• Journal of the Daesoon Academy of Sciences
• /
• v.37
• /
• pp.263-295
• /
• 2021

Until recently, academic consensus held that Zhou Dunyi's Taijitu (Taiji Diagram) originated from Cantongqi. However, a new debate has arisen wherein some scholars question that theory and related theories. They criticize these previous theories because the books and charts used as evidence in those theories were published after the lifetime of Zhou Dunyi, and this disqualifies their influence on his thought. However, identifying certain authors as being of a slightly later period than Zhou Dunyi does not definitively answer whether or not Zhou Dunyi's diagram was based on Cantongqi. I approached this problem from a different perspective. Zhou Dunyi's Taijitu is based on the system of taiji (Taiyi), yin-yang, and the five movements. Consequently, the formation of this system should be traced back historically. In the process of tracing it back, I intended to explain that the main character of Cantongqi is closely related to the formation of the system of taiji (Taiyi), yin-yang, and the five movements. The system of taiji (Taiyi), yin-yang, and the five movements was first established as a religious theological system in the Han Dynasty. In this process, yin-yang and the five movements were combined by Dong Zhongshu, and the five movements were introduced by Han Dynasty scholars as a method of interpreting the I-ching. However, Han Dynasty scholars did not form this system. In the late Han Dynasty, Cantongqi adopted the theological system of yin-yang and the five movements to theoretically form the system of taiji (Taiyi), yin-yang, and the five movements. Cantongqi was able to form this system because of the logic that yin-yang is the essence of the I-ching. Cantongqi does not have the same schematic as Taijitu. However, the system of taiji (Taiyi), yin-yang, and the five movements appears and extracts the components that make up Taijitu. Therefore, I do not think we should hastily agree with the recent claims made by scholars.

• Journal of Environmental Health Sciences
• /
• v.17 no.2
• /
• pp.54-66
• /
• 1991

As a result of measuring illumination and making up a question at home visit directly by investigator who trained over twenty days period from October 4 to 24, 1990, in order to render help which illumination problem against house, society against eyes or framing of health instruction potgram by seizing natural lighting actual conditions of house and actual conditions of wearing spectacles and by investigating interrelationship, I can summarize as follows. 1) In property of investigation subject, woman 66.9%, In an age, the twenties was largest of 27.4%, the forties was 20.2%, the fifties was 18.6%, the thirties was 17.4%. In academic career, those of upper secondary school grauates was largest of 28.6%, those who possess university career was 25.9%, those who middle school career was 20.9%, decoding of Korean alphabet was 2%. 2) By a residence area, a big city was 43.3%, farming and fishing villages were 20.3%, the rest was a small town and the administrative office of town, township. In positon of house, the middle area was 43.6%, resident of suburb area was 38.0%. In form of house, a Korean-style house was 40.8%, a western-style house was 34.8%, an apartment house was 11.0%. In the a standard of living, the middle classes 77.2%, the lower classes were 15.3%. In residential house unit of area, from 21 to 30 unit of area was largest of 31.5%, from 10 to 20 unit of area was 19.9%, from 31 to 40 was 18.7%. 3) The wearing spectacles rate of study user was 44.1%. By the area, those who wearing spectacles was more than a half of 50.8% in the resident of big city area. As passing from the farm area to the city, that is being resident of big city was high wearing spectacles rate. In position of house, as being residence in central street showed high wearing spectacles rate. (central street was 51.5%, the middle area was 44.5% and the suburb area was 40.1%.) It seemed similarity difference a variable by position of house from wearing spectacles in standard of 1%. By form of house, wearing spectacles rate those who resident in apartment house was 49.5%, that rate those who resident in a western-style house was high of 49.0%, that rate those who resident in a Korean-style house was the lowest 39.0%. By social position of resident in room, in students case who study showed very high, as university students were very high of 62.3% idn wearing spectacles rate, middle and high school students 'were 50.0%, members of society were 47.6%, workers 20.3%. It seemed similarity difference from academic career in standard of 1%. By an age, the thirties was high of 54.1% in wearing spectacles rate, the twenties was 43.2%, the teenage was the lowest of 11.8%. 4) In illumination of study, over 200Lux was high of 40.1%. but below 99Lux which inappropriate illumination to see the books was 32.4%. Average by area, below 99Lux was 22.7% and over 400Lux was 50.0% in case of wooden floor. As examine by area, below 99Lux was high of 27.0% a case of wooden floor in the big city area, it was not good in illumination passing from the farm area(15.0%) to the city(19.0%). Average illlumination by area of the main living room below 99Lux was high of 37.5%, less than 200Lux was 58.5% of whole. In general, illumination of the main livingroom was inappropriate. By area, the big city was 32.5% below 99Lux, the middle and small city area were 33.8%, town and township area were 45.0%, farming and fishing area were 42.8%. By area, in the big city, illumination of study was 52.5% over 200Lux and 28.9% below 99Lux. In case of the middle and small city, study user of below 99Lux was 38.8% and over 200Lux was 46.9%. In case of the seat of town township, below 99Lux was 34.1% and over 200Lux was 39.7%. In case of farming and fishing area, illumination of study was 33.4% below 99Lux and 48.4% over 200Lux. It tends to high rate of inappropriate illumination. 5) By position of house, in case of wooden floor, less than 100Lux was 24.5% in central street. It was bad illumination than others position of house. In case of the main livingroom, less than 100Lux was 40.4% in the suburb area. It was bad iliumnation than others position of house. In case of study, less than 100Lux was 35.4% in the middle area, it was worse in illumination. In case of the main living room, is seemed similarity difference in standard of 1%. 6) By form of house, in case of wooden floor, illumination of less than 100Lux was 23.8% in a western-style house, it was bad illumination than others form of house. In case of the main livingroom, illumination of less than 100Lux was 47.4% in a Korean-style house, it was remarkably bad illumination than others form of house. In case of study, a Korean-style house was 38.8%, it was very bad illumination than others form of house. In case of the main livingroom and study, it seemed similatrity difference each as P < 0.01 and P < 0.05 in standard of 1%. 7) The wearing spectacles rate of those who use room of illumination over 400Lux was 40.7%, and that of those who use room of illumination less than 100Lux was 28.1%. It seemed similarity differecce in standard of 1%. 8) In period of wearing spectacles, 21.3% of total investigator-highest-was from before five years, 8.6% was from before three years. Among those who use of illumintion less than 99Lux, 34.0% began to wear spectacles from before two years 31.7% was from before five years, 30.3% was from before four years. It seemed similarity difference from period of wearing spectacles by illumination in standard of 1 %. 9) Among cause which sight grow worse, the first was that it was each 33.2% and 27.4% in response rate because watch TV nearly to wearing spectacles person and non-wearing person. The second was that a lot of seeing books was 25.3% in wearing spectacles person and response rate for dark illumination was 7.4% in nonwearing spectacles person. It seemed similarity difference in standard of 1%. (P < 0.01). 10) In experience which take medicine good for eyes, it was 50.1% in wearing spectacles person and 8.5% in non-wearing spectacles person. It seemed similarity difference in standard of 1%(P < 0.01). As we have seen above, inappropriate illumination can be a cause of wearing spectacles. Nevertheless, actually, is realities to indifferent against illumination of house. So it must learn knowledge about health obstacle of illumination through society instruction and school eduction against students as well as general residents. In case that natural lighting is inappropriate structural of house, we must be able to maintain appropriate illumination through artificial illumination. And so eyes which is core of human life have to be protected, related the authorities, related group, and all health medical personnel will organically cooperate with and make efforts.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.135-149
• /
• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of Distribution Research
• /
• v.15 no.3
• /
• pp.71-110
• /
• 2010

1. Introduction Today Internet is recognized as an important way for the transaction of products and services. According to the data surveyed by the National Statistical Office, the on-line transaction in 2007 for a year, 15.7656 trillion, shows a 17.1%(2.3060 trillion won) increase over last year, of these, the amount of B2C has been increased 12.0%(10.2258 trillion won). Like this, because the entry barrier of on-line market of Korea is low, many retailers could easily enter into the market. So the bigger its scale is, but on the other hand, the tougher its competition is. Particularly due to the Internet and innovation of IT, the existing market has been changed into the perfect competitive market(Srinivasan, Rolph & Kishore, 2002). In the early years of on-line business, they think that the main reason for success is a moderate price, they are awakened to its importance of on-line service quality with tough competition. If it's not sure whether customers can be provided with what they want, they can use the Web sites, perhaps they can trust their products that had been already bought or not, they have a doubt its viability(Parasuraman, Zeithaml & Malhotra, 2005). Customers can directly reserve and issue their air tickets irrespective of place and time at the Web sites of travel agencies or airlines, but its empirical studies about these Web sites for reserving and issuing air tickets are insufficient. Therefore this study goes on for following specific objects. First object is to measure service quality and service recovery of Web sites for reserving and issuing air tickets. Second is to look into whether above on-line service quality and on-line service recovery have an impact on overall service quality. Third is to seek for the relation with overall service quality and customer satisfaction, then this customer satisfaction and loyalty intention. 2. Theoretical Background 2.1 On-line Service Quality Barnes & Vidgen(2000; 2001a; 2001b; 2002) had invented the tool to measure Web sites' quality four times(called WebQual). The WebQual 1.0, Step one invented a measuring item for information quality based on QFD, and this had been verified by students of UK business school. The Web Qual 2.0, Step two invented for interaction quality, and had been judged by customers of on-line bookshop. The WebQual 3.0, Step three invented by consolidating the WebQual 1.0 for information quality and the WebQual2.0 for interactionquality. It includes 3-quality-dimension, information quality, interaction quality, site design, and had been assessed and confirmed by auction sites(e-bay, Amazon, QXL). Furtheron, through the former empirical studies, the authors changed sites quality into usability by judging that usability is a concept how customers interact with or perceive Web sites and It is used widely for accessing Web sites. By this process, WebQual 4.0 was invented, and is consist of 3-quality-dimension; information quality, interaction quality, usability, 22 items. However, because WebQual 4.0 is focusing on technical part, it's usable at the Website's design part, on the other hand, it's not usable at the Web site's pleasant experience part. Parasuraman, Zeithaml & Malhorta(2002; 2005) had invented the measure for measuring on-line service quality in 2002 and 2005. The study in 2002 divided on-line service quality into 5 dimensions. But these were not well-organized, so there needed to be studied again totally. So Parasuraman, Zeithaml & Malhorta(2005) re-worked out the study about on-line service quality measure base on 2002's study and invented E-S-QUAL. After they invented preliminary measure for on-line service quality, they made up a question for customers who had purchased at amazon.com and walmart.com and reassessed this measure. And they perfected an invention of E-S-QUAL consists of 4 dimensions, 22 items of efficiency, system availability, fulfillment, privacy. Efficiency measures assess to sites and usability and others, system availability measures accurate technical function of sites and others, fulfillment measures promptness of delivering products and sufficient goods and others and privacy measures the degree of protection of data about their customers and so on. 2.2 Service Recovery Service industries tend to minimize the losses by coping with service failure promptly. This responses of service providers to service failure mean service recovery(Kelly & Davis, 1994). Bitner(1990) went on his study from customers' view about service providers' behavior for customers to recognize their satisfaction/dissatisfaction at service point. According to them, to manage service failure successfully, exact recognition of service problem, an apology, sufficient description about service failure and some tangible compensation are important. Parasuraman, Zeithaml & Malhorta(2005) approached the service recovery from how to measure, rather than how to manage, and moved to on-line market not to off-line, then invented E-RecS-QUAL which is a measuring tool about on-line service recovery. 2.3 Customer Satisfaction The definition of customer satisfaction can be divided into two points of view. First, they approached customer satisfaction from outcome of comsumer. Howard & Sheth(1969) defined satisfaction as 'a cognitive condition feeling being rewarded properly or improperly for their sacrifice.' and Westbrook & Reilly(1983) also defined customer satisfaction/dissatisfaction as 'a psychological reaction to the behavior pattern of shopping and purchasing, the display condition of retail store, outcome of purchased goods and service as well as whole market.' Second, they approached customer satisfaction from process. Engel & Blackwell(1982) defined satisfaction as 'an assessment of a consistency in chosen alternative proposal and their belief they had with them.' Tse & Wilton(1988) defined customer satisfaction as 'a customers' reaction to discordance between advance expectation and ex post facto outcome.' That is, this point of view that customer satisfaction is process is the important factor that comparing and assessing process what they expect and outcome of consumer. Unlike outcome-oriented approach, process-oriented approach has many advantages. As process-oriented approach deals with customers' whole expenditure experience, it checks up main process by measuring one by one each factor which is essential role at each step. And this approach enables us to check perceptual/psychological process formed customer satisfaction. Because of these advantages, now many studies are adopting this process-oriented approach(Yi, 1995). 2.4 Loyalty Intention Loyalty has been studied by dividing into behavioral approaches, attitudinal approaches and complex approaches(Dekimpe et al., 1997). In the early years of study, they defined loyalty focusing on behavioral concept, behavioral approaches regard customer loyalty as "a tendency to purchase periodically within a certain period of time at specific retail store." But the loyalty of behavioral approaches focuses on only outcome of customer behavior, so there are someone to point the limits that customers' decision-making situation or process were neglected(Enis & Paul, 1970; Raj, 1982; Lee, 2002). So the attitudinal approaches were suggested. The attitudinal approaches consider loyalty contains all the cognitive, emotional, voluntary factors(Oliver, 1997), define the customer loyalty as "friendly behaviors for specific retail stores." However these attitudinal approaches can explain that how the customer loyalty form and change, but cannot say positively whether it is moved to real purchasing in the future or not. This is a kind of shortcoming(Oh, 1995). 3. Research Design 3.1 Research Model Based on the objects of this study, the research model derived is