• Korean Chemical Engineering Research
• /
• v.44 no.6
• /
• pp.650-658
• /
• 2006

The recycling of TDA from solid waste of TDI plant(TDI-R) by near-critical hydrolysis reaction had been studied by means of a statistical design of experiment. The main and interaction effects of process variables had been defined from the experiments in a batch reactor and the correlation equation with process variables for TDA yield had been obtained from the experiments in a continuous pilot plant. It was confirmed that the effects of reaction temperature, catalyst type and concentration, and the weight ratio of water to TDI-R(WR) on TDA yield were significant. TDA yield decreased with increases in reaction temperature and catalyst concentration, and increased with an increase in WR. As a catalyst, NaOH was more effective than $Na_2CO_3$ for TDA yield. The interaction effects between catalyst concentration and temperature, WR and temperature, catalyst type and reaction time on TDA yield had been defined as significant. Although the effect of catalyst concentration on TDA yield at $300^{\circ}C$ as subcritical water was insignificant, the TDA yield decreased with increasing catalyst concentration at $400^{\circ}C$ as supercritical water. On the other hand, the yield increased with an increase in WR at $300^{\circ}C$ but showed negligible effect with WR at $400^{\circ}C$. The optimization of process variables for TDA yield has been explored with a pilot plant for scale-up. The catalyst concentration and WR were selected as process variables with respect to economic feasibility and efficiency. The effects of process variables on TDA yield had been explored by means of central composite design. The TDA yield increased with an increase in catalyst concentration. It showed maximum value at below 2.5 of WR and then decreased with an increase in WR. However, the ratio at which the TDA yield showed a maximum value increased with increasing catalyst concentration. The correlation equation of a quadratic model with catalyst concentration and WR had been obtained by the regression analysis of experimental results in a pilot plant.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.31 no.6 s.165
• /
• pp.974-984
• /
• 2007

The purpose of this study was to find differences in evaluation criteria and to find differences in preferred images based on benefits segmented groups of jeans products consumers. Male and female Korean university students participated in the study. Quota sampling method was used to collect the data based on gender and a residential area of the respondents. Data from 492 questionnaires were used in the analysis. Factor analysis, Cronbach's alpha coefficient, cluster analysis, one-way ANOVA, and post-hoc test were conducted. As a result, respondents who seek multi-benefits considered aesthetic criteria(e.g., color, style, design, fit) and quality performance criteria(e.g., durability, ease of care, contractibility, flexibility) more importantly when evaluating and purchasing jeans products. Respondents who seek brand name considered extrinsic criteria(e.g., brand reputation, status symbol, country of origin, fashionability) more importantly than respondents who seek economic efciency. Respondents who seek multi-benefits such as attractiveness, fashion, individuality, and utility tend to prefer all the images: individual image, active image, sexual image, sophisticated image, and simple image when wearing jeans products. Respondents who seek fashion are likely to prefer individual image, and respondents who seek brand name more prefer both individual image and polished image. Mean while, respondents who seek economical efficiency less prefer sexual image and polished image.

• International Journal of Highway Engineering
• /
• v.11 no.4
• /
• pp.69-78
• /
• 2009

Korea has achieved significant economic growth with building the Gyeongbu Expressway. As the number of new road construction projects has decreased, it becomes more important to maintain optimal status of the current road networks. One of the best ways to accomplish it is weight enforcement as active control measure of traffic load. This study is to develop High-speed Weigh-in-motion System in order to enhance efficiency of weight enforcement, and to analyze patterns of overloaded trucks on highways through the system. Furthermore, it is to review possibilities of developing overweight control system with application of the HS-WIM system. The HS-WIM system developed by this study consists of two sets of an axle load sensor, a loop sensor and a wandering sensor on each lane. A wandering sensor detects whether a travelling vehicle is off the lane or not with the function of checking the location of tire imprint. The sensor of the WIM system has better function of classifying types of vehicles than other existing systems by detecting wheel distance and tire type such as single or dual tire. As a result, its measurement errors regarding 12 types of vehicle classification are very low, which is an advantage of the sensor. The verification tests of the system under all conditions showed that the mean measurement errors of axle weight and gross axle weight were within 15 percent and 7 percent respectively. According to the WIM rate standard of the COST-323, the WIM system of this study is ranked at B(10). It means the system is appropriate for the purpose of design, maintenance and valuation of road infrastructure. The WIM system in testing a 5-axle cargo truck, the most frequently overloaded vehicle among 12 types of vehicles, is ranked at A(5) which means the system is available to control overloaded vehicles. In this case, the measurement errors of axle load and gross axle load were within 8 percent and 5 percent respectively. Weight analysis of all types of vehicles on highways showed that the most frequently overloaded vehicles were type 5, 6, 7 and 12 among 12 vehicle types. As a result, it is necessary to use more effective overweight enforcement system for vehicles which are seriously overloaded due to their lift axles. Traffic volume data depending upon vehicle types is basic information for road design and construction, maintenance, analysis of traffic flow, road policies as well as research.

• Journal of Life Science
• /
• v.26 no.2
• /
• pp.190-197
• /
• 2016

Aflatoxin M₁ can be produced in cow’s milk when cows eat contaminated produce. Milk is a major source of food for infants and for children who have a weak level of immunity, and the detection of Aflatoxin M₁ for risk assessment is necessary in order to reduce the amount of it in milk. In this study, the Aflatoxin M₁ level was monitored for one year in raw milk samples obtained from Chungnam Province, Korea. The milk samples were divided into three categories: 1. milk samples from a standard general farm, 2. milk samples from a HACCP controlled farm, and 3. milk samples from the supply of Aflatoxin M₁ reduced fodder. The average concentrations of Aflatoxin M₁ in milk were 0.023±0.005 ug/l for the standard general farm, 0.017±0.004 ug/l for the HACCP controlled farm, and 0.013±0.003 ug/l for the supply of Aflatoxin M₁ reduction fodder. Milk collected from the supply of Aflatoxin M₁ reduction fodder had the lowest level of Aflatoxin M₁. However, when efficiency and economic aspects are considered the most effective way of reducting Aflatoxin M₁, could be taking milk from the HACCP controlled farm and implementing good feed management. Institutional support from the government, careful management of dairy farming, and a strict farm sanitation program are required in order to lower the level of Aflatoxin M₁ in milk.

• Journal of agriculture & life science
• /
• v.46 no.5
• /
• pp.73-82
• /
• 2012

Allele information from the analysis of the 13 microsatellite (MS) markers, were classified into the $F_0$, $F_1$ and $F_2$ generations, and probabilities of the same individual emergency in each generation was calculated. As a result, the 13 MS markers showed an estimate of $3.84{\times}10^{-23}$ on the premise of the randomly mated group of $F_2$, which implies that the same individuals may emerge by the use of 37 kinds of SNP markers. In this study, the experimental pigs were intercross between only 2 breeds (Korean native pig and Landrace). In addition, the success rate of paternity tests was analyzed on the whole group, by the use of the 13 MS markers and 37 SNP markers. As regards the exclusionary power of the second parent ($PE_{pu}$), MS markers and SNP markers showed 0.97897 and 0.99149, respectively. In relation to the parent exclusion power of both parent (PE), MS markers and SNP markers showed 0.99916 and 0.99949, respectively. In the case of the estimate to identify parental candidates that had the highest probability ($PNE_{pp}$), the two showed 1.00000 all. The Korean pig industry tends to mass produce hogs with limited numbers of alleles in limited parents. Such being the case, there is a need to organize a marker, for which it is imperative to find markers with high efficiency and high economic feasibility of the characteristics of DNA markers, sample size, the accuracy and expenses of genotyping cost, the manageability of data and the compatibility among analysis systems.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.23 no.6
• /
• pp.719-730
• /
• 2017

Recently, oceanic research has been carried out investigating global scientific interests and the territorial management of national marine jurisdictional waters, including exclusive economic zones (EEZ) and the open seas. To meet the needs of ocean researchers pursuing these - objectives, acquiring advanced research infrastructure, including research vessels, large facilities, and equipment, is a top priority in ocean science. However, ocean science is a similar to space science, and securing resources and state-of-the-art technology can be expensive. Faced with these challenges, our study focused on establishing a strategy for the efficient operation and management of research vessels, attempting to establish benchmarks from foreign examples that can be adapted to suit the target context. The results of this study provide ways to identify operating systems that could increase the efficiency of joint-use research vessels. The different systems examined in this study included a joint-use committee-based management system (JCMS, Type 1), private enterprise entrusted operating system (PEOS, Type 2), institutional investment operating system (IIOS, Type 3), and commissioned executive operating system (CEOS, Type 4). The efficiencies of JCMS, PEOS, IIOS and CEOS were 9.17, 5.82, 11.2 and -1.72 %, respectively. Given the total costs involved, the most affordable operating system was IIOS. JCMS was the most cost-effective system based on a quantitative cost-benefit analysis, but IIOS also had an acceptable cost-benefit balance. An operational committee would be required and regulations and guidelines shoulde be established to employ, JCMS, while a strategy to yield independent revenue would be needed to utilize an IIOS system.

• Journal of Korean Society of Forest Science
• /
• v.108 no.3
• /
• pp.429-439
• /
• 2019

The objectives of this study were to gauge the extent of the forestry business through establishing the definition of forestry industry from the perspective of economic convergence and to analyze key components that affect each growth phase of a forestry business entity by classifying them. A total of 1,397 "sixth-sector industry" management entities were certified by the Ministry of Agriculture, Food, and Rural Affairs in South Korea from 2012-2017. Of these, 259 (18.5%) were in the forestry sector. In this study, the 259 forestry management entities were further classified into three phases based on sales distribution: entrance, development, and maturity. The entrance phase (<100 million KRW), development phase (>100 million and <1 billion KRW), and maturity phase (>1 billion KRW) constituted 33.2%, 55.4%, and 12.4% of the total 259 entities, respectively. The results showed that most of the management entities were either in the entrance or development phases, and only a small portion was in the maturity phase. To identify the key variables that affect each of the phases, chi-square analysis was used. We designed the "sixth-sector industry" type as an independent variable, whereas selected region, business organization, manager age group, forest product, processing type, and service type were designated as dependent variables. The results of the analysis showed that the processing and service types influenced all three developmental phases. Moreover, as the phase advanced, processing type showed a higher proportion of health-functional ingredients, such as powder or extract from forest products, which enable to develop and produce a variety of products. Service type also changed from simple experience to integrated experience tourism and finally to tourism education. Distribution and sales channel also turned out to be a significant factor during the development phase. This study provides the basic information needed to guide government support in the implementation of a formal forestry business through convergence as well as to increase the efficiency of business management.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.1-17
• /
• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.48 no.6
• /
• pp.1-15
• /
• 2020

The purpose of this study is to propose a direction for designing and managing a smart park for realizing a smart green city and to present measures in the landscape field to foster related industries. The research process is as follows. First, the concept of a smart park was operationally defined through a literature review, and three principles to be considered in the process of creation and management were established. Second, in terms of the three principles, problems and implications for improvement were derived through an analysis of established cases of smart parks in new and pre-existing cities. Third, a pool of designs and management standards for each spatial component of a smart park was prepared through literature and case studies, and then further refined through brainstorming with experts in related fields. Fourth, measures were suggested to the government, local governments, and the landscape field to promote smart park creation and management. The main findings are as follows. First, the concept of a smart park is defined as "a park that contributes to securing the social, economic, and environmental sustainability of cities and local communities by supporting citizens' safe and pleasant use of parks and improving the management and operational efficiency by utilizing the digital, environment, and material technologies." Second, the three principles of smart parks are to improve the intrinsic value of parks, to improve the innovative functions of parks to solve urban problems, and to make the design, construction, and management process smart. Third, improvement implications were derived through the analysis of cases of smart parks creation in new and pre-existing cities. Fourth, the directions for smart park design and management were suggested in five aspects: green area, hydroponic facility area, road and plaza area, landscape facilities area, and park design method. Fifth, as for policy implications for revitalizing the construction and management of smart parks, the development of smart park policy business models by city growth stage, and park type, the promotion of pilot projects, the promotion of smart park projects in connection with the Korean New Deal policy, and smart park policies led by landscape experts were presented.

• Journal of Bio-Environment Control
• /
• v.31 no.4
• /
• pp.468-475
• /
• 2022

This study was conducted to find out the efficiency of heating initiative temperature and carbon dioxide fertilization in summer squash (Cucurbita moschata D.). The heating start temperature experiment was performed at 9℃, 12℃, and 15℃ using an electric heater and operated when the temperature was lower than the target temperature. The CO₂ fertilization concentration experiment was performed from 7 to 12 with the control, 500 µmol·mol^-1, and 800 µmol·mol^-1 using liquefied carbon dioxide. Investigation items were plant height, stem diameter, number of leaves, leaf area, fresh weight, dry weight, also economic analysis was conducted by surveying only fruits exceeding 100 g. Photosynthesis was measured for the upper leaf position to calculate the saturation point according to the control. The photo saturation point was 587 µmol·m^-2·s^-1, and the CO₂ saturation point was 702 µmol·mol^-1. A_max values by carbon dioxide were 13.4, 17.8, 17.2, 19.6, and 17.5 µmolCO₂·m^-2·s^-1 in the order of 9℃, 12℃, 15℃, 500 µmol·mol^-1, and 800 µmol·mol^-1. In the temperature experiment, 9℃ in growth did not grow normally and no fruiting was performed. 12℃ and 15℃ were higher than 9℃, but there was no significant difference in growth and production. The CO₂ fertilization experiment showed no significant difference between the treatment in growth, but the productivity of 800 µmol·mol^-1 was the best. Comprehensively, the heating initiative temperature of 15℃ was good for crop growth and production, but there is no significant difference from 12℃, so it is good to set the heating start temperature to 12℃ economically, and maintaining of 800 µmol·mol^-1 is effective in increasing production.