Abstract: The purpose of determining impact significance is to
place value on impacts. Environmental impact assessment review is a
process that judges whether impact significance is acceptable or not in
accordance with the scientific facts regarding environmental,
ecological and socio-economical impacts described in environmental
impact statements (EIS) or environmental impact assessment reports
(EIAR). The first aim of this paper is to summarize the criteria of
significance evaluation from the past review results and accordingly
utilize fuzzy logic to incorporate these criteria into scientific facts. The
second aim is to employ data mining technique to construct an EIS or
EIAR prediction model for reviewing results which can assist
developers to prepare and revise better environmental management
plans in advance. The validity of the previous prediction model
proposed by authors in 2009 is 92.7%. The enhanced validity in this
study can attain 100.0%.
Abstract: Increasing concerns over climate change have limited
the liberal usage of available energy technology options. India faces
a formidable challenge to meet its energy needs and provide adequate
energy of desired quality in various forms to users in sustainable
manner at reasonable costs. In this paper, work carried out with an
objective to study the role of various energy technology options
under different scenarios namely base line scenario, high nuclear
scenario, high renewable scenario, low growth and high growth rate
scenario. The study has been carried out using Model for Energy
Supply Strategy Alternatives and their General Environmental
Impacts (MESSAGE) model which evaluates the alternative energy
supply strategies with user defined constraints on fuel availability,
environmental regulations etc. The projected electricity demand, at
the end of study period i.e. 2035 is 500490 MWYr. The model
predicted the share of the demand by Thermal: 428170 MWYr,
Hydro: 40320 MWYr, Nuclear: 14000 MWYr, Wind: 18000 MWYr
in the base line scenario. Coal remains the dominant fuel for
production of electricity during the study period. However, the
import dependency of coal increased during the study period. In
baseline scenario the cumulative carbon dioxide emissions upto 2035
are about 11,000 million tones of CO2. In the scenario of high nuclear
capacity the carbon dioxide emissions reduced by 10 % when nuclear
energy share increased to 9 % compared to 3 % in baseline scenario.
Similarly aggressive use of renewables reduces 4 % of carbon
dioxide emissions.
Abstract: A wideband 2-1-1 cascaded ΣΔ modulator with a
single-bit quantizer in the two first stages and a 4-bit quantizer in the
final stage is developed. To reduce sensitivity of digital-to-analog
converter (DAC) nonlinearities in the feedback of the last stage,
dynamic element matching (DEM) is introduced. This paper presents
two modelling approaches: The first is MATLAB description and the
second is VHDL-AMS modelling of the proposed architecture and
exposes some high-level-simulation results allowing a behavioural
study. The detail of both ideal and non-ideal behaviour modelling are
presented. Then, the study of the effect of building blocks
nonidealities is presented; especially the influences of nonlinearity,
finite operational amplifier gain, amplifier slew rate limitation and
capacitor mismatch. A VHDL-AMS description presents a good
solution to predict system-s performances and can provide sensitivity
curves giving the impact of nonidealities on the system performance.
Abstract: A rigorous two-dimensional model is developed for simulating the operation of a less-investigated type steam reformer having a considerably lower operating Reynolds number, higher tube diameter, and non-availability of extra steam in the feed compared with conventional steam reformers. Simulation results show that reasonable predictions can only be achieved when certain correlations for wall to fluid heat transfer equations are applied. Due to severe operating conditions, in all cases, strong radial temperature gradients inside the reformer tubes have been found. Furthermore, the results show how a certain catalyst loading profile will affect the operation of the reformer.
Abstract: This study applied the Theory of Planned Behavior
model in predicting dietary behavior among Type 2 diabetics in a
Kenyan environment. The study was conducted for three months
within the diabetic clinic at Kisii Hospital in Nyanza Province in
Kenya and adopted sequential mixed methods design combing both
qualitative and quantitative phases. Qualitative data was analyzed
using grounded theory analysis method. Structural equation modeling
using maximum likelihood was used to analyze quantitative data.
The results based on the common fit indices revealed that the theory
of planned behavior fitted the data acceptably well among the Type 2
diabetes and within dietary behavior {χ2 = 223.3, df = 77, p = .02,
χ2/df = 2.9, n=237; TLI = .93; CFI =.91; RMSEA (90CI) = .090(.039,
.146)}. This implies that the Theory of Planned Behavior holds and
forms a framework for promoting dietary practice among Type 2
diabetics.
Abstract: Study of soil properties like field capacity (F.C.) and permanent wilting point (P.W.P.) play important roles in study of soil moisture retention curve. Although these parameters can be measured directly, their measurement is difficult and expensive. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. In this investigation, 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. The data set was divided into two subsets for calibration (80%) and testing (20%) of the models and their normality were tested by Kolmogorov-Smirnov method. Both multivariate regression and artificial neural network (ANN) techniques were employed to develop the appropriate PTFs for predicting soil parameters using easily measurable characteristics of clay, silt, O.C, S.P, B.D and CaCO3. The performance of the multivariate regression and ANN models was evaluated using an independent test data set. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The comparison of RSME for two mentioned models showed that the ANN model gives better estimates of F.C and P.W.P than the multivariate regression model. The value of RMSE and R2 derived by ANN model for F.C and P.W.P were (2.35, 0.77) and (2.83, 0.72), respectively. The corresponding values for multivariate regression model were (4.46, 0.68) and (5.21, 0.64), respectively. Results showed that ANN with five neurons in hidden layer had better performance in predicting soil properties than multivariate regression.
Abstract: This paper presents a vocoder to obtain high quality synthetic speech at 600 bps. To reduce the bit rate, the algorithm is based on a sinusoidally excited linear prediction model which extracts few coding parameters, and three consecutive frames are grouped into a superframe and jointly vector quantization is used to obtain high coding efficiency. The inter-frame redundancy is exploited with distinct quantization schemes for different unvoiced/voiced frame combinations in the superframe. Experimental results show that the quality of the proposed coder is better than that of 2.4kbps LPC10e and achieves approximately the same as that of 2.4kbps MELP and with high robustness.
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: High Speed PM Generators driven by micro-turbines
are widely used in Smart Grid System. So, this paper proposes
comparative study among six classical, optimized and genetic
analytical design cases for 400 kW output power at tip speed 200
m/s. These six design trials of High Speed Permanent Magnet
Synchronous Generators (HSPMSGs) are: Classical Sizing;
Unconstrained optimization for total losses and its minimization;
Constrained optimized total mass with bounded constraints are
introduced in the problem formulation. Then a genetic algorithm is
formulated for obtaining maximum efficiency and minimizing
machine size. In the second genetic problem formulation, we attempt
to obtain minimum mass, the machine sizing that is constrained by
the non-linear constraint function of machine losses. Finally, an
optimum torque per ampere genetic sizing is predicted. All results are
simulated with MATLAB, Optimization Toolbox and its Genetic
Algorithm. Finally, six analytical design examples comparisons are
introduced with study of machines waveforms, THD and rotor losses.
Abstract: A numerical analysis of wave and hydrodynamic models
is used to investigate the influence of WAve and Storm Surge
(WASS) in the regional and coastal zones. The numerical analyzed
system consists of the WAve Model Cycle 4 (WAMC4) and the
Princeton Ocean Model (POM) which used to solve the energy
balance and primitive equations respectively. The results of both
models presented the incorporated surface wave in the regional
zone affected the coastal storm surge zone. Specifically, the results
indicated that the WASS generally under the approximation is not
only the peak surge but also the coastal water level drop which
can also cause substantial impact on the coastal environment. The
wave–induced surface stress affected the storm surge can significantly
improve storm surge prediction. Finally, the calibration of wave
module according to the minimum error of the significant wave height
(Hs) is not necessarily result in the optimum wave module in the
WASS analyzed system for the WASS prediction.
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: 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: Training neural networks to capture an intrinsic
property of a large volume of high dimensional data is a difficult
task, as the training process is computationally expensive. Input
attributes should be carefully selected to keep the dimensionality of
input vectors relatively small.
Technical indexes commonly used for stock market prediction
using neural networks are investigated to determine its effectiveness
as inputs. The feed forward neural network of Levenberg-Marquardt
algorithm is applied to perform one step ahead forecasting of
NASDAQ and Dow stock prices.
Abstract: The Multi-Layered Perceptron (MLP) Neural
networks have been very successful in a number of signal processing
applications. In this work we have studied the possibilities and the
met difficulties in the application of the MLP neural networks for the
prediction of daily solar radiation data. We have used the Polack-Ribière algorithm for training the neural networks. A comparison, in
term of the statistical indicators, with a linear model most used in
literature, is also performed, and the obtained results show that the
neural networks are more efficient and gave the best results.
Abstract: Condition monitoring of electrical power equipment
has attracted considerable attention for many years. The aim of this
paper is to use Labview with Fuzzy Logic controller to build a
simulation system to diagnose transformer faults and monitor its
condition. The front panel of the system was designed using
LabVIEW to enable computer to act as customer-designed
instrument. The dissolved gas-in-oil analysis (DGA) method was
used as technique for oil type transformer diagnosis; meanwhile
terminal voltages and currents analysis method was used for dry type
transformer. Fuzzy Logic was used as expert system that assesses all
information keyed in at the front panel to diagnose and predict the
condition of the transformer. The outcome of the Fuzzy Logic
interpretation will be displayed at front panel of LabVIEW to show
the user the conditions of the transformer at any time.
Abstract: In this paper multivariable predictive PID controller has
been implemented on a multi-inputs multi-outputs control problem
i.e., quadruple tank system, in comparison with a simple multiloop
PI controller. One of the salient feature of this system is an
adjustable transmission zero which can be adjust to operate in both
minimum and non-minimum phase configuration, through the flow
distribution to upper and lower tanks in quadruple tank system.
Stability and performance analysis has also been carried out for this
highly interactive two input two output system, both in minimum
and non-minimum phases. Simulations of control system revealed
that better performance are obtained in predictive PID design.
Abstract: In current common research reports, salient regions
are usually defined as those regions that could present the main
meaningful or semantic contents. However, there are no uniform
saliency metrics that could describe the saliency of implicit image
regions. Most common metrics take those regions as salient regions,
which have many abrupt changes or some unpredictable
characteristics. But, this metric will fail to detect those salient useful
regions with flat textures. In fact, according to human semantic
perceptions, color and texture distinctions are the main characteristics
that could distinct different regions. Thus, we present a novel saliency
metric coupled with color and texture features, and its corresponding
salient region extraction methods. In order to evaluate the
corresponding saliency values of implicit regions in one image, three
main colors and multi-resolution Gabor features are respectively used
for color and texture features. For each region, its saliency value is
actually to evaluate the total sum of its Euclidean distances for other
regions in the color and texture spaces. A special synthesized image
and several practical images with main salient regions are used to
evaluate the performance of the proposed saliency metric and other
several common metrics, i.e., scale saliency, wavelet transform
modulus maxima point density, and important index based metrics.
Experiment results verified that the proposed saliency metric could
achieve more robust performance than those common saliency
metrics.
Abstract: Complexity, as a theoretical background has made it
easier to understand and explain the features and dynamic behavior
of various complex systems. As the common theoretical background
has confirmed, borrowing the terminology for design from the
natural sciences has helped to control and understand urban
complexity. Phenomena like self-organization, evolution and
adaptation are appropriate to describe the formerly inaccessible
characteristics of the complex environment in unpredictable bottomup
systems. Increased computing capacity has been a key element in
capturing the chaotic nature of these systems.
A paradigm shift in urban planning and architectural design has
forced us to give up the illusion of total control in urban
environment, and consequently to seek for novel methods for
steering the development. New methods using dynamic modeling
have offered a real option for more thorough understanding of
complexity and urban processes. At best new approaches may renew
the design processes so that we get a better grip on the complex
world via more flexible processes, support urban environmental
diversity and respond to our needs beyond basic welfare by liberating
ourselves from the standardized minimalism.
A complex system and its features are as such beyond human
ethics. Self-organization or evolution is either good or bad. Their
mechanisms are by nature devoid of reason. They are common in
urban dynamics in both natural processes and gas. They are features
of a complex system, and they cannot be prevented. Yet their
dynamics can be studied and supported.
The paradigm of complexity and new design approaches has been
criticized for a lack of humanity and morality, but the ethical
implications of scientific or computational design processes have not
been much discussed. It is important to distinguish the (unexciting)
ethics of the theory and tools from the ethics of computer aided
processes based on ethical decisions. Urban planning and architecture
cannot be based on the survival of the fittest; however, the natural
dynamics of the system cannot be impeded on grounds of being
“non-human".
In this paper the ethical challenges of using the dynamic models
are contemplated in light of a few examples of new architecture and
dynamic urban models and literature. It is suggested that ethical
challenges in computational design processes could be reframed
under the concepts of responsibility and transparency.
Abstract: The speech signal conveys information about the
identity of the speaker. The area of speaker identification is
concerned with extracting the identity of the person speaking the
utterance. As speech interaction with computers becomes more
pervasive in activities such as the telephone, financial transactions
and information retrieval from speech databases, the utility of
automatically identifying a speaker is based solely on vocal
characteristic. This paper emphasizes on text dependent speaker
identification, which deals with detecting a particular speaker from a
known population. The system prompts the user to provide speech
utterance. System identifies the user by comparing the codebook of
speech utterance with those of the stored in the database and lists,
which contain the most likely speakers, could have given that speech
utterance. The speech signal is recorded for N speakers further the
features are extracted. Feature extraction is done by means of LPC
coefficients, calculating AMDF, and DFT. The neural network is
trained by applying these features as input parameters. The features
are stored in templates for further comparison. The features for the
speaker who has to be identified are extracted and compared with the
stored templates using Back Propogation Algorithm. Here, the
trained network corresponds to the output; the input is the extracted
features of the speaker to be identified. The network does the weight
adjustment and the best match is found to identify the speaker. The
number of epochs required to get the target decides the network
performance.
Abstract: Traffic Management and Information Systems, which rely on a system of sensors, aim to describe in real-time traffic in urban areas using a set of parameters and estimating them. Though the state of the art focuses on data analysis, little is done in the sense of prediction. In this paper, we describe a machine learning system for traffic flow management and control for a prediction of traffic flow problem. This new algorithm is obtained by combining Random Forests algorithm into Adaboost algorithm as a weak learner. We show that our algorithm performs relatively well on real data, and enables, according to the Traffic Flow Evaluation model, to estimate and predict whether there is congestion or not at a given time on road intersections.