• 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.

• Korea Journal of Hospital Management
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• v.10 no.1
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• pp.93-114
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• 2005

The prenatal care is the preventive medical service to help the pregnant mother deliver the healthy baby. It's regular examines give some chances to check-up the healthy conditions. This thesis concentrates on the CRM system to support an effective prenatal care system and prove the effectiveness of it. As CRM is the adapted management related to the customer's own information, it is important to develop the CRM model classified by the patients characteristics. A general hospital in Busan operated the CRM system to carry out the effective prenatal care and there is an analysis to ensure the effectiveness of CRM system for the pregnant women in our maternity ward. The results can be summarized as follows: 1) According to the comparisons with the CRM system, we can conclude the system is desirable. (1) Maternal Age : In the age distribution, the prenatal visit frequency, triple marker freqency, oral GTT and targeted ultrasonography in the experimental group in 30 to 34 years old is higher on the whole. For over 35 years old group, the higher frequency comes out in the oral GTT and targeted ultrasonography and for 25 to 29 years old group the different figure shows just in the targeted ultrasonography. (2) Area of residence: There is a clear difference in all the items in Busan and near area but no sign of difference in prenatal visits and oral GTT in other residencial area. Especially in the targeted ultrasonography the higher figure shows in the experimental group located in the both areas. The targeted ultrasonography is known as the specific examination which should be examined by the specialists, on the contrary the other examinations can be operated in the small clinic. So the public information and seminars related with ultrasonography increases the check-up frequency. The clinic requests some ultrasonographical examinations to the specialists in general hospital. (3) Parity: The clear difference shows that the CRM system causes the prenatal visit frequency to become higher in experimental group. The figure is 9.7 times and 8.6 times each. This is opposite that the past study said multiparity reduced the average prenatal visits. But the result of CRM is considered as the method to help the multiparity understand the importance of the prenatal care. (4) Obstetrical history: In the experimental group of the spontaneous delivery group, the figure is higher in the prenatal visit frequency, triple marker, oral GTT and targeted ultrasonography but the Caesarean section delivery group has higher figure in targeted ultrasonography. (5) In the first check-up, the rate of targeted ultrasonography in under 16 week pregnancy, in the 16 week pregnancy to 32 week pregnancy and the over 32 week pregnancy in the experimental group is upper than the compared one. For the oral GTT, there is a difference in under 16 week pregnancy but no difference in prenatal visits and triple marker. 2) The analysis of characteristics of prenatal care through the decision tree resulted in the fact that the most important variable is the residential area. After the delivery frequency is following, the obstetrical history and maternal age are in order. It is the same result in the triple marker and oral GTT. Consequently it is the same order of important variables in CRM system. The effectiveness of CRM system is proved in this study. The CRM system is a marketing method to control and lead the customers through the segmentation of customer data. It increases the new customer aquisition, maintenance of loyal customers, augmentation of customers value, activation of potential customers and creation of life time customers. So eventually it can enlarge the customers value. The medical institution should make efforts to establish the data base enforced by the customer's information on the underlying ordinary data system to carry out the CRM system effectively. In addition, it should develop the a variety of marketing strategy in order to set up one to one marketing satisfying the needs of individual patients.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6B
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• pp.565-575
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• 2011

The purpose of this study was to quantitatively evaluate the expression of both multi-metric qualitative habitat evaluation index (QHEI) and biological integrity index (IBI) for artificial structures eco-artificial fish reef (EAFR) for fishes asylum and habitat. Especially, both experimental evaluation and biological verification were performed in Water and Environmental Center's outdoor test-bed of Korea Institute of Construction Technology located in Andong-city, Gyeongsangbuk-do. The experimental conditions reflecting the situation of domestic river include the flow rate (e.g., $0.0{\sim}1.5m\;s^{-1}$), the width (e.g., 1.0~3.0 m), the depth (e.g., 0.05~0.70 m), and variable bed materials. Both QHEI and IBI were monitored for 8 months from May to December 2010. Whereas QHEI values were highest at experimental points of the E~F with an average of 83.1, those were lowest at B~C with an average of 78.1. However, QHEI values inside EAFR were more than 98.9, regardelss of space and time, and indicated more than the highest good of the state (Good) in the habitat. Overally, IBI values showed similar trend with QHEI, but were 44.2 in the winter dry season, compared to 32.8 of QHEI values. IBI values Also, IBI values inside EAFR were greater than those at the experimental channel by 5.7 to 11.4% and 18.7 to 34.8% in flow and stagnant conditions, respectively, indicating that EAFR can secure asylum and habitat for fish during the dry season. For comprehensive aquatic ecosystem assessment, the experimental channel showed generally fair conditions (Fair~Good), whereas EAFR showed good conditions (Good), suggesting that EAFR can be applied to aquatic ecosystem restoration and improvement.

• Korean Journal of Environment and Ecology
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• v.31 no.4
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• pp.381-396
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• 2017

Understanding species distribution plays an important role in conservation as well as evolutionary biology. In this study, we applied a species distribution model to predict hotspot areas and habitat characteristics for endangered herpetofauna species in South Korea: the Korean Crevice Salamander (Karsenia koreana), Suweon-tree frog (Hyla suweonensis), Gold-spotted pond frog (Pelophylax chosenicus), Narrow-mouthed toad (Kaloula borealis), Korean ratsnake (Elaphe schrenckii), Mongolian racerunner (Eremias argus), Reeve's turtle (Mauremys reevesii) and Soft-shelled turtle (Pelodiscus sinensis). The Kori salamander (Hynobius yangi) and Black-headed snake (Sibynophis chinensis) were excluded from the analysis due to insufficient sample size. The results showed that the altitude was the most important environmental variable for their distribution, and the altitude at which these species were distributed correlated with the climate of that region. The predicted distribution area derived from the species distribution modelling adequately reflected the observation site used in this study as well as those reported in preceding studies. The average AUC value of the eigh species was relatively high ($0.845{\pm}0.08$), while the average omission rate value was relatively low ($0.087{\pm}0.01$). Therefore, the species overlaying model created for the endangered species is considered successful. When merging the distribution models, it was shown that five species shared their habitats in the coastal areas of Gyeonggi-do and Chungcheongnam-do, which are the western regions of the Korean Peninsula. Therefore, we suggest that protection should be a high priority in these area, and our overall results may serve as essential and fundamental data for the conservation of endangered amphibian and reptiles in Korea.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.2
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• pp.177-192
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• 2018

The mission of the university is to grow young people who will be responsible for the next generation nation to mature society. In particular, the role of universities in the 4th Industrial Revolution era is to foster creative talents. Therefore, innovative changes are required for existing infusion education and employment-oriented college education contents and teaching methodology. In the high youth unemployment rate, entrepreneurship education is spreading to universities to innovate existing organizations and create new jobs. Until now, however, the effects of university entrepreneurship education have been studied mainly in the field of start-up. Therefore, it is very difficult to study various effects on entrepreneurship education. In this study, empirical analysis of the effect of entrepreneurship training on career preparation behaviors of university students who are about to enter the society is verified. The sample of this study was surveyed from August, 2016, and the questionnaire was applied to 393 university students who selected 5 universities and entrepreneurship education. As a result of the analysis, the ability of communicative communication and creative problem solving cultivated through entrepreneurship education had a positive effect on entrepreneurship and self - efficacy, Entrepreneurship and self - efficacy had a positive (+) positive effect on career preparation behavior. However, cooperative communication ability, creative problem solving ability, and effective work behavior ability by entrepreneurship education were not directly related to career preparation behavior. On the other hand, in verifying the mediating effect of entrepreneurship and self - efficacy, it proved that mediating role of positive communication between collaborative communication ability, creative problem solving ability and career preparation behavior. The implications of this study are as follows This study examines the effects of university entrepreneurship education on career preparedness behaviors.In other words, the university proved the direct effect and the mediating effect that affects positively (+) effect on career preparation behaviors by encouraging entrepreneurship and self - efficacy, rather than quantitative extension based on competency. Also, the implication of the parameters (entrepreneurship, self - efficacy) as a key variable for the effect and performance of career preparation behavior was derived.It also suggests that it is necessary to improve the creativity ability of entrepreneurship education so that it can have a direct and meaningful influence on career preparation behavior. First of all, it is urgent to develop a qualitative evaluation index for entrepreneurship education in order to measure these effects. Therefore, further study is required to verify the various implications for future entrepreneurship education, as well as the implications that entrepreneurship education is indispensable for the preparation of university students' careers.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.2
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• pp.203-209
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• 2009

Yield monitoring is one of a precision agriculture technology that is used most widely. It is spatial variability analysis of yield information that should be attained with yield monitoring system development. This experiment was conducted to evaluate spatial variability of yield and grain moisture content in rice paddy field, and their relationships to rice productivity. It is necessary to minimize sampling interval for accurate yield map making or to control cutting width of rice combine. Considering small rice plots such as $0.2{\sim}0.4$ ha, optimum size of sampling plot was below 15 m more than 5 m in with and length. In variable rate treatment field, average yield was similar, but yield variation was reduced than conventional field. Gap of yield by another plot in same field was bigger than half of average yield than yield variation was significantly big. Therefore yield measuring flow sensor must be able to measure at least 300 kg/10a more than 1000 kg/10a. Variation of moisture content in same field was not big and spatial dependance did not appear greatly. But, variation between different field is appeared difference according to weather circumstance before harvesting. Change of spatial dependence of yield was not big, because of field variation of moisture content is not big.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.7
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• pp.2386-2396
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• 1996

A numerical simulation has been performed for isothermal and reacting flows in an exisymmetric, bluff-body research combustor. The present formulation is based on the density-weighted averaged Navier-Stokes equations together with a k-epsilon. turbulence model and a modified eddy-breakup combustion model. The PISO algorithm is employed for solution of thel Navier-Stokes system. Comparison between measurements and predictions are made for a centerline axial velocities, location of stagnation points, strength of recirculation zone, and temperature profile. Even though the numerical simulation gives acceptable agreement with experimental data in many respects, the present model is defictient in predicting the recoveryt rate of a central near-wake region, the non-isotropic turbulence effects, and variation of turbulent Schmidt number. Several possible explanations for these discrepancies have been discussed.

• The Korean Journal of Mycology
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• v.30 no.2
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• pp.86-94
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• 2002

Recently a serious outbreak of weed mould caused by a species of Hypocrea occurred in oyster mushroom (Pleurotus ostreatus) substrates in Korea. The disease was characterized by a rapid infestation of the oyster mushroom substrates by Hypocrea sp. and subsequent inhibition of fructification of the mushroom. In spite of it's serious losses to the oyster mushroom industry in Korea, etiology and ecology of the disease have not been studied. Morphological characteristics of the fungus were examined and molecular characteristics of the fungus were compared with those of the green moulds (Trichoderma spp.) isolated from oyster mushroom bed. Stromata formed superficially on suface of the substrates were pulvinate to effuse or irreguler, initially white but becoming yellowish brown, measuring $6.0{\sim}13.0{\times}3.0{\sim}11.0mm$. Perithecia were globose to subglobose, immersed in stroma, $223{\sim}263\;(Ave.239.9){\times}167.3{\sim}231\;(Ave.204.1){\mu}m$ in size. Asci were unitunicate, cylindrical, nonamyloid, $82.7{\sim}124.8\;(Ave.103.3){\times}4.1{\sim}5.1\;(Ave.4.9){\mu}m$ in size, 16 part-spored. Ascospores were bullet-shaped or somewhat oblong, hyaline, bicellular, roughened or warted, $5.4{\sim}7.4\;(Ave.6.5){\times}3.6{\sim}5.5\;(Ave.4.7){\mu}m$ in size. This fungus readily form the stroma on PDA. Mycelia on PDA nearly invisible and without cottony aerial mycelium. Optimum temperature for mycelial growth of this fungus was $25^{\circ}C$ on PDA and its growth rate was 15 mm per day. This species did not grow at below 10 and above $35^{\circ}C$. Phialides in culture enlarged in the middle and aggregated to penicillate type. They were very variable, shorted ampulliform and occasionally curved when matured, but cylinderical when young, measuring $11.9{\sim}24.3\;(Ave.\;14.7){\times}2.9{\sim}3.9\;(Ave.\;3.4){\mu}m$ when matured and $7.2{\sim}14.0\;(Ave.\;10.8){\times}2.8{\sim}4.9\;(Ave.\;3.5){\mu}m$ when young. Phialosopres were ovoid to ellipsoid, smooth, measuring $3.5{\sim}7.2\;(Ave.\;4.5){\times}2.6{\sim}3.3\;(Ave.\;2.9){\mu}m$. Nineteen isolates of Hypocrea sp. were analyzed on the basis of molecular characteristics and classified into phenotypic groups. On the basis of RAPD, URP-PCR, the fungus was confirm to monoclonal, and was classified as a different taxon from reported species of Hypocrea and Trichoderma and supposed to be a new species not previously reported in literature.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.4
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• pp.215-225
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• 2016

There is growing attention towards the performance of non-profit organizations (NPO) as cases of moral hazard have emerged as social issues and the public becomes interested in the accountability of NPOs. Social welfare is among the representative service areas of NPOs, and it is regarded as a human service because it is delivered by people. Therefore, human capital-measured by criteria such as levels of organizational commitment-significantly affects the performance of social welfare organizations. However, these organizations have a high turnover rate due to inadequate working environments, heavy workloads, intensive work, and poor remuneration. Against this background, we surveyed social welfare organizations located in Busan and examined how workers' organizational commitment affects their turnover intention and the performance of the organization. For data analysis, SPSS WIN 18.0 was used, and variable-specific frequency analysis and technical statistics were used to identify general properties. A Pearson correlation analysis and a multiple regression analysis were conducted to investigate correlations and causal relationships among main variables. The results showed that the social workers' level of organizational commitment was 3.36 for normative commitment, 3.07 for continuance commitment, and 2.75 for affective commitment. The level of normative commitment was slightly higher than moderate, continuance commitment was close to moderate, and affective commitment was lower than moderate. The surveyed social workers' turnover intention was 2.71, and organizational performance was 2.79; both of those results were slightly lower than moderate. It was observed that affective commitment, marital status, workload, and age affected social workers' turnover intention. Their affective commitment, gender, and age affected the organization's performance. These results suggest that, for launching or operating an NPO, it is important to manage human capital effectively, particularly their affective commitment, to lower workers' turnover intention and improve organizational performance.