• 설비공학논문집
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• 제24권8호
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• pp.627-635
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• 2012

This study aims at developing an artificial neural network(ANN)-based predictive and adaptive temperature control method to control the openings at internal and external skins, and heating systems used in a building with double skin envelope. Based on the predicted indoor temperature, the control logic determined opening conditions of air inlets and outlets, and the operation of the heating systems. The optimization process of the initial ANN model was conducted to determine the optimal structure and learning methods followed by the performance tests by the comparison with the actual data measured from the existing double skin envelope. The analysis proved the prediction accuracy and the adaptability of the ANN model in terms of Root Mean Square and Mean Square Errors. The analysis results implied that the proposed ANN-based temperature control logic had potentials to be applied for the temperature control in the double skin envelope buildings.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.892-894
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• 1995

This paper deals with developmeant and application of coating thickness control system in hot dip galvanizing process. According to the line conditions, such as line speed, strip size and target coating weight, a predictive preset model sets the initial oprating conditions. Referring the zine coating informations from the gauge, mean coating value controller adjusts the chamber pressure and horizontal distance between strip and air knife, while coating deviation controller adjusts the lip gap profile of the air knife. All adaptive gains are interactively calculated by numeric models based on the theoretical analysis. The operating result with this system effectively reduces the coating deviation in transverse direction as well as in longitudinal direction.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.43.6-43
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• 2001

In this paper, we describe an actor-critic method as a kind of temporal difference (TD) algorithms. The value function is regarded as a current estimator, in which two value functions have different inputs: one is an actual experience; the other is a simulated experience obtained through a predictive model. Thus, the parameter´s updating for the actor and critic parts is based on actual and simulated experiences, where the critic is constructed by a radial-basis function neural network (RBFNN) and the actor is composed of a kinematic-based controller. As an example application of the present method, a tracking control problem for the position coordinates and azimuth of a nonholonomic mobile robot is considered. The effectiveness is illustrated by a simulation.

• 한국가스학회지
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• 제15권1호
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• pp.1-8
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• 2011

최근 석유 위주의 에너지원에서 가스나 신재생에너지와 같은 새로운 에너지원을 생산 혹은 이용하는 공정들에 대한 연구가 활발한 가운데 이러한 공정들의 상업화를 위해서는 조업 중 에너지 절감과 생산 단가의 경제성 확보가 필수적이며, 이를 위해서는 적합한 공정제어 기법의 도입이 필수적이라 할 수 있다. 본 논문에서는 지난 50여 년간 공정산업에 적용된 다양한 공정제어 기법 중 큰 기틀이 되는 몇몇 기술을 소개하고, 가스 관련 공정의 응용 예 및 앞으로 나아갈 방향에 대해 논하였다.

• KIEAE Journal
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• 제14권3호
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• pp.71-77
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• 2014

This study aimed at developing and evaluating performance of the two logics for respectively operating two-position- and variable-heating systems. Both logics control the heating system and openings of the double skin facade buildings in an integrated manner. Artificial neural network models were applied for the predictive and adaptive controls in order to optimally condition the indoor thermal environment. Numerical computer simulation methods using the MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation) were employed for the performance tests of the logics in the test module. Analysis on the test results revealed that the variable control logic provided more comfortable and stable temperature conditions with the increased comfortable period and the decreased standard deviation from the center of the comfortable range. In addition, the amount of heat supply to the indoor space was significantly reduced by the variable control logic. Thus, it can be concluded that the optimal control method using the artificial neural network model can work more effectively when it is applied to the variable heating systems.

• 대한전자공학회논문지SD
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• 제49권2호
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• pp.23-30
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• 2012

본 논문에서는 음 바이어스 온도 불안정성 (NBTI) 효과에 의해서 야기되는 파워 게이팅 구조의 성능 저하와 증가하는 기상시간을 보상하기위한 새로운 적응형 헤더기반의 파워 게이팅 구조를 제안한다. 제안된 구조는 두 개의 패스 (two-pass)를 가지는 파워 게이팅 구조에 기반을 둔 폭 변화 헤더(header)와 적응형 제어를 위한 새로운 NBTI 센싱 회로로 구성된다. 본 논문의 시뮬레이션 결과는 적응형 제어를 하지 않는 파워 게이팅의 시뮬레이션 결과와 비교되며, 그 결과는 파워 게이팅 구조에서 누설 전력과 돌입 전류(rush current)을 작게 유지하면서 회로 지연과 기상시간에 대한 NBTI 의존성이 단지 3% 와 4% 내로 줄어든다는 것을 보여준다. 본 논문에서는 45nm CMOS 공정과 NBTI 예측 모델이 제안된 회로를 구성하기 위해서 사용된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1603-1623
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• 2020

Multiple Mobile Robots System (MMRS) has shown many attractive features in lots of real-world applications that motivate their rapid and wide diffusion. In MMRS, the Cooperative Localization (CL) is the basis and premise of its high-performance task. However, the statistical characteristics of the system noise should be already known in traditional CL algorithms, which is difficult to satisfy in actual MMRS because of the numerous of disturbances form the complex external environment. So the CL accuracy will be reduced. To solve this problem, an improved Adaptive Active Cooperative Localization (A²CL) algorithm based on information optimization strategy for MMRS is proposed in this manuscript. In this manuscript, an adaptive information fusion algorithm based on the variance component estimation under Extended Kalman filter (VCEKF) method for MMRS is introduced firstly to enhance the robustness and accuracy of information fusion by estimating the covariance matrix of the system noise or observation noise in real time. Besides, to decrease the effect of observation uncertainty on CL accuracy further, an observation optimization strategy based on information theory, the Model Predictive Control (MPC) strategy, is used here to maximize the information amount from observations. And semi-physical simulation experiments were carried out to verity the A²CL algorithm's performance finally. Results proved that the presented A²CL algorithm based on information optimization strategy for MMRS cannot only enhance the CL accuracy effectively but also have good robustness.

• Journal of Computing Science and Engineering
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• 제10권3호
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• pp.75-84
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• 2016

Medical cyber-physical systems (MCPS) integrate sensors, actuators, and software to improve patient safety and quality of healthcare. These systems introduce major challenges to safety analysis because the patient's physiology is complex, nonlinear, unobservable, and uncertain. To cope with the challenge that unidentified physiological parameters may exhibit short-term variances in certain clinical scenarios, we propose a novel run-time predictive safety monitoring technique that leverages a maximal model coupled with online training of a computational virtual subject (CVS) set. The proposed monitor predicts safety-critical events at run-time using only clinically available measurements. We apply the technique to a surgical glucose control case study. Evaluation on retrospective real clinical data shows that the algorithm achieves 96% sensitivity with a low average false alarm rate of 0.5 false alarm per surgery.

• 한국정보처리학회논문지
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• 제7권4호
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• pp.1211-1216
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• 2000

ATM은 패킷 데이터 서비스 통신에 적합한 통신방식으로 데이터, 음성, 영상 등의 서비스를 동시에 지원할 수 있다. 이러한 ATM의 QoS를 보증하기 위해서는 패킷 데이터를 전송하는 소스 비율에 대해 네트워크 초과적조 조건을 조절하는 것이 필요하는데 대부분의 제어알고리즘이 임계값을 기반으로 한 피드백 제어방식이다. 그러니 실시간 음성 트레픽과 같은 서비스는 네트워크 상에서 데이터 서비스 동안에 동적인 연결이 설정되고 종료될 수 있으므로 피드벡 제어정보가 지연된다면 고속으로 서비스된 음성 데이터에 대한 품질은 소스의 목적지 사이의 시간 지연으로 인해 손실될 수 있다. 본 논문에서는 제시된 최소평균제곱오차에 근거를 둔 제어 알고리즘은 예시적인 피드백 제어로 피드백 제어를 위해 미지함수의 기울기와 버퍼크기를 이용하여 미래의 버퍼크기를 예측하려 하였으며 시뮬레이션 질과 본 논문에서 제시한 제어 알고리즘은 효과적임이 증명되었다.

• Nuclear Engineering and Technology
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• 제54권2호
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• pp.608-616
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• 2022

Power peaking factors (PPF) is an important parameter for safe and efficient reactor operation. There are several methods to calculate the PPF at TRIGA research reactors such as MCNP and TRIGLAV codes. However, these methods are time-consuming and required high specifications of a computer system. To overcome these limitations, artificial intelligence was introduced for parameter prediction. Previous studies applied the neural network method to predict the PPF, but the publications using the ANFIS method are not well developed yet. In this paper, the prediction of PPF using the ANFIS was conducted. Two input variables, control rod position, and neutron flux were collected while the PPF was calculated using TRIGLAV code as the data output. These input-output datasets were used for ANFIS model generation, training, and testing. In this study, four ANFIS model with two types of input space partitioning methods shows good predictive performances with R² values in the range of 96%-97%, reveals the strong relationship between the predicted and actual PPF values. The RMSE calculated also near zero. From this statistical analysis, it is proven that the ANFIS could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.