Search results for: Membership function.
1873 PID Parameter Optimization of an UAV Longitudinal Flight Control System
Authors: Kamran Turkoglu, Ugur Ozdemir, Melike Nikbay, Elbrous M. Jafarov
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In this paper, an automatic control system design based on Integral Squared Error (ISE) parameter optimization technique has been implemented on longitudinal flight dynamics of an UAV. It has been aimed to minimize the error function between the reference signal and the output of the plant. In the following parts, objective function has been defined with respect to error dynamics. An unconstrained optimization problem has been solved analytically by using necessary and sufficient conditions of optimality, optimum PID parameters have been obtained and implemented in control system dynamics.Keywords: Optimum Design, KKT Conditions, UAV, Longitudinal Flight Dynamics, ISE Parameter Optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 37281872 A Stochastic Diffusion Process Based on the Two-Parameters Weibull Density Function
Authors: Meriem Bahij, Ahmed Nafidi, Boujemâa Achchab, Sílvio M. A. Gama, José A. O. Matos
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Stochastic modeling concerns the use of probability to model real-world situations in which uncertainty is present. Therefore, the purpose of stochastic modeling is to estimate the probability of outcomes within a forecast, i.e. to be able to predict what conditions or decisions might happen under different situations. In the present study, we present a model of a stochastic diffusion process based on the bi-Weibull distribution function (its trend is proportional to the bi-Weibull probability density function). In general, the Weibull distribution has the ability to assume the characteristics of many different types of distributions. This has made it very popular among engineers and quality practitioners, who have considered it the most commonly used distribution for studying problems such as modeling reliability data, accelerated life testing, and maintainability modeling and analysis. In this work, we start by obtaining the probabilistic characteristics of this model, as the explicit expression of the process, its trends, and its distribution by transforming the diffusion process in a Wiener process as shown in the Ricciaardi theorem. Then, we develop the statistical inference of this model using the maximum likelihood methodology. Finally, we analyse with simulated data the computational problems associated with the parameters, an issue of great importance in its application to real data with the use of the convergence analysis methods. Overall, the use of a stochastic model reflects only a pragmatic decision on the part of the modeler. According to the data that is available and the universe of models known to the modeler, this model represents the best currently available description of the phenomenon under consideration.Keywords: Diffusion process, discrete sampling, likelihood estimation method, simulation, stochastic diffusion equation, trends functions, bi-parameters Weibull density function.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19581871 Mamdani Model based Adaptive Neural Fuzzy Inference System and its Application
Authors: Yuanyuan Chai, Limin Jia, Zundong Zhang
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Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion on Simulation Mechanism Based (SMB) classification method and composite patterns, this paper presents the Mamdani model based Adaptive Neural Fuzzy Inference System (M-ANFIS) and weight updating formula in consideration with qualitative representation of inference consequent parts in fuzzy neural networks. M-ANFIS model adopts Mamdani fuzzy inference system which has advantages in consequent part. Experiment results of applying M-ANFIS to evaluate traffic Level of service show that M-ANFIS, as a new hybrid algorithm in computational intelligence, has great advantages in non-linear modeling, membership functions in consequent parts, scale of training data and amount of adjusted parameters.Keywords: Fuzzy neural networks, Mamdani fuzzy inference, M-ANFIS
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 52271870 Implementation of an Associative Memory Using a Restricted Hopfield Network
Authors: Tet H. Yeap
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An analog restricted Hopfield Network is presented in this paper. It consists of two layers of nodes, visible and hidden nodes, connected by directional weighted paths forming a bipartite graph with no intralayer connection. An energy or Lyapunov function was derived to show that the proposed network will converge to stable states. By introducing hidden nodes, the proposed network can be trained to store patterns and has increased memory capacity. Training to be an associative memory, simulation results show that the associative memory performs better than a classical Hopfield network by being able to perform better memory recall when the input is noisy.Keywords: Associative memory, Hopfield network, Lyapunov function, Restricted Hopfield network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4681869 An Effective Framework for Chinese Syntactic Parsing
Authors: Xing Li, Chengqing Zong
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This paper presents an effective framework for Chinesesyntactic parsing, which includes two parts. The first one is a parsing framework, which is based on an improved bottom-up chart parsingalgorithm, and integrates the idea of the beam search strategy of N bestalgorithm and heuristic function of A* algorithm for pruning, then get multiple parsing trees. The second is a novel evaluation model, which integrates contextual and partial lexical information into traditional PCFG model and defines a new score function. Using this model, the tree with the highest score is found out as the best parsing tree. Finally,the contrasting experiment results are given. Keywords?syntactic parsing, PCFG, pruning, evaluation model.
Keywords: syntactic parsing, PCFG, pruning, evaluation model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12051868 An Estimation of Variance Components in Linear Mixed Model
Authors: Shuimiao Wan, Chao Yuan, Baoguang Tian
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In this paper, a linear mixed model which has two random effects is broken up into two models. This thesis gets the parameter estimation of the original model and an estimation’s statistical qualities based on these two models. Then many important properties are given by comparing this estimation with other general estimations. At the same time, this paper proves the analysis of variance estimate (ANOVAE) about σ2 of the original model is equal to the least-squares estimation (LSE) about σ2 of these two models. Finally, it also proves that this estimation is better than ANOVAE under Stein function and special condition in some degree.Keywords: Linear mixed model, Random effects, Parameter estimation, Stein function.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18071867 Hybrid Function Method for Solving Nonlinear Fredholm Integral Equations of the Second Kind
Authors: jianhua Hou, Changqing Yang, and Beibo Qin
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A numerical method for solving nonlinear Fredholm integral equations of second kind is proposed. The Fredholm type equations which have many applications in mathematical physics are then considered. The method is based on hybrid function approximations. The properties of hybrid of block-pulse functions and Chebyshev polynomials are presented and are utilized to reduce the computation of nonlinear Fredholm integral equations to a system of nonlinear. Some numerical examples are selected to illustrate the effectiveness and simplicity of the method.
Keywords: Hybrid functions, Fredholm integral equation, Blockpulse, Chebyshev polynomials, product operational matrix.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13821866 Improved Robust Stability Criteria of a Class of Neutral Lur’e Systems with Interval Time-Varying Delays
Authors: Longqiao Zhou, Zixin Liu, Shu Lü
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This paper addresses the robust stability problem of a class of delayed neutral Lur’e systems. Combined with the property of convex function and double integral Jensen inequality, a new tripe integral Lyapunov functional is constructed to derive some new stability criteria. Compared with some related results, the new criteria established in this paper are less conservative. Finally, two numerical examples are presented to illustrate the validity of the main results.
Keywords: Lur’e system, Convex function, Jensen integral inequality, Triple-integral method, Exponential stability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15061865 Analysis of Train Passenger Seat Using Ergonomic Function Deployment Method
Authors: Robertoes K. K. Wibowo, Siswoyo Soekarno, Irma Puspitasari
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Indonesian people use trains for their transportation, especially they use economy class train transportation because it is cheaper and has a more precise schedule than any other ground transportation. Nevertheless, the economy class passenger seat raises some inconvenience issues for passengers. This is due to the design of the chair on the economic class of trains that did not adjusted to the shape of anthropometry of Indonesian people. Thus, research needs to be conducted on the design of the seats in the economic class of trains. The purpose of this research is to make the design of economy class passenger seats ergonomic. This research method uses questionnaires and anthropometry measurements. The data obtained is processed using House of Quality of Ergonomic Function Development. From the results of analysis and data processing were obtained important changes from the original design. Ergonomic chair design according to the analysis is a stainless steel frame, seat height 390 mm, with a seat width for each passenger of 400 mm and a depth of 400 mm. Design of the backrest has a height of 840 mm, width of 430 mm and length of 300 mm that can move at the angle of 105-115 degrees. The width of the footrest is 42 mm and 400 mm length. The thickness of the seat cushion is 100 mm.
Keywords: Chair, ergonomics, function development, train passenger.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18091864 An Antibacterial Dental Restorative Containing 3,4-Dichlorocrotonolactone: Synthesis, Formulation and Evaluation
Authors: Dong Xie, Leah Howard, Yiming Weng
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The objective of this study was to synthesize and characterize 5-acryloyloxy-3,4-dichlorocrotonolactone (a furanone derivative), use this derivative to modify a dental restorative, and study the effect of the derivative on the antibacterial activity and compressive strength of the formed restorative. In this study, a furanone derivative was synthesized, characterized, and used to formulate a dental restorative. Compressive strength (CS) and S. mutans viability were used to evaluate the mechanical strength and antibacterial activity of the formed restorative. The fabricated restorative specimens were photocured and conditioned in distilled water at 37oC for 24 h, followed by direct testing for CS or/and incubating with S. mutans for 48 h for antibacterial testing. The results show that the modified dental restorative showed a significant antibacterial activity without substantially decreasing the mechanical strengths. With addition of the antibacterial derivative up to 30%, the restorative kept its original CS nearly unchanged but showed a significant antibacterial activity with 68% reduction in the S. mutans viability. Furthermore, the antibacterial function of the modified restorative was not affected by human saliva. The aging study also indicates that the modified restorative may have a long-lasting antibacterial function. It is concluded that this experimental antibacterial restorative may potentially be developed into a clinically attractive dental filling restorative due to its high mechanical strength and antibacterial function.
Keywords: Antibacterial, dental filling restorative, compressive strength, S. mutans viability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19341863 Power Line Carrier for Power Telemetering
Authors: Tosaphol Ratniyomchai, Uthai Jaithong, Thanatchai Kulworawanichpong
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This paper presents an application of power line carrier (PLC) for electrical power telemetering. This system has a special capability of transmitting the measured values to a centralized computer via power lines. The PLC modem as a passive high-pass filter is designed for transmitting and receiving information. Its function is to send the information carrier together with transmitted data by superimposing it on the 50 Hz power frequency signal. A microcontroller is employed to function as the main processing of the modem. It is programmed for PLC control and interfacing with other devices. Each power meter, connected via a PLC modem, is assigned with a unique identification number (address) for distinguishing each device from one another.Keywords: Power telemetering, Power line carrier, High-passfilter, Digital data transmission
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 29531862 Neural Network Learning Based on Chaos
Authors: Truong Quang Dang Khoa, Masahiro Nakagawa
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Chaos and fractals are novel fields of physics and mathematics showing up a new way of universe viewpoint and creating many ideas to solve several present problems. In this paper, a novel algorithm based on the chaotic sequence generator with the highest ability to adapt and reach the global optima is proposed. The adaptive ability of proposal algorithm is flexible in 2 steps. The first one is a breadth-first search and the second one is a depth-first search. The proposal algorithm is examined by 2 functions, the Camel function and the Schaffer function. Furthermore, the proposal algorithm is applied to optimize training Multilayer Neural Networks.
Keywords: learning and evolutionary computing, Chaos Optimization Algorithm, Artificial Neural Networks, nonlinear optimization, intelligent computational technologies.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17661861 Improved Segmentation of Speckled Images Using an Arithmetic-to-Geometric Mean Ratio Kernel
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In this work, we improve a previously developed segmentation scheme aimed at extracting edge information from speckled images using a maximum likelihood edge detector. The scheme was based on finding a threshold for the probability density function of a new kernel defined as the arithmetic mean-to-geometric mean ratio field over a circular neighborhood set and, in a general context, is founded on a likelihood random field model (LRFM). The segmentation algorithm was applied to discriminated speckle areas obtained using simple elliptic discriminant functions based on measures of the signal-to-noise ratio with fractional order moments. A rigorous stochastic analysis was used to derive an exact expression for the cumulative density function of the probability density function of the random field. Based on this, an accurate probability of error was derived and the performance of the scheme was analysed. The improved segmentation scheme performed well for both simulated and real images and showed superior results to those previously obtained using the original LRFM scheme and standard edge detection methods. In particular, the false alarm probability was markedly lower than that of the original LRFM method with oversegmentation artifacts virtually eliminated. The importance of this work lies in the development of a stochastic-based segmentation, allowing an accurate quantification of the probability of false detection. Non visual quantification and misclassification in medical ultrasound speckled images is relatively new and is of interest to clinicians.Keywords: Discriminant function, false alarm, segmentation, signal-to-noise ratio, skewness, speckle.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16441860 Explicit Solution of an Investment Plan for a DC Pension Scheme with Voluntary Contributions and Return Clause under Logarithm Utility
Authors: Promise A. Azor, Avievie Igodo, Esabai M. Ase
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The paper merged the return of premium clause and voluntary contributions to investigate retirees’ investment plan in a defined contributory (DC) pension scheme with a portfolio comprising of a risk-free asset and a risky asset whose price process is described by geometric Brownian motion (GBM). The paper considers additional voluntary contributions paid by members, charge on balance by pension fund administrators and the mortality risk of members of the scheme during the accumulation period by introducing return of premium clause. To achieve this, the Weilbull mortality force function is used to establish the mortality rate of members during accumulation phase. Furthermore, an optimization problem from the Hamilton Jacobi Bellman (HJB) equation is obtained using dynamic programming approach. Also, the Legendre transformation method is used to transform the HJB equation which is a nonlinear partial differential equation to a linear partial differential equation and solves the resultant equation for the value function and the optimal distribution plan under logarithm utility function. Finally, numerical simulations of the impact of some important parameters on the optimal distribution plan were obtained and it was observed that the optimal distribution plan is inversely proportional to the initial fund size, predetermined interest rate, additional voluntary contributions, charge on balance and instantaneous volatility.
Keywords: Legendre transform, logarithm utility, optimal distribution plan, return clause of premium, charge on balance, Weibull mortality function.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1761859 Particle Swarm Optimization Based Interconnected Hydro-Thermal AGC System Considering GRC and TCPS
Authors: Banaja Mohanty, Prakash Kumar Hota
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This paper represents performance of particle swarm optimisation (PSO) algorithm based integral (I) controller and proportional-integral controller (PI) for interconnected hydro-thermal automatic generation control (AGC) with generation rate constraint (GRC) and Thyristor controlled phase shifter (TCPS) in series with tie line. The control strategy of TCPS provides active control of system frequency. Conventional objective function integral square error (ISE) and another objective function considering square of derivative of change in frequencies of both areas and change in tie line power are considered. The aim of designing the objective function is to suppress oscillation in frequency deviations and change in tie line power oscillation. The controller parameters are searched by PSO algorithm by minimising the objective functions. The dynamic performance of the controllers I and PI, for both the objective functions, are compared with conventionally optimized I controller.
Keywords: Automatic generation control (AGC), Generation rate constraint (GRC), Thyristor control phase shifter (TCPS), Particle swarm optimization (PSO).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21591858 A Bayesian Kernel for the Prediction of Protein- Protein Interactions
Authors: Hany Alashwal, Safaai Deris, Razib M. Othman
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Understanding proteins functions is a major goal in the post-genomic era. Proteins usually work in context of other proteins and rarely function alone. Therefore, it is highly relevant to study the interaction partners of a protein in order to understand its function. Machine learning techniques have been widely applied to predict protein-protein interactions. Kernel functions play an important role for a successful machine learning technique. Choosing the appropriate kernel function can lead to a better accuracy in a binary classifier such as the support vector machines. In this paper, we describe a Bayesian kernel for the support vector machine to predict protein-protein interactions. The use of Bayesian kernel can improve the classifier performance by incorporating the probability characteristic of the available experimental protein-protein interactions data that were compiled from different sources. In addition, the probabilistic output from the Bayesian kernel can assist biologists to conduct more research on the highly predicted interactions. The results show that the accuracy of the classifier has been improved using the Bayesian kernel compared to the standard SVM kernels. These results imply that protein-protein interaction can be predicted using Bayesian kernel with better accuracy compared to the standard SVM kernels.Keywords: Bioinformatics, Protein-protein interactions, Bayesian Kernel, Support Vector Machines.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21511857 Lithofacies Classification from Well Log Data Using Neural Networks, Interval Neutrosophic Sets and Quantification of Uncertainty
Authors: Pawalai Kraipeerapun, Chun Che Fung, Kok Wai Wong
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This paper proposes a novel approach to the question of lithofacies classification based on an assessment of the uncertainty in the classification results. The proposed approach has multiple neural networks (NN), and interval neutrosophic sets (INS) are used to classify the input well log data into outputs of multiple classes of lithofacies. A pair of n-class neural networks are used to predict n-degree of truth memberships and n-degree of false memberships. Indeterminacy memberships or uncertainties in the predictions are estimated using a multidimensional interpolation method. These three memberships form the INS used to support the confidence in results of multiclass classification. Based on the experimental data, our approach improves the classification performance as compared to an existing technique applied only to the truth membership. In addition, our approach has the capability to provide a measure of uncertainty in the problem of multiclass classification.
Keywords: Multiclass classification, feed-forward backpropagation neural network, interval neutrosophic sets, uncertainty.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16221856 Adaptive Digital Watermarking Integrating Fuzzy Inference HVS Perceptual Model
Authors: Sherin M. Youssef, Ahmed Abouelfarag, Noha M. Ghatwary
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An adaptive Fuzzy Inference Perceptual model has been proposed for watermarking of digital images. The model depends on the human visual characteristics of image sub-regions in the frequency multi-resolution wavelet domain. In the proposed model, a multi-variable fuzzy based architecture has been designed to produce a perceptual membership degree for both candidate embedding sub-regions and strength watermark embedding factor. Different sizes of benchmark images with different sizes of watermarks have been applied on the model. Several experimental attacks have been applied such as JPEG compression, noises and rotation, to ensure the robustness of the scheme. In addition, the model has been compared with different watermarking schemes. The proposed model showed its robustness to attacks and at the same time achieved a high level of imperceptibility.Keywords: Watermarking, The human visual system (HVS), Fuzzy Inference System (FIS), Local Binary Pattern (LBP), Discrete Wavelet Transform (DWT).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18041855 Enhancement of Capacity in a MC-CDMA based Cognitive Radio Network Using Non-Cooperative Game Model
Authors: Kalyani J. Kulkarni, Bharat S. Chaudhari
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This paper addresses the issue of resource allocation in the emerging cognitive technology. Focusing the Quality of Service (QoS) of Primary Users (PU), a novel method is proposed for the resource allocation of Secondary Users (SU). In this paper, we propose the unique Utility Function in the game theoretic model of Cognitive Radio which can be maximized to increase the capacity of the Cognitive Radio Network (CRN) and to minimize the interference scenario. Utility function is formulated to cater the need of PUs by observing Signal to Noise ratio. Existence of Nash Equilibrium for the postulated game is established.Keywords: Cognitive Networks, Game Theory, Nash Equilibrium, Resource Allocation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17281854 Effect of Size of the Step in the Response Surface Methodology using Nonlinear Test Functions
Authors: Jesús Everardo Olguín Tiznado, Rafael García Martínez, Claudia Camargo Wilson, Juan Andrés López Barreras, Everardo Inzunza González, Javier Ordorica Villalvazo
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The response surface methodology (RSM) is a collection of mathematical and statistical techniques useful in the modeling and analysis of problems in which the dependent variable receives the influence of several independent variables, in order to determine which are the conditions under which should operate these variables to optimize a production process. The RSM estimated a regression model of first order, and sets the search direction using the method of maximum / minimum slope up / down MMS U/D. However, this method selects the step size intuitively, which can affect the efficiency of the RSM. This paper assesses how the step size affects the efficiency of this methodology. The numerical examples are carried out through Monte Carlo experiments, evaluating three response variables: efficiency gain function, the optimum distance and the number of iterations. The results in the simulation experiments showed that in response variables efficiency and gain function at the optimum distance were not affected by the step size, while the number of iterations is found that the efficiency if it is affected by the size of the step and function type of test used.Keywords: RSM, dependent variable, independent variables, efficiency, simulation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19751853 Ordinal Regression with Fenton-Wilkinson Order Statistics: A Case Study of an Orienteering Race
Authors: Joonas Pääkkönen
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In sports, individuals and teams are typically interested in final rankings. Final results, such as times or distances, dictate these rankings, also known as places. Places can be further associated with ordered random variables, commonly referred to as order statistics. In this work, we introduce a simple, yet accurate order statistical ordinal regression function that predicts relay race places with changeover-times. We call this function the Fenton-Wilkinson Order Statistics model. This model is built on the following educated assumption: individual leg-times follow log-normal distributions. Moreover, our key idea is to utilize Fenton-Wilkinson approximations of changeover-times alongside an estimator for the total number of teams as in the notorious German tank problem. This original place regression function is sigmoidal and thus correctly predicts the existence of a small number of elite teams that significantly outperform the rest of the teams. Our model also describes how place increases linearly with changeover-time at the inflection point of the log-normal distribution function. With real-world data from Jukola 2019, a massive orienteering relay race, the model is shown to be highly accurate even when the size of the training set is only 5% of the whole data set. Numerical results also show that our model exhibits smaller place prediction root-mean-square-errors than linear regression, mord regression and Gaussian process regression.Keywords: Fenton-Wilkinson approximation, German tank problem, log-normal distribution, order statistics, ordinal regression, orienteering, sports analytics, sports modeling.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8021852 Reliability Indices Evaluation of SEIG Rotor Core Magnetization with Minimum Capacitive Excitation for WECs
Authors: Lokesh Varshney, R. K. Saket
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This paper presents reliability indices evaluation of the rotor core magnetization of the induction motor operated as a self excited induction generator by using probability distribution approach and Monte Carlo simulation. Parallel capacitors with calculated minimum capacitive value across the terminals of the induction motor operated as a SEIG with unregulated shaft speed have been connected during the experimental study. A three phase, 4 poles, 50Hz, 5.5 hp, 12.3A, 230V induction motor coupled with DC Shunt Motor was tested in the electrical machine laboratory with variable reactive loads. Based on this experimental study, it is possible to choose a reliable induction machines operated as a SEIG for unregulated renewable energy application in remote area or where grid is not available. Failure density function, cumulative failure distribution function, survivor function, hazard model, probability of success and probability of failure for reliability evaluation of the three phase induction motor operating as a SEIG have been presented graphically in this paper.
Keywords: Residual magnetism, magnetization curve, induction motor, self excited induction generator, probability distribution, Monte Carlo simulation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21141851 GMDH Modeling Based on Polynomial Spline Estimation and Its Applications
Authors: LI qiu-min, TIAN yi-xiang, ZHANG gao-xun
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GMDH algorithm can well describe the internal structure of objects. In the process of modeling, automatic screening of model structure and variables ensure the convergence rate.This paper studied a new GMDH model based on polynomial spline stimation. The polynomial spline function was used to instead of the transfer function of GMDH to characterize the relationship between the input variables and output variables. It has proved that the algorithm has the optimal convergence rate under some conditions. The empirical results show that the algorithm can well forecast Consumer Price Index (CPI).
Keywords: spline, GMDH, nonparametric, bias, forecast.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21141850 Simulation of Robotic Arm using Genetic Algorithm and AHP
Authors: V. K. Banga, Y. Singh, R. Kumar
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In this paper, we have proposed a low cost optimized solution for the movement of a three-arm manipulator using Genetic Algorithm (GA) and Analytical Hierarchy Process (AHP). A scheme is given for optimizing the movement of robotic arm with the help of Genetic Algorithm so that the minimum energy consumption criteria can be achieved. As compared to Direct Kinematics, Inverse Kinematics evolved two solutions out of which the best-fit solution is selected with the help of Genetic Algorithm and is kept in search space for future use. The Inverse Kinematics, Fitness Value evaluation and Binary Encoding like tasks are simulated and tested. Although, three factors viz. Movement, Friction and Least Settling Time (or Min. Vibration) are used for finding the Fitness Function / Fitness Values, however some more factors can also be considered.Keywords: Inverse Kinematics, Genetic Algorithm (GA), Analytical Hierarchy Process (AHP), Fitness Value, Fitness Function.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 29501849 Effects of Virtual Reality on the Upper Extremity Spasticity and Motor Function in Patients with Stroke: A Single Blinded Randomized Controlled Trial
Authors: K. Afsahi, M. Soheilifar, S. H. Hosseini, O. S. Esmaeili, R. Kezemi, N. Mehrbod, N. Vahed, T. Hajiahmad, N. N. Ansari
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Background: Stroke is a disabling neurological disease. Rehabilitative therapies are important treatment methods. This clinical trial was done to compare the effects of virtual reality (VR) beside conventional rehabilitation versus conventional rehabilitation alone on the spasticity and motor function in stroke patients. Materials and methods: In this open-label randomized controlled clinical trial, 40 consecutive patients with stable first-ever ischemic stroke in the past three to 12 months that were referred to a rehabilitation clinic in Tehran, Iran in 2020 were enrolled. After signing the informed written consent form, subjects were randomly assigned by block randomization of five in each block as cases with 1:1 into two groups of 20 cases; conventional plus VR therapy group: 45-minute conventional therapy session plus 15-minute VR therapy, and conventional group: 60-minute conventional therapy session. VR rehabilitation is designed and developed with different stages. Outcomes were Modified Ashworth scale, Recovery Stage score for motor function, range of motion (ROM) of shoulder abduction/wrist extension, and patients’ satisfaction rate. Data were compared after study termination. Results: The satisfaction rate among the patients was significantly better in combination group (P = 0.003). Only wrist extension was varied between groups and was better in combination group. The variables generally had statistically significant difference (P < 0.05). Conclusion: VR plus conventional rehabilitation therapy is superior versus conventional rehabilitation alone on the wrist and elbow spasticity and motor function in patients with stroke.
Keywords: Stroke, virtual therapy, efficacy, rehabilitation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7231848 New Moment Rotation Model of Single Web Angle Connections
Authors: Zhengyi Kong, Seung-Eock Kim
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Single angle connections, which are bolted to the beam web and the column flange, are studied to investigate their moment-rotation behavior. Elastic–perfectly plastic material behavior is assumed. ABAQUS software is used to analyze the nonlinear behavior of a single angle connection. The identical geometric and material conditions with Lipson’s test are used for verifying finite element models. Since Kishi and Chen’s Power model and Lee and Moon’s Log model are accurate only for a limited range of mechanism, simpler and more accurate hyperbolic function models are proposed.Keywords: Single-web angle connections, finite element method, moment and rotation, hyperbolic function models.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22811847 Industrial Effects and Firm's Survival (Case Study: Iran- East Azarbaijan Province)
Authors: Ghaffar Tari
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The aim of this paper is to investigate the effect of mean size of industry on survival of new firms in East-Azarbaijan province through 1981-2006 using hazard function. So the effect of two variables including mean employment of industry and mean capital of industry are investigated on firm's survival. The Industry & Mine Ministry database has used for data gathering and the data are analyzed using the semi-parametric cox regression model. The results of this study shows that there is a meaningful negative relationship between mean capital of industry and firm's survival, but the mean employment of industry has no meaningful effect on survival of new firms.Keywords: Firm's Survival, Hazard Function, Mean Capital of Industry, Mean Employment of Industry.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12871846 Fuzzy Hyperbolization Image Enhancement and Artificial Neural Network for Anomaly Detection
Authors: Sri Hartati, 1Agus Harjoko, Brad G. Nickerson
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A prototype of an anomaly detection system was developed to automate process of recognizing an anomaly of roentgen image by utilizing fuzzy histogram hyperbolization image enhancement and back propagation artificial neural network. The system consists of image acquisition, pre-processor, feature extractor, response selector and output. Fuzzy Histogram Hyperbolization is chosen to improve the quality of the roentgen image. The fuzzy histogram hyperbolization steps consist of fuzzyfication, modification of values of membership functions and defuzzyfication. Image features are extracted after the the quality of the image is improved. The extracted image features are input to the artificial neural network for detecting anomaly. The number of nodes in the proposed ANN layers was made small. Experimental results indicate that the fuzzy histogram hyperbolization method can be used to improve the quality of the image. The system is capable to detect the anomaly in the roentgen image.Keywords: Image processing, artificial neural network, anomaly detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20941845 Image Compression with Back-Propagation Neural Network using Cumulative Distribution Function
Authors: S. Anna Durai, E. Anna Saro
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Image Compression using Artificial Neural Networks is a topic where research is being carried out in various directions towards achieving a generalized and economical network. Feedforward Networks using Back propagation Algorithm adopting the method of steepest descent for error minimization is popular and widely adopted and is directly applied to image compression. Various research works are directed towards achieving quick convergence of the network without loss of quality of the restored image. In general the images used for compression are of different types like dark image, high intensity image etc. When these images are compressed using Back-propagation Network, it takes longer time to converge. The reason for this is, the given image may contain a number of distinct gray levels with narrow difference with their neighborhood pixels. If the gray levels of the pixels in an image and their neighbors are mapped in such a way that the difference in the gray levels of the neighbors with the pixel is minimum, then compression ratio as well as the convergence of the network can be improved. To achieve this, a Cumulative distribution function is estimated for the image and it is used to map the image pixels. When the mapped image pixels are used, the Back-propagation Neural Network yields high compression ratio as well as it converges quickly.Keywords: Back-propagation Neural Network, Cumulative Distribution Function, Correlation, Convergence.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25411844 Unsupervised Clustering Methods for Identifying Rare Events in Anomaly Detection
Authors: Witcha Chimphlee, Abdul Hanan Abdullah, Mohd Noor Md Sap, Siriporn Chimphlee, Surat Srinoy
Abstract:
It is important problems to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Although preventative techniques such as access control and authentication attempt to prevent intruders, these can fail, and as a second line of defence, intrusion detection has been introduced. Rare events are events that occur very infrequently, detection of rare events is a common problem in many domains. In this paper we propose an intrusion detection method that combines Rough set and Fuzzy Clustering. Rough set has to decrease the amount of data and get rid of redundancy. Fuzzy c-means clustering allow objects to belong to several clusters simultaneously, with different degrees of membership. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999) Dataset show that the method is efficient and practical for intrusion detection systems.Keywords: Network and security, intrusion detection, fuzzy cmeans, rough set.
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