Search results for: Error analysis

Authors: Anthony Spiteri Staines

Abstract:

This paper examines the modeling and analysis of a cruise control system using a Petri net based approach, task graphs, invariant analysis and behavioral properties. It shows how the structures used can be verified and optimized.

Keywords: Software Engineering, Real Time Analysis andDesign, Petri Nets, Task Graphs, Parallelism.

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Authors: Faheem Ahmed, Fareed Ahmed, Yongheng Guo, Yong Yang

Abstract:

This paper deals with a high-order accurate Runge Kutta Discontinuous Galerkin (RKDG) method for the numerical solution of the wave equation, which is one of the simple case of a linear hyperbolic partial differential equation. Nodal DG method is used for a finite element space discretization in 'x' by discontinuous approximations. This method combines mainly two key ideas which are based on the finite volume and finite element methods. The physics of wave propagation being accounted for by means of Riemann problems and accuracy is obtained by means of high-order polynomial approximations within the elements. High order accurate Low Storage Explicit Runge Kutta (LSERK) method is used for temporal discretization in 't' that allows the method to be nonlinearly stable regardless of its accuracy. The resulting RKDG methods are stable and high-order accurate. The L1 ,L2 and L∞ error norm analysis shows that the scheme is highly accurate and effective. Hence, the method is well suited to achieve high order accurate solution for the scalar wave equation and other hyperbolic equations.

Keywords: Nodal Discontinuous Galerkin Method, RKDG, Scalar Wave Equation, LSERK

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Authors: M. A. Masnadi-Shirazi, S. A. Banani

Abstract:

In this paper a comprehensive algorithm is presented to alleviate the undesired simultaneous effects of target maneuvering, observed glint noise distribution, and colored noise spectrum using online colored glint noise parameter estimation. The simulation results illustrate a significant reduction in the root mean square error (RMSE) produced by the proposed algorithm compared to the algorithms that do not compensate all the above effects simultaneously.

Keywords: Glint noise, IMM, Kalman Filter, Kinematics, Target Tracking.

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Authors: Reza Lotfalian, Sudarshan Martins, Peter Radziszewski

Abstract:

The effect of reliability on life-cycle cost, including initial and maintenance cost of a system is studied. The failure probability of a component is used to calculate the average maintenance cost during the operation cycle of the component. The standard deviation of the life-cycle cost is also calculated as an error measure for the average life-cycle cost. As a numerical example, the model is used to study the average life-cycle cost of an electric motor.

Keywords: Initial Cost, Life-cycle cost, Maintenance Cost, Reliability.

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Authors: Khalid Minaoui, Thierry Chonavel, Benayad Nsiri, Driss Aboutajdine

Abstract:

This paper deals with efficient quadrature formulas involving functions that are observed only at fixed sampling points. The approach that we develop is derived from efficient continuous quadrature formulas, such as Gauss-Legendre or Clenshaw-Curtis quadrature. We select nodes at sampling positions that are as close as possible to those of the associated classical quadrature and we update quadrature weights accordingly. We supply the theoretical quadrature error formula for this new approach. We show on examples the potential gain of this approach.

Keywords: Gauss-Legendre, Clenshaw-Curtis, quadrature, Peano kernel, irregular sampling.

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Authors: C. B. Vishwakarma

Abstract:

The author presented a method for model order reduction of large-scale time-invariant systems in time domain. In this approach, two modified Hankel matrices are suggested for getting reduced order models. The proposed method is simple, efficient and retains stability feature of the original high order system. The viability of the method is illustrated through the examples taken from literature.

Keywords: Model Order Reduction, Stability, Hankel Matrix, Time-Domain, Integral Square Error.

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Authors: Shuki Dror

Abstract:

This work discusses an innovative methodology for deployment of service quality characteristics. Four groups of organizational features that may influence the quality of services are identified: human resource, technology, planning, and organizational relationships. A House of Service Quality (HOSQ) matrix is built to extract the desired improvement in the service quality characteristics and to translate them into a hierarchy of important organizational features. The Mean Square Error (MSE) criterion enables the pinpointing of the few essential service quality characteristics to be improved as well as selection of the vital organizational features. The method was implemented in an engineering supply enterprise and provides useful information on its vital service dimensions.

Keywords: HOQ, organizational features, service quality.

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Authors: Sorin Soviany, Cristina Soviany, Mariana Jurian

Abstract:

The paper presents a multimodal approach for biometric authentication, based on multiple classifiers. The proposed solution uses a post-classification biometric fusion method in which the biometric data classifiers outputs are combined in order to improve the overall biometric system performance by decreasing the classification error rates. The paper shows also the biometric recognition task improvement by means of a carefully feature selection, as much as not all of the feature vectors components support the accuracy improvement.

Keywords: biometric fusion, multiple classifiers

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Authors: M. Pollar, M. Jaroensutasinee, K. Jaroensutasinee

Abstract:

The population structure of the Tor tambroides was investigated with morphometric data (i.e. morphormetric measurement and truss measurement). A morphometric analysis was conducted to compare specimens from three waterfalls: Sunanta, Nan Chong Fa and Wang Muang waterfalls at Khao Nan National Park, Nakhon Si Thammarat, Southern Thailand. The results of stepwise discriminant analysis on seven morphometric variables and 21 truss variables per individual were the same as from a neural network. Fish from three waterfalls were separated into three groups based on their morphometric measurements. The morphometric data shows that the nerual network model performed better than the stepwise discriminant analysis.

Keywords: Morphometric, Tor tambroides, Stepwise Discriminant Analysis , Neural Network Analysis.

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Authors: D.Elleuch, T.Damak

Abstract:

In this paper, the performance of two adaptive observers applied to interconnected systems is studied. The nonlinearity of systems can be written in a fractional form. The first adaptive observer is an adaptive sliding mode observer for a Lipchitz nonlinear system and the second one is an adaptive sliding mode observer having a filtered error as a sliding surface. After comparing their performances throughout the inverted pendulum mounted on a car system, it was shown that the second one is more robust to estimate the state.

Keywords: Adaptive observer, Lipchitz system, Interconnected fractional nonlinear system, sliding mode.

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Authors: Lily Ingsrisawang, Supawadee Ingsriswang, Saisuda Somchit, Prasert Aungsuratana, Warawut Khantiyanan

Abstract:

This paper presents the methodology from machine learning approaches for short-term rain forecasting system. Decision Tree, Artificial Neural Network (ANN), and Support Vector Machine (SVM) were applied to develop classification and prediction models for rainfall forecasts. The goals of this presentation are to demonstrate (1) how feature selection can be used to identify the relationships between rainfall occurrences and other weather conditions and (2) what models can be developed and deployed for predicting the accurate rainfall estimates to support the decisions to launch the cloud seeding operations in the northeastern part of Thailand. Datasets collected during 2004-2006 from the Chalermprakiat Royal Rain Making Research Center at Hua Hin, Prachuap Khiri khan, the Chalermprakiat Royal Rain Making Research Center at Pimai, Nakhon Ratchasima and Thai Meteorological Department (TMD). A total of 179 records with 57 features was merged and matched by unique date. There are three main parts in this work. Firstly, a decision tree induction algorithm (C4.5) was used to classify the rain status into either rain or no-rain. The overall accuracy of classification tree achieves 94.41% with the five-fold cross validation. The C4.5 algorithm was also used to classify the rain amount into three classes as no-rain (0-0.1 mm.), few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall accuracy of classification tree achieves 62.57%. Secondly, an ANN was applied to predict the rainfall amount and the root mean square error (RMSE) were used to measure the training and testing errors of the ANN. It is found that the ANN yields a lower RMSE at 0.171 for daily rainfall estimates, when compared to next-day and next-2-day estimation. Thirdly, the ANN and SVM techniques were also used to classify the rain amount into three classes as no-rain, few-rain, and moderate-rain as above. The results achieved in 68.15% and 69.10% of overall accuracy of same-day prediction for the ANN and SVM models, respectively. The obtained results illustrated the comparison of the predictive power of different methods for rainfall estimation.

Keywords: Machine learning, decision tree, artificial neural network, support vector machine, root mean square error.

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Authors: Amer Ibrahim Falah Al-Omari

Abstract:

In this paper, we suggest new product-type estimators for the population mean of the variable of interest exploiting the first or the third quartile of the auxiliary variable. We obtain mean square error equations and the bias for the estimators. We study the properties of these estimators using simple random sampling (SRS) and ranked set sampling (RSS) methods. It is found that, SRS and RSS produce approximately unbiased estimators of the population mean. However, the RSS estimators are more efficient than those obtained using SRS based on the same number of measured units for all values of the correlation coefficient.

Keywords: Product estimator, auxiliary variable, simple random sampling, extreme ranked set sampling

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Authors: Md. Ashikur Rahman Khan, M. M. Rahman, K. Kadirgama

Abstract:

Artificial neural networks (ANN) are used in distinct researching fields and professions, and are prepared by cooperation of scientists in different fields such as computer engineering, electronic, structure, biology and so many different branches of science. Many models are built correlating the parameters and the outputs in electrical discharge machining (EDM) concern for different types of materials. Up till now model for Ti-5Al-2.5Sn alloy in the case of electrical discharge machining performance characteristics has not been developed. Therefore, in the present work, it is attempted to generate a model of material removal rate (MRR) for Ti-5Al-2.5Sn material by means of Artificial Neural Network. The experimentation is performed according to the design of experiment (DOE) of response surface methodology (RSM). To generate the DOE four parameters such as peak current, pulse on time, pulse off time and servo voltage and one output as MRR are considered. Ti-5Al-2.5Sn alloy is machined with positive polarity of copper electrode. Finally the developed model is tested with confirmation test. The confirmation test yields an error as within the agreeable limit. To investigate the effect of the parameters on performance sensitivity analysis is also carried out which reveals that the peak current having more effect on EDM performance.

Keywords: Ti-5Al-2.5Sn, material removal rate, copper tungsten, positive polarity, artificial neural network, multi-layer perceptron.

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Authors: Young-Seok Choi

Abstract:

We propose two affine projection algorithms (APA) with variable regularization parameter. The proposed algorithms dynamically update the regularization parameter that is fixed in the conventional regularized APA (R-APA) using a gradient descent based approach. By introducing the normalized gradient, the proposed algorithms give birth to an efficient and a robust update scheme for the regularization parameter. Through experiments we demonstrate that the proposed algorithms outperform conventional R-APA in terms of the convergence rate and the misadjustment error.

Keywords: Affine projection, regularization, gradient descent, system identification.

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Authors: Muhammad Rehan Khalid, Ahmed Farhan Hanif, Adnan Ahmed Khan

Abstract:

This paper investigates the performance of Multiple- Input Multiple-Output (MIMO) feedback system combined with Orthogonal Frequency Division Multiplexing (OFDM). Two types of codebook based channel feedback techniques are used in this work. The first feedback technique uses a combination of both the long-term and short-term channel state information (CSI) at the transmitter, whereas the second technique uses only the short term CSI. The long-term and short-term CSI at the transmitter is used for efficient channel utilization. OFDM is a powerful technique employed in communication systems suffering from frequency selectivity. Combined with multiple antennas at the transmitter and receiver, OFDM proves to be robust against delay spread. Moreover, it leads to significant data rates with improved bit error performance over links having only a single antenna at both the transmitter and receiver. The effectiveness of these techniques has been demonstrated through the simulation of a MIMO-OFDM feedback system. The results have been evaluated for 4x4 MIMO channels. Simulation results indicate the benefits of the MIMO-OFDM channel feedback system over the one without incorporating OFDM. Performance gain of about 3 dB is observed for MIMO-OFDM feedback system as compared to the one without employing OFDM. Hence MIMO-OFDM becomes an attractive approach for future high speed wireless communication systems.

Keywords: MIMO systems, OFDM, Codebooks, Channel Feedback

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Authors: Jaehoon Ha, Byungmo Kang, Kilho Hong, Jungsoo Park

Abstract:

Electro-optical (EO) stabilized platforms have been widely used for surveillance and reconnaissance on various types of vehicles, from surface ships to unmanned air vehicles (UAVs). EO stabilized platforms usually consist of an assembly of structure, bearings, and motors called gimbals in which a gyroscope is installed. EO elements such as a CCD camera and IR camera, are mounted to a gimbal, which has a range of motion in elevation and azimuth and can designate and track a target. In addition, a laser range finder (LRF) can be added to the gimbal in order to acquire the precise slant range from the platform to the target. Recently, a versatile functionality of target localization is needed in order to cooperate with the weapon systems that are mounted on the same platform. The target information, such as its location or velocity, needed to be more accurate. The accuracy of the target information depends on diverse component errors and alignment errors of each component. Specially, the type of moving platform can affect the accuracy of the target information. In the case of flying platforms, or UAVs, the target location error can be increased with altitude so it is important to measure altitude as precisely as possible. In the case of surface ships, target location error can be increased with obliqueness of the elevation angle of the gimbal since the altitude of the EO stabilized platform is supposed to be relatively low. The farther the slant ranges from the surface ship to the target, the more extreme the obliqueness of the elevation angle. This can hamper the precise acquisition of the target information. So far, there have been many studies on EO stabilized platforms of flying vehicles. However, few researchers have focused on ship-borne EO stabilized platforms of the surface ship. In this paper, we deal with a target localization method when an EO stabilized platform is located on the mast of a surface ship. Especially, we need to overcome the limitation caused by the obliqueness of the elevation angle of the gimbal. We introduce a well-known approach for target localization using Unscented Kalman Filter (UKF) and present the problem definition showing the above-mentioned limitation. Finally, we want to show the effectiveness of the approach that will be demonstrated through computer simulations.

Keywords: Target localization, ship-borne electro-optical stabilized platform, unscented Kalman filter.

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Authors: Fadi Eleiwi, Taous Meriem Laleg-Kirati, Sofiane Khelladi, Farid Bakir

Abstract:

This paper presents the use of a semi-classical signal analysis method that has been developed recently for the analysis of turbomachinery flow unsteadiness. We will focus on the correlation between theSemi-Classical Signal Analysis parameters and some physical parameters in relation with turbomachinery features. To demonstrate the potential of the proposed approach, a static pressure signal issued from a rotor/stator interaction of a centrifugal pump is studied. Several configurations of the pump are compared.

Keywords: Semi-classical signal analysis, turbomachines, newindices, physical parameters

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Authors: Taylan Kabbani, Ekrem Duman

Abstract:

Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining  the financial assets price ”prediction” step and the ”allocation” step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solved the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and achieved a 2.68 Sharpe ratio on the test dataset. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages of strategic decision-making.

Keywords: Autonomous agent, deep reinforcement learning, MDP, sentiment analysis, stock market, technical indicators, twin delayed deep deterministic policy gradient.

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Authors: Chao Yuan, Bao Guang Tian

Abstract:

A relative efficiency is defined as Ridge Estimate in the general linear model. The relative efficiency is based on the Mean square error. In this paper, we put forward a parameter of Ridge Estimate and discussions are made on the relative efficiency between the ridge estimation and the General Ridge Estimate. Eventually, this paper proves that the estimation is better than the general ridge estimate, which is based on the MSE.

Keywords: Ridge estimate, generalized ridge estimate, MSE, relative efficiency.

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Authors: Olaf Hallan Graven, Dag Andreas Hals Samuelsen

Abstract:

An implementation of a design for a game based virtual learning environment is described. The game is developed for a course in analogue electronics, and the topic is the design of a power supply. This task can be solved in a number of different ways, with certain constraints, giving the students a certain amount of freedom, although the game is designed not to facilitate trial-and error approach. The use of storytelling and a virtual gaming environment provides the student with the learning material in a MMORPG environment. The game is tested on a group of second year electrical engineering students with good results.

Keywords: analogue electronics, e-learning, computer games for learning, virtual reality

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Authors: Zhu Jiaming, Yu Mengna

Abstract:

In this article, models based on quantitative analysis, physical geometry and regression analysis are established, by using analytic hierarchy process analysis, fuzzy cluster analysis, fuzzy photographic and data fitting. The reasons of various leaf shapes among different species and the differences between the leaf shapes on same tree have been solved by using software, such as Eviews, VB and Matlab. We also successfully estimate the leaf mass of a tree and the correlation with the tree profile.

Keywords: Leaf shape; Mass; Fuzzy cluster; Regression analysis; Eviews; Matlab

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Authors: Said Elkassimi, Said Safi, B. Manaut

Abstract:

This paper presented a study of three algorithms, the equalization algorithm to equalize the transmission channel with ZF and MMSE criteria, application of channel Bran A, and adaptive filtering algorithms LMS and RLS to estimate the parameters of the equalizer filter, i.e. move to the channel estimation and therefore reflect the temporal variations of the channel, and reduce the error in the transmitted signal. So far the performance of the algorithm equalizer with ZF and MMSE criteria both in the case without noise, a comparison of performance of the LMS and RLS algorithm.

Keywords: Adaptive filtering second equalizer, LMS, RLS Bran A, Proakis (B) MMSE, ZF.

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Authors: Zixin Liu, Shu Lü, Shouming Zhong, Mao Ye

Abstract:

This paper studies the pth moment exponential synchronization of a class of stochastic neural networks with mixed delays. Based on Lyapunov stability theory, by establishing a new integrodifferential inequality with mixed delays, several sufficient conditions have been derived to ensure the pth moment exponential stability for the error system. The criteria extend and improve some earlier results. One numerical example is presented to illustrate the validity of the main results.

Keywords: pth Moment Exponential synchronization, Stochastic, Neural networks, Mixed time delays

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Authors: L. Zehtaban, D. Roller

Abstract:

Apart from geometry, functionality is one of the most significant hallmarks of a product. The functionality of a product can be considered as the fundamental justification for a product existence. Therefore a functional analysis including a complete and reliable descriptor has a high potential to improve product development process in various fields especially in knowledge-based design. One of the important applications of the functional analysis and indexing is in retrieval and design reuse concept. More than 75% of design activity for a new product development contains reusing earlier and existing design know-how. Thus, analysis and categorization of product functions concluded by functional indexing, influences directly in design optimization. This paper elucidates and evaluates major classes for functional analysis by discussing their major methods. Moreover it is finalized by presenting a noble hybrid approach for functional analysis.

Keywords: Functional analysis, design reuse, functionalindexing and representation.

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Authors: Sepehr Damavandinejadmonfared, Ali Khalili Mobarakeh, Mohsen Pashna, , Jiangping Gou Sayedmehran Mirsafaie Rizi, Saba Nazari, Shadi Mahmoodi Khaniabadi, Mohamad Ali Bagheri

Abstract:

In this paper a novel algorithm is proposed to merit the accuracy of finger vein recognition. The performances of Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), and Kernel Entropy Component Analysis (KECA) in this algorithm are validated and compared with each other in order to determine which one is the most appropriate one in terms of finger vein recognition.

Keywords: Biometrics, finger vein recognition, PrincipalComponent Analysis (PCA), Kernel Principal Component Analysis(KPCA), Kernel Entropy Component Analysis (KPCA).

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Authors: Keunhong Chae, Seokho Yoon

Abstract:

This paper proposes a cooperative Alamouti space time transmission scheme with low relay complexity for the cooperative communication systems. In the proposed scheme, the source node combines the data symbols to construct the Alamouti-coded form at the destination node, while the conventional scheme performs the corresponding operations at the relay nodes. In simulation results, it is shown that the proposed scheme achieves the second order cooperative diversity while maintaining the same bit error rate (BER) performance as that of the conventional scheme.

Keywords: Space-time transmission, cooperative communication system, MIMO.

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Authors: J. P. Dubois, O. M. Abdul-Latif

Abstract:

Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM is applied to an infrared (IR) binary communication system with different types of channel models including Ricean multipath fading and partially developed scattering channel with additive white Gaussian noise (AWGN) at the receiver. The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these channel stochastic models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to classical binary signal maximum likelihood detection using a matched filter driven by On-Off keying (OOK) modulation. We found that the performance of SVM is superior to that of the traditional optimal detection schemes used in statistical communication, especially for very low signal-to-noise ratio (SNR) ranges. For large SNR, the performance of the SVM is similar to that of the classical detectors. The implication of these results is that SVM can prove very beneficial to IR communication systems that notoriously suffer from low SNR at the cost of increased computational complexity.

Keywords: Least square-support vector machine, on-off keying, matched filter, maximum likelihood detector, wireless infrared communication.

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Authors: M. Zahid Hossain, M. Ashraful Amin

Abstract:

The algorithms of convex hull have been extensively studied in literature, principally because of their wide range of applications in different areas. This article presents an efficient algorithm to construct approximate convex hull from a set of n points in the plane in O(n + k) time, where k is the approximation error control parameter. The proposed algorithm is suitable for applications preferred to reduce the computation time in exchange of accuracy level such as animation and interaction in computer graphics where rapid and real-time graphics rendering is indispensable.

Keywords: Convex hull, Approximation algorithm, Computational geometry, Linear time.

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Authors: Mohammed H. Nafie, Ahmed F. Shalash

Abstract:

With the advent of DSL services, high data rates are now available over phone lines, yet higher rates are in demand. In this paper, we optimize the transmit filters that can be used over wireline channels. Results showing the bit error rates when optimized filters are used, and with a decision feedback equalizer (DFE) employed in the receiver, are given. We then show that significantly higher throughput can be achieved by modeling the channel as a multiple input multiple output (MIMO) channel. A receiver that employs a MIMO-DFE that deals jointly with several users is proposed and shown to provide significant improvement over the conventional DFE.

Keywords: DFE, MIMO Channels, Receiver Architectures, Transmit Filters.

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Authors: Le Thanh Ha, Hye-Soo Kim, Chun-Su Park, Seung-Won Jung, Sung-Jea Ko

Abstract:

Flexible macroblock ordering (FMO), adopted in the H.264 standard, allows to partition all macroblocks (MBs) in a frame into separate groups of MBs called Slice Groups (SGs). FMO can not only support error-resilience, but also control the size of video packets for different network types. However, it is well-known that the number of bits required for encoding the frame is increased by adopting FMO. In this paper, we propose a novel algorithm that can reduce the bitrate overhead caused by utilizing FMO. In the proposed algorithm, all MBs are grouped in SGs based on the similarity of the transform coefficients. Experimental results show that our algorithm can reduce the bitrate as compared with conventional FMO.

Keywords: Data Partition, Entropy Coding, Greedy Algorithm, H.264/AVC, Slice Group.

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