• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.433-440
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• 1998

The purpose of this study is to provide an integrated process control system for plasma etching. The control system is designed to employ neural network for the modeling of plasma etching process and to utilize genetic algorithm to search for the appropriate selection of control input variables, and to provide a control chart to maintain the process output within a desired range in the real plasma etching process. The target equipment is the one operating in DRAM production lines. The result shows that the integrated system developed is practical value in the improved performance of plasma etching process.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.108-110
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• 2006

A prediction model of charge concentration of silicon nitride (SiN) thin films was constructed by using neural network and genetic algorithm. SIN films were deposited by plasma enhanced chemical vapor deposition and the deposition process was characterized by means of $2^{6-1}$ fractional factorial experiment. Effect of five training factors on the model prediction performance was optimized by using genetic algorithm. This was examined as a function of the learring rate. The root mean squared error of optimized model was 0.975, which is much smaller than statistical regression model by about 45%. The constructed model can facilitate a Qualitative analysis of parameter effects on the charge concentration.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.675-678
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• 1997

In this research we continue the study of nuclear power plant steam generator's intelligent modeling. This model represents the input-output behavior and is a preliminary stage for intelligent control. Among many intelligent models available, we study neural network models that have been proven as universal function approximators. We select multilayer perceptrons, circular backpropagation networks, piecewise linearly trained networks and recurrent neural networks as the candidates for the steam generator's intelligent models. We take the input-output pairs from steam generator's reference model and train the neural network models. We validate trained neural network models as intelligent models of steam generator.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.584-587
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• 1997

In this paper, we present a transmission constraints modeling of carrier relay signal over ATM network. Since teleprotection system, which is used for protecting power transmission lines using telecommunications, has strict transmission delay constraints, it is a important problem to transmit teleprotection signals in future utilities' ATM networks when utilites' communications are integrated. Therefore, we considered the transmission constraints of carrier relay signal over ATM network and the transmission model system.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11b
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• pp.1333-1336
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• 2003

최근의 인터넷은 기하급수적으로 폭증하는 트래픽을 원활히 수용하기 위하여 광인터넷으로 급속히 전환되고 있다. 이에 따라 새롭게 제시되는 네트워크 구조를 기반으로 네트워크 설계 단계에서 구축단계까지 새로운 운용관리 체계를 필요로 한다. 광인터넷 시뮬레이션 도구는 이러한 요구사항을 효과적으로 지원할 수 있는 도구로서 네트워크 요소시스템의 기능성 검증 및 네트워크 운용성 검증 등을 제공할 수 있다. 한편 광인터넷 시뮬레이션 도구에 있어서 광인터넷의 표현 및 모델링은 주요한 요구사항 중의 하나이며 이는 DML(Domain Modeling Language) 기반의 네트워크 모델링 컴포넌트를 통하여 구성될 수 있다. DML 은 간단하면서 고급의 모델 정의 기능을 제공하고 인터넷과 같은 대규모 모델로의 확장이 용이하며 이종 엔진과의 시뮬레이션 모델교환 형식으로도 사용이 가능하므로, 광인터넷 시뮬레이션 도구에서 시뮬레이션 모델의 표현에 매우 적합하다. 따라서 이 논문에서는 광인터넷 시뮬레이션 도구에 적용 가능한 DML 기반 네트워크 모델링 컴포넌트를 설계한다.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.2
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• pp.85-93
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• 2003

Generally, it is very difficult to get a modeling equation because multi-variable system has coupling relations between its inputs and outputs. To design an optimal controller without the modeling equation, this paper proposes a neural-network (NN) controller being learned by Jacobian matrix. Another major characteristic is that the controller consists of two separated NN controllers, namely, proportional control part and derivative control part. Simulation results for a catamaran system show that the proposed NN controller is superior to LQR in the regulation and tracking problems.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.363-370
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• 2002

A substrate network model characterizing substrate effect of submicron MOS transistors for RF operation and its parameter extraction with physically meaningful values are presented. The proposed substrate network model includes a single resistance and inductance originated from ring-type substrate contacts around active devices. Model parameters are extracted from S-parameter data measured from common-bulk configured MOS transistors with floating gate and use where needed with out any optimization. The proposed modeling technique has been applied to various-sized MOS transistors. Excellent agreement the measurement data and the simulation results using extracted substrate network model up to 30㎓

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.2
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• pp.105-127
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• 2003

In this paper, four different data mining techniques, two neural networks and two statistical modeling techniques, are compared in terms of prediction accuracy in the context of bankruptcy prediction. In business setting, how to accurately detect the condition of a firm has been an important event in the literature. In neural networks, Backpropagation (BP) network and the Kohonen self-organizing feature map, are selected and compared each other while in statistical modeling techniques, discriminant analysis and logistic regression are also performed to provide performance benchmarks for the neural network experiment. The findings suggest that the BP network is a better choice among the data mining tools compared. This paper also identified some distinctive characteristics of Kohonen self-organizing feature map.

• Journal of Information Science Theory and Practice
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• v.2 no.2
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• pp.16-32
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• 2014

As Topic Modeling has been applied in increasingly various domains, the difficulty in naming and characterizing topics also has been recognized more. This study, therefore, explores an approach of combining text mining with network analysis in a multi-step approach. The concept of synchronization was applied to re-assign the top author keywords in more than one topic category, in order to improve the visibility of the topic-author keyword network, and to increase the topical cohesion in each topic. The suggested approach was applied using 16,548 articles with 2,881 unique author keywords in construction and building engineering indexed by KSCI. As a result, it was revealed that the combined approach could improve both the visibility of the topic-author keyword map and topical cohesion in most of the detected topic categories. There should be more cases of applying the approach in various domains for generalization and advancement of the approach. Also, more sophisticated evaluation methods should also be necessary to develop the suggested approach.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.537-539
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• 1998

In this paper, we construct the on-line model structure for the nonlinear process systems using the adaptive fuzzy-neural network. Adaptive fuzzy-neural network usually consists of two distinct modifiable structure, with both, the premise and the consequent part. These two parts can be adapted by different optimization methods, which are the hybrid learning procedure combining gradient descent method and least square method. To achieve the on-line model structure, we use the recursive least square method for the consequent parameter identification of nonlinear process. We design the interface between PLC and main computer, and construct the monitoring and control simulator for the nonlinear process. The proposed on-line modeling to real process is carried out to obtain the effective and accurate results.