• 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.

• Journal of the Korean Geosynthetics Society
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• v.11 no.4
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• pp.27-35
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• 2012

Facings of mechanically stabilized earth retaining walls have function to fix the reinforcement and prevent backfill loss, but the walls are lack of structural rigidity capable of resisting applied loads. The reinforced subgrade with rigid wall was developed to have the structural functions under train loading. Though it has lots of advantages such as small deformation after construction, its negative side effects of economics and difficult construction were mainly mentioned and not practically used. To apply it for railroad subgrade, this study focus on the construction cost down and the enhancement of constructability without functional loss. To do so, the behaviors of reinforced subgrade with rigid wall were evaluated with the change of the vertical spacing and length of reinforcement. Small scale model tests (1/10 scale) and 3 m full scale tests were performed to evaluate deformation characteristics of reinforced subgrade under simulated train loading. Even though it uses short reinforcement, it showed small horizontal displacement of wall and plastic settlement of subgrade. Also, it was verified that not only 30 cm but also 40 cm of vertical spacing of reinforcement had good performance in serviceability aspects.

• Steel and Composite Structures
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• v.34 no.5
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• pp.743-767
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• 2020

Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R² = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R² = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.

• Journal of Satellite, Information and Communications
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• v.2 no.2
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• pp.69-74
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• 2007

Recently, the demands for the satellite broadband mobile communication services are increased. To provide these services, mobile satellite communication systems for the passengers or crews on the high-speed moving vehicles, are being developed for the last several years especially in the Europe and North America. However, most of these systems can provide only several hundred kbps of transmission rate and this is not enough performance to provide satellite internet service for the group users such as passengers on the high-speed train. Moreover, service availability with these systems is limited to be rather low because they don't have any countermeasure scheme for the N-LOS environment which happens often along the railway. This paper describes mobile broadband satellite communication system, which is on the development, to provide high data-rate internet services to the high-speed trains. This system is applied with the inter-networking scenarios of both satellite/terrestrial network and satellite/gap-filler network so that it can provide seamless service even in the train operating environment, and these inter-networking schemes result in high service availability. And this system also has the countermeasure schemes, such as upper layer FEC and antenna diversity, for the short fading which is occurred periodically on the railway due to the power supplying structures so that it can provide high speed internet services. Mobile DVB-S2 technology which is now being standardized in the DVB is used for the forward-link transmission and DVB-RCS for the return-link.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.537-544
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• 2016

National Competency Standards (NCS) need to be introduced to train newly hired staff and to gradually improve employees' work performance in the information security industry. In particular, the introduction of a new NCS curriculum for new hires is important in order to retain and efficiently manage professionals in the information security field. However, the legacy NCS is not clearly designed for the information security field. So a formal curriculum has been suggested for institutions training the information security workforce. Therefore, this study establishes a competency unit based on the types of personnel, their duties, and required knowledge. To select the competency unit, this study reviewed prior research to understand the required skills and work knowledge, and reviewed recruitment-based NCS that public agencies and public and private companies have carried out, including them in the study. The selected competency unit was classified into a required competency unit and an elective competency unit based on the importance of the duties and the demands of training. Through a verification process for the new, licensed career path model in the NCS information and communications field, this study suggests updated NCS competency units and required courses to provide an appropriate NCS curriculum for newly hired employees in the information security industry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6604-6610
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• 2015

In this paper, an efficient Wireless Access in Vehicular Environment (WAVE) packet transmission scheme for railroad communication is proposed. WAVE communication is a wireless local area network (WLAN) based communication and it is developed to be suitable for vehicular communication. There has been a lot of study on WAVE's applicability to Intelligent Transport System (ITS). As one of main transportation methods, by using WAVE, quality of railroad communication including WLAN based Communication Based Train Control (CBTC) can be enhanced and variety of railroad communication systems can be integrated into WAVE. However, there are technical challenges to adopt WAVE in railroad communications. For the simplest single-PHY WAVE, time division alternation of 50ms between Control Channel (CCH) and Service Channel (SCH) is required. Since there are delay sensitive railroad traffic types, alternation operation of CCH and SCH may cause performance degradation. In this paper, after identifying a couple of problems based on detailed analysis, a novel packet transmission scheme under railroad environment is proposed. In order to verify if the proposed scheme meets the requirement of railroad communication, WAVE transmission is mathematically modeled.

• Journal of the Korean Society for Railway
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• v.18 no.3
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• pp.252-260
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• 2015

Ballasted track on an asphalt roadbed can be beneficial for its various effects such as (i) decreasing of roadbed thickness by dispersing train load; (ii) prevention of both strength reduction and weakening in roadbed system by preventing rainwater penetration; and (iii) reducing maintenance cost by preventing roadbed mud-pumping and frostbite. With these beneficial effects, ballasted track on asphalt roadbed has been widely used in Europe and Japan, and relevant research for applying such ballasted track on asphalt roadbed systems in Korea is ongoing. In this study, full-scale static and dynamic train load tests were performed to compare the performance of ballasted track on asphalt roadbed and ballasted track. The optimum thickness levels of asphalt and reinforced roadbeds, corresponding to the design criteria for reinforced roadbed of high-speed railway, was estimated using the FEM program ABAQUS. Test results show that the earth pressure on reinforced roadbed of ballasted track on the asphalt roadbed was relatively low compared with that of simple ballasted track. The elastic and plastic displacements of simple ballasted track on the asphalt roadbed were also lower than those of ballasted track. These test results may indicate that the use of ballasted track on asphalt roadbed is an advantageous system in view of long-term maintenance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.15-23
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• 2017

The person Re-identification is the most challenging part of computer vision due to the significant changes in human pose and background clutter with occlusions. The picture from non-overlapping cameras enhance the difficulty to distinguish some person from the other. To reach a better performance match, most methods use feature selection and distance metrics separately to get discriminative representations and proper distance to describe the similarity between person and kind of ignoring some significant features. This situation has encouraged us to consider a novel method to deal with this problem. In this paper, we proposed an enhanced recurrent neural network with three-tier hierarchical network for person re-identification. Specifically, the proposed recurrent neural network (RNN) model contain an iterative expectation maximum (EM) algorithm and three-tier Hierarchical network to jointly learn both the discriminative features and metrics distance. The iterative EM algorithm can fully use of the feature extraction ability of convolutional neural network (CNN) which is in series before the RNN. By unsupervised learning, the EM framework can change the labels of the patches and train larger datasets. Through the three-tier hierarchical network, the convolutional neural network, recurrent network and pooling layer can jointly be a feature extractor to better train the network. The experimental result shows that comparing with other researchers' approaches in this field, this method also can get a competitive accuracy. The influence of different component of this method will be analyzed and evaluated in the future research.

• Journal of the Korean Society of Safety
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• v.18 no.2
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• pp.132-138
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• 2003

The use of long rails in high-speed railway bridges causes additional stresses due to nonlinear behaviours between the rail and bridge decks in the neighbourhood of the deck joints. In the seismic response analysis of high-speed railway bridges, since structural response is highly sensitive to properties of the ground motion, spatial variation of the ground excitation affects responses of the bridges, which in turn affect stresses in the rails. In addition, it is shown that high-speed trains need very long distances to stop when braking under seismic occurrence corresponding to operational earthquake performance level so that verification of the safe stoppage of the train is also required. In view of such additional stresses due to long rails, sensibility of structural response to the properties of the ground motion and braking distance needed by the train to stop safely, this paper proposes and establishes a time domain nonlinear dynamic analysis method that accounts for braking loads, spatial variation of the ground motion and material nonlinearities of rails to analyze long rail stresses in high-speed railway bridges subjected to seismic event. The accuracy of the proposed method is demonstrated through an application on a typical site of the Korean high-speed railway.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.483-494
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• 2020

With the advent of the 4th Industrial Revolution, interest in AI education is increasing. In order to cultivate talented people with AI competencies who will lead the future, AI education must be conducted in a sound manner at the school site. Although AI education is being conducted at home and abroad, it was determined that the role of the AI education platform is important to implement better AI education, so this study investigated the perception of experts on the AI education platform. A perception survey was conducted based on five criteria: teaching and learning management, educational contents, accessibility, performance of AI education platform, and level suitability of elementary school students. As a results, the number of 103 educational experts selected 'Entry' as the most proper platform among the eight platforms - 'Machine learning for Kids', 'Teachable Machine', 'AI Oceans(code.org)', 'Entry', 'Genie Block', 'Elice', 'mBlock' and etc. Analysis shows that this is because 'Entry' provides quality educational content, has convenient accessibility, is easy to manage teaching and learning, as well as an AI education platform suitable for the level of elementary school. In order to apply various AI education platforms to the school field, it is necessary to train teachers in AI-related training to train them as AI education experts, and to continuously provide opportunities to experience AI education platforms. In this study, there are limitations to what is called 'a population perception survey'. because only 103 people were surveyed, and most of the experts are working in a specific area(Gyeonggi-do). In the future, it is judged that research targeting experts at the national level should be conducted to supplement these limitations.