• Smart Media Journal
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• v.6 no.4
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• pp.58-64
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• 2017

Modern society is characterized by rapid increase in world population, aging of the rural population, decrease of cultivation area due to industrialization. The food problem is becoming an important issue with the farmers and becomes rural. Recently, the researches about the field of the smart farm are actively carried out to increase the profit of the rural area. The existing smart farm researches mainly monitor the cultivation environment of the crops in the greenhouse, another way like in the case of poor quality t is being studied that the system to control cultivation environmental factors is automatically activated to keep the cultivation environment of crops in optimum conditions. The researches focus on the crops cultivated indoors, and there are not many studies applied to the cultivation environment of crops grown outside. In this paper, we propose a method to improve the harvestability of poor areas by monitoring the areas with bad harvests by using big data analysis, by precisely predicting the harvest timing of fruit trees growing in orchards. Factors besides for harvesting include fruit color information and fruit weight information We suggest that a harvest correlation factor data collected in real time. It is analyzed using the Apache Spark engine. The Apache Spark engine has excellent performance in real-time data analysis as well as high capacity batch data analysis. User device receiving service supports PC user and smartphone users. A sensing data receiving device purpose Arduino, because it requires only simple processing to receive a sensed data and transmit it to the server. It regulates a harvest time of fruit which produces a good quality fruit, it is needful to determine a poor harvest area or concentrate a bad area. In this paper, we also present an architectural model to determine the bad areas of fruit harvest using strong data analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.472-474
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• 2018

봉제 공정에서 노루발 압력 센싱이 중요한 이유는 적정 압력 조건으로 봉제원단을 눌러주지 못할 경우 봉제 스티치의 불량 및 최종 마감 원단의 손실로 이어져 납기시간 증가 및 원가상승에 막대한 영향을 미칠 수 있다. 이러한 점을 사전 예방하여 적기생산 및 양품 생산 데이터를 획득 양산시 반영하도록 하여 궁극적으로 CPS환경의 스마트 팩토리를 실현하는데 본 연구가 필요하다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.306-316
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• 2016

In this paper, an NMOS-diode eFuse OTP (One-Time Programmable) memory cell is proposed using a parasitic junction diode formed between a PW (P-Well), a body of an isolated NMOS (N-channel MOSFET) transistor with the small channel width, and an n+ diffusion, a source node, in a DNW (Deep N-Well) instead of an NMOS transistor with the big channel width as a program select device. Blowing of the proposed cell is done through the parasitic junction formed in the NMOS transistor in the program mode. Sensing failures of '0' data are removed because of removed contact voltage drop of a diode since a NMOS transistor is used instead of the junction diode in the read mode. In addition, a problem of being blown for a non-blown eFuse from a read current through the corresponding eFuse OTP cell is solved by limiting the read current to less than $100{\mu}A$ since a voltage is transferred to BL by using an NMOS transistor with the small channel width in the read mode.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.203-212
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• 2019

Along with the recent fourth industrial revolution, the world's manufacturing powerhouses are pushing for national strategies to revive the sluggish manufacturing industry. Moon Jae-in, the government is in accordance with the trend, called 'advancement of science and technology is leading the fourth round of the Industrial Revolution' strategy. Intelligent information technology such as IoT, Cloud, Big Data, Mobile, and AI, which are key technologies that lead the fourth industrial revolution, is promoting the emergence of new industries such as robots and 3D printing and the smarting of existing major manufacturing industries. Advances in technologies such as smart factories have enabled IoT-based sensing technology to measure various data that could not be collected before, and data generated by each process has also exploded. Thus, this paper uses data generators to generate virtual data that can occur in smart factories, and uses them to analyze the cause of the defect in real time and to predict the replacement cycle of the facility.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.823-824
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• 2016

In general, distribution board, panel board and motor control center can be installed over a wide area such as residence of group, building, schools, factories, ports, airports, water service and sewerage, substation and heavy industries that are used to supply converts the voltages extra high voltage into optimal voltage. There are electrical accidents due to rise of contact temperature, loose contact of bus bar, deterioration of the contact resistance, overtemperatue of the bus bars. In this paper, we propose bus bar joints monitoring system with loose connection of bus bar, measuring the joint resistance of busbars and monitoring internal and external heat. The proposed system can be reduced the electrical accidents by maintenance of busbar joints and the temperature of the conductive contact surface of busbars.