Multi-Agent Simulation of Urban Social Dynamics for Spatial Load Forecasting

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Structural health evaluation by optimization techinique and artificial neural network

This paper presents two different approaches to detect, locate, and characterize structural damage. Both techniques utilize electrical impedance in a first stage to locate the damaged area. In the second stage, to quantify the damage severity, one can use neural network, or optimization technique. The electrical impedance-based, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations, this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors, and therefore, it is able to detect the damage in its early stage. Optimization approaches must be used for the case where a good condensed model is known, while neural network can be also used to estimate the nature of damage without prior knowledge of the model of the structure. The paper concludes with an experimental example in a welded cubic aluminum structure, in order to verify the performance of these two proposed methodologies.

Multi-choice mixed integer goal programming optimization for real problems in a sugar and ethanol milling company

Goal Programming (GP) is an important analytical approach devised to solve many realworld problems. The first GP model is known as Weighted Goal Programming (WGP). However, Multi-Choice Aspirations Level (MCAL) problems cannot be solved by current GP techniques. In this paper, we propose a Multi-Choice Mixed Integer Goal Programming model (MCMI-GP) for the aggregate production planning of a Brazilian sugar and ethanol milling company. The MC-MIGP model was based on traditional selection and process methods for the design of lots, representing the production system of sugar, alcohol, molasses and derivatives. The research covers decisions on the agricultural and cutting stages, sugarcane loading and transportation by suppliers and, especially, energy cogeneration decisions; that is, the choice of production process, including storage stages and distribution. The MCMIGP allows decision-makers to set multiple aspiration levels for their problems in which the more/higher, the better and the less/lower, the better in the aspiration levels are addressed. An application of the proposed model for real problems in a Brazilian sugar and ethanol mill was conducted; producing interesting results that are herein reported and commented upon. Also, it was made a comparison between MCMI GP and WGP models using these real cases. © 2013 Elsevier Inc.

Experience generalization for multi-agent reinforcement learning

On-line learning methods have been applied successfully in multi-agent systems to achieve coordination among agents. Learning in multi-agent systems implies in a non-stationary scenario perceived by the agents, since the behavior of other agents may change as they simultaneously learn how to improve their actions. Non-stationary scenarios can be modeled as Markov Games, which can be solved using the Minimax-Q algorithm a combination of Q-learning (a Reinforcement Learning (RL) algorithm which directly learns an optimal control policy) and the Minimax algorithm. However, finding optimal control policies using any RL algorithm (Q-learning and Minimax-Q included) can be very time consuming. Trying to improve the learning time of Q-learning, we considered the QS-algorithm. in which a single experience can update more than a single action value by using a spreading function. In this paper, we contribute a Minimax-QS algorithm which combines the Minimax-Q algorithm and the QS-algorithm. We conduct a series of empirical evaluation of the algorithm in a simplified simulator of the soccer domain. We show that even using a very simple domain-dependent spreading function, the performance of the learning algorithm can be improved.

Short and long period optimization of drug doses in the treatment of AIDS

Técnicas de otimização numérica são úteis na solução de problemas de determinação da melhor entrada para sistemas descritos por modelos matemáticos e cujos objetivos podem ser expressos de uma maneira quantitativa. Este trabalho aborda o problema de otimizar as dosagens dos medicamentos no tratamento da AIDS em termos de um balanço entre a resposta terapêutica e os efeitos colaterais. Um modelo matemático para descrever a dinâmica do vírus HIV e células CD4 é utilizado para calcular a dosagem ótima do medicamento no tratamento a curto prazo de pacientes com AIDS por um método de otimização direta utilizando uma função custo do tipo Bolza. Os parâmetros do modelo foram ajustados com dados reais obtidos da literatura. Com o objetivo de simplificar os procedimentos numéricos, a lei de controle foi expressa em termos de uma expansão em séries que, após truncamento, permite obter controles sub-ótimos. Quando os pacientes atingem um estado clínico satisfatório, a técnica do Regulador Linear Quadrático (RLQ) é utilizada para determinar a dosagem permanente de longo período para os medicamentos. As dosagens calculadas utilizando a técnica RLQ , tendem a ser menores do que a equivalente terapia de dose constante em termos do expressivo aumento na contagem das células T+ CD4 e da redução da densidade de vírus livre durante um intervalo fixo de tempo.

Multi-Agent Framework for Spatial Load Forecasting

A multi-agent framework for spatial electric load forecasting, especially suited to simulate the different dynamics involved on distribution systems, is presented. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented as development probabilities. With this setting, different kind of agents can be developed to simulate the growth pattern of the loads in distribution systems. This paper presents two different kinds of agents to simulate different situations, presenting some promissory results.

Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes

This work presents an investigation into the use of the finite element method and artificial neural networks in the identification of defects in industrial plants metallic tubes, due to the aggressive actions of the fluids contained by them, and/or atmospheric agents. The methodology used in this study consists of simulating a very large number of defects in a metallic tube, using the finite element method. Both variations in width and height of the defects are considered. Then, the obtained results are used to generate a set of vectors for the training of a perceptron multilayer artificial neural network. Finally, the obtained neural network is used to classify a group of new defects, simulated by the finite element method, but that do not belong to the original dataset. The reached results demonstrate the efficiency of the proposed approach, and encourage future works on this subject.

Development of a micro heat exchanger made with ceramic multi-layers (LTCC) and its setup to gas flow measurements

A green ceramic tape micro heat exchanger was developed using LTCC technology. The device was designed by using a CAD software and 2D and 3D simulations using a CFD package (COMSOL Multiphysics) to evaluate the fluid behavior in the microchannels. The micro heat exchanger is composed of five thermal exchange plates in cross flow arrangement and two connecting plates; heat exchanger dimensions are 26 × 26 × 6 mm3. Preliminary tests were carried out to characterize the device both in atmospheric pressure and in vacuum. The same techniques used in vacuum technology were applied to check the rotameters and to prevent device leakages. Thermal performance of the micro heat exchanger was experimentally tested. © 2009 Elsevier B.V. All rights reserved.

A multi-agent architecture for intelligent data analysis

The present study introduces a multi-agent architecture designed for doing automation process of data integration and intelligent data analysis. Different from other approaches the multi-agent architecture was designed using a multi-agent based methodology. Tropos, an agent based methodology was used for design. Based on the proposed architecture, we describe a Web based application where the agents are responsible to analyse petroleum well drilling data to identify possible abnormalities occurrence. The intelligent data analysis methods used was the Neural Network.

A multi-agent system with a percolation approach to simulate the driving pattern of Plug-In ELectric Vehicles

A multi-agent system with a percolation approach to simulate the driving pattern of Plug-In Electric Vehicle (PEV), especially suited to simulate the PEVs behavior on any distribution systems, is presented. This tool intends to complement information about the driving patterns database on systems where that kind of information is not available. So, this paper aims to provide a framework that is able to work with any kind of technology and load generated of PEVs. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with corresponding load level, and their relationships with the neighboring zones are represented as network probabilities. A percolation approach is used to characterize the autonomy of the battery of the PVEs to move through the city. The methodology is tested with data from a mid-size city real distribution system. The result shows the sub-area where the battery of PEVs will need to be recharge and gives the planners of distribution systems the necessary input for a medium to long term network planning in a smart grid environment. © 2012 IEEE.

Identification of steel bars immersed in reinforced concrete based on experimental results of eddy current testing and artificial neural network analysis

This paper presents an experimental research on the use of eddy current testing (ECT) and artificial neural networks (ANNs) in order to identify the gauge and position of steel bars immersed in concrete structures. The paper presents details of the ECT probe and concrete specimens constructed for the tests, and a study about the influence of the concrete on the values of measured voltages. After this, new measurements were done with a greater number of specimens, simulating a field condition and the results were used to generate training and validation vectors for multilayer perceptron ANNs. The results show a high percentage of correct identification with respect to both, the gauge of the bar and of the thickness of the concrete cover. © 2013 Copyright Taylor and Francis Group, LLC.

Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks

The accurate identification of the nitrogen content in crop plants is extremely important since it involves economic aspects and environmental impacts. Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification involves a lot of nonlinear parametes and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought spectral reflectance of plant leaves using artificial neural networks. The network acts as identifier of relationships among pH of soil, fertilizer treatment, spectral reflectance and nitrogen content in the plants. So, nitrogen content can be estimated and generalized from an input parameter set. This approach can be form the basis for development of an accurate real time nitrogen applicator.

Carbon fiber non-crimp multi-axial reinforcement and epoxy mono-component system composite: fatigue behavior

Fiber reinforced polymer composites have been widely applied in the aeronautical field. However, composite processing, which uses unlocked molds, should be avoided in view of the tight requirements and also due to possible environmental contamination. To produce high performance structural frames meeting aeronautical reproducibility and low cost criteria, the Brazilian industry has shown interest to investigate the resin transfer molding process (RTM) considering being a closed-mold pressure injection system which allows faster gel and cure times. Due to the fibrous composite anisotropic and non homogeneity characteristics, the fatigue behavior is a complex phenomenon quite different from to metals materials crucial to be investigated considering the aeronautical application. Fatigue sub-scale specimens of intermediate modulus carbon fiber non-crimp multi-axial reinforcement and epoxy mono-component system composite were produced according to the ASTM 3039 D. Axial fatigue tests were carried out according to ASTM D 3479. A sinusoidal load of 10 Hz frequency and load ratio R = 0.1. It was observed a high fatigue interval obtained for NCF/RTM6 composites. Weibull statistical analysis was applied to describe the failure probability of materials under cyclic loads and fractures pattern was observed by scanning electron microscopy. (C) 2010 Published by Elsevier Ltd.

Alteration of blue pigment in artificial iris in ocular prosthesis: Effect of paint, drying method and artificial aging

The artificial iris is the structure responsible for the dissimulation and aesthetics of ocular prosthesis. The objective of the present study was to evaluate the color stability of artificial iris of microwaveable polymerized ocular prosthesis, as a function of paint type, drying method and accelerated aging. A total of 40 discs of microwaveable polymerized acrylic resin were fabricated, and divided according to the blue paint type ( n = 5): hydrosoluble acrylic, nitrocellulose automotive, hydrosoluble gouache and oil paints. Paints where dried either at natural or at infrared light bulb method. Each specimen was constituted of one disc in colorless acrylic resin and another colored with a basic sclera pigment. Painting was performed in one surface of one of the discs. The specimens were submitted to an artificial aging chamber under ultraviolet light, during 1008 h. A reflective spectrophotometer was used to evaluate color changes. Data were evaluated by 3-way repeated-measures ANOVA and the Tukey HSD test (alpha = 0.05). All paints suffered color alteration. The oil paint presented the highest color resistance to artificial aging regardless of drying method. (C) 2010 British Contact Lens Association. Published by Elsevier Ltd. All rights reserved.

Color stability of maxillofacial silicone with nanoparticle pigment and opacifier submitted to disinfection and artificial aging

The purpose of this study was to evaluate the color stability of a maxillofacial elastomer with the addition of a nanoparticle pigment and/or an opacifier submitted to chemical disinfection and artificial aging. Specimens were divided into four groups (n = 30): group I: silicone without pigment or opacifier, group II: ceramic powder pigment, group III: Barium sulfate (BaSO(4)) opacifier, and group IV: ceramic powder and BaSO(4) opacifier. Specimens of each group (n = 10) were disinfected with effervescent tablets, neutral soap, or 4% chlorhexidine gluconate. Disinfection was done three times a week during two months. Afterward, specimens were submitted to different periods of artificial aging. Color evaluation was initially done, after 60 days (disinfection period) and after 252, 504, and 1008 h of artificial aging with aid of a reflection spectrophotometer. Data were analyzed by three-way ANOVA and Tukey test (alpha = 0.05). The isolated factor disinfection did not statistically influence the values of color stability among groups. The association between pigment and BaSO(4) opacifier (GIV) was more stable in relationship to color change (Delta E). All values of Delta E obtained, independent of the disinfectant and the period of artificial aging, were considered acceptable in agreement with the norms presented in literature. (C) 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.3625401]