Search

Este artigo tem como objetivo apresentar o problema da evasao como um desafio para a promocao da democratizacao do ensino superior publico. Numa visao historica da educacao brasileira, pode-se verificar que a educacao superior sempre foi privilegio da elite. Com o objetivo de mudar essa situacao, democratizando assim o ensino superior publico para todas as camadas sociais, o Governo Lula criou programas de expansao, como o Programa Expandir e o REUNI. Porem, tais programas ainda sao insuficientes, considerando o total da populacao brasileira que possui graduacao. A importância do estudo da evasao como um obstaculo para a democratizacao do ensino reside na possibilidade de elaboracao de propostas que, ao minimizar a evasao, indiretamente contribuam para a democratizacao do ensino superior. Diante da complexidade do assunto, decidiu-se primeiramente fazer um levantamento da bibliografia academica referente aos temas da democratizacao e da evasao, para depois entao fazer uma busca sobre uma possivel relacao entre esses dois fenomenos. A metodologia consistiu em pesquisa bibliografica. Como resultado, entende-se que o combate a evasao e necessario para se alcancar a democratizacao do ensino superior em seu sentido total - a ampliacao da oferta de vagas, mais a garantia de apoio ao aluno para que ele consiga concluir o curso de graduacao no qual ingressou. Ainda, entende-se que em que pese os esforcos do Governo por meio do Expandir, do REUNI e do PNAES, ainda ha um longo caminho a ser percorrido para que a democratizacao do ensino superior seja uma realidade no Brasil.

Goal: Propose hybrid heuristics combining VNS and Tabu Search (TS) for adding adaptability to University Course Timetabling Problem (UCTTP) resolution. Design / Methodology / Approach: VNS and TS metaheuristics were used isolated and in combination in order to verify which shape acquired the best solutions. Those heuristics were verified using constraints found at two undergraduate courses of Federal Fluminense University. Effectiveness is verified by comparing them with solutions manually elaborated by undergraduate course coordinators. Results: The computational results showed the efficiency of the hybrid VNS-TS heuristics, with emphasis on the heuristic VNS-TS1, which uses tabu search as local search method. Practical implications: The hybrid heuristic VNS-TS1 has been in use since the first half of 2019 by some of UFF undergraduate courses coordinations. Even though each university has specific constraints on its timetable, the use of the proposed heuristics is possible because it has been developed using the framework FINESS that allows for the easy insertion and removal of constraints. Originality / Value: We have the following innovative contributions: Development of heuristics with a high degree of adaptation to the needs of educational institutions; Two proposed VNS-TS heuristics; Proposed VND-TS improvement method.

This paper proposes a new methodology for solving the scheduling problem of center pivot system irrigation timetable based on the Luus–Jaakola optimization method. The new methodology considers as influencing factors the water and energy consumed and the limit of the available flow to the areas that are being irrigated. The methodology allows the implementation of a non-linear model during an acceptable time scenario as it reduces the number of parameters that must be estimated in the optimization problem. The numerical results show that the programmed timetables, which consider a large number of parameters, were successfully processed.

The time required for a vehicle to travel different routes in the daily traffic of large cities varies and changes constantly, impacting the daily lives of everyone dwelling in those cities. Trying to predict such time is essential to evolve in understanding the behavior of vehicle traffic. On the other hand, due to the vast amount of data generated in this context, it is necessary to use new ways of dealing with this problem. This paper presents an exploratory analysis of the behavior of batch and stream learning algorithms for predicting the trip duration time for vehicles going through different routes. We understand that batch learning algorithms are not necessarily adequate for being used in stream mining situations. However, we would like to have a testbed to analyze the behavior of stream learning algorithms. For our experimental analysis, we used real data from three specific routes. The results show that the use of data stream learning for this domain yields promising results.

Smart cities should be built on harmonious relationships with the environment, with rational measures to use environmental resources in many ways. Resources essential to human life such as water and energy must have special attention. With the advance of technologies use, human being becomes one of the main promoters of information and solutions for smart cities. Stream machine learning is an approach where data becomes available sequentially, called data stream, which adapts the prediction model with fixed window-sized data stream. The objective of this work is to analyze the effectiveness of buildings energy consumption using stream learning. To achieve this goal, MOA 11 Massive Online Analysis tool was used to evaluate the performance of the selected stream learning algorithms on an an open database. Batch algorithms were also used as testbed, to analyze the performance of stream algorithms related to them. Our work shows that stream learning can show good results in this scenario.

Intelligent transport support systems have had a major impact on people's urban mobility. In large urban centers, transportation services still need ways to optimize vehicle supply in certain areas, according to the demand in each of them. Optimized distribution of on-demand taxi services can be part of an intelligent urban mobility plan, causing direct impacts on urban traffic, improving transport accessibility, improving safety at taxi standpoints by reduced waiting times, reduce transportation fare etc. Many vehicle-mounted sensors currently generate real-time information that is not used for processing and generating information with value. This paper proposes a Taxi Demand Forecasting methodology using stream machine learning algorithms that tackle concept drift detection on taxi data stream. A real data source made available on the New York open platform feeds a stream learning model, constructed using the Massive Online Analysis (MOA) tool - a framework for data stream mining. The stream model shows promising results in forecasting taxi demand, reaching 78% accuracy. Despite using data from a specific city, the methodology and results of this work can contribute to a more proactive demand management in other cities.

Uma valvula de alivio de pressao e seguranca protege os equipamentos de processo e tubulacoes quanto a sobre pressoes, garantindo que estes nao operem em pressoes superiores as estabelecidas em projeto, visando a seguranca operacional. Este trabalho apresenta a aplicacao da metodologia AHP na identificacao dos fatores operacionais e de projeto que mais impactam no funcionamento das valvulas de alivio de pressao e seguranca, juntamente com a abordagem AHP ratings que possibilitou a analise de um numero de valvulas maior do que seria possivel utilizando o AHP tradicional. Este metodo proposto gera um indice para cada valvula analisada baseada na sua importância operacional e serve de embasamento para o plano de manutencao preventiva da unidade. Atraves deste indice, as valvulas foram classificadas em 03 grupos, cujos intervalos de inspecao foram fixados devido a sua criticidade. Estes intervalos atribuidos teriam um potencial de reducao de custos, devido ao aumento da media de prazos de inspecao, porem com reducao em alguns casos para valvulas mais importantes, aumentando a confiabilidade e seguranca da planta de processo.

The Brazilian carnival samba schools' parade is one of the biggest popular shows in the planet, currently considered a Brazilian mark. Yearly, each carnival association makes a new carnival, building complex structures of floats and costumes in a short period. Although, the costume production scheduling is still performed manually. The purpose of this work is to propose different heuristic solutions for the addressed problem, based on the metaheuristics VNS, GRASP and Simulated Annealing and in hybrid versions of these metaheuristics. In addition to these heuristics, another innovative contribution is a mixed linear integer programming model proposed for the problem. Computational experiments highlight the efficiency of hybrid VNS heuristics, with emphasis on the VNS-GRASP that combines the strategy of multiple restarts of GRASP with the flexibility of VNS search.

Goal: to propose a hybrid method, combining AHP and Fuzzy, for project portfolio management. Originality/value: this paper meets the positive characteristics of both methods, adequately weighing the criteria and contemplating process subjectivity. Limitations that exist in both methods, such as the maximum number of alternatives and the difficulty of inserting new alternatives at the end of the process, are overcome. Design/methodology/approach: the AHP is applied for determining the criteria weights and fuzzy is used to compare the alternatives for each criterion. Results: the results show that the proposed hybrid method allows the ranking of many alternatives and provides higher reliability for decision makers. It must be noted that the system is fed by performance indicators, which minimize the subjectivity in decision-making—an important characteristic in the technology management. Moreover, the results provide the possibility of changing the number of projects at any time without influencing the outcome. Practical implications: the proposed hybrid method can be used in different problems that have many alternatives or subjectivity.

This work proposes a collaborative approach for solving the university course timetabling problem (UCTP). A prototype was developed and used for a computer sciencecourse at the Federal Fluminense University in Brazil. The main idea is that students, professors, and course coordinators contribute collaboratively to course timetabling through an app. These contributions employ heuristics, which is responsible for timetabling to improve the solution to the problem. Results and future works are described herein.

O segmento de óleo e gás executa suas etapas através de unidades marítimas de exploração, produção, armazenamento e suporte. Para que este processo ocorra, é imprescindível a presença, a bordo, do serviço de Hotelaria Marítima. Este serviço é prestado em plataformas ou navios e provê aos tripulantes das embarcações alimentação, higiene e conforto adequados à permanência desses a bordo por longos períodos de tempo. Devido ao rápido crescimento, a empresa objeto deste estudo tem declinado em algumas propostas de possíveis clientes por entender que está em fase de estruturação, tendo atingido o número máximo de contratos. Entretanto, notou-se que algumas propostas recebidas ofereceriam melhores resultados que alguns contratos já existentes, tendo em vista variáveis operacionais e de custo em etapas similares da operação entre cada cliente. O objetivo deste trabalho é apoiar a tomada de decisão gerencial e determinar, através do Método AHP, qual dos cinco contratos com resultados similares, porém com a menor margem de contribuição para a empresa, seria passível de substituição, levando-se em consideração critérios operacionais que impactam no resultado destes contratos. A validação dos dados se deu através da ferramenta Expert Choice®. O método AHP mostrou-se eficaz para auxiliar à tomada de decisão gerencial, trazendo agilidade e melhor embasamento para a decisão, através de análise em equipe, holística e inovadora para a estruturação hierárquica do problema estudado.

Ensuring protection in computer networks is an increasingly difficult task because of the sheer number and variability of threats currently encountered. Intrusion detection systems (IDSs) is usually used to improve the security of information in computers networks, including any content that has value to a person or company. IDS monitor computers or networks to identify malicious activity or unauthorised access. An open issue is how much data is necessary for constructing models for predicting invasion in wireless networks, specially considering that are some scenarios that dataset is not promptly available. Our approach should consider constructing classifiers given a dataset and, as the dataset grows, new classifiers are constructed. Other strategy is explore stream learning algorithms that adapt models along the time. In addition to studying the applicability of stream learning algorithms. This work aims to investigate whether in terms of processing time, stream algorithms are more efficient than batch ones.

Two main characteristics of multi-label dataset are cardinality and density, related to the number of labels of (each instance of) a multi-label dataset. The relation between these characteristics and multi-label learning performance has been observed with different datasets. However, the difference in domain dataset attributes also interfere on multi-label learning performance. In this work, we use a real dataset, named The Million Song Dataset, available in the internet, which presents the property of having too many labels associated to their instances (songs), as well as so many instances. In this work we present the processed datasets used to conduct our experiments, and we also describe our experiments results.

Global competition have made companies optimize your costs, including inventory costs. In the oil & gas industry, the current scenario of profits reduction forces the reduction of costs and optimization of resources. In this context, the supply chain management is fundamental for process optimization and the reduction of acquisition and storage costs. However, the management of spare parts inventory is still a challenge due to the complexity of the issue, which deals with erratic and intermittent demand items. Thus, the current work presents a study of the inventory magement of turbomachinery spare parts of an oil & gas company workshop. An exploratory research was carried out about the problem and real data were collected in order to elaborate a discrete simulation model to indicate better policies and methods of workshop inventory management. Alternative scenarios were experimented through simulation and significant improvement of service levels was achieved through the use of Croston and SES forecasting methods. It can be concluded that these forecasting methods can increase the availability of equipments without increasing inventory costs.

Este artigo apresenta o desenvolvimento e a construção de um sistema inteligente para o processamento de eventos dentro de uma subestação. Este sistema é baseado em redes neurais artificiais e sistemas especialistas, em que a saída representará a resposta a cada padrão de eventos enviados pelo sistema de controle e aquisição de dados, após a ocorrência de um distúrbio na subestação. Esta resposta irá auxiliar o operador na identificação do(s) componente(s) defeituoso(s) após o distúrbio, podendo ser analisado como um problema de reconhecimento de padrões. Também é descrito o desenvolvimento do protótipo, além dos testes realizados para validação da metodologia proposta.

This paper proposes a genetic algorithm-based methodology for network parameter estimation and correction. Network parameter errors may come from many different sources, such as: imprecise data provided by manufacturers, poor estimation of transmission lines lengths and changes in transmission network design which are not adequately updated in the corresponding database. Network parameter data are employed by almost all power system analysis tools, from real time monitoring to long-term planning. The presence of parameter errors contaminates the results obtained by these tools and compromises decision-making processes. To get rid of single or multiple network parameter errors, a methodology that combines genetic algorithms and power system state estimation is proposed. Tests with the IEEE 14-bus system and a real Brazilian system are performed to illustrate the proposed method.

Neste trabalho foi desenvolvido um modelo de simulação para o Sistema de Corte, Carregamento e Transporte da cana-de-açúcar (conhecido como CCT) das frentes de corte até uma usina localizada no norte do Estado do Rio de Janeiro. O objetivo do modelo foi analisar parâmetros do sistema, a partir do carregamento de cana-de-açúcar, sendo estes influenciados por operações de descarregamento na usina. Foi avaliado, então, o impacto do tempo de descarregamento da cana na usina e as suas implicações no sistema de corte, carregamento e transporte, considerando diferentes cenários.

Este trabalho apresenta a aplicação de um modelo neuro-fuzzy para o processamento de alarmes e localização de defeitos em sistemas de potência. Diferentes técnicas são investigadas para o estabelecimento de relações fuzzy entre padrões de alarme e defeitos no sistema. Uma base de dados contendo as relações fuzzy é empregada para o treinamento de redes neurais artificiais. Os vetores de entrada das redes neurais são constituídos por padrões de alarmes associados a diferentes eventos e cada neurônio de saída é responsável por estimar o grau de pertinência de um componente específico na classe de componentes defeituosos. Testes são realizados utilizando um sistema de 7 barras e também um sistema real.

This work presents a methodology that combines the use of artificial neural networks and fuzzy logic for alarm processing and identification of faulted components in electrical power systems. Fuzzy relations are established and form a database employed to train artificial neural networks. The artificial neural networks inputs are alarm patterns, while each output neuron is responsible for estimating the degree of membership of a specific system component into the class of faulted components. The proposed method allows good interpretation of the results, even in the presence of difficult corrupted alarm patterns. Tests are performed with a test system and with part of a real Brazilian system.

This paper proposes efficient methods for correcting suspicious power system network parameters. State estimation is used for tackling the parameter estimation problem. The proposed methods explore the concept of irrelevant/barely relevant branches to eliminate/mitigate temporarily the participation of suspicious parameters in the state estimation process, until the suspicions are cleared up. Different situations, e.g. the occurrence of single/multiple parameter errors under unavailability of measurements, are handled. The methods distinguish themselves by the construction of a parameter estimation algorithm which does not modify an existing state estimation process, but interacts with it. Results with the IEEE-14 bus system, benchmark network for state estimation studies, and with a real power system are presented to illustrate the application of the proposed methods.

This work presents an optimal meter placement methodology for real-time power distribution systems monitoring. The proposed methodology is flexible and enables to take into account aspects such as: different network configurations, use of pseudomeasurements, already existent meters and remote terminal units, among others. The Genetic algorithms technique is employed to achieve a trade-off between investment costs and real-time monitoring capability.

This work investigates the construction of fuzzy relations for alarm processing and fault location in electrical power systems. Several data aggregation classes are tested and compared. Fuzzy relations are established with the aid of the knowledge on protection devices operation for faults involving different system components. Tests are performed with a 7-bus test system and with part of a real Brazilian power system.

Straightforward, efficient, offline methods for dealing with suspicious power network branch parameters are proposed. State estimation (SE) is the natural tool for tackling the parameter estimation problem. The proposed methods explore the concept of irrelevant/barely relevant branches to eliminate/mitigate temporarily the participation of suspicious parameters in the SE process, until the suspicions are remedied. Strategies are built to handle different situations, such as occurrence of single or multiple network parameter errors and redundancy reduction of available measurements. Suspicious parameter values can be corrected through procedures which interact with an existing conventional state estimator. This is achieved according to different strategies, depending on the situations previously described. Results with the IEEE-14 bus test system illustrate the application of the proposed methodology.