• Physical Therapy Korea
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• v.30 no.3
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• pp.230-236
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• 2023

Background: The globalization of the healthcare industry and the increasing demand for skilled professionals in the global healthcare industry have opened up opportunities for specialized biotech healthcare professionals to seek overseas employment and career advancement. Objects: This study aimed to develop educational content essential for the overseas employment of digital bio-health professionals. Methods: A survey was conducted among 196 participants. Google Forms (Google) were utilized to create and administer the survey, employing purposive sampling, a non-probability sampling method. Data analysis was performed using IBM SPSS 25.0 (IBM Co.), including Cronbach's α and independent sample t-tests to assess significant differences. Results: About half of college students are interested in overseas employment and international careers, while the other half had not. The most common reason for wanting to work or go overseas was "foreign experience will be useful for future activities in Korea." Students who had experience taking courses from the Bio-health Convergence Open Sharing University preferred overseas programs more than those who did not have that experience. In terms of the degree of desire for overseas education courses provided by universities, contents related to human health were the highest, followed by bio-health big data. Conclusion: Many students wanted to work and go overseas if there is sufficient support from the university. The findings in this study suggest that universities are necessary to play an important role in supporting students' aspirations to work or go overseas by providing language education, education and training programs, information on overseas jobs, and mentoring programs.

• Genomics & Informatics
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• v.21 no.1
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• pp.12.1-12.8
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• 2023

A wave of new technologies has created opportunities for the cost-effective generation of high-throughput profiles of biological systems, foreshadowing a "data-driven science" era. The large variety of data available from biological research is also a rich resource that can be used for innovative endeavors. However, we are facing considerable challenges in big data deposition, integration, and translation due to the complexity of biological data and its production at unprecedented exponential rates. To address these problems, in 2020, the Korean government officially announced a national strategy to collect and manage the biological data produced through national R&D fund allocations and provide the collected data to researchers. To this end, the Korea Bioinformation Center (KOBIC) developed a new biological data repository, the Korea BioData Station (K-BDS), for sharing data from individual researchers and research programs to create a data-driven biological study environment. The K-BDS is dedicated to providing free open access to a suite of featured data resources in support of worldwide activities in both academia and industry.

• Journal of Bio-Environment Control
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• v.23 no.3
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• pp.192-198
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• 2014

To establish the design criteria for seasonal heating load calculation in greenhouses, standard weather data are required. However, they are being provided only at seven regions in Korea. So, instead of using standard weather data, in order to find the method to build design weather data for seasonal heating load calculation, heating degree-hour and heating degree-day were analyzed and compared by methods of fundamental equation, Mihara's equation and modified Mihara's equation using normal and thirty years from 1981 to 2010 hourly weather data provided by KMA and standard weather data provided by KSES. Average heating degree-hours calculated by fundamental equation using thirty years hourly weather data showed a good agreement with them using standard weather data. The 24 times of heating degree-day showed relatively big differences with heating degree-hour at the low setting temperature. Therefore, the heating degree-hour was considered more appropriate method to estimate the seasonal heating load. And to conclude, in regions which are not available standard weather data, we suggest that design weather data should be analyzed using thirty years hourly weather data. Average of heating degree-hours derived from every year hourly weather data during the whole period can be established as environmental design standards, and also minimum and maximum of them can be used as reference data for energy estimation.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.255-260
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• 2022

This study aimed to develop a service model that uses a deep learning algorithm for detecting diseases and pests in strawberries through image data. In addition, the pest detection performance of deep learning models was further improved by proposing segmented image data sets specialized in disease and pest symptoms. The CNN-based YOLO deep learning model was selected to enhance the existing R-CNN-based model's slow learning speed and inference speed. A general image data set and a proposed segmented image dataset was prepared to train the pest and disease detection model. When the deep learning model was trained with the general training data set, the pest detection rate was 81.35%, and the pest detection reliability was 73.35%. On the other hand, when the deep learning model was trained with the segmented image dataset, the pest detection rate increased to 91.93%, and detection reliability was increased to 83.41%. This study concludes with the possibility of improving the performance of the deep learning model by using a segmented image dataset instead of a general image dataset.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1035-1045
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• 2015

Recent studies in Big Data Analysis are showing promising results, utilizing the main memory for rapid data processing. In-memory computing technology can be highly advantageous when used with high-performing servers having tens of gigabytes of RAM with multi-core processors. The constraint in network in these infrastructure can be lessen by combining in-memory technology with distributed parallel processing. This paper discusses the research in the aforementioned concept applying to a test taxi hailing application without disregard to its underlying RDBMS structure. The application of IMDG technology in the application's backend API without restructuring the database schema yields 6 to 9 times increase in performance in data processing and throughput. Specifically, the change in throughput is very small even with increase in data load processing.

• Progress in Medical Physics
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• v.30 no.2
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• pp.49-58
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• 2019

Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.11a
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• pp.55-56
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• 2018

In order to reduce the risk of accidents, we proposed a construction safety management system combined with wearable device and LoRa (Low-Range Wireless Network) communication method to apply the usefulness of Internet (IoT) technology which means "everything connected". to construction safety management Management system. The proposed wearable safety device is a device that relays information exchange between wearable safety device and safety management server by LoRa wireless communication method. The safety management server can store workers bio-data and perform big data analysis. If a risk factor is determined from the analysis result, a warning is sent to the wearable safety device and the manager's application. The goal of this system is to prevent construction workers from entering the dangerous area that is not suitable for work, and to prevent safety accidents caused by human cause by detecting abnormal condition during work.

• Quantitative Bio-Science
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• v.37 no.2
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• pp.73-79
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• 2018

As high frequency (HF, for short) time series is now prevalent in the presence of real time big data, volatility computations based on traditional ARCH/GARCH models need to be further developed to suit the high frequency characteristics. This article reviews realized volatilities (RV) and multivariate GARCH (MGARCH) to deal with high frequency volatility computations. As a (functional) infinite dimensional models, the fARCH and fGARCH are introduced to accommodate ultra high frequency (UHF) volatilities. The fARCH and fGARCH models are developed in the recent literature by Hormann et al. [1] and Aue et al. [2], respectively, and our discussions are mainly based on these two key articles. Real data applications to domestic UHF financial time series are illustrated.

• Journal of Arbitration Studies
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• v.31 no.2
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• pp.119-135
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• 2021

South Korea has the most advanced technology in the Fourth Industrial Revolution era because of its high-speed Internet commercialization. However, the industry is shrinking due to its various regulations in building and its utilization of personal information as big data. Currently, South Korea's personal data utilization business is in its early stages. In the era of the 4th Industrial Revolution, it is difficult for startups to use data. There are various causes here. Above all, legal regulations to protect personal information are emphasized. This study confirms that transactions of personal medical records through My Data can be made. Moreover, it confirms that there is a need for a mediating role between stakeholders. This study lacks statistical access in the process of performing stakeholder roles. However, personal medical records will be traded safely in the future, and new subjects will enter the market. Furthermore, the domestic bio-industry will develop. Through this study, various problems were derived in establishing Medical MyData in Korea. Moreover, it looks forward to continuing various studies in the health care sector in the future.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.275-284
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• 2023

Gene expression data can be utilized in various studies, including the prediction of disease prognosis. However, there are challenges associated with collecting enough data due to cost constraints. In this paper, we propose a gene expression data generation model based on Conditional Variational Autoencoder. Our results demonstrate that the proposed model generates synthetic data with superior quality compared to two other state-of-the-art models for gene expression data generation, namely the Wasserstein Generative Adversarial Network with Gradient Penalty based model and the structured data generation models CTGAN and TVAE.