• Journal of Digital Convergence
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• v.12 no.1
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• pp.319-324
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• 2014

In recent years, high-performance flying drones attract attention for many peoples. In particular, the drone equipped with multi-rotor is expanding its range of utilization in video imaging, aerial rescue, logistics, monitoring, measurement, military field, etc. However, the control function of its controller is very simple. In this study, using a G-sensor mounted on a mobile device, implements an enhanced controller to control flying drones through the intuitive gesture of user. The implemented controller improves the gesture recognition performance using a neural network algorithm.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.2
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• pp.315-326
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• 2017

Four quadrotor UAVs are maneuvered to guide four animals into their pen within the minimum time by creating noises of predators modeled with an exponential function. The quadrotor UAVs are controlled via PID controllers, follow time optimal trajectories, and avoid collisions through altitude separations. The stability of the proposed PID controller is analyzed and verified using MATLAB/Simulink based simulations. Proposed step by step strategies would be practical solutions of actual cattle roundup problems.

• The Journal of Information Technology
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• v.5 no.4
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• pp.73-79
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• 2002

In this paper, I will propose the Niche-Meta Genetic Algorithm that has a multi-mutation operator for design of fuzzy controller. The gene in the proposed algorithm is formed by several parameters that represent the crossover rate, mutation rate and input-output membership functions. The optimization of fuzzy membership function is performed with local search on sub-population and the optimal structure is constructed with global search on total-population. The multi-mutation is selected under basis of the result of local evolution. A simulation for 2 D.O.F wheeled-mobile robot is showed to prove the efficiency of the proposed algorithm

• Journal of the Korean Institute of Navigation
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• v.22 no.2
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• pp.47-52
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• 1998

Recently , there have been considerable researches about the fusion of fuzzy logic and neural networks. The propose of thise researches is to combine the advantages of both. After the function of approximation using GMDP (Generalized Multi-Denderite Product)neural network for defuzzification operation of fuzzy controller, a new fuzzy-neural network is proposed. Fuzzy membership function of the proposed fuzzy-neural network can be adjusted by learning in order to be adaptive to the variations of a parameter or the external environment. To show the applicability of the proposed fuzzy-nerual network, the proposed model is applied to a speed control o fDC sevo motor. By the hardware implementation, we obtained the desriable results.

• Journal of the Semiconductor & Display Technology
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• v.2 no.3
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• pp.25-30
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• 2003

In this study, the controller using TMS320LF2407 low voltage DSP for motor control is designed and realized. It has 40 MIPS calculating ability and its driving voltage is 3.3 V for low power. The peripheral elements, however usually use 5 V and they need voltage transfer interface. In this study, voltage transformation and reducing noise are studied and space vector PWM is adopted as a motor control scheme. According to these methods, the efforts for software programming and calculation processes are reduced. In addition, the hardware is also simplified by substituting the current control part with software programming. Through this study, the DSP based servo controller increases its ability for high performance multi-function on semiconductor transportation equipments..

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.65-69
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• 1992

In this paper, an implementation of neuro-controller with an application of artificial neural network for an adjustment and tuning process for the completed electronics devices is presented. Multi-layer neural network model is employed with the learning method of error back-propagation. For the intelligent control of adjustment and tuning process, the neural network emulator (NNE) and the neural network controller(NNC) are developed. Computer simulation reveals that the intelligent controllers designed can function very effectively as tools for computer aided adjustment system. The applications of the controllers to the real systems are also demonstrated.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.135-143
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• 2003

In this study, a real-time simulation model was developed for tracked vehicles with in-arm type semi-active hydro-pneumatic suspension unit using MATLAB S-functions. Since the vehicle model uses relative coordinates and massless link elements, the developed model has an enhanced analytic time performance. Through the comparison of simulation results with multi-body software(DADS), the vehicle model is verified. A controller using on-off skyhook control algorithm is designed with the pilot-centre]led proportional valve based on conventional damper characteristics. Exploiting the developed tracked vehicle model with other subsystem model such as a controller model, a suspension unit model, and a test road model, computer simulations are carried out. Control simulation results with the developed tracked vehicle model show that the semi-active suspension control system has a better performance than the conventional suspension system.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1756-1757
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• 2007

어느 정도 규모를 가진 실험 설비를 자동화하기 위해서 분산 I/O를 지원해 주는 자동화 컨트롤러를 사용하는 것이 편리하며 이러한 용도로 PLC(Programmable Logic Controller), PAC(Programmable Automation Controller) 등을 사용하고 있다. 하지만 기능적으로 필요로 하는 I/O 이외에 전원장치, 백프레임, 그리고 메인콘트롤러 등의 항목이 추가된다. 이러한 제한은 필요로 하는 자동화 대상인 설비의 I/O의 접점수가 많지 않지만 이들을 분산하여 설치될 필요가 있을 경우 전체 시스템을 자동화 하는데 소요되는 비용을 필요 이상으로 높이게 된다. 본 연구는 현재 한국원자력연구원에서 시험 운전 중인 TS-NBI 장치의 자동화에 이용할 목적으로 자동화 대상 설비의 I/O 접점수가 많지 않지만 분산 I/O로 구성하여야 하는 경우에 적용할 해결책을 찾기 위해서 수행 하였다.

• Korean Journal of Agricultural Science
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• v.37 no.1
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• pp.123-130
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• 2010

A steering control system based on CAN(Controller Area Network) for autonomous tractor was developed to reduce duty of a central processing computer and to improve performance of steering control in terms of reduced control interval and error. The steering control system consisted of a SCU (Steering Control Unit), an EHPS system, and a potentiometer. The SCU consisted of an MCU (Micro Controller unit), an A/D converter, and a DC-DC converter, and a PID controller was used to control steering angle. The steering control system was communicated with the computer by CAN-bus. Each actuator and implement was connected to a multi-function board interfacing with the computer through a USB cable. Without CAN, control interval of the autonomous tractor was 1.5 seconds. When the CAN-based steering control system was combined with the autonomous tractor, however, control interval of the integrated system was reduced to those less than 0.05 seconds. When the autonomous tractor was operated with 1.5-s and 0.05-s control cycles at a 0.63-m/s travelling speed, the trajectories were close to straight lines for both of the control cycles. For a 1.34-m/s traveling speed, tractor trajectory was close to sine wave with a 1.5-s control cycle, but was straight line with a 0.05-s control cycle.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.265-273
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• 2016

A water wall system is one of the most important components of a boiler in a thermal power plant, and it is a nonlinear Multi-Input and Multi-Output (MIMO) system, with 6 inputs and 3 outputs. Three models are developed and comp for the controller design, including a linear model, a multilayer feed-forward neural network (MFNN) model and an Echo State Network (ESN) model. First, the linear model is developed by linearizing a given nonlinear model and is analyzed as a function of the operating point. Second, the MFNN and the ESN are developed by using training data from the nonlinear model. The three models are validated using Matlab with nonlinear input-output data that was not used during training.