Search results for: propagation channel

Authors: Han Nguyen

Abstract:

The objectives of this study is to investigate the impact of culture on advertising appeals in mobile phone industry via social media channel in UK, Brazil and India. Content analysis on Samsung and Nokia commercials in YouTube is conducted. The result indicates that the advertising appeals are both congruent and incongruent with cultural dimensions in UK, Brazil and India. The result suggests that Hofstede and value paradoxes might be the tools to predict the relationship between cultural values and advertising appeals.

Keywords: Advertising appeal, international advertising, mobile phone advertising, social media advertising.

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Authors: Ram N. Singh, Abraham K. George, Dawood N. Al-Namaani

Abstract:

The Euler-s equation of motion is extended to include the viscosity stress tensor leading to the formulation of Navier– Stokes type equation. The latter is linearized and applied to investigate the rotational motion or vorticity in a viscous fluid. Relations for the velocity of viscous waves and attenuation parameter are obtained in terms of viscosity (μ) and the density (¤ü) of the fluid. μ and ¤ü are measured experimentally as a function of temperature for two different samples of light and heavy crude oil. These data facilitated to determine the activation energy, velocity of viscous wave and the attenuation parameter. Shear wave velocity in heavy oil is found to be much larger than the light oil, whereas the attenuation parameter in heavy oil is quite low in comparison to light one. The activation energy of heavy oil is three times larger than light oil.

Keywords: Activation Energy, Attenuation, Crude Oil, Navier- Stokes Equation, Viscosity.

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Authors: Tao Nie, Weiqiang Liu

Abstract:

In order to obtain an accurate result of the heat transfer of the rib in the internal cooling Rectangular channel, using separation of variables, analytical solutions of three dimensional steady-state heat conduction in rectangular ribs are given by solving three dimensional steady-state function of the rectangular ribs. Therefore, we can get solution of three dimensional temperature field in the rib. Based on the solution, we can get how the Bi number affected on heat transfer. Furthermore, comparisons of the analytical and numerical results indicate agreement on temperature field in the rib.

Keywords: variable separation method, analytical solution, rib, heat transfer

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Authors: M. Khaouani, A. Guen-Bouazza, B. Bouazza, Z. Kourdi

Abstract:

The integrity and issues related to electrostatic performance associated with scaling Si MOSFET bulk sub 10nm channel length promotes research in new device architectures such as SOI, double gate and GAA MOSFET. In this paper, we present some novel characteristic of horizontal rectangular gate\gate all around MOSFETs with dual metal of gate we obtained using SILVACO TCAD tools. We will also exhibit some simulation results we obtained relating to the influence of some parameters variation on our structure, that having a direct impact on their threshold voltage and drain current. In addition, our TFET showed reasonable I_ON/I_OFF ratio of (10⁴) and low drain induced barrier lowering (DIBL) of 39 mV/V.

Keywords: GAA, SILVACO, QUANTUM, MOSFETs.

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Authors: Hamdi. A. Awad, Mohamed A. Al-Zorkany

Abstract:

Developing techniques for mobile robot navigation constitutes one of the major trends in the current research on mobile robotics. This paper develops a local model network (LMN) for mobile robot navigation. The LMN represents the mobile robot by a set of locally valid submodels that are Multi-Layer Perceptrons (MLPs). Training these submodels employs Back Propagation (BP) algorithm. The paper proposes the fuzzy C-means (FCM) in this scheme to divide the input space to sub regions, and then a submodel (MLP) is identified to represent a particular region. The submodels then are combined in a unified structure. In run time phase, Radial Basis Functions (RBFs) are employed as windows for the activated submodels. This proposed structure overcomes the problem of changing operating regions of mobile robots. Read data are used in all experiments. Results for mobile robot navigation using the proposed LMN reflect the soundness of the proposed scheme.

Keywords: Mobile Robot Navigation, Neural Networks, Local Model Networks

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Authors: Irfan Ahmad, Tahir Siddiqui, Rashid Ahmad Khan, Tahir Munir Butt

Abstract:

An effort to find out the smaller size of cuttings for propagation of Morus alba was made in experimental area Department of Forestry, Range Management and Wildlife, University of Agriculture, Faisalabad, Pakistan. Different size of cuttings i.e. 2", 4", 6" and 8" were planted in polythene tubes of 3.5"x7". The effort was also made to compare the performance of cuttings in open air and in polythene low tunnel. Root length, number of root branches, root diameter and root fresh and dry weight were found maximum in two inches cuttings while minimum in four inches cuttings. Root growth was found maximum in open air as compared to under polythene sheet.

Keywords: cutting sizes Morus alba, Open air and polythene sheet, root growth

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Authors: Mario Mastriani

Abstract:

We describe a new filtering approach in the wavelet domain for image denoising and compression, based on the projections of details subbands coefficients (resultants of the splitting procedure, typical in wavelet domain) onto the approximation subband coefficients (much less noisy). The new algorithm is called Projection Onto Approximation Coefficients (POAC). As a result of this approach, only the approximation subband coefficients and three scalars are stored and/or transmitted to the channel. Besides, with the elimination of the details subbands coefficients, we obtain a bigger compression rate. Experimental results demonstrate that our approach compares favorably to more typical methods of denoising and compression in wavelet domain.

Keywords: Compression, denoising, projections, wavelets.

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Authors: Young-Su Ryu, Su-Hyun Jung, Myoung-Jin Kim, Hyoung-Kyu Song

Abstract:

In this paper, improved performance scheme for joint transmission (JT) is proposed in downlink (DL) coordinated multi-point (CoMP) in case of the constraint transmission power. This scheme is that a serving transmission point (TP) requests the JT to an inter-TP and it selects a precoding technique according to the channel state information (CSI) from user equipment (UE). The simulation results show that the bit error rate (BER) and the throughput performances of the proposed scheme provide the high spectral efficiency and the reliable data at the cell edge.

Keywords: CoMP, joint transmission, minimum mean square error, zero-forcing, zero-forcing dirty paper coding.

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Authors: Ahmed Kayad Abdourazak, Abderafi Souad, Zejli Driss, Idriss Abdoulkader Ibrahim

Abstract:

In this paper Artificial Neural Network (ANN) is used to predict the solar irradiation in Djibouti for the first Time that is useful to the integration of Concentrating Solar Power (CSP) and sites selections for new or future solar plants as part of solar energy development. An ANN algorithm was developed to establish a forward/reverse correspondence between the latitude, longitude, altitude and monthly solar irradiation. For this purpose the German Aerospace Centre (DLR) data of eight Djibouti sites were used as training and testing in a standard three layers network with the back propagation algorithm of Lavenber-Marquardt. Results have shown a very good agreement for the solar irradiation prediction in Djibouti and proves that the proposed approach can be well used as an efficient tool for prediction of solar irradiation by providing so helpful information concerning sites selection, design and planning of solar plants.

Keywords: Artificial neural network, solar irradiation, concentrated solar power, Lavenberg-Marquardt.

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Authors: Salvatore Marra, Francesco C. Morabito

Abstract:

In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.

Keywords: Elman neural networks, sunspot, solar activity, time series prediction.

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Authors: Andras Szekrenyes

Abstract:

This work presents the mixed-mode II/III prestressed split-cantilever beam specimen for the fracture testing of composite materials. In accordance with the concept of prestressed composite beams one of the two fracture modes is provided by the prestressed state of the specimen, and the other one is increased up to fracture initiation by using a testing machine. The novel beam-like specimen is able to provide any combination of the mode-II and mode-III energy release rates. A simple closed-form solution is developed using beam theory as a data reduction scheme and for the calculation of the energy release rates in the new configuration. The applicability and the limitations of the novel fracture mechanical test are demonstrated using unidirectional glass/polyester composite specimens. If only crack propagation onset is involved then the mixed-mode beam specimen can be used to obtain the fracture criterion of transparent composite materials in the GII - GIII plane in a relatively simple way.

Keywords: Composite, fracture mechanics, toughness testing, mixed-mode II/III fracture.

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Authors: Harpreet Singh Dhillon, Abhijit Mitra

Abstract:

A reduced-bit multiplication algorithm based on the ancient Vedic multiplication formulae is proposed in this paper. Both the Vedic multiplication formulae, Urdhva tiryakbhyam and Nikhilam, are first discussed in detail. Urdhva tiryakbhyam, being a general multiplication formula, is equally applicable to all cases of multiplication. It is applied to the digital arithmetic and is shown to yield a multiplier architecture which is very similar to the popular array multiplier. Due to its structure, it leads to a high carry propagation delay in case of multiplication of large numbers. Nikhilam Sutra, on the other hand, is more efficient in the multiplication of large numbers as it reduces the multiplication of two large numbers to that of two smaller numbers. The framework of the proposed algorithm is taken from this Sutra and is further optimized by use of some general arithmetic operations such as expansion and bit-shifting to take advantage of bit-reduction in multiplication. We illustrate the proposed algorithm by reducing a general 4x4-bit multiplication to a single 2 x 2-bit multiplication operation.

Keywords: Multiplication, algorithm, Vedic mathematics, digital arithmetic, reduced-bit.

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Authors: A. Karsenty, A. Chelly

Abstract:

Ultrathin (UTD) and Nanoscale (NSD) SOI-MOSFET devices, sharing a similar W/L but with a channel thickness of 46nm and 1.6nm respectively, were fabricated using a selective “gate recessed” process on the same silicon wafer. The electrical transport characterization at room temperature has shown a large difference between the two kinds of devices and has been interpreted in terms of a huge unexpected series resistance. Electrical characteristics of the Nanoscale device, taken in the linear region, can be analytically derived from the ultrathin device ones. A comparison of the structure and composition of the layers, using advanced techniques such as Focused Ion Beam (FIB) and High Resolution TEM (HRTEM) coupled with Energy Dispersive X-ray Spectroscopy (EDS), contributes an explanation as to the difference of transport between the devices.

Keywords: Nanoscale Devices, SOI MOSFET, Analytical Model, Electron Transport.

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Authors: Supriya Pal, Kalyan Adhikari, Somnath Mukherjee, Sudipta Ghosh

Abstract:

This study presents the application of artificial neural network for modeling the phenolic compound migration through vertical soil column. A three layered feed forward neural network with back propagation training algorithm was developed using forty eight experimental data sets obtained from laboratory fixed bed vertical column tests. The input parameters used in the model were the influent concentration of phenol(mg/L) on the top end of the soil column, depth of the soil column (cm), elapsed time after phenol injection (hr), percentage of clay (%), percentage of silt (%) in soils. The output of the ANN was the effluent phenol concentration (mg/L) from the bottom end of the soil columns. The ANN predicted results were compared with the experimental results of the laboratory tests and the accuracy of the ANN model was evaluated.

Keywords: Modeling, Neural Networks, Phenol, Soil media

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Authors: Fatima Zohra Rahou, A. Guen Bouazza, B. Bouazza

Abstract:

Invention of transistor is the foundation of electronics industry. Metal Oxide Semiconductor Field Effect Transistor (MOSFET) has been the key for the development of nanoelectronics technology. In the first part of this manuscript, we present a new generation of MOSFET transistors based on SOI (Silicon-On-Insulator) technology. It is a partially depleted Silicon-On-Insulator (PD SOI MOSFET) transistor simulated by using SILVACO software. This work was completed by the presentation of some results concerning the influence of parameters variation (channel length L and gate oxide thickness Tox) on our PDSOI n-MOSFET structure on its drain current and kink effect.

Keywords: SOI technology, PDSOI MOSFET, FDSOI MOSFET, Kink Effect, SILVACO TCAD.

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Authors: Tales H. J. Moreira, Thiago M. R. Dias, Gray F. Moita

Abstract:

The adviser-advised relationship, in addition to the evident propagation of knowledge, can provide an increase in the scientific production of the advisors. Specifically, in post-graduate programs, in which the advised submit diverse papers in different means of publication, these end up boosting the production of their advisor, since in general the advisors appear as co-authors, responsible for instructing and assisting in the development of the work. Therefore, to visualize the orientation process and the scientific production resulting from this relation is another important way of analyzing the scientific collaboration in the different areas of knowledge. In this work, are used the data of orientations and postgraduate supervisions from the Lattes curricula, from the main advisors who work in the Engineering area, to obtain an overview of the process of orientation of this group, and even, to produce Academic genealogical trees, where it is possible to verify how knowledge has spread in the diverse areas of engineering.

Keywords: Academic genealogy, advice, engineering, lattes platform.

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Authors: M. S. Thakur, Mohit Shukla

Abstract:

A channel/tunnel, which carries the water to the penstock/pressure shaft is called headrace tunnel (HRT). It is necessary to know the general topography, geology of the area, state of stress and other mechanical properties of the strata. For this certain topographical and geological investigations, in-situ and laboratory tests, and observations are required to be done. These investigations play an important role in a tunnel design as these help in deciding the optimum layout, shape and size and support requirements of the tunnel. The paper includes inputs from Nathpa Jhakri Hydeoelectric project which is India’s highest capacity (1500 MW) operating hydroelectric project. The paper would help the design engineers with various new concepts and preparedness against geological surprises.

Keywords: Tunnelling, geology, head race tunnel, rockmass.

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Authors: Alexandra Leukauf, Alexander Schirrer, Emir Talic

Abstract:

Numerical computation of wave propagation in a large domain usually requires significant computational effort. Hence, the considered domain must be truncated to a smaller domain of interest. In addition, special boundary conditions, which absorb the outward travelling waves, need to be implemented in order to describe the system domains correctly. In this work, the linear one dimensional wave equation is approximated by utilizing the Fourier Galerkin approach. Furthermore, the artificial boundaries are realized with absorbing boundary conditions. Within this work, a systematic work flow for setting up the wave problem, including the absorbing boundary conditions, is proposed. As a result, a convenient modal system description with an effective absorbing boundary formulation is established. Moreover, the truncated model shows high accuracy compared to the global domain.

Keywords: Absorbing boundary conditions, boundary control, Fourier Galerkin approach, modal approach, wave equation.

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Authors: Md. Fazlul Karim, Ahmad Izani Ismail, Mohammed Ashaque Meah

Abstract:

This paper focuses on the development of a 2-D boundary fitted and nested grid (BFNG) model to compute the tsunami propagation of Indonesian tsunami 2004 along the coastal region of Penang in Peninsular Malaysia.

In the presence of a curvilinear coastline, boundary fitted grids are suitable to represent the model boundaries accurately. On the other hand, when large gradient of velocity within a confined area is expected, the use of a nested grid system is appropriate to improve the numerical accuracy with the least grid numbers.

This paper constructs a shallow water nested and orthogonal boundary fitted grid model and presents computational results of the tsunami impact on the Penang coast due to the Indonesian tsunami of 2004. The results of the numerical simulations are compared with available data.

Keywords: Boundary Fitted Nested Model, Tsunami, Penang Island, 2004 Indonesian Tsunami.

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Authors: Nureddin Dinler, Nuri Yucel

Abstract:

Improving the performance of internal combustion engines is one of the major concerns of researchers. Experimental studies are more expensive than computational studies. Also using computational techniques allows one to obtain all the required data for the cylinder, some of which could not be measured. In this study, an axisymmetric homogeneous charged spark ignition engine was modeled. Fluid motion and combustion process were investigated numerically. Turbulent flow conditions were considered. Standard k- ε turbulence model for fluid flow and eddy break-up model for turbulent combustion were utilized. The effects of valve angle on the fluid flow and combustion are analyzed for constant air/fuel and compression ratios. It is found that, velocities and strength of tumble increases in-cylinder flow and due to increase in turbulence strength, the flame propagation is faster for small valve angles.

Keywords: CFD simulation, eddy break-up model, k-εturbulence model, reciprocating engine flow and combustion.

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Authors: Mat Syai'in, Adi Soeprijanto

Abstract:

An Optimal Power Flow based on Improved Particle Swarm Optimization (OPF-IPSO) with Generator Capability Curve Constraint is used by NN-OPF as a reference to get pattern of generator scheduling. There are three stages in Designing NN-OPF. The first stage is design of OPF-IPSO with generator capability curve constraint. The second stage is clustering load to specific range and calculating its index. The third stage is training NN-OPF using constructive back propagation method. In training process total load and load index used as input, and pattern of generator scheduling used as output. Data used in this paper is power system of Java-Bali. Software used in this simulation is MATLAB.

Keywords: Optimal Power Flow, Generator Capability Curve, Improved Particle Swarm Optimization, Neural Network

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Authors: Morteza Moazami-Goudarzi, Mohammad H. Moradi, Ali Taheri

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In this paper we introduce an effective ECG compression algorithm based on two dimensional multiwavelet transform. Multiwavelets offer simultaneous orthogonality, symmetry and short support, which is not possible with scalar two-channel wavelet systems. These features are known to be important in signal processing. Thus multiwavelet offers the possibility of superior performance for image processing applications. The SPIHT algorithm has achieved notable success in still image coding. We suggested applying SPIHT algorithm to 2-D multiwavelet transform of2-D arranged ECG signals. Experiments on selected records of ECG from MIT-BIH arrhythmia database revealed that the proposed algorithm is significantly more efficient in comparison with previously proposed ECG compression schemes.

Keywords: ECG signal compression, multi-rateprocessing, 2-D Multiwavelet, Prefiltering.

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Authors: A. Аndreev, Yu. Rozhdestvenskii, К. Platonov, R. Salomaa

Abstract:

This paper considers various channels of gammaquantum generation via an ultra-short high-power laser pulse interaction with different targets.We analyse the possibilities to create a pulsed gamma-radiation source using laser triggering of some nuclear reactions and isomer targets. It is shown that sub-MeV monochromatic short pulse of gamma-radiation can be obtained with pulse energy of sub-mJ level from isomer target irradiated by intense laser pulse. For nuclear reaction channel in light- atom materials, it is shown that sub-PW laser pulse gives rise to formation about million gamma-photons of multi-MeV energy.

Keywords: High power laser, short pulse, fast particles, isomertarget.

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Authors: Khenfer-Koummich Fatima, Mesbahi Larbi, Hendel Fatiha

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This paper presents a recognition system for isolated words like robot commands. It’s carried out by Time Delay Neural Networks; TDNN. To teleoperate a robot for specific tasks as turn, close, etc… In industrial environment and taking into account the noise coming from the machine. The choice of TDNN is based on its generalization in terms of accuracy, in more it acts as a filter that allows the passage of certain desirable frequency characteristics of speech; the goal is to determine the parameters of this filter for making an adaptable system to the variability of speech signal and to noise especially, for this the back propagation technique was used in learning phase. The approach was applied on commands pronounced in two languages separately: The French and Arabic. The results for two test bases of 300 spoken words for each one are 87%, 97.6% in neutral environment and 77.67%, 92.67% when the white Gaussian noisy was added with a SNR of 35 dB.

Keywords: Neural networks, Noise, Speech Recognition.

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Authors: Kong Fah Tee, Lutfor Rahman Khan, Hongshuang Li

Abstract:

An advanced Monte Carlo simulation method, called Subset Simulation (SS) for the time-dependent reliability prediction for underground pipelines has been presented in this paper. The SS can provide better resolution for low failure probability level with efficient investigating of rare failure events which are commonly encountered in pipeline engineering applications. In SS method, random samples leading to progressive failure are generated efficiently and used for computing probabilistic performance by statistical variables. SS gains its efficiency as small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment. It is hoped that the development work can promote the use of SS tools for uncertainty propagation in the decision-making process of underground pipelines network reliability prediction.

Keywords: Underground pipelines, Probability of failure, Reliability and Subset Simulation.

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Authors: Paul O'Leary, Matthew Harker

Abstract:

This paper presents unified theory for local (Savitzky- Golay) and global polynomial smoothing. The algebraic framework can represent any polynomial approximation and is seamless from low degree local, to high degree global approximations. The representation of the smoothing operator as a projection onto orthonormal basis functions enables the computation of: the covariance matrix for noise propagation through the filter; the noise gain and; the frequency response of the polynomial filters. A virtually perfect Gram polynomial basis is synthesized, whereby polynomials of degree d = 1000 can be synthesized without significant errors. The perfect basis ensures that the filters are strictly polynomial preserving. Given n points and a support length ls = 2m + 1 then the smoothing operator is strictly linear phase for the points xi, i = m+1. . . n-m. The method is demonstrated on geometric surfaces data lying on an invariant 2D lattice.

Keywords: Gram polynomials, Savitzky-Golay Smoothing, Discrete Polynomial Moments

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Authors: Alexander Sutor, David Demetz

Abstract:

We present methods for developing wireless and traceable sensors for photobioreactors or photoreactors in general. The main focus of application are reactors which are wirelessly powered. Due to the promising properties of the propagation of magnetic fields under water we implemented an inductive link with an on/off switched hartley-oscillator as transmitter and an LC-tank as receiver. For this inductive link we used a carrier frequency of 298 kHz. With this system we performed measurements to demonstrate the independence of the magnetic field from water or salty water. In contrast we showed the strongly reduced range of RF-transmitter-receiver systems at higher frequencies (433 MHz and 2.4 GHz) in water and in salty water. For implementing the traceability of the sensors, we performed measurements to show the well defined orientation of the magnetic field of a coil. This information will be used in future work for implementing an inductive link based traceability system for our sensors.

Keywords: Wireless sensors, traceable sensors, photoreactor, internal illumination, wireless power.

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Authors: Sarbjot Singh Sandhu

Abstract:

This paper presents the experimental results of a single cylinder Enfield engine using an electronically controlled fuel injection system which was developed to carry out exhaustive tests using neat CNG, and mixtures of hydrogen in compressed natural gas (HCNG) as 0, 5, 10, 15 and 20% by energy. Experiments were performed at 2000 and 2400 rpm with wide open throttle and varying the equivalence ratio. Hydrogen which has fast burning rate, when added to compressed natural gas, enhances its flame propagation rate. The emissions of HC, CO, decreased with increasing percentage of hydrogen but NOx was found to increase. The results indicated a marked improvement in the brake thermal efficiency with the increase in percentage of hydrogen added. The improved thermal efficiency was clearly observed to be more in lean region as compared to rich region. This study is expected to reduce vehicular emissions along with increase in thermal efficiency and thus help in reduction of further environmental degradation.

Keywords: Hydrogen, CNG, HCNG, Emissions.

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Authors: Ε. Giovanis

Abstract:

In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer after the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model in the out-of-sample period. The idea behind this approach is to propose a parametric regression with weighted variables in order to test for the statistical significance and the magnitude of the estimated autoregressive coefficients and simultaneously to estimate the forecasts.

Keywords: Autoregressive model, Error back-propagation Feed-Forward neural networks, , Gross Domestic Product

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Authors: E. Giovanis

Abstract:

In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer of the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model. Moreover this technique can be used in Autoregressive-Moving Average models, with and without exogenous inputs, as also the training process with genetics algorithms optimization can be replaced by the error back-propagation algorithm.

Keywords: Autoregressive model, Feed-Forward neuralnetworks, Genetic Algorithms, Gross Domestic Product

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