783 resultados para Dynamic artificial neural network
Resumo:
Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.
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A uniform chronology for foraminifera-based sea surface temperature records has been established in more than 120 sediment cores obtained from the equatorial and eastern Atlantic up to the Arctic Ocean. The chronostratigraphy of the last 30,000 years is mainly based on published d18O records and 14C ages from accelerator mass spectrometry, converted into calendar-year ages. The high-precision age control provides the database necessary for the uniform reconstruction of the climate interval of the Last Glacial Maximum within the GLAMAP-2000 project.
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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.
Resumo:
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.
Resumo:
The Artificial Neural Networks (ANN), which is one of the branches of Artificial Intelligence (AI), are being employed as a solution to many complex problems existing in several areas. To solve these problems, it is essential that its implementation is done in hardware. Among the strategies to be adopted and met during the design phase and implementation of RNAs in hardware, connections between neurons are the ones that need more attention. Recently, are RNAs implemented both in application specific integrated circuits's (Application Specific Integrated Circuits - ASIC) and in integrated circuits configured by the user, like the Field Programmable Gate Array (FPGA), which have the ability to be partially rewritten, at runtime, forming thus a system Partially Reconfigurable (SPR), the use of which provides several advantages, such as flexibility in implementation and cost reduction. It has been noted a considerable increase in the use of FPGAs for implementing ANNs. Given the above, it is proposed to implement an array of reconfigurable neurons for topologies Description of artificial neural network multilayer perceptrons (MLPs) in FPGA, in order to encourage feedback and reuse of neural processors (perceptrons) used in the same area of the circuit. It is further proposed, a communication network capable of performing the reuse of artificial neurons. The architecture of the proposed system will configure various topologies MLPs networks through partial reconfiguration of the FPGA. To allow this flexibility RNAs settings, a set of digital components (datapath), and a controller were developed to execute instructions that define each topology for MLP neural network.
Resumo:
The Artificial Neural Networks (ANN), which is one of the branches of Artificial Intelligence (AI), are being employed as a solution to many complex problems existing in several areas. To solve these problems, it is essential that its implementation is done in hardware. Among the strategies to be adopted and met during the design phase and implementation of RNAs in hardware, connections between neurons are the ones that need more attention. Recently, are RNAs implemented both in application specific integrated circuits's (Application Specific Integrated Circuits - ASIC) and in integrated circuits configured by the user, like the Field Programmable Gate Array (FPGA), which have the ability to be partially rewritten, at runtime, forming thus a system Partially Reconfigurable (SPR), the use of which provides several advantages, such as flexibility in implementation and cost reduction. It has been noted a considerable increase in the use of FPGAs for implementing ANNs. Given the above, it is proposed to implement an array of reconfigurable neurons for topologies Description of artificial neural network multilayer perceptrons (MLPs) in FPGA, in order to encourage feedback and reuse of neural processors (perceptrons) used in the same area of the circuit. It is further proposed, a communication network capable of performing the reuse of artificial neurons. The architecture of the proposed system will configure various topologies MLPs networks through partial reconfiguration of the FPGA. To allow this flexibility RNAs settings, a set of digital components (datapath), and a controller were developed to execute instructions that define each topology for MLP neural network.
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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.
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Lung cancer is the most common of malignant tumors, with 1.59 million new cases worldwide in 2012. Early detection is the main factor to determine the survival of patients affected by this disease. Furthermore, the correct classification is important to define the most appropriate therapeutic approach as well as suggest the prognosis and the clinical disease evolution. Among the exams used to detect lung cancer, computed tomography have been the most indicated. However, CT images are naturally complex and even experts medical are subject to fault detection or classification. In order to assist the detection of malignant tumors, computer-aided diagnosis systems have been developed to aid reduce the amount of false positives biopsies. In this work it was developed an automatic classification system of pulmonary nodules on CT images by using Artificial Neural Networks. Morphological, texture and intensity attributes were extracted from lung nodules cut tomographic images using elliptical regions of interest that they were subsequently segmented by Otsu method. These features were selected through statistical tests that compare populations (T test of Student and U test of Mann-Whitney); from which it originated a ranking. The features after selected, were inserted in Artificial Neural Networks (backpropagation) to compose two types of classification; one to classify nodules in malignant and benign (network 1); and another to classify two types of malignancies (network 2); featuring a cascade classifier. The best networks were associated and its performance was measured by the area under the ROC curve, where the network 1 and network 2 achieved performance equal to 0.901 and 0.892 respectively.
Resumo:
Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.
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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
Resumo:
Diese Arbeit beschäftigt sich mit nicht in Rechnung stellbaren Wasserverlusten in städtischen Versorgungsnetzen in Entwicklungsländern. Es soll das Wissen über diese Verluste erweitert und aufgezeigt werden, ob diese auf ein ökonomisch vertretbares Maß reduziert werden können. Die vorliegende Doktorarbeit untersucht solche unberechneten Wasserverluste und versucht, neben der Quantifizierung von Leckagen auch Entscheidungswerkzeuge für ein verbessertes Management der Versorgungsnetze in Entwicklungsländern zu erarbeiten. Als Fallstudie dient Harare, die Hauptstadt von Simbabwe. Wasserverluste in Verteilungsnetzen sind unvermeidbar, sollten aber auf ein ökonomisch tragbares Niveau reduziert werden, wenn ein nachhaltiger Betrieb erreicht werden soll. Wasserverluste können sowohl durch illegale und ungenehmigte Anschlüsse oder durch Undichtigkeiten im Verteilnetz, als auch durch mangelhafte Mess- und Berechnungssysteme entstehen. Es sind bereits viele Ansätze zur Verringerung von Verlusten in Wasserverteilsystemen bekannt geworden, entsprechend existieren dazu auch zahlreiche Methoden und Werkzeuge. Diese reichen von computergestützten Verfahren über gesetzliche und politische Vorgaben sowie ökonomische Berechnungen bis hin zu Maßnahmen der Modernisierung der Infrastruktur. Der Erfolg dieser Anstrengungen ist abhängig von der Umsetzbarkeit und dem Umfeld, in dem diese Maßnahmen durchgeführt werden. Die Bewertung der Arbeitsgüte einer jeden Wasserversorgungseinheit basiert auf der Effektivität des jeweiligen Verteilungssystems. Leistungs- und Bewertungszahlen sind die meist genutzten Ansätze, um Wasserverteilsysteme und ihre Effizienz einzustufen. Weltweit haben sich zur Bewertung als Indikatoren die finanzielle und die technische Leistungsfähigkeit durchgesetzt. Die eigene Untersuchung zeigt, dass diese Indikatoren in vielen Wasserversorgungssystemen der Entwicklungsländer nicht zur Einführung von Verlust reduzierenden Managementstrategien geführt haben. Viele durchgeführte Studien über die Einführung von Maßnahmen zur Verlustreduzierung beachten nur das gesamte nicht in Rechnung stellbare Wasser, ohne aber den Anteil der Leckagen an der Gesamthöhe zu bestimmen. Damit ist keine Aussage über die tatsächliche Zuordnung der Verluste möglich. Aus diesem Grund ist ein Bewertungsinstrument notwendig, mit dem die Verluste den verschiedenen Ursachen zugeordnet werden können. Ein solches Rechenwerkzeug ist das South African Night Flow Analysis Model (SANFLOW) der südafrikanischen Wasser-Forschungskommission, das Untersuchungen von Wasserdurchfluss und Anlagendruck in einzelnen Verteilbezirken ermöglicht. In der vorliegenden Arbeit konnte nachgewiesen werden, dass das SANFLOW-Modell gut zur Bestimmung des Leckageanteiles verwendet werden kann. Daraus kann gefolgert werden, dass dieses Modell ein geeignetes und gut anpassbares Analysewerkzeug für Entwicklungsländer ist. Solche computergestützte Berechnungsansätze können zur Bestimmung von Leckagen in Wasserverteilungsnetzen eingesetzt werden. Eine weitere Möglichkeit ist der Einsatz von Künstlichen Neuronalen Netzen (Artificial Neural Network – ANN), die trainiert und dann zur Vorhersage der dynamischen Verhältnisse in Wasserversorgungssystemen genutzt werden können. Diese Werte können mit der Wassernachfrage eines definierten Bezirks verglichen werden. Zur Untersuchung wurde ein Mehrschichtiges Künstliches Neuronales Netz mit Fehlerrückführung zur Modellierung des Wasserflusses in einem überwachten Abschnitt eingesetzt. Zur Bestimmung des Wasserbedarfes wurde ein MATLAB Algorithmus entwickelt. Aus der Differenz der aktuellen und des simulierten Wassernachfrage konnte die Leckagerate des Wasserversorgungssystems ermittelt werden. Es konnte gezeigt werden, dass mit dem angelernten Neuronalen Netzwerk eine Vorhersage des Wasserflusses mit einer Genauigkeit von 99% möglich ist. Daraus lässt sich die Eignung von ANNs als flexibler und wirkungsvoller Ansatz zur Leckagedetektion in der Wasserversorgung ableiten. Die Untersuchung zeigte weiterhin, dass im Versorgungsnetz von Harare 36 % des eingespeisten Wassers verloren geht. Davon wiederum sind 33 % auf Leckagen zurückzuführen. Umgerechnet bedeutet dies einen finanziellen Verlust von monatlich 1 Millionen Dollar, was 20 % der Gesamteinnahmen der Stadt entspricht. Der Stadtverwaltung von Harare wird daher empfohlen, aktiv an der Beseitigung der Leckagen zu arbeiten, da diese hohen Verluste den Versorgungsbetrieb negativ beeinflussen. Abschließend wird in der Arbeit ein integriertes Leckage-Managementsystem vorgeschlagen, das den Wasserversorgern eine Entscheidungshilfe bei zu ergreifenden Maßnahmen zur Instandhaltung des Verteilnetzes geben soll.
Resumo:
This paper provides an overview of IDS types and how they work as well as configuration considerations and issues that affect them. Advanced methods of increasing the performance of an IDS are explored such as specification based IDS for protecting Supervisory Control And Data Acquisition (SCADA) and Cloud networks. Also by providing a review of varied studies ranging from issues in configuration and specific problems to custom techniques and cutting edge studies a reference can be provided to others interested in learning about and developing IDS solutions. Intrusion Detection is an area of much required study to provide solutions to satisfy evolving services and networks and systems that support them. This paper aims to be a reference for IDS technologies other researchers and developers interested in the field of intrusion detection.
Resumo:
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
Resumo:
Recent years have seen an astronomical rise in SQL Injection Attacks (SQLIAs) used to compromise the confidentiality, authentication and integrity of organisations’ databases. Intruders becoming smarter in obfuscating web requests to evade detection combined with increasing volumes of web traffic from the Internet of Things (IoT), cloud-hosted and on-premise business applications have made it evident that the existing approaches of mostly static signature lack the ability to cope with novel signatures. A SQLIA detection and prevention solution can be achieved through exploring an alternative bio-inspired supervised learning approach that uses input of labelled dataset of numerical attributes in classifying true positives and negatives. We present in this paper a Numerical Encoding to Tame SQLIA (NETSQLIA) that implements a proof of concept for scalable numerical encoding of features to a dataset attributes with labelled class obtained from deep web traffic analysis. In the numerical attributes encoding: the model leverages proxy in the interception and decryption of web traffic. The intercepted web requests are then assembled for front-end SQL parsing and pattern matching by applying traditional Non-Deterministic Finite Automaton (NFA). This paper is intended for a technique of numerical attributes extraction of any size primed as an input dataset to an Artificial Neural Network (ANN) and statistical Machine Learning (ML) algorithms implemented using Two-Class Averaged Perceptron (TCAP) and Two-Class Logistic Regression (TCLR) respectively. This methodology then forms the subject of the empirical evaluation of the suitability of this model in the accurate classification of both legitimate web requests and SQLIA payloads.