• Journal of Korean Society of Rural Planning
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• v.26 no.4
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• pp.107-116
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• 2020

The purpose of this study is to analyze the characteristics of the 11 southeastern states in the United States by using regional characteristics variables and to classify the regions. First, 19 variables from four categories of population, society, industry-economy and urban service were selected and factor analysis were conducted, and the result showed five major factors of population, economic condition, job and commuting. Based on the following factor scores, a cluster analysis was conducted, and eight types of big city, medium-sized city, bed town, small town, urban hinterland, retirement town, and rural village were derived. These types of spatial distribution characteristics showed big cities were by different types of regions and they formed metropolitan areas. Each types of classified regions were located along the road network with hierarchy. The study focused on cases in the southeastern regions of the United States and can be used as a comparison with Korean cases. If the same research method is applied to Korea in the future, or if the time series of changes is tracked by analyzing different time points, it will greatly help identify the characteristics of urban and rural mixed areas.

• Korean Journal of Construction Engineering and Management
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• v.22 no.1
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• pp.106-115
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• 2021

Classifying work trades using images can serve an important role in a multitude of advanced applications in construction management and automated progress monitoring. However, images obtained from work sites may not always be clean. Defective images can damage an image classifier's accuracy which gives rise to a needs for a method to enhance a work trade image classifier's performance. We propose a method that uses work dependency information to aid image classifiers. We show that using work dependency can enhance the classifier's performance, especially when a base classifier is not so great in doing its job.

• Journal of Sport and Applied Science
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• v.7 no.2
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• pp.53-60
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• 2023

Purpose: This study examines the role of leaders of sport organizations from the perspectives of rank-and-file volunteers. Specifically, the study explores which factors are important in leading volunteers and how rank-and-filers interact with their leaders. Research design, data, and methodology: This study reviews a comprehensive literature on volunteer and leadership theories which are trait theory, behavior theory, and contingency theory. Given the comprehension of prior structure of knowledge on leadership, the study provides a structure of knowledge on volunteer and leadership in sport context and discusses managerial implications for leaders in sport organization. Results: With an exploration of sport leadership, this study proposes a volunteer classification model which presents four-volunteer types: professional volunteer, company volunteer, general volunteer, and school volunteer. Furthermore, this study discussed managerial implications for sport organization leaders. Conclusions: Paid employees may be prepared to accept a job and its requirements mainly due to economic benefits. Volunteers, however, do not pursue economic benefits through their activity. Different types of motivation between paid employees and volunteers bring to surface how a leader influences volunteer effectively. A conceptual volunteer clarification model could be examined in real world situations. Insights for future studies were discussed.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.167-174
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• 2023

Currently, the budget for vocational competency development training has been expanded, but the number of participants has decreased. As the budget for the Vocational Competency Development Project increases, the participation of a large number of people becomes necessary. This study aims to derive factors that affect satisfaction by selecting factors related to respondent characteristics, training institutions, training types, and job performance for satisfaction with vocational competency development training, and to study ways to improve satisfaction. Data were collected through focus group interviews (FGI), and logistic regression analysis was conducted through feasibility review and reliability analysis. As a result, in the case of the model, it was confirmed that the degree of agreement between the case actually measured and the case predicted by the model was low in the Hosmer and Lemeshow test, but the overall classification accuracy was classified as 96.0% in the classification accuracy table. As for the influence of the factors, the result was derived that the application of knowledge technology, training institution facility equipment, Business Collaboration, long-term work plan, and satisfaction with work performed have an influence in the order.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.744-751
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• 2022

Construction is among the most dangerous industries with numerous accidents occurring at job sites. Following an accident, an investigation report is issued, containing all of the specifics. Analyzing the text information in construction accident reports can help enhance our understanding of historical data and be utilized for accident prevention. However, the conventional method requires a significant amount of time and effort to read and identify crucial information. The previous studies primarily focused on analyzing related objects and causes of accidents rather than the construction activities. This study aims to extract construction activities taken by workers associated with accidents by presenting an automated framework that adopts a deep learning-based approach and natural language processing (NLP) techniques to automatically classify sentences obtained from previous construction accident reports into predefined categories, namely TRADE (i.e., a construction activity before an accident), EVENT (i.e., an accident), and CONSEQUENCE (i.e., the outcome of an accident). The classification model was developed using Convolutional Neural Network (CNN) showed a robust accuracy of 88.7%, indicating that the proposed model is capable of investigating the occurrence of accidents with minimal manual involvement and sophisticated engineering. Also, this study is expected to support safety assessments and build risk management systems.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.306-315
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• 2011

The study defines the job characteristics anxiety of the private security workers and the effect of the performance. While the study selected the private guards working at the private security companies registered at the National Police Agency in Seoul 2011 and sampled total 300 people by using the judgment sampling method, the final input case number is 266 people. The study used alpha value of the reliability analysis and the maximum-likelihood classification of the covariation structure analysis in order to verify the validity of the survey and the reliability. With the research method and the process the result of the study is as follows. First, the task importance of the private security workers affects the minus influence to the characteristic anxiety. Second, the feedback of the private security workers affects the minus influence to the characteristic anxiety. Third, the job autonomy of the private security workers affects the minus influence to the characteristic anxiety. Fourth, the feedback of the private security workers affects the plus influence to the task performance. Fifth, the job autonomy of the private security workers affects the plus influence to the task performance. Sixth, the skill variety of the private security workers affects the plus influence to the task performance. Seventh, the characteristic anxiety of the private security workers affects the plus influence to the task performance.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.21-29
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• 2023

In this paper, we propose an AI-based system for automatically classifying industry and occupation codes in the population census. The accurate classification of industry and occupation codes is crucial for informing policy decisions, allocating resources, and conducting research. However, this task has traditionally been performed by human coders, which is time-consuming, resource-intensive, and prone to errors. Our system represents a significant improvement over the existing rule-based system used by the statistics agency, which relies on user-entered data for code classification. In this paper, we trained and evaluated several models, and developed an ensemble model that achieved an 86.76% match accuracy in industry and 81.84% in occupation, outperforming the best individual model. Additionally, we propose process improvement work based on the classification probability results of the model. Our proposed method utilizes an ensemble model that combines transfer learning techniques with pre-trained models. In this paper, we demonstrate the potential for AI-based systems to improve the accuracy and efficiency of population census data classification. By automating this process with AI, we can achieve more accurate and consistent results while reducing the workload on agency staff.

• (The)Study of the Eastern Classic
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• no.69
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• pp.9-44
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• 2017

Seonsaengan(先生案) is the predecessor's lists. The list includes the names of the predecessor, the date of the appointment, the date of return, the previous job, and the next job. Therefore, previous studies on the local recruitment and Jungin (中人) that can not be found in general personnel information of the Joseon dynasty were conducted. However, the status and classification of the list has not been achieved yet. So this study aims to clarify the status, classification and data characteristics of the list. 176 books, are the Joseon dynasty lists of predecessors, remain to this day. These lists are in Jangseogak(47 cases), Kyujanggak(80 cases), the National Library of Korea(24 cases) and other collections(25 cases). Jangseogak has lists of royal government officials, Kyujanggak has lists of central government officials, and the National Library of Korea and other collections have lists of local government officials. However, this paper focuses on accessible Jangseogak list of 47 cases. As I mentioned earlier, the Jangsaegak lists are generally related to the royal government officails. This classification includes 18 central government officials, 5 local government officials, and 24 royal government officails. If the list is classified as contents, it can be classified into six rituals and diplomatic officials, 12 royal government officials, 5 local government officials, 14 royal tombs officials, and 10 royal education officials. Through the information on the list, the following six characteristics can be summarized. First, it can be finded the basic personal information about the recorded person. Second, the period of office and reasons for leaving the office and office can be known. Third, changes in the office system can be confirmed. Fourth, it can be looked at one aspect of the personnel administration system of the Joseon Dynasty through the previous workplace and the next job. Fifth, it is possible to know days that are particularly important for each government. Sixth, the contents of work evaluation can be confirmed. This is the reality of the Joseon Dynasty, which is different from the contents recorded in the Code. Through this, it is possible to look at the personnel administration system of the Joseon Dynasty. However, in order to carry out a precise review, it is necessary to make a database for 176 lists. In addition, if data is analyzed in connection with existing genealogy data, it will be possible to establish a basis for understanding the personnel administration system of the Joseon Dynasty.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.471-477
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• 2021

In the field of display, a smart factory means more efficient display manufacturing using AI/BIG DATA technology not only for job automation, but also for existing process management, moving facilities, process abnormalities, and defect classification. In the past, when defects appeared in the display manufacturing process, the classification of defects and coping with process abnormalities were different, a lot of time was consumed for this. However, in the field of display manufacturing, advanced process equipment must be used, and it can be said that the competitiveness of the display manufacturing industry is to quickly identify the cause of defects and increase the yield. In this paper, we will summarize the cases in which smart factory AI/BIG DATA technology is applied to domestic display manufacturing, and analyze what advantages can be derived compared to existing methods. This information can be used as prior knowledge for improved smart factory development in the field of display manufacturing using AI/BIG DATA.

• Agribusiness and Information Management
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• v.6 no.2
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• pp.1-12
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• 2014

HMR is a home-style food product designed for convenience and cooked outside the home leaving out cumbersome cooking process and consumed at home. The present paper aims to find out factors that influence the consumption of HMR by analyzing data on food consumption during the 3 years between December 2010 and November 2013. Following the classification of Costa et al. (2001), this study categorized HMR products as 3 types as follows: C1 (ready to eat), C2 (ready to heat) and C3 (ready to cook), and examined factors affecting purchase rate and per capita purchase price for each type of HMR product. The results of our analysis show that only the purchase rate of C3 products was influenced by whether the purchaser was housewife with job or not. For those who do not live together with parents, per capita purchase price for HMR was high; and the more they ate out, the higher the purchase rate of HMR was.