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Call number: LD2668 .T4 1986 W34 / Master of Science / Electrical and Computer Engineering

In the event of a nuclear power plant accident, fission products could be released into the atmosphere potentially affecting the health of local citizens. In order to triage the possibly large number of people impacted, a detection device is needed that can acquire data quickly and that is sensitive to internal contamination. The portal monitor TPM-903B was investigated for use in the event of a fission product release. A list of fission products released from a Pressurized Water Reactor (PWR) was generated and separated into two groups--Group 1 (gamma- and beta-emitting fission products) and Group 2 (strictly beta-emitting fission products.) Group one fission products were used in the previously validated Monte Carlo N-Particle Transport Code (MCNP) model of the portal monitor. Two MIRD anthropomorphic phantom types were implemented in the MCNP model--the Adipose Male and Child phantoms. Dose and Risk Calculation software (DCAL) provided inhalation biokinetic data that were applied to the output of the MCNP modeling to determine the radionuclide concentrations in each organ as a function of time. For each phantom type, these data were used to determine the total body counts associated with each individual gamma-emitting fission product. Corresponding adult and child dose coefficients were implemented to determine the total body counts per 250 mSv. A weighted sum of all of the isotopes involved was performed. The ratio of dose associated with gamma-emitting fission products to the total of all fission products was determined based on corresponding dose coefficients and relative abundance. This ratio was used to project the total body counts corresponding to 250mSv for the entire fission product release inhalation--including all types of radiation. The developed procedure sheets will be used by first response personnel in the event of a fission product release.

[pt] Em grandes indústrias, como construção, metalúrgica e petróleo, trabalhadores são continuamente expostos a vários tipos de perigos em seus locais de trabalho. Segundo a Organização Internacional do Trabalho (OIT), anualmente ocorrem cerca de 340 milhões de acidentes de trabalho. Equipamentos de Proteção Individual (EPI) são utilizados para garantir a proteção essencial da saúde e segurança dos trabalhadores. Com isto, há um grande esforço para garantir que esses tipos de equipamentos sejam usados de maneira adequada em ambientes de trabalho. Em tais ambientes, é comum ter câmeras de circuito fechado de televisão (CFTV) para monitorar os trabalhadores, pois essas podem ser usadas para verificar o uso adequado de EPIs. Alguns trabalhos presentes na literatura abordam o problema de verificação automática de EPIs usando imagens de CFTV como entrada; no entanto, muitos destes trabalhos não conseguem lidar com a detecção de uso seguro de múltiplos equipamentos e outros até mesmo pulam a fase de verificação, fazendo apenas a detecção. Neste trabalho, propomos um novo componente de análise de segurança cognitiva para um sistema de monitoramento. Este componente atua para detectar o uso adequado de EPIs em tempo real, usando fluxo de dados de câmeras de CFTV comuns. Construímos este componente do sistema com base nas melhores técnicas de Aprendizado Profundo voltadas para a tarefa de detecção de objetos. A metodologia proposta é robusta com resultados consistentes e promissores em termos da métrica Mean Average Precision (mAP) e pode atuar em tempo real. / [en] In large industries, such as construction, metallurgy, and oil, workers are continually exposed to various hazards in their workplace. Accordingly to the International Labor Organization (ILO), there are 340 million occupational accidents annually. Personal Protective Equipment (PPE) is used to ensure the essential protection of workers health and safety. There is a great effort to ensure that these types of equipment are used properly. In such an environment, it is common to have closed-circuit television (CCTV) cameras to monitor workers, as those can be used to verify the PPE s proper usage. Some works address this problem using CCTV images; however, they frequently can not deal with multiples safe equipment usage detection and others even skip the verification phase, making only the detection. In this paper, we propose a novel cognitive safety analysis component for a monitoring system. This component acts to detect the proper usage of PPE s in real-time using data stream from regular CCTV cameras. We built the system component based on the top of state-of-art deep learning techniques for object detection. The methodology is robust with consistent and promising results for Mean Average Precision (mAP) and can act in real-time.

In my thesis I go about principles and methods of security systems, then I go about analysis of avalaible types of security systems (EPS, EZS and CCTV). I have written about their possibile use in design security building. I have described levels of project documentation and its different parts including process service of production a project documentation for security system. I used all knowledges in the end of my thesis, when I designed and integrated security system for a special building.