• Smart Media Journal
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• v.6 no.3
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• pp.68-74
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• 2017

Big data processing processes various types of data such as files, images, and video to solve problems and provide insightful useful information. Currently, various platforms are used for big data processing, but many organizations and enterprises are using Hadoop for big data processing due to the simplicity, productivity, scalability, and fault tolerance of Hadoop. In addition, Hadoop can build clusters on various hardware platforms and handle big data by dividing into a name node (master) and a data node (slave). In this paper, we use a fully distributed mode used by actual institutions and companies as an operation mode. We have constructed a Hadoop cluster using a low-power and low-cost single board for smooth experiment. The performance analysis of Name node is compared through the same data processing using single board and laptop as name nodes. Analysis of influence by number of data nodes increases the number of data nodes by two times from the number of existing clusters. The effect of the above experiment was analyzed.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.989-1009
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• 2018

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.168-173
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• 2022

As the function of a product is advanced and the process is refined, the yield in the fine manufacturing process becomes an important variable that determines the cost and quality of the product. Since a fine manufacturing process generally produces a product through many steps, it is difficult to find which process or equipment has a defect, and thus it is practically difficult to ensure a high yield. This paper presents the system architecture of how to build a smart manufacturing system to analyze the big data of the manufacturing plant, and the equipment factor analysis methodology to increase the yield of products in the smart manufacturing system. In order to improve the yield of the product, it is necessary to analyze the defect factor that causes the low yield among the numerous factors of the equipment, and find and manage the equipment factor that affects the defect factor. This study analyzed the key factors of abnormal equipment that affect the yield of products in the manufacturing process using the data mining technique. Eventually, a methodology for finding key factors of abnormal equipment that directly affect the yield of products in smart manufacturing systems is presented. The methodology presented in this study was applied to the actual manufacturing plant to confirm the effect of key factors of important facilities on yield.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.21-26
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• 2018

In this study, HRD-NET (http://hrd.go.kr), a vocational and training integrated computer network operated by the Ministry of Employment and Labor, is used to confirm whether job training information required by job seekers is being provided smoothly The question bulletin board was extracted using 'R' program which is optimized for big data technique. Therefore, the effectiveness, appropriateness, visualization, frequency analysis and association analysis of the vocational training system were conducted through this, The results of the study are as follows. First, the issue of vocational training card, video viewing, certificate issue, registration error, Second, management and processing procedures of learning cards for tomorrow 's learning cards are complicated and difficult. In addition, it was analyzed that the training cost system and the refund structure differentiated according to the training occupation, the process, and the training institution in the course of the training. Based on this paper, we will study not only the training system of the Ministry of Employment and Labor but also the improvement of the various training computer system of the government department through the analysis of big data.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.377-392
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• 2015

In today's data-driven society, we've been hearing a great deal about the power of Big Data over the last couple of years. At the same time, it has become the most important issue that the problems is caused by the data collection, management and utilization. Moreover, Big Data has a wide applications ranging from situation awareness, decision-making to the area to enable for the foreseeable future with man-made and analysis of data. It is necessary to process data into meaningful information given that the huge amount of structured and unstructured data being created in the private and the public sector, even in disaster management. This data should be public and private sector at the same time for the appropriate linkage analysis for effective disaster management. In this paper, we conducted a literature review and case study efficient Big Data to derive the revitalization of national disaster management. The study obtained data on the role and responsibility of the public sector and the private sector to leverage Big Data for promotion of national disaster management plan. Both public and private sectors should promote common development challenges related to the openness and sharing of Big Data, technology and expansion of infrastructure, legal and institutional maintenance. The implications of the finding were discussed.

• Journal of Korean Society of Rural Planning
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• v.25 no.2
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• pp.15-21
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• 2019

The agricultural environment is changing and becoming more advanced due to the influence of the 4th Industrial Revolution. From the basic plan of Rural Informatics to the current level of 2nd generation smart farms aimed at improving productivity using Big data, cloud network and more IoT technology. We are continuing to provide support and research and development. However, many problems remain to be solved in order to supply and settle smart farms in Korea. The purpose of this study is to provide a method of collecting and sharing data on farming environment and to help improve the income and productivity of farmers based on collected data. In the case of hog farm, the multiple sensors for environmental data like temperature, humidity and gases and the network environment for connecting the internet were established. The environment sensor was made using the ESP8266 Node MCU board as micro-controller, DHT22 sensor for temperature and humidity, and MQ series sensors for various gases in the hog pens. The network sensor was applied experimentally for one month and the environmental data of the hog farm was stored on a web database. This study is expected to raise the importance of collecting and managing the agricultural and environmental data, for the next generation farmers to understand the smart farm more easily and to try it by themselves.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.690-692
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• 2014

Conventional one-way cramming education at schools has the disadvantage of poor student interest in learning, immersion, and learning efficiency as well as a limitation in realizing collective intelligence and collaborative learning. Therefore, an educational content service platform using a near-field communication(NFC) technology is required as a tool for encouraging the voluntary learning participation of students and increasing learning effectiveness through self-directed studying. This study focuses on the development of an educational content production system that creates high-quality education contents suitable for smart schools. In these schools, students and teachers generally communicate through an electronic blackboard using Bluetooth, which is an NFC technology. Further, the lecture notes of individual students are reproduced and collected as big data, which will facilitate the sharing of these notes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2153-2159
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• 2013

In order to drive a vehicle safely, driver needs to check status of the car. Many moderns are having trouble to spare time to visit auto mechanic and have car mechanics to check their car other than their office hours. If the car status cannot be inspected regularly, it is likely to cause a big accident threatening the surroundings as well as driver's life. Inspection tool and system help driver to check their own vehicle status personally are required for preventing it. In this paper, it designed and realized system that records driving information based on changing data of vehicle (location and automotive internal data) and allows driver can check the vehicle status easily and further, driver can share the driving information with repair shop via the Internet to receive detailed inspection service for car status.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.726-734
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• 2019

The current maritime industry is expected to have a significant impact on the role of maritime-related technologies and systems, especially seafarers, in the rapidly changing Fourth Industrial Revolution. The Maritime Autonomous Surface Ship (MASS) aims to reduce the number of safety accidents and improve seafarers' working environment. With regard to MASS, the International Maritime Organization has been trying to minimize unexpected impact in the maritime education and training sector by establishing international conventions such as the Standards of Training, Certification and Watchkeeping for Seafarers. However, domestic designated educational institutions have not yet established an education and training scheme to develop seafarers who will be on board for MASS. Therefore, this paper reviews the technology of MASS, analyzes the changes in education and training in order to upgrade the qualifications, and suggests the competencies of smart seafarers equipped with the integrated management ability required for Artificial Intelligence, Big Data, Cybersecurity, and the Digital System Revolution through education and training. In addition, this study provides basic information for the education and training of seafarers who are optimized for the rapidly changing technological environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1144-1151
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• 2021

The pressing process is a compression process in which a product is made by applying force to a heated or unheated material to transform it into the desired shape. Due to the characteristics of press equipment that produces products through continuous compression for a short time, product defects occur continuously, and systems for solving these problems are being developed using various technologies. This paper proposes a real-time defect detection system based on an artificial intelligence algorithm that detects defects. By attaching various sensors to the press device, the relationship between equipment status and defects is defined and collected based on a big data platform. By developing an artificial intelligence algorithm based on the collected data and implementing the developed algorithm using an embedded board, we will show the practicality of the system by applying it to the actual field.