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Voltage magnitudes in transmission power systems must be kept within the limits defined by regulatory agencies for safe operations. Thus, this paper proposes an approach to determine and apply a sequence of adjustments to control the voltage magnitude in such systems. The adjustments follow two methodologies based on a reduced Jacobian matrix (J qv ) of the system power balance equations. Computational simulations made in the IEEE (Institute of Electrical and Electronic Engineers) 57-bus system evaluated the proposed approach for operational scenarios with voltage magnitude violations. The results obtained provide the reactive power dispatch to correct voltage magnitude violations, voltage profiles and active power losses as the recommended adjustments are executed, showing that the control methodologies were able to manage the test system across different states without voltage magnitude violations.

The hydrothermal coordination can be defined as a problem to determine the optimum usage of the hydroelectric and thermoelectric resources available during a period. In hydrothermal generation systems with a predominance of hydroelectric power plants, like in the Brazilian system, the problem consists in replacing the thermal generation by hydropower generation to minimize the system operational costs. Therefore, this paper presents a model based on Particle Swarm Optimization, and Network Flow applied to the problem of hydrothermal coordination. The goal is to determine an optimal operational strategy for the reservoirs of the hydroelectric power plants considering each hydroelectric power plant separately, its operational constraints and guaranteeing the applicability of the solutions, in order to minimize the operating cost of the system. The proposed approach is compared with four other optimization methods: a Genetic Algorithm (GA), a model based on Genetic Algorithms and the Takagi-Sugeno Fuzzy Inference System (GA+Fuzzy), a model based only on PSO and an optimization algorithm that employs Network Flow and Reduced Gradient (NF+RG). A hydroelectric system composed of three hydroelectric power plants and six different hydrological scenarios were used to test the algorithms. The primary objective was to illustrate the viability and applicability of the proposed algorithm, and, based on the obtained results, show the efficacy and energy gains that are possible.

In sensor clouds environments, the provisioning process is a crucial task since it is responsible for selecting physical sensors nodes that will be allocated to compose virtual sensors. In literature, most works consider the allocation of all sensors within the region of interest. However, this approach causes serious problems such as wasted energy consumption. Therefore, the objective of this paper is to present ACOSIM, an approach to minimize the overall sensor cloud energy consumption by selecting only a subset of sensor nodes to compose the virtual sensors. Results from initial experiments show that the approach reduces the sensor cloud energy consumption the by 73.97%, providing a solution to be considered in sensor cloud scenarios.

Pervasive mobile games utilise contextual data about players and their environment to explore new means of interaction and enhance the gaming experience. However, the inherent imperfection of contextual data acquisition poses a challenge for developers and designers of pervasive games. In these games, both sensor inaccuracies and uncertainties need to be identified and properly handled to prevent disrupting the gaming experience. This paper presents a model for the development of pervasive mobile games based on fuzzy systems. It supports the use of fuzzy set theory to represent contextual uncertainty and applies fuzzy logic to design game rules. A pervasive mobile game, called Radar, is presented to showcase the model's applicability; the game exploits contextual data from GPS and motion sensors to control the animation frequency of a simulated radar using a type-I Mamdani fuzzy inference system. Thus, the proposed model is capable of handling sensor inaccuracies while providing an intuitive approach to the design of pervasive games.

The Demand Response (DR) program is used by public electric utilities to encourage consumers to change their consumption profiles in order to improve the reliability and efficiency of the electric power system (EPS). However, operational particularities due to different categories of residential appliances, consumer satisfaction and comfort are not usually taken into consideration when designing a DR model, since the main aim is to minimize electricity costs. This article presents an optimized DR model for residential consumers, which is based on the real-time price (RTP) of electricity to minimize electricity costs associated with consumption as well as considering the operational aspects of the residential appliances. The results of the inconvenience values show that the different categories of residential appliances that were taken into consideration in this work did not undermine the satisfaction/comfort of the consumer.

Glaucoma is an eye disease that causes irreversible vision loss. Retinography is done manually by the ophthalmologist and is the cheapest, least invasive and most effective way to diagnose glaucoma. The ratio between the diameter of the outer part of the Optic Disc (OD) and the cup (internal part) called CDR (cup-to-disc ratio) is an important indicator of glaucoma presence in patients. This paper proposes a semiautomatic approach that includes the segmentation of OD and cup regions. The proposed approach consists of four stages. The first stage consists of preprocessing the retinal image, in order to remove blood vessels and a possible influence in the segmentation stage. In the second stage we apply the Seeded Fuzzy C-means algorithm to segment the preprocessed image in order to indentify cup and OD. The third step involves the application of a post-processing so that non-segmented regions are filled. Finally, the last step calculates the value of the CDR associated with the retinal image. To verify the applicability of the proposed approach, we carried out tests in two public image databases: DRISHTI-GS and RIM-ONE r3. The results obtained illustrate the feasibility of applying the approach in order to effectively assist ophthalmologists in the segmentation of cup and OD, as well as the calculation of the CDR.

Among common disturbances in Electric Power Quality, the most relevant and with greatest rate of occurrence are voltage sags, which cause substantial economical loss to concessionary companies and to customers. Constant monitoring is an essential part of identifying existing disturbances. However, the costs involved make monitoring the complete system infeasible, thus only a reduced number of monitors are available, which need to be installed in strategic positions in order to cover the greatest number of possible events. This work presents an approach for solving the problem of allocating monitors of electric power quality, considering various aspects of the problem, such as topological coverage, voltage sags that have happened but have not been monitored and total cost of equipment installed. The Monte Carlo Method was used for modeling the time series of faults in the distribution system and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was used in the construction of this model. The approach was submitted to IEEE 13, 34 and 37-bus distribution systems, which were simulated using DigSILENT Power Factory 15.1 software. The results allow the user to make decisions regarding the amount and the position of the monitors to be installed, in order to seek adequacy to the financial reality of the power supplies' and as to avoid unnecessary costs that would not result in improvements in monitoring performance.