• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• The Korean Journal of Archival Studies
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• no.22
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• pp.157-199
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• 2009

The organization for managing records and archives did not emerge together with the founding of the Chinese Communist Party. Such management became active with the establishment of the Department of Documents (文書科) and its affiliated offices overseeing reading and safekeeping of official papers, after the formation of the Central Secretariat(中央秘書處) in 1926. Improving the work of the Secretariat's organization became the focus of critical discussions in the early 1930s. The main criticism was that the Secretariat had failed to be cognizant of its political role and degenerated into a mere "functional organization." The solution to this was the "politicization of the Secretariat's work." Moreover, influenced by the "Rectification Movement" in the 1940s, the party emphasized the responsibility of the Resources Department (材料科) that extended beyond managing documents to collecting, organizing and providing various kinds of important information data. In the mean time, maintaining security with regard to composing documents continued to be emphasized through such methods as using different names for figures and organizations or employing special inks for document production. In addition, communications between the central political organs and regional offices were emphasized through regular reports on work activities and situations of the local areas. The General Secretary not only composed the drafts of the major official documents but also handled the reading and examination of all documents, and thus played a central role in record processing. The records, called archives after undergoing document processing, were placed in safekeeping. This function was handled by the "Document Safekeeping Office(文件保管處)" of the Central Secretariat's Department of Documents. Although the Document Safekeeping Office, also called the "Central Repository(中央文庫)", could no longer accept, beginning in the early 1930s, additional archive transfers, the Resources Department continued to strengthen throughout the 1940s its role of safekeeping and providing documents and publication materials. In particular, collections of materials for research and study were carried out, and with the recovery of regions which had been under the Japanese rule, massive amounts of archive and document materials were collected. After being stipulated by rules in 1931, the archive classification and cataloguing methods became actively systematized, especially in the 1940s. Basically, "subject" classification methods and fundamental cataloguing techniques were adopted. The principle of assuming "importance" and "confidentiality" as the criteria of management emerged from a relatively early period, but the concept or process of evaluation that differentiated preservation and discarding of documents was not clear. While implementing a system of secure management and restricted access for confidential information, the critical view on providing use of archive materials was very strong, as can be seen in the slogan, "the unification of preservation and use." Even during the revolutionary movement and wars, the Chinese Communist Party continued their efforts to strengthen management and preservation of records & archives. The results were not always desirable nor were there any reasons for such experiences to lead to stable development. The historical conditions in which the Chinese Communist Party found itself probably made it inevitable. The most pronounced characteristics of this process can be found in the fact that they not only pursued efficiency of records & archives management at the functional level but, while strengthening their self-awareness of the political significance impacting the Chinese Communist Party's revolution movement, they also paid attention to the value possessed by archive materials as actual evidence for revolutionary policy research and as historical evidence of the Chinese Communist Party.

• Korean Journal of Heritage: History & Science
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• v.50 no.4
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• pp.4-19
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• 2017

Kim Jeong-gi (pen-name: Changsan, Mar. 31, 1930 - Aug. 26, 2015) made a major breakthrough in the history of cultural property excavation in Korea: In 1959, he began to develop an interest in cultural heritage after starting work as an employee of the National Museum of Korea. For about thirty years until he retired from the National Research Institute of Cultural Heritage in 1987, he devoted his life to the excavation of our country's historical relics and artifacts and compiled countless data about them. He continued striving to identify the unique value and meaning of our cultural heritage in universities and excavation organizations until he passed away in 2015. Changsan spearheaded all of Korea's monumental archeological excavations and research. He is widely known at home and abroad as a scholar of Korean archeology, particularly in the early years of its existence as an academic discipline. As such, he has had a considerable influence on the development of Korean archeology. Although his multiple activities and roles are meaningful in terms of the country's archaeological history, there are limits to his contributions nevertheless. The Deoksugung Palace period (1955-1972), when the National Museum of Korea was situated in Deoksugung Palace, is considered to be a time of great significance for Korean archeology, as relics with diverse characteristics were researched during this period. Changsan actively participated in archeological surveys of prehistoric shell mounds and dwellings, conducted surveys of historical relics, measured many historical sites, and took charge of photographing and drawing such relics. He put to good use all the excavation techniques that he had learned in Japan, while his countrywide archaeological surveys are highly regarded in terms of academic history as well. What particularly sets his perspectives apart in archaeological terms is the fact that he raised the possibility of underwater tombs in ancient times, and also coined the term "Haemi Culture" as part of a theory of local culture aimed at furthering understanding of Bronze Age cultures in Korea. His input was simply breathtaking. In 1969, the National Research Institute of Cultural Heritage (NRICH) was founded and Changsan was appointed as its head. Despite the many difficulties he faced in running the institute with limited financial and human resources, he gave everything he had to research and field studies of the brilliant cultural heritages that Korea has preserved for so long. Changsan succeeded in restoring Bulguksa Temple, and followed this up with the successful excavation of the Cheonmachong Tomb and the Hwangnamdaechong Tomb in Gyeongju. He then explored the Hwangnyongsa Temple site, Bunhwangsa Temple, and the Mireuksa Temple site in order to systematically evaluate the Buddhist culture and structures of the Three Kingdoms Period. We can safely say that the large excavation projects that he organized and carried out at that time not only laid the foundations for Korean archeology but also made significant contributions to studies in related fields. Above all, in terms of the developmental process of Korean archeology, the achievements he generated with his exceptional passion during the period are almost too numerous to mention, but they include his systematization of various excavation methods, cultivation of archaeologists, popularization of archeological excavations, formalization of survey records, and promotion of data disclosure. On the other hand, although this "Excavation King" devoted himself to excavations, kept precise records, and paid keen attention to every detail, he failed to overcome the limitations of his era in the process of defining the nature of cultural remains and interpreting historical sites and structures. Despite his many roles in Korean archeology, the fact that he left behind a controversy over the identity of the occupant of the Hwangnamdaechong Tomb remains a sore spot in his otherwise perfect reputation.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.67-83
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• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.

• Korean Journal of Heritage: History & Science
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• v.46 no.3
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• pp.230-250
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• 2013

possessed by Dong-A University Museum is designated as Treasure No. 595, and has been known for a more exquisite, delicate and realistic expression and a colorful three-dimensional structure compared to the 'grass and insect painting' work and its value in art history. However, it has not been analyzed and studied in fabric craft despite it being an embroidered work. This study used scientific devices to examine and analyze the Screen's fabric, thread colors, and embroidery techniques to clarify its patterns and fabric craft characteristics for its value in the history of fabric craft. As a result, consists of eight sides and its subject matters and composition are similar to those of the general paintings of grass and insects. The patterns on each side of the 'grass and insect painting' include cucumber, cockscomb, day lily, balsam pear, gillyflower, watermelon, eggplant, and chrysanthemums from the first side. Among these flowers, the balsam pear is a special material not found in the existing paintings of grass and insect. The eighth side only has the chrysanthemums with no insects and reptiles, making it different from the typical forms of the paintings of grass and insect. The fabric of the Screen uses black that is not seen in other decorative embroideries to emphasize and maximize various colors of threads. The fabric used the weave structure of 5-end satin called Gong Dan [non-patterned satin]. The threads used extremely slightly twisted threads that are incidentally twisted. Some threads use one color, while other threads use two or mixed colors in combination for three-dimensional expressions. Because the threads are severely deterioration and faded, it is impossible to know the original colors, but the most frequently used colors are yellow to green and other colors remaining relatively prominently are blue, grown, and violet. The colors of day lily, gillyflower, and strawberries are currently remaining as reddish yellow, but it is anticipated that they were originally orange and red considering the existing paintings of grass and insects. The embroidery technique was mostly surface satin stitch to fill the surfaces. This shows the traditional women's wisdom to reduce the waste of color threads. Satin stitch is a relatively simple embroidery technique for decorating a surface, but it uses various color threads and divides the surfaces for combined vertical, horizontal, and diagonal stitches or for the combination of long and short stitches for various textures and the sense of volume. The bodies of insects use the combination of buttonhole stitch, outline stitch, and satin stitch for three-dimensional expressions, but the use of buttonhole stitch is particularly noticeable. In addition to that, decorative stitches were used to give volume to the leaves and surface pine needle stitches were done on the scouring rush to add more realistic texture. Decorative stitches were added on top of gillyflower, strawberries, and cucumbers for a more delicate touch. is valuable in the history of paintings and art and bears great importance in the history of Korean embroidery as it uses outstanding technique and colors of Korea to express the Shin Sa-im-dang's 'Grass and Insect Painting'.

• Korean Journal of Heritage: History & Science
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• v.49 no.2
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• pp.172-189
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• 2016

In this research, literature research on the Odong material, mixture ratio, casting method and casting facility was conducted on contemporary documents, such as Cheongong Geamul. Also, a long sword was produced using the Odong inlay technique. The sword reproduction steps were as follows; Odong alloying, silver soldering alloying, Odong plate and Silver plate production, hilt and sheath production, metal frame and decorative elements, such as a Dugup (metal frame), production, Odong inlay assembly and final assembly. For the Odong alloy production, the mixture ratio of the true Odong, which has copper and gold ratio of 20:1, was used. This is traditional ratio for high quality product according to $17^{th}$ century metallurgy instruction manual. The silver soldering alloy was produced with silver and brass(Cu 7 : Zn 3) ratio of 5:1 for inlay purpose and 5:2 ratio for simple welding purpose. The true Odong alloy laminated with silver plate was used to produce hilt and sheath. The alloy went through annealing and forging steps to make it into 0.6 mm thick plate and its backing layer, which is a silver plate, had the matching thickness. After the two plates were adhered, the laminated plate went through annealing, forging, engraving, silver inlaying, shaping, silver welding, finishing and polishing steps. During the Odong colouring process, its red surface turns black by induced corrosion and different hues can be achieved depending on its quality. To accomplish the silver inlay Odong techniques, a Hanji saturated with thirty day old urine is wrapped around a hilt and sheath material, then it is left at warm room temperature for two to three hours. The Odong's surface will turn black when silver inlay remains unchanged. Various scientific analysis were conducted to study composition of recreated Odong panel, silver soldering, silver plate and the colouring agent on Odong's surface. The recreated Odong had average out at Cu 95.57 wt% Au 4.16wt% and Cu 98.04 wt% Au 1.95wt%, when documented ratio in the old record is Cu 95wt% and Au 5wt%. The recreated Odong was prone to surface breakage during manufacturing process unlike material made with composition ratio written in the old record. On the silver plate of the silver and Odong laminate, 100wt% Ag was detected and between the two layers Cu, Ag and Au were detected. This proves that the adhesion between the two layers was successfully achieved. The silver soldering had varied composition of Ag depending on the location. This shows uneven composition of the silver welding. A large quantities of S, that was not initially present, was detected on the surface of the black Odong. This indicates that presence of S has influence on Odong colour. Additional study on the chromaticity, additional chemical compounds and its restoration are needed for the further understanding of the origin of Odong colour. The result of Odong alloy testing and recreation, Odong silver inlay long sword production, scientific analysis of the Odong black colouring agent will form an important foundation of knowledge for conservation of Odong artifact.

• 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.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.107-116
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• 2018

Purpose : Intensity-modulated radiation therapy(IMRT) has been widely used for radiation therapy of Prostate Cancer because it can reduce radiation adverse effects on normal tissues and deliver more dose to the Prostate than 3D radiation therapy. Volumetric modulated arc therapy(VMAT) has been widely used due to recent advances in equipment and treatment techniques. VMAT can reduce treatment time by up to 55 % compared to IMRT, minimizing motion error during treatment. Materials and Methods : In this study, compared the MU and DVH values of 10 patients with prostate cancer by classifying them into 4 groups with 5 LN-Prostate groups and 5 Only-Prostate. And DQA measurements were performed using ArcCHECK and MapCHECK. Results : The results of Target and OAR dose distribution of Prostate patients are as follows. $D_{max}$ was in the range of 100~110 % in 4 groups, and more than 110 % of hot spot was not seen. Only-Prostate ($P_1$, $P_2$) without LN had a satisfactory dose distribution for the target dose, but slightly better for 2 arc plan($P_2$) than 1 arc plan($P_1$). The target dose $D_{98%}$ distribution in the LN-Prostate ($P_{L1}$, $P_{L2}$) group showed better 2 arc plan($P_{L2}$) than 1 arc plan($P_{L1}$), But in the case of 1 arc plan($P_{L1}$), the target dose $D_{98%}$ value was not enough. In OAR, the dose distribution of 1 Arc($P_1$) Plan and 2 Arc($P_2$) Plan in the Only-Prostate ($P_1$, $P_2$) Group satisfied the prescribed dose value. But, The dose distribution of 1 arc($P_1$) was slightly higher. In LN-Prostate OAR, 1 Arc($P_{L1}$) Plan showed higher dose than the prescribed dose. The Gamma evaluation pass rate of ArcCHECK and MapCHECK calculated from the DQA measurements was slightly higher than 99 % and the mean error range of the point dose measurements using the CC04 ion chamber was less than 1 %. Conclusion : In this study, Only-Prostate ($P_1$, $P_2$) group, the dose of 2 Arc plan was better. However, considering the treatment time and MU value, 1 Arc treatment method was more suitable. In the LN-Prostate ($P_{L1}$, $P_{L2}$) group, 2 Arc($P_{L2}$) treatment method showed better results and satisfied with Target $D_{98%}$ and OAR prescription dose.