Desenvolvimento de um protocolo de comunicação para automação de subestações móveis via satélite

One of the most important decisions to turn a substation automatic and no attended it relates to the communication media between this substation and Operation Center. Generally energy companies uses radio or optic fiber, depending of distances and infrastructure of each situation. This rule applies to common substations. Mobile substations are a particular case, therefore they are conceived for use at provisional situations, emergencies, preventive or corrective maintenance. Thus the telecommunication solution used at common substations are not applied so easily to mobile substations, due absence of infrastructure (media) or difficulty to insert the mobile substation data in existing automation network not long. The ideal media must supply covering in a great geographic area to satisfy presented requirements. The implantation costs of this big infrastructure are expensive, however a existing operator may be used. Two services that fulfill that requirements are satellite and cellular telephony. This work presents a solution for automation of mobile substations through satellite. It was successfully implanted at a brazilian electric energy concessionaire named COSERN. The operation became transparent to operators. Other gotten benefits had been operational security, quality in the supply of electric energy and costs reduction. The project presented is a new solution, designed to substations and general applications where few data should be transmitted, but there is difficulties in relation to the media. Despite the satellite having been used, the same resulted can be gotten using celullar telephony, through Short Messages or packet networks as GPRS or EDGE.

Um sistema para gestão do conhecimento em ameaças, vulnerabilidades e seus efeitos

Attacks to devices connected to networks are one of the main problems related to the confidentiality of sensitive data and the correct functioning of computer systems. In spite of the availability of tools and procedures that harden or prevent the occurrence of security incidents, network devices are successfully attacked using strategies applied in previous events. The lack of knowledge about scenarios in which these attacks occurred effectively contributes to the success of new attacks. The development of a tool that makes this kind of information available is, therefore, of great relevance. This work presents a support system to the management of corporate security for the storage, retrieval and help in constructing attack scenarios and related information. If an incident occurs in a corporation, an expert must access the system to store the specific attack scenario. This scenario, made available through controlled access, must be analyzed so that effective decisions or actions can be taken for similar cases. Besides the strategy used by the attacker, attack scenarios also exacerbate vulnerabilities in devices. The access to this kind of information contributes to an increased security level of a corporation's network devices and a decreased response time to occurring incidents

Estudo avaliativo de um algoritmo genético auto-organizável e multiobjetivo utilizando aprendizado de máquina para aplicações de telecomunicações

This paper presents an evaluative study about the effects of using a machine learning technique on the main features of a self-organizing and multiobjective genetic algorithm (GA). A typical GA can be seen as a search technique which is usually applied in problems involving no polynomial complexity. Originally, these algorithms were designed to create methods that seek acceptable solutions to problems where the global optimum is inaccessible or difficult to obtain. At first, the GAs considered only one evaluation function and a single objective optimization. Today, however, implementations that consider several optimization objectives simultaneously (multiobjective algorithms) are common, besides allowing the change of many components of the algorithm dynamically (self-organizing algorithms). At the same time, they are also common combinations of GAs with machine learning techniques to improve some of its characteristics of performance and use. In this work, a GA with a machine learning technique was analyzed and applied in a antenna design. We used a variant of bicubic interpolation technique, called 2D Spline, as machine learning technique to estimate the behavior of a dynamic fitness function, based on the knowledge obtained from a set of laboratory experiments. This fitness function is also called evaluation function and, it is responsible for determining the fitness degree of a candidate solution (individual), in relation to others in the same population. The algorithm can be applied in many areas, including in the field of telecommunications, as projects of antennas and frequency selective surfaces. In this particular work, the presented algorithm was developed to optimize the design of a microstrip antenna, usually used in wireless communication systems for application in Ultra-Wideband (UWB). The algorithm allowed the optimization of two variables of geometry antenna - the length (Ls) and width (Ws) a slit in the ground plane with respect to three objectives: radiated signal bandwidth, return loss and central frequency deviation. These two dimensions (Ws and Ls) are used as variables in three different interpolation functions, one Spline for each optimization objective, to compose a multiobjective and aggregate fitness function. The final result proposed by the algorithm was compared with the simulation program result and the measured result of a physical prototype of the antenna built in the laboratory. In the present study, the algorithm was analyzed with respect to their success degree in relation to four important characteristics of a self-organizing multiobjective GA: performance, flexibility, scalability and accuracy. At the end of the study, it was observed a time increase in algorithm execution in comparison to a common GA, due to the time required for the machine learning process. On the plus side, we notice a sensitive gain with respect to flexibility and accuracy of results, and a prosperous path that indicates directions to the algorithm to allow the optimization problems with "η" variables

Uma arquitetura para sistemas supervisórios industriais e sua aplicação em processos de elevação artificial de petróleo

The using of supervision systems has become more and more essential in accessing, managing and obtaining data of industrial processes, because of constant and frequent developments in industrial automation. These supervisory systems (SCADA) have been widely used in many industrial environments to store process data and to control the processes in accordance with some adopted strategy. The SCADA s control hardware is the set of equipments that execute this work. The SCADA s supervision software accesses process data through the control hardware and shows them to the users. Currently, many industrial systems adopt supervision softwares developed by the same manufacturer of the control hardware. Usually, these softwares cannot be used with other equipments made by distinct manufacturers. This work proposes an approach for developing supervisory systems able to access process information through different control hardwares. An architecture for supervisory systems is first defined, in order to guarantee efficiency in communication and data exchange. Then, the architecture is applied in a supervisory system to monitor oil wells that use distinct control hardwares. The implementation was modeled and verified by using the formal method of the Petri networks. Finally, experimental results are presented to demonstrate the applicability of the proposed solution

Classificação de padrões através de um comitê de máquinas aprimorado por aprendizagem por reforço

Reinforcement learning is a machine learning technique that, although finding a large number of applications, maybe is yet to reach its full potential. One of the inadequately tested possibilities is the use of reinforcement learning in combination with other methods for the solution of pattern classification problems. It is well documented in the literature the problems that support vector machine ensembles face in terms of generalization capacity. Algorithms such as Adaboost do not deal appropriately with the imbalances that arise in those situations. Several alternatives have been proposed, with varying degrees of success. This dissertation presents a new approach to building committees of support vector machines. The presented algorithm combines Adaboost algorithm with a layer of reinforcement learning to adjust committee parameters in order to avoid that imbalances on the committee components affect the generalization performance of the final hypothesis. Comparisons were made with ensembles using and not using the reinforcement learning layer, testing benchmark data sets widely known in area of pattern classification

Desenvolvimento de software para simulação de motores com dispositivos de partida baseada na integração do atp com o toprede

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Roteamento em Redes de Sensores Sem Fios Com Base Em Aprendizagem Por Reforço

The use of wireless sensor and actuator networks in industry has been increasing past few years, bringing multiple benefits compared to wired systems, like network flexibility and manageability. Such networks consists of a possibly large number of small and autonomous sensor and actuator devices with wireless communication capabilities. The data collected by sensors are sent directly or through intermediary nodes along the network to a base station called sink node. The data routing in this environment is an essential matter since it is strictly bounded to the energy efficiency, thus the network lifetime. This work investigates the application of a routing technique based on Reinforcement Learning s Q-Learning algorithm to a wireless sensor network by using an NS-2 simulated environment. Several metrics like energy consumption, data packet delivery rates and delays are used to validate de proposal comparing it with another solutions existing in the literature

Classificação Automática de Modulação Digital com uso de Correntropia para Ambientes de Rádio Cognitivo

Modern wireless systems employ adaptive techniques to provide high throughput while observing desired coverage, Quality of Service (QoS) and capacity. An alternative to further enhance data rate is to apply cognitive radio concepts, where a system is able to exploit unused spectrum on existing licensed bands by sensing the spectrum and opportunistically access unused portions. Techniques like Automatic Modulation Classification (AMC) could help or be vital for such scenarios. Usually, AMC implementations rely on some form of signal pre-processing, which may introduce a high computational cost or make assumptions about the received signal which may not hold (e.g. Gaussianity of noise). This work proposes a new method to perform AMC which uses a similarity measure from the Information Theoretic Learning (ITL) framework, known as correntropy coefficient. It is capable of extracting similarity measurements over a pair of random processes using higher order statistics, yielding in better similarity estimations than by using e.g. correlation coefficient. Experiments carried out by means of computer simulation show that the technique proposed in this paper presents a high rate success in classification of digital modulation, even in the presence of additive white gaussian noise (AWGN)

Extração e Representação de Conhecimento de Séries Temporais de Demanda de Energia Elétrica Usando TSKR

The opening of the Brazilian market of electricity and competitiveness between companies in the energy sector make the search for useful information and tools that will assist in decision making activities, increase by the concessionaires. An important source of knowledge for these utilities is the time series of energy demand. The identification of behavior patterns and description of events become important for the planning execution, seeking improvements in service quality and financial benefits. This dissertation presents a methodology based on mining and representation tools of time series, in order to extract knowledge that relate series of electricity demand in various substations connected of a electric utility. The method exploits the relationship of duration, coincidence and partial order of events in multi-dimensionals time series. To represent the knowledge is used the language proposed by Mörchen (2005) called Time Series Knowledge Representation (TSKR). We conducted a case study using time series of energy demand of 8 substations interconnected by a ring system, which feeds the metropolitan area of Goiânia-GO, provided by CELG (Companhia Energética de Goiás), responsible for the service of power distribution in the state of Goiás (Brazil). Using the proposed methodology were extracted three levels of knowledge that describe the behavior of the system studied, representing clearly the system dynamics, becoming a tool to assist planning activities

Uma Aplicação do Algoritmo QT Clustering para Marcação Colaborativa de Pontos Perigosos em Vias Públicas

This work proposes a collaborative system for marking dangerous points in the transport routes and generation of alerts to drivers. It consisted of a proximity warning system for a danger point that is fed by the driver via a mobile device equipped with GPS. The system will consolidate data provided by several different drivers and generate a set of points common to be used in the warning system. Although the application is designed to protect drivers, the data generated by it can serve as inputs for the responsible to improve signage and recovery of public roads

Controle Robusto Aplicado a Conversor Buck-Boost em Sistemas Fotovoltaicos

The need to implement a software architecture that promotes the development of a SCADA supervisory system for monitoring industrial processes simulated with the flexibility of adding intelligent modules and devices such as CLP, according to the specifications of the problem, it was the motivation for this work. In the present study, we developed an intelligent supervisory system on a simulation of a distillation column modeled with Unisim. Furthermore, OLE Automation was used as communication between the supervisory and simulation software, which, with the use of the database, promoted an architecture both scalable and easy to maintain. Moreover, intelligent modules have been developed for preprocessing, data characteristics extraction, and variables inference. These modules were fundamentally based on the Encog software

Escalabilidade Paralela de um Algoritmo de Migração Reversa no Tempo (RTM) Pré-empilhamento

The seismic method is of extreme importance in geophysics. Mainly associated with oil exploration, this line of research focuses most of all investment in this area. The acquisition, processing and interpretation of seismic data are the parts that instantiate a seismic study. Seismic processing in particular is focused on the imaging that represents the geological structures in subsurface. Seismic processing has evolved significantly in recent decades due to the demands of the oil industry, and also due to the technological advances of hardware that achieved higher storage and digital information processing capabilities, which enabled the development of more sophisticated processing algorithms such as the ones that use of parallel architectures. One of the most important steps in seismic processing is imaging. Migration of seismic data is one of the techniques used for imaging, with the goal of obtaining a seismic section image that represents the geological structures the most accurately and faithfully as possible. The result of migration is a 2D or 3D image which it is possible to identify faults and salt domes among other structures of interest, such as potential hydrocarbon reservoirs. However, a migration fulfilled with quality and accuracy may be a long time consuming process, due to the mathematical algorithm heuristics and the extensive amount of data inputs and outputs involved in this process, which may take days, weeks and even months of uninterrupted execution on the supercomputers, representing large computational and financial costs, that could derail the implementation of these methods. Aiming at performance improvement, this work conducted the core parallelization of a Reverse Time Migration (RTM) algorithm, using the parallel programming model Open Multi-Processing (OpenMP), due to the large computational effort required by this migration technique. Furthermore, analyzes such as speedup, efficiency were performed, and ultimately, the identification of the algorithmic scalability degree with respect to the technological advancement expected by future processors

Uma arquitetura baseada em SCTP e SIP para suporte a aplicações VoIP móveis e a especificação formal do seu módulo de controle

New versions of SCTP protocol allow the implementation of handover procedures in the transport layer, as well as the supply of a partially reliable communication service. A communication architecture is proposed herein, integrating SCTP with the session initiation protocol, SIP, besides additional protocols. This architecture is intended to handle voice applications over IP networks with mobility requirements. User localization procedures are specified in the application layer as well, using SIP, as an alternative mean to the mechanisms used by traditional protocols, that support mobility in the network layer. The SDL formal specification language is used to specify the operation of a control module, which coordinates the operation of the system component protocols. This formal specification is intended to prevent ambiguities and inconsistencies in the definition of this module, assisting in the correct implementation of the elements of this architecture

Análise de desempenho experimental de redes IEEE 802.3.

The Ethernet technology dominates the market of computer local networks. However, it was not been established as technology for industrial automation set, where the requirements demand determinism and real-time performance. Many solutions have been proposed to solve the problem of non-determinism, which are based mainly on TDMA (Time Division Multiple Access), Token Passing and Master-Slave. This work of research carries through measured of performance that allows to compare the behavior of the Ethernet nets when submitted with the transmissions of data on protocols UDP and RAW Ethernet, as well as, on three different types of Ethernet technologies. The objective is to identify to the alternative amongst the protocols and analyzed Ethernet technologies that offer to a more satisfactory support the nets of the industrial automation and distributed real-time application

Controle fuzzy espacialmente diferenciado para um sistema de irrigação

Traditional irrigation projects do not locally determine the water availability in the soil. Then, irregular irrigation cycles may occur: some with insufficient amount that leads to water deficit, other with excessive watering that causes lack of oxygen in plants. Due to the nonlinear nature of this problem and the multivariable context of irrigation processes, fuzzy logic is suggested to replace commercial ON-OFF irrigation system with predefined timing. Other limitation of commercial solutions is that irrigation processes either consider the different watering needs throughout plant growth cycles or the climate changes. In order to fulfill location based agricultural needs, it is indicated to monitor environmental data using wireless sensors connected to an intelligent control system. This is more evident in applications as precision agriculture. This work presents the theoretical and experimental development of a fuzzy system to implement a spatially differentiated control of an irrigation system, based on soil moisture measurement with wireless sensor nodes. The control system architecture is modular: a fuzzy supervisor determines the soil moisture set point of each sensor node area (according to the soil-plant set) and another fuzzy system, embedded in the sensor node, does the local control and actuates in the irrigation system. The fuzzy control system was simulated with SIMULINK® programming tool and was experimentally built embedded in mobile device SunSPOTTM operating in ZigBee. Controller models were designed and evaluated in different combinations of input variables and inference rules base