• Journal of the Korean Applied Science and Technology
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• v.37 no.4
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• pp.870-877
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• 2020

The purpose of this study is to compare and analyze the effects of stepping-in-place tempo and roundhouse kick types on response time. Fifteen males participated in this experiment. All have over ten years of experience and hold a forth dan(degree) black belt in taekwondo. The task is when the participants are doing stepping in place they respond to the light stimulus as fast as they can do roundhouse kick. Five different stepping in place tempos (100, 120, 140, 160, and 180 bpm) and four different types of roundhouse kick(front leg body roundhouse kick, front leg head roundhouse kick, back leg body roundhouse kick, and back leg head roundhouse kick) were used. Three measurements were taken for each of the different combinations of conditions for a total of 60 measurements. For data analysis, two-way ANOVA with repeated measures was used and pair-wise comparisons were performed using bonferroni statistics. The results show that there was significant difference interaction effect between stepping in place tempo and roundhouse kick type in the response time. And, there were significant difference in main effect of response time in accordance with stepping in place tempo and roundhouse kick type. The response time of roundhouse kick was the fastest at 160 bpm of stepping in place tempo, but there was no significant difference between 140 and 160 bpm. Front leg body roundhouse kick was the fastest. And, the response time was the fastest when front leg body roundhouse kicked at 140 bpm of stepping in place tempo. Stepping in place tempo between 140 and 160 bpm is the most effective to optimize the response time. And, More effective response time was front leg roundhouse kick as compared with back leg roundhouse kick and, body roundhouse kick as compared with head roundhouse kick. The findings in this study will provide useful information for performance improvement and will help with strategy for taekwondo competition.

• Journal of Astronomy and Space Sciences
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• v.26 no.4
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• pp.425-438
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• 2009

We reevaluate the Burton equation (Burton et al. 1975) of predicting Dst index using high quality hourly solar wind data supplied by the ACE satellite for the period from 1998 to 2006. Sixty magnetic storms with monotonously decreasing main phase are selected. In order to determine the injection term (Q) and the decay time ($\tau$) of the equation, we examine the relationships between $Dst^*$ and $VS_s$, ${\Delta}Dst^*$ and $VS_s$, and ${\Delta}Dst^*$ and $Dst^*$ during the magnetic storms. For this analysis, we take into account one hour of the propagation time from the ACE satellite to the magnetopause, and a half hour of the response time of the magnetosphere/ring current to he solar wind forcing. The injection term is found to be $Q(nT/h)\;=\;-3.56VS_s$ for $VS_s$ > 0.5mV/m and Q(nT=h) = 0 for $VB_s\;{\leq}\;0.5mV/m$. The $\tau$ (hour) is estimated as $0.060Dst^*\;+\;16.65$ for $Dst^*$ > -175nT and 6.15 hours for $Dst^*\;{\leq}\;-175nT$. Based on these empirical relationships, we predict the 60 magnetic storms and find that the correlation coefficient between the observed and predicted $Dst^*$ is 0.88. To evaluate the performance of our prediction scheme, the 60 magnetic storms are predicted again using the models by Burton et al. (1975) and O'Brien & McPherron (2000a). The correlation coefficients thus obtained are 0.85, the same value for both of the two models. In this respect, our model is slightly improved over the other two models as far as the correlation coefficients is concerned. Particularly our model does a better job than the other two models in predicting intense magnetic storms ($Dst^*\;{< \atop \sim}\;-200nT$).

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.131-146
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• 2011

It is becoming more essential than ever for office professionals to become competent in information collection/gathering and problem solving in today's global business society. In particular, office professionals do not only assist simple chores but are also forced to make decisions as quickly and efficiently as possible in problematic situations that can end in either profit or loss to their company. Since office professionals rely heavily on their tacit knowledge to solve problems that arise in everyday business situations, it is truly helpful and efficient to refer to similar business cases from the past and share or reuse such previous business knowledge for better performance results. Case-based reasoning(CBR) is a problem-solving method which utilizes previous similar cases to solve problems. Through CBR, the closest case to the current business situation can be searched and retrieved from the case or knowledge base and can be referred to for a new solution. This reduces the time and resources needed and increase success probability. The main purpose of this study is to design a system called COKRS(Case-based reasoning Office Knowledge Recommender System) and develop a prototype for it. COKRS manages cases and their meta data, accepts key words from the user and searches the casebase for the most similar past case to the input keyword, and communicates with users to collect information about the quality of the case provided and continuously apply the information to update values on the similarity table. Core concepts like system architecture, definition of a case, meta database, similarity table have been introduced, and also an algorithm to retrieve all similar cases from past work history has also been proposed. In this research, a case is best defined as a work experience in office administration. However, defining a case in office administration was not an easy task in reality. We surveyed 10 office professionals in order to get an idea of how to define a case in office administration and found out that in most cases any type of office work is to be recorded digitally and/or non-digitally. Therefore, we have defined a record or document case as for COKRS. Similarity table was composed of items of the result of job analysis for office professionals conducted in a previous research. Values between items of the similarity table were initially set to those from researchers' experiences and literature review. The results of this study could also be utilized in other areas of business for knowledge sharing wherever it is necessary and beneficial to share and learn from past experiences. We expect this research to be a reference for researchers and developers who are in this area or interested in office knowledge recommendation system based on CBR. Focus group interview(FGI) was conducted with ten administrative assistants carefully selected from various areas of business. They were given a chance to try out COKRS in an actual work setting and make some suggestions for future improvement. FGI has identified the user-interface for saving and searching cases for keywords as the most positive aspect of COKRS, and has identified the most urgently needed improvement as transforming tacit knowledge and knowhow into recorded documents more efficiently. Also, the focus group has mentioned that it is essential to secure enough support, encouragement, and reward from the company and promote positive attitude and atmosphere for knowledge sharing for everybody's benefit in the company.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.129-143
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• 2012

Recently, the rapid progress of a number of standardized web technologies and the proliferation of web users in the world bring an explosive increase of producing and consuming information documents on the web. In addition, most companies have produced, shared, and managed a huge number of information documents that are needed to perform their businesses. They also have discretionally raked, stored and managed a number of web documents published on the web for their business. Along with this increase of information documents that should be managed in the companies, the need of a solution to locate information documents more accurately among a huge number of information sources have increased. In order to satisfy the need of accurate search, the market size of search engine solution market is becoming increasingly expended. The most important functionality among much functionality provided by search engine is to locate accurate information documents from a huge information sources. The major metric to evaluate the accuracy of search engine is relevance that consists of two measures, precision and recall. Precision is thought of as a measure of exactness, that is, what percentage of information considered as true answer are actually such, whereas recall is a measure of completeness, that is, what percentage of true answer are retrieved as such. These two measures can be used differently according to the applied domain. If we need to exhaustively search information such as patent documents and research papers, it is better to increase the recall. On the other hand, when the amount of information is small scale, it is better to increase precision. Most of existing web search engines typically uses a keyword search method that returns web documents including keywords which correspond to search words entered by a user. This method has a virtue of locating all web documents quickly, even though many search words are inputted. However, this method has a fundamental imitation of not considering search intention of a user, thereby retrieving irrelevant results as well as relevant ones. Thus, it takes additional time and effort to set relevant ones out from all results returned by a search engine. That is, keyword search method can increase recall, while it is difficult to locate web documents which a user actually want to find because it does not provide a means of understanding the intention of a user and reflecting it to a progress of searching information. Thus, this research suggests a new method of combining ontology-based search solution with core search functionalities provided by existing search engine solutions. The method enables a search engine to provide optimal search results by inferenceing the search intention of a user. To that end, we build an ontology which contains concepts and relationships among them in a specific domain. The ontology is used to inference synonyms of a set of search keywords inputted by a user, thereby making the search intention of the user reflected into the progress of searching information more actively compared to existing search engines. Based on the proposed method we implement a prototype search system and test the system in the patent domain where we experiment on searching relevant documents associated with a patent. The experiment shows that our system increases the both recall and precision in accuracy and augments the search productivity by using improved user interface that enables a user to interact with our search system effectively. In the future research, we will study a means of validating the better performance of our prototype system by comparing other search engine solution and will extend the applied domain into other domains for searching information such as portal.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.12
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• pp.803-812
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• 2014

This study assesses the strategy and performance of Eco-industrial Park (EIP) initiative implemented by Korea Industrial Complex Corporation (KICOX) with the support of Ministry of Trade, Industry and Energy (MOTIE), Korea since 2005 to 2013 and recommends future directions. After the concept of EIP based on industrial symbiosis (IS) is introduced, the background and implementation procedure of the EIP initiative are described. Then, economic and environmental achievement was assessed. During the project periods (2005-2013), 449 industrial symbiosis project were explored, among which 296 projects have been implemented. Among (Of these 296 projects,) them, 244 projects have been completed in which 118 projects have been commercialized which shows 48% commercialization rate of the completed projects. Through these commercialized projects, around 311.1 billion won/year of economic benefits and reduction of waste by-products of 828,113 tons/year, wastewater of 215,517 tons/year, reduction in energy consumption of 250,475 toe/year and GHG emission reduction of 1,107,189 $tCO_2/year$ were achieved. This results confirmed that EIP initiative based on industrial symbiosis can enhance eco-efficiency of industrial parks and harmonize economy and environment. However, there are obstacles like absence of interagency coordination and cooperation, laws and institutional barriers, increased demand for local governments, funding for project investment. Thus, to utilize EIP initiative as a strategic tool for competiveness and environmental management of industrial parks, it needs intergovernmental collaboration and interdisciplinary approach to lower barrier in implementation.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.33 no.4
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• pp.231-238
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• 2007

In the previous study, the anti-oxidant activity of extract/fraction of Sueada asparagoides (SA) was investigated and the results showed that the ethylacetate (EtOAc) fraction and its aglycone fraction had the best performance on the free radical scavenging activity, reactive oxygen species scavenging (ROS) activity and cell protective activity (J. Soc. Cosme. Scientists Korea, 33(3), 145 (2007)). In this study, the stability of cream containing 0.3% SA EtOAc extract (called extract below) was evaluated. pH, viscosity and absorbance (363 nm) were measured under the 4 different temperatures ($0^{\circ}C,\;25{\circ}C,\;37{\circ}C\;and\;45{\circ}C$) and under the sun light at the 4 week intervals during the 12 weeks in total. The control cream without containing the extract did not show pH change under the different temperatures mentioned above. However, the pH of the cream the extract was decreased 0.08 at the temperature ranges of $0^{\circ}C\;to\;37^{\circ}C$. Under the $45^{\circ}C$ and sun light condition, the pH was decreased 0.51 and 0.66, respectively. The cream containing the extract did not show absorbance change at the temperature ranges of 0 to $37^{\circ}C$ for 12 weeks. Instead, the absorbance of the cream treated under $45^{\circ}C$ and sun light condition was decreased 7.6 % and 7.4 %, respectively. This decrease in absorbance is relatively small compared to the 48.3 % decrease of the extract sampled from the cream using ethanol solution. This indicates that the extract is stabilized in the cream. After treating the cream for 12 weeks under the different temperatures, the viscosity was measured for the cream containing the extract and control cream. The values were increased by 1,748 cPs in average compared to the initial value for the former and by 951 cPs in average for the latter. On the other hand, the viscosity of control cream treated under the sun light for 12 weeks was significantly decreased (4,022 cPs) relative to the cream containing the extract, which showed 2,484 cPs increase in viscosity. This indicates that the SA extract contributes to the stability of the emulsion product by protective effect to maintain the viscosity of the cream against sun light. In addition, any change in color or smell was not observed through 12 weeks of the experimental time period. Thus, it is concluded that it is still not clear in the stability of the cream containing the extract when it is stored for the long time. Accordingly, it is suggested that further study is needed to provide more information to the manufactures, who are seeking for the application of the extract to improve the anti-oxidant activity and stability of cosmetic products.

• Journal of the Korean Wood Science and Technology
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• v.48 no.3
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• pp.271-282
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• 2020

Sewage treatment plants are social infrastructure of cities. The sewage distribution rate in Korea is reaching 94% based on the sewage statistics based in the year of 2017. In Korean sewage treatment plants, use of PAC (Poly Aluminum Chloride) accounts for 58%. It contains a large amount of impurities (heavy metal) according to the quality standards, however, there have been insufficient efforts to reinforce the standards or technically improve the quality, which resulted in secondary pollution problems from injecting excessive coagulant. Also, the increase in the use of chemicals is leading to the increases in the annual amount of sewage sludge generated in 2017 and the need to reuse sludge. As such, this study aims to verify the possibility of reusing sludge by evaluating the stability of heavy metals based on the injection of coagulant mixture during water treatment which uses the torrefield wood powder and natural materials, and evaluating the sedimentation and heating value of sewage sludge. As a result of analyzing heavy metals (Cr, Fe, Zn, Cu, Cd, As, Pb, and Ni) from the coagulant mixture and PAC (10%), Cr, Cd, Pb, Ni, and Hg were not detected. As for Zn, while its concentration notified in the quality standards for drinking water is 3 mg/L, only a small amount of 0.007 mg/L was detected in the coagulant mixture. Maximum amounts of over double amounts of Fe, Cu, and As were found with PAC (10%) compared to the coagulant mixture. Also, an analysis of sludge sedimentation found that the coagulant mixture showed a better performance of up to double the speed of the conventional coagulant, PAC (10%). The dry-basis lower heating value of sewage sludge produced by injecting the coagulant mixture was 3,378 kcal/kg, while that of sewage sludge generated due to PAC (10%) was 3,171 kcal/kg; although both coagulants met the requirements to be used as auxiliary fuel at thermal power plants, the coagulant mixture developed in this study could secure heating values 200 kal/kg higher than the counterpart. Therefore, utilization of the coagulant mixture for water treatment rather than PAC (10%) is expected to be more environmentally stable and effective, as it helps generating sludge with better stability against heavy metals, having a faster sludge sedimentation, and higher heating value.

• Food Science and Preservation
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• v.19 no.1
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• pp.95-103
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• 2012

The objective of this study was to develop models for the predict of the milk properties (fat, protein, SNF, lactose, MUN) of unhomogenized milk using the visible and near-infrared (NIR) spectroscopic technique. A total of 180 milk samples were collected from dairy farms. To determine optimal measurement temperature, the temperatures of the milk samples were kept at three levels ($5^{\circ}C$, $20^{\circ}C$, and $40^{\circ}C$). A spectrophotometer was used to measure the reflectance spectra of the milk samples. Multilinear-regression (MLR) models with stepwise method were developed for the selection of the optimal wavelength. The preprocessing methods were used to minimize the spectroscopic noise, and the partial-least-square (PLS) models were developed to prediction of the milk properties of the unhomogenized milk. The PLS results showed that there was a good correlation between the predicted and measured milk properties of the samples at $40^{\circ}C$ and at 400~2,500 nm. The optimal-wavelength range of fat and protein were 1,600~1,800 nm, and normalization improved the prediction performance. The SNF and lactose were optimized at 1,600~1,900 nm, and the MUN at 600~800 nm. The best preprocessing method for SNF, lactose, and MUN turned out to be smoothing, MSC, and second derivative. The Correlation coefficients between the predicted and measured fat, protein, SNF, lactose, and MUN were 0.98, 0.90, 0.82, 0.75, and 0.61, respectively. The study results indicate that the models can be used to assess milk quality.

• The Journal of Society for e-Business Studies
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• v.17 no.4
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• pp.69-93
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• 2012

Various influence-related relationships in Social Network Services (SNS) among users, posts, and user-and-post, can be expressed using links. The current research evaluates the influence of specific users or posts by analyzing the link structure of relevant social network graphs to identify influential users. We applied the concept of mutual interactions proposed for ranking semantic web resources, rather than the voting notion of Page Rank or HITS, to blogosphere, one of the early SNS. Through many experiments with network models, where the performance and validity of each alternative approach can be analyzed, we showed the applicability and strengths of our approach. The weight tuning processes for the links of these network models enabled us to control the experiment errors form the link weight differences and compare the implementation easiness of alternatives. An additional example of how to enter the content scores of commercial or spam posts into the graph-based method is suggested on a small network model as well. This research, as a starting point of the study on identifying influential users in SNS, is distinctive from the previous researches in the following points. First, various influence-related properties that are deemed important but are disregarded, such as scraping, commenting, subscribing to RSS feeds, and trusting friends, can be considered simultaneously. Second, the framework reflects the general phenomenon where objects interacting with more influential objects increase their influence. Third, regarding the extent to which a bloggers causes other bloggers to act after him or her as the most important factor of influence, we treated sequential referencing relationships with a viewpoint from that of PageRank or HITS (Hypertext Induced Topic Selection).