• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• SUVANNABHUMI
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• v.10 no.2
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• pp.181-213
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• 2018

How to study Southeast Asia (SEA)? The need to explore and identify methodologies for studying SEA are inherent in its multifaceted subject matter. At a minimum, the region's rich cultural diversity inhibits both the articulation of decisive defining characteristics and the training of scholars who can write with confidence beyond their specialisms. Consequently, the challenges of understanding the region remain and a consensus regarding the most effective approaches to studying its history, identity and future seem quite unlikely. Furthermore, "Area Studies" more generally, has proved to be a less attractive frame of reference for burgeoning scholarly trends. This paper will propose a new tool to help address these challenges. Even though the science of artificial intelligence (AI) is in its infancy, it has already yielded new approaches to many commercial, scientific and humanistic questions. At this point, AI has been used to produce news, generate better smart phones, deliver more entertainment choices, analyze earthquakes and write fiction. The time has come to explore the possibility that AI can be put at the service of the study of SEA. The paper intends to lay out what would be required to develop SEABOT. This instrument might exist as a robot on the web which might be called upon to make the study of SEA both broader and more comprehensive. The discussion will explore the financial resources, ownership and timeline needed to make SEABOT go from an idea to a reality. SEABOT would draw upon artificial neural networks (ANNs) to mine the region's "Big Data", while synthesizing the information to form new and useful perspectives on SEA. Overcoming significant language issues, applying multidisciplinary methods and drawing upon new yields of information should produce new questions and ways to conceptualize SEA. SEABOT could lead to findings which might not otherwise be achieved. SEABOT's work might well produce outcomes which could open up solutions to immediate regional problems, provide ASEAN planners with new resources and make it possible to eventually define and capitalize on SEA's "soft power". That is, new findings should provide the basis for ASEAN diplomats and policy-makers to develop new modalities of cultural diplomacy and improved governance. Last, SEABOT might also open up avenues to tell the SEA story in new distinctive ways. SEABOT is seen as a heuristic device to explore the results which this instrument might yield. More important the discussion will also raise the possibility that an AI-driven perspective on SEA may prove to be even more problematic than it is beneficial.

• Korea Trade Review
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• v.41 no.3
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• pp.135-156
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• 2016

This study, with reference to data on economic conditions in Shandong Province, China, looked into trade and investment activities in Korea and major cities of Shandong - Qingdao, Yantai, Weihai and Jinan - and investigated claim cases between the two countries by type. In addition, we investigated the matter empirically by conducting a survey administered to 300 Korean companies investing in Shandong Province and, based on the data, tested hypotheses for inferential analysis. The findings are as follows: i) while hypotheses in which the size of a firm, represented by import and export volume, has a positive relation with the frequency of trade claim filings (H1) and with the financial value of the trade claims (H2) were quoted, company size proved to have a significantly negative relation with the time required to obtain a claim decision, which rejects the third hypothesis (H3) in which the relation was thought to be positive: ii) while products, as represented by the type of business, showed a clearly significant difference with the frequency of trade claim filings (H4) and with methods of preventing and responding to claims (H6), they did not show a significant link to the type of trade claim (H5). This study is a theoretical and empirical overview of Korean companies based in Shandong Province of China, and can be used to address the practical needs of the Korean companies looking to start business in Shandong Province.

• 한국노년학
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• v.29 no.2
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• pp.645-656
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• 2009

The purpose of this study was to address the current status of hospital-based home care(HBHC). We analyzed the data on HBHC from national electronic data information of Health Insurance Review Agency. Beside, we surveyed 75 hospital-based home care agency. In 2006, 20,343 elderly(64.0% from all HBHC user) used 333,889 visits(76.8%from all visits). Medical diagnosis was composed of circulatory disease including cerebrovascular diseases 41.3%, endocrine system disease including Diabetes mellitus 10.4%, neoplasm 9.7%. Some of subjects used HBHC in excess of maximum covered 8 visits a month by National Health Insurance, decubitus 7.0%, the cancer 5.4%, the diabetes 2.5%, the hypertension 1.1%, and the stroke 0.9%. This results will contribute to expand the coverage of hospital-based home care by National Health Insurance. There was distribution difference in medical diagnosis and nursing intervention between HBHC and Public health center-based home care(PBHC) subjects. Therefore, HBHC subjects had more severe medical diagnosis, and were intervened more injections, examinations, than PBHC subjects. These differences must be considered to set up functional role among the three types of home visit care.

• Economic Analysis
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• v.25 no.4
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• pp.58-108
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• 2019

This paper analyzes the determining factors in the unemployment rate among young people in their 20s by studying data from 30 OECD countries between 2000 and 2017. It identifies reasons why Korea has a higher youth unemployment rate than Japan, and assesses what implications Japan's youth unemployment measures could have on Korea. The study highlights the variables that have meaningful impacts on youth unemployment. They include the unemployment rate among the working-age population, the percentage of each age bracket in the overall population, the GDP growth rate, the percentage of wage laborers in each age group, the percentage of elderly people, and the percentage of part-time workers. This paper also finds that a decline in the youth population, especially among people in their 20s, does not help to address the issue of youth unemployment. Secondly, this paper explains the additional factors behind Korea's higher youth unemployment rates. One is Korea's disadvantageous employment environment, compared to that in Japan, in terms of wage earnings. Other factors include the existence of fewer decent corporate jobs than in Japan, and wide disparities in wages between large and small corporate jobs. Therefore, while making efforts to resolve long-term and structural problems, it is necessary to actively promote policy measures to solve short-term mismatch problems of youth employment by referring to Japanese policy examples.

• Asia Marketing Journal
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• v.12 no.1
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• pp.51-79
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• 2010

This study is based on recent advances in celebrity traits and self-determination researches to address how consumers develop the strong attachment to a "star brand," and the effect of consumer's attachment to star brand on evaluation of product brand sponsored by the star. We use the consumers' need satisfaction and star traits as major causal factors that influence consumer's attachment to a star brand. Specifically this article uses autonomy need, relatedness need, competence need as the dimensions of need satisfaction and expertness, trustworthiness, likeability, and similarity as the key star traits. The purpose of this study is to investigate the influence of these factors on the consumer's attachment to star brand and how consumer's evaluation of product brand sponsored by star is moderated by the fitness level of between the image of star brand and the image of sponsored product brand. To collect the data, survey was taken in a University located in Incheon. Collected data are analysed using SPSS 15.0 and AMOS 7.0. The results show that when the star brand is perceived as more trustworthy and likeable, and satisfies autonomy need and relatedness need, the consumer is more likely to become strongly attached to him or her. The hypothesis that consumer's attachment to star brand will have the positive influence on the evaluation of product brand sponsored by star is supported. And hypothesis that consumer's attitude to a sponsored product brand is moderated by the fitness level of between the image of star brand and the image of sponsored product brand is also supported. The empirical results imply that star brand to which consumers are attached may have the significant positive impact on the consumers' evaluation process of endorsed product brand and purchase behavior, and that marketers should consider need dimensions which target consumers want to satisfy and the fitness level of between the image of star brand being considered as endorser and the image of product brand.

• Korean Journal of Culture and Social Issue
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• v.29 no.4
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• pp.477-496
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• 2023

The purpose of this study is to examine the relationship between rejection sensitivity and reactive aggression among college students, as well as to determine the mediating effects of self-concept clarity and hostile attribution bias on the relationship between rejection sensitivity and reactive aggression. A self-report questionnaire was conducted online for the purpose of gathering data from university students aged 18 years and older. A total of 250 participants were included in the analysis. SPSS 27.0 was used for data analysis to check the basic statistics of the variables, frequency analysis, reliability analysis, and correlation analysis. In addition, the model fit was checked using Amos 21.0, and the bootstrapping method verified the significance of the indirect effect. The results of this study are as follows. The results of this study are as follows. First, rejection sensitivity positively affects reactive aggression through self-concept clarity. Second, rejection sensitivity increases the hostile attribution bias, leading to an increase in reactive aggression. Third, rejection sensitivity positively influences reactive aggression in an indirect way by sequentially affecting self-concept clarity and hostile attribution bias. These findings have implications as they identify psychological factors that affect reactive aggression in college students. This suggests the importance of utilizing psychological interventions to address reactive aggression associated with social problems, such as crime, and provides a foundation for both treatment and prevention. Finally, implications for further research and limitations of this study are suggested.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.185-205
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• 2023

As the tourism industry recovers post the COVID-19 pandemic, an increasing number of tourists are utilizing various platforms to leave reviews. However, amidst the vast amount of data, finding useful information remains challenging, often leading to time and cost inefficiencies in selecting travel destinations. Despite ongoing research, there are limitations due to the absence of ratings or the presence of different rating formats across platforms. Moreover, inconsistencies between ratings and the content of reviews pose challenges in developing recommendation models. To address these issues, this study utilized 7,104 reviews of tourist spots in Jeju Island to develop a specialized satisfaction index for Jeju tourist attractions and employed this index to construct a 'Rating Prediction Model.' To validate the model's performance, we predicted the ratings of 700 experimental data points using both the developed model and an LSTM approach. The proposed model demonstrated superior performance with a weighted accuracy of 73.87%, which is approximately 4.67% higher than that of the LSTM. The results of this study are expected to resolve the discrepancies between ratings and review contents, standardize ratings in reviews without ratings or in various formats, and provide reliable rating indicators applicable across all areas of travel in different domains.

• Advances in nano research
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• v.16 no.4
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• pp.375-394
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• 2024

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.