• Journal of Korean Society of Forest Science
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• v.76 no.1
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• pp.11-16
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• 1987

With the purpose of pursuing the increase and decrease of chlorophyll a and chlorophyll b caused by application of the simulated acid rain treatment on Pinus koraiensis seedlings and Ligustrum obtusifolium cuttings, the experimental design of randomized block arrangement with three replications was implemented in the experimental field of Yesan National Agricultural Junior College, during the growing season of 1985. Pinus koraiensis seeds stratified in cool and moist condition were sown on pots, and in case of Ligustrum obtusifolium, C1/1 cuttings were potted for experimental use in the early spring. The regime of artificial acid rain, based upon precipitation frequency and density, was simulated from the learning of climatological data averaged from 30 years records. The spray of acid water containing pH values of 4.0 and 2.0 was initiated from the 1st of May and ended on the 31st of August. As control, ground water was also treated at the same time. To analyse the chlorophyll content, those leaves looking representative and unaffected by other harmful agents were sampled on the 18th of September, and UV-visible spectrophotometer was used. With decrease in pH values of acid rain, the content of chlorophyll a and chlorophyll b decreased in both species. The decrease in chlorophyll a could be confirmed through statistical significance, but not in chlorophyll b. And when we discussed the chlorophyll decrease index which was explained in detail in the paper, an attention might be given to similarly decreasing values in both chlorophyll a and chlorophyll b, according as pH levels of acid rain decreased. The ratios of chlorophyll a to chlorophyll b in both species were not affected by different pH leaves of acid rain.

• Sleep Medicine and Psychophysiology
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• v.11 no.1
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• pp.29-36
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• 2004

Objectives: Sound and light (SL) stimulation has been used as a method to induce some useful mental states in the fields of psychology and psychiatry. It is believed that sound and light entrainment device (SLED) has some specific effects through synchronization of EEG in patients who use it. Theta frequency is believed to stimulate deep relaxation and short term memory processing. This study was conducted to evaluate if 4-10 Hz SL stimulation can induce relaxation and improve short term memory function. Methods: Ten medical students with no medical or psychiatric problems participated in this study. Subjects were randomly divided into two groups. One group was applied with real SLED was applied to one group (R group) and pseudo SLED to the other group (P group). The two groups were exposed to SL stimulation with SLED 15 minutes a day for 5 days, and after two days rest the two groups were switched over. The Korean Wechsler Adult Intelligence Scale (K-WAIS), Academic Motivation Tests (AMT), Test Anxiety Scale (TAS), Korean Auditory Verbal Learning Test (K-AVLT), and digit span were used to evaluate short term memory. Spielberger's State-Trait anxiety inventory and heart rate variability (HRV) test were used to evaluate degree of relaxation. Results: Compared with S group, R group showed a significant improvement in K-AVLT and digit span after a single application of SL stimulation. But 5-day long application did not reveal any differences between the two groups. A significant change in HRV was observed in 5-day long application of SL stimulation after being switched over to other SLED. Conclusion: This pilot study suggests that 4-10 Hz SL stimulation has some positive influences on short term memory and relaxation.

• Journal of Service Research and Studies
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• v.10 no.3
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• pp.23-42
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• 2020

This study conceptualized the essence and propensity of modern management in service age, based on philosophy, and developed items to evaluate the conceptualized content. It was carried out as a new study to deepen the study of management philosophy and management theory by the new management framework. In order to establish the philosophical foundation of the modern management, the essence of the modern management was conceptualized based on the fundamental ideas of the East and West, and then an evaluation item was developed to put the essence and propensity of the modern management into practical use through analytical and empirical methods. After analyzing the representative ideas of mankind, it was derived that the Book of Change has the qualification as a philosophical model that can derive the essence of modern management. The Book of Change explains the reasoning of the world in the structure of two opposing parties, such as Taiji or Yin and Yang, and the process of acknowledging the contradictions within each opposing party and overcoming the contradictions through change is the central idea. Because you can see. After conducting a conceptual study, through empirical research, the essence and propensity of a modern manager should be conceptualized. The concept of essence and empirical study of the modern management using the leading role was conducted in two stages. First, a qualitative study using repetitive comparative analysis (CCM), focus group interview (FGI), and text mining was conducted to derive the essential and propensity conceptualization items that modern managers should possess. In addition, a quantitative study using factor analysis to develop sample items and develop measurement items through literature review and FGI was conducted to derive the essential concept of the modern management. Finally, the essence of modern management was derived: learning, preparation, challenge, inclusion, trust, morality, and sacrifice. In the future, it is necessary to conduct empirical research on the effectiveness of the essence of modern management for global and Korean representative companies.

• Korean Journal of Heritage: History & Science
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• v.48 no.2
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• pp.142-157
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• 2015

This study examines how traditional knowledge functions in the specific techniques to make pottery in terms of the traditional knowledge on the pottery techniques of Onggi potters. It focuses on how traditional pottery manufacturing skills are categorized and what aspects are observed with regard to the techniques. The pottery manufacturing process is divided into the preparation step of raw material, the molding step of pottery, and the final plasticity step. Each step involves unique traditional knowledge. The preparation step mainly comprises the knowledge on different kinds of mud. The knowledge is about the colors and properties of mud, the information on the regional distribution of quality mud, and the techniques to optimize mud for pottery manufacturing. The molding step mainly involves the structure and shape of spinning wheels, the techniques to accumulate mud, ways to use different kinds of tools, the techniques to dry processed pottery. The plasticity step involves the knowledge on kilns and the scheme to build kilns, the skills to stack pottery inside of the kilns, the knowledge on firewood and efficient ways of wood burning, the discrimination of different kinds of fire and the techniques to stoke the kilns. These different kinds of knowledge may be roughly divided into three categories : the preparation of raw material, molding, and plasticity. They are closely connected with one another, which is because it becomes difficult to manufacture quality pottery even with only one incorrect factor. The contents of knowledge involved in the manufacturing process of pottery focused are mainly about raw material, color, shape, distribution aspect, fusion point, durability, physical property, etc, which are all about science. They are rather obtained through the experimental learning process of apprenticeship, not through the official education. It is not easy to categorize the knowledge involved. Most of the knowledge can be understood in the category of ethnoscience. In terms of the UNESCO world heritage of intangible cultural assets, the knowledge is mainly about 'the knowledge on nature and universe'. Unique knowledge and skills are, however, identified in the molding step. They can be referred to 'body techniques', which unify the physical stance of potters, tools they employ, and the conceived pottery. Potters themselves find it difficult to articulate the knowledge. In case stated, it cannot be easily understood without the experience and knowledge on the field. From the preparation of raw material to the complete products, the techniques and traditional knowledge involved in the process of manufacturing pottery are closely connected, employing numerous categories and levels. Such an aspect can be referred to as a 'techniques chain'. Here the techniques mean not only the scientific techniques but also, in addition to the skills, the knowledge of various techniques and levels including habitual, unconscious behaviors of potters.

• The Journal of Korean Philosophical History
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• no.32
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• pp.79-112
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• 2011

Since Noron(老論) had organized in the period of Sookjong(肅宗), it constantly had led the political situation of Choson until Choson(朝鮮) perished as the grasping political power. Studies and thoughts development of Noron can be devided into four periods. First, the term of politics of faction of the period of Sookjong. Second, a period of Youngjo(英祖) and Joungjo(正祖). Third, a period of politics of power(勢道政治). Fourth, the latter term of 19century. We can look into an origin and development aspect in outline by dividing like this. The general character of Noron can be summarized by the respect of Song Si-yeol(宋時烈, 1607-1689), the theory of a party of a man of virtue(君子黨論) based on the theory of moral civilization of Choson(朝鮮中華論), the succession of Lee i(李珥; 1636-1684)'s neo-confucianism, rejecting all teaching that does not conform to neoconfucianism and protecting right studies, and oppression of Roman Catholic. The noticeable scholars of Noron were Kwon sang Ha(權尙夏; 1641~1721), Kim chang hyup(金昌協; 1651~1708), Lee jea(李縡; 1680~1746) etc. These scholars of Noron following Song Si-yeol had tried to raise "Learning of the Way"(正明道) by respecting Zushi and removing injustice(尊朱子攘夷狄), also believed people should embody moral values in their society and country. and possessed an will guiding to stabilize the country by rejecting uncivilization(尊王攘夷). Above all, they insisted, the King of Choson should rule with 'lighting heavenly reason'(明天理). Also they insisted the King and countrymen should together strive to recover civilization of moral humanity and destroy uncivilzation. But gradually they lost the motive and purpose of moral politics in the seventeenth century. Finally Noron Byeokpa(?派) take over the reins of government. It resulted in the bad effect of politics of autocrat(勢道政治) having their own way to use power of authority after death of Jungjo(正祖). The peculiar character of Noron politics can valued as the extreme aspect of 'according of politics and scholarship'(政學一致).

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.17-32
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• 2021

Speaker recognition is generally divided into speaker identification and speaker verification. Speaker recognition plays an important function in the automatic voice system, and the importance of speaker recognition technology is becoming more prominent as the recent development of portable devices, voice technology, and audio content fields continue to expand. Previous speaker recognition studies have been conducted with the goal of automatically determining who the speaker is based on voice files and improving accuracy. Speech is an important sociolinguistic subject, and it contains very useful information that reveals the speaker's attitude, conversation intention, and personality, and this can be an important clue to speaker recognition. The final ending used in the speaker's speech determines the type of sentence or has functions and information such as the speaker's intention, psychological attitude, or relationship to the listener. The use of the terminating ending has various probabilities depending on the characteristics of the speaker, so the type and distribution of the terminating ending of a specific unidentified speaker will be helpful in recognizing the speaker. However, there have been few studies that considered speech in the existing text-based speaker recognition, and if speech information is added to the speech signal-based speaker recognition technique, the accuracy of speaker recognition can be further improved. Hence, the purpose of this paper is to propose a novel method using speech style expressed as a sentence-final ending to improve the accuracy of Korean speaker recognition. To this end, a method called sentence sequencing that generates vector values by using the type and frequency of the sentence-final ending appearing in the utterance of a specific person is proposed. To evaluate the performance of the proposed method, learning and performance evaluation were conducted with a actual drama script. The method proposed in this study can be used as a means to improve the performance of Korean speech recognition service.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.35-52
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• 2019

As public services are provided in various forms, including e-government, the level of public demand for public service quality is increasing. Although continuous measurement and improvement of the quality of public services is needed to improve the quality of public services, traditional surveys are costly and time-consuming and have limitations. Therefore, there is a need for an analytical technique that can measure the quality of public services quickly and accurately at any time based on the data generated from public services. In this study, we analyzed the quality of public services based on data using process mining techniques for civil licensing services in N city. It is because the N city's building license complaint service can secure data necessary for analysis and can be spread to other institutions through public service quality management. This study conducted process mining on a total of 3678 building license complaint services in N city for two years from January 2014, and identified process maps and departments with high frequency and long processing time. According to the analysis results, there was a case where a department was crowded or relatively few at a certain point in time. In addition, there was a reasonable doubt that the increase in the number of complaints would increase the time required to complete the complaints. According to the analysis results, the time required to complete the complaint was varied from the same day to a year and 146 days. The cumulative frequency of the top four departments of the Sewage Treatment Division, the Waterworks Division, the Urban Design Division, and the Green Growth Division exceeded 50% and the cumulative frequency of the top nine departments exceeded 70%. Higher departments were limited and there was a great deal of unbalanced load among departments. Most complaint services have a variety of different patterns of processes. Research shows that the number of 'complementary' decisions has the greatest impact on the length of a complaint. This is interpreted as a lengthy period until the completion of the entire complaint is required because the 'complement' decision requires a physical period in which the complainant supplements and submits the documents again. In order to solve these problems, it is possible to drastically reduce the overall processing time of the complaints by preparing thoroughly before the filing of the complaints or in the preparation of the complaints, or the 'complementary' decision of other complaints. By clarifying and disclosing the cause and solution of one of the important data in the system, it helps the complainant to prepare in advance and convinces that the documents prepared by the public information will be passed. The transparency of complaints can be sufficiently predictable. Documents prepared by pre-disclosed information are likely to be processed without problems, which not only shortens the processing period but also improves work efficiency by eliminating the need for renegotiation or multiple tasks from the point of view of the processor. The results of this study can be used to find departments with high burdens of civil complaints at certain points of time and to flexibly manage the workforce allocation between departments. In addition, as a result of analyzing the pattern of the departments participating in the consultation by the characteristics of the complaints, it is possible to use it for automation or recommendation when requesting the consultation department. In addition, by using various data generated during the complaint process and using machine learning techniques, the pattern of the complaint process can be found. It can be used for automation / intelligence of civil complaint processing by making this algorithm and applying it to the system. This study is expected to be used to suggest future public service quality improvement through process mining analysis on civil service.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.