Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

Comportamento dinâmico não-linear em fenômenos de colheita de energia usando dispositivos baseados em materiais piezoelétricos em estruturas aporticadas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Contribuições na modelagem dinâmica e no controle neural de um braço robótico com elos flexíveis

Em geral, estruturas espaciais e manipuladores robóticos leves têm uma característica similar e inerente que é a flexibilidade. Esta característica torna a dinâmica do sistema muito mais complexa e com maiores dificuldades para a análise de estabilidade e controle. Então, braços robóticos bastantes leves, com velocidade elevada e potencia limitada devem considerar o controle de vibração causada pela flexibilidade. Por este motivo, uma estratégia de controle é desejada não somente para o controle do modo rígido mas também que seja capaz de controlar os modos de vibração do braço robótico flexível. Também, redes neurais artificiais (RNA) são identificadas como uma subespecialidade de inteligência artificial. Constituem atualmente uma teoria para o estudo de fenômenos complexos e representam uma nova ferramenta na tecnologia de processamento de informação, por possuírem características como processamento paralelo, capacidade de aprendizagem, mapeamento não-linear e capacidade de generalização. Assim, neste estudo utilizam-se RNA na identificação e controle do braço robótico com elos flexíveis. Esta tese apresenta a modelagem dinâmica de braços robóticos com elos flexíveis, 1D no plano horizontal e 2D no plano vertical com ação da gravidade, respectivamente. Modelos dinâmicos reduzidos são obtidos pelo formalismo de Newton-Euler, e utiliza-se o método dos elementos finitos (MEF) na discretização dos deslocamentos elásticos baseado na teoria elementar da viga. Além disso, duas estratégias de controle têm sido desenvolvidas com a finalidade de eliminar as vibrações devido à flexibilidade do braço robótico com elos flexíveis. Primeiro, utilizase um controlador neural feedforward (NFF) na obtenção da dinâmica inversa do braço robótico flexível e o calculo do torque da junta. E segundo, para obter precisão no posicionamento... (Resumo completo, clicar acesso eletrônico abaixo)

The influence of self-weight of elastic 2D structures in topology optimization via numerical technique Smooth Evolutionary Structural Optimization (SESO)

This paper deals with topology optimization in plane elastic-linear problems considering the influence of the self weight in efforts in structural elements. For this purpose it is used a numerical technique called SESO (Smooth ESO), which is based on the procedure for progressive decrease of the inefficient stiffness element contribution at lower stresses until he has no more influence. The SESO is applied with the finite element method and is utilized a triangular finite element and high order. This paper extends the technique SESO for application its self weight where the program, in computing the volume and specific weight, automatically generates a concentrated equivalent force to each node of the element. The evaluation is finalized with the definition of a model of strut-and-tie resulting in regions of stress concentration. Examples are presented with optimum topology structures obtaining optimal settings. (C) 2012 CIMNE (Universitat Politecnica de Catalunya). Published by Elsevier Espana, S.L.U. All rights reserved.

Analysis of Structural Integrity Using an ARTMAP-Fuzzy Artificial Neural Network

This paper presents the application of artificial neural networks in the analysis of the structural integrity of a building. The main objective is to apply an artificial neural network based on adaptive resonance theory, called ARTMAP-Fuzzy neural network and apply it to the identification and characterization of structural failure. This methodology can help professionals in the inspection of structures, to identify and characterize flaws in order to conduct preventative maintenance to ensure the integrity of the structure and decision-making. In order to validate the methodology was modeled a building of two walk, and from this model were simulated various situations (base-line condition and improper conditions), resulting in a database of signs, which were used as input data for ARTMAP-Fuzzy network. The results show efficiency, robustness and accuracy.

Programação linear no planejamento do tratamento de câncer por radiocirurgia

Medical Physics has been reaching an important role among several lines in Science, providing means for the improvement of several theories and procedures. Currently, its main application is related with the use of ionizing radiations, specially, in treatment procedures such as Radiotherapy. Radiosurgery is a Radiotherapy technique which consists in administering a single tumoricidal dose of radiation exclusively to the tumorous lesion. It becomes then an interesting alternative to surgical treatment, mainly in cerebral metastases, which are the most frequent cerebral tumors in the central nervous system. The radio neurosurgical team works out a planning for the Radiosurgery treatment, aiming for obtaining an appropriate ideal treatment for each case. For the working out of this treatment planning, Computed Tomography images of the region to be treated are obtained, digitalized and later, fused with nuclear magnetic resonance images. Through these images, critical structures, organs at risk and lesions are localized. After this, calculations are made to determine three-dimensional positions of isocenters, isodose curves, prescribed dose, collimators sizes, position, numbers and respective weight of isocentric conformal fields, and others. The treatment planning is commonly based in desired levels of dose for specific types of tumors and organs at risk concerning the irradiated region. Theses levels of dose are chosen in a way that a high probability of cure may be achieved and meanwhile, that the probability of complications, in whichever organ at risk, may be minimal. Thus, many researches have been carried out, showing that mathematical techniques may help to obtain an optimal planning for the treatment of cerebral metastases. Among the methods of optimization in the study...(Complete abstract click electronic access below)

Extensometria: estudo das microdeformações ao redor de três implantes de hexágono interno, com posicionamento linear, sob a influência da fundição de coifas plásticas e usinadas

The objective of this in vitro study was quantify the micro strain development around the internal hexagon implants, varying the type of prosthetic coping. For This reason, three implants of internal hexagon were inserted into one polyurethane block in line placement. Microunit abutments were screwed onto the implants. Tangentially the implants were bonded the strain gauges, two to the center implant. Ten structures, each one containing three copings were cast in Co-Cr alloy, that were divided into groups in the first group, plastic copings were used, and in the second group machined copings were used. The superstructure’s occlusal screws were tightened onto Microunit abutments with 10 Ncm torque, the magnitude of micro strain was recorded. The mean values of each strain gauge of each plastic copings were 363,37 ± 237,66 and the machined copings were 338,12 ± 223,01. The data were analyzed statistically by t- Student test. No statistically significant difference was found between the prosthetic copings (p= 0,867). It was concluded that to internal hexagon implants in line placement, the type of copings presented similar magnitude of micro strain after prosthetic occlusal screw was tightened

Análise de Tópicos Relevantes em Programação Linear e Aplicações no Ensino de Engenharia

Pós-graduação em Engenharia Elétrica - FEIS

General characterization of Tityus fasciolatus scorpion venom. Molecular identification of toxins and localization of linear B-cell epitopes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Non-parametric identification of a non-linear buckled beam using discrete-time Volterra Series

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Caracterização experimental de sistemas mecânicos com comportamento não-linear

Pós-graduação em Engenharia Mecânica - FEIS

Artificial neural networks applied to bandsawing process control

In the search for productivity increase, industry has invested on the development of intelligent, flexible and self-adjusting method, capable of controlling processes through the assistance of autonomous systems, independently whether they are hardware or software. Notwithstanding, simulating conventional computational techniques is rather challenging, regarding the complexity and non-linearity of the production systems. Compared to traditional models, the approach with Artificial Neural Networks (ANN) performs well as noise suppression and treatment of non-linear data. Therefore, the challenges in the wood industry justify the use of ANN as a tool for process improvement and, consequently, add value to the final product. Furthermore, Artificial Intelligence techniques such as Neuro-Fuzzy Networks (NFNs) have proven effective, since NFNs combine the ability to learn from previous examples and generalize the acquired information from the ANNs with the capacity of Fuzzy Logic to transform linguistic variables in rules.

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

Evaluation of perforated steel plates as connection in glulam-concrete composite structures

This paper shows the results of an experimental investigation carried out on a connection element of glulam and concrete composite structures, through double-sided push-out shear tests. The connection system was composed of perforated steel plates glued with epoxy adhesive. Five specimens were made and tested under shear forces. This innovative connection system showed an average initial slip modulus equivalent to 339.4 kN/mm. In addition, the connection system was evaluated by means of numerical simulations and the software ANSYS was used for this purpose. The numerical simulations demonstrated good agreement with the experimental data, especially in the regime of elastic-linear behavior of materials. (C) 2011 Elsevier Ltd. All rights reserved.

Fuzzy Connectedness Image Segmentation in Graph Cut Formulation: A Linear-Time Algorithm and a Comparative Analysis

A deep theoretical analysis of the graph cut image segmentation framework presented in this paper simultaneously translates into important contributions in several directions. The most important practical contribution of this work is a full theoretical description, and implementation, of a novel powerful segmentation algorithm, GC(max). The output of GC(max) coincides with a version of a segmentation algorithm known as Iterative Relative Fuzzy Connectedness, IRFC. However, GC(max) is considerably faster than the classic IRFC algorithm, which we prove theoretically and show experimentally. Specifically, we prove that, in the worst case scenario, the GC(max) algorithm runs in linear time with respect to the variable M=|C|+|Z|, where |C| is the image scene size and |Z| is the size of the allowable range, Z, of the associated weight/affinity function. For most implementations, Z is identical to the set of allowable image intensity values, and its size can be treated as small with respect to |C|, meaning that O(M)=O(|C|). In such a situation, GC(max) runs in linear time with respect to the image size |C|. We show that the output of GC(max) constitutes a solution of a graph cut energy minimization problem, in which the energy is defined as the a"" (a) norm ayenF (P) ayen(a) of the map F (P) that associates, with every element e from the boundary of an object P, its weight w(e). This formulation brings IRFC algorithms to the realm of the graph cut energy minimizers, with energy functions ayenF (P) ayen (q) for qa[1,a]. Of these, the best known minimization problem is for the energy ayenF (P) ayen(1), which is solved by the classic min-cut/max-flow algorithm, referred to often as the Graph Cut algorithm. We notice that a minimization problem for ayenF (P) ayen (q) , qa[1,a), is identical to that for ayenF (P) ayen(1), when the original weight function w is replaced by w (q) . Thus, any algorithm GC(sum) solving the ayenF (P) ayen(1) minimization problem, solves also one for ayenF (P) ayen (q) with qa[1,a), so just two algorithms, GC(sum) and GC(max), are enough to solve all ayenF (P) ayen (q) -minimization problems. We also show that, for any fixed weight assignment, the solutions of the ayenF (P) ayen (q) -minimization problems converge to a solution of the ayenF (P) ayen(a)-minimization problem (ayenF (P) ayen(a)=lim (q -> a)ayenF (P) ayen (q) is not enough to deduce that). An experimental comparison of the performance of GC(max) and GC(sum) algorithms is included. This concentrates on comparing the actual (as opposed to provable worst scenario) algorithms' running time, as well as the influence of the choice of the seeds on the output.