• 한국정밀공학회지
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• 제14권1호
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• pp.101-107
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• 1997

Recently, as the raw water quality becomes to be polluted and the seasonal and local variation of water quality becomes to be severe, an exact control of coagulant dosing have been required in the water treat- ment plant. The amounts of coagulant is related to the raw water quality such as turbidity, alkalinity, water temperature, pH and edectrical conductivity. However the process of chemical reaction has not been clarified so far, so the dosing rate has been decided by jar-test, which is taken one or two hours. For the sake of this coagulant dosing control, fuzzy neural network to fuse fuzzy logic and neural network was proposed, and the scheme was applied to automatic determination of coagulant dosing rate. This controller can automatically identify the if-then rules and tune the membership functions by utilizing expert's cintrol data. It is shown that determination of coagulant dosing rate according to real time sensing of water quality is very effect.

• International journal of advanced smart convergence
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• 제11권4호
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• pp.10-19
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• 2022

AI-based Network Intrusion Detection Systems (AI-NIDS) detect network attacks using machine learning and deep learning models. Recently, unsupervised AI-NIDS methods are getting more attention since there is no need for labeling, which is crucial for building practical NIDS systems. This paper aims to test the impact of designing autoencoder models that can be applied to unsupervised an AI-NIDS in real network systems. We collected security events of legacy network security system and carried out an experiment. We report the results and discuss the findings.

• Water Engineering Research
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• 제1권1호
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• pp.63-74
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• 2000

This paper presents a two- phase search scheme for optimal pipe expansion of expansion of existing water distribution systems. In pipe network problems, link flows affect the total cost of the system because the link flows are not uniquely determined for various pipe diameters. The two-phase search scheme based on stochastic optimization scheme is suggested to determine the optimal link flows which make the optimal design of existing pipe network. A sample pipe network is employed to test the proposed method. Once the best tree network is obtained, the link flows are perturbed to find a near global optimum over the whole feasible region. It should be noted that in the perturbation stage the loop flows obtained form the sample existing network are employed as the initial loop flows of the proposed method. It has been also found that the relationship of cost-hydraulic gradient for pipe expansion of existing network affects the total cost of the sample network. The results show that the proposed method can yield a lower cost design than the conventional design method and that the proposed method can be efficiently used to design the pipe expansion of existing water distribution systems.

• 한국산업융합학회 논문집
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• 제14권3호
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• pp.121-125
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• 2011

EMTP-RV is the very powerful program to analyze the dynamic operation of the power system. To use this package in the large complex power system, it is very important to simplify the power system to simple equivalent network. In our study the 100 MVA STATCOM is placed at 345kV "MIGUM" which is the one of the 345kV substations of the Korean Electric Power System that is consist of more than 1000-bus. MIGUM substation is connected with 7 separated transmission lines to main Korean Electric power system. We developed a new method to simplify the network except the substation that we want to analysis. The power system outside the 345kV substation is modeled into the equivalent network. The loop network outside the substation can be modeled to simplified Thevenin equivalent network. The proposed method is applied to IEEE-14 Reliability Test System and the results shows the effectiveness of the method.

• 한국정보통신학회논문지
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• 제17권2호
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• pp.515-524
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• 2013

신뢰성 있는 소프트웨어 개발을 위해서 가장 중요한 단계 중의 하나가 소프트웨어 테스트이다. 최근에 점진적이고 반복적인 소프트웨어 개발 방법론이 각광을 받으면서, 소프트웨어의 작은 변경에 따른 회귀 테스트의 중요성이 점점 커지고 있다. 또한 센서네트워크와 같은 다수의 노드 환경에서 동작하는 소프트웨어를 검증하기 위한 시뮬레이터 환경이 필요하다. 본 논문에서는 네트워크 프레임워크와 통합시뮬레이터 간의 인터페이스 모듈을 구현하여, 네트워크 프레임워크로 구현한 센서네트워크 어플리케이션을 다양한 가상의 환경에서 단위테스트하기 위한 환경을 제공한다.

• 한국산학기술학회논문지
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• 제10권2호
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• pp.280-286
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• 2009

NGN(Next-Generation Network)의 구조는 전 IP 유무선 통합망으로서 다양한 관점에서 연구되어져왔으며 이러한 네트워크의 성능은 가장 중요한 쟁점의 하나이다. 본 연구에서는 다양한 유무선 통합망에서 음성 서비스를 제공할 때에 발생할 수 있는 문제점과 요인들을 발견하기 위하여 OPnet 시뮬레이터를 활용하여 성능분석을 수행하였다. 단일의 유선망이나 무선망과는 달리 유무선 통합망에서만 보이는 특성을 측정하고 분석하였다. 특히, 유무선 통합망에서는 네트워크의 전송이 빈번하지 않은 환경에서도 전반적인 망의 성능이 저하되는 결과를 측정하였으며 그밖에도 노드수의 증가와 전송량의 변화에 따른 망의 성능을 비교 분석하였다.

• Smart Structures and Systems
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• 제8권1호
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• pp.69-92
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• 2011

This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

• 산경연구논집
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• 제8권7호
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• pp.21-29
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• 2017

Purpose - The purpose of this study is to investigate the impact of organizational politics on employees' social network service addiction and how it influences their job satisfaction and organizational citizenship behavior. And this study explores if leader-member exchange can moderate the relationship between organizational politics and social network service addiction. Research, design, data, and methodology - For this, this study collected data from 305 employees in Korean companies through a survey method and uses SPSS 18.0 for hierarchical regression analysis in the hypothesis test. Results - First, organizational politics increases immersion, compulsion and association among the sub-factors of social network service addiction. Second, each phenomena of social network service addiction such as salience, compulsion and association decrease each relevant factors of job satisfaction and organizational citizen behavior. Third, compulsion and association among the sub-factors of social network service addiction play the mediating roles between organizational politics and each relevant factors of job satisfaction/organizational citizen behavior. Finally, some of sub-factors of leader-member exchange decrease the effect of each characteristics of organizational politics on immersion, compulsion and association among the sub-factors of social network service addiction. Conclusions - This study provides some of managerial implications to corporate executives who try to manage organizational attitude.

• 지식경영연구
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• 제16권2호
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• pp.67-89
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• 2015

Improving productivity of knowledge workers is an important issue in the 21st century referred as knowledge-based society. The core key word is knowledge sharing among constituents of an organization. The purpose of this study is to combine the social network position factors with attitude and behavior factors, and develop an integrated research model for the knowledge sharing among members of an organization. This study adopted the integrated theoretical framework based on social capital, self-efficacy, transactive memory, and knowledge sharing. Surveys were conducted to 42 organizational members from a department in a leading IT outsourcing company to empirically test the proposed research model. In order to validate the proposed research model, social network analysis tool, UCINET, a structural equation modeling tool, SmartPLS, were utilized. The empirical result showed that, first of all, organizational members' familiarity network position had significant influence on knowledge self-efficacy and transactive memory capability. Second, knowledge self-efficacy and transactive memory capability affected knowledge sharing intention. Third, knowledge sharing intention also had an impact on the job performance. However, organizational members' expertise network position had no significant influence on knowledge self-efficacy and transactive memory capability. This finding reveals the importance of the emotional approach rather than the rational approach in knowledge management. The theoretical and practical implications on the research findings were discussed along with limitations.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 춘계정기학술대회 e-Business를 위한 지능형 정보기술 / 한국지능정보시스템학회
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• pp.235-241
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• 2000

This article suggests integrated neural network models for the interest rate forecasting using change point detection. The basic concept of proposed model is to obtain intervals divided by change point, to identify them as change-point groups, and to involve them in interest rate forecasting. the proposed models consist of three stages. The first stage is to detect successive change points in interest rate dataset. The second stage is to forecast change-point group with data mining classifiers. The final stage is to forecast the desired output with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. This article is then to examine the predictability of integrated neural network models for interest rate forecasting using change-point detection.