• Horticultural Science & Technology
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• v.34 no.2
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• pp.324-330
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• 2016

To determine whether FT-IR spectral analysis based on multivariate analysis for whole cell extracts can be used to discriminate between artichoke (Cynara cardunculus var. scolymus L.) plants at the metabolic level, leaves of ten artichoke plants were subjected to Fourier transform infrared(FT-IR) spectroscopy. FT-IR spectral data from leaves were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and $1,100-950cm^{-1}$, respectively. These spectral regions reflect the quantitative and qualitative variations of amide I, II from amino acids and proteins ($1,700-1,500cm^{-1}$), phosphodiester groups from nucleic acid and phospholipid ($1,500-1,300cm^{-1}$) and carbohydrate compounds ($1,100-950cm^{-1}$). PCA revealed separate clusters that corresponded to their species relationship. Thus, PCA could be used to distinguish between artichoke species with different metabolite contents. PLS-DA showed similar species classification of artichoke. Furthermore these metabolic discrimination systems could be used for the rapid selection and classification of useful artichoke cultivars.

• Analytical Science and Technology
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• v.24 no.5
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• pp.387-394
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• 2011

This study was performed to investigate the contents and leaching characteristics of hazardous wastes from the paint industry. In order to establish a hazardous waste list, samples from industrial discharge have been analyzed for 8 non-regulated inorganic hazardous substances (i.e., Sb, Ni, F, V, Ba, Zn, Be, Se). In more detail, hazardous waste samples from a total of 64 workplaces, e.g. manufacture, formulation, supply and use (MFSU) of coatings, adhesives, sealants and printing inks processing, have been chosen and analyzed. Contents and leaching tests for inorganic metal species in samples show that the non-regulated hazardous substances satisfy all the criteria, while quantitative analyses reveal that some samples of the discharged wastes exceeded the criteria proposed by NIER (National Instituted of Environmental Research). In conclusion, we expect the outcome of this study to align the classification system of hazardous waste management in South Korea with international legislations, and consequently contribute to reduce environmental pollution as well as health risks by toxic wastes.

• Analytical Science and Technology
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• v.26 no.4
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• pp.276-286
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• 2013

This study was performed to investigate the contents characteristics of hazardous oil wastes and wastes of liquid fuels from different industrial process. In order to establish a hazardous waste list, samples of various industrial discharge have been analyzed for 16 non-regulated inorganic hazardous substances (i.e., Cu, Pb, Cd, CN, Hg, As, T-Cr, $Cr^{6+}$, Sb, Ni, F, V, Ba, Zn, Be, Se). In more detail, hazardous waste samples including waste hydraulic oils, waste engine, gear and lubricating oils, waste insulating and heat transmission oils, bilge oils, oil/water separator contents processing were collected from 37 workplaces and analyzed. We observed that the most of the inorganic substances exceeded the proposed criteria in many samples. Especially the concentration of Sb in heat transmission oil, bilge oil and gear & lubricating oils were ranged from 6 to 419 mg/kg whereas the proposed criteria is 50 mg/kg. The assessment result of hazardous waste in Korea according to the EWC showed that the out of 24 processes, 16 belongs to absolute entry and 8 belongs to mirror entry. In conclusion, we expect the outcome of this study to align the classification system of hazardous waste management in South Korea with international legislations, and consequently contribute to reduce environmental pollution as well as health risks by toxic wastes.

• 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 Korean Society of Forest Science
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• v.109 no.1
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• pp.81-98
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• 2020

In this study, forest-related research papers published in North Korean journals were analyzed to understand the research trends in North Korean forest science. The Korea Science and Technology Information Institute (KISTI) North Korea Science and Technology Network (NKtech) is constructing a database related to science and technology in North Korea. From this, a total of 1,389 articles published from 1962 to 2016 were collected with forest science key words based on the South Korean National Science and Technology Standard Classification System. The topics were divided into four categories: afforestation, forest protection, forest use, and forest management. In the field of afforestation, research activities on nursery and agroforestry were active, and the survival rate was emphasized. In the forest protection field, there was a significant research effort into forest pests, and efforts were being made to reduce soil erosion through agroforestry. In the field of forest use, research activities on pulp/paper and mushrooms were active. In the forest management field, activities related to "ecological information" were conspicuous, and efforts were being made to reduce carbon. These results suggest that the perspective of North Korean forest research has changed from nature reorganization to nature protection. Thus, a comparative study on forest science and technology in each sub-sector of the forest research field, along with analysis of the relationship between policy direction and research direction of North Korea over time, would be worthwhile future investigations. To overcome the problem of technical terminology, a compilation/dictionary of inter-Korean forestry terminology would be useful for effective communication between the two Koreas.

• Economic and Environmental Geology
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• v.49 no.3
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• pp.213-224
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• 2016

Non-conventional resource and alternative energy were researched for predicting oil peak. In this study, one of many non-conventional resources, specifically oil-sands, was investigated due to the increasing interest of oil-sands plant modularization in permaforst areas for reducing the construction periods through modular transportation while limiting local construction workers. Hence, tehcnological trends were analyzed for oil-sand plant modularization. Data used were between 1994 and 2015 for patent analysis while targets included Korea, US, Japan, Europe and Canada. Technology classification system consisted of mining, steam assisted gravity drainage(SAGD), separation/upgrading/tailors ponds, module design/packaging, module transportation and material/maintenance. Result of patent analysis, patent application accounts 89% in US and Canada. The main competitive companies were Shell, Suncor and Exxon-mobil. Unlike other oil developments, oil-sands have a long-term stable production characteristic, hence, it is important to ensure the competitiveness of oil-sands for obtaining a patent in the long run.