• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 춘계학술대회 학술발표 논문집 제14권 제1호
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• pp.125-128
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• 2004

Q-learning은 강화학습의 한 방법으로서, 여러 분야에 널리 응용되고 있는 기법이다. 최근에는 Linear Quadratic Regulation (이하 LQR) 문제에 성공적으로 적용된 바 있다. 특히 시스템 모델의 파라미터에 대한 구체적인 정보가 없는 상태에서 적절한 입력과 출력만을 가지고, 학습을 통해 문제를 해결할 수 있어서 상황에 따라서 매우 실용적인 대안이 될 수 있다. 이에 따라 본 논문에서는 이러한 일반적인 LQR Q-learning(이하 LQRQL) 학습방법에 퍼지 모델을 이용하여 제어기를 설계하는 방법을 고려하고, 일반적인 LQROL 기법과 본 논문에서 제시한 방법의 결과를 비교하여 응용 가능성을 살펴보았다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.778_779
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• 2009

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. The paper is proposed maximum torque control of induction motor drive using adaptive learning neuro fuzzy controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d, q axis current $_i_{ds}$, $i_{qs}$ for maximum torque operation is derived. The proposed control algorithm is applied to induction motor drive system controlled adaptive learning neuro fuzzy controller and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the adaptive learning neuro fuzzy controller and ANN controller.

• 한국지능시스템학회논문지
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• 제16권1호
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• pp.1-7
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• 2006

본 논문에서는 6개의 특징점을 이용하는 가버 웨이블릿 신경망 기반 적응 표정인식 시스템을 제안한다. 특징 추출부를 포함하는 초기 네트워크의 구성은 Levenberg-Marquardt 기반의 학습방법이 사용되며, 따라서 특징 추출부 결정에 있어서 경험적 요소를 배재시킬 수 있다. 또한 새로운 사용자에 대한 적응 네트워크를 구성하기 위해서 개선된 보상함수를 가지는 Q-학습과, 비지도 퍼지 신경망 모델을 사용하였다. Q-학습을 통해서는 개인 사용자에 대해 분리도가 좋은 특징벡터를 얻을 수 있는 가버필터 세트를 얻을 수 있으며, 퍼지 신경망을 통해서는 사용자의 얼굴변화에 맞게 인식기를 변화시킬 수 있다. 따라서 제안된 시스템은 사용자의 얼굴변화를 따라갈 수 있는 좋은 적응 성능을 보이고 있다.

• 한국지능시스템학회논문지
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• 제16권1호
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• pp.79-85
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• 2006

본 논문은 인접한 두 로봇의 위치와 역할에 따라 로봇의 행동을 결정하는 퍼지 로직 중계자를 사용한 로봇 축구의 전략 및 전술을 제안한다. 기존의 Q 학습 알고리즘은 로봇의 수에 따라 상태의 수가 기하급수적으로 증가하여, 많은 연산을 필요로 하기 때문에 실시간 연산을 필요로 하는 로봇 축구 시스템에 알맞지 않다. Modular Q 학습 알고리즘은 해당 지역을 분할하는 방법으로 상태수를 줄였는데, 여기에는 로봇들 간의 협력을 위하여 따로 중재자 알고리즘이 사용되었다. 제안된 방법은 퍼지 규칙을 사용하여 로봇들 간의 협력을 위한 중재자 알고리즘을 구현하였고, 사용된 퍼지 규칙이 간단하기 때문에 계산 량이 작아 실시간 로봇 축구에 적합하다. MiroSot 시뮬레이션을 통하여 제안된 방법의 가능성을 보인다.

• 한국항해항만학회지
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• 제47권2호
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• pp.75-84
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• 2023

Ships need to ensure safety during their navigation, which makes route determination highly important. It must be accompanied by a route following controller that can accurately follow the route. This study proposes a method for automatically generating the ship route based on deep reinforcement learning algorithm and following it using a route following controller. To generate a ship route, under keel clearance was applied to secure the ship's safety and navigation chart information was used to apply ship navigation related regulations. For the experiment, a target ship with a draft of 8.23 m was designated. The target route in this study was to depart from Busan port and arrive at the pilot boarding place of the Ulsan port. As a route following controller, a velocity type fuzzy P ID controller that could compensate for the limitation of a linear controller was applied. As a result of using the deep Q network, a route with a total distance of 62.22 km and 81 waypoints was generated. To simplify the route, the Douglas-Peucker algorithm was introduced to reduce the total distance to 55.67 m and the number of way points to 3. After that, an experiment was conducted to follow the path generated by the target ship. Experiment results revealed that the velocity type fuzzy P ID controller had less overshoot and fast settling time. In addition, it had the advantage of reducing the energy loss of the ship because the change in rudder angle was smooth. This study can be used as a basic study of route automatic generation. It suggests a method of combining ship route generation with the route following control.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권3호
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• pp.264-269
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• 2006

This paper presents the strategy for searching a hidden object in an unknown area for using by multiple distributed autonomous robotic systems (DARS). To search the target in Markovian space, DARS should recognize th ε ir surrounding at where they are located and generate some rules to act upon by themselves. First of all, DARS obtain 6-distances from itself to environment by infrared sensor which are hexagonally allocated around itself. Second, it calculates 6-areas with those distances then take an action, i.e., turn and move toward where the widest space will be guaranteed. After the action is taken, the value of Q will be updated by relative formula at the state. We set up an experimental environment with five small mobile robots, obstacles, and a target object, and tried to research for a target object while navigating in a un known hallway where some obstacles were placed. In the end of this paper, we present the results of three algorithms - a random search, an area-based action making process to determine the next action of the robot and hexagon-based Q-learning to enhance the area-based action making process.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.776_777
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• 2009

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed a novel efficiency optimization control of SynRM considering iron loss using neural network(NN). The optimal current ratio between torque current and exciting current is analytically derived to drive SynRM at maximum efficiency. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism fuzzy-neural networks(ALM-FNN) controller that is implemented using fuzzy control and neural networks. The objective of the efficiency optimization control is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 추계학술대회 논문집
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• pp.359-362
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• 2009

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed a novel efficiency optimization control of SynRM considering iron loss using multi adaptive fuzzy learning controller(AFLC). The optimal current ratio between torque current and exciting current is analytically derived to drive SynRM at maximum efficiency. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization control is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• 조명전기설비학회논문지
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• 제24권5호
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• pp.44-54
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• 2010

SynRM 효율최적화 제어는 다른 교류전동기에 비해 SynRM의 효율이 낮기 때문에 에너지 절약과 환경보존의 관점에서 매우 중요하다. 본 논문에서는 다중 AFLC를 이용하여 철손을 고려한 SynRM의 새로운 효율 최적화 제어를 제안하였다. 최대효율에서 SynRM을 구동하기 위해 토크전류와 여자전류사이의 최적전류비를 분석하여 구한다. 본 논문에서는 동손과 철손을 최소로 하는 SynRM의 효율 최적화 제어를 제안하였다. 특정한 모터토크를 제공하는 d축과 q축 전류의 다양한 조합이 존재한다. 효율 최적화의 목적은 정상상태에서 최소 손실을 제공하는 d축과 q축 전류의 조합을 찾는 것이며, 제안된 제어기의 제어 성능은 다양한 동작조건의 분석을 통해 평가되었다. 분석된 결과는 제안된 알고리즘의 타당성을 입증한다.

• 정보처리학회지
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• 제11권6호
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• pp.56-75
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• 2004

In this article we address some system characteristics and challenging issues in developing Context-aware Middleware for Ubiquitous Computing. The functionalities of a Context-aware Middleware includes gathering context data from hardware/software sensors, reasoning and inferring high-level context data, and disseminating/delivering appropriate context data to interested applications/services. The Middleware should facilitate the query, aggregation, and discovery for the contexts, as well as facilities to specify their privacy policy. Following a formal context model using ontology would enable syntactic and semantic interoperability, and knowledge sharing between different domains. Moddleware should also provide different kinds of context classification mechanical as pluggable modules, including rules written in different types of logic (first order logic, description logic, temporal/spatial logic, fuzzy logic, etc.) as well as machine-learning mechanical (supervised and unsupervised classifiers). Different mechanisms have different power, expressiveness and decidability properties, and system developers can choose the appropriate mechanism that best meets the reasoning requirements of each context. And finally, to promote the context-trigger actions in application level, it is important to provide a uniform and platform-independent interface for applications to express their need for different context data without knowing how that data is acquired. The action could involve adapting to the new environment, notifying the user, communicating with another device to exchange information, or performing any other task.