• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.25-32
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• 2013

Due to increasing competition of globalization and fast technological improvements the appropriate method for evaluating and selecting IS-personnel is one of the key factors for an organization's success. Personnel selection is a multi-criteria decision-making (MCDM) problem which consists of both qualitative and quantitative metrics. Although many articles have discussed various knowledge and skills IS personnel should possess, no specific model for IS personnel selection and evaluation, to our knowledge, has been published up to now. After reviewing the IS personnel's important characteristics, we propose an approach for categorizing the IS personnel based on their skills, ability, and knowledge during evaluation and selection process. Our proposed approach is derived from a model of neural network algorithm. We have adapted and implemented the fuzzy ART algorithm with Jaccard choice function. The result of an illustrative numerical example is proposed to demonstrate the easiness and effectiveness of our approach.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.8
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• pp.88-100
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• 1997

This paper proposes a new concept for an optimal traffic signal cycle method which will reduce the average vehicle waiting time and improve average vehicle speed. Electro sensitive traffic system can extend the traffic cycle when there ar emany vehicles in the road or it can reduce the traffic consider vehicle length, so it can cause oveflow and reduce average vechicel waiting time at the intersection, we propose on optimal traffic cycle with fuzzy ruels and neural network. Computer simulation results prove that reducing the average vehicle waiting time which proposed considering passing vehicle's length for the optimal traffic cycle better than fixe dsignal method dosen't consider vehicle length.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.65-73
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• 2006

It is very difficult for the teacher to know who understands the lecture or not in the classroom. Therefore, in this paper, it proposed the algorithm of student score evaluation algorithm using full duplex method. Moreover, it confirms that full duplex method using fuzzy rules and neural network can tell where misunderstanding of the problems in the test. The computer simulation results shows that the full duplex virtual learning system has been proven to be much more efficient than one way traditional method which unfortunately does not consider the students understanding.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.161-165
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• 2005

In order to produce an optimal traffic cycle. We must first check how many waiting cars are at the lower intersection, because waiting queue is bigger than the length of upper traffic intersection. Start up delay time and vehicle waiting time occurs. To reduce vehicle waiting time, in this paper, we present an optimal green time algorithm using fuzzy neural network. Through computer simulation has been proven to be improved average vehicle speed than fixed traffic signal light which do not consider different intersection conditions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.101-104
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• 2006

유비쿼터스 시대가 다가오면서 앞으로 가정 및 회사 등 인간이 거주하며 생활하는 공간에서의 좀 더 편리하고 효율적인 다양한 정보를 인간에게 인지시켜 줄 수 있는 환경이 구축되어야한다. 이를 기반으로 유비쿼터스 주변장치들의 네트워크와 인간에게 많은 정보와 편리성이 좀 더 효율적으로 이루어져야 할 것이다. 이를 위해 본 논문에서는 센서모듈에서 추출되는 데이터를 신경망과 퍼지 알고리즘을 사용해 동작인식의 패턴을 분류하여 인간의 사고를 움직임 파악한다. 이러한 패턴의 분류를 통해 홈네트워크 시스템과의 센서모듈의 통신제어가 가능하게 된다 이를 바탕으로 패턴이 분류된 행동들의 명령으로 미리 지정된 간단한 손동작으로 여러 가전기기라든지 홈네트워크 시스템의 제어방식을 더욱 간단히 제어하며, 인간의 건강상태를 파악함으로써 인간행동과 상태에 따른 유비쿼터스 환경의 제어가 이루어 질 수 있는 시스템을 제안한다.

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.77-86
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• 2004

In this study, we applied the Bayesian Network for the case of the mode choice models using the Seoul metropolitan area's house trip survey Data. Sex and age were used lot the independent variables for the explanation or the mode choice, and the relationships between the mode choice and the travellers' social characteristics were identified by the Bayesian Network. Furthermore, trip and mode's characteristics such as time and fare were also used for independent variables and the mode choice models were developed. It was found that the Bayesian Network were useful tool to overcome the problems which were in the traditional mode choice models. In particular, the various transport policies could be evaluated in the very short time by the established relation-ships. It is expected that the Bayesian Network will be utilized as the important tools for the transport analysis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.10a
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• pp.144-148
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• 2001

자동차의 발전과 함께 유지 보수를 위한 사용자의 요구는 급증하고 있으나 정비사의 부족으로 인해 경제성 및 신속성 등 이 문제가 되고 있고 이를 해결하기 위해 현재 개발되고 있는 장치들은 대부분 전자 제어 유닛에서 발생시키는 신호를 분석하거나 운전자와의 대화를 통하여 진단하는 방식으로 고장으로 인한 소음이나 진동등 운전자들의 주관적인 평가대상에 대해서는 적절한 해결책으로 제시해 주지 못하고 있다. 그러므로 계측에 의한 소음과 진동 데이터를 이용하여 전문가의 판단을 가지고 고장의 원인을 규명하며 운전자를 위한 오디오적인 표현을 해 줄 수 있는 진단 전문가 시스템이 필요하게 되었다. 본 논문에서는 자동차의 여러 단품중 쇼크 옵서버와 에어컨에 대하여 소음 진동 현상의 정상 및 이상 증상과 신호 계측 방법을 연구하였고 계측된 신호에 대해 패턴 화하여 인공 신경 회로망과 퍼지 추론을 통한 진단을 할 수 있는 알고리즘을 개발하였으며 차후 계속되는 연구에 사용될 정상 및 이상신호에 대한 기본적인 데이터 베이스를 구축하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.49-54
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• 2011

In this paper, we represent a mobile robot which can recognize chemical odor, measure concentration, and track its source indoors. The mobile robot has the function of smell that can sort several gases in experiment such as ammonia, ethanol, and their mixture with neural network algorithm and measure each gas concentration with fuzzy rules. In addition, it can not only navigate to the desired position with vision system by avoiding obstacles but also transmit odor information and warning messages earned from its own operations to other nodes by multi-hop communication in wireless sensor network. We suggest the way of odor sorting, concentration measurement, and source tracking for a mobile robot in wireless sensor network using a hybrid algorithm with vision system and gas sensors. The experimental studies prove that the efficiency of the proposed algorithm for odor recognition, concentration measurement, and source tracking.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.2
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• pp.71-83
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• 2013

Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1647-1652
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• 2009

Generally the neural network and the fuzzy compensative algorithm are applied to forecast the time series for power demand with a characteristic of non-linear dynamic system, but it has a few prediction errors relatively. It also makes long term forecast difficult for sensitivity on the initial condition. On this paper, we evaluate the chaotic characteristic of electrical power demand with analysis methods of qualitative and quantitative and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction, time series forecast for multi dimension using Lyapunov exponent quantitatively. We compare simulated results with the previous method and verify that the purpose one being more practice and effective than it.