• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.276-292
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• 2018

This study was conducted to investigate the trends in domestic and international disaster-related research through a systematic review of the literature and to establish a basis for future disaster-related countermeasures and development directions. A related literature search was conducted through the domestic and foreign databases through the combination of disaster-related terms from 2000 until February 28, 2017, and 79 articles were used in the analysis based on selection and exclusion criteria of 177 total documents. As a result of the research, 31.6% of disaster research type was quantitative studies, and 29.1% of the major disciplines were medical research. In addition, there were engineering(18.9%), public administration(13.9%), and nursing(11.4%). In foreign literature, there are many triage studies for the classification of patients in multiple lesions. On the other hand, only 30.4% of total triage studies in Korea were detected. Most of them were related to triage development, triage evaluation, triage research, and reviews. In addition, according to the disaster nursing capacity framework of the International Council of Nurses, 72.3% of studies were related to the response phase. Future research on disasters requires interdisciplinary convergence, patient classification, and technology integration to improve the survival rate of multiple injuries, and an integrated system based on the results of collaborative research among interdisciplinary groups is needed.

• The Journal of Society for e-Business Studies
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• v.23 no.1
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• pp.1-22
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• 2018

The value chain has been utilized as a strategic tool to improve competitive advantage, mainly at the enterprise level and at the industrial level. However, in order to conduct value chain analysis at the enterprise level, the client companies of the parent company should be classified according to whether they belong to it's value chain. The establishment of a value chain for a single company can be performed smoothly by experts, but it takes a lot of cost and time to build one which consists of multiple companies. Thus, this study proposes a model that automatically classifies the companies that form a value chain based on actual transaction data. A total of 19 transaction attribute variables were extracted from the transaction data and processed into the form of input data for machine learning method. The proposed model was constructed using the Random Forest algorithm. The experiment was conducted on a automobile parts company. The experimental results demonstrate that the proposed model can classify the client companies of the parent company automatically with 92% of accuracy, 76% of F1-score and 94% of AUC. Also, the empirical study confirm that a few transaction attributes such as transaction concentration, transaction amount and total sales per customer are the main characteristics representing the companies that form a value chain.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.183-191
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• 2021

Non-intrusive load monitoring is a technology that can be used for predicting and classifying the type of appliances through real-time monitoring of user power consumption, and it has recently got interested as a means of energy-saving. In this paper, we propose a system for classifying appliances from user consumption data by combining GAF(Gramian angular field) technique that can be used for converting one-dimensional data to the two-dimensional matrix with convolutional neural networks. We use REDD(residential energy disaggregation dataset) that is the public appliances power data and confirm the classification accuracy of the GASF(Gramian angular summation field) and GADF(Gramian angular difference field). Simulation results show that both models showed 94% accuracy on appliances with binary-state(on/off) and that GASF showed 93.5% accuracy that is 3% higher than GADF on appliances with multi-state. In later studies, we plan to increase the dataset and optimize the model to improve accuracy and speed.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• 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 Society of Marine Environment & Safety
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• v.24 no.2
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• pp.146-156
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• 2018

The ship's chart system was changed from the use of paper chart to the ENC (Electronic Navigational Chart) using ECDIS (Electronic Chart Display and Information System). The introduction of ENC in ships is necessary for ship automation and for the digitalizing of data and integration of information, but unexpected various problems have occurred and are posing a great threat to safe navigation in the transitional period when the new system has been applied to the sea. In this paper, to assess whether ECDIS contributes to the safety of navigation for its intended purposes as new navigation equipment, a usability test of ECDIS was conducted on masters and crew who have used ECDIS on ocean-going vessels. The result was verified with a paired sample T-test, and it was significantly analyzed with the effectiveness of a simplified task; cost efficiency was decreased since ECDIS was used. By analyzing 'MSC.1/Circ.1503 ECDIS - Guidance for good practice', we found that the effects of the maintenance of ECDIS software, operating anomalies identified within ECDIS, differences between raster chart display system (RCDS) and ECDIS, and matters of identification were compounded by the overlapping information on the safety of ships. The anomalies were also grouped according to their characteristics, and we proposed suitable improvements accordingly. The reason for the reduction in efficiency in the usability test was that the problems with ECDIS were intended to be solved only with the careful use of navigational officers who did not have systematic solutions. To solve these problems, the maintenance of software, the improvement of ECDIS anomalies, the reliable ENC issuance including the global oceans, and S-mode development are a priority.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.22-34
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• 2016

The Project Delivery System (PDS) is used in mixed way without clear classification from tendering system and the standard itself that can be selected is set with project budget or estimated cost only. Essentially, the PDS should consider and reflect project characteristics and types, internal and external factors for the purpose of improving the lives of citizens and their welfare. However, the current status is not operated flexibly due to the given budget, period and uniform laws and regulations. In order to solve this problem, this study suggests a Decision Support Model to select the optimal PDS for public construction projects. The current problem of the PDS for public construction projects were identified and the application of a decision support model was proposed. Subsequently a decision-making model was suggested for each PDS using the identified factors and linear discriminant function of discriminant analysis. An additional questionnaire survey and actual practical case analysis were carried out to verify the effectiveness and applicability of the model to actual work. It can be used by adjusting the decision support model and detailed factors according to the specific characteristics of public organization, ability of person in charge and project type.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.365-372
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• 2022

Compared to sensors mainly used in human activity recognition (HAR) systems, inertial measurement unit (IMU) sensors are small and light, so can achieve lightweight system at low cost. Therefore, in this paper, we propose a binary neural network (BNN) based gait pattern analysis system using IMU sensor, and present the design and implementation results of an FPGA-based accelerator for computational acceleration. Six signals for gait are measured through IMU sensor, and a spectrogram is extracted using a short-time Fourier transform. In order to have a lightweight system with high accuracy, a BNN-based structure was used for gait pattern classification. It is designed as a hardware accelerator structure using FPGA for computation acceleration of binary neural network. The proposed gait pattern analysis system was implemented using 24,158 logics, 14,669 registers, and 13.687 KB of block memory, and it was confirmed that the operation was completed within 1.5 ms at the maximum operating frequency of 62.35 MHz and real-time operation was possible.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.157-176
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• 2016

For a long time, mobile phone had a sole function of communication. Recently however, abrupt innovations in technology allowed extension of the sphere in mobile phone activities. Development of technology enabled realization of almost computer-like environment even on a very small device. Such advancement yielded several forms of new high-tech devices such as smartphone and tablet PC, which quickly proliferated. Simultaneously with the diffusion of the mobile devices, mobile applications for those devices also prospered and soon became deeply penetrated in consumers' daily lives. Numerous mobile applications have been released in app stores yielding trillions of cumulative downloads. However, a big majority of the applications are disregarded from consumers. Even after the applications are purchased, they do not survive long in consumers' mobile devices and are soon abandoned. Nevertheless, it is imperative for both app developers and app-store operators to understand consumer behaviors and to develop marketing strategies aiming to make sustainable business by first increasing sales of mobile applications and by also designing surviving strategy for applications. Therefore, this research analyzes consumers' mobile application usage behavior in a frame of substitution/supplementary of application categories and several explanatory variables. Considering that consumers of mobile devices use multiple apps simultaneously, this research adopts multivariate probit models to explain mobile application usage behavior and to derive correlation between categories of applications for observing substitution/supplementary of application use. The research adopts several explanatory variables including sociodemographic data, user experiences of purchased applications that reflect future purchasing behavior of paid applications as well as consumer attitudes toward marketing efforts, variables representing consumer attitudes toward rating of the app and those representing consumer attitudes toward app-store promotion efforts (i.e., top developer badge and editor's choice badge). Results of this study can be explained in hedonic and utilitarian framework. Consumers who use hedonic applications, such as those of game and entertainment-related, are of young age with low education level. However, consumers who are old and have received higher education level prefer utilitarian application category such as life, information etc. There are disputable arguments over whether the users of SNS are hedonic or utilitarian. In our results, consumers who are younger and those with higher education level prefer using SNS category applications, which is in a middle of utilitarian and hedonic results. Also, applications that are directly related to tangible assets, such as banking, stock and mobile shopping, are only negatively related to experience of purchasing of paid app, meaning that consumers who put weights on tangible assets do not prefer buying paid application. Regarding categories, most correlations among categories are significantly positive. This is because someone who spend more time on mobile devices tends to use more applications. Game and entertainment category shows significant and positive correlation; however, there exists significantly negative correlation between game and information, as well as game and e-commerce categories of applications. Meanwhile, categories of game and SNS as well as game and finance have shown no significant correlations. This result clearly shows that mobile application usage behavior is quite clearly distinguishable - that the purpose of using mobile devices are polarized into utilitarian and hedonic purpose. This research proves several arguments that can only be explained by second-hand real data, not by survey data, and offers behavioral explanations of mobile application usage in consumers' perspectives. This research also shows substitution/supplementary patterns of consumer application usage, which then explain consumers' mobile application usage behaviors. However, this research has limitations in some points. Classification of categories itself is disputable, for classification is diverged among several studies. Therefore, there is a possibility of change in results depending on the classification. Lastly, although the data are collected in an individual application level, we reduce its observation into an individual level. Further research will be done to resolve these limitations.

• Korean Journal of Ecology and Environment
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• v.51 no.2
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• pp.149-159
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• 2018

Twenty-nine wetlands (20 river type and 9 lake type wetlands) in Gyeongsangnam-do were investigated to understand the characteristics of fish assemblages by the wetland type and to suggest management strategies. As a result, $10.3{\pm}4.8$ species were collected from river type wetlands on average (${\pm}SD$) and $9.1{\pm}4.1$ species from lake type wetlands. Thus, there was no significant difference in the number of species between them (Mann-Whitney U test, P>0.05). However, the species that constitute the fish assemblage showed statistically significant differences between the two wetland types (PERMANOVA, Pseudo-F=2.9555, P=0.007). Furthermore, the species that contribute the most to each type of fish assemblage were Zacco koreanus (river type, 28.51%) and Lepomis macrochirus (lake type, 23.21%), respectively (SIMPER). The results of the NMDS analysis using the fish assemblage by place classified the species into three groups (river type, lake type, and others). The current wetland management is only focused on endangered species, but this study shows a difference in fish assemblage by wetland type. Therefore, a management system based information on endemic species, exotic species and major contribution species should be provided. Furthermore, the classification of some types of wetlands based on the present topography was found to be ambiguous, and wetland classification using living creatures can be used as a complementary method. This study has limitations because only two types of wetlands were analyzed. Therefore, a detailed management method that can represent every type of wetland should be prepared through the research of all types of wetlands in the future.