• Korean Journal of Social Welfare Studies
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• v.47 no.3
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• pp.187-221
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• 2016

This study examines determining factors of effectiveness and satisfaction of Veteran Medical Service Delivery System. Especially, the association between the relevant variables of the effectiveness of the Veteran Medical Service Delivery System and the variables of the satisfaction from the perspective of beneficiaries was studied. Multi-level analysis was utilized to separate results of the evaluation of effectiveness in organizational-level and the evaluation of satisfaction in individual-level. This study tests key posited hypothesis by using survey data collected from 5 medical center of country(Seoul, Busan, Daejeon, Daegu, Gwangju). In terms of the result of the hypothesis testing on the effectiveness variable, integrity(${\beta}=.156$), accountability(${\beta}=.376$, financial sufficiency(${\beta}=.109$), and adequacy (${\beta}=.367$) are the determinants among various factors in evaluating veteran medical service delivery system, statistically reflecting the perception of directors of the veteran medical service delivery facilities on effectiveness. In other word, professionalism variable(${\beta}=0.99$) and effectiveness variable(${\beta}=-1.09$) are statistically reflecting the perception of directors of the beneficiaries satisfaction with employee. The findings suggests that the theoretical and practical implications will improve Effectiveness and Satisfaction of Veteran Medical Service Delivery System.

• Food Science and Industry
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• v.54 no.2
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• pp.93-101
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• 2021

Hazard Analysis Critical Control Point (HACCP) systems were developed to ensure a high level of food safety and reduced risk of foodborne illness. This paper focuses on significant issues associated with the implementation of HACCP; it provides an overview on recent literature. The structure of the paper follows six groupings of issues in the international literature of HACCP: (1) comparative studies and unification plan between HACCP and other food safety regulations; (2) verification of the HACCP system's effectiveness in improving food safety; (3) establishment of critical control point (CCP) for various foods HACCP model development; (4) expansion of HACCP application in the various fields and small businesses;(5) the impacts of HACCP on consumer's preferences and firms' financial performance in food industry; (6) HACCP and technological changes. The paper concludes with some suggestions for the future research in order to promote safe food supply chain for global customers.

• International Area Studies Review
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• v.21 no.1
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• pp.221-243
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• 2017

In this study, I examine overall conditions and problems of personal asset management processes by the old age people in Korea from the global perspectives. Major recommended policy implications for those are as follows.. First, the IRR (income replacement ratio) of public pensions in Korea is found to rank nearly the lowest among the OECD member countries. The relatively low fund performance compared to that of developed countries as well as this low IRR can be pointed out as major problems of public pension in Korea. It is recommended to reinforce specialty in fund management as a top priority to solve out these problems related with public pensions in Korea. Second, it is needed to set retirement pensions to be mandatory for almost all the firms in Korea to substitute for the above lower IRR of public pensions and to recover from the highest elderly poverty ratio among the OECD countries. Third, it is required to discuss about the expansion of tax refund policy application in the individual pension sector and many financial investment products under the correction of current budget control to motivate voluntary subscription for individual pension planning and to stabilize elderly lives of ordinary people in Korea. Fourth, it is required to induce market mechanism in controling price and longevity risk of reverse mortgages for the long-run sustainability.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.153-166
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• 2011

Recent trends in mobile device market whose services are rapidly expanding to provide wireless internet access are drawing people's attention to mobile security. Especially, since threats to information leakage are reaching to the critical level due to the frequent interchange of important data such as personal and financial information through wireless internet, various encryption algorithms has been developed to protect them. The encryption algorithms confront the serious threats by the appearance of side channel attack (SCA) which uses the physical leakage information such as timing, and power consumption, though the their robustness to threats is theoretically verified. Against the threats of SCA, researches including the performance and development direction of SCA should precede. Among tile SCA methods, the power analysis (PA) attack overcome this misalignment problem. The conventional methods require large computational power and they do not effectively deal with the delay changes in a power trace. To overcome the limitation of the conventional methods, we proposed a novel alignment method using peak matching. By computer simulations, we show the advantages of the proposed method compared to the conventional alignment methods.

• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.9-16
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• 2021

In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.602-609
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• 2021

Under accrual basic accounting, financial statements may be less reliable compared to cash basis accounting. The purpose of this study is to conduct an empirical analysis to determine the possibility of profit adjustment through the increase and decrease of deferred tax accounts. For our empirical analysis, a dummy variable of '1' was used as a dependent variable when the deferred tax net assets increased from the previous year and '0' when the deferred tax net assets decreased. Meanwhile, the variables of interest were discretionary accruals and ROA variation compared to the previous year. Logistic regression analysis was performed to establish the relevance between variables. Results found larger discretionary accruals related to lower net deferred tax assets compared to the previous year. In addition, there was a correlation between ROA and net deferred tax assets only if the ROA increased and net profit was greater than '0'. Study results will enable deferred tax information to be used in investment decision-making, and supervisory institutions can establish policies to prevent profit adjustments and enhance reporting standards.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.268-277
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• 2020

This study emphasizes that the survival strategy of universities should be a co-evolution strategy based on ecological thinking. Therefore, the purpose of the research is to present a theoretical framework for dividing the university innovation ecosystem into four stages and building a co-evolution strategy for each step, as universities play a prominent role in regional innovation ecosystems. Thus, our research method focused on literature research, and the theoretical framework for the university innovation ecosystem used Moore's Enterprise Ecosystem Model (1996). The university's ecological innovation strategy is divided into four stages of development, and a step-by-step co-evolution strategy is presented. Findings are summarized as follows. The pioneering stage involves the creation of values of the university-led innovation ecosystem. The expansion stage focuses on the establishment of critical mass. The authority stage covers maintaining authority and bargaining power. The renewal stage features continuous performance improvement. In particular, this theoretical model of the university-regional innovation ecosystem is meaningful in that it provides a theoretical basis for enhancing the effectiveness of government financial support projects, and for individual universities, it provides a framework for strategies suitable for their ecosystem building process.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.62-73
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• 2022

As the competition among the autonomous vehicle (AV, here after) developers are getting fierce, Korean government has been supporting developers by deregulating safety standards and providing financial subsidies. Recently, some OEMs announced their plans to market Lv3 and Lv4 automated driving systems. However, these market changes raised concern among public road management sectors for monitoring road conditions and alleviating hazardous conditions for AVs and human drivers. In this regards, the authors proposed a methodology for monitoring road infrastructure to identify hazardous factors for AVs and categorizing the hazards based on their level of impact. To evaluate the degrees of the harm on AVs, the authors suggested a methodology for managing road hazard factors based on vehicle performance features including vehicle body, sensors, and algorithms. Furthermore, they proposed a method providing AVs and road management authorities with potential risk information on road by delivering them on the monitoring map with node and link structure.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.263-278
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• 2022

With the recent advent of IoT technology, automatic pet feeders are being distributed so that owners can feed their companion animals while they are out. However, due to behaviors of pets, the method of measuring weight, which is important in automatic feeding, can be easily damaged and broken when using the scale. The 3D camera method has disadvantages due to its cost, and the 2D camera method has relatively poor accuracy when compared to 3D camera method. Hence, the purpose of this study is to propose a deep learning approach that can accurately estimate weight while simply using a 2D camera. For this, various convolutional neural networks were used, and among them, the ResNet101-based model showed the best performance: an average absolute error of 3.06 grams and an average absolute ratio error of 3.40%, which could be used commercially in terms of technical and financial viability. The result of this study can be useful for the practitioners to predict the weight of a standardized object such as feed only through an easy 2D image.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.