• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.3
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• pp.332-336
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• 2024

High-energy bandgap material silicon carbide (SiC) is gaining attention as a next-generation power semiconductor material, and in particular, SiC-based MOSFETs are developed as representative power semiconductors to increase the breakdown voltage (BV) of conventional planar structures. However, as the size of SJ (Super Junction) MOSFET devices decreases and the depth of pillars increases, it becomes challenging to uniformly form the doping concentration of pillars. Therefore, a structure with different doping concentrations segmented within the pillar is being researched. Using Silvaco TCAD simulation, a SJ VVD (vertical variation doping profile) MOSFET with three different doping concentrations in the pillar was studied. Simulations were conducted for the width of the pillar and the doping concentration of N-epi, revealing that as the width of the pillar increases, the depletion region widens, leading to an increase in on-specific resistance (R_on,sp) and breakdown voltage (BV). Additionally, as the doping concentration of N-epi increases, the number of carriers increases, and the depletion region narrows, resulting in a decrease in R_on,sp and BV. The optimized SJ VVD MOSFET exhibits a very high figure of merit (BFOM) of 13,400 KW/cm², indicating excellent performance characteristics and suggesting its potential as a next-generation highperformance power device suitable for practical applications.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• Journal of Powder Materials
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• v.31 no.2
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• pp.169-179
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• 2024

Colloidal quantum dot (QDs) have emerged as a crucial building block for LEDs due to their size-tunable emission wavelength, narrow spectral line width, and high quantum efficiency. Tremendous efforts have been dedicated to improving the performance of quantum dot light-emitting diodes (QLEDs) in the past decade, primarily focusing on optimization of device architectures and synthetic procedures for high quality QDs. However, despite these efforts, the commercialization of QLEDs has yet to be realized due to the absence of suitable large-scale patterning technologies for high-resolution devices., This review will focus on the development trends associated with transfer printing, photolithography, and inkjet printing, and aims to provide a brief overview of the fabricated QLED devices. The advancement of various quantum dot patterning methods will lead to the development of not only QLED devices but also solar cells, quantum communication, and quantum computers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.3
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• pp.48-66
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• 2024

According to the three amended Laws of the Data Economy and the Data Industry Act of Korea, systems for pseudonymous data integration and Data Safe Zones have been operated separately by selected agencies, eventually causing a burden of use in SMEs, startups, and general users because of complicated and ineffective procedures. An over-stringent pseudonymization policy to prevent data breaches has also compromised data quality. Such trials should be improved to ensure the convenience of use and data quality. This paper proposes a prototype system of the Integrated Data Safe Zone based on redesigned and optimized pseudonymization workflows. Conventional workflows of pseudonymization were redesigned by applying the amended guidelines and selectively revising existing guidelines for business process redesign. The proposed prototype has been shown quantitatively to outperform the conventional one: 6-fold increase in time efficiency, 1.28-fold in cost reduction, and 1.3-fold improvement in data quality.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.4
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• pp.333-339
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• 2016

The objective of this study was to optimize analytical methods for ochratoxin A (OTA) and zearalenone (ZEA) in rice straw silage and winter forage crops using ultra-high performance liquid chromatography (UHPLC). Samples free of mycotoxins were spiked with $50{\mu}g/kg$, $250{\mu}g/kg$, or $500{\mu}g/kg$ of OTA and $300{\mu}g/kg$, $1500{\mu}g/kg$, or $3000{\mu}g/kg$ of ZEA. OTA and ZEA were extracted by acetonitrile and cleaned-up using an immunoaffinity column. They were then subjected to analysis with UHPLC equipped with a fluorescence detector. The correlation coefficients of calibration curves showed high linearity ($R^2{\geq_-}0.9999$ for OTA and $R^2{\geq_-}0.9995$ for ZEA). The limit of detection and quantification were $0.1{\mu}g/kg$ and $0.3{\mu}g/kg$, respectively, for OTA and $5{\mu}g/kg$ and $16.7{\mu}g/kg$, respectively, for ZEA. The recovery and relative standard deviation (RSD) of OTA were as follows: rice straw = 84.23~95.33%, 2.59~4.77%; Italian ryegrass = 79.02~95%, 0.86~5.83%; barley = 74.93~97%, 0.85~9.19%; rye = 77.99~96.67%, 0.33~6.26%. The recovery and RSD of ZEA were: rice straw = 109.6~114.22%, 0.67~7.15%; Italian ryegrass = 98.01~109.44%, 1.65~4.81%; barley = 98~113.53%, 0.25~5.85%; rye = 90.44~108.56%, 2.5~4.66%. They both satisfied the standards of European Commission criteria (EC 401-2006) for quantitative analysis. These results showed that the optimized methods could be used for mycotoxin analysis of forages.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.21 no.12
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• pp.715-732
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• 2009

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2008. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) Research trends in thermal and fluid engineering have been surveyed in the categories of general fluid flow, fluid machinery and piping, new and renewable energy, and fire. Well-developed CFD technologies were widely applied in developing facilities and their systems. New research topics include fire, fuel cell, and solar energy. Research was mainly focused on flow distribution and optimization in the fields of fluid machinery and piping. Topics related to the development of fans and compressors had been popular, but were no longer investigated widely. Research papers on micro heat exchangers using nanofluids and micro pumps were also not presented during this period. There were some studies on thermal reliability and performance in the fields of new and renewable energy. Numerical simulations of smoke ventilation and the spread of fire were the main topics in the field of fire. (2) Research works on heat transfer presented in 2008 have been reviewed in the categories of heat transfer characteristics, industrial heat exchangers, and ground heat exchangers. Research on heat transfer characteristics included thermal transport in cryogenic vessels, dish solar collectors, radiative thermal reflectors, variable conductance heat pipes, and flow condensation and evaporation of refrigerants. In the area of industrial heat exchangers, examined are research on micro-channel plate heat exchangers, liquid cooled cold plates, fin-tube heat exchangers, and frost behavior of heat exchanger fins. Measurements on ground thermal conductivity and on the thermal diffusion characteristics of ground heat exchangers were reported. (3) In the field of refrigeration, many studies were presented on simultaneous heating and cooling heat pump systems. Switching between various operation modes and optimizing the refrigerant charge were considered in this research. Studies of heat pump systems using unutilized energy sources such as sewage water and river water were reported. Evaporative cooling was studied both theoretically and experimentally as a potential alternative to the conventional methods. (4) Research papers on building facilities have been reviewed and divided into studies on heat and cold sources, air conditioning and air cleaning, ventilation, automatic control of heat sources with piping systems, and sound reduction in hydraulic turbine dynamo rooms. In particular, considered were efficient and effective uses of energy resulting in reduced environmental pollution and operating costs. (5) In the field of building environments, many studies focused on health and comfort. Ventilation. system performance was considered to be important in improving indoor air conditions. Due to high oil prices, various tests were planned to examine building energy consumption and to cut life cycle costs.

• Economic and Environmental Geology
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• v.50 no.3
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• pp.215-224
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• 2017

Arsenic (As) is known to be the most toxic element and frequently detected in groundwater environment. Inorganic As exists as arsenite [As(III)] and arsenate [As(V)] in reduced and oxidized environments, respectively. It has been reported that the toxicity of arsenite is much higher than that of arsenate and furthermore arsenite shows relatively higher mobility in aqueous environments. For this reason, there have been numerous researches on the process for oxidation of arsenite to arsenate to reduce the toxicity of arsenic. In particular, photooxidation has been considered to be simple, economical, and efficient to attain such goal. This study was conducted to evaluate the applicability of naturally-occurring goethite as a photocatalyst to substitute for $TiO_2$ which has been mostly used in the photooxidation processes so far. In addition, the effects of several factors on the overall performance of arsenite photocatalytic oxidation process were evaluated. The results show that the efficiency of the process was affected by total concentration of dissolved cations rather than by the kind of those cations and also the relatively higher pH conditions seemed to be more favorable to the process. In the case of coexistence of arsenite and arsenate, the removal tendency by adsorption onto goethite appeared to be different between arsenite and arsenate due to their different affinities with goethite, but any effect on the photocatalytic oxidation of arsenite was not observed. In terms of effect of humic acid on the process, it is likely that the higher concentration of humic acid reduced the overall performance of the arsenite photocatalytic oxidation as a result of competing interaction of activated oxygen species, such as hydroxyl and superoxide radicals, with arsenite and humic acid. In addition, it is revealed that the injection of oxygen gas improved the process because oxygen contributes to arsenite oxidation as an electron acceptor. Based on the results of the study, consequently, the photocatalytic oxidation of aqueous arsenite using goethite seems to be greatly feasible with the optimization of process.

• Clean Technology
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• v.27 no.4
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• pp.332-340
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• 2021

With the recent reinforcement of emission standards, it is necessary to make efforts to reduce NOx from air pollutant-emitting workplaces. The NOx reduction method mainly used in industrial facilities is selective catalytic reduction (SCR), and the most commercial SCR catalyst is the ceramic honeycomb catalyst. This study was carried out to reduce the NOx emitted from steel plants by applying De-NOx catalyst coated on metallic monolith. The De-NOx catalyst was synthesized through the optimized coating technique, and the coated catalyst was uniformly and strongly adhered onto the surface of the metallic monolith according to the air jet erosion and bending test. Due to the good thermal conductivity of metallic monolith, the De-NOx catalyst coated on metallic monolith showed good De-NOx efficiency at low temperatures (200 ~ 250 ℃). In addition, the optimal amount of catalyst coating on the metallic monolith surface was confirmed for the design of an economical catalyst. Based on these results, the De-NOx catalyst of commercial grade size was tested in a semi-pilot De-NOx performance facility under a simulated gas similar to the exhaust gas emitted from a steel plant. Even at a low temperature (200 ℃), it showed excellent performance satisfying the emission standard (less than 60 ppm). Therefore, the De-NOx catalyst coated metallic monolith has good physical and chemical properties and showed a good De-NOx efficiency even with the minimum amount of catalyst. Additionally, it was possible to compact and downsize the SCR reactor through the application of a high-density cell. Therefore, we suggest that the proposed De-NOx catalyst coated metallic monolith may be a good alternative De-NOx catalyst for industrial uses such as steel plants, thermal power plants, incineration plants ships, and construction machinery.

• Journal of Wetlands Research
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• v.20 no.4
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• pp.424-431
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• 2018

Stormwater runoff containing considerable amounts of pollutants such as particulates, organics, nutrients, and heavy metals contaminate natural bodies of water. At present, best management practices (BMP) intended to reduce the volume and treat pollutants from stormwater runoff were devised to serve as cost-effective measures of stormwater management. However, improper design and lack of proper maintenance can lead to degradation of the facility, making it unable to perform its intended function. This study evaluated an infiltration trench (IT) that went through a series of maintenance operations. 41 monitored rainfall events from 2009 to 2016 were used to evaluate the pollutant removal capabilities of the IT. Assessment of the water quality and hydrological data revealed that the inflow volume was the most relative factor affecting the unit pollutant loads (UPL) entering the facility. Seasonal variations also affected the pollutant removal capabilities of the IT. During the summer season, the increased rainfall depths and runoff volumes diminished the pollutant removal efficiency (RE) of the facility due to increased volumes that washed off larger pollutant loads and caused the IT to overflow. Moreover, the system also exhibited reduced pollutant RE for the winter season due to frozen media layers and chemical-related mechanisms impacted by the low winter temperature. Maintenance operations also posed considerable effects of the performance of the IT. During the first two years of operation, the IT exhibited a decrease in pollutant RE due to aging and lack of proper maintenance. However, some events also showed reduced pollutant RE succeeding the maintenance as a result of disturbed sediments that were not removed from the geotextile. Ultimately, the presented effects of maintenance operations in relation to the pollutant RE of the system may lead to the optimization of maintenance schedules and procedures for BMP of same structure.