• Journal of Popular Narrative
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• v.25 no.1
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• pp.315-347
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• 2019

(1986) and (1987) represent the society of 1980s in which the professional baseball game was initiated to cover the irrational military culture. The love and marriage of sports players were the headlines of the media, and the yearly salary of the players was the hottest issue of conversation. The military culture is represented in the scenes where the coaches train the failures and inapt players in extreme drills. The films pinpoint the absurdity of military culture and win-at-all-costs mentality. The collapse of the dictatorial leadership at the end of the films is a metaphor for the collapse of the fifth Republic of Korea. The episodes where the players talk about contract money, and the trade of players and sports business were a new phenomenon of the 1980's. The fact that Oh Hyesung of chooses love instead of victory deals a big blow to the secular ambition for money, victory and dictatorial leadership. His option provides catharsis for an audience oppressed under military leadership and success driven ideology. On the other hand, Oh Hyesung of dies right at the moment of winning the world champion. He achieves neither love nor success. While Oh Hyesung of is a symbol of pure love and gives spiritual comfort to the audience, Oh Hyesung of gives a sense of hopelessness to the audience. Both of the two sports films reflect the representation of the 1980's but received opposing reviews from audiences.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Korean Chemical Engineering Research
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• v.61 no.3
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• pp.388-393
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• 2023

The analysis of engineering data using neural network based on supervised learning has been utilized in various engineering fields such as optimization of chemical engineering process, concentration prediction of particulate matter pollution, prediction of thermodynamic phase equilibria, and prediction of physical properties for transport phenomena system. The supervised learning requires training data, and the performance of the supervised learning is affected by the composition and the configurations of the given training data. Among the frequently observed engineering data, the data is given in log-scale such as length of DNA, concentration of analytes, etc. In this study, for widely distributed log-scaled training data of virtual 100×100 images, available loss functions were quantitatively evaluated in terms of (i) confusion matrix, (ii) maximum relative error and (iii) mean relative error. As a result, the loss functions of mean-absolute-percentage-error and mean-squared-logarithmic-error were the optimal functions for the log-scaled training data. Furthermore, we figured out that uniformly selected training data lead to the best prediction performance. The optimal loss functions and method for how to compose training data studied in this work would be applied to engineering problems such as evaluating DNA length, analyzing biomolecules, predicting concentration of colloidal suspension.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.261-261
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• 2020

물관리의 기본이 되는 연속적인 유량 자료 확보를 위해서는 정확도 높은 수위-유량 관계 곡선식 개발이 필수적이다. 수위-유량 관계곡선식은 모든 수문시설 설계의 기초가 되며 홍수, 가뭄 등 물재해 대응을 위해서도 중요한 의미를 가지고 있다. 그러나 일반적으로 유량 측정은 많은 비용과 시간이 들고, 식생성장, 단면변화 등의 통제특성(control)이 변함에 따라 구간분리, 기간분리와 같은 비선형적인 양상이 나타나 자료 해석에 어려움이 존재한다. 특히, 국내 하천의 경우 자연적 및 인위적인 환경 변화가 다양하여 지점 및 기간에 따라 세밀한 분석이 요구된다. 머신러닝(Machine Learning)이란 데이터를 통해 컴퓨터가 스스로 학습하여 모델을 구축하고 성능을 향상시키는 일련의 과정을 뜻한다. 기존의 수위-유량 관계곡선식은 개발자의 판단에 의해 데이터의 종류와 기간 등을 설정하여 회귀식의 파라미터를 산출한다면, 머신러닝은 유효한 전체 데이터를 이용해 스스로 학습하여 자료 간 상관성을 찾아내 모델을 구축하고 성능을 지속적으로 향상 시킬 수 있다. 머신러닝은 충분한 수문자료가 확보되었다는 전제 하에 복잡하고 가변적인 수자원 환경을 반영하여 유량 추정의 정확도를 지속적으로 향상시킬 수 있다는 이점을 가지고 있다. 본 연구는 머신러닝의 대표적인 알고리즘들을 활용하여 유량을 추정하는 모델을 구축하고 성능을 비교·분석하였다. 대상지역은 안정적인 수량을 확보하고 있는 한강수계의 거운교 지점이며, 사용자료는 2010~2018년의 시간, 수위, 유량, 수면폭 등 이다. 프로그램은 파이썬을 기반으로 한 머신러닝 라이브러리인 사이킷런(sklearn)을 사용하였고 알고리즘은 랜덤포레스트 회귀, 의사결정트리, KNN(K-Nearest Neighbor), rgboost을 적용하였다. 학습(train) 데이터는 입력자료 종류별로 조합하여 6개의 세트로 구분하여 모델을 구축하였고, 이를 적용해 검증(test) 데이터를 RMSE(Roog Mean Square Error)로 평가하였다. 그 결과 모델 및 입력 자료의 조합에 따라 3.67~171.46로 다소 넓은 범위의 값이 도출되었다. 그 중 가장 우수한 유형은 수위, 연도, 수면폭 3개의 입력자료를 조합하여 랜덤포레스트 회귀 모델에 적용한 경우이다. 비교를 위해 동일한 검증 데이터를 한국수문조사연보(2018년) 내거운교 지점의 수위별 수위-유량 곡선식을 이용해 유량을 추정한 결과 RMSE가 3.76이 산출되어, 머신러닝이 세분화된 수위-유량 곡선식과 비슷한 수준까지 성능을 내는 것으로 확인되었다. 본 연구는 양질의 유량자료 생산을 위해 기 구축된 수문자료를 기반으로 머신러닝 기법의 적용 가능성을 검토한 기초 연구로써, 국내 효율적인 수문자료 측정 및 수위-유량 곡선 산출에 도움이 될 수 있을 것으로 판단된다. 향후 수자원 환경 및 통제특성에 영향을 미치는 다양한 영향변수를 파악하기 위해 기상자료, 취수량 등의 입력 자료를 적용할 필요가 있으며, 머신러닝 내 비지도학습인 딥러닝과 같은 보다 정교한 모델에 대한 추가적인 연구도 수행되어야 할 것이다.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.71-83
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• 2023

Since last year, the government has enforced the 'Act on the Punishment of Severe Accidents, Etc.' (hereafter referred to as the 'Serious Accident Punishment Act'), which punishes business owners and business managers who fail to fulfill their duty of safety measures with 'imprisonment of one year or more' and the Occupational Safety and Health Act. Based on this, various occupational safety and health policies were developed, including the operation of a system related to entrusting the work of safety managers. Despite these efforts, the effect of implementing the Severe Accident Punishment Act is a groundbreaking change in the current disaster prevention policy, which has increased by 0.02%P and 0.03‱P, respectively, from the previous year to 0.65% of the total accident rate and 1.10‱ of the death rate per 10,000 people as of 2022. As the need emerged, attention was paid to 'collaboration and governance with safety management institutions' in the 'Severe Disaster Reduction Roadmap' announced by the Ministry of Employment and Labor in November 2022. In this study, a meaningful result was derived by comparing and analyzing the industrial accident status of workplaces entrusted by "A" safety management institutions with the national average based on the industrial accident survey table, and the types of industrial accidents that occurred in consigned workplaces were selected as intensive management targets. The policy direction for industrial accident prevention was established. It is necessary to develop safety management work manuals based on the results of this study, expertise, discover best cases of risk assessment and develop guides, and educate and train consigned workers. In addition, it suggests that the government's guidance and supervision are needed to advance the professionalism of safety management entrusted tasks, and that safety management institutions should strengthen their roles and functions for preventing and reducing industrial accidents. However, due to difficulties in disclosing information of specialized safety management institutions, the limitation of the provision, collection, and viewing of research-related data to "A" specialized safety management institutions remains a limitation of the research. It seems likely that more thorough research will be conducted.

• Convergence Security Journal
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• v.22 no.4
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• pp.35-44
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• 2022

Among the five promotion strategies of Defense Innovation 4.0(DI 4.0), the military structure/operation optimization strategy aims to innovate the military structure based on advanced science&technology(S&T), and to integrate advanced S&T in the field of defense operation such as education&training and human resource development. As the future battlefield expands to AI-based unmanned/robot combat systems, space, cyberspace, and electromagnetic fields, it is necessary to train officers with the capabilities required in these battlefields. It is necessary to develop capabilities from junior officers who will lead the future battlefield to operating core advanced power based on the 4^th industrial revolution S&T. We review the education system of the military in universities and propose a method of redesigning the education system that is compatible with DI 4.0 and can develop technology-intensive capabilities based on advanced S&T. We propose a operation plan of major and extra-programs that can develop the capabilities of junior officers required for the future battlefield, and also suggest ways to support the army's practical training.

• The Journal of Bigdata
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• v.8 no.1
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• pp.49-59
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• 2023

There are various items in the safety and health standards of the manufacturing industry, but they can be divided into work-related diseases and musculoskeletal diseases according to the standards for sickness and accident victims. Musculoskeletal diseases occur frequently in manufacturing and can lead to a decrease in labor productivity and a weakening of competitiveness in manufacturing. In this paper, to detect the musculoskeletal harmful factors of manufacturing workers, we defined the musculoskeletal load work factor analysis, harmful load working postures, and key points matching, and constructed data for Artificial Intelligence(AI) learning. To check the effectiveness of the suggested dataset, AI algorithms such as YOLO, Lite-HRNet, and EfficientNet were used to train and verify. Our experimental results the human detection accuracy is 99%, the key points matching accuracy of the detected person is @AP0.5 88%, and the accuracy of working postures evaluation by integrating the inferred matching positions is LEGS 72.2%, NECT 85.7%, TRUNK 81.9%, UPPERARM 79.8%, and LOWERARM 92.7%, and considered the necessity for research that can prevent deep learning-based musculoskeletal diseases.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.839-848
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• 2006

Korea has become the 5th country to own and operate the high speed railroad in 2004. However, there were many difficulties until Koreans enjoy the first bullet train service with the average hourly speed of 300km. The high speed railroad requires elevated quality standards differently from the traditional railways. In addition to the technical difficulties, the construction project itself was an unpleasant case with huge delays and cost overruns mainly due to the lack of experiences, deficiency of owner$^{\circ}{\O}$s role, and increase of public resistances triggered by environmental concerns. This paper analyzes the reasons for delays on this mega-project. With respect to the characteristics of the whole project level, it is very complicated/linear project, whose total length is around 412 km with the composition of various sections in the route of the railway which have basically different conditions. For that reason, the analysis is performed in both macro and micro level. First, macroscopic analysis is performed to find critical subdivisions in the railway route that induces the significant delay in the opening due date. Then, microscopic analysis is followed to quantify the causes and effects of delays focused on these critical subdivisions in more detailed way. Finally, this paper provides lessons learned from this project to avoid the decisive delays in performing the similar large-scaled projects.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.2
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• pp.151-162
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• 2009

This study proposes a new methodology for quantitative rock classification in unsampled rock zone, which occupies the most of tunnel design area. This methodology is to train an ANN (artificial neural network) by using results from a drilling investigation combined with electric resistivity survey in sampled zone, and then apply the trained ANN to making a prediction of grade of rock classification in unsampled zone. The prediction is made at the center point of a shifting window by using a number of electric resistivity values within the window as input reference information. The ANN training in this study was carried out by the RPROP (Resilient backpropagation) training algorithm and Early-Stopping method for achieving a generalized training. The proposed methodology is then applied to generate a rock grade distribution on a real tunnel site where drilling investigation and resistivity survey were undertaken. The result from the ANN based prediction is compared with one from a conventional kriging method. In the comparison, the proposed ANN method shows a better agreement with the electric resistivity distribution obtained by field survey. And it is also seen that the proposed method produces a more realistic and more understandable rock grade distribution.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.565-571
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• 2006

This study aimed to analyze access mode choice behavior for KTX Passengers. To fulfill the aims of this study, Dong-Daegu Station was selected as a station for a case study. This study takes place in two stages. These are (i) descriptive statistical analysis of transportation status before and after introduction of the KTX, (ii) empirical model estimation for analyzing access mode choice behavior. This study makes use of the data from travel survey from Daegu metropolitan area. The main part of the survey was carried out in the KTX Dong-Daegu station. The data was collected from a sample of 1,800 individuals. The survey data includes the information on travel from Dong-Daegu station to Seoul. From descriptive statistical analysis of transportation status before and after introduction of the KTX, it is found that revealed demand of the KTX is lower than that expected. Moreover, it is found that the low demand of the KTX stems from high cost for the KTX itself and inconvenience( including travel time and cost) of access mode. In order to analyze mode choice behavior for accessing Dong-Daegu station, multinomial logit model structure is used. For the model specification, a variety of behavioral assumptions about the factors which affect the access mode choice, were considered. From the empirical model estimation, it si found that access travel time and access travel cost are significant in choosing access mode. Given the empirical evidence, we see that improvement of access transportation system for Dong-Daegu station is very important for enhancing the use of KTX.