• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.241-251
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• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.

• Journal of the Korea Computer Graphics Society
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• v.28 no.1
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• pp.1-9
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• 2022

Non-small cell lung cancer (NSCLC) accounts for a high proportion of 85% among all lung cancer and has a significantly higher mortality rate (22.7%) compared to other cancers. Therefore, it is very important to predict the prognosis after surgery in patients with non-small cell lung cancer. In this study, the types of preoperative chest CT image patches for non-small cell lung cancer patients with tumor as a region of interest are diversified into five types according to tumor-related information, and performance of single classifier model, ensemble classifier model with soft-voting method, and ensemble classifier model using 3 input channels for combination of three different patches using pre-trained ResNet and EfficientNet CNN networks are analyzed through misclassification cases and Grad-CAM visualization. As a result of the experiment, the ResNet152 single model and the EfficientNet-b7 single model trained on the peritumoral patch showed accuracy of 87.93% and 81.03%, respectively. In addition, ResNet152 ensemble model using the image, peritumoral, and shape-focused intratumoral patches which were placed in each input channels showed stable performance with an accuracy of 87.93%. Also, EfficientNet-b7 ensemble classifier model with soft-voting method using the image and peritumoral patches showed accuracy of 84.48%.

• Journal of Korean Neurosurgical Society
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• v.65 no.2
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• pp.315-324
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• 2022

Objective : The aim of this study was to verify the equivalence and effectiveness of the tablet-administered Korean Repeatable Battery for the Assessment of Neuropsychological Status (K-RBANS) for the prevention and early detection of dementia. Methods : Data from 88 psychiatry and neurology patient samples were examined to evaluate the equivalence between tablet and paper administrations of the K-RBANS using a non-randomly equivalent group design. We calculated the prediction scores of the tablet-administered K-RBANS based on demographics and covariate-test scores for focal tests using norm samples and tested format effects. In addition, we compared the receiver operating characteristic curves to confirm the effectiveness of the K-RBANS for preventing and detecting dementia. Results : In the analysis of raw scores, line orientation showed a significant difference (t=-2.94, p<0.001), and subtests showed small to large effect sizes (0.04-0.86) between paper- and tablet-administered K-RBANS. To investigate the format effect, we compared the predicted scaled scores of the tablet sample to the scaled scores of the norm sample. Consequently, a small effect size (d≤0.20) was observed in most of the subtests, except word list and story recall, which showed a medium effect size (d=0.21), while picture naming and subtests of delayed memory showed significant differences in the one-sample t-test. In addition, the area under the curve of the total scale index (TSI) (0.827; 95% confidence interval, 0.738-0.916) was higher than that of the five indices, ranging from 0.688 to 0.820. The sensitivity and specificity of TSI were 80% and 76%, respectively. Conclusion : The overall results of this study suggest that the tablet-administered K-RBANS showed significant equivalence to the norm sample, although some subtests showed format effects, and it may be used as a valid tool for the brief screening of patients with neuropsychological disorders in Korea.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.48-55
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• 2022

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In 'offset printing' mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called 'spot color' ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through 'Delta E' provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.1
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• pp.1-8
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• 2023

At construction sites, interest in the production of precast materials is increasing due to off-site conditions due to changes in construction site conditions due to increased labor costs and the Act on the Punishment of Serious Accidents. In particular, the precast prestressed hollow slab has a hollow shape in the cross section, so structural performance is secured by reducing weight and controlling deflection through stranded wires. With the application of structural standards, the urgency of securing fire resistance performance is emerging. In this study, a fire-resistance cross section was developed by reducing the concrete filling rate in the cross section and improving the upper and lower flange shapes by optimizing the hollow shape in the cross section of the slab to have the same or better structural performance and economic efficiency compared to the existing hollow slab. The PC hollow slab to which this was applied was subjected to a two-hour fire resistance test using the cross-sectional thickness as a variable, and as a result of the test, fire resistance performance (load bearing capacity, heat shielding property, flame retardance property) was secured. Based on the experimental results, it is determined that fire resistance modeling can be established through numerical analysis simulation, and prediction of fire resistance analysis is possible according to the change of the cross-sectional shape in the future.

• The Journal of Engineering Geology
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• v.33 no.1
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• pp.189-200
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• 2023

As urbanization and industrialization increase development in downtown areas, damage due to ground settlement continues to occur. Building collapse in urban has a high risk of leading to large-scale damage to life and property. However, there has rarely been studied on measurement data analysis methods when uneven loads are applied to the excavated ground and no prior knowledge of the ground. Accordingly, it was attempted to analyze the relative settlement behavior and correlation by processing the time-series surface settlement of construction sites in the urban. In this paper, the average index of difference in settlement and average of relative difference in settlement are defined and calculated, then plotted in the coordinate system to analyze the relative settlement behavior over time. In addition, since there was no prior knowledge of the ground, a standard to classify the clusters was needed, and the observation points were classified into using k-means clustering and Dunn Index. As a result of the analysis, it was confirmed that all the clusters moved to the stable region as the settlement amount converges. The clusters were segmented. Based on the analysis results, it was possible to distinguish between the independent displacement area and same behavior area by analyzing the correlation between measurement points. If possible to analyze the relative settlement behavior between the stations and classify the behavior areas, it can be helpful in settlement and stability management, such as uplift of the surrounding area, prediction of ground failure area, and prevention of activity failure.

• Journal of Environmental Impact Assessment
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• v.32 no.1
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• pp.29-40
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• 2023

The need for improvement is raised due to limitations with environmental impact assessment, and the importance for data-based environmental impact assessment is increasing. In this study, data demand was derived by analyzing Agreed Terms and Conditions in the Water Environment field (Water Quality, Hydraulic & Hydrologic Conditions, and Marine Environment) of environmental impact assessment. Agreed Terms and Conditions on environmental impact assessment in the water environment field were classified and categorized by environmental impact assessment stage (addition to status survey, impact prediction and evaluation, establishment of reduction measures, post-environmental impact survey), and data demand for each type of consultation opinion was linked. As a result of the categorization of Agreed Terms and Conditions, it was classified into 18 types in the water quality, 15 types in the hydraulic & hydrologic conditions, and 17 types in the marine environment. As a result of linking data demand, the total number of data demand was 236 in the water quality, 98 in the hydraulic & hydrologic conditions, and 73 in the marine environment. The highest number of Agreed Terms and Conditions and data demands were found in the water quality for the evaluation item and establishment of reduction measures, specifically establishment of non-point source pollution reduction measures, for the stage. The numbers were judged to be linked to the relative importance of the items and the primary purpose of environmental impact assessment. The derivation of data demand through the analysis of Agreed Terms and Conditions in the environmental impact assessment can contribute to the advancement of the preparation of environmental impact assessment reports and is expected to increase data utilization by various decision-makers by establishing a systematic database.

• Korean Journal of Clinical Laboratory Science
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• v.55 no.1
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• pp.29-36
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• 2023

This study proposes a spirometry reference equation suitable for Asian migrant workers undergoing special health examinations. The study participants were divided according to their region: Central Asia, Northeast Asia, and South Asia Pacific. We confirmed results of the spirometry analysis of migrant workers and the interpretation consistency between the prediction equations. Based on this data, we propose a reference equation suitable for domestic migrant workers and suggest a scaling factor applicable to the spirometer wherein the reference equation is not easily applicable. The kappa-values obtained for men and women, respectively, between the global lung function initiative 2012 (GLI2012)-Southeast Asian and the Southeast Asian equations were 0.819 and 0.770, between the GLI2012-Southeast Asian and the South Asian equations were 0.881 and 0.866, and between the GLI2012-Northeast Asian and the Central Asian equations were 0.831 and 0.833. We propose applying the GLI2012-Northeast Asian equation for Northeast Asian and Central Asian countries, whereas the GLI2012-Southeast Asian equation was found to be more suitable for predicting Southeast Asian and South Asian populations. For spirometry, we recommend applying a scaling factor of 0.87 to the Dr. Choi equation, wherein the GLI2012-Southeast Asian equation is not applicable.

• Journal of Life Science
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• v.33 no.1
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• pp.25-33
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• 2023

The fermentation process of kimchi, which is a traditional Korean food, influences the resulting compo- sition of microorganisms, such as the genera Leuconostoc, Weissella, and Lactobacillus. In addition, several factors, including the type of kimchi, fermentation conditions, materials, and ingredients, can influence the distribution of the kimchi microbial community. In this study, next-generation sequencing (NGS) of kimchi samples obtained from central (Gangwon-do and Gyeonggi-do) and southern (Jeolla-do and Gyeongsang-do) regions in Korea was performed, and the microbial communities in samples from the two regions were compared. Good's coverage prediction for all samples was higher than 99%, indicating that there was sufficient reliability for comparative analysis. However, in a α -diversity analysis, there was no significant difference in species richness and diversity between samples. The Firmicutes phylum was common in both regions. At the species level, Weissella kandleri dominated in central (46.5%) and southern (30.8%) regions. Linear discriminant analysis effect size (LEfSe) analysis was performed to identify biomarkers representing the microbial community in each region. The LEfSe results pointed to statistically significant differences between the two regions in community composition, with Leuconostocaceae (71.4%) dominating in the central region and Lactobacillaceae (61.0%) dominating in the southern region. Based on these results, it can be concluded that the microbial communities of kimchi are significantly influenced by regional properties and that it can provide more useful scientific data to study the relationship between regional characteristics of kimchi and their microbial distribution.

• Journal of Korean Society on Water Environment
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• v.39 no.2
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• pp.162-174
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• 2023

In order to prevent water pollution caused by organic matter, Total Organic Carbon(TOC) has been adopted indicator and monitored. TOC can be divided into Dissolved Organic Carbon(DOC) and Particulate Organic Carbon(POC). POC is largely precipitated and removed during stream flow, which making DOC environmentally significant. However, there are lack of studies to define spatio-temporal distributions of DOC in stream affected by various land use. Therefore, it is necessary to estimate the past DOC concentration using other water quality indicators to evaluate status of watershed management. In this study, DOC was estimated by correlation and regression analysis using three different organic matter indicators monitored in mixed land-use watersheds. The results of correlation analysis showed that DOC has the highest correlation with TOC. Based on the results of the correlation analysis, the single- and multiple-regression models were developed using Biochemical Oxygen Demand(BOD), Chemical Oxygen Demand(COD), and TOC. The results of the prediction accuracy for three different regression models showed that the single-regression model with TOC was better than those of the other multiple-regression models. The trend analysis using extended average concentration DOC data shows that DOC tends to decrease reflecting watershed management. This study could contribute to assessment and management of organic water pollution in mixed land-use watershed by suggesting methods for assessment of unmeasured DOC concentration.