• Korean Journal of Cognitive Science
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• v.7 no.4
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• pp.141-162
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• 1996

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.1
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• pp.55-65
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• 2021

In this paper, a methodology for reinforcing reverse logistics (RL) activities in a closed-loop supply chain (CLSC) model is proposed. For the methodology, the activities of the recovery center (RC) which can be considered as one of the facilities in the RL are reinforced. By the reinforced activities in the RC, the recovered parts and products after checking and recovering processes of the returned product from customer can be reused in the forward logistics (FL) of the CLSC model. A mathematical formulation is suggested for representing the CLSC model with reinforced RL activities, and implemented using a hybrid genetic algorithm (HGA) approach. In numerical experiment, two different scales of the CLSC model are presented and the performance of the HGA approach is compared with those of some conventional approaches. The experimental results show that the former outperforms the latter in most of performance measures. The robustness of the CLSC model is also proved by regulating various rates of the recovered parts and products in the RC.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.7
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• pp.51-57
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• 2012

This paper proposes a group key design based on ECDH(Elliptic Curve Diffie Hellman) which guarantees secure V2V and V2I communication. The group key based on ECDH generates the VGK(Vehicular Group key) which is a group key between vehicles, the GGK(Global Group Key) which is a group key between vehicle groups, and the VRGK(Vehicular and RSU Group key) which is a group key between vehicle and RSUs with ECDH algorithm without an AAA server being used. As the VRGK encrypted with RGK(RSU Group Key) is transferred from the current RSU to the next RSU through a secure channel, a perfect forward secret security is provided. In addition, a Sybil attack is detected by checking whether the vehicular that transferred a message is a member of the group with a group key. And the transmission time of messages and the overhead of a server can be reduced because an unnecessary network traffic doesn't happen by means of the secure communication between groups.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.