A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs

Unmanned aerial vehicles (UAVs) are a relatively new technology. Their application can often involve complex and unseen problems. For instance, they can work in a cooperative-based environment under the supervision of a ground station to speed up critical decision-making processes. However, the amount of information exchanged among the aircraft and ground station is limited by high distances, low bandwidth size, restricted processing capability, and energy constraints. These drawbacks restrain large-scale operations such as large area inspections. New distributed state-of-the-art processing architectures, such as fog computing, can improve latency, scalability, and efficiency to meet time constraints via data acquisition, processing, and storage at different levels. Under these amendments, this research work proposes a mathematical model to analyze distribution-based UAVs topologies and a fog-cloud computing framework for large-scale mission and search operations. The tests have successfully predicted latency and other operational constraints, allowing the analysis of fog-computing advantages over traditional cloud-computing architectures.Comment: Volume 2019, Article ID 7497924, 14 page

Surveillance Architecture for Human Activity Recognition using Unmanned Aerial Vehicle / Arquitetura de vigilância para reconhecimento de atividade humana usando veículo aéreo não tripulado

There is intensive growth in researches regarding surveillance and threat detection. Surveillance tasks often involve several actors with multiple interactions. Thus, modeling a complex activity becomes challenging. This work proposes an architecture comprised of low, middle, and high levels. The low-level recognizes characteristics, positioning of objects, and time of occurrences utilizing a camera and Unmanned Aerial Vehicle (UAV) sensors. The middle-level is responsible for structuring the information from the low-level using Deterministic Finite Automata (DFA). An expert system attached in the high-level module performs inference over the organized information to enables the system to have simple reasoning modules, assisting the operator decision. The architecture is embedded in a UAV to reduce the number of cameras and to reach difficult areas. The experiments showed that the proposed system updated the grammatical structure effectively, given a sequence of information computed by the vision modules

Programming of thermoelectric generation systems based on a heuristic composition of ant colonies

a b s t r a c t Studies related to biologically inspired optimization techniques, which are used for daily operational scheduling of thermoelectric generation systems, indicate that combinations of biologically inspired computation methods together with other optimization techniques have an important role to play in obtaining the best solutions in the shortest amount of processing time. Following this line of research, this article uses a methodology based on optimization by an ant colony to minimize the daily scheduling cost of thermoelectric units. The proposed model uses a Sensitivity Matrix (SM) based on the information provided by the Lagrange multipliers to improve the biologically inspired search process. Thus, a percentage of the individuals in the colony use this information in the evolutionary process of the colony. The results achieved through the simulations indicate that the use of the SM results in quality solutions with a reduced number of individuals

NLP based model for individual plant dispatch in long term hydrothermal planning

This paper presents a method to the hydrothermal dispatch using optimization techniques based on non linear programming techniques. To do so, the expected cost-to-go functions from a long term operation plannning strategic decision model are used. This decision model is based on stochastic dual dynamic programming and energy equivalent reservoirs. The proposed method considers a set of historical water inflow scenarios to the hydroelectric reservoirs. Those scenarios are used to simulate the long term operation planning to a given horizon. The results obtained from this disaggregation model (MIUH) are compared with those from the model officially adopted in the Brazilian power system, SUISHI-O. The latter is based on operation heuristics aiming at operating the reservoir maintaining the water storag e in similar levels, that is, trying to operate them in parallel.Este trabalho apresenta um modelo de despacho hidrotérmico à usinas individualizadas, utilizando métodos de otimização baseados em programação não linear. Para tanto, considera-se funções de custo futuro geradas por um modelo de decisão estratégica baseado em programação dinâmica e sistemas equivalentes de energia. O modelo proposto considera diversos cenários históricos de afluências hidrológicas às usinas hidrelétricas, os quais são simulados para um horizonte de planejamento da operação de médio/longo prazo. Os resultados obtidos através do modelo proposto, denominado Modelo Individualizado de Usinas Hidráulicas (MIUH), são comparados com os resultados obtidos a partir da utilização do modelo SUISHI-O adotado pelo Operador Nacional do Sistema Elétrico Brasileiro (ONS)

Stochastic Dynamic Programming Applied to Hydrothermal Power Systems Operation Planning Based on the Convex Hull Algorithm

This paper presents a new approach for the expected cost-to-go functions modeling used in the stochastic dynamic programming (SDP) algorithm. The SDP technique is applied to the long-term operation planning of electrical power systems. Using state space discretization, the Convex Hull algorithm is used for constructing a series of hyperplanes that composes a convex set. These planes represent a piecewise linear approximation for the expected cost-to-go functions. The mean operational costs for using the proposed methodology were compared with those from the deterministic dual dynamic problem in a case study, considering a single inflow scenario. This sensitivity analysis shows the convergence of both methods and is used to determine the minimum discretization level. Additionally, the applicability of the proposed methodology for two hydroplants in a cascade is demonstrated. With proper adaptations, this work can be extended to a complete hydrothermal system

A novel sEMG data augmentation based on WGAN-GP

The classification of sEMG signals is fundamental in applications that use mechanical prostheses, making it necessary to work with generalist databases that improve the accuracy of those classifications. Therefore, synthetic signal generation can be beneficial in enriching a database to make it more generalist. This work proposes using a variant of generative adversarial networks to produce synthetic biosignals of sEMG. A convolutional neural network (CNN) was used to classify the movements. The results showed good performance with an increase of 4.07% in a set of movement classification accuracy when 200 synthetic samples were included for each movement. We compared our results to other methodologies, such as Magnitude Warping and Scaling. Both methodologies did not have the same performance in the classification