• 로봇학회논문지
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• 제17권3호
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• pp.288-295
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• 2022

This study shows a system identification method of a balancing loading device for a stair climbing delivery robot. The balancing loading device is designed as a 2-axes gimbal structure and is interpreted as two independent pendulum structures for simplifying. The loading device's properties such as mass, moment of inertia, and position of the center of gravity are changeable for luggage. The system identification process of the loading device is required, and the controller should be optimized for the system in real-time. In this study, the system identification method is based on least squares method to estimate the unknown parameters of the loading device's dynamic equation. It estimates the unknown parameters by calculating them that minimize the error function between the real system's motion and the estimated system's motion. This study improves the accuracy of parameter estimation using a null space solution. The null space solution can produce the correct parameters by adjusting the parameter's relative sizes. The proposed system identification method is verified by the simulation to determine how close the estimated unknown parameters are to the real parameters.

• 제어로봇시스템학회논문지
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• 제17권6호
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• pp.587-595
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• 2011

In this paper, we focus on the issue of automatic selection for multi-level threshold, and we greatly improve the efficiency of Otsu's method for image segmentation based on genetic algorithm. We have investigated and evaluated the performance of the Otsu and Valley-emphasis threshold methods. Based on this observation we propose a method for automatic threshold method that segments an image into more than two regions with high performance and processing in real-time. Our paper introduced new peak detection, combines with evolution algorithm using MAGA (Modified Adaptive Genetic Algorithm) and HCA (Hill Climbing Algorithm), to find the best threshold automatically, accurately, and quickly. The experimental results show that the proposed evolutionary algorithm achieves a satisfactory segmentation effect and that the processing time can be greatly reduced when the number of thresholds increases.

• 전기전자학회논문지
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• 제22권2호
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• pp.460-475
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• 2018

In the variable-speed wind energy system, to achieve maximum power point tracking (MPPT), the wind turbine should run close to its optimal angular speed according to the wind speed. Non-linear control methods that consider the dynamic behavior of wind speed are generally used to provide maximum power and improved efficiency. In this perspective, the mechanical power is estimated using Kalman filter. And then, from the estimated mechanical power, the wind speed is estimated with Newton-Raphson method to achieve maximum power without anemometer. However, the blade shape and air density get changed with time and the generator efficiency is also degraded. This results in incorrect estimation of wind speed and MPPT. It causes not only the power loss but also incorrect wind resource assessment of site. In this paper, the adaptive maximum power point tracking control algorithm for wind turbine system based on the estimation of wind speed is proposed. The proposed method applies correction factor to wind turbine system to have accurate wind speed estimation for exact MPPT. The proposed method is validated with numerical simulations and the results show an improved performance.

• Journal of Power Electronics
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• 제16권2호
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• pp.666-674
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• 2016

In this paper, a high efficiency satellite electrical power supply system is proposed. The increased efficiency of the power supply system allows for downscaling of the solar array and battery weight, which are among the most important satellite design considerations. The satellite power supply system comprises two units, namely a generation unit and a storage unit. To increase the efficiency of the solar array, a maximum power point tracker (MPPT) is used in the power generation unit. In order to improve the MPPT performance, a novel algorithm is proposed on the basis of the hill climbing method. This method can track the main peak of the array power curve in satellites with long duration missions under unpredicted circumstances such as a part of the array being damaged or the presence of a shadow. A lithium-ion battery is utilized in the storage unit. An algorithm for calculating the optimal rate of battery charging is proposed where the battery is charged with the maximum possible efficiency considering the situation of the satellite. The proposed system is designed and manufactured. In addition, it is compared to the conventional power supply systems in similar satellites. Results show a 12% increase in the overall efficiency of the power supply system when compared to the conventional method.

• 한국경영과학회지
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• 제32권4호
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• pp.1-18
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• 2007

In this paper, we develop an adaptive genetic algorithm (aGA). The aGA has an adaptive scheme which can automatically determine the use of local search technique and adaptively regulate the rates of crossover and mutation operations during its search process. For the adaptive scheme, the ratio of degree of dispersion resulting from the various fitness values of the populations at continuous two generations is considered. For the local search technique, an improved iterative hill climbing method is used and incorporated into genetic algorithm (GA) loop. In order to demonstrate the efficiency of the aGA, i) a canonical GA without any adaptive scheme and ii) several conventional aGAs with various adaptive schemes are also presented. These algorithms, including the aGA, are tested and analyzed each other using various test problems. Numerical results by various measures of performance show that the proposed aGA outperforms the conventional algorithms.

• 한국경영과학회지
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• 제29권4호
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• pp.117-134
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• 2004

This study suggests a data driven optimization approach, which simulates the models of human learning processes from cognitive sciences. It shows how the human learning processes can be simulated and applied to solving combinatorial optimization problems. The main advantage of using this method is in applying it into problems, which are very difficult to simulate. 'Undecidable' problems are considered as best possible application areas for this suggested approach. The concept of an 'undecidable' problem is redefined. The learning models in human learning and decision-making related to combinatorial optimization in cognitive and neural sciences are designed, simulated, and implemented to solve an optimization problem. We call this approach 'SLO : simulated learning for optimization.' Two different versions of SLO have been designed: SLO with position & link matrix, and SLO with decomposition algorithm. The methods are tested for traveling salespersons problems to show how these approaches derive new solution empirically. The tests show that simulated learning for optimization produces new solutions with better performance empirically. Its performance, compared to other hill-climbing type methods, is relatively good.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.635-638
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• 2000

It is necessary to work on a vertical plane of workpiece in order to produce a large structure like a ship. These works can be automated by using the robot with permanent magnet wheels. We developed the permanent magnet wheel which can be used by a mobile robot and easily detached. We enhanced an adhesive power by restricting the occurrence direction of magnetic flow. And we also developed a method which weakens adhesive magnetic force by changing magnetic flow with metal pins. We used the load cell and the gaussmeter to measure the characteristics of the adhesive force and magnetic force. We obtained the result that the adhesive power is reduced to 1/3 of normal state by using 4 inducing pins.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.617-620
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• 1999

In this paper, a new autofocusing technique which is resistive to noise generated by the CCD of video cameras is proposed. In the proposed scheme, the frequency selective weighted median (FSWM) filter is utilized to estimate the degree of focus and the fast hill-climbing search (HCS) strategy is exploited to determine the best focused image. Since the FSWM filter can not only extract high frequency components from the image, but also eliminate impulsive noise, the proposed autofocusing method employing the FSWM criterion function can estimate the degree of focus precisely. Furthermore, the proposed real-time HCS algorithm enables the video camera to continuously focus on dynamic images. Experimental results demonstrate that the proposed technique outperforms existing techniques by enhancing the accuracy of the focus value of the video camera without the influence of noise.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 Proceedings ICPE 98 1998 International Conference on Power Electronics
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• pp.966-971
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• 1998

The studies on the photovoltaic system are extensively exhaustible and broadly available resourse as a future energy supply. In this paper, a new maximum power point tracker(MPPT) using neural network theory is proposed to improve energy conversion efficiency. The boost converter and neural network controller(NNC) were employed so that the operating point of solar cell was located at the Maximum Power Point. And the back propagation algorithm with one input layer of two inputs(E, CE) and output layer(cnntrol value) was applied to train a neural network. Simulation and experimental results show that the performance of NNC in MPPT of photovoltaic array is better than that of controller based upon the Hill Climbing Method.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2016년도 춘계학술발표대회
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• pp.858-860
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• 2016

최근 스포츠와 ICT 기술의 융합으로 스크린 스포츠라는 분야가 등장하였고, 스포츠 클라이밍도 스크린 스포츠로 구현하려는 시도가 이루어지고 있다. 이를 위해서는 실내 인공암벽에서 등반자의 모습을 추적해야 하는데, 이에 앞서 사람의 영역을 검출할 필요가 있다. 사람의 영역을 검출하는 데에는 키넥트를 이용하는데, 스켈레톤 정보와 영상 정보를 이용할 수 있다. 본 논문에서는 깊이 영상을 사용하여 등반자의 영역을 검출하는 방법을 제시한다. 실내 인공암벽 환경에서는 등반자 이외에 주변환경의 변화가 없으므로 등반자의 영역 검출을 위해 차영상을 적용한다.