• Title/Summary/Keyword: 화재판정

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A Study of Evaluation Certification on Electronic Power u-IT Convergence Equipment (전력 u-IT 융복합화 기기의 평가와 인증 연구)

  • Yi, Jeong-Hoon;Park, Dea-Woo;Kim, Eung-Sik;Kim, Hong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.11
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    • pp.2433-2440
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    • 2009
  • Whole system and network for supply of electric power and electricity safety are essential element. Electricity safety technology need rating for product and research about certification as product development that is done electric power u-IT cotton flannel mixed with development of u-IT, u-City base technology consists. Study on serve to develop electricity safety integration supervision system to apply product to u-City electric power appliance and cotton flannel mixed of u-IT appliance, Connection badness sensing instrument made device built-in electric power u-IT cotton flannel mixed in outlet that is used most in electric power appliance terminal. Using sensor on ZigBee, RFID performance estimation of communication module about function of product for remote safety check of electricity safety integration supervision system. A performance experiment and estimation in electric leakage, high voltage, Arc, fire detection diagnosis system and certification KS, electricity safety about product that get fitness finding.

Online Game Identity Theft Detection Model based on Hacker's Behavior Analysis (온라인게임 계정도용 탐지모델에 관한 연구)

  • Choi, Hwa-Jae;Woo, Ji-Young;Kim, Huy-Kang
    • Journal of Korea Game Society
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    • v.11 no.6
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    • pp.81-93
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    • 2011
  • Identity theft happens frequently in popular MMORPG(Massively Multi-player Online Role Playing Games) where profits can be gained easily. In spite of the importance of security about identity theft in MMORPG, few methods to prevent and detect identity theft in online games have been proposed. In this study, we investigate real identity theft cases of an online game and define the representative patterns of identity theft as the speedy type, cautious type, and bold type. We then propose the automatic identity theft detection model based on the multi-class classification. We verify the system with one of the leading online games in Korea. The multi-class detection model outperforms the existing binary-class one(hacked or not).

A Study on Measurements of Autoignition and Activation Energy of Superabsorbent Polymers (고흡수성 중합체의 자연발화와 활성화에너지 측정에 관한 연구)

  • Jong-Man Heo;Jae-Wook Choi
    • Journal of the Society of Disaster Information
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    • v.19 no.2
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    • pp.292-304
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    • 2023
  • Purpose: This study was conducted to obtain experimental data for the establishment of preventive measures against fire, as large and small fire accidents occur at production and storage sites of superabsorbent polymers developed for the convenience of daily life. Method: The sample container was fixed at 0.2m in both length and width, and was shaped into a rectangular cuboid with heights of 3cm, 5cm, 7cm, and 14cm to access an infinite flat plane. The sample container was fixed in the center of a thermostatic bath that was heated to a predetermined temperature according to a preset temperature control program. If the central temperature of the sample rose more than 20℃ above the set temperature, it was determined to have 'ignited', and if it remained similar to the set temperature, it was determined to have 'unignited'. Result: The critical autoignition temperature was calculated to be 212.5℃ for a sample container with a height of 3cm, 202.5℃ for 5cm, 192.5℃ for 7cm, and 177.5℃ for 14cm. The ignition induction time to reach the highest temperature was approximately 42hours for 3cm, 91hours for 5cm, 151hours for 7cm, and 300hours for 14cm. Conclusion:① As the size of the sample container increased, the autoignition temperature decreased and the ignition induction time to reach the highest temperature increased. ② The apparent activation energy was calculated to be 39.30kcal/mol, with a correlation of 99.5%.

Analysis of Applicability of Rapid Hardening Composite Mat to Railway Sites (초속경 복합매트의 철도현장 적용성 분석)

  • Jang, Seong Min;Yoo, Hyun Sang;Oh, Dong Wook;Batchimeg, Banzragchgarav;Jung, Hyuk Sang
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.44 no.1
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    • pp.109-116
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    • 2024
  • The Rapid Hardening Composite Mat (RHCM) is a product that improves the initial strength development speed of conventional Geosynthetic Cementitious Composite Mats (GCCM). It offers the advantage of quickly securing sufficient strength in railway slopes with insufficient formation level, and provides benefits such as preventing slope erosion and inhibiting vegetation growth. In this study, an analysis of the practical applicability of RHCM in railway settings was conducted through experimentation. The on-site applicability was assessed by categorizing it into fire resistance, durability, and stability, and conducting combustibility test, ground contact pressure test, and daily displacement analyses. In the case of South Korea, where a significant portion of the territory is composed of forested areas, the prevention of slope fires is imperative. To analyze the fire resistance of RHCM, combustibility tests were conducted as an essential measure. Durability was assessed through ground contact pressure tests to analyze the deformation and potential damage of RHCM caused by the inevitable use of small to medium-sized equipment on the construction surface. Furthermore, daily displacement analysis was conducted to evaluate the structural stability by comparing and analyzing the displacement and behavior occurring during the application of RHCM with railway slope maintenance criteria. As a result of the experiments, the RHCM was analyzed to meet the criteria for heat release rate and gas toxicity. Furthermore, the ground contact pressure was observed to be consistently above 50 kPa during the curing period of 4 to 24 hours under all conditions. Additionally, the daily displacement analyzed through field site experiments ranged from -1.7 mm to 1.01 mm, confirming compliance with the criteria.

Development of deep learning network based low-quality image enhancement techniques for improving foreign object detection performance (이물 객체 탐지 성능 개선을 위한 딥러닝 네트워크 기반 저품질 영상 개선 기법 개발)

  • Ki-Yeol Eom;Byeong-Seok Min
    • Journal of Internet Computing and Services
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    • v.25 no.1
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    • pp.99-107
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    • 2024
  • Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.