• Korean journal of health promotion
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• v.18 no.4
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• pp.184-193
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• 2018

Background: Despite increasing the number of newly licensed nurses across Korea, shortages caused by geographical imbalances remains a significant concern. Therefore, understanding nursing students' attitudes to working and living, factors influencing where they first choose to work after graduation is useful in formulating appropriate interventions to retain nurses in regional areas. Methods: A total of 329 senior nursing students from areas outside Metropolitan Seoul completed self-report questionnaires. Data were analyzed using t-test, chi-square test and multiple logistic regression analysis. Results: Of the respondents, 57.8% reported that they planned to work in the region in which their school was located. The three factors ranked as having the greatest influence on their decision to work in non-metropolitan regions were: the cost of living, housing costs, and the proximity to family. Enjoyable aspects of rural life contributed positively to students' intentions to work in non-metropolitan regions, whereas isolation and socialization problems negatively affected their intentions to work in such areas. Conclusions: Greater consideration should be given to improving working conditions and housing environments in non-metropolitan regions.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.1-9
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• 2021

In this paper, we propose a training algorithm of support vector machine (SVM) with a sensitive variable. Although machine learning models enable automatic decision making in the real world applications, regulations prohibit sensitive information from being used to protect privacy. In particular, the privacy protection of the legally protected attributes such as race, gender, and disability is compulsory. We present an efficient least square SVM (LSSVM) training algorithm using a fully homomorphic encryption (FHE) to protect a partial sensitive attribute. Our framework posits that data owner has both non-sensitive attributes and a sensitive attribute while machine learning service provider (MLSP) can get non-sensitive attributes and an encrypted sensitive attribute. As a result, data owner can obtain the encrypted model parameters without exposing their sensitive information to MLSP. In the inference phase, both non-sensitive attributes and a sensitive attribute are encrypted, and all computations should be conducted on encrypted domain. Through the experiments on real data, we identify that our proposed method enables to implement privacy-preserving sensitive LSSVM with FHE that has comparable performance with the original LSSVM algorithm. In addition, we demonstrate that the efficient sensitive LSSVM with FHE significantly improves the computational cost with a small degradation of performance.

• Journal of Environmental Impact Assessment
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• v.30 no.1
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• pp.49-60
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• 2021

Environmental impact assessment (EIA) practitioners play a very pivotal role in establishing EIA policies, and when implementing EIA environmental conflicts can be prevented and resolved by sharing information with stakeholders and coordinating opinions. For this reason, grasping the perceptions of stakeholders including practitioners about the overall system, such as EIA policies and implementation, can be helpful in setting improvement directions and policy directions for EIA. However, there is an insufficient information on the perception and understanding of stakeholders about the EIA system and operation currently in effect in Korea. Therefore, this study diagnoses operational and procedural problems for the EIA system, which is a decision-making tool and a precautionary technique that can minimize adverse effects through the environmental information analysis method, and improvement points and systems of the EIA system in the future. We tried to find a complement of an online survey of 37 questions,responses from 95 responses from stakeholders of EIA were summarized. Stakeholders were aware of the problems of the operation of the current system and the preparation of the evaluation form, and this was reflected. Period and cost of preparation of EIS (49%), the introduction of a new method (26%) and the items of collecting opinions and conflict management (41%), which showed high negative response rates (dissatisfied and very dissatisfied), are considered to be areas that we need to supplement further in the future. As society develops rapidly, the system needs to be supplemented accordingly, and policy improvement efforts are needed for items with high negative responses as a result of the survey.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.475-484
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• 2021

The optimal design for water distribution system (WDS) is not only satisfying the minimum required water pressure of the nodes, but also minimizing pipe cost, etc. The number of designs of WDS increases exponentially due to the arrangement of various pipes. Various optimization algorithms were applied to propose an optimized design of WDS. In this study, Modified Hybrid Vision Correction Algorithm (MHVCA) with improved self-adapting parameter was applied to optimal design of WDS. The performance was improved by changing the Hybrid Rate (HR) of the existing Hybrid Vision Correction Algorithm (HVCA) to nonlinear HR. To verify the performance of the proposed MHVCA, it applied to mathematical problems consisting of 2 and 30 decision variables and constrained mathematical problems. In order to review the application results of MHVCA, it was compared with Harmony Search (HS), Improved Harmony Search (IHS), Vision Correction Algorithm (VCA) and HVCA. Finally, MHVCA was applied to the optimal design problem of WDS and the results were compared with other algorithms. MHVCA showed better results than other algorithms in mathematical problems and WDS problem. MHVCA will be able to show good results by applying to various water resource engineering problems as well as problems applied in this study.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.562-569
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• 2022

The alarm monitoring technology applied to existing operating ships manages data items such as temperature and pressure with AMS (Alarm Monitoring System) and provides an alarm to the crew should these sensing data exceed the normal level range. In addition, the maintenance of existing ships follows the Planned Maintenance System (PMS). whereby the sensing data measured from the equipment is monitored and if it surpasses the set range, maintenance is performed through an alarm, or the corresponding part is replaced in advance after being used for a certain period of time regardless of whether the target device has a malfunction or not. To secure the reliability and operational safety of ship engine operation, it is necessary to enable advanced diagnosis and prediction based on real-time condition monitoring data. To do so, comprehensive measurement of actual ship data, creation of a database, and implementation of a condition diagnosis monitoring system for condition-based predictive maintenance of auxiliary equipment and piping must take place. Furthermore, the system should enable management of auxiliary equipment and piping status information based on a responsive web, and be optimized for screen and resolution so that it can be accessed and used by various mobile devices such as smartphones as well as for viewing on a PC on board. This update cost is low, and the management method is easy. In this paper, we propose CBM (Condition Based Management) technology, for autonomous ships. This core technology is used to identify abnormal phenomena through state diagnosis and monitoring of pumps and purifiers among ship auxiliary equipment, and seawater and steam pipes among pipes. It is intended to provide performance diagnosis and failure prediction of ship auxiliary equipment and piping for convergence analysis, and to support preventive maintenance decision-making.

• Korean Journal of Construction Engineering and Management
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• v.23 no.5
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• pp.108-116
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• 2022

Started in 1989 as Public Permanent Rental Housing scheme, public rental housing lease policy is increasing target residents and supply in each government by introducing new supply types. However, public housing business entities have difficulties in expanding the supply due to cumulated deficit. The research suggested long-term public rental housing reconstruction business as a method to preserve the cumulated deficit from the previous. Minimum floor area ratio mathematical model was suggested by defining the floor area ratio of reconstruction business as minimum, since housing sales profit after reconstruction could preserve aggregated deficit, and mathematically approached by considering the traits of long-term public rental housing reconstruction. The determinant for minimum floor area ratio mathematical model comprise cumulated deficit of the existing long-term public rental housing, land size of reconstructed sale housing, housing sales price per unit area, and business cost per unit area. Minimum floor area ratio mathematical model is expected to be the milestone for supporting decision making regarding the economic part of old long-term public lease housings' reconstruction scale, and expanding housing supply within urban area.

• Knowledge Management Research
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• v.23 no.4
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• pp.275-294
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• 2022

In recent years, with the continuous integrative development of e-commerce and social media, social commerce, as a trust-centered social transaction mode, has become an important performance form of e-commerce. The good experience of online community and abundant user-generated content (UGC) attract more and more users and businesses to participate in the community contribution. In this context, the cost of accessing information is continuously decreasing, which not only makes the purchase process more concise and efficient, but also greatly increases the possibility of consumers' impulsive consumption. However, there are very few empirical studies on the internal influencing mechanism of consumers' impulsive consumption based on the characteristics of UGC for social commerce. In view of this, based on S-O-R model, this study constructs a model of consumers' impulsive consumption in the context of social commerce from the characteristics of UGC, with perceived risk as the mediating variable and tie strength as the moderating variable. The results show that content authenticity, content usefulness, and content valence of UGC have significant negative impacts on consumers' risk perception in the process of purchase decision-making, and consumers' perceived risk has a significant negative impact on consumers' impulsive consumption. Meanwhile, the tie strength between UGC producer and UGC receiver plays a moderating role between content usefulness and perceived risk, as well as between perceived risk and impulsive consumption. Finally, combined with the above findings, this study provides effective suggestions for relevant participants in social commerce in terms of business management.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.85-100
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• 2023

Corporate ESG performance (environmental, social, and corporate governance) reflecting a company's strategic sustainability has emerged as one of the main factors in today's investment decisions. The traditional ESG performance rating process is largely performed in a qualitative and subjective manner based on the institution-specific criteria, entailing limitations in reliability, predictability, and timeliness when making investment decisions. This study attempted to predict the corporate ESG rating through automated machine learning based on quantitative and disclosed corporate financial information. Using 12 types (21,360 cases) of market-disclosed financial information and 1,780 ESG measures available through the Korea Institute of Corporate Governance and Sustainability during 2019 to 2021, we suggested a deep neural network prediction model. Our model yielded about 86% of accurate classification performance in predicting ESG rating, showing better performance than other comparative models. This study contributed the literature in a way that the model achieved relatively accurate ESG rating predictions through an automated process using quantitative and publicly available corporate financial information. In terms of practical implications, the general investors can benefit from the prediction accuracy and time efficiency of our proposed model with nominal cost. In addition, this study can be expanded by accumulating more Korean and international data and by developing a more robust and complex model in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2D
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• pp.153-161
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• 2010

The city planning of a local government is a continuous process that does not end with the creation of a plan but proceeds through decision-making, monitoring and evaluation phases. As a new city planning is changed and confirmed, there is a chance to construct a large scale road that is connected with an under constructed road. In this case, the expansion of the width and length of road, the addition of bridges or tunnels, and the change of the size and location of interchanges lead to many changes on road design and construction. In the past, the consignment contracts for a road construction have been made in limited numbers and for limited civil works. Now, it is growing in numbers and is making for large scale multi-works. However, the standard process and guidelines for the consignment contracts have not been suggested yet, so there is difficulty in performing the consigned road construction effectively. In this paper, the important factors for the consignment contracts are determined by construction document reviews and expert interviews. Based on these results, a standard process for the consigned contracts and a guideline for agreeing on construction cost are suggested. The costs that should be paid by a consignor are also defined.

• Economic Analysis
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• v.24 no.3
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• pp.1-36
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• 2018

When it comes to explaining the relationship between inventory investment and business fluctuations, the production smoothing theory and the stock-out avoidance theory take contradictory stances. Decision-making related to inventory investments of corporations is thought to be influenced by both motives, but the relative sizes or directions of their respective influences can differ depending upon the phase of the business cycle. Against this backdrop, this paper differs from existing studies in that it theoretically tests the relative significances of the production smoothing and stock-out avoidance motives in the inventory investment dynamics, while placing its analytical focus on determining the existence and patterns of the asymmetric dynamics of inventory investment over the business cycle phases. To this end this paper sets up a non-linear model that is expanded from the existing linear inventory investment model, and checks whether its predictive power is better than that of the existing model. The results of analysis confirm the nature of the asymmetric dynamics of inventory investment over the business cycle phases. A stock-out avoidance motive appears but there is no significant production smoothing motive in boom times. In downturns, in contrast, the stock-out avoidance motive is insignificant, but a quality of asymmetric dynamics in which changes in inventory cause the deepening of recessions, due to the non-convexity of production costs proposed by Ramey (1991), is detected. This paper confirms that a model considering the asymmetric dynamics of inventory investment can have better predictive power than one that does not consider it, through within-sample and out-of-sample predictions and various predictive power tests. These research results are expected to be useful for economic forecasting, through their enhancement of the understandings of the inventory investment dynamics and of the nature of its business cycle destabilization.