• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1711-1720
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• 2020

Particulate matter (PM) that has been artificially generated during the recent of rapid industrialization and urbanization moves and disperses according to weather conditions, and adversely affects the human skin and respiratory systems. The purpose of this study is to predict the PM10 concentration in Seoul using meteorological factors as input dataset for multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models, and compared and evaluated the performance of the models. First, the PM10 concentration data obtained at 39 air quality monitoring sites (AQMS) in Seoul were divided into training and validation dataset (8:2 ratio). The nine meteorological factors (mean, maximum, and minimum temperature, precipitation, average and maximum wind speed, wind direction, yellow dust, and relative humidity), obtained by the automatic weather system (AWS), were composed to input dataset of models. The coefficients of determination (R2) between the observed PM10 concentration and that predicted by the MLR, SVM, and RF models was 0.260, 0.772, and 0.793, respectively, and the RF model best predicted the PM10 concentration. Among the AQMS used for model validation, Gwanak-gu and Gangnam-daero AQMS are relatively close to AWS, and the SVM and RF models were highly accurate according to the model validations. The Jongno-gu AQMS is relatively far from the AWS, but since PM10 concentration for the two adjacent AQMS were used for model training, both models presented high accuracy. By contrast, Yongsan-gu AQMS was relatively far from AQMS and AWS, both models performed poorly.

• Women's Health Nursing
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• v.29 no.4
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• pp.317-327
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• 2023

Purpose: This study investigated the influence of cervical cancer knowledge, human papillomavirus (HPV) knowledge, self-efficacy, and uncertainty on the intention to engage in cervical cancer preventive behavior in HPV-infected women. Methods: This descriptive correlational study was conducted among 129 adult women aged 20 to 65 years who received positive HPV results at a general hospital in Changwon, Korea. The dataset was analyzed using descriptive statistics, the independent t-test, analysis of variance, the Pearson correlation coefficient, and multiple regression. Results: The mean score for the intention to engage in cervical cancer preventive behavior was high (4.43±0.65). This intention was significantly different according to age at first sexual intercourse (F=7.38, p=.001), HPV type (F=4.79, p=.010), vaccination (t=3.19, p=.002), and condom use (t=3.03, p=.003). The intention to engage in cervical cancer preventive behavior showed significant, weak-to-moderate positive correlations with HPV knowledge (r=.22, p=.012) and self-efficacy (r=.42, p<.001). Self-efficacy (β=.46, p<.001), first sexual intercourse at <20 years (β=.45, p<.001), first sexual intercourse at 20-24 years (β=.29, p=. 018), HPV high- and low-risk group infection (β=.26, p=.019), HPV high-risk group infection (β=.26, p=.026), and vaccination (β=.21, p=.007) significantly influenced the intention to engage in cervical cancer preventive behavior. These variables explained 34.6% of variance in intention. Conclusion: Study findings support the need to develop a program that effectively conveys accurate information about cervical cancer prevention to HPV-infected women and helps them enhance self-efficacy to boost the intention to engage in cervical cancer preventive behavior.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.550-559
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• 2018

The effectiveness of deep neural networks (DNNs) for detection and classification of micro-Doppler signals generated by human walking and background noise sources is investigated. Previous research included a complex process for extracting meaningful features that directly affect classifier performance, and this feature extraction is based on experiences and statistical analysis. However, because a DNN gradually reconstructs and generates features through a process of passing layers in a network, the preprocess for feature extraction is not required. Therefore, binary classifiers and multiclass classifiers were designed and analyzed in which multilayer perceptrons (MLPs) and DNNs were applied, and the effectiveness of DNNs for recognizing micro-Doppler signals was demonstrated. Experimental results showed that, in the case of MLPs, the classification accuracies of the binary classifier and the multiclass classifier were 90.3% and 86.1%, respectively, for the test dataset. In the case of DNNs, the classification accuracies of the binary classifier and the multiclass classifier were 97.3% and 96.1%, respectively, for the test dataset.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.237-251
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• 2023

This study explores the possibility of automated scoring for scientific graph answers by designing an automated scoring model using convolutional neural networks and applying it to students' kinematics graph answers. The researchers prepared 2,200 answers, which were divided into 2,000 training data and 200 validation data. Additionally, 202 student answers were divided into 100 training data and 102 test data. First, in the process of designing an automated scoring model and validating its performance, the automated scoring model was optimized for graph image classification using the answer dataset prepared by the researchers. Next, the automated scoring model was trained using various types of training datasets, and it was used to score the student test dataset. The performance of the automated scoring model has been improved as the amount of training data increased in amount and diversity. Finally, compared to human scoring, the accuracy was 97.06%, the kappa coefficient was 0.957, and the weighted kappa coefficient was 0.968. On the other hand, in the case of answer types that were not included in the training data, the s coring was almos t identical among human s corers however, the automated scoring model performed inaccurately.

• Journal of Environmental Health Sciences
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• v.47 no.5
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• pp.454-461
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• 2021

Background: Fish and fishery products (FFPs) unintentionally contaminated with various environmental pollutants are major exposure pathways for humans. To protect human health from the consumption of contaminated FFPs, it is essential to develop a systematic tool for evaluating exposure and risks. Objectives: To regularly, accurately, and quickly evaluate adverse health outcomes due to FFPs contamination, we developed an automated dietary exposure and risk assessment system called HERA (the Human Exposure and Risk Assessment system for chemicals in FFPs). The aim of this study was to develop an overall architecture design and demonstrate the major features of the HERA system. Methods: For the HERA system, the architecture framework consisted of multi-layer stacks from infrastructure to fish exposure and risk assessment layers. To compile different contamination levels and types of seafood consumption datasets, the data models were designed for the classification codes of FFP items, contaminants, and health-based guidance values (HBGVs). A systematic data pipeline for summarizing exposure factors was constructed through down-scaling and preprocessing the 24-hour dietary recalls raw dataset from the Korea National Health and Nutrition Examination Survey (KNAHES). Results: According to the designed data models for the classification codes, we standardized 167 seafood items and 2,741 contaminants. Subsequently, we implemented two major functional workflows: 1) preparation and 2) main process. The HERA system was developed to enable risk assessors to accumulate the concentration databases sustainably and estimate exposure levels for several populations linked to seafood consumption data in KNAHES in a user-friendly manner and in a local PC environment. Conclusions: The HERA system will support policy-makers in making risk management decisions based on a nation-wide risk assessment for FFPs.

• IMMUNE NETWORK
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• v.16 no.3
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• pp.189-194
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• 2016

Obesity is characterized as an accumulation of adipose tissue mass represented by chronic, low-grade inflammation. Obesity-derived inflammation involves chemokines as important regulators contributing to the pathophysiology of obesity-related diseases such as cardiovascular disease, diabetes and some cancers. The obesity-driven chemokine network is poorly understood. Here, we identified the profiles of chemokine signature between human preadipocytes and adipocytes, using PCR arrays and qRT-PCR. Both preadipocytes and adipocytes showed absent or low levels in chemokine receptors in spite of some changes. On the other hand, the chemokine levels of CCL2, CCL7-8, CCL11, CXCL1-3, CXCL6 and CXCL10-11 were dominantly expressed in preadipocytes compared to adipocytes. Interestingly, CXCL14 was the most dominant chemokine expressed in adipocytes compared to preadipocytes. Moreover, there is significantly higher protein level of CXCL14 in conditioned media from adipocytes. In addition, we analyzed the data of the chemokine signatures in adipocytes obtained from healthy lean and obese postmenopausal women based on Gene Expression Omnibus (GEO) dataset. Adipocytes from obese individuals had significantly higher levels in chemokine signature as follows: CCL2, CCL13, CCL18-19, CCL23, CCL26, CXCL1, CXCL3 and CXCL14, as compared to those from lean ones. Also, among the chemokine networks, CXCL14 appeared to be the highest levels in adipocytes from both lean and obese women. Taken together, these results identify CXCL14 as an important chemokine induced during adipogenesis, requiring further research elucidating its potential therapeutic benefits in obesity.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.81-88
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• 2022

The major educational goal of reading part, which occupies important portion in Korean language in Korean SAT, is to evaluated whether a given text can be fully understood. Therefore given questions in the exam must be able to solely solvable by given text. In this paper we developed a datatset based on Korean SAT's reading part in order to evaluate whether a deep learning language model can classify if the given question is true or false, which is a binary classification task in NLP. In result, by applying language model solely according to the passages in the dataset, we were able to acquire better performance than 59.2% in F1 score for human performance in most of language models, that KoELECTRA scored 62.49% in our experiment. Also we proved that structural limit of language models can be eased by adjusting data preprocess.

• Journal of Biomedical Engineering Research
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• v.45 no.1
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• pp.1-9
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• 2024

Communication with others plays an important role in human social interaction and information exchange in modern society. However, some individuals have difficulty in communicating due to dysarthria. Therefore, it is necessary to develop effective diagnostic techniques for early treatment of the dysarthria. In the present study, we propose a mobile device-based methodology that enables to automatically classify dysarthria type. The light-weight CNN model was trained by using the open audio dataset of Korean patients with dysarthria. The trained CNN model can successfully classify dysarthria into related subtype disease with 78.8%~96.6% accuracy. In addition, the user-friendly mobile application was also developed based on the trained CNN model. Users can easily record their voices according to the selected inspection type (e.g. word, sentence, paragraph, and semi-free speech) and evaluate the recorded voice data through their mobile device and the developed mobile application. This proposed technique would be helpful for personal management of dysarthria and decision making in clinic.

• Bulletin of the Korean Chemical Society
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• v.32 no.7
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• pp.2397-2404
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• 2011

Variable selection k nearest neighbor QSAR modeling approach was applied to a data set of 80 3-arylisoquinolines exhibiting cytotoxicity against human lung tumor cell line (A-549). All compounds were characterized with molecular topology descriptors calculated with the MolconnZ program. Seven compounds were randomly selected from the original dataset and used as an external validation set. The remaining subset of 73 compounds was divided into multiple training (56 to 61 compounds) and test (17 to 12 compounds) sets using a chemical diversity sampling method developed in this group. Highly predictive models characterized by the leave-one out cross-validated $R^2$ ($q^2$) values greater than 0.8 for the training sets and $R^2$ values greater than 0.7 for the test sets have been obtained. The robustness of models was confirmed by the Y-randomization test: all models built using training sets with randomly shuffled activities were characterized by low $q^2{\leq}0.26$ and $R^2{\leq}0.22$ for training and test sets, respectively. Twelve best models (with the highest values of both $q^2$ and $R^2$) predicted the activities of the external validation set of seven compounds with $R^2$ ranging from 0.71 to 0.93.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.9
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• pp.1224-1231
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• 2018

Automatic age estimation has been used in many social network applications, practical commercial applications, and human-computer interaction visual-surveillance biometrics. However, it has rarely been explored. In this paper, we propose an automatic age estimation system, which includes face detection and convolutional deep learning based on an inception module. The latter is a 22-layer-deep network that serves as the particular category of the inception design. To evaluate the proposed approach, we use 4,000 images of eight different age groups from the Adience age dataset. k-fold cross-validation (k = 5) is applied. A comparison of the performance of the proposed work and recent related methods is presented. The results show that the proposed method significantly outperforms existing methods in terms of the exact accuracy and off-by-one accuracy. The off-by-one accuracy is when the result is off by one adjacent age label to the above or below. For the exact accuracy, the age label of "60+" is classified with the highest accuracy of 76%.