Search results for: nonlinear signal prediction
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3116

Search results for: nonlinear signal prediction

2396 Modeling and Stability Analysis of Delayed Game Network

Authors: Zixin Liu, Jian Yu, Daoyun Xu

Abstract:

This paper aims to establish a delayed dynamical relationship between payoffs of players in a zero-sum game. By introducing Markovian chain and time delay in the network model, a delayed game network model with sector bounds and slope bounds restriction nonlinear function is first proposed. As a result, a direct dynamical relationship between payoffs of players in a zero-sum game can be illustrated through a delayed singular system. Combined with Finsler-s Lemma and Lyapunov stable theory, a sufficient condition guaranteeing the unique existence and stability of zero-sum game-s Nash equilibrium is derived. One numerical example is presented to illustrate the validity of the main result.

Keywords: Game networks, zero-sum game, delayed singular system, nonlinear perturbation, time delay.

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2395 Reliability Analysis for Cyclic Fatigue Life Prediction in Railroad Bolt Hole

Authors: Hasan Keshavarzian, Tayebeh Nesari

Abstract:

Bolted rail joint is one of the most vulnerable areas in railway track. A comprehensive approach was developed for studying the reliability of fatigue crack initiation of railroad bolt hole under random axle loads and random material properties. The operation condition was also considered as stochastic variables. In order to obtain the comprehensive probability model of fatigue crack initiation life prediction in railroad bolt hole, we used FEM, response surface method (RSM), and reliability analysis. Combined energy-density based and critical plane based fatigue concept is used for the fatigue crack prediction. The dynamic loads were calculated according to the axle load, speed, and track properties. The results show that axle load is most sensitive parameter compared to Poisson’s ratio in fatigue crack initiation life. Also, the reliability index decreases slowly due to high cycle fatigue regime in this area.

Keywords: Rail-wheel tribology, rolling contact mechanic, finite element modeling, reliability analysis.

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2394 Distortion Estimation in Digital Image Watermarking using Genetic Programming

Authors: Labiba Gilani, Asifullah Khan, Anwar M. Mirza

Abstract:

This paper introduces a technique of distortion estimation in image watermarking using Genetic Programming (GP). The distortion is estimated by considering the problem of obtaining a distorted watermarked signal from the original watermarked signal as a function regression problem. This function regression problem is solved using GP, where the original watermarked signal is considered as an independent variable. GP-based distortion estimation scheme is checked for Gaussian attack and Jpeg compression attack. We have used Gaussian attacks of different strengths by changing the standard deviation. JPEG compression attack is also varied by adding various distortions. Experimental results demonstrate that the proposed technique is able to detect the watermark even in the case of strong distortions and is more robust against attacks.

Keywords: Blind Watermarking, Genetic Programming (GP), Fitness Function, Discrete Cosine Transform (DCT).

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2393 A Grid Synchronization Method Based on Adaptive Notch Filter for SPV System with Modified MPPT

Authors: Priyanka Chaudhary, M. Rizwan

Abstract:

This paper presents a grid synchronization technique based on adaptive notch filter for SPV (Solar Photovoltaic) system along with MPPT (Maximum Power Point Tracking) techniques. An efficient grid synchronization technique offers proficient detection of various components of grid signal like phase and frequency. It also acts as a barrier for harmonics and other disturbances in grid signal. A reference phase signal synchronized with the grid voltage is provided by the grid synchronization technique to standardize the system with grid codes and power quality standards. Hence, grid synchronization unit plays important role for grid connected SPV systems. As the output of the PV array is fluctuating in nature with the meteorological parameters like irradiance, temperature, wind etc. In order to maintain a constant DC voltage at VSC (Voltage Source Converter) input, MPPT control is required to track the maximum power point from PV array. In this work, a variable step size P & O (Perturb and Observe) MPPT technique with DC/DC boost converter has been used at first stage of the system. This algorithm divides the dPpv/dVpv curve of PV panel into three separate zones i.e. zone 0, zone 1 and zone 2. A fine value of tracking step size is used in zone 0 while zone 1 and zone 2 requires a large value of step size in order to obtain a high tracking speed. Further, adaptive notch filter based control technique is proposed for VSC in PV generation system. Adaptive notch filter (ANF) approach is used to synchronize the interfaced PV system with grid to maintain the amplitude, phase and frequency parameters as well as power quality improvement. This technique offers the compensation of harmonics current and reactive power with both linear and nonlinear loads. To maintain constant DC link voltage a PI controller is also implemented and presented in this paper. The complete system has been designed, developed and simulated using SimPower System and Simulink toolbox of MATLAB. The performance analysis of three phase grid connected solar photovoltaic system has been carried out on the basis of various parameters like PV output power, PV voltage, PV current, DC link voltage, PCC (Point of Common Coupling) voltage, grid voltage, grid current, voltage source converter current, power supplied by the voltage source converter etc. The results obtained from the proposed system are found satisfactory.

Keywords: Solar photovoltaic systems, MPPT, voltage source converter, grid synchronization technique.

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2392 Influence of Slenderness Ratio on the Ductility of Reinforced Concrete Portal Structures

Authors: Kahil Amar, Nekmouche Aghiles, Titouche Billal, Hamizi Mohand, Hannachi Naceur Eddine

Abstract:

The ductility is an important parameter in the nonlinear behavior of portal structures reinforced concrete. It may be explained by the ability of the structure to deform in the plastic range, or the geometric characteristics in the map may influence the overall ductility. Our study is based on the influence of geometric slenderness (Lx / Ly) on the overall ductility of these structures, a study is made on a structure has 05 floors with varying the column section of 900 cm², 1600 cm² and 1225 cm². A slight variation in global ductility is noticed as (Lx/Ly) varies; however, column sections can control satisfactorily the plastic behavior of buildings.

Keywords: Ductility, nonlinear behavior, pushover analysis, geometric slenderness, structural behavior.

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2391 Predicting Protein-Protein Interactions from Protein Sequences Using Phylogenetic Profiles

Authors: Omer Nebil Yaveroglu, Tolga Can

Abstract:

In this study, a high accuracy protein-protein interaction prediction method is developed. The importance of the proposed method is that it only uses sequence information of proteins while predicting interaction. The method extracts phylogenetic profiles of proteins by using their sequence information. Combining the phylogenetic profiles of two proteins by checking existence of homologs in different species and fitting this combined profile into a statistical model, it is possible to make predictions about the interaction status of two proteins. For this purpose, we apply a collection of pattern recognition techniques on the dataset of combined phylogenetic profiles of protein pairs. Support Vector Machines, Feature Extraction using ReliefF, Naive Bayes Classification, K-Nearest Neighborhood Classification, Decision Trees, and Random Forest Classification are the methods we applied for finding the classification method that best predicts the interaction status of protein pairs. Random Forest Classification outperformed all other methods with a prediction accuracy of 76.93%

Keywords: Protein Interaction Prediction, Phylogenetic Profile, SVM , ReliefF, Decision Trees, Random Forest Classification

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2390 'Performance-Based' Seismic Methodology and Its Application in Seismic Design of Reinforced Concrete Structures

Authors: Jelena R. Pejović, Nina N. Serdar

Abstract:

This paper presents an analysis of the “Performance-Based” seismic design method, in order to overcome the perceived disadvantages and limitations of the existing seismic design approach based on force, in engineering practice. Bearing in mind, the specificity of the earthquake as a load and the fact that the seismic resistance of the structures solely depends on its behaviour in the nonlinear field, traditional seismic design approach based on force and linear analysis is not adequate. “Performance-Based” seismic design method is based on nonlinear analysis and can be used in everyday engineering practice. This paper presents the application of this method to eight-story high reinforced concrete building with combined structural system (reinforced concrete frame structural system in one direction and reinforced concrete ductile wall system in other direction). The nonlinear time-history analysis is performed on the spatial model of the structure using program Perform 3D, where the structure is exposed to forty real earthquake records. For considered building, large number of results were obtained. It was concluded that using this method we could, with a high degree of reliability, evaluate structural behavior under earthquake. It is obtained significant differences in the response of structures to various earthquake records. Also analysis showed that frame structural system had not performed well at the effect of earthquake records on soil like sand and gravel, while a ductile wall system had a satisfactory behavior on different types of soils.

Keywords: Ductile wall, frame system, nonlinear time-history analysis, performance-based methodology, RC building.

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2389 A Comparative Study of Various Tone Mapping Methods

Authors: YasirSalih, AamirSaeed Malik, Wazirahbt.Md-Esa

Abstract:

In the recent years, high dynamic range imaging has gain popularity with the advancement in digital photography. In this contribution we present a subjective evaluation of various tone production and tone mapping techniques by a number of participants. Firstly, standard HDR images were used and the participants were asked to rate them based on a given rating scheme. After that, the participant was asked to rate HDR image generated using linear and nonlinear combination approach of multiple exposure images. The experimental results showed that linearly generated HDR images have better visualization than the nonlinear combined ones. In addition, Reinhard et al. and the exponential tone mapping operators have shown better results compared to logarithmic and the Garrett et al. tone mapping operators.

Keywords: tone mapping, high dynamic range, low dynamic range, bits per pixel.

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2388 Urban Big Data: An Experimental Approach to Building-Value Estimation Using Web-Based Data

Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

Abstract:

Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: Big data, building-value analysis, machine learning, price prediction.

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2387 Photonic Crystals for Novel Applications in Integrated-Optic Communication Systems and Devices

Authors: Vijay Janyani, Neetu Joshi, Jigyasa Pagaria, Parul Pathak

Abstract:

Photonic Crystal (PhC) based devices are being increasingly used in multifunctional, compact devices in integrated optical communication systems. They provide excellent controllability of light, yet maintaining the small size required for miniaturization. In this paper, the band gap properties of PhCs and their typical applications in optical waveguiding are considered. Novel PhC based applications such as nonlinear switching and tapers are considered and simulation results are shown using the accurate time-domain numerical method based on Finite Difference Time Domain (FDTD) scheme. The suitability of these devices for novel applications is discussed and evaluated.

Keywords: Band gap engineering, Nonlinear switching, Photonic crystals, PhC tapers, waveguides.

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2386 Sparse Frequencies Extracting from Partial Phase-Only Measurements

Authors: R. Fan, Q. Wan, H. Chen, Y.L. Liu, Y.P. Liu

Abstract:

This paper considers a robust recovery of sparse frequencies from partial phase-only measurements. With the proposed method, sparse frequencies can be reconstructed, which makes full use of the sparse distribution in the Fourier representation of the complex-valued time signal. Simulation experiments illustrate the proposed method-s advantages over conventional methods in both noiseless and additive white Gaussian noise cases.

Keywords: Sparse signal recovery, phase-only measurements, Compressive sensing, convex relaxation.

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2385 Dispenser Longitudinal Movement ControlDesign Based on Auto - Disturbances –Rejection - Controller

Authors: Qiaozhen Song

Abstract:

Based on the feature of model disturbances and uncertainty being compensated dynamically in auto – disturbances-rejection-controller (ADRC), a new method using ADRC is proposed for the decoupling control of dispenser longitudinal movement in big flight envelope. Developed from nonlinear model directly, ADRC is especially suitable for dynamic model that has big disturbances. Furthermore, without changing the structure and parameters of the controller in big flight envelope, this scheme can simplify the design of flight control system. The simulation results in big flight envelope show that the system achieves high dynamic performance, steady state performance and the controller has strong robustness.

Keywords: ADRC, ESO, nonlinear system

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2384 EEG Waves Classifier using Wavelet Transform and Fourier Transform

Authors: Maan M. Shaker

Abstract:

The electroencephalograph (EEG) signal is one of the most widely signal used in the bioinformatics field due to its rich information about human tasks. In this work EEG waves classification is achieved using the Discrete Wavelet Transform DWT with Fast Fourier Transform (FFT) by adopting the normalized EEG data. The DWT is used as a classifier of the EEG wave's frequencies, while FFT is implemented to visualize the EEG waves in multi-resolution of DWT. Several real EEG data sets (real EEG data for both normal and abnormal persons) have been tested and the results improve the validity of the proposed technique.

Keywords: Bioinformatics, DWT, EEG waves, FFT.

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2383 Approximated Solutions of Two-Point Nonlinear Boundary Problem by a Combination of Taylor Series Expansion and Newton Raphson Method

Authors: Chinwendu. B. Eleje, Udechukwu P. Egbuhuzor

Abstract:

One of the difficulties encountered in solving nonlinear Boundary Value Problems (BVP) by many researchers is finding approximated solutions with minimum deviations from the exact solutions without so much rigor and complications. In this paper, we propose an approach to solve a two point BVP which involves a combination of Taylor series expansion method and Newton Raphson method. Furthermore, the fourth and sixth order approximated solutions are obtained and we compare their relative error and rate of convergence to the exact solution. Finally, some numerical simulations are presented to show the behavior of the solution and its derivatives.

Keywords: Newton Raphson method, non-linear boundary value problem, Taylor series approximation, Michaelis-Menten equation.

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2382 On the Seismic Response of Collided Structures

Authors: George D. Hatzigeorgiou, Nikos G. Pnevmatikos

Abstract:

This study examines the inelastic behavior of adjacent planar reinforced concrete (R.C.) frames subjected to strong ground motions. The investigation focuses on the effects of vertical ground motion on the seismic pounding. The examined structures are modeled and analyzed by RUAUMOKO dynamic nonlinear analysis program using reliable hysteretic models for both structural members and contact elements. It is found that the vertical ground motion mildly affects the seismic response of adjacent buildings subjected to structural pounding and, for this reason, it can be ignored from the displacement and interstorey drifts assessment. However, the structural damage is moderately affected by the vertical component of earthquakes.

Keywords: Nonlinear seismic behavior, reinforced concrete structures, structural pounding, vertical ground motions.

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2381 Capability Prediction of Machining Processes Based on Uncertainty Analysis

Authors: Hamed Afrasiab, Saeed Khodaygan

Abstract:

Prediction of machining process capability in the design stage plays a key role to reach the precision design and manufacturing of mechanical products. Inaccuracies in machining process lead to errors in position and orientation of machined features on the part, and strongly affect the process capability in the final quality of the product. In this paper, an efficient systematic approach is given to investigate the machining errors to predict the manufacturing errors of the parts and capability prediction of corresponding machining processes. A mathematical formulation of fixture locators modeling is presented to establish the relationship between the part errors and the related sources. Based on this method, the final machining errors of the part can be accurately estimated by relating them to the combined dimensional and geometric tolerances of the workpiece – fixture system. This method is developed for uncertainty analysis based on the Worst Case and statistical approaches. The application of the presented method is illustrated through presenting an example and the computational results are compared with the Monte Carlo simulation results.

Keywords: Process capability, machining error, dimensional and geometrical tolerances, uncertainty analysis.

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2380 Weight Functions for Signal Reconstruction Based On Level Crossings

Authors: Nagesha, G. Hemantha Kumar

Abstract:

Although the level crossing concept has been the subject of intensive investigation over the last few years, certain problems of great interest remain unsolved. One of these concern is distribution of threshold levels. This paper presents a new threshold level allocation schemes for level crossing based on nonuniform sampling. Intuitively, it is more reasonable if the information rich regions of the signal are sampled finer and those with sparse information are sampled coarser. To achieve this objective, we propose non-linear quantization functions which dynamically assign the number of quantization levels depending on the importance of the given amplitude range. Two new approaches to determine the importance of the given amplitude segment are presented. The proposed methods are based on exponential and logarithmic functions. Various aspects of proposed techniques are discussed and experimentally validated. Its efficacy is investigated by comparison with uniform sampling.

Keywords: speech signals, sampling, signal reconstruction, asynchronousdelta modulation, non-linear quantization.

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2379 Complex-Valued Neural Network in Signal Processing: A Study on the Effectiveness of Complex Valued Generalized Mean Neuron Model

Authors: Anupama Pande, Ashok Kumar Thakur, Swapnoneel Roy

Abstract:

A complex valued neural network is a neural network which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in signal processing. In Neural networks, generalized mean neuron model (GMN) is often discussed and studied. The GMN includes a new aggregation function based on the concept of generalized mean of all the inputs to the neuron. This paper aims to present exhaustive results of using Generalized Mean Neuron model in a complex-valued neural network model that uses the back-propagation algorithm (called -Complex-BP-) for learning. Our experiments results demonstrate the effectiveness of a Generalized Mean Neuron Model in a complex plane for signal processing over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error required on a Generalized Mean neural network model. Some inherent properties of this complex back propagation algorithm are also studied and discussed.

Keywords: Complex valued neural network, Generalized Meanneuron model, Signal processing.

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2378 Mixed Frequency Excitation of an Electrostatically Actuated Resonator

Authors: Mixed Frequency Excitation of an Electrostatically Actuated Resonator

Abstract:

We investigate experimentally and theoretically the dynamics of a capacitive resonator under mixed frequency excitation of two AC harmonic signals. The resonator is composed of a proof mass suspended by two cantilever beams. Experimental measurements are conducted using a laser Doppler Vibrometer to reveal the interesting dynamics of the system when subjected to twosource excitation. A nonlinear single-degree-of-freedom model is used for the theoretical investigation. The results reveal combination resonances of additive and subtractive type, which are shown to be promising to increase the bandwidth of the resonator near primary resonance frequency. Our results also demonstrate the ability to shift the combination resonances to much lower or much higher frequency ranges. We also demonstrate the dynamic pull-in instability under mixed frequency excitation.

Keywords: Nonlinear electrostatically actuated resonator.

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2377 Blood Glucose Measurement and Analysis: Methodology

Authors: I. M. Abd Rahim, H. Abdul Rahim, R. Ghazali

Abstract:

There is numerous non-invasive blood glucose measurement technique developed by researchers, and near infrared (NIR) is the potential technique nowadays. However, there are some disagreements on the optimal wavelength range that is suitable to be used as the reference of the glucose substance in the blood. This paper focuses on the experimental data collection technique and also the analysis method used to analyze the data gained from the experiment. The selection of suitable linear and non-linear model structure is essential in prediction system, as the system developed need to be conceivably accurate.

Keywords: Invasive, linear, near-infrared (Nir), non-invasive, non-linear, prediction system.

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2376 An Integrated Predictor for Cis-Regulatory Modules

Authors: Darby Tien-Hao Chang, Guan-Yu Shiu, You-Jie Sun

Abstract:

Various cis-regulatory module (CRM) predictors have been proposed in the last decade. Several well-established CRM predictors adopted different categories of prediction strategies, including window clustering, probabilistic modeling and phylogenetic footprinting. Appropriate integration of them has a potential to achieve high quality CRM prediction. This study analyzed four existing CRM predictors (ClusterBuster, MSCAN, CisModule and MultiModule) to seek a predictor combination that delivers a higher accuracy than individual CRM predictors. 465 CRMs across 140 Drosophila melanogaster genes from the RED fly database were used to evaluate the integrated CRM predictor proposed in this study. The results show that four predictor combinations achieved superior performance than the best individual CRM predictor.

Keywords: Cis-regulatory module, transcription factor binding site.

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2375 Hyperspectral Imaging and Nonlinear Fukunaga-Koontz Transform Based Food Inspection

Authors: Hamidullah Binol, Abdullah Bal

Abstract:

Nowadays, food safety is a great public concern; therefore, robust and effective techniques are required for detecting the safety situation of goods. Hyperspectral Imaging (HSI) is an attractive material for researchers to inspect food quality and safety estimation such as meat quality assessment, automated poultry carcass inspection, quality evaluation of fish, bruise detection of apples, quality analysis and grading of citrus fruits, bruise detection of strawberry, visualization of sugar distribution of melons, measuring ripening of tomatoes, defect detection of pickling cucumber, and classification of wheat kernels. HSI can be used to concurrently collect large amounts of spatial and spectral data on the objects being observed. This technique yields with exceptional detection skills, which otherwise cannot be achieved with either imaging or spectroscopy alone. This paper presents a nonlinear technique based on kernel Fukunaga-Koontz transform (KFKT) for detection of fat content in ground meat using HSI. The KFKT which is the nonlinear version of FKT is one of the most effective techniques for solving problems involving two-pattern nature. The conventional FKT method has been improved with kernel machines for increasing the nonlinear discrimination ability and capturing higher order of statistics of data. The proposed approach in this paper aims to segment the fat content of the ground meat by regarding the fat as target class which is tried to be separated from the remaining classes (as clutter). We have applied the KFKT on visible and nearinfrared (VNIR) hyperspectral images of ground meat to determine fat percentage. The experimental studies indicate that the proposed technique produces high detection performance for fat ratio in ground meat.

Keywords: Food (Ground meat) inspection, Fukunaga-Koontz transform, hyperspectral imaging, kernel methods.

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2374 Heart Rate Variability Analysis for Early Stage Prediction of Sudden Cardiac Death

Authors: Reeta Devi, Hitender Kumar Tyagi, Dinesh Kumar

Abstract:

In present scenario, cardiovascular problems are growing challenge for researchers and physiologists. As heart disease have no geographic, gender or socioeconomic specific reasons; detecting cardiac irregularities at early stage followed by quick and correct treatment is very important. Electrocardiogram is the finest tool for continuous monitoring of heart activity. Heart rate variability (HRV) is used to measure naturally occurring oscillations between consecutive cardiac cycles. Analysis of this variability is carried out using time domain, frequency domain and non-linear parameters. This paper presents HRV analysis of the online dataset for normal sinus rhythm (taken as healthy subject) and sudden cardiac death (SCD subject) using all three methods computing values for parameters like standard deviation of node to node intervals (SDNN), square root of mean of the sequences of difference between adjacent RR intervals (RMSSD), mean of R to R intervals (mean RR) in time domain, very low-frequency (VLF), low-frequency (LF), high frequency (HF) and ratio of low to high frequency (LF/HF ratio) in frequency domain and Poincare plot for non linear analysis. To differentiate HRV of healthy subject from subject died with SCD, k –nearest neighbor (k-NN) classifier has been used because of its high accuracy. Results show highly reduced values for all stated parameters for SCD subjects as compared to healthy ones. As the dataset used for SCD patients is recording of their ECG signal one hour prior to their death, it is therefore, verified with an accuracy of 95% that proposed algorithm can identify mortality risk of a patient one hour before its death. The identification of a patient’s mortality risk at such an early stage may prevent him/her meeting sudden death if in-time and right treatment is given by the doctor.

Keywords: Early stage prediction, heart rate variability, linear and non linear analysis, sudden cardiac death.

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2373 Nonlinear Modelling of Sloshing Waves and Solitary Waves in Shallow Basins

Authors: Mohammad R. Jalali, Mohammad M. Jalali

Abstract:

The earliest theories of sloshing waves and solitary waves based on potential theory idealisations and irrotational flow have been extended to be applicable to more realistic domains. To this end, the computational fluid dynamics (CFD) methods are widely used. Three-dimensional CFD methods such as Navier-Stokes solvers with volume of fluid treatment of the free surface and Navier-Stokes solvers with mappings of the free surface inherently impose high computational expense; therefore, considerable effort has gone into developing depth-averaged approaches. Examples of such approaches include Green–Naghdi (GN) equations. In Cartesian system, GN velocity profile depends on horizontal directions, x-direction and y-direction. The effect of vertical direction (z-direction) is also taken into consideration by applying weighting function in approximation. GN theory considers the effect of vertical acceleration and the consequent non-hydrostatic pressure. Moreover, in GN theory, the flow is rotational. The present study illustrates the application of GN equations to propagation of sloshing waves and solitary waves. For this purpose, GN equations solver is verified for the benchmark tests of Gaussian hump sloshing and solitary wave propagation in shallow basins. Analysis of the free surface sloshing of even harmonic components of an initial Gaussian hump demonstrates that the GN model gives predictions in satisfactory agreement with the linear analytical solutions. Discrepancies between the GN predictions and the linear analytical solutions arise from the effect of wave nonlinearities arising from the wave amplitude itself and wave-wave interactions. Numerically predicted solitary wave propagation indicates that the GN model produces simulations in good agreement with the analytical solution of the linearised wave theory. Comparison between the GN model numerical prediction and the result from perturbation analysis confirms that nonlinear interaction between solitary wave and a solid wall is satisfactorilly modelled. Moreover, solitary wave propagation at an angle to the x-axis and the interaction of solitary waves with each other are conducted to validate the developed model.

Keywords: Even harmonic components of sloshing waves, Green–Naghdi equations, nonlinearity, solitary waves.

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2372 Urban Planning Formulation Problems in China and the Corresponding Optimization Ideas under the Vision of the Hypercycle Theory

Authors: Hong Dongchen, Chen Qiuxiao, Wu Shuang

Abstract:

Systematic Science reveals the complex nonlinear mechanisms of behavior in urban system. However, when confronted with such system, most city planners in China are still utilizing simple linear thinking to learn and understand this open complex giant system. In this paper, the hypercycle theory was introduced, which is one of the basis theories of systematic science. Based on the analysis of the reasons for the failure of current urban planning in China, and in consideration of the nonlinear characteristics of the urban system as well, optimization ideas for urban planning formulation were presented such as the shift from blueprint planning to progressive planning and from the rigid urban planning management control to its dynamically monitor and in time feedback.

Keywords: Systematic science, hypercycle theory, urban planning.

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2371 Modeling and Control of a 4DoF Robotic Assistive Device for Hand Rehabilitation

Authors: Christopher Spiewak, M. R. Islam, Mohammad Arifur Rahaman, Mohammad H. Rahman, Roger Smith, Maarouf Saad

Abstract:

For those who have lost the ability to move their hand, going through repetitious motions with the assistance of a therapist is the main method of recovery. We have been developed a robotic assistive device to rehabilitate the hand motions in place of the traditional therapy. The developed assistive device (RAD-HR) is comprised of four degrees of freedom enabling basic movements, hand function, and assists in supporting the hand during rehabilitation. We used a nonlinear computed torque control technique to control the RAD-HR. The accuracy of the controller was evaluated in simulations (MATLAB/Simulink environment). To see the robustness of the controller external disturbance as modelling uncertainty (±10% of joint torques) were added in each joints.

Keywords: Biorobotics, rehabilitation, nonlinear control, robotic assistive device, exoskeleton.

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2370 A Nonlinear ODE System for the Unsteady Hydrodynamic Force – A New Approach

Authors: Osama A. Marzouk

Abstract:

We propose a reduced-ordermodel for the instantaneous hydrodynamic force on a cylinder. The model consists of a system of two ordinary differential equations (ODEs), which can be integrated in time to yield very accurate histories of the resultant force and its direction. In contrast to several existing models, the proposed model considers the actual (total) hydrodynamic force rather than its perpendicular or parallel projection (the lift and drag), and captures the complete force rather than the oscillatory part only. We study and provide descriptions of the relationship between the model parameters, evaluated utilizing results from numerical simulations, and the Reynolds number so that the model can be used at any arbitrary value within the considered range of 100 to 500 to provide accurate representation of the force without the need to perform timeconsuming simulations and solving the partial differential equations (PDEs) governing the flow field.

Keywords: reduced-order model, wake oscillator, nonlinear, ODEsystem

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2369 Development of Accident Predictive Model for Rural Roadway

Authors: Fajaruddin Mustakim, Motohiro Fujita

Abstract:

This paper present the study carried out of accident analysis, black spot study and to develop accident predictive models based on the data collected at rural roadway, Federal Route 50 (F050) Malaysia. The road accident trends and black spot ranking were established on the F050. The development of the accident prediction model will concentrate in Parit Raja area from KM 19 to KM 23. Multiple non-linear regression method was used to relate the discrete accident data with the road and traffic flow explanatory variable. The dependent variable was modeled as the number of crashes namely accident point weighting, however accident point weighting have rarely been account in the road accident prediction Models. The result show that, the existing number of major access points, without traffic light, rise in speed, increasing number of Annual Average Daily Traffic (AADT), growing number of motorcycle and motorcar and reducing the time gap are the potential contributors of increment accident rates on multiple rural roadway.

Keywords: Accident Trends, Black Spot Study, Accident Prediction Model

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2368 Classifying Turbomachinery Blade Mode Shapes Using Artificial Neural Networks

Authors: Ismail Abubakar, Hamid Mehrabi, Reg Morton

Abstract:

Currently, extensive signal analysis is performed in order to evaluate structural health of turbomachinery blades. This approach is affected by constraints of time and the availability of qualified personnel. Thus, new approaches to blade dynamics identification that provide faster and more accurate results are sought after. Generally, modal analysis is employed in acquiring dynamic properties of a vibrating turbomachinery blade and is widely adopted in condition monitoring of blades. The analysis provides useful information on the different modes of vibration and natural frequencies by exploring different shapes that can be taken up during vibration since all mode shapes have their corresponding natural frequencies. Experimental modal testing and finite element analysis are the traditional methods used to evaluate mode shapes with limited application to real live scenario to facilitate a robust condition monitoring scheme. For a real time mode shape evaluation, rapid evaluation and low computational cost is required and traditional techniques are unsuitable. In this study, artificial neural network is developed to evaluate the mode shape of a lab scale rotating blade assembly by using result from finite element modal analysis as training data. The network performance evaluation shows that artificial neural network (ANN) is capable of mapping the correlation between natural frequencies and mode shapes. This is achieved without the need of extensive signal analysis. The approach offers advantage from the perspective that the network is able to classify mode shapes and can be employed in real time including simplicity in implementation and accuracy of the prediction. The work paves the way for further development of robust condition monitoring system that incorporates real time mode shape evaluation.

Keywords: Modal analysis, artificial neural network, mode shape, natural frequencies, pattern recognition.

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2367 Temperature-Based Detection of Initial Yielding Point in Loading of Tensile Specimens Made of Structural Steel

Authors: Aqsa Jamil, Hiroshi Tamura, Hiroshi Katsuchi, Jiaqi Wang

Abstract:

Yield point represents the upper limit of forces which can be applied on a specimen without causing any permanent deformation. After yielding, the behavior of specimen suddenly changes including the possibility of cracking or buckling. So, the accumulation of damage or type of fracture changes depending on this condition. As it is difficult to accurately detect yield points of the several stress concentration points in structural steel specimens, an effort has been made in this research work to develop a convenient technique using thermography (temperature-based detection) during tensile tests for the precise detection of yield point initiation. To verify the applicability of thermography camera, tests were conducted under different loading conditions and measuring the deformation by installing various strain gauges and monitoring the surface temperature with the help of thermography camera. The yield point of specimens was estimated by the help of temperature dip which occurs due to the thermoelastic effect during the plastic deformation. The scattering of the data has been checked by performing repeatability analysis. The effect of temperature imperfection and light source has been checked by carrying out the tests at daytime as well as midnight and by calculating the signal to noise ratio (SNR) of the noised data from the infrared thermography camera, it can be concluded that the camera is independent of testing time and the presence of a visible light source. Furthermore, a fully coupled thermal-stress analysis has been performed by using Abaqus/Standard exact implementation technique to validate the temperature profiles obtained from the thermography camera and to check the feasibility of numerical simulation for the prediction of results extracted with the help of thermographic technique.

Keywords: Signal to noise ratio, thermoelastic effect, thermography, yield point.

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