• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.65-76
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• 2024

With the recent activation of the offshore wind power industry, there has been a development of power plants with a scale exceeding 400MW, comparable to traditional thermal power plants. Renewable energy, characterized by intermittency depending on the energy source, is a prominent feature of modern renewable power generation facilities, which are structured based on controllable inverter technology. As the integration of renewable energy sources into the grid expands, the grid codes for power system connection are progressively becoming more defined, leading to active discussions and evaluations in this area. In this paper, we propose a method for selecting optimal reactive power compensation capacity when multiple offshore wind farms are integrated and connected through a shared interconnection facility to comply with grid codes. Based on the requirements of the grid code, we analyze the reactive power compensation and excessive stability of the 400MW wind power generation site under development in the southwest sea of Jeonbuk. This analysis involves constructing a generation site database using PSS/E (Power System Simulation for Engineering), incorporating turbine layouts and cable data. The study calculates reactive power due to charging current in internal and external network cables and determines the reactive power compensation capacity at the interconnection point. Additionally, static and dynamic stability assessments are conducted by integrating with the power system database.

• Journal of Korean Society of Disaster and Security
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• v.17 no.1
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• pp.1-8
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• 2024

In Korea, mountainous areas cover 60% of the land, leading to increased factors such as concentrated heavy rainfall and typhoons, which can result in debris flow and landslide. Despite the high risk of disasters like landslides and debris flow, there has been a tendency in most regions to focus more on post-damage recovery rather than preventing damage. Therefore, in this study, precise topographic data was constructed by conducting on-site surveys and drone measurements in areas where debris flow actually occurred, to analyze the risk zones for such events. The numerical analysis program RAMMS model was utilized to perform debris flow analysis on the areas prone to debris flow, and the actual distribution of debris flow was compared and analyzed to evaluate the applicability of the model. As a result, the debris flow generation area calculated by the RAMMS model was found to be 18% larger than the actual area, and the travel distance was estimated to be 10% smaller. However, the simulated shape of debris flow generation and the path of movement calculated by the model closely resembled the actual data. In the future, we aim to conduct additional research, including model verification suitable for domestic conditions and the selection of areas for damage prediction through debris flow analysis in unmeasured watersheds.

• Information Systems Review
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• v.18 no.3
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• pp.155-183
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• 2016

Research on personalized recommender service that uses big data has gained considerable attention given the increasing volume of contents being created. This development indicates the need for service providers to collect personal information and content rating data to personalize content recommendations. Previous studies on this topic proposed algorithms to offer improved recommendations using minimal rating data or service designs and increase the number of ratings. However, limited studies have been conducted on the factors that motivate the ratings input of users, as well as the factors that influence their continuous usage of recommender service. The present study explored the factors that motivate users to enter ratings by conducting in-depth interviews with users who use recommender services. The meanings of these ratings were also explored. Results show that the meaning and usage range of ratings differed based on the stage of a user's with utilization of the service. When users input an initial rating, they treat such a rating as a database to save the impression of a past experience. Such a rating is then used as a tool to reflect the current feeling and thoughts of a user. In the end, users were not only interested in their own rating system, but they also actively sought out the meaning of the rating systems of others and utilized them. Users also expressed mistrust in the recommendations of the service because they were aware of the limitation of the algorithms. This study identified a number of practical implications regarding recommender services.

• Journal of Ginseng Culture
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• v.6
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• pp.116-134
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• 2024

This study aimed to identify the issues in Korean ginseng exports through analyzing the ginseng market. Therefore, the study examined the current ginseng production status in South Korea and China, the major ginseng-producing countries in Northeast Asia, including cultivated areas, harvested areas, and production volumes. For South Korea, specific data on ginseng, such as average prices, operating costs, and production costs, were compiled to demonstrate the production competitiveness of Korean ginseng from a production perspective. Furthermore, as major ginseng-exporting countries, South Korea, China, and Hong Kong's export trends, including export quantities, export values, and export prices, as well as crucial export items and tariff rates, were summarized to showcase the export competitiveness of Korean ginseng. Additionally, this study aimed to understand the consumption patterns of ginseng in China, Hong Kong, and Taiwan by presenting various cases and events in these countries. Based on information related to production, export, and consumption, this study identified obstacles in the ginseng export process, including market downturns, weakened price competitiveness of Korean ginseng, increased market share of competing products like Chinese and Western ginseng, a lack of promotion and marketing, and insufficient development and export of various ginseng products. In response, strategies for overcoming these obstacles were proposed, including diversifying exports, establishing effective production systems, enhancing quality and branding, strengthening promotion and marketing efforts, and developing various ginseng products.

• Knowledge Management Research
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• v.25 no.1
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• pp.135-154
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• 2024

As the digital transformation accelerates, the demand for professionals with competencies in various digital technologies such as artificial intelligence, big data is increasing in the industry. In response, the government is developing various educational programs to nurture talent in these emerging technology fields. However, the lack of a clear definition of competencies, which is the foundation of curriculum development and operation, has posed challenges in effectively designing digital technology education programs. This study systematically reviews the definitions and characteristics of competencies presented in prior research based on a literature review. Subsequently, in-depth interviews were conducted with 30 experts in emerging technology fields to derive a definition of competencies suitable for technology education programs. This research defines competencies for the development of technology education programs as 'a set of one or more knowledge and skills required to perform effectively at the expected level of a given task.' Additionally, the study identifies the elements of competencies, including knowledge and skills, as well as the principles of competency construction. The definition and characteristics of competencies provided in this study can be utilized to create more systematic and effective educational programs in emerging technology fields and bridge the gap between education and industry practice.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.115-127
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• 2024

This research aimed to construct models with various structures based on the Transformer module and to perform land cover classification, thereby examining the applicability of the Transformer module. For the classification of land cover, the Unet model, which has a CNN structure, was selected as the base model, and a total of four deep learning models were constructed by combining both the encoder and decoder parts with the Transformer module. During the training process of the deep learning models, the training was repeated 10 times under the same conditions to evaluate the generalization performance. The evaluation of the classification accuracy of the deep learning models showed that the Model D, which utilized the Transformer module in both the encoder and decoder structures, achieved the highest overall accuracy with an average of approximately 89.4% and a Kappa coefficient average of about 73.2%. In terms of training time, models based on CNN were the most efficient. however, the use of Transformer-based models resulted in an average improvement of 0.5% in classification accuracy based on the Kappa coefficient. It is considered necessary to refine the model by considering various variables such as adjusting hyperparameters and image patch sizes during the integration process with CNN models. A common issue identified in all models during the land cover classification process was the difficulty in detecting small-scale objects. To improve this misclassification phenomenon, it is deemed necessary to explore the use of high-resolution input data and integrate multidimensional data that includes terrain and texture information.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.192-202
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• 2024

Ocean reanalysis data are extensively used in ocean circulation and climate research by integrating observational data with numerical models. This approach overcomes the spatial and temporal limitations of observational data and provides high-resolution gridded information that considers the physical interactions between ocean variables. In this study, I extended the previously produced 12-year (2011-2022) Northwest Pacific regional ocean reanalysis data to create a long-term reanalysis dataset (K-ORA22E) with a horizontal resolution of 1/24° spanning 30 years (1993-2022). These data were analyzed to diagnose long-term ocean climate change in the Korean marginal seas. Analysis of the K-ORA22E data revealed that the axis of the Kuroshio extension has shifted northward by approximately 6 km per year over the past 30 years, with a significant increase in sea surface temperature north of the Kuroshio axis. Among the waters surrounding the Korean Peninsula, the East Sea exhibited the most significant temperature increase. In the East Sea, the temperature increase was more pronounced in the middle layer than in the surface layer, with the East Korea Warm Current showing a rate two to three times higher than the global average. In the central Yellow Sea, where the Yellow Sea Bottom Cold Water appears, temperatures increased over the long-term, but decreased along the west and south coasts of the Korean Peninsula. These spatial differences in long-term temperature changes appear to be closely related to the heat transport pathways of warm water from the Kuroshio Current. High-resolution regional ocean reanalysis data, such as the K-ORA22E produced in this study, are essential foundational data for understanding long-term variability in the Korean marginal seas and analyzing the impacts of climate change.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.109-121
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• 2024

This paper adopts a resource-based approach to analyze why some universities have a greater number of faculty startups, and how this impacts on performance, in terms of indictors such as the number of employees and revenue sales. More specifically, we propose 9 hypotheses which link institutional resources to faculty startups and their performance, and compare 5 different groups of university resources for cross-college variation, using data from 134 South Korean four-year universities from 2017 to 2020. We find that the institutional factors impacting on performance of faculty startups differ from other categories of startups. The results show that it is important for universities to provide a more favorable environment, incorporating more flexible personnel policies and accompanying startup support infrastructure, for faculty startups, whilest it is more effective to have more financial resources and intellectual property for other categories of startups. Our findings also indicate that university technology-holding company and technology transfer programs are crucial to increase the number of faculty startups and their performance. Our analysis results have implications for both university and government policy-makers, endeavoring to facilitate higher particaption of professors in startup formation and ultimate commercialization of associated teachnologies.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.83-92
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• 2024

Recently, the importance of HR analytics for data-based decision-making in establishing and operating an effective human resource management system for companies is increasing. In addition, there is growing interest in the effect of employees' perceptions of organizational justice on positive organizational behavior. Therefore, in this study, among the various factors affecting teamwork behavior, we analyzed the impact on teamwork behavior such as perception of organizational justice and organizational culture. Organizational justice has a significant impact on the formation of members' attitudes, but its meaning may vary depending on the organizational context. In this study, we divided organizational justice into four types (procedural, distributive, interpersonal, and informational fairness) and confirmed their impact on teamwork behavior. In addition, organizational culture was divided into hierarchy culture and innovation culture, and how to regulate these relationships was examined. To analyze these relationships, individual-level data collected from 657 people at domestic companies were used for analysis. According to the analysis results, in a hierarchical culture, procedural justice and information justice had a positive influence on teamwork behavior through the mediating process of job satisfaction, and in an innovative culture, interpersonal justice and information justice had a positive influence on teamwork behavior through job satisfaction. It was confirmed to have a (+) effect. These research results provide implications for people management by indicating that, although organizational justice is important to members and organizations, it may be perceived differently and have different meanings depending on the organizational context. Through the use of the information presented in this study, we will provide value that can effectively and efficiently implement a company's human resource management system.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.2
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• pp.133-144
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• 2006

In order to catch out such Bond Strength, the preceding researchers had ever examined the Bond Strength of FRP Plate through their experimentations by setting up of various fluent. However, since the experiment for research on such Bond Strength takes much of expenditure for equipment structure and time-consuming, also difficult to carry out, it is conducting limitedly. This Study purposes to develop the most suitable Artificial Neural Network Model by application of various Neural Network Model and Algorithm to the adhering experiment data of the preceding researchers. Output Layer of Artificial Neural Network Model, and Input Layer of Bond Strength were performed the learning by selection as the variable of the thickness, width, adhered length, the modulus of elasticity, tensile strength, and the compressive strength of concrete, tensile strength, width, respectively. The developed Artificial Neural Network Model has applied Back-Propagation, and its error was learnt to be converged within the range of 0.001. Besides, the process for generalization has dissolved the problem of Over-Fitting in the way of more generalized method by introduction of Bayesian Technique. The verification on the developed Model was executed by comparison with the resulted value of Bond Strength made by the other preceding researchers which was never been utilized to the learning as yet.