• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.80-81
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• 2022

자율운항선박 원격제어시스템의 실증을 위하여 기존 육상제어센터에서 선박을 제어하는 것은 현재 개발중인 시스템으로 인한 선박의 안전성 확보에 어려움이 있다. 이에 본 연구에서는 원격제어시스템을 시험하기 위하여 선박에서 육상제어센터로 명령을 주어 선박과 육상제어센터 내 모사장치를 통한 원격제어시스템을 테스트하였다. 본선의 통신네트워크(LTE, VSAT)을 통해 선장의 명령으로 선박의 육상제어센터의 모사장치를 제어하였다. 1차 소각도를 이용한 원격제어시험과 2차 Zig-zag 테스트를 실시하여, 개별 시스템의 문제점을 식별하고, 개별 시스템간의 통합을 위한 방안을 제시하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.11a
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• pp.12-13
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• 2018

For navigation safety, navigators have to be familiar with maneuverabilities. When a vessel encounters a danger of collisions and stranding, maneuverability is essential for the safety of ship. It is composed of turning, course change, speed change, etc. In the latter part, maneuverabilities and motion of Training Ship HANBADA are provided by full-scale measurement in the $10^{\circ}/10^{\circ}$ Zig-Zag Test, $20^{\circ}/20^{\circ}$ Zig-Zag Test and Turning Circle Test(Port and Starboard). It aims to provide information on maneuverabilities and motion of Training Ship HANBADA so that the navigators can take proper action to avoid.

• 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.