• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.32-41
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• 2023

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald's (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.165-173
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• 2022

Recently, competition to provide specialized services for the information and communications industry has accelerated in terms of future technology and investment strategy of the Fourth Industrial Revolution. Technological innovations related to information and communications can be seen as increasing productivity, creating added value for companies, and playing a pivotal role in the development of economic growth. Therefore, efficient management of the information and communications industry is important. This study analyzes management efficiency stones for the top 30% of KOSDAQ-listed information and communications companies using dea, presents projected values of companies for efficient operation, and confirms benchmarking company references. We evaluated CCR efficiency, BCC efficiency, ranking, Scale Efficiency(SE), and Return To Scale (RTS) of KOSDAQ-listed information and communications businesses. The analysis showed CCR efficiency of 1, 14 companies, and 1 BCC efficiency, and Nanatan companies were analyzed as ten companies. Scale yield was analyzed by the IRS as four companies, 13 CRS and 13 DRS companies.

• Korean Journal of Materials Research
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• v.33 no.12
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• pp.550-557
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• 2023

In zinc-air batteries, the gel polymer electrolyte (GPE) is an important factor for improving performance. The rigid physical properties of polyvinyl alcohol reduce ionic conductivity, which degrades the performance of the batteries. Zinc acetate is an effective additive that can increase ionic conductivity by weakening the bonding structure of polyvinyl alcohol. In this study, polymer electrolytes were prepared by mixing polyvinyl alcohol and zinc acetate dihydride. The material properties of the prepared polymer electrolytes were analyzed by Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and thermogravimetric analysis (TGA). Also, Electrochemical impedance spectroscopy was used to calculate ionic conductivity. The electrolyte resistances of GPE, 0.2 GPE, 0.4 GPE, and 0.6 GPE were 0.394, 0.338, 0.290, and 0.213 Ω, respectively. In addition, 0.6 GPE delivered 0.023 S/cm high ionic conductivity. Among all of the polymer electrolytes tested, 0.6 GPE showed enhanced cycle life performance and the highest specific discharge capacity of 11.73 mAh/cm² at 10 mA. These results verified that 0.6 GPE improves the performance of zinc-air batteries.

• Textile Coloration and Finishing
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• v.35 no.4
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• pp.214-220
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• 2023

Poly(vinyl alcohol) (PVA) is a common hydrophilic polymer that is synthesized through the saponification reaction of poly(vinyl ester)-based polymers, mostly using poly(vinyl acetate) (PVAc) as a precursor. The heterogeneous saponification reaction of poly(vinyl ester)-based films leads to PVA films with new surface properties. Cellulose acetate (CA), in which the hydroxyl group of cellulose is replaced by an acetyl group, is a typical cellulose derivative capable of overcoming the low processability of cellulose due to strong hydrogen bonding. In this study, P(VAc/VPi)/CA blended films were prepared by the solvent casting, and then PVA/Cellulose blended films with improved surface properties were prepared by heterogeneous saponification. The structural changes caused by heterogeneous saponification were confirmed by FT-IR analysis, where both saponification and deacetylation reactions occurred in the saponification solution. In addition, the surface property changes were analyzed by FE-SEM and contact angle analyses, and the transmittance changes of the modified films were also assessed.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.503-512
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• 2024

This study examines the essays of academic background, written by students from a university, which is classified into prestigious universities in Korean society. By Latent Dirichlet Allocation, 172 essays were analyzed to explore the students' perspectives of the academic fractionalism. The analysis identified five topics such as, functional aspects (Topic 1), double-edged nature (Topic 2), power communities (Topic 3), symbols of victory (Topic 4), and dysfunctional aspects (Topic 5). The most frequently appearing keywords are 'individual,' 'status,' and 'means' in Topic 1, 'definition,' 'school,' and 'meaning' in Topic 2, 'people,' 'origin,' and 'power' in Topic 3, 'university,' 'ability,' and 'effort' in Topic 4, and 'academic achievement,' 'South Korea,' and 'origin' in Topic 5. By exploring the topics, we found that students regarded class reproduction by education as important social issues and they showed little interest in other factors influencing academic fractionalism, such as race or ethnicity. these findings suggest that professars, who teach the impact of education on academic fractionalism, deal with the influence of diverse factors on academic fractionalism.

• Journal of Microbiology and Biotechnology
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• v.34 no.2
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• pp.330-339
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• 2024

Corn cobs were fermented with Aspergillus niger to produce soluble dietary fiber (SDF) of high quality and excellent food safety. In this work, the fermentation process was optimized by single-factor test and response surface methodology (RSM). The optimal fermentation conditions were determined to be a material-liquid ratio of 1:30, an inoculum concentration of 11%, a temperature of 32℃, a time of 6 days, and a shaking speed of 200 r/min. Under these conditions, the SDF yield of corn cob increased from 2.34% to 11.92%, and the ratio of soluble dietary fiber to total dietary fiber (SDF/TDF) reached 19.08%, meeting the requirements for high-quality dietary fiber (SDF/TDF of more than 10%). Scanning electron microscopy (SEM) and Fourier-transformed infrared spectroscopy (FT-IR) analysis revealed that the fermentation effectively degraded part of cellulose and hemicellulose, resulting in the formation of a loose and porous structure. After fermentation the water swelling capacity, water-holding capacity, and oil-holding capacity of the corn cob SDF were significantly improved and the adsorption capacity of glucose, cholesterol, and nitrite ions all increased by more than 20%. Moreover, the total phenolic content increased by 20.96%, which correlated with the higher antioxidant activity of SDF. Overall, the fermentation of corn cobs by A. niger increased the yield and enhanced the functional properties of dietary fiber (DF) as well.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2258-2265
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• 2024

The present investigation aims to study the role of Pr₂O₃ on the structural, physical, and radiation shielding properties of a dense cadmium lead borate glass. The XRD was used to affirm the glassy amorphous structure of fabricated sample materials. Moreover, the FTIR was used to record the change in the FT-IR spectra due to the addition of Pr₂O₃ in the wavenumber interval between 400 and 4000 cm^-1. The features of glass surfaces and the elemental analyses for the synthesized Pr₂O₃-reinforced cadmium lead borate glasses were performed using a SEM, supported by an energy-dispersive spectrometer. The γ-ray protection capacity was evaluated using the Monte Carlo method in a wide energy interval ranging between 0.015 and 15 MeV. The linear attenuation coefficient (LAC) at 1 MeV was reduced by a factor of 10 % from 0.372 cm^-1 to 0.340 cm^-1. The decrease in the LAC values negatively affected the other shielding properties such as half-value thickness and the transmission factor. Although the linear attenuation coefficient is decreased slightly with the partial substitution of CdO by Pr₂O₃ compound, the fabricated glass samples still have a high shielding capacity compared to the traditional commercial glasses as well as previous similar reported glasses.

• Analytical Science and Technology
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• v.28 no.6
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• pp.398-408
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• 2015

Carbon monoxide (CO) intoxication, arising from CO from an ignited charcoal briquette (ICB), is a popular means of committing suicide in Korea. Most CO intoxications are related to suicide attempts; however, the possibility of a homicide disguised as a suicide cannot be ruled out. Therefore, forensic investigation of the deceased and the crime scene is crucial to confirm that the deceased committed suicide. Detection of the components of an ICB on the objects suspected of being contacted by the deceased, such as the hands, nostrils, and doorknobs, is essential for linking the crime scene to the victim in the case of suicides by ignited ICBs. The traces from an ICB were analyzed by investigating the morphological characteristics and obtaining elemental compositions. The ICBs were completely different from blackened wood, as detected by discriminant analysis with the elements of carbon and oxygen. We analyzed one case of CO intoxication to demonstrate an excellent procedure for verifying whether a suicide occurred with an ICB. We employed SEM-EDX for the analysis of an ICB, microscope-FT/IR and pyrolysis-GC/MS for a partly burnt resin-type substance, GC/MS for diphenhydramine (a sleeping drug), and GC/TCD for the CO-Hb level. We detected traces of an ICB on the hands, nostrils, and doorknobs, which were all discriminated into an ICB group. Detection of ICB traces from the nostrils could indicate that the deceased started the fire themselves to commit suicide. The partially burnt black material was analyzed as an acrylronitrilestyrene polymer, which is normally used to make bags for carrying or wrapping and could be assumed to have been used to transport the ICB. Diphenhydramine, a sleeping drug, was detected at a level of 2.3 mg/L in the blood, which was lower than that in fatal cases (8-31 mg/L; mean 16 mg/L). A CO-Hb level of 79% was found in the blood, which means that the cause of death was CO intoxication. The steps shown here could represent an ideal method for reaching a verdict of suicide by CO intoxication produced by burning an ICB in a sealed room or a car.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.2
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• pp.31-36
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• 2017

Purpose Recently, the performance of PET/CT scanner has been improved and various techniques have been developed to increase the image quality such as Sensitivity and Resolution. The purpose of this study is to evaluate the usefulness of Q.Clear (a fully convergent iterative reconstruction) technique of GE Discovery IQ equipment to enhance the image quality. Materials and Methods All scans were acquired by Discovery IQ (GE Healthcare, MI, USA). In NEMA IEC Body Phantom test, Background to Hot-sphere (10 mm, 13 mm, 17 mm, 22 mm) ratio was 1:4 and scan time was 3 minutes. The images were reconstructed by VPHDs (VUE Point High-Definition + SharpIR) and Q.Clear to evaluate each Contrast. We injected 18F-FDG 187 M㏃ to PET/SPECT Performance Phantom. And then it was scanned for 4 minutes to evaluate Resolution and Uniformity. T-test statistical analysis was performed on SUVmax of small lesions less than 2 cm in 100 clinical patients regardless of disease type. Results In the NEMA IEC Body Phantom, the Contrast was $63.6{\pm}5.7%$ (VPHDs) and $75{\pm}4.8%$ (Q.Clear). In the PET/SPECT Performance Phantom, the Resolution was 9.2 mm (VPHDs) and 7.3 mm (Q.Clear). Uniformity of Q.Clear was 10.8% better than VPHDs. T-test statistic of the clinical patients showed a significant difference of p value of 0.021. Conclusion Both the phantom test and the clinical results showed that the quality of the image was improved in Q.Clear was applied. The SUVmax was highly measured in Q.Clear and the lesions were clearly distinguished visually. Therefore Q.Clear can be useful in various aspects such as dose-reduction, patients evaluation and image analysis.