Near-optimal state assignment in a finite state machine

In this research, the application of the Simulated Annealing algorithm to solve the state assignment problem in finite state machines is investigated. The state assignment is a classic NP-Complete problem in digital systems design and impacts directly on both area and power costs as well as on the design time. The solutions found in the literature uses population-based methods that consume additional computer resources. The Simulated Annealing algorithm has been chosen because it does not use populations while seeking a solution. Therefore, the objective of this research is to evaluate the impact on the quality of the solution when using the Simulated Annealing approach. The proposed solution is evaluated using the LGSynth89 benchmark and compared with other approaches in the state-of-the-art. The experimental simulations point out an average loss in solution quality of 14.29%, while an average processing performance of 58.67%. The results indicate that it is possible to have few quality losses with a significant increase in processing performance.

Exploring Super-Resolution for Face Recognition

Biometric recognition is part of many aspects of modern society. With the popularization of smartphones, facial recognition gains space in this environment of biometric technologies. With the diversity of image capture devices, of different brands and qualities, the images will not always be in the ideal standard to be recognized. This article tests and compares different scenarios and situations to assess the results obtained by facial recognition in different environments. For this, the quantity method of data analysis was used. In the first scenario, all images were submitted without changes. In the following, we have the reduction of image resolution, which may or may not be followed by enlargement to the original resolution via bicubic interpolation or through the Image Super-Resolution algorithm, these images can be all, or only that undergo tests. Results indicate that the first scenario obtained the best performance, followed by only the tests images change. The worst performance occurs where the properties of all images are affected. In situations where there is a reduction and enlargement are optional, the enlargement option performs better, so the bicubic enlargement has an advantage over the ISR, the situation in which only the reduction occurs has the worst performance.

Development of an AI-based bot to Tibia MMORPG

Artificial Intelligence has always been used in designing of automated agents for playing games such as Chess, Go, Defense of the Ancients 2, Snake Game, billiard and many others. In this work, we present the development and performance evaluation of an automated bot that mimics a real life player for the RPG Game Tibia. The automated bot is built using a combination of AI techniques such as graph search algorithm A* and computer vision tools like template matching. Using four algorithms to get global position of player in game, handle its health and mana, target monsters and walk through the game, we managed to develop a fully automated Tibia bot based in raw input image. We evaluated the performance of the agent in three different scenarios, collecting and analyzing metrics such as XP Gain, Supplies Usage and Balance. The simulation results shows that the developed bot is capable of producing competitive results according to in-game metrics when compared to human players.

Using Latent Semantic Indexing as a metric for evaluating research potentialities through Innovation Public Policies

Public innovation policies usually define strategies for Public research organizations, such as universities, in order to guide the next research projects of such organizations. Sometimes, it is difficult to know the actual state of an organization when a new policy is released by the government. The objective of this paper is to present the application of Latent Semantic Analysis, a technique of information retrieval, in order to create an index and automatically classify research projects, using text fields like title and abstract, to areas and subareas defined by related terms. It is also proposed a case study of about 200 projects from five graduate programs of the Universidade Federal do Tocantins. The proposed solution was capable of satisfactorily classify each project to the areas and subareas of a recent policy from the Science, Technology, Innovations, and Communications Ministry. In this way, the university could have some decision-making information, and the results could sustain for which internal policies could be implemented to maximize its actuation faced to the national innovation policy.

Evaluation of a Sliding Window mechanism as DataAugmentation over Emotion Detection on Speech

Emotion analysis is an important field of study, with many applications for security, financial, and politician. Despite beinga subjective branch of study, emotion analysis can be simulated by Machine Learning algorithms that are trained for this purpose, throughcataloged audio datasets, they can recognize patterns in these media that could be related to corresponding emotion. Neural NetworkAlgorithms are able to work on the recognition of these emotions, with a focus only on audio, known as Speech Emotion Recognition(SER). Neural Network Algorithms generally obtain unequal averages of referring results such as recognition of emotions when applied todifferent audio datasets. This research evaluates a Data Augmentation method called Slide Window, which generates more data samplesin order to increase the averages of classification rates. The method has been applied to three public datasets: EMO-DB, SAVEE, and RAVEDESS. The experiments have shown effectiveness in the increasing of the recognition rates of about to 11.95% on the EMO-DBbase, 22.76% on SAVEE, and 18.82% on RAVEDESS when compared to other approaches in the literature.

Day-Ahead Hourly Operation Planning of Distribution Networks with Photovoltaic Generation Integrated by Smart Inverters via Mixed-Integer Volt/Var Optimization Problems.

A penetração da geração fotovoltaica distribuída nas redes de distribuição tornou-se uma realidade; entretanto, ainda impõe desafios a serem superados, como o funcionamento dessas redes com desvios mínimos nos perfis de magnitude de tensão. Assim, este trabalho propõe o planejamento diário da operação horária de redes de distribuição visando minimizar o desvio nos perfis de magnitude de tensão por meio de ajustes de tap ótimos de transformadores em subestações, ajustes ótimos de bancos de capacitores chaveados remotamente ao longo dos alimentadores primários e injeções de potência ativa e reativa da geração fotovoltaica distribuída por inversores inteligentes. Em tal problema, as variáveis de controle discretas associadas aos taps de transformadores e susceptâncias equivalentes de bancos de capacitores chaveados remotamente são tratadas por função penalidade senoidal que modificam o problema de programação não linear inteira mista original em um problema de programação não linear com apenas variáveis de decisão contínuas. Os resultados obtidos para redes de distribuição com 69 e 135 nós mostram que o modelo OVV proposto e sua metodologia de resolução são eficientes na redução do desvio nos perfis de magnitude de tensão e, ao mesmo tempo, na redução da demanda geral de potência reativa da rede de distribuição.

Analysis of the application of Insulating Vegetable Oil in electric power distribution transformers

Distribution transformers are present in large quantities in the electrical system, in addition to being essential equipment for delivering quality and reliable energy to consumers. This distribution transformers have a dielectric insulation and refrigeration system, with Insulating Mineral Oil (IMO) being the most used. However, the large-scale use of IMO entails a very high environmental risk, because in addition to its fossil origin (petroleum), it also has low biodegradability and is highly toxic and polluting to the environment. In view of this, substitution alternatives for the IMO began to be sought, where, over the years and with technological advances, Insulating Vegetable Oil (IVO) with characteristics similar to the IMO emerged, in addition to being highly biodegradable and providing an alternative sustainable development for the construction of transformers. Thus, the objective of this work is to carry out an analytical study about the characteristics and benefits of using the vegetable fluid for the equipment and for the environment, in addition to comparing the efficiency and cost parameters of IVO and IMO according to current standards. As a contribution, this paper seeks to provide a broad theoretical foundation on IVO and IMO in order to provide conceptualization to agroenergy researchers.