• KNOM Review
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• v.24 no.2
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• pp.24-34
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• 2021

Service providers using edge computing provide a high level of service. As a result, devices store important information in inner storage and have become a target of the latest cyberattacks, which are more difficult to detect. Although experts use a security system such as intrusion detection systems, the existing intrusion systems have low detection accuracy. Therefore, in this paper, we proposed a machine learning model for more accurate intrusion detections of devices in edge computing. The proposed model is a hybrid model that combines a stacked sparse autoencoder (SSAE) and a convolutional neural network (CNN) to extract important feature vectors from the input data using sparsity constraints. To find the optimal model, we compared and analyzed the performance as adjusting the sparsity coefficient of SSAE. As a result, the model showed the highest accuracy as a 96.9% using the sparsity constraints. Therefore, the model showed the highest performance when model trains only important features.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.312-318
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• 2022

This paper proposes a model which classifies the type of guns and information about sound source location using deep neural network. The proposed classification model is composed of convolutional neural networks (CNN) and long short-term memory (LSTM). For training and test the model, we use the Gunshot Audio Forensic Dataset generated by the project supported by the National Institute of Justice (NIJ). The acoustic signals are transformed to Mel-Spectrogram and they are provided as learning and test data for the proposed model. The model is compared with the control model consisting of convolutional neural networks only. The proposed model shows high accuracy more than 90 %.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.181-188
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• 2022

This study analyzed the effectiveness of sharing activities through software creative sharing classes. The purpose is to find a way for related activities to be carried out continuously. For this study, pre and post were test on changes in self-esteem, responsibility, and sense of community targeting 25 university students in Chungnam. The collected data were analyzed with SPSS 24. The results derived from this study are as follows. First, the self-esteem was significantly higher after the software creative sharing class than before. Second, the responsibility was significantly higher after the software creative sharing class compared to the prior. Third, the sense of community was found to be significantly higher after the software creative sharing class than before. Therefore, it was found that the software creative sharing class had a positive effect on self-esteem responsibility and sense of community. Based on these data, a method to expand continuous participation in talent sharing was suggested.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.333-340
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• 2023

Efficient prediction of corporate bankruptcy is an important part of making appropriate lending decisions for financial institutions and reducing loan default rates. In many studies, classification models using artificial intelligence technology have been used. In the financial industry, even if the performance of the new predictive models is excellent, it should be accompanied by an intuitive explanation of the basis on which the result was determined. Recently, the US, EU, and South Korea have commonly presented the right to request explanations of algorithms, so transparency in the use of AI in the financial sector must be secured. In this paper, an artificial intelligence-based interpretable classification prediction model was proposed using corporate bankruptcy data that was open to the outside world. First, data preprocessing, 5-fold cross-validation, etc. were performed, and classification performance was compared through optimization of 10 supervised learning classification models such as logistic regression, SVM, XGBoost, and LightGBM. As a result, LightGBM was confirmed as the best performance model, and SHAP, an explainable artificial intelligence technique, was applied to provide a post-explanation of the bankruptcy prediction process.

• Journal of Forest and Environmental Science
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• v.39 no.2
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• pp.105-117
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• 2023

Climate change and invasive alien plant species (IAPs) are having a significant impact on mountain ecosystems. The combination of climate change and socio-economic development is exacerbating the invasion of IAPs, which are a major threat to biodiversity loss and ecosystem functioning. Species distribution modelling has become an important tool in predicting the invasion or suitability probability under climate change based on occurrence data and environmental variables. MaxEnt modelling was applied to predict the current suitable distribution of most noxious weed A. adenophora (Spreng) R. King and H. Robinson and analysed the changes in distribution with the use of current (year 2000) environmental variables and future (year 2050) climatic scenarios consisting of 3 representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5) in Bhutan. Species occurrence data was collected from the region of interest along the road side using GPS handset. The model performance of both current and future climatic scenario was moderate in performance with mean temperature of wettest quarter being the most important variable that contributed in model fit. The study shows that current climatic condition favours the A. adenophora for its invasion and RCP 2.6 climatic scenario would promote aggression of invasion as compared to RCP 4.5 and RCP 8.5 climatic scenarios. This can lead to characterization of the species as preferring moderate change in climatic conditions to be invasive, while extreme conditions can inhibit its invasiveness. This study can serve as reference point for the conservation and management strategies in control of this species and further research.

• Korean Journal of Organic Agriculture
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• v.31 no.2
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• pp.135-155
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• 2023

The purpose of this study is to investigate the relationship between the service quality of WPL (Work Place Learning) and the revisit intention through customer satisfaction, targeting users who use the WPL. Data to achieve the purpose of this study were conducted for trainees who had received on-the-job training at 4 selected WPL in Jeollabuk-do. Out of the 210 copies of questionnaires, 170 were picked up, and all of them were used for analysis. As a result of the analysis, First, as a result of analyzing the relationship between service quality and customer satisfaction in WPL, it was discovered that among the service quality components, Responsiveness, Assurance, and Empathy had a substantial influence on customer satisfaction, while Tangibles and Reliability of service quality did not appear to have any significant effect. Second, it was discovered that customer contentment had a considerable influence on revisit intention after evaluating the link between customer satisfaction and revisit intention. It can be shown that the higher the level of client happiness, the greater the likelihood of returning. Third, as a result of analyzing the relationship between service quality and revisit intention of WPL, among service quality factors, Reliability, Responsiveness, and Empathy were found to have a significant effect on revisit intention. As a result of verifying the mediating effect of service quality at WPL on revisit intention through customer satisfaction, responsiveness, assurance, and empathy of service quality were found to be significant in their relationship with revisit intention.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.73-80
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• 2009

It is difficult that expertise human supply and demand for industry requires by imbalance of industry necessity human and profession organs of education's Skill Mismatch. Industry can prove productivity though reeducate school graduation person in spot and master correct technology in industry special quality. This paper is research that accommodate Demand Education that industry requires and make out full text caution Curriculum Specializing Vocational High School in e-Business field. Analysis e-Business industrial classification and occupational classification. Analysis knowledge and technological level that require in industry about e-Business education and investigate and analyze the demand. Base industry, Support industry, Apply e-Business Curriculum that is examined by practical use industry to learning, Do to estimate satisfaction about Demand Education Curriculum of industry and confirm Success special quality with research and investigation and application wave. Suggested for e-Business Curriculum's basis model in this paper and school subject Curriculum. Wish to contribute in nation development through productivity elevation through e-Business education of industry request.

• Smart Media Journal
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• v.11 no.1
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• pp.46-57
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• 2022

In this paper, multi-region based Radial Graph Convolutional Network (MRGCN) algorithm which can perform end-to-end action recognition using the optical flow and gradient of input image is described. Because this method does not use information of skeleton that is difficult to acquire and complicated to estimate, it can be used in general CCTV environment in which only video camera is used. The novelty of MRGCN is that it expresses the optical flow and gradient of the input image as directional histograms and then converts it into six feature vectors to reduce the amount of computational load and uses a newly developed radial type network model to hierarchically propagate the deformation and shape change of the human body in spatio-temporal space. Another important feature is that the data input areas are arranged being overlapped each other, so that information is not spatially disconnected among input nodes. As a result of performing MRGCN's action recognition performance evaluation experiment for 30 actions, it was possible to obtain Top-1 accuracy of 84.78%, which is superior to the existing GCN-based action recognition method using skeleton data as an input.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.55.3-55.3
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• 2019

We present a pipeline to estimate baryonic properties of a galaxy inside a dark matter (DM) halo in DM-only simulations using a machine trained on high-resolution hydrodynamic simulations. As an example, we use the IllustrisTNG hydrodynamic simulation of a (75 h^-1 Mpc)³ volume to train our machine to predict e.g., stellar mass and star formation rate in a galaxy-sized halo based purely on its DM content. An extremely randomized tree (ERT) algorithm is used together with multiple novel improvements we introduce here such as a refined error function in machine training and two-stage learning. Aided by these improvements, our model demonstrates a significantly increased accuracy in predicting baryonic properties compared to prior attempts --- in other words, the machine better mimics IllustrisTNG's galaxy-halo correlation. By applying our machine to the MultiDark-Planck DM-only simulation of a large (1 h^-1 Gpc)³ volume, we then validate the pipeline that rapidly generates a galaxy catalogue from a DM halo catalogue using the correlations the machine found in IllustrisTNG. We also compare our galaxy catalogue with the ones produced by popular semi-analytic models (SAMs). Our so-called machine-assisted semi-simulation model (MSSM) is shown to be largely compatible with SAMs, and may become a promising method to transplant the baryon physics of galaxy-scale hydrodynamic calculations onto a larger-volume DM-only run. We discuss the benefits that machine-based approaches like this entail, as well as suggestions to raise the scientific potential of such approaches.

• Convergence Security Journal
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• v.22 no.5
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• pp.107-113
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• 2022

The AR/VR-based education and training system is expected to contribute greatly to accident prevention and budget reduction as well as practical training effects similar to the battlefield environment. Research to use AR/VR for learning is ongoing, and technology can be improved without experiencing failures that can occur in the real world. Major advanced countries in defense recognized the advantages of AR/VR technology early on, and developed and utilized systems using them in various fields, from mastery of individual weapon system operation to comprehensive combat training systems, war history education, and post-traumatic stress treatment. Therefore, the purpose of this study is to examine the cases of AR/VR application education and training in advanced defense countries and to draw implications for the South Korean military.