• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1305-1308
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• 2003

A new technique is presented to construct a predictive model of plasma etch process. This was accomplished by combining a backpropagation neural network (BPNN) and a genetic algorithm (GA). The predictive model constructed in this way is referred to as a GA-BPNN. The GA played a role of controlling training factors simultaneously. The training factors to be optimized are the hidden neuron, training tolerance, initial weight magnitude, and two gradients of bipolar sigmoid and linear functions. Each etch response was optimized separately. The proposed scheme was evaluated with a set of experimental plasma etch data. The etch process was characterized by a $2^3$ full factorial experiment. The etch responses modeled are aluminum (A1) etch rate, silica profile angle, A1 selectivity, and dc bias. Additional test data were prepared to evaluate model appropriateness. The GA-BPNN was compared to a conventional BPNN. Compared to the BPNN, the GA-BPNN demonstrated an improvement of more than 20% for all etch responses. The improvement was significant in the case of A1 etch rate.

• 전자공학회논문지B
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• 제33B권6호
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• pp.64-72
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• 1996

In this paper, we propose predictive motion estimatin algorithm which can predict motion without additional side information considering spatio-tempral correlatio of motion vector. This method performs motion prediction of current block using correlation of the motion vector for two spatially adjacent blocks and a temporally adjacent block. Form predicted motion, the position of searhc area is determined. Then in this searhc area, we estimate motion vector of current block using block matching algoirthm. Considering spatial an temporal correlation of motion vector, the proposed method can predict motion precisely much more. Especially when the motion of objects is rapid, this method can estimate motion more precisely without reducing block size or increasing search area. Futhrmore, the proposed method has computation time the same as conventional block matching algorithm. And as it predicts motion from adjacent blocks, it does not require additional side information for adjacent block. Computer simulation results show that motion estimation of proposed method is more precise than that of conventioanl method.

• 대한의용생체공학회:의공학회지
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• 제39권2호
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• pp.69-79
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• 2018

The present study proposes an algorithm that can discriminate between normal subjects and paroxysmal atrial fibrillation (PAF) patients, which is conducted using electrocardiogram (ECG) without PAF events. For this, time-domain features and random forest classifier are used. Time-domain features are obtained from Poincare plot, Lorenz plot of ${\delta}RR$ interval, and morphology analysis. Afterward, three features are selected in total through feature selection. PAF patients and normal subjects are classified using random forest. The classification result showed that sensitivity and specificity were 81.82% and 95.24% respectively, the positive predictive value and negative predictive value were 96.43% and 76.92% respectively, and accuracy was 87.04%. The proposed algorithm had an advantage in terms of the computation requirement compared to existing algorithm, so it has suggested applicability in the more efficient prediction of PAF.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.377-382
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• 2005

This paper presents a new method for the attitude control of planar space robots. In order to control highly constrained non-linear system such as a 3D space robot, the analytical formulation for the system with complex dynamics and effective control methodology based on the formulation, are not always obtainable. In the proposed method, correspondingly, a non-analytical but effective self-organizing modeling method for controlling a highly constrained system is proposed based on a polynomial data mining algorithm. In order to control the attitude of a planar space robot, it is well known to require inputs characterized by a special pattern in time series with a non-deterministic length. In order to correspond to this type of control paradigm, we adopt the Model Predictive Control (MPC) scheme where the length of the non-deterministic horizon is determined based on implementation cost and control performance. The optimal solution to finding the size of the input pattern is found by a solving two-stage programming problem.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.933-935
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• 1999

In this paper, we present model predictive control scheme based on neural network to control discrete-time chaotic systems. We use a feedforward neural network as nonlinear prediction model. The training algorithm used is an adaptive backpropagation algorithm that tunes the connection weights. And control signal is obtained by using gradient descent (GD), some kind of LMS method. We identify that the system identification results through model prediction control have a great effect on control performance. Finally, simulation results show that the proposed control algorithm performs much better than the conventional controller.

• 전기학회논문지
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• 제56권12호
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• pp.2251-2256
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• 2007

In this paper, the wavelet transform based lossless image compression algorithm is proposed. The proposed algorithm transforms the input image using 9/7 ICFB and S+P filter, and eliminates the spacious correlation of the subband coefficients, applying the context modeling predictive technique based on the multi-resolution structure and the feedback of the prediction error. The prediction context exploits the subordination and direction property of the different level subband in the vertical, horizontal, and diagonal subband coefficients. The simulation result of the high frequency images such as PEPPERS, BOAT, and AIRPLANE shows that the proposed algorithm efficiently predicts the edge area of each multi-resolution subband.

• 제어로봇시스템학회논문지
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• 제12권12호
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• pp.1202-1208
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• 2006

Development of a model predictive control(MPC) algorithm and its application to the condensate recycle process of a cogeneration power station has been conducted. The cogeneration power station has different characteristics from other industrial processes where MPC has been dominantly applied in that the operating mode changes continuously with seasons and we Ether. Such a characteristic makes it difficulty, a linearized model was derived from mass and pressure balances and linearization. The MPC algorithm has been developed so that the controller tuning is easy with one tuning knob for each output and the constrained optimization is solved by an interior point method. Performance of the MPC algorithm has been verified with the numerically simulated process under various disturbance scenarios and mode changes.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1374-1377
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• 1993

In this paper, a predictive fuzzy control algorithm to supervise the elevator system with plural elevator cars is developed and its performance is evaluated. Elevator group controller must decide which of the cars is suitable for responding the registered hall call and allocate it to the selected car controller. In most cases, the purpose of group control is to minimize waiting time of passengers and occurrence of long wait as much as possible. The proposed algorithm ensures the efficient operations of the group cars and provides the improved level of service, coping with multiple control objects and uncertainty of system state. The feasibility of the proposed control algorithm is evaluated by graphic simulator on computer.

• 한국멀티미디어학회논문지
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• 제14권7호
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• pp.917-923
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• 2011

본 논문에서는 JPEG2000 부호화 시스템의 과도한 메모리 요구 사항을 감소시키기 위해 예측 부호화 기반의 새로운 임베디드 압축(Embedded Compression, EC) 알고리즘을 제안한다. 본 논문의 EC 기법은 EC가 적용되지 않은 DWT 프로세서와 비교하여 DWT 과정에서 발생하는 임시적인 저주파 웨이블릿 계수들의 메모리 접근 및 크기를 50 %로 줄일 수 있다. 무손실의 영상 압축 시스템에 널리 쓰이면서 단순하지만 좋은 성능을 갖는 LOCO-I(LOw COmplexity LOssless COmpression for Image)와 MAP(Median Adaptive Predictor) 예측기를 제안한 EC 알고리즘에 적용하였다. 제안한 예측 기반의 EC 알고리즘은 예측 오차 값들을 인코딩하기 위하여 포워드 적응형 양자화와 고정 길이 코드를 사용한다. 시뮬레이션 결과를 통해 예측기가 LOCO-I와 MAP인 경우, 본 논문에서 제안한 EC 알고리즘에 의한 평균적인 PSNR 저하는 각각 0.48 dB와 0.26 dB임을 알 수 있다. 선행 논문 [9]에서 제안한 하다마드 변환(MHT) 기반의 EC 알고리즘과 비교하여 평균적인 PSNR이 약 1.39 dB 향상된다.

• Nuclear Engineering and Technology
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• 제35권5호
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• pp.399-411
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• 2003

A model predictive control method is applied to design an automatic controller for thermal power control in a reactor core. The basic concept of the model predictive control is to solve an optimization problem for a finite future at current time and to implement as the current control input only the first optimal control input among the solutions of the finite time steps. At the next time step, the second optimal control input is not implemented and the procedure to solve the optimization problem is then repeated. The objectives of the proposed model predictive controller are to minimize the difference between the output and the desired output and the variation of the control rod position. The nonlinear PWR plant model (a nonlinear point kinetics equation with six delayed neutron groups and the lumped thermal-hydraulic balance equations) is used to verify the proposed controller of reactor power. And a controller design model used for designing the model predictive controller is obtained by applying a parameter estimation algorithm at an initial stage. From results of numerical simulation to check the controllability of the proposed controller at the $5\%/min$ ramp increase or decrease of a desired load and its $10\%$ step increase or decrease which are design requirements, the performances of this controller are proved to be excellent.