• Land and Housing Review
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• v.15 no.2
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• pp.57-71
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• 2024

This study aims to develop an evaluation method for solar power facilities considering disaster impacts and to analyse the vulnerabilities of existing facilities. Haenam-gun in Jeollanam-do, where the reassessment of existing facilities is urgent, was selected as the study area. To evaluate the vulnerability from a more objective perspective, principal component analysis and entropy methods were utilised. Seven vulnerability assessment indicators were selected: maximum hourly rainfall, maximum wind speed, number of typhoon occurrence days, number of rainfall days lasting more than five days, maximum daily rainfall, impermeable area ratio, and population density. Among these, maximum hourly rainfall, maximum wind speed, maximum daily rainfall, and number of rainfall days lasting more than five days were found to have the highest weights. The overlay of the derived weights showed that the southeastern regions of Haenam-eup and Bukil-myeon were classified as Grade 1 and 2, whereas the northern regions of Hwawon-myeon, Sani-myeon, and Munnae-myeon were classified as Grade 4 and 5, indicating differences in vulnerability. Of the 2,133 facilities evaluated, 91.1% were classified as Grade 3 or higher, indicating a generally favourable condition. However, there were more Grade 1 facilities than Grade 2, highlighting the need for countermeasures. This study is significant in that it evaluates solar power facilities considering urban disaster resilience and is expected to be used as a basic resource for the installation of new facilities or the management and operation of existing ones.

• Information Systems Review
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• v.21 no.4
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• pp.47-68
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• 2019

As cloud services and deployment models become diverse, there are a growing number of cloud computing selection options. Therefore, financial companies need a methodology to select the appropriated cloud for each financial computing system. This study adopted the Balanced Scorecard (BSC) framework to classify factors for the introduction of cloud computing in financial companies. Using Analytic Hierarchy Process (AHP), the evaluation items are layered into the performance perspective and the cloud consideration factor and a comprehensive decision model is proposed. To verify the proposed research model, a system of financial company is divided into three: account, information, and channel system, and the result of decision making by both financial business experts and technology experts from two financial companies were collected. The result shows that some common factors are important in all systems, but most of the factors considered are very different from system to system. We expect that our methodology contributes to the spread of cloud computing adoption.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.2
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• pp.147-156
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• 2024

The sustainable management and enhancement of marine resources are becoming increasingly important issues worldwide. This study was conducted in response to these challenges, focusing on the development and performance comparison of fish detection and classification models as part of a deep learning-based technique for assessing the effectiveness of marine resource enhancement projects initiated by the Korea Fisheries Resources Agency. The aim was to select the optimal model by training various sizes of YOLOv8-Seg models on a fish image dataset and comparing each performance metric. The dataset used for model construction consisted of 36,749 images and label files of 12 different species of fish, with data diversity enhanced through the application of augmentation techniques during training. When training and validating five different YOLOv8-Seg models under identical conditions, the medium-sized YOLOv8m-Seg model showed high learning efficiency and excellent detection and classification performance, with the shortest training time of 13 h and 12 min, an of 0.933, and an inference speed of 9.6 ms. Considering the balance between each performance metric, this was deemed the most efficient model for meeting real-time processing requirements. The use of such real-time fish detection and classification models could enable effective surveys of marine resource enhancement projects, suggesting the need for ongoing performance improvements and further research.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.383-388
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• 2024

This study conducted voluntary focus group user observations to empirically validate the efficacy of the self-developed psychological support mobile application, "Mindful Now". The app is structured as an interactive game format, enabling individuals to activate self-awareness of mindfulness states anytime, anywhere. It consists of a 3-step process of sensory/emotional/consciousness awareness, facilitating the expression of non-judgmental awareness. To demonstrate the effectiveness of this mindful activation in enhancing psychological well-being such as happiness and stress reduction, voluntary mindfulness mobile app usage was tracked among 49 university students. The results revealed significant improvements, with a 14.4% increase in SWLS happiness index and a 17.1% decrease in PSS-10 stress levels among 12 users who used the app continuously for over 60 days to practice mindfulness awareness. Particularly, higher app engagement was observed among students who initially reported relatively lower indices before using the app. The utilization of mobile apps that promote mindful activation aligns with various therapeutic paradigms based on mindfulness and meditation, contributing to advancements in digital therapeutic interventions for psychological support.

• Journal of Service Research and Studies
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• v.14 no.3
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• pp.188-205
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• 2024

The food truck business, which involves selling various types of food from mobile vehicles, has gained significant popularity in urban centers and at events. These food trucks have rapidly expanded due to their relatively low initial investment and high flexibility, attracting customers with unique menus and personalized services. However, as competition increases, the need to manage service quality to boost customer satisfaction and encourage repeat visits has become more critical. Despite this growing importance, there has been limited empirical research on the topic. This study aims to analyze customer experiences with food truck services to gain strategic insights for improving service quality. By applying structural topic modeling to customer review data, the study identified 50 key topics. The process included a comprehensive evaluation of model diagnostics and interpretability to determine the optimal number of topics, ultimately selecting the most relevant ones related to service experiences. The impact of these identified topics on overall customer satisfaction was empirically tested using regression analysis. The results showed that aspects such as "Food Taste," "Friendly Staff," and "Positive Emotion" had a positive influence on customer satisfaction, whereas "Delayed Service," "Negative Emotion," and "Beverage Service" had a negative impact. Based on this analysis, the study proposes concrete methods for food truck operators to systematically analyze customer feedback and use it to drive service improvements and innovation. This research highlights the importance of data-driven decision-making in small business environments like food trucks and contributes to expanding the application of topic modeling in the service industry.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.4
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• pp.16-36
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• 2024

This study, developed a dynamic hard shoulder running(HSR) algorithm that includes ending speed and minimum operation time in addition to the starting speed for HSR, and presented an operation plan. The first stage of the algorithm was red, which means vehicles are prohibited from HSR. The second stage is red/amber, in which drivers are notified of HSR, and operators are given time to check whether there is any obstacle to HSR. Stage 3 is green, which vehicles are permitted for HSR. Stage 4 is amber, in which a signal is given to drivers that the end of HSR is imminent. In addition, a minimum time is applied to green and red, but if congestion is severe, red is terminated early to prevent congestion from worsening. The upstream and downstream traffic flow is managed stably through main line ramp metering and lane number matching. The operating standard speed reflects the characteristics of vehicles and drivers, and based on simulation results, 7090 was selected as the optimal operating standard speed considering traffic flow and safety aspects. Therefore it is desirable to apply the travel time divided by the minimum speed of the HSR link as the minimum operating time in order to ensure continuity of traffic flow

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.4
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• pp.87-101
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• 2024

This study aims to provide insights for the efficient utilization of resources by analyzing the entrepreneurship support efficiency of Korean universities. To identify the factors influencing the number of entrepreneurs, which is the primary goal of university entrepreneurship support, a multiple regression analysis was conducted, identifying five effective independent variables. Using these five identified independent variables as input variables and the number of entrepreneurs as the output variable, the DEA method was used to analyze the efficiency of entrepreneurship support for each university as of 2023. The analysis of 150 four-year universities in Korea showed that nine universities exhibited complete efficiency in both CCR and BCC models. Among the remaining 141 universities that showed inefficiency, the cause was scale for five universities, technology for two universities, and both scale and technology for 134 universities. Regarding the returns to scale, nine universities exhibited CRS, 79 exhibited IRS, and 62 exhibited DRS. Additionally, reference groups that could serve as benchmarks for improving the efficiency of inefficient universities were identified, and target values(projections) for each variable to achieve efficiency were also presented. Despite the limitations of the DEA model, this study helps each university identify the causes of inefficiency in their entrepreneurship support and derive specific improvements to enhance efficiency. This facilitates more efficient resource management and can positively impact the ultimate goals of university entrepreneurship support, such as regional economic development and job creation.

• Journal of the Korean Society of Radiology
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• v.18 no.5
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• pp.557-566
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• 2024

Ultrasound-guided regional anesthesia is one of the most common techniques used in peripheral nerve blockade by enhancing pain control and recovery time. However, accurate Brachial Plexus (BP) nerve detection and identification remains a challenging task due to the difficulty in data acquisition such as speckle and Doppler artifacts even for experienced anesthesiologists. To mitigate the issue, we introduce a BP nerve small target segmentation network by incorporating BP object detection and U-Net based semantic segmentation into a single deep learning framework based on the multi-scale approach. To this end, the current BP detection and identification was estimated: 1) A RetinaNet model was used to roughly locate the BP nerve region using multi-scale based feature representations, and 2) U-Net was then used by feeding plural BP nerve features for each scale. The experimental results demonstrate that our proposed model produces high quality BP segmentation by increasing the accuracies of the BP nerve identification with the assistance of roughly locating the BP nerve area compared to competing methods such as segmentation-only models.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.35-52
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• 2019

As public services are provided in various forms, including e-government, the level of public demand for public service quality is increasing. Although continuous measurement and improvement of the quality of public services is needed to improve the quality of public services, traditional surveys are costly and time-consuming and have limitations. Therefore, there is a need for an analytical technique that can measure the quality of public services quickly and accurately at any time based on the data generated from public services. In this study, we analyzed the quality of public services based on data using process mining techniques for civil licensing services in N city. It is because the N city's building license complaint service can secure data necessary for analysis and can be spread to other institutions through public service quality management. This study conducted process mining on a total of 3678 building license complaint services in N city for two years from January 2014, and identified process maps and departments with high frequency and long processing time. According to the analysis results, there was a case where a department was crowded or relatively few at a certain point in time. In addition, there was a reasonable doubt that the increase in the number of complaints would increase the time required to complete the complaints. According to the analysis results, the time required to complete the complaint was varied from the same day to a year and 146 days. The cumulative frequency of the top four departments of the Sewage Treatment Division, the Waterworks Division, the Urban Design Division, and the Green Growth Division exceeded 50% and the cumulative frequency of the top nine departments exceeded 70%. Higher departments were limited and there was a great deal of unbalanced load among departments. Most complaint services have a variety of different patterns of processes. Research shows that the number of 'complementary' decisions has the greatest impact on the length of a complaint. This is interpreted as a lengthy period until the completion of the entire complaint is required because the 'complement' decision requires a physical period in which the complainant supplements and submits the documents again. In order to solve these problems, it is possible to drastically reduce the overall processing time of the complaints by preparing thoroughly before the filing of the complaints or in the preparation of the complaints, or the 'complementary' decision of other complaints. By clarifying and disclosing the cause and solution of one of the important data in the system, it helps the complainant to prepare in advance and convinces that the documents prepared by the public information will be passed. The transparency of complaints can be sufficiently predictable. Documents prepared by pre-disclosed information are likely to be processed without problems, which not only shortens the processing period but also improves work efficiency by eliminating the need for renegotiation or multiple tasks from the point of view of the processor. The results of this study can be used to find departments with high burdens of civil complaints at certain points of time and to flexibly manage the workforce allocation between departments. In addition, as a result of analyzing the pattern of the departments participating in the consultation by the characteristics of the complaints, it is possible to use it for automation or recommendation when requesting the consultation department. In addition, by using various data generated during the complaint process and using machine learning techniques, the pattern of the complaint process can be found. It can be used for automation / intelligence of civil complaint processing by making this algorithm and applying it to the system. This study is expected to be used to suggest future public service quality improvement through process mining analysis on civil service.

• Journal of Korean Home Economics Education Association
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• v.21 no.3
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• pp.45-64
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• 2009

The purpose of this study was to develope issue-centered teaching learning process plan for environment-friendly housing education and to apply it to the housing section of Technology Home Economics in a middle school. PRO-CON cooperative group model was used for the teaching learning process plans of 2-session lessons according to the ADDIE model. In the development stage, 7 activity materials and 20 teaching learning materials (4 reading texts, 12 pictures and photos, & 5 moving pictures) were developed for 2-session lessons. The plans applied to the 7 classes, 222 students, in the third grade of the G middle school in Gyeonggi-do during July 10th-17th, 2008. The results showed that the final pro-con was influenced by the rationals of juries' pro-con of each team and the representative's discussion besides one's environmental perspective. The intention to implement environment-friendly housing activities was significantly increased between before and after the lessons. The contents, methods, goals, and process of the 2-session lessons were evaluated over medium to somewhat higher levels. Those evaluations except methods and general satisfaction with the lessons were differed by sex, students' and their families' interests in environments but not by the type of housing. These results might support that pro-con cooperative group model of controversial issues on parking lot would be appropriate to environment-friendly housing lessons and could apply to broad issues on housing and various schools in other areas.