• 한국정밀공학회지
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• 제11권4호
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• pp.138-147
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• 1994

Performance management of computer networks is intended to improve a given network performance in order for more efficient information exchange between subsystems of an integrated large-scale system. Improtance of performance management is growing as many function of the large- scale system depend on the quality of communication services provided by the network. The role of performance management is to manipulate the adjustable protocol parameters on line so that the network can adapt itself to a dynamic environment. This can be divided into two subtasks : performance evaluation to find how changes in protocol parameters affect the network performance and decision making to detemine the magnitude and direction of parameter adjustment. This paper is the second part of the two papers focusing on conceptual design, development, and evaluation of performance management for token bus networks. This paper specifically deals with the task of decision making which utilizes the principles of stochastic optimization and learning automata. The developed algorithm can adjuxt four timer settings of a token bus protocol based on the result of performance evaluation. The overall performance management has been evaluated for its efficacy on a network testbed.

• Structural Engineering and Mechanics
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• 제13권2호
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• pp.187-209
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• 2002

The infrastructure system in the United States has been aging faster than the resource available to restore them. Therefore decision for allocating the resources is based in part on the condition of the structural system. This paper proposes to use neural network to predict the overall rating of the structural system because of the successful applications of neural network to other fields which require a "symptom-diagnostic" type relationship. The goal of this paper is to illustrate the potential of using neural network in civil engineering applications and, particularly, in bridge evaluations. Data collected by the Tennessee Department of Transportation were used as "test bed" for the study. Multi-layer feed forward networks were developed using the Levenberg-Marquardt training algorithm. All the neural networks consisted of at least one hidden layer of neurons. Hyperbolic tangent transfer functions were used in the first hidden layer and log-sigmoid transfer functions were used in the subsequent hidden and output layers. The best performing neural network consisted of three hidden layers. This network contained three neurons in the first hidden layer, two neurons in the second hidden layer and one neuron in the third hidden layer. The neural network performed well based on a target error of 10%. The results of this study indicate that the potential for using neural networks for the evaluation of infrastructure systems is very good.

• ETRI Journal
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• 제31권5호
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• pp.554-564
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• 2009

In information security and network management, attacks based on vulnerabilities have grown in importance. Malicious attackers break into hosts using a variety of techniques. The most common method is to exploit known vulnerabilities. Although patches have long been available for vulnerabilities, system administrators have generally been reluctant to patch their hosts immediately because they perceive the patches to be annoying and complex. To solve these problems, we propose a security vulnerability evaluation and patch framework called PKG-VUL, which evaluates the software installed on hosts to decide whether the hosts are vulnerable and then applies patches to vulnerable hosts. All these operations are accomplished by the widely used simple network management protocol (SNMP). Therefore, system administrators can easily manage their vulnerable hosts through PKG-VUL included in the SNMP-based network management systems as a module. The evaluation results demonstrate the applicability of PKG-VUL and its performance in terms of devised criteria.

• 한국경영과학회지
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• 제19권2호
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• pp.129-151
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• 1994

Recently, Decision Support Systems (DSS) research has seen a more to combine Artificial Intelligence (AI) including neural network techniques with traditional DSS concepts and technologies to build an intelligent DSS or a knowledge-based DSS. This article proposes a Management Evaluation and its Support System (MESS) as a knowledge-based DSS. The management evaluation of a firm means the performance of all managerial operations is appraised by considering the situations of the firm. A neural network is used to represent the management evaluation structure as a suitable means of management knowledge representation. Finally a case study in a telecommunication corporation is presented.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2010년도 춘계 학술발표회
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• pp.1342-1348
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• 2010

Currently available evaluation checklists are developed for specific purposed using different parameters and items determined by different weighting factors. Those items with different weighting are sometimes said that they are based on the engineering judgement and leap of faith and, therefore, there is a limitation to adapt those checklists for slope-stability evaluation in the field. This study reviews factors affecting slope stability, analyze the relationship between those factors and slope failures using artificial neural network, and proposed a slope-stability evaluation model for adequate weighting for the factors.

• 한국공작기계학회논문집
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• 제10권6호
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• pp.95-101
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• 2001

This work is concerned with construction of the intelligence stress predictor far compression strength evaluation using neural network-ultrasonic waves. The contact pressure in jointed plates was measured by using ultrasonic technique. Neural network is used to evaluate and predict contact pressure from the results of the calibration curves. The organized neural system was leaned with the accuracy of 99%, as a result of learning the ultrasonic echo ratio to the contact pressure measurement between SM45C and STS410 materials. And it could be evaluated and predicted with the accuracy of 90% in the evaluation of ultrasonic echo ratio difference in the same surface roughness and contact pressure, and 85% in the prediction of virtual ultrasonic echo ratio. Thus the proposed stress predictor is very useful for the evaluation and prediction of the contact pressure between SM45C and STS410 materials.

• ETRI Journal
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• 제36권4호
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• pp.572-580
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• 2014

Mobile operators in saturated markets increasingly favor mobile network sharing (MNS) over facility-based competition. Previous research examining MNS primarily focused on its positive effects, while the negative effects were largely overlooked. This study proposes a decision-making model using an analytic hierarchy process technique to evaluate decision-making criteria among various types of MNS policies. The decision-making model was applied to Wireless Broadband services in Korea to determine the relative importance of both positive and negative evaluation criteria and preference among multiple types of MNS policies. Positive evaluation criteria (that is, efficiency) were far greater in importance than negative evaluation criteria (that is, competition harm). The preference for adopting MNS among five alternative approaches was also revealed. The study findings offer immediate policy insights in Korea and provide a decision-making framework for policy makers in other countries to utilize.

• Journal of Electrical Engineering and Technology
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• 제9권4호
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• pp.1188-1195
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• 2014

This paper presents an analytical technique for reliability evaluation of electrical distribution network (EDN) containing distributed generation (DG). Based on hierarchical levels of circuit breaker controlling zones and feeder sections, a directed-relation-graph (DRG) for an END is formed to describe the hierarchical structure of the EDN. The reliability indices of EDN and load points can be evaluated directly using the formed DRG, and the reliability evaluation of an EDN containing DGs can also be done without re-forming the DRG. The proposed technique incorporates multi-state models of photovoltaic and diesel generations, as well as weather factors. The IEEE-RBTS Bus 6 EDN is used to validate the proposed technique; and a practical campus EDN containing DG was also analyzed using the proposed technique.

• 대한교통학회지
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• 제28권4호
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• pp.141-156
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• 2010

본 연구에서는 기존의 차량 및 보행환경의 평가에 있어서 종합적이고 통합적인 접근방식에 한계가 있다고 진단하고, 이를 고려하는 평가 방법 개발에 중점을 두었다. 여기에서의 통합적인 평가라 함은 차량환경과 보행환경에서 쓰이고 있는 수단별 평가 지표에 대한 통합과 더불어, 공간적 개념에서 선/축의 평가방식을 포함한 면적인 차원의 평가라 할 수 있겠다. 본 연구에서는 통합 평가를 위한 이론적 배경으로 Grey System Theory(GST)를 적용하였으며, 대상지역은 보행우선구역 시범사업지로 하여 차량 및 보행환경에 대한 평가를 수행하였다. GST에 의한 종합평가 및 분석결과, 같은 서비스수준 또는 상대적으로 차량 및 보행환경의 운영지표상(도로용량편람)에서는 보다 양호한 상태라 할지라도 관측된 데이터가 Grey Category 범위에 따라 통합 평가시에는 평가지표 간 가중치 등이 고려되어 산정되었기에 Category 상에서의 종합점수는 달라지는 것을 알 수 있었다. 이러한 점을 종합적으로 고려해 볼 때, 도로운영상의 지표들이 상대적으로 좋다고 하더라도 안전상의 지표들이 나쁠 경우에는 그에 따라Grey Category상의 점수들도 변화하는 것을 알 수 있었으며, 본 평가방법을 통해 향후 도시가로 형태별로 현재 상태를 진단할 뿐 아니라 노선별(축별), 면적인 개념에서의 평가와 더불어 평가지표간 통합평가가 이루어 질 것으로 기대하고 있다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.816-819
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• 1996

This paper focuses on development of simulation model for SAE J1850 and performance evaluation of a J1850 network. The simulation model has been developed by using discrete event simulation language. SAE J1850 is one of Class B network protocol for general data sharing applications. Through numerous simulation experiments, several important performance factors such as the probability of a successful transmission, average queue delay, and throughput have been evaluated.