• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.33-39
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• 2019

In this paper, we propose a parametric model for analyzing the motion information obtained from the acceleration sensors to measure the activity of the human body. The motion of the upper body and the lower body does not occur at the same time, and the motion analysis method using a single motion sensor involves a lot of errors. In this study, the 3-axis accelerometer is attached to the arms and legs, the body's activity data are measured, the momentum of the arms and legs are calculated for each channel, and the linear predictive coefficient is obtained for each channel. The periodicity of the upper body and the lower body is determined by analyzing the correlation between the channels. The linear predictive coefficient and the periodic value are used as data to measure the type of exercise and the amount of exercise. In the proposed method, we measured four types of movements such as walking, stair climbing, slow hill climbing, and fast hill descending. In order to verify the usefulness of the parameters, the recognition results are presented using the linear predictive coefficient and the periodic value for each motion as the neural network input.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.23-34
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• 2023

The purpose of this paper is to review global stochastic optimization algorithms(GSOA) in case binary response experimentation is used and to compare the performances of them. GSOAs utilise estimator of probability of success $\^p$ instead of population probability of success p, since p is unknown and only known by its estimator which has stochastic characteristics. Hill climbing algorithm algorithm, simple random search, random search with random restart, random optimization, simulated annealing and particle swarm algorithm as a population based algorithm are considered as global stochastic optimization algorithms. For the purpose of comparing the algorithms, two types of test functions(one is simple uni-modal the other is complex multi-modal) are proposed and Monte Carlo simulation study is done to measure the performances of the algorithms. All algorithms show similar performances for simple test function. Less greedy algorithms such as Random optimization with Random Restart and Simulated Annealing, Particle Swarm Optimization(PSO) based on population show much better performances for complex multi-modal function.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.2
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• pp.44-51
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• 2014

This paper describes system definition of the extended range EV and presents cruising strategy of EV mode and ER mode. Also high voltage battery strategic SOC could be indicated and compared depends on various generator working cycles. A C-segment SUV has been produced and carried out cruising test in order to validate on highway, city and hill climbing road. This paper shows advantages and disadvantages of SOC variation on each road environments and presents the strategies as the cruising test results. On the basis of the test result, this paper suggests future works and research directions for strategy of battery management to extended range EV.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.256-262
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• 2021

In order for small electric vehicles to drive on hilly roads in Korea, methods to improve the climbing ability and power performance of vehicles should be taken. In order to improve the power performance of small electric vehicles, the performance of motors mounted on electric vehicles should be improved. However, if the performance of the motor is improved to improve the power performance of the electric vehicle, it is possible to lower the price competitiveness accordingly. In addition, the power consumption of the battery is rapidly increased to drive the high-performance motor, so in order to introduce the small electric vehicle into the domestic market, various problems must be overcome. In order to commercialize small electric vehicles that do not emit harmful exhaust gases to the human body in the hilly domestic terrain, it is effective to introduce a separate continuously variable transmission system that can improve the climbing ability and power transmission ability. In this study, we propose a proprietary model of continuously variable transmissions that can be applied to small electric vehicles. The proposed continuously variable transmission is equipped with a spring in the driving pulley and the driven pulley, and has the advantage of performing a shift that increases torque in a situation where the vehicle needs to increase torque when driving on a hill. In addition, the basic design for commercialization of the proposed continuously variable transmission was carried out, and the prototype manufactured and attached to the body of a small electric vehicle.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.617-621
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• 2001

This paper presents a maximum power point tracking algorithm for Photovoltaic array using only instantaneous output current information. The conventional Hill climbing method of peak power tracking has a disadvantage of oscillations about the maximum power point. To overcome this problem, we have developed a algorithm, that will estimate the duty ratio corresponding to maximum power operation of solar cell. The estimation of the optimal duty ratio involves, finding the duty ratio at which integral value of output current is maximum. For the estimation, we have used the well know Lagrange's interpolation method. This method can track maximum power point quickly even for changing solar insolations and avoids oscillations after reaching the maximum power point.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.4
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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.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.48
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• pp.299-310
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• 1998

Actually mixed assembly line is mixed with open and close type workstation. This workstation is called hybridworkstation. The propose of this paper is to determine the sequencing of model that minimize line length for actual(hybridworkstation) mixed model assembly line. we developed three mathematical formulation of the problem to minimize the overall length of a line with hybrid station. Mathematical formulation classified model by operato schedule. Mixed model assembly line is combination program and NP-hard program. Thus computation time is often a critical factor in choosing a method of determining the sequence. This study suggests a tabu search technique which can provide a near optimal solution in real time and use the hill climbing heuristic method for selecting initial solution. Modified tabu search method is compared with MIP(Mixed Integer Program). Numerical results are reported to demonstrate the efficiency of the method.

• Proceedings of the IEEK Conference
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• 1999.11a
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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.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.356-361
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• 1990

This paper presents a new self organizing fuzzy linguistic control (SOFLC) strategy for application to an arc welding process control. The proposed SOFLC is based on on-line modification of the control rules according to the extent of deviation of the one step ahead predictive output of the process from the desired output. The Predictive output of the process is estimated by a fuzzy predictor which is updated from the input and output data of the process. The rule base of the fuzzy subsets describing the control rules is modified by the improving mechanism based on the hill climbing approach. Simulation results show that this proposed SOFLC improves the response of the process in presence of the variation of the process dynamic characteristics.