• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.59
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• pp.1-10
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• 2000

The traveling salesman problem is a representative NP-Complete problem. It needs lots of time to get a solution as the number of city increase. So, we need an efficient heuristic algorithm that gets good solution in a short time. Almost edges that participate in optimal path have somewhat low value cost. This paper discusses the property of nearest neighbor and 3-opt. This paper uses nearest neighbor's property to select candidate edge. Candidate edge is a set of edge that has high probability to improve cycle path. We insert edge that is one of candidate edge into intial cycle path. As two cities are connected. It does not satisfy hamiltonian cycle's rule that every city must be visited and departed only one time. This paper uses 3-opt's method to sustain hamiltonian cycle while inserting edge into cycle path. This paper presents a highly efficient heuristic algorithm verified by numerous experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.154-162
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• 2003

In this paper, Associative Memory with Cellular Neural Networks classifies and recognizes image patterns as an operator applied to image process. CNN processes nonlinear data in real-time like neural networks, and made by cell which communicates with each other directly through its neighbor cells as the Cellular Automata does. It is applied to the optimization problem, associative memory, pattern recognition, and computer vision. Image processing with CNN is appropriate to 2-D images, because each cell which corresponds to each pixel in the image is simultaneously processed in parallel. This paper shows the method for designing the structure of associative memory based on CNN and getting output image by choosing the most appropriate weight pattern among the whole learned weight pattern memories. Each template represents weight values between cells and updates them by learning. Hebbian rule is used for learning template weights and LMS algorithm is used for classification.

• Journal of the Korea Institute of Building Construction
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• v.24 no.5
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• pp.541-552
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• 2024

This study introduces a new analytical model known as the Cellular Automaton Method(CAM) designed to predict the degree of deterioration in concrete, taking into account its complex pore structure. The CAM model assesses the impacts of moisture migration, driven by capillary action and pressure differentials at the gas-liquid interface, which are influenced by the distribution of pores. It also evaluates how porosity and diffusion coefficients affect the penetration of chloride ions. The model's application revealed distinct moisture movement patterns in concrete structures, distinguishing between those with porosity levels below and above 40 percent. Additionally, it facilitated a comparison and analysis of chloride ion diffusion phenomena, based on diffusion coefficients in areas penetrated by moisture, against results obtained from the Finite Element Method(FEM). The comparison showed a maximum deviation of only 0.989 percent between the predicted outcomes of the FEM and CAM, demonstrating substantial agreement and validating CAM's efficacy in simulating the diffusion processes of chloride ions within concrete under actual salt damage conditions. Thus, CAM proves to be a reliable tool for modeling and anticipating deterioration in concrete structures exposed to saline environments.