• Title/Summary/Keyword: Electric Fire

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A Study of Industrial Patients from Selected General in the Kyung Pook and Taegu City areas (일부지역 산업재해환자 실태 조사 연구 -대구${\cdot}$경북지역 일부 종합병원 중심으로-)

  • Huh, Choon-Bok
    • The Journal of Korean Physical Therapy
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    • v.3 no.1
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    • pp.151-174
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    • 1991
  • The purpose of this study is to research the actual conditions of industrial accident patients and to produce worker satisfaction and a rational and effective counter measure plan. Direct interviews with 179 cases (in and out patients) were carried out during a three month period from April to July 1990, at six hospitals : two general hospitals Sun Lin and Sung Mo in Po Hang, and four general hospitals in Taegu : Kyung pooh University Hospital, Dong San Medical Center, Young Nam Medical Center and Catholic Hospital. The results of this study are summarized as fellows : 1. Among the 179 cases, $51.6\%$ were male and $48.4\%$ were female. The two largest age groups were 30-39, $31.8\%$ and 20-29, $27.4\%$. Among the 179 cases, $51.6\%$ were married, the largest family number was 2 to 3, $41.1\%$ and 4 to 5, $25.6\%$. Educationally, graduation from high school was the largest group, $46.4\%$ among ,the patients, followed by middle school and primary school. The largest group income level was from 40-69 만원, $45.2\%$. The largest group of patients who worked over 50 hrs. a week was $52.0\%$. The largest group of patients who worked less than 1 year was $44.7\%$, of the patients in work places of less than 100 people, $60.3\%$ were injured and in work places of 100-299 people, $20.1\%$ were injured. In manufacturing, the largest group injured was $55.3\%$, the next group was transport, storage, communication. The largest group of production workers injured was $40.2\%$. 2. The cause of injury in the largest group was facility problems, $33.5\%$. The next group was unsafe habits, $30.2\%$ ; a lack of safety knowledge, $17.9\%$ ; and insufficient supervision, $12.3\%$. The 30-39 year age group head the highest number of injuries, $40.4\%$ ; work places with more than 10 years of work, $44.4\%$ ; work places with more than 1000 people, $56.3\%$ and mining accidents, $80.0\%$. Among. these groups the highest cause of injury was due to facility problems. 3. The accident pattern showed machinery injuries $28.5\%$ as the largest group, followed by falls & falling objects $17.3\%$, fire & electric $15.1\%$, strucke by an object $14.5\%$, followed by overaction and vehicular accidents. The accident pattern showed $46.4\%$ among workers over the 50 year age group, workers in the 5-10 year group, $50.0\%$ ; places employing more than 1000 workers, $35.3\%$ ; construction $73.7\%$, and construction workers $57.1\%$, among these fall & falling objects caused the greatest number of injuries. 4. The largest group of injuries was fractures $54.8\%$, trauma $14.5\%$, amputation $11.7\%$, open wound, and burns. The largest number of fractures occurred in people in the 30-39 year age group, $63.2\%$ : over 10 years of work, $55.0\%$ ; in work places of 300-490 people, $63.6\%$ ; construction $63.2\%$ and general workers $57.2\%$. 5. The largest group of injuries was upper extremity $45.3\%$, lower extremity $24.0\%$, trunk $18.5\%$ and head or neck $12.2\%$. Of these groups, upper extremity injuries were the highest in those less than 20 years old $75.0\%$, less than 1 year or work $59.5\%$, in work places of 500-999 people $60.0\%$, manufacturing $56.6\%$ and production workers $55.6\%$. 6. Periods of injury showed 34 people injured in September, to be the largest followed by October, 32 ; August, 22 people : July, 19 people and the lowest December, 2 people. During the week, Friday had the largest group injured, 35 people ; followed by Saturday, 26 people and the lowest was Wednesday, 17 people, During the day 1400 hours had the largest group injured, 38 people ; followed by 800 hours, 31 people. 7. On a basis of 5 as the highest mark, the average, according to worker satisfaction showed facility safety 3.55, work environment 3.47, income 3.44, job 3.21 and treatment 2.98. 8. The correlation between general characteristics and injury showed that age was directly correlated to the duration of work (r=2591) p<0.01, age was directly correlated to industry (r=2311) p<0.01, and the duration was directly correlated to occupation (r=4372) p<0.001.

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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.