• Journal of Microbiology and Biotechnology
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• 제12권4호
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• pp.687-691
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• 2002

To enhance the production of flavonoids [baicalin, wogonin-7-Ο-glucuronic acid (GA)], which are secondary metabolites of Scutellaria baicalensis Georgi(G.) plant cells, a multilayer perceptron control system was applied to regulate the substrate feeding in a fed-batch cultivation. The optimal profile for the substrate feeding rate in a fed-batch culture of S. baicalensis G. was determined by simulating a kinetic model using a genetic algorithm. Process variable profiles were then prepared for the construction of a multilayer perceptron controller that included massive parallelism, trainability, and fault tolerance. An error back-propagation algorithm was applied to train the multiplayer perceptron. The experimental results showed that neurocontrol incorporated with a genetic algorithm improved the flavonoid production compared with a simple fuzzy logic control system. Furthermore, the specific production yield and flavonoid productivity also increased.

• 제어로봇시스템학회논문지
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• 제20권10호
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• pp.1036-1043
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• 2014

In this paper, a new haptic device is proposed for the teleoperation, which can recognize the invisible environment of a mobile robot. With this new device, it is possible for the user to identify the location of an obstacle and to avoid it. The haptic device has been attached on the top of a joystick so that the user can remotely control the mobile robot to avoid the obstacles which are recognized by the ultrasonic sensors. Also, the invisible environment is recognized more accurately overlapping the data from the ultrasonic sensors. There are five vibration motors in the haptic device to indicate the direction of the obstacle. So the direction of the obstacle can be recognized by the vibration at the finger on each vibration motor. For various situations and surrounding environments, experiments are performed using fuzzy controller and overlapping ultrasonic sensors. The results demonstrate the effectiveness of the proposed haptic joystick.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.432-434
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• 1998

Recently, For the material transport is increased, the AGV(Automated Guided Vehicle) is the most important part in the industrial factory. So we treat the navigation control problem and experimental results using microprocessor. In navigation control, we have faced with velocity control problem related to guide path tracking problem. Carefully, In the straight line, the AGV moves at its high speed, but in the curve line, especially when the radian ratio is very big it is difficult to follow guide line. So, Using fuzzy controller we have simulated the guide path following AGV according to the varying velocity and experimented it with microprocessor.(Intel 80C196KC) Now, If we use the AGV industrial factory, we will improve the product and efficiency in spite of changing the factory environment.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권3호
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• pp.327-336
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• 2004

In distributed autonomous mobile robot system, each robot (predator or prey) must behave by itself according to its states and environments, and if necessary, must cooperate with other robots in order to carry out a given task. Therefore it is essential that each robot have both learning and evolution ability to adapt to dynamic environment. This paper proposes a pursuing system utilizing the artificial life concept where virtual robots emulate social behaviors of animals and insects and realize their group behaviors. Each robot contains sensors to perceive other robots in several directions and decides its behavior based on the information obtained by the sensors. In this paper, a neural network is used for behavior decision controller. The input of the neural network is decided by the existence of other robots and the distance to the other robots. The output determines the directions in which the robot moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behaviors fit adequately to the goal and can express group behaviors. The validity of the system is verified through simulation. Besides, in this paper, we could have observed the robots' emergent behaviors during simulation.

• 한국공작기계학회논문집
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• 제16권6호
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• pp.71-78
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• 2007

There have been continuous efforts to automate welding processes. This automation process could be said to fall into two categories, weld seam tracking and weld quality evaluation. Recently, the attempts to achieve these two functions simultaneously are on the increase. For the study presented in this paper, a vision sensor is made, and using this, the 3 dimensional geometry of the bead is measured in real time. For the application in welding, which is the characteristic of nonlinear process, a fuzzy controller is designed. And with this, an adaptive control system is proposed which acquires the bead height and the coordinates of the point on the bead along the horizontal fillet joint, performs seam tracking with those data, and also at the same time, controls the bead geometry to a uniform shape. A communication system, which enables the communication with the industrial robot, is designed to control the bead geometry and to track the weld seam. Experiments are made with varied offset angles from the pre-taught weld path, and they showed the adaptive system works favorable results.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 춘계종합학술대회
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• pp.295-299
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• 2005

본 연구는 개선된 거리변환 알고리즘을 사용하여 로봇이 이동해야 할 경로를 생성하고, 퍼지 시스템을 사용하여 경로추적 행위기와 장애물 회피 행위기를 구현해서 로봇이 그 경로를 안전하고 효율적으로 추적하는 것을 목적으로 하였다. 개선된 거리변환 알고리즘을 이용하여 로봇의 유연한 주행을 가능하게 했으며, 또한 주행거리와 주행시간도 단축되는 것을 확인할 수 있었다. 경로생성 후 경로를 추적할 때는 퍼지 시스템을 이용한 경로추적 행위기와 장애물 회피 행위기를 선택적으로 사용해서 경로추적 중에 예상치 못한 장애물이 발견되었을 경우에도 장애물을 회피한 후에 계속해서 경로를 추적하는 것을 확인함으로써 경로생성 후의 외부환경변화에도 강인함을 볼 수 있었다.

• 조명전기설비학회논문지
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• 제27권9호
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• pp.74-82
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• 2013

This paper presents a novel maximum power point tracking for a photovoltaic power (PV) system with a direct control plan. Maximum power point tracking (MPPT) must usually be integrated with photovoltaic (PV) power systems so that the photovoltaic arrays are able to deliver maximum available power. The maximum available power is tracked using specialized algorithms such as Perturb and Observe (P&O) and incremental Conductance (indCond) methods. The proposed method has the direct control of the MPPT algorithm to change the duty cycle of a dc-dc converter. The main difference of the proposed system to existing MPPT systems includes elimination of the proportional-integral control loop and investigation of the effect of simplifying the control circuit. The proposed method thus has not only faster dynamic performance but also high tracking accuracy. Without a conventional controller, this method can control the dc-dc converter. A simulation model and the direct control of MPPT algorithm for the PV power system are developed by Matlab/Simulink, SimPowerSystems and Matlab/Stateflow.

• Structural Engineering and Mechanics
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• 제81권5호
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• pp.633-645
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• 2022

The active tuned mass damper (ATMD) is an efficient and reliable structural control system for mitigating the dynamic response of structures. The inertial force that an ATMD exerts on a structure to attenuate its otherwise large kinetic energy and undesirable vibrations and displacements is proportional to its excursion. Achieving a balance between the inertial force and excursion requires a control law or feedback mechanism. This study presents a technique for the optimum design of a sliding mode controller (SMC) as the control law for ATMD-equipped structures subjected to earthquakes. The technique includes optimizing an SMC under an artificial earthquake followed by testing its performance under real earthquakes. The SMC of a real 11-story shear building is optimized to demonstrate the technique, and its performance in mitigating the displacements of the building under benchmark near- and far-fault earthquakes is compared against that of a few other techniques (proportional-integral-derivative [PID], linear-quadratic regulator [LQR], and fuzzy logic control [FLC]). Results indicate that the optimum SMC outperforms PID and LQR and exhibits performance comparable to that of FLC in reducing displacements.

• Earthquakes and Structures
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• 제24권1호
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• pp.1-9
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• 2023

A model for calculating structure interacted mechanics is proposed. A structural interaction model and controller design based on tuned mass damping (TMD) was developed to control the induced vibration. A key point is to introduce a new analytical model to evaluate the properties of the TMD that recognizes the motion-dependent nonlinear response observed in the simulations. Aiming at the problem of increased current harmonics and low efficiency of permanent magnet synchronous motors for electric vehicles due to dead time effect, a dead time compensation method based on neural network filter and current polarity detection is proposed. Firstly, the DC components and the higher harmonic components of the motor currents are obtained by virtue of what the neural network filters and the extracted harmonic currents are adjusted to the required compensation voltages by virtue of what the neural network filters. Then, the extracted DC components are used for current polarity dead time compensation control to avert the false compensation when currents approach zero. The neural network filter method extracts the required compensation voltages from the speed component and the current polarity detection compensation method obtains the required compensation voltages by discriminating the current polarity. The combination of the two methods can more precisely compensate the dead time effect of the control system to improve the control performance. Furthermore, based on the relaxed method, the intelligent approach of stability criterion can be regulated appropriately and the artificial TMD was found to be effective in reducing cross-wind vibrations.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.531-534
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• 2022

승차 공유, 카풀, 렌터카의 이용률이 증가하면서 많은 사용자가 동일한 차량에 로컬 액세스 할 수 있는 시나리오가 더욱 보편화됨에 따라 차량 네트워크에 대한 공격 가능성이 커지고 있다. 차량용 CAN Bus Network에 대한 DoS(Denial of Service), Fuzzy Attack 및 Replay Attack과 같은 공격은 일부 ECU(Electronic Controller Unit) 비활성 및 작동 불능 상태를 유발한다. 에어백, 제동 시스템과 같은 필수 시스템이 작동 불가 상태가 되어 운전자에게 치명적인 결과를 초래할 수 있다. 차량 네트워크 침입 탐지를 위하여 많은 연구가 진행되고 있으나, 기존 화이트리스트를 이용한 탐지 방법은 새로운 유형의 공격이 발생하거나 희소성이 높은 공격일 때 탐지하기 어렵다. 본 논문에서는 인공신경망 기반의 CAN 버스 네트워크 침입 탐지 기법을 제안한다. 제안하는 침입 탐지 기법은 2단계로 나누어 진다. 1단계에서 정상 패킷 분포를 학습한 VAE 모형이 이상 탐지를 수행한다. 이상 패킷으로 판정될 경우, 2단계에서 인코더로부터 추출된 잠재변수와 VAE의 재구성 오차를 이용하여 공격 유형을 분류한다. 분류 결과의 신뢰점수(Confidence score)가 임계치보다 낮을 경우 학습하지 않은 공격으로 판단한다. 본 연구 결과물은 정보보호 연구·개발 데이터 첼린지 2019 대회의 차량 이상징후 탐지 트랙에서 제공하는 정상 및 3종의 차량 공격시도 패킷 데이터를 대상으로 성능을 평가하였다. 실험을 통해 자동차 제조사의 규칙이나 정책을 사전에 정의하지 않더라도 낮은 오탐율로 비정상 패킷을 탐지해 낼 수 있음을 확인할 수 있다.