• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Membrane Journal
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• v.32 no.4
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• pp.235-252
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• 2022

Forward osmotic pressure-free reverse osmosis (Δ𝜋=0 RO) was invented in 2013. The first patent (US 9,950,297 B2) was registered on April 18, 2018. The "Osmotic Pressure of Concentrated Solutions" in JACS (1908) by G.N. Lewis of MIT was used for the estimation. The Chang's RO system differs from conventional RO (C-RO) in that two-chamber system of osmotic pressure equalizer and a low-pressure RO system while C-RO is based on a single chamber. Chang claimed that all aqueous solutions, including salt water, regardless of its osmotic pressure can be separated into water and salt. The second patent (US 10.953.367B2, March 23, 2021) showed that a low-pressure reverse osmosis is possible for 3.0% input at Δ𝜋 of 10 to 12 bar. Singularity ZERO reverse osmosis from his third patent (Korea patent 10-22322755, US-PCT/KR202003595) for a 3.0% NaCl input, 50% more water recovery, use of 1/3 RO membrane area, and 1/5th of theoretical energy. These numbers come from Chang's laboratory experiments and theoretical analysis. Relative residence time (RRT) of feed and OE chambers makes Δ𝜋 to zero or negative by recycling enriched feed flow. The construction cost by S-ZERO was estimated to be around 50~60% of the current RO system.

• Korean Journal of Health Education and Promotion
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• v.2 no.1
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• pp.31-61
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• 2000

Introduction. Despite the fact that half of premature deaths are caused by unhealthy lifestyles such as smoking tobacco, sedentary lifestyle, alcohol and drug abuse and poor nutrition, there are no theoretical models which accurately explain these health promotion related behaviors. This study tests a new model of health behavior called the Model of Health Promotion Behavior. This model draws on elements and frameworks suggested by the Health Belief Model, Social Cognitive Theory, the Theory of Planned Action and the Health Promotion Model. This model is intended as a general model of behavior but this first test of the model uses amount of exercise as the outcome behavior. Design. This study utilized a cross sectional mail-out, mail-back survey design to determine the elements within the model that best explained intentions to exercise and those that best explained amount of exercise. A follow-up questionnaire was mailed to all respondents to the first questionnaire about 10 months after the initial survey. A pretest was conducted to refine the questionnaire and a pilot study to test the protocols and assumptions used to calculate the required sample size. Sample. The sample was drawn from 2000 eligible participants at two blue collar (utility company and part of a hospital) and two white collar (bank and pharmaceutical) companies located in Southeastern Michigan. Both white collar site had employee fitness centers and all four sites offered health promotion programs. In the first survey, 982 responses were received (49.1％) after two mailings to non-respondents and one additional mailing to secure answers to missing data, with 845 usable cases for the analyzing current intentions and 918 usable cases for the explaining of amount of current exercise analysis. In the follow-up survey, questionnaires were mailed to the 982 employees who responded to the initial survey. After one follow-up mailing to non-respondents, and one mailing to secure answers to missing data, 697 (71.0％) responses were received, with 627 (63.8％) usable cases to predict intentions and 673 (68.5％) usable cases to predict amount of exercise. Measures. The questionnaire in the initial survey had 15 scales and 134 items; these scales measured each of the variables in the model. Thirteen of the scales were drawn from the literature, all had Cronbach's alpha scores above .74 and all but three had scores above .80. The questionnaire in the second mailing had only 10 items, and measured only outcome variables. Analysis. The analysis included calculation of scale scores, Cronbach's alpha, zero order correlations, and factor analysis, ordinary least square analysis, hierarchical tests of interaction terms and path analysis, and comparisons of results based on a random split of the data and splits based on gender and employer site. The power of the regression analysis was .99 at the .01 significance level for the model as a whole. Results. Self efficacy and Non-Health Benefits emerged as the most powerful predictors of Intentions to exercise, together explaining approximately 19％ of the variance in future Intentions. Intentions, and the interaction of Intentions with Barriers, with Support of Friends, and with Self Efficacy were the most consistent predictors of amount of future exercise, together explaining 38％ of the variance. With the inclusion of Prior Exercise History the model explained 52％ of the variance in amount of exercise 10 months later. There were very few differences in the variables that emerged as important predictors of intentions or exercise in the different employer sites or between males and females. Discussion. This new model is viable in predicting intentions to exercise and amount of exercise, both in absolute terms and when compared to existing models.

• Korean Journal of Culture and Social Issue
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• v.21 no.3
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• pp.551-573
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• 2015

The rise of single living has been one of the most important demographic shifts of recent decades. The solo household is a little less than 40% in Europe areas and that of Tokyo is over 45%. Being impacted this figure, the formation of single economy is the key word in World Economic Forum(WEF) 2008. Seoul' single household is increasing rapidly. Between 2000 and 2005, the growth of single person is around 34%, the population of single person reached 700,000 people. Now 20% of total household in Seoul is Single household. Living alone or solo living is not exceptional or special in Seoul Metropolitan City. The rise in single living will create pressures towards poverty and inequality and so on. Seoul should develop and prepare the urban policy for single household. We figured out the four key trends which composed of single household in Seoul. Four types of single person are like below : Gold Mr and Miss, Reserved labor forces, depressed single and silver generation. Gold group is amonst people aged 30 and 40 who is working in the area of white collar and professional. They are usually elective single person household who have chosen solo living. Reserved labor forces group is usually among 20s people who have not get the regular hob. For this group, job acquiring is the most important issue. Depressed single person household group is among people aged late 30s and 40s. Its group is the result from the broken family. The silver group is among aged over 65 that is the main issue of the aged society. In this research, we stressed that people living alone can be split into two types - elective single person households who have chosen single living, and forced single person household who have been constrained to this lifestyle by circumstances. Except gold group, the rest of the group is the forced single household who are faced to poverty. The monthly income of single person household is almost under 2 million won. Single person household is usually working in the blue collar job and service area. So, except gold group that is the smallest part of single person household, almost single person is not the target of private market, but the object of public policy.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.1
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• pp.54-60
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• 2022

The planting date of corn for silage has been delayed because of spring drought and double cropping system in Korea. This experiment was conducted to evaluate agronomic characteristics, forage production and feed value of corn at April and May in 2019. Experimental design was a split-plot with three replications. Planting dates (12 April and 10 May) were designated to the main plot, and corn hybrids ('P0928', 'P1543' and 'P2088') to the subplot. The silking days of the early planting date (12 April) was 79 days and that of the late planting date (10 May) was 66 days (p<0.0001), however, there were no significant differences among the corn hybrids. Ear height of the late planting date was higher than that of the early planting (p<0.05), while there were no significant differences in plant height of corn. Insect resistance at the early planting was lower than that of late planting (p<0.05), however, lodging resistance was no significant difference at planting date. The rice black streaked virus (RBSDV) infection of early planting was 3.7% and that of late planting was 0.3% (p<0.001). Dry matter (DM) contents of stover, ear and whole plant had significant difference at planting date (p<0.05). And differences in ear percentages were observed among the corn hybrids (p<0.01). And ear percentages of early maturing corn ('P0928') was higher than for other hybrids. Ear percentage at the early planting date was higher than that at the late planting date (p<0.01). DM and total digestible nutrients (TDN) yields had significant difference at planting date, however, there were no significant differences among the corn hybrids. DM and TDN yields at the late planting (21,678 kg/ha and 14,878 kg/ha) were higher than those of the early planting (13,732 kg/ha and 9,830 kg/ha). Crude protein content at the early planting date was higher than that of the late planting. Acid detergent fiber content of the late planting was lower than that of the early planting date (p<0.01), while there were no significant neutral detergent fiber content difference among the corn tested. Calculated net energy for lactation (NE_L) and TDN at the early planting were higher than those of at the late planting (p<0.01). Results of this our study indicate that the late planting date (May) is better than early planting date (April) in forage yield and feed value of corn. Therefore, the delay of planting date by May was more suitable for use in cropping system.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.4
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• pp.305-330
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• 2023

Unlike NATM tunnels, Shield TBM tunnels have split linings. Therefore, the stress distribution of the lining is different even if the lining is under the same load. Representative methods for analyzing the stress generated in lining in Shield TBM tunnels include Non-joint Mode that does not consider connections and a 2-ring beam-spring model that considers ring-to-ring joints and segment connections. This study is an analysis method by Break-joint Mode. However, we do not consider the structural role of segment lining connections. The effectiveness of the modeling is verified by analyzing behavioral characteristics against vibration loads by modeling with segment connection interfaces to which vertical stiffness and shear stiffness, which are friction components, are applied. Unlike the Non-joint mode, where the greatest stress occurs on the crown for static loads such as earth pressure, the stress distribution caused by contact between segment lining and friction stiffness produced the smallest stress in the crown key segment where segment connections were concentrated. The stress distribution was clearly distinguished based on segment connections. The results of static analysis by earth pressure, etc., produced up to seven times the stress generated in Non-joint mode compared to the stress generated by Break-joint Mode. This result is consistent with the stress distribution pattern of the 2-ring beam-spring model. However, as for the stress value for the train vibration load, the stress of Break-joint Mode was greater than that of Non-joint mode. This is a different result from the static mechanics concept that a segment ring consisting of a combination of short members is integrated in the circumferential direction, resulting in a smaller stress than Non-joint mode with a relatively longer member length.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.43-54
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• 2023

Due to climate change, drought and flood occurrences have been increasing. Accurate projections of watershed discharges are imperative to effectively manage natural disasters caused by climate change. However, climate change and hydrological model uncertainty can lead to imprecise analysis. To address this issues, we used two lumped models, IHACRES and GR4J, to compare and analyze the changes in discharges under climate stress scenarios. The Hapcheon and Seomjingang dam basins were the study site, and the Nash-Sutcliffe efficiency (NSE) and the Kling-Gupta efficiency (KGE) were used for parameter optimizations. Twenty years of discharge, precipitation, and temperature (1995-2014) data were used and divided into training and testing data sets with a 70/30 split. The accuracies of the modeled results were relatively high during the training and testing periods (NSE>0.74, KGE>0.75), indicating that both models could reproduce the previously observed discharges. To explore the impacts of climate change on modeled discharges, we developed climate stress scenarios by changing precipitation from -50 % to +50 % by 1 % and temperature from 0 ℃ to 8 ℃ by 0.1 ℃ based on two decades of weather data, which resulted in 8,181 climate stress scenarios. We analyzed the yearly maximum, abundant, and ordinary discharges projected by the two lumped models. We found that the trends of the maximum and abundant discharges modeled by IHACRES and GR4J became pronounced as changes in precipitation and temperature increased. The opposite was true for the case of ordinary water levels. Our study demonstrated that the quantitative evaluations of the model uncertainty were important to reduce the impacts of climate change on water resources.

• Exercise Science
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• v.23 no.1
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• pp.1-11
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• 2014

The purpose of this study was to develop and validate regression models to estimate maximal oxygen uptake (VO₂max) from the 20 m Progressive Shuttle Run Test (20 m PSRT) in Korean middle-school girls aged 13-15 years. The 20 m PSRT and VO₂max were assessed in a sample of 194 participants. The sample was randomly split into validation (n=127) and test-retest reliability (n=99, 32 out of 127 participants also performed validity test) groups. 127 participants performed a graded exercise test (GXT, stationary gas analyser) and the 20 m PSRT (portable gas analyser) once to develop a VO₂max prediction model and to analyze the validity of the modified 20 m PSRT protocol (starting at 7.5 km/h and increasing by 0.5 km/h every 1 min). 99 participants performed the 20 m PSRT twice for test-retest reliability purpose. Mean measured VO₂max (39.2±5.1 ml/kg/min) from the potable gas analyzer was significantly increased from that measured during the GXT from stationary gas analyzer (37.7±5.7 ml/kg/min, p=.001) using the modified 20 m PSRT protocol. But it was a narrow range (1.5 ml/kg/min). The measured VO₂max from the potable and stationary gas analyzers correlated at r=.88(p<.001). Test-retest of the 20 m PSRT yielded comparable results (Laps r=.88 & final speed r=.85). New regression equations were developed from present data to predict VO₂max for middle-school girls: y=.231×Laps-.311×weight(in kg)+46.201 (r=.74, SEE=4.29 ml/kg/min). It is concluded that (a) the modified 20 m PSRT protocol is a valid and reliable test and (b) this equation developed in this study provides valid estimates of VO₂max of Korean middle-school girl aged 13-15 years.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of The Korean Society of Grassland and Forage Science
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• v.5 no.1
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• pp.22-32
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• 1985

Optimum pasture management during the summer season is an important factor to maintain good regrowth and persistence of pasture in Korea. This experiment was carried out to investigate the effects of the cutting management on the dead plant, weed appearance, regrowth and carbohydrate reserves in stubble, and dry matter yield of tall fescue dominated pasture during the mid summer season. For the test, a split plot design with 4 replications was treated with 2 different the third cutting times (July 12 and Aug. 4) as the mainplots, and 3 different cutting heights (3, 6 and 9 cm) at the third cut as the subplots, and the experiment was done at the experimental field of the Livestock Experiment Station, in Suweon, 1984. The results obtained are summarized as follows: 1. Considering the meteorological conditions during the experimental period, the temperature was a little higher by $2^{\circ}C$ than that of average year, especially the first and second decade of August were high. And the precipitation of 1984 tended to be low when compared with the average year. 2. Temperature of soil surface and underground tended to increase by $1-3^{\circ}C$ as the stubble height was low during the summer season. 3. Regrowth leaf length and leaf area after the third cut increased significantly with the high cutting height at the third cut. 4. A significant higher total nonstructural carbohydrate (TNC) content in stubble after the third cut was observed in the high stubble cut on July 12. The results indicate that the high stubble height reserves more carbohydrates for early regrowth stage after the third cut when compared with the low stubble. On Aug. 4, however, the recovery of TNC contents after the third cut was not effective due to high temperature and rainfall. 5. The percentage of dead plant after the third cut was found to be high with the low cutting height during the mid summer season (p<0.05). 6. With the low stubble height on July 12 cut, it was appeared that the percentage of weed was significantly increased (p<0.05), and main weeds appeared after the third cut were Echinochloa crusgalli>Digitaria sanguinalis>Cyperus iria>Rumex crispus, and so on. In case of cut on Aug. 4, weed appearance was no difference at three cutting heights. 7. Dry matter yield at the third cut was increased in the plot of cutting on Aug. 4 and stubble height (p<0.05). However, yields at the fourth and fifth cut were increased with high stubble height (p<0.05), regardless of harvest time. 8. In total dry matter yield after the third cut, there was no significant difference between the cutting time and forage yield. However, total yield on July 12 was increased with the high stubble height (p<0.05). 9. From the above results, it is suggested that the 9 cm cutting height during the mid summer season is the most effective for good regrowth, weed control and forage yield of tall fescue dominated pasture.