• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.217-222
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• 2011

Genetic algorithms are one of the most important methods used to solve the Traveling Salesman Problem. Therefore, many researchers have tried to improve the Genetic Algorithm by using different methods and operations in order to find the optimal solution within reasonable time. This paper intends to find a new approach that adopts an improved genetic algorithm to solve the Traveling Salesman Problem, and compare with the well known heuristic method, namely, Kohonen Self-Organizing Map by using different data sets of symmetric TSP from TSPLIB. In order to improve the search process for the optimal solution, the proposed approach consists of three strategies: two separate tour segments sets, the improved crossover operator, and the improved mutation operator. The two separate tour segments sets are construction heuristic which produces tour of the first generation with low cost. The improved crossover operator finds the candidate fine tour segments in parents and preserves them for descendants. The mutation operator is an operator which can optimize a chromosome with mutation successfully by altering the mutation probability dynamically. The two improved operators can be used to avoid the premature convergence. Simulation experiments are executed to investigate the quality of the solution and convergence speed by using a representative set of test problems taken from TSPLIB. The results of a comparison between the new approach using the improved genetic algorithm and the Kohonen Self-Organizing Map show that the new approach yields better results for problems up to 200 cities.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.11
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• pp.1201-1206
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• 2014

This paper presents the development of an event-based operator model of a wheel loader for virtual V-pattern working. The objective of this study is to analyze the performance and dynamic behavior of the wheel loader for a typical V-pattern. The proposed typical V-pattern working is divided into four stages. The developed operator model is based on eight events, and the operator's inputs are occurred sequentially by event. A 3D dynamic simulation model of the wheel loader is developed and used to analyze the dynamic behavior during working, and the simulation results are compared with the experimental data of V-pattern working. The proposed 3D dynamic simulation model and operator model are constructed using MATLAB/Simulink. The proposed operator model for V-pattern working is expected to enable evaluation of the working performance and dynamic behavior of the wheel loader.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.709-717
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• 2014

The objective of this study is to analyze the degree of correspondences among corners found in building facade imagery when the projective transformation parameters are applied to. Additionally, an appropriate corner detection operator is determined through experiments. Modeling of the shape of a building has been studied in numerous approaches using various type of data such as aerial imagery, aerial lidar scanner imagery, terrestrial imagery, and terrestrial lidar imagery. This study compared the Harris operator with FAST operator and found that the Harris operator is superior in extracting major corner points. After extracting corners using the Harris operator and assessing the degree of correspondence among corners in difference images, real corresponding corners were found to be located in the closest distance. The experiment of the projective transformation with varying corners shows that more corner control points with a good distribution enhances the accuracy of the correspondences.

• Journal of Korean Society of Rural Planning
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• v.19 no.2
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• pp.173-181
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• 2013

The importance of human resources empowerment for operation and management is increasing for sustainable effects and improvement in mountain eco village development projects. This study aimed to understand the cognition characteristics of operator who works for mountain eco villages as part of the mountain village development and to suggest improvement methods in empowerment training aspects. The survey contained operator's empowerment and operator systems in mountain eco villages and the results were analyzed for the study. Operators who joined the mountain eco village operator training course by Korea Forest Service were conducted the survey on March 12th~13th in 2012 and March 13th~15th in 2013. 69 and 58 of questionnaires were collected respectively and analyzed for the study. T-test was applied to Intergroup cognition difference and regression analysis was used for influential factors in necessity of operator's role. Collected data was analyzed by statistical package programme SPSS 18.0 version. According to the comparison of empowerment cognition with contingent upon training experience, 'harmony with residents' showed significantly difference at p<0.05 level. In the recognition comparison for prospect of future mountain eco village development, 'various training experiences' was significantly difference at p<0.01 level between positive and negative prospect group. Regression analysis revealed that 'communication with village leader', 'harmony with residents', and 'idea related to the project' have an effect on necessity of operator's empowerment significantly. Based on the results, the study suggests improved directions for operator's empowerment training as a horizontal leader who conduces a mountain village.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.1
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• pp.37-48
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• 2000

In this paper, we propose a new connected operator Using morphological grayscale reconstruction for region-based coding First, an effective method of reference-image creation lis proposed, which is based on the Size as well as the contrast. This improves the performance of simplification, because It preserves perceptually important components and removes unnecessary components The conventional connected operators are good for removing small regions, but have a serious drawback for low-contrast regions that are larger than the structuring element. That is, when the conventional connected operators are applied to tills region, the simplification becomes less effective or several meaningful regions are merged to one region to avoid this, the conventional geodesic dilation is modified to propose an adaptive operator to reduce the effect of inappropriate propagation, pixels reconstructed to the original values are excluded m the dilation operation Experimental results have shown that the proposed algorithm achieves better performance In terms of the reconstruction of flat zones. The Picture quality has also been improved by about 7dB, compared to the conventional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.11 no.4
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• pp.280-286
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• 1986

A newmethod of fast mask operators for edge detection is proposed, which is based on the matrix factorization. The output of each component in the multi-directional mask operator is obtained adding every image pixels in the mask area weighting by corresponding mask element. Therefore, it is same as the result of matrix-vector multiplication like one dimensional transform, i, e, , trasnform of an image vector surrounded by mask with a transform matrix consisted of all the elements of eack mask row by row. In this paper, for the Sobel and Prewitt operators, we find the transform matrices, add up the number of operations factoring these matrices and compare the performances of the proposed method and the standard method. As a result, the number of operations with the proposed method, for Sobel and prewitt operators, without any extra storage element, are reduced by 42.85% and 50% of the standard operations, respectively and in case of an image having 100x100 pixels, the proposed Sobel operator with 301 extra storage locations can be computed by 35.93% of the standard method.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.205-216
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• 2022

One of the many tools used for distributed deep learning training is Kubeflow, which runs on Kubernetes, a container orchestration tool. TensorFlow jobs can be managed using the existing operator provided by Kubeflow. However, when considering the distributed deep learning training jobs based on the parameter server architecture, the scheduling policy used by the existing operator does not consider the task affinity of the distributed training job and does not provide the ability to dynamically allocate or release resources. This can lead to long job completion time and low resource utilization rate. Therefore, in this paper we proposes a new operator that efficiently schedules distributed deep learning training jobs to minimize the job completion time and increase resource utilization rate. We implemented the new operator by modifying the existing operator and conducted experiments to evaluate its performance. The experiment results showed that our scheduling policy improved the average job completion time reduction rate of up to 84% and average CPU utilization increase rate of up to 92%.

• Bulletin of the Korean Chemical Society
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• v.33 no.3
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• pp.1071-1074
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• 2012

• Journal of the Korean Mathematical Society
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• v.37 no.2
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• pp.309-320
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• 2000

• Journal of the Korean Mathematical Society
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• v.17 no.1
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• pp.123-129
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• 1980