• Information Systems Review
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• v.25 no.3
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• pp.59-81
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• 2023

Elderly suicide problem has become worse in South Korea. With a rapid aging of the population, the trend of suicide among the elderly is expected to accelerate, preventing elderly suicide has been considered an important societal problem. Thus, we aim to investigate various factors that explain suicidal ideation and to develop a predictive model for suicidal ideation in the context of elderly people in South Korea. To this end, this study contributes to addressing the elderly suicide problem. By using seven-year panel data from the Korea Welfare Panel Survey, we extract various potential causal factors for elderly suicidal ideation based on interpersonal theory of suicide and social disorganization theory. Then a panel logit model was employed to assess the impacts of potential factors on suicidal ideation and deep learning and machine learning algorithms were used to develop a predictive model for suicidal ideation of elderly people. The results of our study provide practical implications for preventing elderly suicide by identifying causal factors of suicidal ideation and a high suicidal risk group of the elderly. This study sheds light on synergy of mixed methodology and provides various academic implications.

• 대한공업교육학회지
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• v.38 no.2
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• pp.156-175
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• 2013

The purpose of this study was to verify that the impacts of experience of donation for education to improve the teaching efficacy of pre-technology teacher. The Invention experience of donation for education was performed with Invent-touring sponsored by Chunnam National University Invention Education Center for Teachers and was included by development of creative problem solving program, program execution and evaluation. Research participants were Technology education Majors and minors 20 students. The active locations were D children community center, K alternative school, D Elementary School and D middle school. For the study, various literature researches were reviewed intensively about donation for education and teaching efficacy. The instrument for the study was the modified STEBI(Science Teaching Efficacy Beliefs Instrument) for technology education by 3 experts. This study was designed by single group pre and post test design (One-Group Pretest-Posttest Design) and was conducted by the pre-test and post-test. Check the reliability of the tool was conducted with Cronbach ${\alpha}$ coefficient analysis, pre-test 0.840, post-test 0.746. The analysis of data from the 5% significance level, paired sample t-test was performed using the SPSS 19.0 statistical tool. The results were as follows: 1. Teaching efficacy of pre-technology teachers who participated in the invention experience for educational donation technology has improved. 72. The qualitative study was performed by the interviews with students who participated in. Humanism was positively change and learning opportunity was provided to develop the competence of technology education teacher. Based upon the conclusion of this study, the donation activity for invention education need to use learning strategies for pre-technology teacher to improve teaching efficacy.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.

• Asia pacific journal of information systems
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• v.21 no.4
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• pp.63-104
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• 2011

The study examines the relationships among employee's goal orientation, IT personnel competency, personal effectiveness. The goal orientation includes learning goal orientation, performance approach goal orientation, and performance avoid goal orientation. Personal effectiveness consists of personal work satisfaction and personal work performance. In general, IT personnel competency refers to IT expert's skills, expertise, and knowledge required to perform IT activities in organizations. However, due to the advent of the internet and the generalization of IT, IT personnel competency turns out to be an important competency of technological experts as well as employees in organizations. While the competency of IT itself is important, the appropriate harmony between IT personnel's business capability and technological capability enhances the value of human resources and thus provides organizations with sustainable competitive advantages. The rapid pace of organization change places increased pressure on employees to continually update their skills and adapt their behavior to new organizational realities. This challenge raises a number of important questions concerning organizational behavior? Why do some employees display remarkable flexibility in their behavioral responses to changes in the organization, whereas others firmly resist change or experience great stress when faced with the need to alter behavior? Why do some employees continually strive to improve themselves over their life span, whereas others are content to forge through life using the same basic knowledge and skills? Why do some employees throw themselves enthusiastically into challenging tasks, whereas others avoid challenging tasks? The goal orientation proposed by organizational psychology provides at least a partial answer to these questions. Goal orientations refer to stable personally characteristics fostered by "self-theories" about the nature and development of attributes (such as intelligence, personality, abilities, and skills) people have. Self-theories are one's beliefs and goal orientations are achievement motivation revealed in seeking goals in accordance with one's beliefs. The goal orientations include learning goal orientation, performance approach goal orientation, and performance avoid goal orientation. Specifically, a learning goal orientation refers to a preference to develop the self by acquiring new skills, mastering new situations, and improving one's competence. A performance approach goal orientation refers to a preference to demonstrate and validate the adequacy of one's competence by seeking favorable judgments and avoiding negative judgments. A performance avoid goal orientation refers to a preference to avoid the disproving of one's competence and to avoid negative judgements about it, while focusing on performance. And the study also examines the moderating role of work career of employees to investigate the difference in the relationship between IT personnel competency and personal effectiveness. The study analyzes the collected data using PASW 18.0 and and PLS(Partial Least Square). The study also uses PLS bootstrapping algorithm (sample size: 500) to test research hypotheses. The result shows that the influences of both a learning goal orientation (${\beta}$ = 0.301, t = 3.822, P < 0.000) and a performance approach goal orientation (${\beta}$ = 0.224, t = 2.710, P < 0.01) on IT personnel competency are positively significant, while the influence of a performance avoid goal orientation(${\beta}$ = -0.142, t = 2.398, p < 0.05) on IT personnel competency is negatively significant. The result indicates that employees differ in their psychological and behavioral responses according to the goal orientation of employees. The result also shows that the impact of a IT personnel competency on both personal work satisfaction(${\beta}$ = 0.395, t = 4.897, P < 0.000) and personal work performance(${\beta}$ = 0.575, t = 12.800, P < 0.000) is positively significant. And the impact of personal work satisfaction(${\beta}$ = 0.148, t = 2.432, p < 0.05) on personal work performance is positively significant. Finally, the impacts of control variables (gender, age, type of industry, position, work career) on the relationships between IT personnel competency and personal effectiveness(personal work satisfaction work performance) are partly significant. In addition, the study uses PLS algorithm to find out a GoF(global criterion of goodness of fit) of the exploratory research model which includes a mediating variable, IT personnel competency. The result of analysis shows that the value of GoF is 0.45 above GoFlarge(0.36). Therefore, the research model turns out be good. In addition, the study performs a Sobel Test to find out the statistical significance of the mediating variable, IT personnel competency, which is already turned out to have the mediating effect in the research model using PLS. The result of a Sobel Test shows that the values of Z are all significant statistically (above 1.96 and below -1.96) and indicates that IT personnel competency plays a mediating role in the research model. At the present day, most employees are universally afraid of organizational changes and resistant to them in organizations in which the acceptance and learning of a new information technology or information system is particularly required. The problem is due' to increasing a feeling of uneasiness and uncertainty in improving past practices in accordance with new organizational changes. It is not always possible for employees with positive attitudes to perform their works suitable to organizational goals. Therefore, organizations need to identify what kinds of goal-oriented minds employees have, motivate them to do self-directed learning, and provide them with organizational environment to enhance positive aspects in their works. Thus, the study provides researchers and practitioners with a matter of primary interest in goal orientation and IT personnel competency, of which they have been unaware until very recently. Some academic and practical implications and limitations arisen in the course of the research, and suggestions for future research directions are also discussed.

• Korean Journal of Environment and Ecology
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• v.32 no.5
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• pp.469-477
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• 2018

Hanabusaya asiatica is an endemic species whose distribution is limited in the mid-eastern part of the Korean peninsula. Due to its narrow range and small population, it is necessary to protect its habitats by identifying it as Key Biodiversity Areas (KBAs) adopted by the International Union for Conservation of Nature (IUCN). In this paper, we estimated potential natural habitats for H. asiatica using maximum entropy model (MaxEnt) and identified candidate sites for KBA based on the model results. MaxEnt is a machine learning algorithm that can predict habitats for species of interest unbiasedly with presence-only data. This property is particularly useful for the study area where data collection via a field survey is unavailable. We trained MaxEnt using 38 locations of H. asiatica and 11 environmental variables that measured climate, topography, and vegetation status of the study area which encompassed all locations of the border region between South and North Korea. Results showed that the potential habitats where the occurrence probabilities of H. asiatica exceeded 0.5 were $778km^2$, and the KBA candidate area identified by taking into account existing protected areas was $1,321km^2$. Of 11 environmental variables, elevation, annual average precipitation, average precipitation in growing seasons, and the average temperature in the coldest month had impacts on habitat selection, indicating that H. asiatica prefers cool regions at a relatively high elevation. These results can be used not only for identifying KBAs but also for the reference to a protection plan for H. asiatica in preparation of Korean reunification and climate change.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Korean Journal of Social Welfare
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• v.63 no.3
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• pp.109-131
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• 2011

This qualitative study aimed to explore and understand the impacts of the poor neighborhood on children from their own perspectives. To this end, the meaning and the structure of children's lived experiences were explored. The participants were recruited from elementary school high grade students living in poor neighborhood. Focused group interviews were conducted in each of the 24 World Visions social work agencies. The collected data were analysed qualitatively. According to the results, the following main themes could be extracted from the children's lived experiences: "Conflict between connection and differentiation", "Resigning to the fate of being neglected in danger" "Learning to live in the dark cave", "Anticipating a bowl of trust from the neighborhood". The children living in poor neighborhood were threatened by the physical and emotional danger highly probable in weak social infrastructure. Also, they have internalized negative self-identity by chaotic environment and, even the negative role models from alcoholic and sometimes violent adults. However, they were experiencing positive caring from and solidarity with surrounding neighborhood, and were participating in volunteered activities for asserting their citizen rights. These may be the keys to empowerment and, ultimately, to change for themselves and the community. The implications of the study results for the policy making and the actual practice were discussed focusing on the children's rights and well-being.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.335-345
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• 1999

오늘날 날로 증가하는 물류비는 개별 기업은 물론 국가 전체의 수출 경쟁력을 약화시키는 주요 원인으로 지적되고 있다. 그러나 그동안 우리나라에서는 물류비 절감을 위한 종합적이고 체계적인 대책이 이루어지지 못하였다. 특히 본 논문의 연구대상인 육상물류의 경우 그 비중이 전체 화물 운송의 60% 이상을 차지함에도 불구하고 심각한 교통체증 및 물류기반 시설의 미비, 효율적인 정보시스템의 미비 등으로 인하여 물류비가 계속 증가하는 양상을 보여 왔다. 따라서 본 논문에서는 우리나라 육상물류시스템이 안고 있는 문제점의 해결을 위한 방안들 중의 하나로 정보기술의 활용에 관한 내용을 다루고 있다. 즉 영세한 기업들도 누구나 손쉽게 이용할 수 있도록 인터넷을 이용한 육상물류중개시스템의 개발에 관한 내용을 소개하고 있다. 육상물류중개시스템은 복합화물주선업체인 (주) 대형물류와 함께 개발한 시스템으로 인터넷을 통하여 화주의 화물 운송의뢰를 접수받아 이를 여러 운송업체에게 제공해주는 역할을 수행하게 된다. 특히 육상물류중개시스템은 화물의 운송과 관련하여 발생하는 다양한 정보들을 데이터베이스에 저장하여 두었다가 세관을 비롯한 터미날에 대한 각종 신고업무에 이용할 수 있으며, 이밖에도 교통정보 및 화물 위치정보 등 다양한 서비스를 제공해줄 수 있다. 따라서 운송업체의 공차율을 줄이고 화주에게는 자신의 화물에 대한 정보를 실시간으로 전달해 줄 수 있다는 장점이 있다. 또한 이러한 육상물류중개시스템은 현재 개발중인 통합데이터베이스를 기반으로 한 항만물류원스톱서비스 시스템과 연계되어 차후에는 물류원스톱시스템으로 발전할 수 있을 것이다. 연구가 진행되고 있는 인공신경망과의 모형결합을 통해 기존연구와는 다른 새로운 통합예측방법론을 제시하고자 한다. 본 연구에서 제시하는 통합방법론은 크게 2단계 과정을 거쳐 예측모형으로 완성이 된다. 즉, 1차 모형단계에서 원시 재무시계열은 먼저 웨이블릿분석을 통해서 노이즈가 필터링 되는 동시에, 과거 재무시계열의 프랙탈 구조, 즉 비선형적인 움직임을 보다 잘 반영시켜 주는 다차원 주기요소를 가지는 시계열로 분해, 생성되며, 이렇게 주기에 따라 장단기로 분할된 시계열들은 2차 모형단계에서 신경망의 새로운 입력변수로서 사용되어 최종적인 인공 신경망모델을 구축하는 데 반영된다.ocioeconomic impacts are resulted from the program. It would be useful for the means of (ⅰ) fulfillment of public accountability to legitimate the program and to reveal the expenditure of pubic fund, and (ⅱ) managemental and strategical learning to give information necessary to improve the making. program and policy decision making, The objectives of the study are to develop the methodology of modeling the socioeconomic evaluation, and build up the practical socioeconomic ev

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.180-190
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• 2014

Social network services are increasingly being used in organizational settings to improve relationships among employees and enhance prospects for information exchange and cooperative work. Social Networking Service(SNS) has deeply penetrated organizational job settings, influencing multiple aspects of employee's life. This study is designed to explore the impact of SNS engagement on the job performance mediated as team cooperation and decision making quality effects. Data were collected from 146 employees who use organizational SNS in there company. Factor analysis and structural equation method are employed. Results from a survey accompanied by the substantial impacts of organizational employee's social networking engagement on social learning processes and outcomes. SNS engagement not only directly influences organizational employee's job performance, but also helps their team cooperation and decision making quality from others and adapt to organizational culture, both of which play prominent roles in improving their job performance.