• 한국정보처리학회논문지
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• 제4권2호
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• pp.401-408
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• 1997

인간이 지식을 얻는 대부분의 수단은, 눈으로 사물을 보거나 귀로 소리를 들어 입력되는 패턴.영상또는 소리.을 인식하고 그것을 지식으로 축적하는 연속적인 과정이다. 그중 문자인식은 시각정보를 통하여 문제를 인식하고 나아가 의미를 이해하는 인간의 능력을 컴퓨터로 실현하려는 패턴인식의 한분야로서 신경망을 사용한 패턴인식 시스템으로 발전되고 있다. 신경망의 학습에 있어서를 출력값을 재사용하는 신경망모델로는, 순환신경망( Recurrent Neural Netwrek)이 있다. 최근 들어서 이러한 순환신경망을 오프라인 필기체 문자와 같은 정적인 패턴의 분류에 적용하려는 연구가 많이 진행되고 있다. 그러나 이러한 방법들의 대부분든 오프라인 필기체문자와 같은 정적인 패턴의 분류에 있어서는 효과적으로 적용되지 않는다. 이에 본 연구에서는 오프라인 필기체문자와 같은 정적인 패턴을 효과적으로 분르하기 위한 새로운 형태의 순환신경망을 제안한다.본논문에서는 Jordan과 Elman Model 을 확정 결합한 새로운 J-E(Jordan-Elman) 신경망 모델을 사용하여 숫자 및 필기체 문자와 같은 정적인 패턴의 인식에서 기존의 신명망보다 성능이 향상되었음을 보여 준다.

• 한국정보통신학회논문지
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• 제14권8호
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• pp.1843-1848
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• 2010

키 교환 방식은 안전한 암호 통신을 위하여 매우 중요하다. 키 교환 프로토콜은 안전성, 키 확신, 키 신선도 등의 요구사항을 만족해야 한다. 본 논문에서는 두 개의 인증된 키 교환 프로토콜로 EKE-E 와 EKE-S를 제안한다. 프로토콜들의 기본적인 생각은 암호가 단위 추가 N에 의하여 나타내어질 수 있는 것이고, 암호를 나타내는 가능한 단위 추가 N 수는 $2^N$ 이다. EKE-E는 main-in-the-middle 공격과 오프라인 사전 공격을 포함하고, 실행은 또 다른 것과 비교해서 우수하며 중요한 교환 프로토콜들의 신임도를 인증한다. EKE-S는 EKE-E에 대한 약간의 변형이다. EKE-S는 EKE-E의 공격을 보존하는 동안에 오프라인 사전 공격을 하지 못하고 암호를 습득하기 위하여 평가 실행 불가를 제공한다.

• Journal of Power Electronics
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• 제13권4호
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.616-621
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• 2003

Until now, many networked robots have been connected to the Internet for the various applications. With these networked robots, very long distance teleoperation can be possible through the Internet. However, the promising area of the Internet-based teleoperation may be distance learning, because of several reasons such as the unpredictable characteristics of the Internet. In robotics class, students learn many theories about robots, but it is hard to perform the actual experiments for all students due to the rack of the real robots and safety problems. Some classes may introduce the virtual robot simulator for students to program the virtual robot and upload their program to operate the real robot through the off-line programming method. However, the students may also visit the laboratory when they want to use the real robot for testing their program. In this paper, we developed an Internet-based robot education system. The developed system was composed of two parts, the robotics class materials and the web-based Java3d robot simulator. That is, this system can provide two services for distance learning to the students through the Internet. The robotics class materials can be provided to the student as the multimedia contents on the web page. As well, the web-based robot simulator as the real experiment tool can help the students get good understanding about certain subject. So, the students can learn the required robotics theories and perform the real experiments from their web browser when they want to study themselves at any time.

• 대한기계학회논문집A
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• 제26권6호
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• pp.1092-1098
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• 2002

This paper discusses the composition of the theory of reinforcement teaming, which is applied in real-time teaming, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line teaming method. The individuals are reduced in order to team the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It is possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because of the teaming process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 추계학술대회 논문집 학회본부
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• pp.56-59
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• 1988

The main purpose of Set-up control of hot strip mill plant is to obtain the most regular thickness. Then the learning or adaptive computer control in hot strip rolling mill has been developed. But it is very difficult to keep the inter-stands load distribution ratio uniform; so that the deviation of strip flatness is not avoidable. This leads to the degradation of quality of the products. In this report, an improved method base on the steepest descent method including the computation of optimum step size. This method is applied to the off-line simulation. In consequence, the better balances of inter-stands load distribution is achieved in addition to improvements of output thickness of hot strip mill in POSCO.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.242-244
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• 1992

Conventionally in the industrial control, PlD controller has been used because of its robustness, and nonlinear characteristic of a system under control. Although the PlD controller produce suitable parameter of the each system and also variable of PlD controller should be changed according to environment, disturbance, load. In this paper, the convergence and learning accuracy of the back-propagation(BP) method in neural network are investigated by analyzing the reason for decelerating the convergence of BP method. and examining the rapid deceleration of the convergence when the learning is executed on the part of sigmoid activation function with the very small first derivative. The modified logistic activation function it proposed by defining the convergence factor based on the analysis and applied to the position and speed control of a DC-servo motor. This paper revealed for experimental, a neural network and a PD controller combined off-line system using developed the position and speed characteristics of a DC-servo motor.

• 컴퓨터교육학회논문지
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• 제4권2호
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• pp.97-103
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• 2001

본 논문은 STS(Science-Technology-Society)학습 이론과 구성주의적 관점을 적용하여 현실 생활에서 재현과 관찰이 힘든 초등학교 4학년 자연과 화석 학습 내용을 주제로 멀티미디어를 활용한 웹 코스웨어를 구현하였다. STS이론을 적용한 학습 자료의 효율성이 검증된 연구가 다수 있다. 그러나 STS이론을 적용한 웹 코스웨어를 설계, 구현하여 그 효과에 대하여 알아보고자 하였다. 그리고 학습자 스스로 가상 공간에서 관찰 실험하고 의견교환을 통해 다양한 경험과 사고를 할 수 있도록 STS학습 웹 코스웨어를 구현하였다. 웹이 주는 기능적 환경(전자우편, 정보검색, 공유, 채팅등)을 이용하여 협동학습을 원활히 함으로써 STS학습의 효율성이 증대되는 것으로 실험 결과가 나타났다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.57-60
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• 1999

An optimal control technique designed for simultaneous tracking and quality control for batch processes. The proposed technique is designed by transforming quadratic-criterion based iterative learning control(Q-ILC) into linear quadratic control problem. For real-time quality inferential control, the quality is modeled by linear combination of control input around target qualify and then the relationship between quality and control input can be transformed into time-varying linear state space model. With this state space model, the real-time quality inferential control can be incorporated to LQ control Problem. As a consequence, both the quality variable as well as other controlled variables can progressively reduce their control error as the batch number increases while rejecting real-time disturbances, and finally reach the best achievable states dictated by a quadratic criterion even in case that there is significant model error Also the computational burden is much reduced since the most computation is calculated in off-line. The Proposed control technique is applied to a semi-batch reactor model where series-parallelreactions take place.

• 대한전기학회논문지:전력기술부문A
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• 제49권3호
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• pp.107-117
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• 2000

This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models. The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized model. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptrons, radial basis function networks, and neuro-fuzzy models without the structure learning.