Dissertação PPG.EGPSA
URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/3
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127 resultados
Resultados da Pesquisa
Item Sistema de inferência fuzzy para avaliação dos impactos ambientais sobre o aspecto da caminhabilidade(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) TOLEDO, Ulli Guerreiro de; CAMPOS, Paola SoutoThis dissertation investigates the applicability of a fuzzy inference system to evaluate environmental impacts on urban walkability in Manaus, Amazonas. The central problem lies in the need for precise methodologies to measure pedestrian infrastructure quality and guide sustainable public policies. The main objective of the study is to develop a fuzzy inference model capable of classifying the level of pedestrian mobility in different city zones, using the walkability indicators (iCam) from the Institute for Transportation and Development Policy (ITDP). The methodology involved the collection of primary and secondary data, the application of walkability indicators in four city zones, and the modeling of the fuzzy system using MATLAB software. The results demonstrated that the developed model is effective in identifying areas with different levels of walkability, contributing to the formulation of public policies aimed at sustainable urban development. It is concluded that the fuzzy inference system is a useful tool for assessing urban walkability and improving the quality of life in cities.Item Avaliação de imunidade eletromagnética radiada do protocolo IOT LORA(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) RIBEIRO, Jhonatan; SANTOS, Eliton Smith dosIndustrial automation, historically based on wired technologies, has evolved toward wireless solutions, driven by the increasing interconnection of devices and the need to overcome logistical and range limitations. In Industry 4.0, wireless communication plays a central role by integrating sensors, actuators, and control systems into an intelligent production environment. This integration enables real-time data collection and analysis, facilitating automation, optimization, and process flexibility. Despite the benefits, adopting wireless technologies faces significant challenges, particularly electromagnetic interference (EMI), which can compromise the operation of devices and systems. This study presented an analysis of the electromagnetic immunity of the LoRa protocol, using the Heltec V2 board, which incorporates the SX1278 chip, to evaluate its robustness in environments subject to electromagnetic interference. The tests, conducted according to the IEC 61000-4-3 standard, identified critical failure frequencies such as 232.4 MHz, 412 MHz, and 615.2 MHz under different parameter configurations. In these conditions, partial or total communication loss was observed, depending on the intensity of the irradiated field, demonstrating that different configurations can significantly impact the system's immunity. Solutions such as using robust protocols, including LoRaWAN and NB-IoT, spectral spreading techniques, electromagnetic shielding, and filters can mitigate the effects of EMI. Furthermore, future strategies include developing adaptive algorithms to dynamically adjust communication parameters and signal failures, enhancing the resilience and reliability of the LoRa protocol in complex industrial environments. The results of this research contribute to improving wireless communications in Industry 4.0, highlighting LoRa as an essential technology for robust and efficient connectivity.Item Técnicas da indústria 4.0 aplicadas na melhoria do processo de corte dos terminais das baterias de íon-lítio(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) MACIEL, Lincoln Fábio Luiz; NASCIMENTO, Manoel Henrique ReisThe transition to Industry 4.0 has brought significant advances in production processes, where the application of Artificial Intelligence (AI) technologies has transformed entire sectors, including the manufacturing of lithium-ion batteries. This work addresses the development of an intelligent system focused on improving the cutting of battery terminals, using AI algorithms to optimize precision and reduce defect rates. The central problem is the imprecision in cutting the terminals of lithium-ion batteries, leading to a high rate of defective or unusable products. This issue not only increases production costs due to material waste but also negatively impacts the overall efficiency of the manufacturing process. To achieve this goal, a methodology was used for the application of machine learning and real-time data analysis, which enabled automatic adjustments in the cutting process, promoting a flexible and adaptable manufacturing environment. The results obtained from the application of the system indicate a significant reduction in the rate of defective batteries, as well as an increase in the quality and uniformity of the produced terminals. This efficiency gain demonstrates the potential of AI to meet the requirements of quality and precision while simultaneously reducing production costs. The integration of AI technologies in the process of cutting lithium-ion battery terminals not only promotes improvements in quality and sustainability but can also generate a significant competitive advantage for the renewable energy and storage sector in flexible manufacturing processes.Item A proteção ambiental como princípio da gestão pública: uma análise sob a perspectiva do direito administrativo(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) FERREIRA JÚNIOR, Edinaldo Inocêncio; NASCIMENTO, Manoel Henrique ReisEnvironmental protection in public management in the state of Amazonas plays a central role in conserving natural resources and biodiversity, given the global importance of the Amazon region. This study investigated the effectiveness of environmental protection policies implemented in the state, employing a methodological approach based on literature review and document analysis. The objectives included analyzing the application and outcomes of these policies, identifying gaps and challenges in sustainable management, and proposing strategic recommendations to improve practices. Additionally, the cases of the Belo Monte and Balbina hydroelectric plants were examined, highlighting their environmental impacts and implications for public policy formulation in the Amazon region. The findings revealed significant progress but also exposed structural and operational deficiencies, such as ineffective enforcement and poor coordination across governance levels. The proposed recommendations aim to align economic development with environmental preservation, contributing to a more efficient and sustainable public management framework. This research also emphasizes the importance of multidisciplinary and participatory approaches to addressing socio-environmental challenges, ensuring a sustainable future for the Amazon and its populations. Moreover, it offers valuable insights that may serve as a reference for other regions facing similar environmental challenges.Item Sistema integrado para detecção de falhas do processo de montagem de placas utilizando ferramenta de Business Intelligence para maturidade da Indústria 4.0.(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) PAIXÃO, Elisete da Silva; CAMPOS, Paola SoutoThe plate assembly process in a company has a target of producing 2000 plates per day; however, 10% of this production presents failures, compromising the management-defined goals. This study aims to implement an intelligent system for identifying and correcting failures in the board assembly process, based on Business Intelligence, aiming at the evolution of Industry 4.0 practices. The methodology included process mapping, analysis of requirements for implementing the integrated system, and evaluation of the system's effectiveness. The integrated system enabled communication between the various systems in the production process, providing more accurate information to improve usability performance and ensure data security. The implementation of this system represented a significant advancement for the evolution of Industry 4.0 practices, providing improvements in quality, efficiency, and cost reduction.Item Desenvolvimento de um dispositivo inteligente para monitoramento de subestações elétricas integrado à plataforma SGE: uma aplicação da Indústria 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SANTOS JUNIOR, Hélio Andrade dos; ALENCAR, David Barbosa deThe dissertation focuses on the development of a portable device for monitoring electrical substations, integrated with the SGE platform, utilizing Industry 4.0 technologies like the Internet of Things (IoT) and real-time data analysis. The primary objective was to create a solution to identify and mitigate energy losses, optimize consumption, and reduce operational costs. To achieve this, the research employed an inference model based on fuzzy logic and developed a real-time process control (RTPC) framework. The device and its monitoring software were validated through field tests in real substations, showing positive results in reducing losses and improving energy efficiency. This work contributes to the modernization of electrical substation management, aligning with sustainability trends and technological advancements in the energy sector.Item Utilização de Inteligência Artificial e IoT para Inspeção e Detecção de Defeitos em Placas de Circuito Impresso(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SOARES, Karen Kettelen Souza; GUIMARÃES, Gil EduardoThe increasing complexity in the manufacturing of printed circuit boards (PCBs) has posed significant challenges for industries, particularly in the inspection and detection of defects. This study proposes the use of Artificial Intelligence (AI) and the Internet of Things (IoT) as tools to optimize fault detection in PCBs, focusing on reducing human errors and improving operational efficiency. The research is based on the implementation of an automated inspection system, utilizing neural networks for defect classification and IoT sensors for real-time monitoring of manufacturing conditions. The main goal of the dissertation is to develop an efficient analysis model to identify early faults, enhance product quality, and reduce operational costs. The study involved building a functional prototype that integrates AI and IoT, validated in a real production environment. The results showed that the proposed approach could detect defects with higher accuracy, leading to significant improvements in the efficiency of the PCB production line.Item Implementação de práticas sustentáveis: avaliação fuzzy do desempenho e conscientização ambiental em crianças com deficiência(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) BEZERRA, Igor Felipe Oliveira; CAMPOS, Paola SoutoThis study aimed to develop a fuzzy logic-based model to assess the impact of interventions in Adapted Physical Education and Environmental Education on the physical, cognitive, and environmental awareness development of children with disabilities. The need for inclusive educational strategies and accurate tools to capture individual progress underpins this work, which seeks to go beyond traditional assessment approaches. Using scenario simulations that replicate authentic conditions, the study incorporated variables such as motor coordination, strength, endurance, as well as attention and understanding of environmental concepts. Interventions were analyzed using fuzzy rules to assess the expected impacts in physical, cognitive, and environmental awareness dimensions. Although the model was not implemented in a real setting with children, its validation suggests future applicability in practical studies. The results indicate that well-integrated interventions, especially those that balance physical and cognitive development, can lead to substantial improvements. Fuzzy logic proved to be a valuable tool, allowing for detailed and gradual progress assessment. The proposed model is a significant addition to inclusive pedagogical practices and can guide educational policies to better meet the needs of children with disabilities, emphasizing the importance of sustainable environmental education.Item Aplicação de Tecnologia da Indústria 4.0 na Melhoria dos Processos de Injeção Plástica em uma Empresa no Polo Industrial de Manaus(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) FERREIRA, Marcos Sidney Castro; CAMPOS, Paola Souto; LEITE, Jandecy CabralOptimizing lead times ensures that customer deadlines are met, thereby promoting customer satisfaction and loyalty. Additionally, reducing lead time leads to more efficient resource management, directly impacting the reduction of operating costs. This study aimed to implement Industry 4.0 technologies to improve plastic injection processes at a company in the Manaus Industrial Estate (PIM), reducing manufacturing costs, accelerating the lead time for device construction, and optimizing the time for developing new products. The methodology involved data collection on new production processes and the evaluation of the technology's impact on reducing manufacturing costs and improving lead time. This was done by mapping the current process flow, analyzing production loss reports, and examining production performance data. The results show a significant reduction in lead time, from 35 to 9 days. 3D printing also enabled the creation of more complex and precise devices, with less material waste, directly contributing to cost reduction and improved product quality.Item Smart energy: aplicação do sistema fotovoltaico utilizando algoritmos genéticos para tomada de decisão na Indústria 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) WASCHINGTON, Adriana Carneiro; SILVA, Simone daThe global energy transition and the need for energy efficiency in industrial environments are driven by the search for sustainability and the reduction of environmental impacts. This work addresses the application of genetic algorithms in the management of photovoltaic systems within the context of Industry 4.0, highlighting the concept of Smart Energy. The main objective is to investigate the benefits and impacts of this approach on energy efficiency, environmental sustainability, and the reduction of operating costs at the Manaus Industrial Estate (PIM). To achieve the objectives, methods based on computer simulation and analysis of real cases were used. The research included the modeling and development of genetic algorithms capable of optimizing variables such as energy generation, storage, and consumption in photovoltaic systems. Data was collected based on local climatic conditions, energy demand profiles, and industrial operating parameters. The results indicated that the genetic algorithms enabled significant gains in energy efficiency, with an average reduction of 20% in energy waste and 15% in operating costs. In addition, the model developed proved to be effective in adapting to climate variations and industrial demands, reducing dependence on non-renewable sources and greenhouse gas emissions. The conclusion is that integrating photovoltaic systems with genetic algorithms is a promising solution for energy management in Industry 4.0, promoting sustainability and industrial competitiveness, especially in regions with high solar incidence like the Amazon. The research highlights the relevance of technological innovation in the transition to a low-carbon economy.