• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.434-440
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• 2024

SIZE KOREA updates body measurement data every five years, providing essential information for the fashion industry. This anthropometric data is widely used to diagnose consumer body shapes and develop optimal clothing sizes. Artificial intelligence, particularly machine learning, excels in predicting such body shape classifications. This study seeks to enhance the suitability of clothing design by applying the new analytical methodology of machine learning techniques to better capture and classify the unique body shapes of Korean women. In this study, machine learning techniques such as K-means clustering, Silhouette analysis, and Decision Tree analysis were used to classify the lower body shapes of Korean women in their twenties and identify standard body shapes useful for slacks design. The results showed that the lower body of the age group could be classified into three categories: 'small stature' (the majority), 'tall with an average lower body volume,' and 'medium height with a fuller lower body' (the smallest share). The three-cluster approach is validated through Silhouette analysis, which minimizes misclassification. Decision Tree analysis then further defines the criteria for these clusters, highlighting waist height and hip depth as the most significant factors, achieving a classification accuracy of 90.6%. While this study is not directly related to Robotic Process Automation, its detailed analysis of body shapes for slacks patterns can aid RPA in clothing production. Future research should continue integrating machine learning in human body and fashion design studies.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.18-24
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• 2024

Efficient path planning for mobile crane lifting operations in the construction industry is essential for ensuring smooth machinery operation, worker safety, and the timely completion of projects. The inherently complex construction sites, characterized by dynamic environments, constantly changing conditions, and numerous static and mobile obstacles, underscore the necessity for advanced algorithms capable of generating optimal paths under various constraints. Mobile crane path planning algorithms have been researched extensively and possess the potential to resolve the challenges presented by construction sites. However, the application of these algorithms in actual construction sites is rare, suggesting a need for ongoing research and development in this field. This paper begins by systematically identifying and analyzing relevant research papers using predetermined keywords, providing a comprehensive review of the current state of mobile crane path planning algorithms. Specifically, it categorizes mobile crane path planning algorithms into four main groups: Graph search-based algorithms, Sampling-based algorithms, Nature-inspired algorithms, and Newly developed algorithms. It performs a critical analysis of each category, offering guidance to researchers exploring path planning solutions suitable for the dynamic and complex environments of construction sites. Through this review, we affirm the need for continued interest and attempts at new methodologies in mobile crane path planning, suggesting improvements for further research and practical application of these algorithms.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.2
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• pp.59-75
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• 2019

This study was carried out to explore the 4th Industrial Revolution technology from the perspective of the landscape industry to provide the basic data necessary to increase the virtuous circle value. The 4th Industrial Revolution, the characteristics of the landscape industry and urban regeneration were considered and the methodology was established and studied including the technical classification system suitable for systematic research, which was selected as a framework. First, the 4th Industrial Revolution technology based on digital data was selected, which could be utilized to increase the value of the virtuous circle for the landscape industry. From 'Element Technology Level', and 'Core Technology' such as the Internet of Things, Cloud Computing, Big Data, Artificial Intelligence, Robot, 'Peripheral Technology', Virtual or Augmented Reality, Drones, 3D 4D Printing, and 3D Scanning were highlighted as the 4th Industrial Revolution technology. It has been shown that it is possible to increase the value of the virtuous circle when applied at the 'Trend Level', in particular to the landscape industry. The 'System Level' was analyzed as a general-purpose technology, and based on the platform, the level of element technology(computers, and smart devices) was systematically interconnected, and illuminated with the 4th Industrial Revolution technology based on digital data. The application of the 'Trend Level' specific to the landscape industry has been shown to be an effective technology for increasing the virtuous circle values. It is possible to realize all synergistic effects and implementation of the proposed method at the trend level applying the element technology level. Smart gardens, smart parks, etc. have been analyzed to the level they should pursue. It was judged that Smart City, Smart Home, Smart Farm, and Precision Agriculture, Smart Tourism, and Smart Health Care could be highly linked through the collaboration among technologies in adjacent areas at the Trend Level. Additionally, various utilization measures of related technology applied at the Trend Level were highlighted in the process of urban regeneration, public service space creation, maintenance, and public service. In other words, with the realization of ubiquitous computing, Hyper-Connectivity, Hyper-Reality, Hyper-Intelligence, and Hyper-Convergence were proposed, reflecting the basic characteristics of digital technology in the landscape industry can be achieved. It was analyzed that the landscaping industry was effectively accommodating and coordinating with the needs of new characters, education and consulting, as well as existing tasks, even when participating in urban regeneration projects. In particular, it has been shown that the overall landscapig area is effective in increasing the virtuous circle value when it systems the related technology at the trend level by linking maintenance with strategic bridgehead. This is because the industrial structure is effective in distributing data and information produced from various channels. Subsequent research, such as demonstrating the fusion of the 4th Industrial Revolution technology based on the use of digital data in creation, maintenance, and service of actual landscape space is necessary.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 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 Korean Academy of Nursing Administration
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• v.1 no.2
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• pp.388-407
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• 1995

This study is (a) to describe the history of Total Quality Management (TQM) generated in the industry, health care service, and nursing society ; (b) to define the concept, total quality management including the definition of quality ; (C) to explain the each principle of TQM theory developed by main theorists, E. Deming, J. Juran, and B. Crosby ; (d) to give the examples related to TQM implementation at the health care organization ; and (e) to mention the extent to which the health care organizations are able to evaluate their cultural organization toward TQM and have had the way to measure the effect of TQM implementation. TQM referred to Continuous Quality Improvement(CQI), Quality Improvement(QI), and Total Quality Improvement(TQI), was not recognized by experts in the United States industry, but by economists in Japan until the end of the 1970's. However, the United States' government led to introduce the principles of TQM to general industry as well as health care service area so that TQM became a main philosophy to manage the organizations in health care service. TQM is a structured, systematic process for creating organization-wide participation in planning and implementing continuous improvement in quality. E. Deming established the "Chain reaction in Quality" and the fourteen point of TQM. The Chain reaction in quality is to describe the relationship among the reduction of waste, rework, and delay, quality improvement, customer satisfaction, and productivity. There are fourteen points to explain the principles of TQM by E. Deming. Juran defined the "Quality Trilogy" to improve the level of quality in any organization. Quality Trilogy has three steps such as quality planning, quality control, and quality improvement for implementing the TQM projects. Crosby describes his TQM theory by establishing "Four Absolutes" and "Fourteen steps in TQM" implementation. Until now, most healthcare organizations have made efforts to organize the TQM task team and to implement TQM principles with various issues. There are three priorities to select the TQM issues : High-volume, High-risk, and Problem-prone. However, there is no absolute, credible measurement yet to evaluate the effects of TQM implementation in health care organization regardless of the classification of health care organizations, geographical background, and social influence. Thus, developing the evaluation way in terms of TQM is the foremost task in health service area. The most important thing for TQM implementation in the organization is to settle up the concept, cultural transformation from traditional management toward quality.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.687-697
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• 2014

This study ranges from planning phase to the detailed design phase of steel box girder bridge and proposes ways to generate semantic information of components through Industry Foundation Classes (IFC), a data model for Building Information Modeling (BIM). The classification of components of steel box girder bridge was performed to define information items required for identifying semantic information based on IFC, and spatial information items based on topology and physical information items based on functions of components were classified to create additional properties that does not support IFC by applying user-defined property set within the IFC framework. Steel box girder bridge information model based on IFC was implemented through BIM software and semantic information input interface, which was developed in this study to examine the effectiveness of the additionally created user-defined property. Furthermore, the quantity take-off of components was performed through information model of steel box girder bridge, and the applicability of the proposed method was tested by comparing the quantity take-off based on design document with the result.

• Journal of Distribution Science
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• v.12 no.11
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• pp.77-83
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• 2014

Purpose - The National Agricultural Cooperative Federation of Korea and National Fisheries Cooperative Federation of Korea have prosecuted both financial and retail businesses. As cooperatives are public institutions and receive government support, their sound management is required by the Financial Supervisory Service in Korea. This is mainly managed by CAEL, which is changed by CAMEL. However, NFFC's business section, managing the finance and retail businesses, is unified and evaluated; the CAEL model has an insufficient classification to evaluate the retail industry. First, there is discrimination power as regards CAEL. Although the retail business sector union can receive a higher rating on a CAEL model, defaults have often been reported. Therefore, a default prediction model is needed to support a CAEL model. As we have the default prediction model using a subdivision of indexes and statistical methods, it can be useful to have a prevention function through the estimation of the retail sector's default probability. Second, separating the difference between the finance and retail business sectors is necessary. Their businesses have different characteristics. Based on various management indexes that have been systematically managed by the National Fisheries Cooperative Federation of Korea, our model predicts retail default, and is better than the CAEL model in its failure prediction because it has various discriminative financial ratios reflecting the retail industry situation. Research design, data, and methodology - The model to predict retail default was presented using logistic analysis. To develop the predictive model, we use the retail financial statements of the NFCF. We consider 93 unions each year from 2006 to 2012 to select confident management indexes. We also adapted the statistical power analysis that is a t-test, logit analysis, AR (accuracy ratio), and AUROC (Area Under Receiver Operating Characteristic) analysis. Finally, through the multivariate logistic model, we show that it is excellent in its discrimination power and higher in its hit ratio for default prediction. We also evaluate its usefulness. Results - The statistical power analysis using the AR (AUROC) method on the short term model shows that the logistic model has excellent discrimination power, with 84.6%. Further, it is higher in its hit ratio for failure (prediction) of total model, at 94%, indicating that it is temporally stable and useful for evaluating the management status of retail institutions. Conclusions - This model is useful for evaluating the management status of retail union institutions. First, subdividing CAEL evaluation is required. The existing CAEL evaluation is underdeveloped, and discrimination power falls. Second, efforts to develop a varied and rational management index are continuously required. An index reflecting retail industry characteristics needs to be developed. However, extending this study will need the following. First, it will require a complementary default model reflecting size differences. Second, in the case of small and medium retail, it will need non-financial information. Therefore, it will be a hybrid default model reflecting financial and non-financial information.

• Korean Journal of Health Education and Promotion
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• v.21 no.3
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• pp.195-211
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• 2004

Stress is a primary health promotion issue in worksite research because psychological distress is closely related not only to workers' health status but also to their job performance. This study identified the significant health behaviors affecting workers' job-related stress in Korean manufacturing industry with the national survey data conducted by the Korean Occupational Safety and Health Agency in 2003. A total of 7,818 factory workers in 1,562 manufacturing companies participated in the Korean nation-wide occupational health survey and 3,390 workers answered that they had any stressors in their workplace among the 7,818 workers finally participated in the analysis. Participants were selected by the stratified proportional sampling process by manufacturing industry classification, company size, and company locations (8 metropolitan and 8 non-metropolitan regions) in Korea. Trained interviewers visited the target companies and interviewed the factory workers randomly selected in each company. Smoking, drinking, weight control, exercise, sleeping, break time at work, and perceived fatigue were included in the health behavior construct. Stress symptoms was consisted of physical and psychological stress with 8 items. All survey responses were anonymously coded into the SPSS statistical program and testified using stepwise multiple regression analysis. Male workers were 73.5% and the 30s were 40.0% among the age groups. The married and the high school graduate were majority with 52.1% and 61.8% each. Current smokers were 44.7% and More than 50% of the participants drank alcohol sometimes. No exercise group was 59.3% and the participants who dissatisfied with their daily sleeping hours were 43.5%. In t-test and analysis of variance, the significant general characteristics associated with physical and psychological job stress were young age (p<0.001), single marital status (p<0.001), and short working period at the present company (p<0.001). The health behaviors related to physical job stress were current smoking, weight change during the past one year (p<0.001), weight control effort (p<0.001), exercise (p<0.001), daily sleeping dissatisfaction (p<0.001), break time, and perceived fatigue (p<0.001). All 10 health behavior factors were significantly associated with psychological job stress (p<0.05). Weight change, weight control effort, exercise, daily sleeping dissatisfaction, little break at work, and high perceived fatigue were significant factors affecting job stress. Daily sleeping dissatisfaction, little break at work, little exercise, weight change for the past one year and young age were selected as the significant health behavior and general factors affecting physical job stress symptoms in stepwise multiple regression analysis. The five factors explained 18.9% of the physical stress score variance. Six factors were selected as the significant health behaviors affecting psychological job stress: daily sleeping dissatisfaction, little exercise, frequent drinking alcohol, high perceived fatigue, little break at work, and little weight control effort. The six factors explained 10.6% of the psychological stress score variance.

• Asian Journal of Turfgrass Science
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• v.23 no.2
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• pp.253-264
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• 2009

The purpose of seed certification is to preserve the genetic purity and identity of seed varieties. This study is to provide information concerning seed certification procedures and certification standards of Kentucky bluegrass especially used in golf courses. We analyzed data from the seed certification standards of three states (Washington, Idaho and Oregon) in U.S.A. The certification processes both field inspection and laboratory requirement satisfying the minimum seed quality standards. The seed harvesting field must be propagated with the specified class of seeds and requires an adequate isolated distance from other crops. Moreover, the field should be clean and free from the objectionable weeds. The seed analysis tests include a germination rate, a percentage of pure seed, contents of other crop seed, weed seed, and inert matter. The certification standards of the certified seed and the sod quality seed showed general similarity in all three states. The certification standards of the sod quality seed should have less than 0.02% of maximum weed seed. The certified seed should have less than 0.3% of maximum weed seeds. Those certification standards of seed quality should guaranty the quality of turfgrass establishment of golf course.

• Economic and Environmental Geology
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• v.54 no.5
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• pp.573-580
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• 2021

This study analyzed the induced effects of the aggregate and stone sectors using the industry association table. First, the added value of the aggregate and stone sectors was summarized, and then the intermediate input structure and induced effect were analyzed. In terms of value-added structure, aggregate and stone showed a higher employee remuneration rate compared to the manufacturing industry, and a higher rate of operating surplus compared to other mining industries. The intermediate input structure summarizes the sector using aggregate and stone products as intermediate inputs and their input ratio. The proportion of the intermediate element input structure was confirmed. In addition, the main input sectors of ready-mixed concrete, the largest consumer of aggregate and stone, are also summarized. The production-inducing effect of aggregate and stone showed a higher influence coefficient than the sensitivity coefficient, confirming that they had a relatively large rear chain effect. The production inducement effect was reviewed by reconstructing the industry association table, and it was found to show a relative superiority in the influence coefficient, similar to the results derived according to the provisional classification of the Bank of Korea.