to the caster in steel-making companies. Mathematical modeling of the temperature decreases between the processes is difficult, since the reaction mechanism by which the temperature changes in a molten steel batch is dynamic, uncertain and complex. Three soft computing techniques are examined using the same data, namely the multiple regression, fuzzy regression, and neural net (NN) models. To compare the accuracy of these three models, a limited number of input variables are selected from those variables significantly affecting the temperature decrease. The results show that the difference in accuracy between the three models is not statistically significant. Nonetheless, the NN model is recommended because of its adaptive ability and robustness. The method presented in this paper allows the temperature decrease to be predicted without requiring any precise metallurgical knowledge.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.654-658
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• 2002

The purpose of this paper is to establish an effective featured based inspection planning system for OMM(On-Machine Measurement) process. In this system, an effective inspection process planning is accomplished by determining the number of measuring points, their locations and probing paths using fuzzy logic, Hammersley method and TSP problem. Also, an effective collision-free algorithm Is proposed based on the EZ-map analysis. All the inspection planning processes are performed based on the defined inspection features those are derived from the CAD database. Proposed inspection planning method is simulated for the given sample wrokpieces, and the results are analyzed. The results show that the proposed method can be successfully implemented in real OMM process.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.133-136
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• 1996

클러스터의 타당성 정도를 계산하기 위한 측정자로서, 퍼지 분할된 데이터의 서로 다른 클래스 사이의 분리성과 한 클래스안에서의 밀접성의 비율, G를 정의하였다. 본 논문에서는 이렇게 정의된 G로부터, 각 클러스터가 가지는 데이터 수의 차이점을 고려하여 하나의 데이터 집합에 대하여 서로 다른 분할들을 비교할 수 있도록 하기 위하여, IG를 재정의하였다. 기존의 클러스터 타당성 전략은 클러스터 수의 함수로서, 주어진 척도의 값을 계산하여 기록한 후 그 값의 변화가 가장 큰 경우를 최적의 클러스터의 수로서 선택하였다. 이때 그 값의 변화를 고려하기 위한 주관적인 해석이 필요하게 된다. 본 논문에서는 주관적인 해석 없이 IG를 이용하여 최적의 클러스터 수를 결정하기 위한 방법을 제안하고자 한다. 제안된 방법은 널리 알려진 Iris data와 서로 다른 클러스터 인구수를 가지는 가상의 데이터 집합에 적용하여 그 타당성을 보인다.