• Environmental Engineering Research
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• 제14권2호
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• pp.102-110
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• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

• 대한산업공학회지
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• 제20권4호
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• pp.113-134
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• 1994

A radial basis hybrid neural network (RHNN) is presented for an on-line detection of machine condition change. Two-phase modeling by RHNN is designed for describing a machine condition process and for predicting future signal. A moving block procedure is also designed for detecting a process change. A fast on-line learning algorithm, the recursive least square estimation, is introduced. Experimental results showed the RHNN could be utilized efficiently for on-line machine condition monitoring.

• Structural Engineering and Mechanics
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• 제45권1호
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• pp.53-67
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• 2013

This study aimed to develop a model to accurately predict the acceleration of structural systems during an earthquake. The acceleration and applied force of a structure were measured at current time step and the velocity and displacement were estimated through linear integration. These data were used as input to predict the structural acceleration at next time step. The computation tool used was the Volterra/Wiener neural network (VWNN) which contained the mathematical model to predict the acceleration. For alleviating problems of relatively large-dimensional and nonlinear systems, the VWNN model was utilized as the signal processing tool, including the Taylor series components in the input nodes of the neural network. The number of the intermediate layer nodes in the neural network model, containing the training and simulation stage, was evaluated and optimized. Discussions on the influences of the gradient descent with adaptive learning rate algorithm and the Levenberg-Marquardt algorithm, both for determining the network weights, on prediction errors were provided. During the simulation stage, different earthquake excitations were tested with the optimized settings acquired from the training stage to find out which of the algorithms would result in the smallest error, to determine a proper simulation model.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.113.1-113
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• 2001

The robust position control with the bound function of neural network structure is proposed for uncertain robot manipulators. The neural network structure presents the bound function and does not need the concave property of the bound function, The robust approach is to solve this problem as uncertainties are included in a model and the controller can achieve the desired properties in spite of the imperfect modeling. Simulation is performed to validate this law for four-axis SCARA type robot manipulators.

• Structural Engineering and Mechanics
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• 제16권6호
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• pp.643-654
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• 2003

Two real-time modeling prediction (RMP) schemes are presented in this paper for analyzing the behavior of deep excavations during construction. The first RMP scheme is developed from the traditional AR(p) model. The second is based on the simplified Elman-style recurrent neural networks. An on-line learning algorithm is introduced to describe the dynamic behavior of deep excavations. As a case study, in-situ measurements of an excavation were recorded and the measured data were used to verify the reliability of the two schemes. They proved to be both effective and convenient for predicting the behavior of deep excavations during construction. It is shown through the case study that the RMP scheme based on the neural network is more accurate than that based on the traditional AR(p) model.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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• pp.172-175
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• 2004

Generally, the yield rate of semiconductors is the major factor that affects directly the price of semiconductors. For a high yield rate of semiconductors, the air inside clean room is needed to be purified and high efficient filters are used for this. The filter are made of super-fine fiber and certain pinholes can be easily produced on the filter's surface by inadvertent manufacturing. As these pinholes are not easily detected with the bare sight, these pinholes exert a negative impact to filtration performance of the filter. In this research, not only the automatic test equipment for detecting pinholes is proposed, but also inverse modeling scheme based on artificial neural network is applied for tuning of its important parameters.

• 치위생과학회지
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• 제9권2호
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• pp.181-187
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• 2009

본 연구는 구조화된 설문을 통하여 구강건강 유지 및 증진을 위해 구강건강실태를 조사 분석하여 치통을 일으키는 요인을 기반으로 치통예측모형을 개발하였다(n=110). 1. 연구대상자는 총 110명 남성 27명, 여성 83명이었다. 신장 평균은 남성 172.59cm, 여성 161.95cm, 체중 평균은 남성 64.33kg, 여성 53.81kg이었다. BMI (Body Mass Index)는 남성이 $21.58{\pm}1.84$, 여성 $20.51{\pm}2.00$으로 성별에 따라 유의하였다(p=0.004). 2. 식습관0 조사 결과 선호하는 맞은 남성 51.8%가 짠맛을 선호하는 반면, 여성 62.7%는 보통(중간)의 맛을 선호하는 것으로 나타나 성별에 따른 차이가 나타났다(p=0.009). 본인의 식사가 균형이 있는지 인식을 조사한 결과 남성 76.9%는 '그렇다'라고 한 반면, 여성 49.4%만 균형 있는 식사를 하고 있다고 하여 성별에 따른 차이가 있었다(p = 0.011). 3. 운동 및 기호에 대한 조사결과 일주일 동안 운동 시간에 대한 결과 남성 55.6%, 여성 55.5%가 주당 4시간미만 운동하는 것으로 나타났다. 지금 운동의 적절성에 대한 인식 결과 남성 82.6%, 여성 66.7%가 적정한 운동이라고 답하였다. 흡연은 남성 77.8%, 여성 100%가 전혀 흡연을 한 경험이 없는 것으로 나타났다(p < 0.001). 4. 구강 건강 습관 조사결과 조사시점에 치통 유무는 남성 11.5%, 여성 20.7%가 있는 것으로 나타났다. 칫솔질 교육은 남성 55.6%, 여성 69.9%가 받았다고 하였다. 하루 3번 칫솔질하는 횟수는 남성 50.0%, 여성 66.3%로 나타났다. 5. 잇몸수술 경험은 없으며, 칫솔횟수는 하루 4회하며 균형 있는 식습관을 하고 있으며, 약간 단맛을 선호하는 사람이 치통을 더 느끼는 것으로 나타났다. 6. 치통 예측 모델링에 대한 결과 신경망 모델을 사용한 상대적 중요도가 높은 독립변수는 선호 맛, 스트레스 합, 흡연 유무, 잇몸수술, BMI, 균형 있는 식사 인식, 나이였으며, 치통발생 모형의 정확도는 88.75%이었다.

• 제어로봇시스템학회논문지
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• 제6권2호
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• pp.127-137
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• 2000

This paper presents a novel neural based controller which controls the water level of the nuclear power plant steam generator. The controller consists of a model reference feedback linearization controller and a PI controller for stabilizing the feedback linearization controller. The feedback linearization controller consists of a neural network model and an inversing module which uses the neural network model for computing the control input to the steam generator. We chose Piecewise Linearly Trained Network(PLTN) and Recurrent Neural Netwrok(RNN) for an approximator of the plant and used these approximators in calculating the input from the feedback linearization controller. Combining the above two controllers gives a result of better performance than the case which uses only a PI controller Each control result of PLTN and RNN is given.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.27-34
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• 1996

GENIE is a learning-based engine for building intelligent systems. Learning in GENIE proceeds by incrementally modeling its human or technical environment using a neural network and a genetic algorithm. The neural network is used to represent the knowledge for solving a given task and has the ability to grow its structure. The genetic algorithm provides the neural network with training examples by actively exploring the example space of the problem. Integrated into the training examples by actively exploring the example space of the problem. Integrated into the GENIE system architecture, the genetic algorithm and the neural network build a virtually self-teaching autonomous learning system. This paper describes the structure of GENIE and its learning components. The performance is demonstrated on a robot learning problem. We also discuss the lessons learned from experiments with GENIE and point out further possibilities of effectively hybridizing genetic algorithms with neural networks and other softcomputing techniques.

• 한국농공학회논문집
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• 제49권2호
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• pp.27-36
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• 2007

This study, the present study wishes to develop a model that estimates shear strength characteristics of steel fiber reinforced concrete using artifical neural network models. Neural network models, developed as mathematical models, are being widely used not only in its original purpose of pattern recognition, but also in application fields by the function's nonlinear loaming and interpolar ability Neural network has a repetitive rotation algorithm that can cyclically and repeatedly estimate system conditions and parameter ideal values, and it can be used in the modeling of the nonlinear system by nonlinear characteristic functions that construct the system.