Abstract: The paper provides biomasses characteristics by
proximate analysis (volatile matter, fixed carbon and ash) and
ultimate analysis (carbon, hydrogen, nitrogen and oxygen) for the
prediction of the heating value equations. The heating value
estimation of various biomasses can be used as an energy evaluation.
Thirteen types of biomass were studied. Proximate analysis was
investigated by mass loss method and infrared moisture analyzer.
Ultimate analysis was analyzed by CHNO analyzer. The heating
values varied from 15 to 22.4MJ kg-1. Correlations of the calculated
heating value with proximate and ultimate analyses were undertaken
using multiple regression analysis and summarized into three and two
equations, respectively. Correlations based on proximate analysis
illustrated that deviation of calculated heating values from
experimental heating values was higher than the correlations based
on ultimate analysis.
Abstract: Artificial Neural Network (ANN)s are best suited for
prediction and optimization problems. Trained ANNs have found
wide spread acceptance in several antenna design systems. Four
parameters namely antenna radiation resistance, loss resistance, efficiency,
and inductance can be used to design an antenna layout though
there are several other parameters available. An ANN can be trained
to provide the best and worst case precisions of an antenna design
problem defined by these four parameters. This work describes the
use of an ANN to generate the four mentioned parameters for a loop
antenna for the specified frequency range. It also provides insights
to the prediction of best and worst-case design problems observed
in applications and thereby formulate a model for physical layout
design of a loop antenna.
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.
Abstract: Safer driver behavior promoting is the main goal of this paper. It is a fact that drivers behavior is relatively safer when being monitored. Thus, in this paper, we propose a monitoring system to report specific driving event as well as the potentially aggressive events for estimation of the driving performance. Our driving monitoring system is composed of two parts. The first part is the in-vehicle embedded system which is composed of a GPS receiver, a two-axis accelerometer, radar sensor, OBD interface, and GPRS modem. The design considerations that led to this architecture is described in this paper. The second part is a web server where an adaptive hierarchical fuzzy system is proposed to classify the driving performance based on the data that is sent by the in-vehicle embedded system and the data that is provided by the geographical information system (GIS). Our system is robust, inexpensive and small enough to fit inside a vehicle without distracting the driver.
Abstract: A preliminary evaluation of the urban land system is
presented in the article together with the instruments of land policy in
Serbia. The main reason for the analysis is demand for
definition of reform framework for urban land management in
Serbia in the period of transition towards market-led system. It
is concluded that due to the limitations of the current regulation it
will be impossible in the future to apply market principles in the
urban land policy (supply and demand of land, land capitalization,
investment efficiency, et al.). Based on the estimation that the urban
land system and land policy are key factors of competitiveness
between regions and towns in Serbia, it is necessary to initiate
changes in this field. There are indicated on an option of privatization
of urban public land and possible establishment of leasehold land. A
comparative analysis of the possibilities of the reform urban land
system in Serbia has been carried out in relation to two approaches of
market systems: (a) with dominant private ownership of urban land
(neo/liberal approach) and (b) with dominant public ownership of
urban land (system of leasehold)whose findings can be a basis for
further study of the new system in Serbia.. The attanied results are
part of studies matter for the making of Strategy of territorial
development of Serbia.
Abstract: This paper presents a method to estimate load profile
in a multiple power flow solutions for every minutes in 24 hours per
day. A method to calculate multiple solutions of non linear profile is
introduced. The Power System Simulation/Engineering (PSS®E) and
python has been used to solve the load power flow. The result of this
power flow solutions has been used to estimate the load profiles for
each load at buses using Independent Component Analysis (ICA)
without any knowledge of parameter and network topology of the
systems. The proposed algorithm is tested with IEEE 69 test bus
system represents for distribution part and the method of ICA has
been programmed in MATLAB R2012b version. Simulation results
and errors of estimations are discussed in this paper.
Abstract: This paper introduces an intelligent system, which can be applied in the monitoring of vehicle speed using a single camera. The ability of motion tracking is extremely useful in many automation problems and the solution to this problem will open up many future applications. One of the most common problems in our daily life is the speed detection of vehicles on a highway. In this paper, a novel technique is developed to track multiple moving objects with their speeds being estimated using a sequence of video frames. Field test has been conducted to capture real-life data and the processed results were presented. Multiple object problems and noisy in data are also considered. Implementing this system in real-time is straightforward. The proposal can accurately evaluate the position and the orientation of moving objects in real-time. The transformations and calibration between the 2D image and the actual road are also considered.
Abstract: Many supervised induction algorithms require discrete
data, even while real data often comes in a discrete
and continuous formats. Quality discretization of continuous
attributes is an important problem that has effects on speed,
accuracy and understandability of the induction models. Usually,
discretization and other types of statistical processes are applied
to subsets of the population as the entire population is practically
inaccessible. For this reason we argue that the discretization
performed on a sample of the population is only an estimate of
the entire population. Most of the existing discretization methods,
partition the attribute range into two or several intervals using
a single or a set of cut points. In this paper, we introduce a
technique by using resampling (such as bootstrap) to generate
a set of candidate discretization points and thus, improving the
discretization quality by providing a better estimation towards
the entire population. Thus, the goal of this paper is to observe
whether the resampling technique can lead to better discretization
points, which opens up a new paradigm to construction of
soft decision trees.
Abstract: This work proposes an accurate crosstalk noise estimation method in the presence of multiple RLC lines for the use in design automation tools. This method correctly models the loading effects of non switching aggressors and aggressor tree branches using resistive shielding effect and realistic exponential input waveforms. Noise peak and width expressions have been derived. The results obtained are at good agreement with SPICE results. Results show that average error for noise peak is 4.7% and for the width is 6.15% while allowing a very fast analysis.
Abstract: Cyclic delay diversity (CDD) is a simple technique to
intentionally increase frequency selectivity of channels for orthogonal
frequency division multiplexing (OFDM).This paper proposes a residual
carrier frequency offset (RFO) estimation scheme for OFDMbased
broadcasting system using CDD. In order to improve the RFO
estimation, this paper addresses a decision scheme of the amount of
cyclic delay and pilot pattern used to estimate the RFO. By computer
simulation, the proposed estimator is shown to benefit form propoerly
chosen delay parameter and perform robustly.
Abstract: Number of breakdowns experienced by a machinery is a highly under-dispersed count random variable and its value can be attributed to the factors related to the mechanical input and output of that machinery. Analyzing such under-dispersed count observations as a function of the explanatory factors has been a challenging problem. In this paper, we aim at estimating the effects of various factors on the number of breakdowns experienced by a passenger car based on a study performed in Mauritius over a year. We remark that the number of passenger car breakdowns is highly under-dispersed. These data are therefore modelled and analyzed using Com-Poisson regression model. We use quasi-likelihood estimation approach to estimate the parameters of the model. Under-dispersion parameter is estimated to be 2.14 justifying the appropriateness of Com-Poisson distribution in modelling under-dispersed count responses recorded in this study.
Abstract: The process for predicting the ballistic properties of a liquid rocket engine is based on the quantitative estimation of idealized performance deviations. In this aim, an equilibrium chemistry procedure is firstly developed and implemented in a Fortran routine. The thermodynamic formulation allows for the calculation of the theoretical performances of a rocket thrust chamber. In a second step, a computational fluid dynamic analysis of the turbulent reactive flow within the chamber is performed using a finite volume approach. The obtained values for the “quasi-real" performances account for both turbulent mixing and chemistryturbulence coupling. In the present work, emphasis is made on the combustion efficiency performance for which deviation is mainly due to radial gradients of static temperature and mixture ratio. Numerical values of the characteristic velocity are successfully compared with results from an industry-used code. The results are also confronted with the experimental data of a laboratory-scale rocket engine.
Abstract: We estimate snow velocity and snow drift density on hilly terrain under the assumption that the drifting snow mass can be represented using a micro-continuum approach (i.e. using a nonclassical mechanics approach assuming a class of fluids for which basic equations of mass, momentum and energy have been derived). In our model, the theory of coupled stress fluids proposed by Stokes [1] has been employed for the computation of flow parameters. Analyses of bulk drift velocity, drift density, drift transport and mass transport of snow particles have been carried out and computations made, considering various parametric effects. Results are compared with those of classical mechanics (logarithmic wind profile). The results indicate that particle size affects the flow characteristics significantly.
Abstract: The analytical solution of functionally graded
piezoelectric hollow cylinder which is under radial electric potential
and non-axisymmetric thermo-mechanical loads, are presented in this
paper. Using complex Fourier series and estimation of power law for
variations of material characterizations through the thickness, the
electro thermo mechanical behavior of the FGPM cylinder is
obtained. The stress and displacement distributions and the effect of
electric potential field on the cylinder behavior are also presented and
some applicable results are offered at the end of the paper.
Abstract: The development and extension of large cities induced
a need for shallow tunnel in soft ground of building areas. Estimation
of ground settlement caused by the tunnel excavation is important
engineering point. In this paper, prediction of surface subsidence
caused by tunneling in one section of seventh line of Tehran subway
is considered. On the basis of studied geotechnical conditions of the
region, tunnel with the length of 26.9km has been excavated applying
a mechanized method using an EPB-TBM with a diameter of 9.14m.
In this regard, settlement is estimated utilizing both analytical and
numerical finite element method. The numerical method shows that
the value of settlement in this section is 5cm. Besides, the analytical
consequences (Bobet and Loganathan-Polous) are 5.29 and 12.36cm,
respectively. According to results of this study, due tosaturation of
this section, there are good agreement between Bobet and numerical
methods. Therefore, tunneling processes in this section needs a
special consolidation measurement and support system before the
passage of tunnel boring machine.
Abstract: Speed estimation is one of the important and practical tasks in machine vision, Robotic and Mechatronic. the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis has generated a great deal of interest in machine vision algorithms. Numerous approaches for speed estimation have been proposed. So classification and survey of the proposed methods can be very useful. The goal of this paper is first to review and verify these methods. Then we will propose a novel algorithm to estimate the speed of moving object by using fuzzy concept. There is a direct relation between motion blur parameters and object speed. In our new approach we will use Radon transform to find direction of blurred image, and Fuzzy sets to estimate motion blur length. The most benefit of this algorithm is its robustness and precision in noisy images. Our method was tested on many images with different range of SNR and is satisfiable.
Abstract: This paper introduces a new variable step-size APA with decorrelation of AR input process is based on the MSD analysis. To achieve a fast convergence rate and a small steady-state estimation error, he proposed algorithm uses variable step size that is determined by minimising the MSD. In addition, experimental results show that the proposed algorithm is achieved better performance than the other algorithms.
Abstract: This paper presents a formant-tracking linear prediction
(FTLP) model for speech processing in noise. The main focus of this
work is the detection of formant trajectory based on Hidden Markov
Models (HMM), for improved formant estimation in noise. The
approach proposed in this paper provides a systematic framework for
modelling and utilization of a time- sequence of peaks which satisfies
continuity constraints on parameter; the within peaks are modelled
by the LP parameters. The formant tracking LP model estimation
is composed of three stages: (1) a pre-cleaning multi-band spectral
subtraction stage to reduce the effect of residue noise on formants
(2) estimation stage where an initial estimate of the LP model of
speech for each frame is obtained (3) a formant classification using
probability models of formants and Viterbi-decoders. The evaluation
results for the estimation of the formant tracking LP model tested
in Gaussian white noise background, demonstrate that the proposed
combination of the initial noise reduction stage with formant tracking
and LPC variable order analysis, results in a significant reduction in
errors and distortions. The performance was evaluated with noisy
natual vowels extracted from international french and English vocabulary
speech signals at SNR value of 10dB. In each case, the
estimated formants are compared to reference formants.
Abstract: This work presents a matched field processing (MFP)
algorithm based on Dopplerlet transform for estimating the motion
parameters of a sound source moving along a straight line and with a
constant speed by using a piecewise strategy, which can significantly
reduce the computational burden. Monte Carlo simulation results and
an experimental result are presented to verify the effectiveness of the
algorithm advocated.
Abstract: In this paper we consider the issue of distributed adaptive estimation over sensor networks. To deal with more realistic scenario, different variance for observation noise is assumed for sensors in the network. To solve the problem of different variance of observation noise, the proposed method is divided into two phases: I) Estimating each sensor-s observation noise variance and II) using the estimated variances to obtain the desired parameter. Our proposed algorithm is based on a diffusion least mean square (LMS) implementation with linear combiner model. In the proposed algorithm, the step-size parameter the coefficients of linear combiner are adjusted according to estimated observation noise variances. As the simulation results show, the proposed algorithm considerably improves the diffusion LMS algorithm given in literature.