Search results for: Modular neural control
3653 Mathematical Expression for Machining Performance
Authors: Md. Ashikur Rahman Khan, M. M. Rahman
Abstract:
In electrical discharge machining (EDM), a complete and clear theory has not yet been established. The developed theory (physical models) yields results far from reality due to the complexity of the physics. It is difficult to select proper parameter settings in order to achieve better EDM performance. However, modelling can solve this critical problem concerning the parameter settings. Therefore, the purpose of the present work is to develop mathematical model to predict performance characteristics of EDM on Ti-5Al-2.5Sn titanium alloy. Response surface method (RSM) and artificial neural network (ANN) are employed to develop the mathematical models. The developed models are verified through analysis of variance (ANOVA). The ANN models are trained, tested, and validated utilizing a set of data. It is found that the developed ANN and mathematical model can predict performance of EDM effectively. Thus, the model has found a precise tool that turns EDM process cost-effective and more efficient.
Keywords: Analysis of variance, artificial neural network, material removal rate, modelling, response surface method, surface finish.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7313652 A Web Pages Automatic Filtering System
Authors: O. Nouali, A. Saidi, H. Chahrat, A. Krinah, B. Toursel
Abstract:
This article describes a Web pages automatic filtering system. It is an open and dynamic system based on multi agents architecture. This system is built up by a set of agents having each a quite precise filtering task of to carry out (filtering process broken up into several elementary treatments working each one a partial solution). New criteria can be added to the system without stopping its execution or modifying its environment. We want to show applicability and adaptability of the multi-agents approach to the networks information automatic filtering. In practice, most of existing filtering systems are based on modular conception approaches which are limited to centralized applications which role is to resolve static data flow problems. Web pages filtering systems are characterized by a data flow which varies dynamically.Keywords: Agent, Distributed Artificial Intelligence, Multiagents System, Web pages filtering.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13773651 Methods of Geodesic Distance in Two-Dimensional Face Recognition
Authors: Rachid Ahdid, Said Safi, Bouzid Manaut
Abstract:
In this paper, we present a comparative study of three methods of 2D face recognition system such as: Iso-Geodesic Curves (IGC), Geodesic Distance (GD) and Geodesic-Intensity Histogram (GIH). These approaches are based on computing of geodesic distance between points of facial surface and between facial curves. In this study we represented the image at gray level as a 2D surface in a 3D space, with the third coordinate proportional to the intensity values of pixels. In the classifying step, we use: Neural Networks (NN), K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). The images used in our experiments are from two wellknown databases of face images ORL and YaleB. ORL data base was used to evaluate the performance of methods under conditions where the pose and sample size are varied, and the database YaleB was used to examine the performance of the systems when the facial expressions and lighting are varied.
Keywords: 2D face recognition, Geodesic distance, Iso-Geodesic Curves, Geodesic-Intensity Histogram, facial surface, Neural Networks, K-Nearest Neighbor, Support Vector Machines.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18153650 Chattering-free Sliding Mode Control for an Active Magnetic Bearing System
Authors: Abdul Rashid Husain, Mohamad Noh Ahmad, Abdul Halim Mohd Yatim
Abstract:
In this paper, a few chattering-free Sliding Mode Controllers (SMC) are proposed to stabilize an Active Magnetic Bearing (AMB) system with gyroscopic effect that is proportional to the rotor speed. The improved switching terms of the controller inherited from the saturation-type function and boundary layer control technique is shown to be able to achieve bounded and asymptotic stability, respectively, while the chattering effect in the input is attenuated. This is proven to be advantageous for AMB system since minimization of chattering results in optimized control effort. The performance of each controller is demonstrated via result of simulation in which the measurement of the total consumed energy and maximum control magnitude of each controller illustrates the effectiveness of the proposed controllers.
Keywords: Active Magnetic Bearing (AMB), Sliding Mode Control (SMC), chattering-free SMC.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21453649 Investigation of the GFR2400 Reactivity Control System
Authors: Ján Haščík, Štefan Čerba, Jakub Lüley, Branislav Vrban
Abstract:
The presented paper is related to the design methods and neutronic characterization of the reactivity control system in the large power unit of Generation IV Gas cooled Fast Reactor – GFR2400. The reactor core is based on carbide pin fuel type with the application of refractory metallic liners used to enhance the fission product retention of the SiCcladding. The heterogeneous design optimization of control rod is presented and the results of rods worth and their interferences in a core are evaluated. In addition, the idea of reflector removal as an additive reactivity management option is investigated and briefly described.
Keywords: Control rods design, GFR2400, hot spot, movable reflector, reactivity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18083648 Design of Smith-like Predictive Controller with Communication Delay Adaptation
Authors: Jasmin Velagic
Abstract:
This paper addresses the design of predictive networked controller with adaptation of a communication delay. The networked control system contains random delays from sensor to controller and from controller to actuator. The proposed predictive controller includes an adaptation loop which decreases the influence of communication delay on the control performance. Also, the predictive controller contains a filter which improves the robustness of the control system. The performance of the proposed adaptive predictive controller is demonstrated by simulation results in comparison with PI controller and predictive controller with constant delay.Keywords: Predictive control, adaptation, communication delay, communication network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18543647 Prediction of Compressive Strength Using Artificial Neural Network
Authors: Vijay Pal Singh, Yogesh Chandra Kotiyal
Abstract:
Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-destructive techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.
Keywords: Rebound, ultra-sonic pulse, penetration, ANN, NDT, regression.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 43853646 Optimizing the Capacity of a Convolutional Neural Network for Image Segmentation and Pattern Recognition
Authors: Yalong Jiang, Zheru Chi
Abstract:
In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.Keywords: CNN, capsule network, capacity optimization, character recognition, data augmentation; semantic segmentation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7013645 Frequent and Systematic Timing Enhancement of Congestion Window in Typical Transmission Control Protocol
Authors: Ghassan A. Abed, Akbal O. Salman, Bayan M. Sabbar
Abstract:
Transmission Control Protocol (TCP) among the wired and wireless networks, it still has a practical problem; where the congestion control mechanism does not permit the data stream to get complete bandwidth over the existing network links. To solve this problem, many TCP protocols have been introduced with high speed performance. Therefore, an enhanced congestion window (cwnd) for the congestion control mechanism is proposed in this article to improve the performance of TCP by increasing the number of cycles of the new window to improve the transmitted packet number. The proposed algorithm used a new mechanism based on the available bandwidth of the connection to detect the capacity of network path in order to improve the regular clocking of congestion avoidance mechanism. The work in this paper based on using Network Simulator 2 (NS-2) to simulate the proposed algorithm.
Keywords: TCP, cwnd, Congestion Control, NS-2.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16603644 Global Electricity Consumption Estimation Using Particle Swarm Optimization (PSO)
Authors: E.Assareh, M.A. Behrang, R. Assareh, N. Hedayat
Abstract:
An integrated Artificial Neural Network- Particle Swarm Optimization (PSO) is presented for analyzing global electricity consumption. To aim this purpose, following steps are done: STEP 1: in the first step, PSO is applied in order to determine world-s oil, natural gas, coal and primary energy demand equations based on socio-economic indicators. World-s population, Gross domestic product (GDP), oil trade movement and natural gas trade movement are used as socio-economic indicators in this study. For each socio-economic indicator, a feed-forward back propagation artificial neural network is trained and projected for future time domain. STEP 2: in the second step, global electricity consumption is projected based on the oil, natural gas, coal and primary energy consumption using PSO. global electricity consumption is forecasted up to year 2040.
Keywords: Particle Swarm Optimization, Artificial NeuralNetworks, Fossil Fuels, Electricity, Forecasting.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15043643 Discrete Vector Control for Induction Motor Drives with the Rotor Time Constant Update
Authors: A.Larabi, M.S. Boucherit
Abstract:
In this paper, we investigated vector control of an induction machine taking into account discretization problems of the command. In the purpose to show how to include in a discrete model of this current control and with rotor time constant update. The results of simulation obtained are very satisfaisant. That was possible thanks to the good choice of the values of the parameters of the regulators used which shows, the founded good of the method used, for the choice of the parameters of the discrete regulators. The simulation results are presented at the end of this paper.
Keywords: Induction motor, discrete vector control, PIRegulator, transformation of park, PWM.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15123642 DocPro: A Framework for Processing Semantic and Layout Information in Business Documents
Authors: Ming-Jen Huang, Chun-Fang Huang, Chiching Wei
Abstract:
With the recent advance of the deep neural network, we observe new applications of NLP (natural language processing) and CV (computer vision) powered by deep neural networks for processing business documents. However, creating a real-world document processing system needs to integrate several NLP and CV tasks, rather than treating them separately. There is a need to have a unified approach for processing documents containing textual and graphical elements with rich formats, diverse layout arrangement, and distinct semantics. In this paper, a framework that fulfills this unified approach is presented. The framework includes a representation model definition for holding the information generated by various tasks and specifications defining the coordination between these tasks. The framework is a blueprint for building a system that can process documents with rich formats, styles, and multiple types of elements. The flexible and lightweight design of the framework can help build a system for diverse business scenarios, such as contract monitoring and reviewing.
Keywords: Document processing, framework, formal definition, machine learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6393641 An Approximation Method for Exact Boundary Controllability of Euler-Bernoulli System
Authors: Abdelaziz Khernane, Naceur Khelil, Leila Djerou
Abstract:
The aim of this work is to study the numerical implementation of the Hilbert Uniqueness Method for the exact boundary controllability of Euler-Bernoulli beam equation. This study may be difficult. This will depend on the problem under consideration (geometry, control and dimension) and the numerical method used. Knowledge of the asymptotic behaviour of the control governing the system at time T may be useful for its calculation. This idea will be developed in this study. We have characterized as a first step, the solution by a minimization principle and proposed secondly a method for its resolution to approximate the control steering the considered system to rest at time T.Keywords: Boundary control, exact controllability, finite difference methods, functional optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14873640 Experimental Investigation of a Novel Reaction in Reduction of Sulfates by Natural Gas as a Reducing Agent
Authors: Ali Ghiaseddin , Akram Nemati
Abstract:
In a pilot plant scale of a fluidized bed reactor, a reduction reaction of sodium sulfate by natural gas has been investigated. Natural gas is applied in this study as a reductant. Feed density, feed mass flow rate, natural gas and air flow rate (independent parameters)and temperature of bed and CO concentration in inlet and outlet of reactor (dependent parameters) were monitored and recorded at steady state. The residence time was adjusted close to value of traditional reaction [1]. An artificial neural network (ANN) was established to study dependency of yield and carbon gradient on operating parameters. Resultant 97% accuracy of applied ANN is a good prove that natural gas can be used as a reducing agent. Predicted ANN model for relation between other sources carbon gradient (accuracy 74%) indicates there is not a meaningful relation between other sources carbon variation and reduction process which means carbon in granule does not have significant effect on the reaction yield.Keywords: reduction by natural gas, fluidized bed, sulfate, sulfide, artificial neural network
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15223639 Using Support Vector Machine for Prediction Dynamic Voltage Collapse in an Actual Power System
Authors: Muhammad Nizam, Azah Mohamed, Majid Al-Dabbagh, Aini Hussain
Abstract:
This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.Keywords: Dynamic voltage collapse, prediction, artificial neural network, support vector machines
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18163638 Improvement in Power Transformer Intelligent Dissolved Gas Analysis Method
Authors: S. Qaedi, S. Seyedtabaii
Abstract:
Non-Destructive evaluation of in-service power transformer condition is necessary for avoiding catastrophic failures. Dissolved Gas Analysis (DGA) is one of the important methods. Traditional, statistical and intelligent DGA approaches have been adopted for accurate classification of incipient fault sources. Unfortunately, there are not often enough faulty patterns required for sufficient training of intelligent systems. By bootstrapping the shortcoming is expected to be alleviated and algorithms with better classification success rates to be obtained. In this paper the performance of an artificial neural network, K-Nearest Neighbour and support vector machine methods using bootstrapped data are detailed and shown that while the success rate of the ANN algorithms improves remarkably, the outcome of the others do not benefit so much from the provided enlarged data space. For assessment, two databases are employed: IEC TC10 and a dataset collected from reported data in papers. High average test success rate well exhibits the remarkable outcome.Keywords: Dissolved gas analysis, Transformer incipient fault, Artificial Neural Network, Support Vector Machine (SVM), KNearest Neighbor (KNN)
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27393637 Machine Learning Techniques in Bank Credit Analysis
Authors: Fernanda M. Assef, Maria Teresinha A. Steiner
Abstract:
The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.
Keywords: Artificial Neural Networks, ANNs, classifier algorithms, credit risk assessment, logistic regression, machine learning, support vector machines.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12813636 Robust Heart Sounds Segmentation Based on the Variation of the Phonocardiogram Curve Length
Authors: Mecheri Zeid Belmecheri, Maamar Ahfir, Izzet Kale
Abstract:
Automatic cardiac auscultation is still a subject of research in order to establish an objective diagnosis. Recorded heart sounds as Phonocardiogram (PCG) signals can be used for automatic segmentation into components that have clinical meanings. These are the first sound, S1, the second sound, S2, and the systolic and diastolic components, respectively. In this paper, an automatic method is proposed for the robust segmentation of heart sounds. This method is based on calculating an intermediate sawtooth-shaped signal from the length variation of the recorded PCG signal in the time domain and, using its positive derivative function that is a binary signal in training a Recurrent Neural Network (RNN). Results obtained in the context of a large database of recorded PCGs with their simultaneously recorded Electrocardiograms (ECGs) from different patients in clinical settings, including normal and abnormal subjects, show on average a segmentation testing performance average of 76% sensitivity and 94% specificity.
Keywords: Heart sounds, PCG segmentation, event detection, Recurrent Neural Networks, PCG curve length.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3213635 Forecasting Electricity Spot Price with Generalized Long Memory Modeling: Wavelet and Neural Network
Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem
Abstract:
This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.Keywords: k-factor, GARMA, LLWNN, G-GARCH, electricity price, forecasting.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9953634 Actuator Fault Detection and Fault Tolerant Control of a Nonlinear System Using Sliding Mode Observer
Authors: R. Loukil, M. Chtourou, T. Damak
Abstract:
In this work, we use the Fault detection and isolation and the Fault tolerant control based on sliding mode observer in order to introduce the well diagnosis of a nonlinear system. The robustness of the proposed observer for the two techniques is tested through a physical example. The results in this paper show the interaction between the Fault tolerant control and the Diagnosis procedure.Keywords: Fault detection and isolation “FDI”, Fault tolerant control “FTC”, sliding mode observer, nonlinear system, robustness, stability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16533633 The Stability Analysis and New Torque Control Strategy of Direct-Driven PMSG Wind Turbines
Authors: Jun Liu, Feihang Zhou, Gungyi Wang
Abstract:
This paper expounds on the direct-driven PMSG wind power system control strategy, and analyses the stability conditions of the system. The direct-driven PMSG wind power system may generate the intense mechanical vibration, when wind speed changes dramatically. This paper proposes a new type of torque control strategy, which increases the system damping effectively, mitigates mechanical vibration of the system, and enhances the stability conditions of the system. The simulation results verify the reliability of the new torque control strategy.
Keywords: Damping, direct-driven PMSG wind power system, mechanical vibration, torque control.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13913632 Nonlinear Model Predictive Swing-Up and Stabilizing Sliding Mode Controllers
Authors: S. Kahvecioglu, A. Karamancioglu, A. Yazici
Abstract:
In this paper, a nonlinear model predictive swing-up and stabilizing sliding controller is proposed for an inverted pendulum-cart system. In the swing up phase, the nonlinear model predictive control is formulated as a nonlinear programming problem with energy based objective function. By solving this problem at each sampling instant, a sequence of control inputs that optimize the nonlinear objective function subject to various constraints over a finite horizon are obtained. Then, this control drives the pendulum to a predefined neighborhood of the upper equilibrium point, at where sliding mode based model predictive control is used to stabilize the systems with the specified constraints. It is shown by the simulations that, due to the way of formulating the problem, short horizon lengths are sufficient for attaining the swing up goal.Keywords: Inverted pendulum, model predictive control, swingup, stabilization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21933631 Evaluation of New Product Development Projects using Artificial Intelligence and Fuzzy Logic
Authors: Orhan Feyzioğlu, Gülçin Büyüközkan
Abstract:
As a vital activity for companies, new product development (NPD) is also a very risky process due to the high uncertainty degree encountered at every development stage and the inevitable dependence on how previous steps are successfully accomplished. Hence, there is an apparent need to evaluate new product initiatives systematically and make accurate decisions under uncertainty. Another major concern is the time pressure to launch a significant number of new products to preserve and increase the competitive power of the company. In this work, we propose an integrated decision-making framework based on neural networks and fuzzy logic to make appropriate decisions and accelerate the evaluation process. We are especially interested in the two initial stages where new product ideas are selected (go/no go decision) and the implementation order of the corresponding projects are determined. We show that this two-staged intelligent approach allows practitioners to roughly and quickly separate good and bad product ideas by making use of previous experiences, and then, analyze a more shortened list rigorously.Keywords: Decision Making, Neural Networks, Fuzzy Theory and Systems, Choquet Integral, New Product Development.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28333630 Dynamic Admission Control for Quality of Service in IP Networks
Authors: J. Kasigwa, V. Baryamureeba, D. Williams
Abstract:
The goal of admission control is to support the Quality of Service demands of real-time applications via resource reservation in IP networks. In this paper we introduce a novel Dynamic Admission Control (DAC) mechanism for IP networks. The DAC dynamically allocates network resources using the previous network pattern for each path and uses the dynamic admission algorithm to improve bandwidth utilization using bandwidth brokers. We evaluate the performance of the proposed mechanism through trace-driven simulation experiments in view point of blocking probability, throughput and normalized utilization.Keywords: Bandwidth broker, dynamic admission control(DAC), IP networks, quality of service, real-time flows.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12943629 Evaluation of the ANN Based Nonlinear System Models in the MSE and CRLB Senses
Authors: M.V Rajesh, Archana R, A Unnikrishnan, R Gopikakumari, Jeevamma Jacob
Abstract:
The System Identification problem looks for a suitably parameterized model, representing a given process. The parameters of the model are adjusted to optimize a performance function based on error between the given process output and identified process output. The linear system identification field is well established with many classical approaches whereas most of those methods cannot be applied for nonlinear systems. The problem becomes tougher if the system is completely unknown with only the output time series is available. It has been reported that the capability of Artificial Neural Network to approximate all linear and nonlinear input-output maps makes it predominantly suitable for the identification of nonlinear systems, where only the output time series is available. [1][2][4][5]. The work reported here is an attempt to implement few of the well known algorithms in the context of modeling of nonlinear systems, and to make a performance comparison to establish the relative merits and demerits.Keywords: Multilayer neural networks, Radial Basis Functions, Clustering algorithm, Back Propagation training, Extended Kalmanfiltering, Mean Square Error, Nonlinear Modeling, Cramer RaoLower Bound.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16463628 Predicting Protein Interaction Sites Based on a New Integrated Radial Basis Functional Neural Network
Authors: Xiaoli Shen, Yuehui Chen
Abstract:
Interactions among proteins are the basis of various life events. So, it is important to recognize and research protein interaction sites. A control set that contains 149 protein molecules were used here. Then 10 features were extracted and 4 sample sets that contained 9 sliding windows were made according to features. These 4 sample sets were calculated by Radial Basis Functional neutral networks which were optimized by Particle Swarm Optimization respectively. Then 4 groups of results were obtained. Finally, these 4 groups of results were integrated by decision fusion (DF) and Genetic Algorithm based Selected Ensemble (GASEN). A better accuracy was got by DF and GASEN. So, the integrated methods were proved to be effective.Keywords: protein interaction sites, features, sliding windows, radial basis functional neutral networks, genetic algorithm basedselected ensemble.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14213627 GSM-Based Approach for Indoor Localization
Authors: M.Stella, M. Russo, D. Begušić
Abstract:
Ability of accurate and reliable location estimation in indoor environment is the key issue in developing great number of context aware applications and Location Based Services (LBS). Today, the most viable solution for localization is the Received Signal Strength (RSS) fingerprinting based approach using wireless local area network (WLAN). This paper presents two RSS fingerprinting based approaches – first we employ widely used WLAN based positioning as a reference system and then investigate the possibility of using GSM signals for positioning. To compare them, we developed a positioning system in real world environment, where realistic RSS measurements were collected. Multi-Layer Perceptron (MLP) neural network was used as the approximation function that maps RSS fingerprints and locations. Experimental results indicate advantage of WLAN based approach in the sense of lower localization error compared to GSM based approach, but GSM signal coverage by far outreaches WLAN coverage and for some LBS services requiring less precise accuracy our results indicate that GSM positioning can also be a viable solution.Keywords: Indoor positioning, WLAN, GSM, RSS, location fingerprints, neural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27473626 Dynamic Modelling and Virtual Simulation of Digital Duty-Cycle Modulation Control Drivers
Authors: J. Mbihi
Abstract:
This paper presents a dynamic architecture of digital duty-cycle modulation control drivers. Compared to most oversampling digital modulation schemes encountered in industrial electronics, its novelty is founded on a number of relevant merits including; embedded positive and negative feedback loops, internal modulation clock, structural simplicity, elementary building operators, no explicit need of samples of the nonlinear duty-cycle function when computing the switching modulated signal, and minimum number of design parameters. A prototyping digital control driver is synthesized and well tested within MATLAB/Simulink workspace. Then, the virtual simulation results and performance obtained under a sample of relevant instrumentation and control systems are presented, in order to show the feasibility, the reliability, and the versatility of target applications, of the proposed class of low cost and high quality digital control drivers in industrial electronics.
Keywords: Dynamic architecture, virtual simulation, duty-cycle modulation, digital control drivers, industrial electronics.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11383625 Artificial Neural Network Modeling of a Closed Loop Pulsating Heat Pipe
Authors: Vipul M. Patel, Hemantkumar B. Mehta
Abstract:
Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.
Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11843624 Traffic Signal Coordinated Control Optimization: A Case Study
Authors: Pengdi Diao, Zhuo Wang, Zundong Zhang, Hua Cheng
Abstract:
In the urban traffic network, the intersections are the “bottleneck point" of road network capacity. And the arterials are the main body in road network and the key factor which guarantees the normal operation of the city-s social and economic activities. The rapid increase in vehicles leads to seriously traffic jam and cause the increment of vehicles- delay. Most cities of our country are traditional single control system, which cannot meet the need for the city traffic any longer. In this paper, Synchro6.0 as a platform to minimize the intersection delay, optimizesingle signal cycle and split for Zhonghua Street in Handan City. Meanwhile, linear control system uses to optimize the phase for the t arterial road in this system. Comparing before and after use the control, capacities and service levels of this road and the adjacent road have improved significantly.Keywords: linear control system; delay mode; signal optimization; synchro6.0 simulation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2125