• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.105-114
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• 2019

As the Industrial Revolution progressed, Big Data technologies were used to develop a system that instantly identified the consequences of older vehicles using mobile devices. First, data from the vehicle was collected using the OBD2 sensor, and the data collected was stored in the raspberry pie, giving it the same situation that the raspberry pie was driving. In the event that vehicle data is generated, the data is collected in real time, stored in multiple nodes, and visualized and printed based on the processed, refined, processed and processed data. We can use Big Data in this process and quickly process vehicle data to identify it effectively through mobile devices.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.1-9
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• 2018

In the fourth industrial revolution, which has become an issue now, we have been able to receive instant analysis results faster than the existing slow speed through various Big Data technologies, and to conduct real-time monitoring on mobile and web. First, various irregular sensor Data is generated using IoT device, Raspberry Pi. Sensor Data is collected in real time, and the collected data is distributed and stored using several nodes. Then, the stored Sensor Data is processed and refined. Visualize and output the analysis result after analysis. By using these methods, we can train the human resources required for Big Data and mobile related fields using IoT, and process data efficiently and quickly. We also provide information that can confirm the reliability of research results through real time monitoring.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.117-126
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• 2019

The data generated in the IoT environment is very diverse. Especially, the development of the fourth industrial revolution has made it possible to increase the number of fixed and unstructured data generated in manufacturing facilities such as Smart Factory. With Big Data related solutions, it is possible to collect, store, process, analyze and visualize various large volumes of data quickly and accurately. Therefore, in this paper, we will directly generate data using Raspberry Pi used in IoT environment, and analyze using various Big Data solutions. Collected by using an Sqoop solution collected and stored in the database to the HDFS, and the process is to process the data by using the solutions available Hive parallel processing is associated with Hadoop. Finally, the analysis and visualization of the processed data via the R programming will be used universally to end verification.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.1-6
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• 2020

Road movement pollutant air environment big data is a link between real-time traffic data such as vehicle type, speed, and load using AVC, VDS, WIM, and DTG, which are always traffic volume survey equipment, and road shape (uphill, downhill, turning section) data using GIS. It consists of traffic flow data. Also, unlike general data, a lot of data per unit time is generated and has various formats. In particular, since about 7.4 million cases/hour or more of large-scale real-time data collected as detailed traffic flow information are collected, stored and processed, a system that can efficiently process data is required. Therefore, in this study, an open source-based data parallel processing performance optimization study is conducted for the visualization of big data in the air environment of road transport pollution.

• Economic and Environmental Geology
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• v.55 no.3
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• pp.273-280
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• 2022

The purpose of this study is to explore the social perception on geoscience using a big data analysis and to propose a way to enhance people's perception on geoscience. For this, 5,044 media articles including geoscience produced by 54 media companies from January 1, 2010 to April 14, 2022. were analyzed. Big data analyses were applied. The results of analyses are as follows: Media articles consist of key words of research institute, some countries of America, China and Japan, City of Pohang, CEO of KIGAM. And geology, industry, development of mineral resources, environment, energy, nuclear power, and groundwater are highlighted as key words. Also, it is confirmed that topics related to geoscience such as expert, environment and research institute are not individually isolated, but interconnected and linked to topics in the center of future, industry, and global. Based on this result, ways to enhance people's perception on geoscience were discussed.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.8
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• pp.491-498
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• 2016

This paper proposes a visualization method that expresses users' MBTI personality types based on the categorization method called 'Big Five Personality Traits' and their representative celebrities' facial images. This system has significance for visualizing traits from texts to images effectively. The entire system is composed of making backgrounds to represent traits of each MBTI category using 'Big Five Personality Traits' and showing facial images of celebrities with corresponding MBTI personality types. First, 'Big Five Personality Traits' classifies 16 MBTI types into four categories and each of category traits lays the foundation to make background using visual elements such as colors and lines. Second, celebrities' facial images are substituted for the corresponding literal explanation of each MBTI type. By adjusting the color saturation, users can distinguish relationship between the types intuitively. As a result, this system enables users to take information for their own / opposite / similar MBTI types simultaneously.

• Science of Emotion and Sensibility
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• v.26 no.1
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• pp.115-132
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• 2023

Previous studies on emotion have repeatedly suggested that alexithymia is an important predictor of depression. Therefore, this study examined whether the relationship between these two variables can be moderated by personality factors, focusing on the Big five personality traits. An online survey of 312 college students, including alexithymia, depression, and Big five personality traits (Extraversion, Agreeableness, Conscientiousness, Emotional Stability, and Openness to Experience) was conducted. As the results, as expected, alexithymia predicted depression, which was in line with the existing research flow. In addition, moderation analysis revealed that agreeableness, emotional stability, and openness to experience moderated the relationship between alexithymia and depression, while extraversion and conscientiousness did not. This study has an important implication in that it discriminated which specific personality traits function as moderators in the process of increasing depression due to alexithymia.

• Animal Bioscience
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• v.37 no.11
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• pp.1848-1862
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• 2024

Objective: Asparagine synthetase (ASNS) is an aminotransferase responsible for the biosynthesis of aspartate by using aspartic acid and glutamine. ASNS is highly expressed in fast-growing broilers, but few studies have reported the regulatory role of ASNS in muscle development. Methods: To explore the function of ASNS in chicken muscle development, the expression of ASNS in different chicken breeds and tissues were first performed by real-time quantitative reverse transcription polymerase chain reaction (RT-PCR). Then, using real-time quantitative RT-PCR, western blot, EdU assay, cell cycle assay and immunofluorescence, the effects of ASNS on the proliferation and differentiation of chicken skeletal muscle satellite cell (SMSC) were investigated. Finally, potential mechanisms by which ASNS influences chicken muscle fiber differentiation were identified through RNA-Seq. Results: The mRNA expression pattern of ASNS in muscles mirrors trends in muscle fiber cross-sectional area, average daily weight gain, and muscle weight across different breeds. ASNS knockdown inhibited SMSC proliferation, while overexpression showed the opposite. Moreover, ASNS attenuated SMSC differentiation by activating the adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) pathway. Additionally, 5-aminoimidazole-4-carboxamide1-β-D-ribofuranoside (AICAR) treatment suppressed the cell differentiation induced by siRNA-ASNS. RNA-Seq identified 1,968 differentially expressed genes (DEGs) during chicken SMSC differentiation when overexpression ASNS. Gene ontology (GO) enrichment analysis revealed that these DEGs primarily participated in 8 biological processes, 8 cellular components, and 4 molecular functions. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis identified several significantly enriched signaling pathways, such as the JAK-STAT signaling pathway, tumor necrosis factor signaling pathway, toll-like receptor signaling pathway, and PI3K-Akt signaling pathway. Conclusion: ASNS promotes proliferation while inhibits the differentiation of chicken SMSCs. This study provides a theoretical basis for studying the role of ASNS in muscle development.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.202-207
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• 2023

In this paper, we discuss the design and implementation of a recommendation platform actually built in the field. We survey deep learning-based recommendation models that are effective in reflecting individual user characteristics. The recently proposed RNN-based sequential recommendation models reflect individual user characteristics well. The recommendation platform we proposed has an architecture that can collect, store, and process big data from a company's commercial services. Our recommendation platform provides service providers with intuitive tools to evaluate and apply timely optimized recommendation models. In the model evaluation we performed, RNN-based sequential recommendation models showed high scores.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.126-134
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• 2019

Nowadays, since there are so many big data available everywhere, those big data can be used to find useful information to improve design and operation by using various analysis methods such as data mining. Especially if we have event log data that has execution history data of an organization such as case_id, event_time, event (activity), performer, etc., then we can apply process mining to discover the main process model in the organization. Once we can find the main process from process mining, we can utilize it to improve current working environment. In this paper we developed a new method to find a final diagnosis of a patient, who needs several procedures (medical test and examination) to diagnose disease of the patient by using process mining approach. Some patients can be diagnosed by only one procedure, but there are certainly some patients who are very difficult to diagnose and need to take several procedures to find exact disease name. We used 2 million procedure log data and there are 397 thousands patients who took 2 and more procedures to find a final disease. These multi-procedure patients are not frequent case, but it is very critical to prevent wrong diagnosis. From those multi-procedure taken patients, 4 procedures were discovered to be a main process model in the hospital. Using this main process model, we can understand the sequence of procedures in the hospital and furthermore the relationship between diagnosis and corresponding procedures.