• Environmental and Resource Economics Review
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• v.29 no.4
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• pp.499-520
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• 2020

Input ratio of electricity to other production inputs in the Korean manufacturing sector has been higher than for the other OECD countries. In addition, electricity prices in Korea has been relatively lower than the average of OECD countries. Moreover, electricity sector is responsible for most CO2 emissions in Korea as coal and natural gas account 41.9% and 26.8% of electricity production as of 2018. Therefore, it looks inevitable to raise the electricity tariff for the manufacturing sector in Korea, but there is a concern that increase in the electricity tariff might affect small and medium enterprises (SMEs) more than large firms. This study estimates electricity demand's price and output elasticities for large firms and SMEs in steel industry by employing a time varying parameter model (Kalman filter). The analysis shows that changes in output levels regardless of firms' size affect electricity demands more significantly than do changes in electricity prices. Second, large firms have higher variances for both price and output elasticities of electricity demand. Third, large firms have higher price elasticity but lower output elasticity of electricity demand relative to SMEs. Policy implications are suggested in association with how to reduce electricity demands in the energy-intensive industry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.9-17
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• 2021

Curved beams are increasingly used in buildings, vehicles, ships, and aircraft, which has resulted in considerable effort being directed toward developing an accurate method for analyzing the dynamic behavior of such structures. The stability behavior of elastic circular arches has been the subject of a large number of investigations. One of the efficient procedures for the solution of ordinary differential equations or partial differential equations is the differential quadrature method DQM. This method has been applied to a large number of cases to overcome the difficulties of the complex computer algorithms, as well as excessive use of storage due to conditions of non-linear geometries, loadings, or material properties. This study uses DQM to analyze the in-plane vibration of the circular arches considering the effects of midsurface extension and rotatory inertia. Fundamental frequency parameters are calculated for the member with various parameter ratios, boundary conditions, and opening angles. The solutions from DQM are compared with exact solutions or other numerical solutions for cases in which they are available and given to analyze the effects of midsurface extension and rotatory inertia on the frequency parameters of the circular arches.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.363-372
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• 2022

Since the recent COVID-19 Pandemic, the ransomware fandom has intensified along with the expansion of remote work. Currently, anti-virus vaccine companies are trying to respond to ransomware, but traditional file signature-based static analysis can be neutralized in the face of diversification, obfuscation, variants, or the emergence of new ransomware. Various studies are being conducted for such ransomware detection, and detection studies using signature-based static analysis and behavior-based dynamic analysis can be seen as the main research type at present. In this paper, the frequency of ".text Section" Opcode and the Native API used in practice was extracted, and the association between feature information selected using K-means Clustering algorithm, Cosine Similarity, and Pearson correlation coefficient was analyzed. In addition, Through experiments to classify and detect worms among other malware types and Cerber-type ransomware, it was verified that the selected feature information was specialized in detecting specific ransomware (Cerber). As a result of combining the finally selected feature information through the above verification and applying it to machine learning and performing hyper parameter optimization, the detection rate was up to 93.3%.

• Animal Bioscience
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• v.36 no.1
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• pp.75-83
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• 2023

Objective: The objective of this study was to describe a methodological procedure to quantify the heat production (HP) partitioning in basal metabolism or fasting heat production (FHP), heat production due to physical activity (HPA), and the thermic effect of feeding (TEF) in roosters. Methods: Eighteen 54-wk-old Hy Line Brown roosters (2.916±0.15 kg) were allocated in an open-circuit chamber of respirometry for O₂ consumption (VO₂), CO₂ production (VCO₂), and physical activity (PA) measurements, under environmental comfort conditions, following the protocol: adaptation (3 d), ad libitum feeding (1 d), and fasting conditions (1 d). The Brouwer equation was used to calculate the HP from VO₂ and VCO₂. The plateau-FHP (parameter L) was estimated through the broken line model: HP = U×(R-t)×I+L; I = 1 if t<R or I = 0 if t>R; Where the broken-point (R) was assigned as the time (t) that defined the difference between a short and long fasting period, I is conditional, and U is the decreasing rate after the feed was withdrawn. The HP components description was characterized by three events: ad libitum feeding and short and long fasting periods. Linear regression was adjusted between physical activity (PA) and HP to determine the HPA and to estimate the standardized FHP (st-FHP) as the intercept of PA = 0. Results: The time when plateau-FHP was reached at 11.7 h after withdrawal feed, with a mean value of 386 kJ/kg^0.75/d, differing in 32 kJ from st-FHP (354 kJ/kg^0.75/d). The slope of HP per unit of PA was 4.52 kJ/mV. The total HP in roosters partitioned into the st-FHP, termal effect of feeding (TEF), and HPA was 56.6%, 25.7%, and 17.7%, respectively. Conclusion: The FHP represents the largest fraction of energy expenditure in roosters, followed by the TEF. Furthermore, the PA increased the variation of HP measurements.

• Membrane Journal
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• v.32 no.4
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• pp.253-263
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• 2022

This study suggests a guideline for designing unit process of wastewater reuse in terms of a maintenance of the process based on critical parameters to draw a high quality performance of RO unit. Defining the parameters was done by applying membrane integrity test (MIT) in pretreatment process utilizing lab-scale MF. SDI is utilized for judging whether permeate is suitable to RO unit. However, result said TOC concentration matching with particle count analysis is better for judging the permeate condition. When membrane test pressure (P_test) was measured to derive log removal value in PDT, virgin state of membrane fiber was used to measure dynamic contact angle utilizing surface tension of the membrane fiber. Actually, foulant affects to the state of membrane surface, and it decreases the P_test value along with time elapsed. Consequently, LRV_DIT is also affected by P_test value. Thus, sensitivity of direct integrity test descends with result of P_test value change, so P_test value should be considered not the virgin state of the membrane but its current state. Overall, this study focuses on defining design parameters suitable to MF pretreatment for RO process in wastewater reuse by assessing its impact. Therefore, utilities can acknowledge that the membrane surface condition must be considered when users conduct the direct integrity test so that P_test and other relative parameter used to calculate LRV_DIT are adequately measured.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• The Korean Journal of Nuclear Medicine
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• v.32 no.4
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• pp.332-343
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• 1998

Purpose: The purpose of this study was to assess the diagnostic accuracy of various quantitation methods using F-18-fluorodeoxyglucose (FDG) in patients with malignant or benign lung lesion. Materials and Methods: 22 patients (13 malignant including 5 bronchoalverolar cell cancer; 9 benign lesions including 1 hamartoma and 8 active inflammation) were studied after overnight fasting. We performed dynamic PET imaging for 56 min after injection of 370 MBq (10 mCi) of FDG. Standardized uptake values normalized to patient's body weight and plasma glucose concentration (SUVglu) were calculated. The uptake rate constant of FDG and glucose metabolic rate were quantified using Patlak graphical analysis (Kpat and MRpat), three compartment-five parameter model (K5p, MR5p), and six parameter model taking into account heterogeneity of tumor tissue (K6p, MR6p). Areas under receiver operating characteristic curves (ROC) were calculated for each method. Results: There was no significant difference of rate constant or glucose metabolic rate measured by various quantitation methods between malignant and benign lesions. The area under ROC curve were 0.73 for SUVglu, 0.66 for Kpat, 0.77 for MRpat, 0.71 for K5p, 0.73 for MR5p, 0.70 for K6p, and 0.78 for MR6p. No significant difference of area under the ROC curve between these methods was observed except the area between Kpat vs. MRpat (p<0.05). Conclusion: Quantitative methods did not improve diagnostic accuracy in comparison with nonkinetic methods. However, the clinical utility of these methods needs to be evaluated further in patients with low pretest likelihood of active inflammation or bronchoalveolar cell carcinoma.

• Korean Journal of Food Science and Technology
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• v.32 no.3
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• pp.590-597
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• 2000

The rheological properties of ${\beta}-glucans$ isolated from non-waxy and waxy barley were investigated. ${\beta}-Glucan$ solutions showed pseudoplastic properties and their behaviors were explained by applying Power law model in the range of concentrations$(1{\sim}4%)$ and temperatures$(20{\sim}65^{\circ}C)$. The effects of temperature and concentration on the apparent viscosity at $700\;s^{-1}$ shear rate were examined by applying Arrhenius equation and power law equation, and their effect was more pronounced in waxy ${\beta}-glucan$ solutions. The activation energy for flow of ${\beta}-glucan$ solutions decreased with the increase of concentration, and the concentration-dependent constant A increased with the increase of temperature. The intrinsic viscosity of waxy ${\beta}-glucan$ was higher than that of non-waxy ${\beta}-glucan$. The transition from dilute to concentrate region occurred at a critical coil overlap parameter $C^*[{\eta}]=0.02.$ The slopes of non-waxy and waxy ${\beta}-glucan$ at $C[{\eta}] were similar, but the slope of waxy ${\beta}-glucan$ at $C[{\eta}]>C^*[{\eta}]$ was higher than that of non-waxy ${\beta}-glucan$. Dynamic viscoelasticity measurement showed that cross-over happened, and storage modulus was higher than loss modulus at frequency range above cross-over. ${\beta}-Glucan$ solutions formed weak gels after stored for 24 hr.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.2
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• pp.63-77
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• 2018

In the rapidly to change global market in recent years, as the era of merging and integrating industries and the evolution of technology have come to an era in which everything can not be solved as a single company, it is evolving into competition for the enterprise network rather than the competition for the enterprise unit. In a competitive business environment, it is necessary to provide not only for the efforts as an individual companies but also the mutual development efforts to enhance output through the innovation activities based on the interrelationship with the business partners. In spite of the recent efforts and research through core competencies and innovation activities, some of business activities were unable to achieve enough progress in business performance and this study mainly focused to improve business performance for those companies. This study targeted CEOs and Directors who participates in "manufacturing performance innovation partnership project" carried by The foundation of Large, SMEs, Agriculture, Fisheries cooperation Korea and studied the influences of innovation activities to the core competencies and business performance. Detailed variables in this study were extracted from the previous research and used for verification. The study is designed to determine the influence of individual innovation activities to the core competencies and business performance. Innovation activities as a parameter, the relationship between core competencies and business performance was examined. In the examination of the innovation activities as a meditated effect, those activities carried by SMEs (Collaboration in Technology, Manufacturing, and Management innovations with Large Scale Business) through partnership in manufacturing innovation is significantly related business performance. Therefore, the result reveals that the individual SMEs are having own limitation in the achievement of significant progress in business performance with their own capabilities, and using the innovation activities act as catalyst through the collaboration with large scale businesses would result significant progress in business performance. Mutual effort in collaborative innovation activities between large scale businesses and SMEs is one of the most critical issues in recent years in Korea and the main focus of this study is to provide analysis which demonstrates where the SMEs are required to focus in their innovation activities.

• The Korean Journal of Nuclear Medicine
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• v.37 no.5
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• pp.288-300
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• 2003

Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from ${H_2}^{15}O$ PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency. Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS). ${H_2}^{15}O$ dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In audition, parametric images from ${H_2}^{15}O$ dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data. Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for $128{\times}128{\times}46$ images on Pentium III processor. Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.