• Proceedings of the KSPS conference
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• 대한음성학회 1996년도 10월 학술대회지
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• pp.246-246
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• 1996

Purpose: Some of the properties of the prosodic phrasing and some acoustic and phonological effects of contrastive focus on the tonal pattern of Seoul Korean is explored based on a brief experiment of analyzing the fundamental frequency(=FO) contour of the speech of the author. Data Base and Analysis Procedures: The examples were chosen to contain mostly nasal and liquid consonants, since it is difficult to track down the formants in stops and fricatives during their corresponding consonantal intervals and stops may yield an effect of unwanted increase in the FO value due to their burst into the following vowel. All examples were recorded three times and the spectrum of the most stable repetition was generated, from which the FO contour of each sentence was obtained, the peaks with a value higher than 250Hz being interpreted as a high tone (=H). The result is then discussed within the prosodic hierarchy framework of Selkirk (1986) and compared with the tonal pattern of the Northern Kyungsang dialect of Korean reported in Kenstowicz & Sohn (1996). Prosodic Phrasing: In N.K. Korean, H never appears both on the object and on the verb in a neutral sentence, which indicates the object and the verb form a single Phonological Phrase ($={\phi}$), given that there is only one pitch peak for each $={\phi}$. However, Seoul Korean shows that both the object and the verb have H of their own, indicating that they are not contained in one $={\phi}$. This violates the Optimality constraint of Wrap-XP (=Enclose a lexical head and its arguments in one $={\phi}$), while N.K. Korean obeys the constraint by grouping a VP in a single $={\phi}$. This asymmetry can be resolved through a constraint that favors the separate grouping of each lexical category and is ranked higher than Wrap-XP in Seoul Korean but vice versa in N.K. Korean; $Align-x^{lex}$ (=Align the left edge of a lexical category with that of a $={\phi}$). (1) nuna-ka manll-ll mEk-nIn-ta ('sister-NOM garlic-ACC eat-PRES-DECL') a. (LLH) (LLH) (HLL) ----Seoul Korean b. (LLH) (LLL LHL) ----N.K. Korean Focus and Phrasing: Two major effects of contrastive focus on phonological phrasing are found in Seoul Korean: (a) the peak of an Intonatioanl Phrase (=IP) falls on the focused element; and (b) focus has the effect of deleting all the following prosodic structures. A focused element always attracts the peak of IP, showing an increase of approximately 30Hz compared with the peak of a non-focused IP. When a subject is focused, no H appears either on the object or on the verb and a focused object is never followed by a verb with H. The post-focus deletion of prosodic boundaries is forced through the interaction of StressFocus (=If F is a focus and DF is its semantic domain, the highest prominence in DF will be within F) and Rightmost-IP (=The peak of an IP projects from the rightmost $={\phi}$). First Stress-F requires the peak of IP to fall on the focused element. Then to avoid violating Rightmost-IP, all the boundaries after the focused element should delete, minimizing the number of $={\phi}$'s intervening from the right edge of IP. (2) (omitted) Conclusion: In general, there seems to be no direct alignment constraints between the syntactically focused element and the edge of $={\phi}$ determined in phonology; all the alignment effects come from a single requirement that the peak of IP projects from the rightmost $={\phi}$ as proposed in Truckenbrodt (1995).

• Journal of Intelligence and Information Systems
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• 제16권2호
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• pp.129-141
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• 2010

An Internet shopping mall for clothing operates a warehouse for packing and shipping products to fulfill its orders. All the products in the warehouse are put into the boxes of same brands and the boxes are stored in a row on shelves equiped in the warehouse. To make picking and managing easy, boxes of the same brands are located side by side on the shelves. When new products arrive to the warehouse for storage, the products of a brand are put into boxes and those boxes are located adjacent to the boxes of the same brand. If there is not enough space for the new coming boxes, however, some boxes of other brands should be moved away and then the new coming boxes are located adjacent in the resultant vacant spaces. We want to minimize the movement of the existing boxes of other brands to another places on the shelves during the warehousing of new coming boxes, while all the boxes of the same brand are kept side by side on the shelves. Firstly, we define the adjacency of boxes by looking the shelves as an one dimensional series of spaces to store boxes, i.e. cells, tagging the series of cells by a series of numbers starting from one, and considering any two boxes stored in the cells to be adjacent to each other if their cell numbers are continuous from one number to the other number. After that, we tried to formulate the problem into an integer programming model to obtain an optimal solution. An integer programming formulation and Branch-and-Bound technique for this problem may not be tractable because it would take too long time to solve the problem considering the number of the cells or boxes in the warehouse and the computing power of the Internet shopping mall. As an alternative approach, we designed a fast heuristic method for this reallocation problem by focusing on just the unused spaces-empty cells-on the shelves, which results in an assignment problem model. In this approach, the new coming boxes are assigned to each empty cells and then those boxes are reorganized so that the boxes of a brand are adjacent to each other. The objective of this new approach is to minimize the movement of the boxes during the reorganization process while keeping the boxes of a brand adjacent to each other. The approach, however, does not ensure the optimality of the solution in terms of the original problem, that is, the problem to minimize the movement of existing boxes while keeping boxes of the same brands adjacent to each other. Even though this heuristic method may produce a suboptimal solution, we could obtain a satisfactory solution within a satisfactory time, which are acceptable by real world experts. In order to justify the quality of the solution by the heuristic approach, we generate 100 problems randomly, in which the number of cells spans from 2,000 to 4,000, solve the problems by both of our heuristic approach and the original integer programming approach using a commercial optimization software package, and then compare the heuristic solutions with their corresponding optimal solutions in terms of solution time and the number of movement of boxes. We also implement our heuristic approach into a storage location assignment system for the Internet shopping mall.

• Journal of Intelligence and Information Systems
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• 제25권1호
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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.