• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1122-1133
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• 2019

We have designed and implemented a new data processing framework called MOHA(Mtc On HAdoop) which can effectively support Many-Task Computing(MTC) applications in a YARN-based Hadoop platform. MTC applications can be composed of a very large number of computational tasks ranging from hundreds of thousands to millions of tasks, and each MTC application may have different resource usage patterns. Therefore, we have implemented MOHA-TaskExecutor(a pilot-job that executes real MTC application tasks)'s Adaptive Parallel Computability which can adaptively execute multiple tasks simultaneously, in order to improve the parallel computability of a YARN container and the overall system throughput. We have implemented multi-threaded version of TaskExecutor which can "independently and dynamically" adjust the number of concurrently running tasks, and in order to find the optimal number of concurrent tasks, we have employed Hill-Climbing algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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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.

• 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.

• 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.

• Proceedings of the KIPE Conference
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• 1998.10a
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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.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1141-1149
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• 2000

This paper proposes new cost error measurement method and analyzes the optimistic and pessimistic cost errors statistically which is resulted from an asynchronous parallel Simulated annealing (SA) in distributed memory multicomputers. The traditional cost error measurement scheme has inherent problems which are corrected in the new method. At each temperature the new method predicts the amount of cost error that an algorithm will tolerate and still converge by the hill-climbing nature of SA. This method also explains three interesting phenomenon of he cost error analytically. So the new cost error measurement method provides a single mechanism for the occurrence of cost error and its control.

• International Journal of CAD/CAM
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• v.9 no.1
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• pp.31-36
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• 2010

3D point data acquired from laser scan or stereo vision can be quite noisy. A preprocessing step is often needed before a surface reconstruction algorithm can be applied. In this paper, we propose a nonparametric approach for noisy point data preprocessing. In particular, we proposed an anisotropic kernel based nonparametric density estimation method for outlier removal, and a hill-climbing line search approach for projecting data points onto the real surface boundary. Our approach is simple, robust and efficient. We demonstrate our method on both real and synthetic point datasets.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.40-45
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• 2015

Online social networks(OSN) are very popular nowadays. As OSNs grows, the commercial markets are expanding their social commerce by applying Influence Maximization. However, in reality, there exist more than two players(e.g., commercial companies or service providers) in this same market sector. To address the Influence Maximization problem between adversaries, we first introduced Influence Maximization against the social adversaries' problem. Then, we proposed an algorithm that could efficiently solve the problem efficiently by utilizing social network properties such as Betweenness Centrality, Clustering Coefficient, Local Bridge and Ties and Triadic Closure. Moreover, our algorithm performed orders of magnitudes better than the existing Greedy hill climbing algorithm.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2146-2156
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• 2017

When a failure occurs in active distribution system, it will be isolated through the action of circuit breakers and sectionalizing switches. As a result, the network might be divided into several connected components, in which distributed generations could supply power for customers. Aimed at decreasing customer interruption cost, this paper proposes a theoretically optimal island partition model for such connected components, and a simplified but more practical model is also derived. The model aims to calculate a dynamic island partition schedule during the failure recovery time period, instead of a static islanding status. Fluctuation and stochastic characteristics of the renewable distributed generations and loads are considered, and the interruption cost functions of the loads are fitted. To solve the optimization model, a heuristic search algorithm based on the hill climbing method is proposed. The effectiveness of the proposed model and algorithm is evaluated by comparing with an existing static island partitioning model and intelligent algorithms, respectively.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.10
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• pp.1029-1037
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• 2009

Recently, the technologies of mobile robots have been growing rapidly in the fields such as cleaning robot, explosive ordnance disposal robot, patrol robot, etc. However, the researches about the autonomous underwater robots have not been done so much, and they still remain at the low level of technology. This paper describes a model of 3-joint (4 links) fish robot type. Then we calculate the dynamic motion equation of this fish robot and use Singular Value Decomposition (SVD) method to reduce the divergence of fish robot's motion when it operates in the underwater environment. And also, we analysis response characteristic of fish robot according to the parameters of input torque function and compare characteristic of fish robot with 3 joint and fish robot with 2 joint. Next, fish robot's maximum velocity is optimized by using the combination of Hill Climbing Algorithm (HCA) and Genetic Algorithm (GA). HCA is used to generate the good initial population for GA and then use GA is used to find the optimal parameters set that give maximum propulsion power in order to make fish robot swim at the fastest velocity.