• Environmental and Resource Economics Review
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• v.28 no.1
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• pp.121-145
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• 2019

This paper investigates the relationship between the oil price and the major petroleum products prices at the trading hubs such as Singapore, North West Europe and the US New York Harbor. We focus on the lead-lag relationship between the weekly petroleum prices from 2009 to 2016 based on the vector error correction model. We find that the oil price leads the prices of petroleum products in the long term, while there is bidirectional causality in the short term. On the other hand, prices of petroleum products in regions with high import dependency, such as Europe gas oil and jet fuel price, are exogenous in the long term. We also present evidence that prices of petroleum products in region with a large global-market share lead prices in other regions. However, if the region is in an over-production situation and low industry concentration, it may lose its price leadership due to intense competition. The result in this study can provide a useful information to petroleum refining companies in forecasting fluctuations of product price, and hence in planning their regional arbitrage trading activities.

• Journal of Practical Engineering Education
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• v.9 no.1
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• pp.69-75
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• 2017

In this paper, we analyzed the factors influencing the size of Korea Polytechnic as a public vocational education and training institution through analysis of demand, region, industry, and demand with established existing campus and new campus in Korea Polytechnic. By analyzing data on admission, training, and employment for 3 years out of 37 campuses, we have sampled 5 campuses by type of Korea Polytechnic, fused with the results derived from the literature analysis and in-depth analysis results, so that the regional campus will play a leading role and the direction of development. The selection of five campuses by type is a precedent study to analyze 37 campuses in the future. As a result of the study, the demand analysis through objective indicators such as the number of high school graduates, the number of employed persons, the presence of nearby industrial complexes, and policy variables is very important and reflects the reality well. Therefore, it is necessary to analyze the demand through the objective indicators in decision making related to the new campus at the pre-analysis stage. In addition to the general data proposed in this paper, that is, common variables in all regions, it is important to consider the factors that can reflect local demand characteristics when considering specific locations.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.67-75
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• 2022

Medical information is variously generated not only from medical devices but also from electronic devices. Recently, related convergence technologies from big data collection in healthcare to medical AI products for patient's condition analysis are rapidly increasing. However, there are difficulties in applying them because of independent developmental procedures. In this paper, we propose an intelligent hospital information system (iHIS) model to simplify and integrate research, development and application of medical AI technology. The proposed model includes (1) real-time patient data management, (2) specialized data management for medical AI development, and (3) real-time monitoring for patient. Using this, real-time biometric data collection and medical AI specialized data generation from patient monitoring devices, as well as specific AI applications of camera-based patient gait analysis and brain MRA-based cerebrovascular disease analysis will be introduced. Based on the proposed model, it is expected that it will be used to improve the HIS by increasing security of data management and improving practical use through consistent interface platformization.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• Clean Technology
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• v.28 no.3
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• pp.232-237
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• 2022

Biorefineries, in which renewable resources are utilized, are an eco-friendly alternative based on biomass feedstocks. Alginic acid, a major component of brown algae, which is a type of marine biomass, is widely used in various industries and can be converted into value-added chemicals such as sugars, sugar alcohols, furans, and organic acids via catalytic hydrothermal decomposition under certain conditions. In this study, ruthenium-supported activated carbon and magnesium oxide were mixed and applied to the depolymerization of alginic acid in a batch reactor. The addition of magnesium oxide as a basic promoter had a strong influence on product distribution. In this heterogeneous catalytic system, the separation and purification processes are also simplified. After the reaction, low molecular weight alcohols and organic acids with 5 or fewer carbons were produced. Specifically, under the optimal reaction conditions of 30 mL of 1 wt% alginic acid aqueous solution, 100 mg of ruthenium-supported activated carbon, 100 mg of magnesium oxide, 210 ℃ of reaction temperature, and 1 h of reaction time, total carbon yields of 29.8% for alcohols and 43.8% for a liquid product were obtained. Hence, it is suggested that this catalytic system results in the enhanced hydrogenolysis of alginic acid to value-added chemicals.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.1019-1034
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• 2022

Malware attacks become more prevalent in the hyper-connected society of the 4th industrial revolution. To respond to such malware, automation of malware detection using artificial intelligence technology is attracting attention as a new alternative. However, using artificial intelligence without collateral for its reliability poses greater risks and side effects. The EU and the United States are seeking ways to secure the reliability of artificial intelligence, and the government announced a reliable strategy for realizing artificial intelligence in 2021. The government's AI reliability has five attributes: Safety, Explainability, Transparency, Robustness and Fairness. We develop four elements of safety, explainable, transparent, and fairness, excluding robustness in the malware detection model. In particular, we demonstrated stable generalization performance, which is model accuracy, through the verification of external agencies, and developed focusing on explainability including transparency. The artificial intelligence model, of which learning is determined by changing data, requires life cycle management. As a result, demand for the MLops framework is increasing, which integrates data, model development, and service operations. EXE-executable malware and documented malware response services become data collector as well as service operation at the same time, and connect with data pipelines which obtain information for labeling and purification through external APIs. We have facilitated other security service associations or infrastructure scaling using cloud SaaS and standard APIs.

• Membrane Journal
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• v.32 no.5
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• pp.348-356
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• 2022

With the development of the bio industry, membrane chromatography with a high adsorption efficiency is emerging to replace the existing column chromatography used in the downstream processes of pharmaceuticals, food, etc. In this study, through the deacetylation reaction of two commercial cellulose acetate (CA) membranes with different pore sizes, the porous regenerated cellulose (RC) supports for membrane chromatography were obtained to attach the anion exchange ligands. The adsorptive membranes for anion exchange were prepared by attaching an anion exchange ligand ([3-(methacryloylamino) propyl] trimethylammonium chloride) containing quaternary ammonium groups on the RC supports by grafting and UV polymerization. The protein adsorption capacities of the prepared membranes were obtained through both the static binding capacity (SBC) and the dynamic adsorption capacity (DBC) measurement. As a result, the membrane chromatography with the smaller the pore size, the larger the surface area showed the highest protein adsorption capacity. Membrane chromatography which was prepared by using deacetylated commercial CA support with MAPTAC ligand (i.e., RC 0.8 + MAPTAC: 43.69 mg/ml, RC 3.0 + MAPTAC: 36.33 mg/ml) showed a higher adsorption capacity compared to commercial membrane chromatography (28.38 mg/ml).

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.71-86
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• 2023

The purpose of this exploratory study is to compare the differences in public perceptual characteristics of Korean and Chinese societies regarding the metaverse using big data analysis. Due to the environmental impact of the COVID-19 pandemic, technological progress, and the expansion of new consumer bases such as generation Z and Alpha, the world's interest in the metaverse is drawing attention, and related academic studies have been also in full swing from 2021. In particular, Korea and China have emerged as major leading countries in the metaverse industry. It is a timely research question to discover the difference in social awareness using big data accumulated in both countries at a time when the amount of mentions on the metaverse has skyrocketed. The analysis technique identifies the importance of key words by analyzing word frequency, N-gram, and TF-IDF of clean data through text mining analysis, and analyzes the density and centrality of semantic networks to determine the strength of connection between words and their semantic relevance. Python 3.9 Anaconda data science platform 3 and Textom 6 versions were used, and UCINET 6.759 analysis and visualization were performed for semantic network analysis and structural CONCOR analysis. As a result, four blocks, each of which are similar word groups, were driven. These blocks represent different perspectives that reflect the types of social perceptions of the metaverse in both countries. Studies on the metaverse are increasing, but studies on comparative research approaches between countries from a cross-cultural aspect have not yet been conducted. At this point, as a preceding study, this study will be able to provide theoretical grounds and meaningful insights to future studies.

• The Journal of Bigdata
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• v.7 no.2
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• pp.153-172
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• 2022

Online shopping has transformed and rapidly grown the entire market at the forefront of wholesale and retail services as an effective solution to issues such as digital transformation and social distancing policy (COVID-19 pandemic). Small business owners, who form the majority at the center of the online shopping industry, are constantly collecting policy changes and market trend information to overcome these problems and use them for marketing and other sales activities in order to overcome these problems and continue to grow. Objective and refined information that is more closely related to the business is also needed. Therefore, in this paper, through the collection and analysis of big data information, which is the core technology of digital transformation, key variables are set in product classification, sales trends, consumer preferences, and review information of online shopping malls, and a method of using them for competitor comparison analysis and business sustainability evaluation has been prepared and we would like to propose it as a service. If small and medium-sized businesses can benchmark competitors or excellent businesses based on big data and identify market trends and consumer tendencies, they will clearly recognize their level and position in business and voluntarily strive to secure higher competitiveness. In addition, if the sustainable growth of the online shopping mall operator can be confirmed as an indicator, more efficient policy establishment and risk management can be expected because it has an improved measurement method.

• Smart Media Journal
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• v.12 no.10
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• pp.71-84
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• 2023

Paralichthys olivaceus accounts for a large proportion, accounting for more than half of Korea's aquaculture industry. However, about 25-30% of the total breeding volume throughout the year occurs due to diseases, which has a very bad impact on the economic feasibility of fish farms. For the economic growth of Paralichthys olivaceus farms, it is necessary to quickly and accurately diagnose disease symptoms by automating the diagnosis of Paralichthys olivaceus diseases. In this study, we create training data using innovative data collection methods, refining data algorithms, and techniques for partitioning dataset, and compare the Paralichthys olivaceus disease symptom detection performance of four object detection deep learning models(such as YOLOv8, Swin, Vitdet, MvitV2). The experimental findings indicate that the YOLOv8 model demonstrates superiority in terms of average detection rate (mAP) and Estimated Time of Arrival (ETA). If the performance of the AI model proposed in this study is verified, Paralichthys olivaceus farms can diagnose disease symptoms in real time, and it is expected that the productivity of the farm will be greatly improved by rapid preventive measures according to the diagnosis results.