• Journal of Korea Multimedia Society
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• v.18 no.5
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• pp.652-662
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• 2015

It is necessary to develop small, implantable bio-telemetry systems which can measure and transmit patients' bio-signals from internal body to external receiver. When measuring bio-signals, like electrical bio-signals, acoustic bio-signal measurement has also a big clinical usefulness. But, sound signal has larger frequency bandwidth than any other bio-signals. When considering these issues, a wireless telemetry system which has rapid data transmission rate proportional to wide frequency bandwidth is necessary to be developed. The bluetooth module is used to overcome the data rate limitation caused by the large frequency bandwidth. In this paper, a novel multimedia bluetooth biotelemetry system was developed which consists of transmitter module located in the body and receiver device located outside of the body. The transmitter consists of microphone, bluetooth, and wireless charging device. And the receiver consists of bluetooth and codec system. The sound inside the skin is captured by microphone and sent to receiver by bluetooth while charging. The wireless charging system constantly supplies the electric power to the system. To verify the performance of the developed system, an in vitro experiment has been performed. The results show that the proposed biotelemetry system has ability to acquire the sound signals under the skin.

• The Korean Journal of Community Living Science
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• v.27 no.spc
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• pp.665-679
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• 2016

This study examined the main factors for activating rural tourism of Jeollabuk-do using big data analysis. The tourism big data was gathered from public open data sources and social network services (SNS), and the analysis tools, 'Opinion Mining', 'Text Mining', and 'Social Network Analysis(SNA)' were used. The opinion mining and text mining analysis identified the key local contents of the 14 areas of Jeollabuk-do and the evaluations of customers on rural tourism. Social network analysis detected the relationships between their contents and determined the importance of the contents. The results of this research showed that each location in Jeollabuk-do had their specific contents attracting visitors and the number of contents affected the scale of tourists. In addition, the number of visitors might be large when their tourism contents were strongly correlated with the other contents. Hence, strong connections among their contents are a point to activate rural tourism. Social network analysis divided the contents into several clusters and derived the eigenvector centralities of the content nodes implying the importance of them in the network. Tourism was active when the nodes at high value of the eigenvector centrality were distributed evenly in every cluster; however the results were contrary when the nodes were located in a few clusters. This study suggests an action plan to extend rural tourism that develop valuable contents and connect the content clusters properly.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.75-80
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• 2018

In this paper, we propose heterogeneous lifelog mining model in health big-data platform. It is an ontology-based mining model for collecting user's lifelog in real-time and providing healthcare services. The proposed method distributes heterogeneous lifelog data and processes it in real time in a cloud computing environment. The knowledge base is reconstructed by an upper ontology method suitable for the environment constructed based on the heterogeneous ontology. The restructured knowledge base generates inference rules using Jena 4.0 inference engines, and provides real-time healthcare services by rule-based inference methods. Lifelog mining constructs an analysis of hidden relationships and a predictive model for time-series bio-signal. This enables real-time healthcare services that realize preventive health services to detect changes in the users' bio-signal by exploring negative or positive correlations that are not included in the relationships or inference rules. The performance evaluation shows that the proposed heterogeneous lifelog mining model method is superior to other models with an accuracy of 0.734, a precision of 0.752.

• Journal of Ecology and Environment
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• v.41 no.3
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• pp.78-84
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• 2017

Recently, great demands are rising around the globe for monitoring and studying of long-term ecological changes. To go with the stream, many researchers in South Korea have attempted to share and integrate ecological data for practical use. Although some achievements were made in the meantime, we still have to overcome a big obstacle that existing ecological data in South Korea are mostly spread all over the country in various formats of computer files. In this study, we aim to handle the situation by developing a semi-automatic data conversion tool for Korean ecological data standardization, based on some predefined protocols for ecological data collection and management. The current implementation of this tool works on only five species (libythea celtis, spittle bugs, mosquitoes, pinus, and quercus mongolica), helping data managers to quickly and efficiently obtain a standardized format of ecological data from raw collection data. With this tool, the procedure of data conversion is divided into four steps: data file and protocol selection step, species selection step, attribute mapping step, and data standardization step. To find the usability of this tool, we utilized it to conduct the standardization of raw five species data collected from six different observatory sites of Korean National Parks. As a result, we could obtain a common form of standardized data in a relatively short time. With the help of this tool, various ecological data could be easily integrated into the nationwide common platform, providing broad applicability towards solving many issues in ecological and environmental system.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.133-154
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• 2016

Interdisciplinary approach has been recognized as one of key strategies to address various and complex research problems in modern science. The purpose of this study is to investigate the interdisciplinary characteristics and structure of the field of big data. Among the 1,083 journals related to the field of big data, multiple Subject Categories (SC) from the Web of Science were assigned to 420 journals (38.8%) and 239 journals (22.1%) were assigned with the SCs from different fields. These results show that the field of big data indicates the characteristics of interdisciplinarity. In addition, through bibliographic coupling network analysis of top 56 journals, 10 clusters in the network were recognized. Among the 10 clusters, 7 clusters were from computer science field focusing on technical aspects such as storing, processing and analyzing the data. The results of cluster analysis also identified multiple research works of analyzing and utilizing big data in various fields such as science & technology, engineering, communication, law, geography, bio-engineering and etc. Finally, with measuring three types of centrality (betweenness centrality, nearest centrality, triangle betweenness centrality) of journals, computer science journals appeared to have strong impact and subjective relations to other fields in the network.

• Journal of the Korean Society of Food Culture
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• v.38 no.4
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• pp.185-190
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• 2023

This paper elucidates the novel direction of food research in the era of the 4^th Industrial Revolution characterized by personalized approaches. Since conventional approaches for identifying novel food materials for health benefits are expensive and time-consuming, there is a need to shift towards AI-based approaches which offer more efficient and cost-effective methods, thus accelerating progress in the field of food science. However, relevant research papers in this field present several challenges such as regional and ethnic differences and lack of standardized data. To tackle this problem, our study proposes to address the issues by acquiring and normalizing food and biological big data. In addition, the paper demonstrates the association between heath status and biological big data such as metabolome, epigenome, and microbiome for personalized healthcare. Through the integration of food-health-bio data with AI technologies, we propose solutions for personalized healthcare that are both effective and validated.

• Journal of Bio-Environment Control
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• v.30 no.1
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• pp.19-26
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• 2021

In this study, analysis was performed to identify three greenhouses located in the same area using principal component analysis (PCA) and linear discrimination analysis (LDA). The environmental data in the greenhouse were from 3 farms in the same area, and the values collected at 1 hour intervals for a total of 4 weeks from April 1 to April 28 were used. Before analyzing the data, it was pre-processed to normalize the data, and the analysis was performed by dividing it into 80% of the training data and 20% of the test data. As a result of PCA and LDA analysis, it was found that PCA classification accuracy was 57.51% and LDA classification was 67.06%, indicating that it can be classified by greenhouse. Based on the farmhouse data classified in advance, the data of the new environment can be classified into specific groups to determine the tendency of the data. Such data is judged to be a way to increase the utilization of data by facilitating identification.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• Genomics & Informatics
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• v.18 no.1
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• pp.8.1-8.10
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• 2020

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www. bioexpress.re.kr/.

• The Journal of the Korea Contents Association
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• v.16 no.6
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• pp.260-269
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• 2016

With the development of genomics, wearable device and IT/NT, a vast amount of bio-medical data are generated recently. Also, healthcare industries based on big-data are booming and big-data technology based on bio-medical data is rising rapidly as a core technology for improving the national health and aged society. Also, oriental medicine research is focused with modern research technology and validate it's various biochemical effect by combining with molecular biology technology. However there are few searching system for finding biochemical mechanism which is related to major compounds in oriental medicine. Therefore, in this paper, we collected papers related with medical herbs from PubMed and constructed a medical herbs database to store and manage chemical, gene/protein and biological interaction information extracted by a literature analysis on the papers. Also, to supporting a multidimensional analysis on the database, we developed a network analysis system based on a hierarchy structure of chemical, gene/protein and biological interaction information. Finally, we expect this system will be used the major tool to discover various biochemical effect by combining with molecular biology technology.