Detecção e diagnostico de agarramento em válvulas posicionadoras

Valve stiction, or static friction, in control loops is a common problem in modern industrial processes. Recently, many studies have been developed to understand, reproduce and detect such problem, but quantification still remains a challenge. Since the valve position (mv) is normally unknown in an industrial process, the main challenge is to diagnose stiction knowing only the output signals of the process (pv) and the control signal (op). This paper presents an Artificial Neural Network approach in order to detect and quantify the amount of static friction using only the pv and op information. Different methods for preprocessing the training set of the neural network are presented. Those methods are based on the calculation of centroid and Fourier Transform. The proposal is validated using a simulated process and the results show a satisfactory measurement of stiction.

Influência de mudanças de fase no ciclo claro-escuro sobre o controle autonômico cardíaco de ratos

Introduction: The circadian system has neural projections for the Autonomic Nervous System (ANS), directly interfering with sympathetic-vagal modulation of the cardiovascular system. Disturbances in the circadian system, such as phase changes in light-dark cycle (LD), has been related to the risk of development of cardiovascular diseases due to increased sympathetic tone and reduction o Heart Rate Variability (HRV - RR intervals). Purpose: Investigate the interaction between Circadian Timing System and cardiac autonomic control in rats. Materials and methods: We used 18 Wistar rats (♀, age = 139.9 ± 32.1 days, weight = 219.5 ± 16.2 g), divided into three distinct groups: Control (CG), phase delay of 6h (GDe) and phase advance of 6h (GAd). Three animals were excluded during data collection (CG/GDe/GAd - n=5). Telemeters were surgically implanted in each animal for continuous acquisition of electrocardiographic (ECG) signals (duration of 21 days in the CG and 28 days in GDe/ GAd). A LD cycle was established 12h: 12h, beginning of light at18:00h and dark at 06:00h. The animals remained in the same CG LD cycle throughout the experimental period, while, on the 14th day of registration, the GDe and GAd underwent a delay and an advance in 6h, respectively. Throughout the experimental period, the locomotor activity (LA), the mean heart rate (mHR) and variables related to iRR [mean RR (mRR), SDNN, RMSSD, LF, HF and LF/ HF ratio ] were recorded. All data were analyzed in blocks of 3 and 7 days, for the presence of circadian rhythm, values of Cosinor - mesor, amplitude and acrophase (paired t test), phase relationship, differences between light and dark (t test independent), averages every 30 minutes along each time series (two-way ANOVA with post hoc Bonferroni). The data block B1,M1 and M2 in CG served as benchmarks for comparisons between series of analysis of the GAT/GAV. Results: We observed circadian rhythmicity in the variables LA, mRR and mFC(p<0.01). mRR and mFC showed phase relationship with the LA in all three groups, being less stable in GAd. In the CG, no significant differences between blocks were found in any of the analyzes(p>0.05). Among the 7 day blocks, there was a significant reduction in mRR(p=0.04) and mFC(p=0.03) in GDe and significant reduction in HF mean(p=0.02) in GAd; and between 3 day blocks, a significant increase of LF/HF(p= 0.04) in the GDe; besides mRR(p=0.03), SDNN(p=0.04), RMSSD (p=0.04), LF (p=0.01) and HF(p=0.02) significant increase in the GAd. It was found that the differences between the means of the mRR, LA and mFC in light and dark phases were not significant after phase changes in some of the blocks/moments (GDe and GAd). No significant results were found when comparing rhythmic variables means every 30 minutes over the blocks, except for a significant decrease in mRR at the middle of the dark phase (B2) and the start of light phase (B3) - (p<0.01). Conclusion: phase advances and delays (6h) altered cardiac autonomic control in the experimental groups by temporarily HRV decrease. Phase advances apparently had greater negative interference in this process, in relation to the phase delays.

A neuromodulação do córtex pré-frontal dorsolateral na percepção de tempo em contexto neutro ou emocionalmente ativo

The time perception is critical for environmental adaptation in humans and other species. The temporal processing, has evolved through different neural systems, each responsible for processing different time scales. Among the most studied scales is that spans the arrangement of seconds to minutes. Evidence suggests that the dorsolateral prefrontal (DLPFC) cortex has relationship with the time perception scale of seconds. However, it is unclear whether the deficit of time perception in patients with brain injuries or even "reversible lesions" caused by transcranial magnetic stimulation (TMS) in this region, whether by disruption of other cognitive processes (such as attention and working memory) or the time perception itself. Studies also link the region of DLPFC in emotional regulation and specifically the judgment and emotional anticipation. Given this, our objective was to study the role of the dorsolateral prefrontal cortex in the time perception intervals of active and emotionally neutral stimuli, from the effects of cortical modulation by transcranial direct current stimulation (tDCS), through the cortical excitation (anodic current), inhibition (cathode current) and control (sham) using the ranges of 4 and 8 seconds. Our results showed that there is an underestimation when the picture was presented by 8 seconds, with the anodic current in the right DLPFC, there is an underestimation and with cathodic current in the left DLPFC, there is an overestimation of the time reproduction with neutral ones. The cathodic current over the left DLPFC leads to an inverse effect of neutral ones, an underestimation of time with negative pictures. Positive or negative pictures improved estimates for 8 second and positive pictures inhibited the effect of tDCS in DLPFC in estimating time to 4 seconds. With this work, we conclude that the DLPFC plays a key role in the o time perception and largely corresponds to the stages of memory and decision on the internal clock model. The left hemisphere participates in the perception of time in both active and emotionally neutral contexts, and we can conclude that the ETCC and an effective method to study the cortical functions in the time perception in terms of cause and effect.

Ferramenta para avaliação e inferência de parâmetros de redes industriais sem fio

Wireless sensor networks (WSN) have gained ground in the industrial environment, due to the possibility of connecting points of information that were inaccessible to wired networks. However, there are several challenges in the implementation and acceptance of this technology in the industrial environment, one of them the guaranteed availability of information, which can be influenced by various parameters, such as path stability and power consumption of the field device. As such, in this work was developed a tool to evaluate and infer parameters of wireless industrial networks based on the WirelessHART and ISA 100.11a protocols. The tool allows quantitative evaluation, qualitative evaluation and evaluation by inference during a given time of the operating network. The quantitative and qualitative evaluation are based on own definitions of parameters, such as the parameter of stability, or based on descriptive statistics, such as mean, standard deviation and box plots. In the evaluation by inference uses the intelligent technique artificial neural networks to infer some network parameters such as battery life. Finally, it displays the results of use the tool in different scenarios networks, as topologies star and mesh, in order to attest to the importance of tool in evaluation of the behavior of these networks, but also support possible changes or maintenance of the system.

Critério de correntropia no treinamento de redes fuzzy wavelet neural networks para identificação de sistemas dinâmicos não lineares

The great interest in nonlinear system identification is mainly due to the fact that a large amount of real systems are complex and need to have their nonlinearities considered so that their models can be successfully used in applications of control, prediction, inference, among others. This work evaluates the application of Fuzzy Wavelet Neural Networks (FWNN) to identify nonlinear dynamical systems subjected to noise and outliers. Generally, these elements cause negative effects on the identification procedure, resulting in erroneous interpretations regarding the dynamical behavior of the system. The FWNN combines in a single structure the ability to deal with uncertainties of fuzzy logic, the multiresolution characteristics of wavelet theory and learning and generalization abilities of the artificial neural networks. Usually, the learning procedure of these neural networks is realized by a gradient based method, which uses the mean squared error as its cost function. This work proposes the replacement of this traditional function by an Information Theoretic Learning similarity measure, called correntropy. With the use of this similarity measure, higher order statistics can be considered during the FWNN training process. For this reason, this measure is more suitable for non-Gaussian error distributions and makes the training less sensitive to the presence of outliers. In order to evaluate this replacement, FWNN models are obtained in two identification case studies: a real nonlinear system, consisting of a multisection tank, and a simulated system based on a model of the human knee joint. The results demonstrate that the application of correntropy as the error backpropagation algorithm cost function makes the identification procedure using FWNN models more robust to outliers. However, this is only achieved if the gaussian kernel width of correntropy is properly adjusted.

Modelagem do comportamento à fadiga de compósitos de fibra de vidro a partir de um modelo misto de RNA

This work consists basically in the elaboration of an Artificial Neural Network (ANN) in order to model the composites materials’ behavior when submitted to fatigue loadings. The proposal is to develop and present a mixed model, which associate an analytical equation (Adam Equation) to the structure of the ANN. Given that the composites often shows a similar behavior when subject to float loadings, this equation aims to establish a pre-defined comparison pattern for a generic material, so that the ANN fit the behavior of another composite material to that pattern. In this way, the ANN did not need to fully learn the behavior of a determined material, because the Adam Equation would do the big part of the job. This model was used in two different network architectures, modular and perceptron, with the aim of analyze it efficiency in distinct structures. Beyond the different architectures, it was analyzed the answers generated from two sets of different data – with three and two SN curves. This model was also compared to the specialized literature results, which use a conventional structure of ANN. The results consist in analyze and compare some characteristics like generalization capacity, robustness and the Goodman Diagrams, developed by the networks.

Modelagem da degradação de fenol em efluentes aquosos através do processo de foto-fenton

Advanced Oxidation Processes (AOP) are techniques involving the formation of hydroxyl radical (HO•) with high organic matter oxidation rate. These processes application in industry have been increasing due to their capacity of degrading recalcitrant substances that cannot be completely removed by traditional processes of effluent treatment. In the present work, phenol degrading by photo-Fenton process based on addition of H2O2, Fe2+ and luminous radiation was studied. An experimental design was developed to analyze the effect of phenol, H2O2 and Fe2+ concentration on the fraction of total organic carbon (TOC) degraded. The experiments were performed in a batch photochemical parabolic reactor with 1.5 L of capacity. Samples of the reactional medium were collected at different reaction times and analyzed in a TOC measurement instrument from Shimadzu (TOC-VWP). The results showed a negative effect of phenol concentration and a positive effect of the two other variables in the TOC degraded fraction. A statistical analysis of the experimental design showed that the hydrogen peroxide concentration was the most influent variable in the TOC degraded fraction at 45 minutes and generated a model with R² = 0.82, which predicted the experimental data with low precision. The Visual Basic for Application (VBA) tool was used to generate a neural networks model and a photochemical database. The aforementioned model presented R² = 0.96 and precisely predicted the response data used for testing. The results found indicate the possible application of the developed tool for industry, mainly for its simplicity, low cost and easy access to the program.

Modelagem da degradação de fenol em efluentes aquosos através do processo de foto-fenton

Advanced Oxidation Processes (AOP) are techniques involving the formation of hydroxyl radical (HO•) with high organic matter oxidation rate. These processes application in industry have been increasing due to their capacity of degrading recalcitrant substances that cannot be completely removed by traditional processes of effluent treatment. In the present work, phenol degrading by photo-Fenton process based on addition of H2O2, Fe2+ and luminous radiation was studied. An experimental design was developed to analyze the effect of phenol, H2O2 and Fe2+ concentration on the fraction of total organic carbon (TOC) degraded. The experiments were performed in a batch photochemical parabolic reactor with 1.5 L of capacity. Samples of the reactional medium were collected at different reaction times and analyzed in a TOC measurement instrument from Shimadzu (TOC-VWP). The results showed a negative effect of phenol concentration and a positive effect of the two other variables in the TOC degraded fraction. A statistical analysis of the experimental design showed that the hydrogen peroxide concentration was the most influent variable in the TOC degraded fraction at 45 minutes and generated a model with R² = 0.82, which predicted the experimental data with low precision. The Visual Basic for Application (VBA) tool was used to generate a neural networks model and a photochemical database. The aforementioned model presented R² = 0.96 and precisely predicted the response data used for testing. The results found indicate the possible application of the developed tool for industry, mainly for its simplicity, low cost and easy access to the program.

Reconstrução e reconhecimento de imagens binárias utilizando o algoritmo Máquina de Boltzmann

The objective of this work is to use algorithms known as Boltzmann Machine to rebuild and classify patterns as images. This algorithm has a similar structure to that of an Artificial Neural Network but network nodes have stochastic and probabilistic decisions. This work presents the theoretical framework of the main Artificial Neural Networks, General Boltzmann Machine algorithm and a variation of this algorithm known as Restricted Boltzmann Machine. Computer simulations are performed comparing algorithms Artificial Neural Network Backpropagation with these algorithms Boltzmann General Machine and Machine Restricted Boltzmann. Through computer simulations are analyzed executions times of the different described algorithms and bit hit percentage of trained patterns that are later reconstructed. Finally, they used binary images with and without noise in training Restricted Boltzmann Machine algorithm, these images are reconstructed and classified according to the bit hit percentage in the reconstruction of the images. The Boltzmann machine algorithms were able to classify patterns trained and showed excellent results in the reconstruction of the standards code faster runtime and thus can be used in applications such as image recognition.

Implementação de classificador de tarefas mentais baseado em EEG

BARBOSA, André F. ; SOUZA, Bryan C. ; PEREIRA JUNIOR, Antônio ; MEDEIROS, Adelardo A. D.de, . Implementação de Classificador de Tarefas Mentais Baseado em EEG. In: CONGRESSO BRASILEIRO DE REDES NEURAIS, 9., 2009, Ouro Preto, MG. Anais... Ouro Preto, MG, 2009

Implementação de classificador de tarefas mentais baseado em EEG

BARBOSA, André F. ; SOUZA, Bryan C. ; PEREIRA JUNIOR, Antônio ; MEDEIROS, Adelardo A. D.de, . Implementação de Classificador de Tarefas Mentais Baseado em EEG. In: CONGRESSO BRASILEIRO DE REDES NEURAIS, 9., 2009, Ouro Preto, MG. Anais... Ouro Preto, MG, 2009

Avaliação do comportamento ingestivo utilizando eletromiografia de superfície do músculo masseter

Physiologists and animal scientists try to understand the relationship between ruminants and their environment. The knowledge about feeding behavior of these animals is the key to maximize the production of meat and milk and their derivatives and ensure animal welfare. Within the area called precision farming, one of the goals is to find a model that describes animal nutrition. Existing methods for determining the consumption and ingestive patterns are often time-consuming and imprecise. Therefore, an accurate and less laborious method may be relevant for feeding behaviour recognition. Surface electromyography (sEMG) is able to provide information of muscle activity. Through sEMG of the muscles of mastication, coupled with instrumentation techniques, signal processing and data classification, it is possible to extract the variables of interest that describe chewing activity. This work presents a new method for chewing pattern evaluation, feed intake prediction and for the determination of rumination, food and daily rest time through ruminant animals masseter muscle sEMG signals. Short-term evaluation results are shown and discussed, evidencing employed methods viability.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.

Social buffering of fear in zebrafish

Tese de Doutoramento em Biologia Comportamental apresentada ao ISPA - Instituto Universitário

Padrões temporais e espaciais na detecção de resíduos e contaminantes na produção de aves e suínos no Brasil

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Agronomia e Medicina Veterinária, 2016.