Abstract: Power system stability enhancement by simultaneous tuning of a Power System Stabilizer (PSS) and a Static Var Compensator (SVC)-based controller is thoroughly investigated in this paper. The coordination among the proposed damping stabilizers and the SVC internal voltage regulators has also been taken into consideration. The design problem is formulated as an optimization problem with a time-domain simulation-based objective function and Real-Coded Genetic Algorithm (RCGA) is employed to search for optimal controller parameters. The proposed stabilizers are tested on a weakly connected power system with different disturbances and loading conditions. The nonlinear simulation results are presented to show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions and disturbances. Further, the proposed design approach is found to be robust and improves stability effectively even under small disturbance and unbalanced fault conditions.
Abstract: Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness prediction before milling process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 84 samples were run in this study by using FANUC CNC Milling α-Τ14ιE. Those samples were randomly divided into two data sets- the training sets (m=60) and testing sets(m=24). ANOVA analysis showed that at least one of the population regression coefficients was not zero. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using Multiple Regression Method equation, the average percentage deviation of the testing set was 9.8% and 9.7% for training data set. This showed that the statistical model could predict the surface roughness with about 90.2% accuracy of the testing data set and 90.3% accuracy of the training data set.
Abstract: Time series forecasting is an important and widely
popular topic in the research of system modeling. This paper
describes how to use the hybrid PSO-RLSE neuro-fuzzy learning
approach to the problem of time series forecasting. The PSO
algorithm is used to update the premise parameters of the
proposed prediction system, and the RLSE is used to update the
consequence parameters. Thanks to the hybrid learning (HL)
approach for the neuro-fuzzy system, the prediction performance
is excellent and the speed of learning convergence is much faster
than other compared approaches. In the experiments, we use the
well-known Mackey-Glass chaos time series. According to the
experimental results, the prediction performance and accuracy in
time series forecasting by the proposed approach is much better
than other compared approaches, as shown in Table IV. Excellent
prediction performance by the proposed approach has been
observed.
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: The paper represents a reflection on how to select proper indicators to assess the progress of regional contexts towards a knowledge-based society. Taking the first research methodologies elaborated at an international level (World Bank, OECD, etc.) as a reference point, this work intends to identify a set of indicators of the knowledge economy suitable to adequately understand in which manner and to which extent the territorial development dynamics are correlated with the knowledge-base of the considered local society. After a critical survey of the variables utilized within other approaches adopted by international or national organizations, this paper seeks to elaborate a framework of variables, named Regional Knowledge Economy Indicators (ReKEI), necessary to describe the knowledge-based relations of subnational socio-economic contexts. The realization of this framework has a double purpose: an analytical one consisting in highlighting the regional differences in the governance of knowledge based processes, and an operative one consisting in providing some reference parameters for contributing to increasing the effectiveness of those economic policies aiming at enlarging the knowledge bases of local societies.
Abstract: Tool wear and surface roughness prediction plays a
significant role in machining industry for proper planning and control
of machining parameters and optimization of cutting conditions. This
paper deals with developing an artificial neural network (ANN)
model as a function of cutting parameters in turning steel under
minimum quantity lubrication (MQL). A feed-forward
backpropagation network with twenty five hidden neurons has been
selected as the optimum network. The co-efficient of determination
(R2) between model predictions and experimental values are 0.9915,
0.9906, 0.9761 and 0.9627 in terms of VB, VM, VS and Ra
respectively. The results imply that the model can be used easily to
forecast tool wear and surface roughness in response to cutting
parameters.
Abstract: Renewable and non-renewable resource constraints have been vast studied in theoretical fields of project scheduling problems. However, although cumulative resources are widespread in practical cases, the literature on project scheduling problems subject to these resources is scant. So in order to study this type of resources more, in this paper we use the framework of a resource constrained project scheduling problem (RCPSP) with finish-start precedence relations between activities and subject to the cumulative resources in addition to the renewable resources. We develop a branch and bound algorithm for this problem customizing precedence tree algorithm of RCPSP. We perform extensive experimental analysis on the algorithm to check its effectiveness and performance for solving different instances of the problem in question.
Abstract: Risk Assessment Tool (RAT) is an expert system that
assesses, monitors, and gives preliminary treatments automatically
based on the project plan. In this paper, a review was taken out for
the current project time management risk assessment tools for SME
software development projects, analyze risk assessment parameters,
conditions, scenarios, and finally propose risk assessment tool (RAT)
model to assess, treat, and monitor risks. An implementation prototype
system is developed to validate the model.
Abstract: We present in this paper an acquisition and treatment system designed for semi-analog Gamma-camera. It consists of a nuclear medical Image Acquisition, Treatment and Display chain(IATD) ensuring the acquisition, the treatment of the signals(resulting from the Gamma-camera detection head) and the scintigraphic image construction in real time. This chain is composed by an analog treatment board and a digital treatment board. We describe the designed systems and the digital treatment algorithms in which we have improved the performance and the flexibility. The digital treatment algorithms are implemented in a specific reprogrammable circuit FPGA (Field Programmable Gate Array).interface for semi-analog cameras of Sopha Medical Vision(SMVi) by taking as example SOPHY DS7. The developed system consists of an Image Acquisition, Treatment and Display (IATD) ensuring the acquisition and the treatment of the signals resulting from the DH. The developed chain is formed by a treatment analog board and a digital treatment board designed around a DSP [2]. In this paper we have presented the architecture of a new version of our chain IATD in which the integration of the treatment algorithms is executed on an FPGA (Field Programmable Gate Array)
Abstract: This manuscript presents, palmprint recognition by
combining different texture extraction approaches with high accuracy.
The Region of Interest (ROI) is decomposed into different frequencytime
sub-bands by wavelet transform up-to two levels and only the
approximate image of two levels is selected, which is known as
Approximate Image ROI (AIROI). This AIROI has information of
principal lines of the palm. The Competitive Index is used as the
features of the palmprint, in which six Gabor filters of different
orientations convolve with the palmprint image to extract the orientation
information from the image. The winner-take-all strategy
is used to select dominant orientation for each pixel, which is
known as Competitive Index. Further, PCA is applied to select highly
uncorrelated Competitive Index features, to reduce the dimensions of
the feature vector, and to project the features on Eigen space. The
similarity of two palmprints is measured by the Euclidean distance
metrics. The algorithm is tested on Hong Kong PolyU palmprint
database. Different AIROI of different wavelet filter families are also
tested with the Competitive Index and PCA. AIROI of db7 wavelet
filter achievs Equal Error Rate (EER) of 0.0152% and Genuine
Acceptance Rate (GAR) of 99.67% on the palm database of Hong
Kong PolyU.
Abstract: This study assesses the vulnerability of Bulgarian
agriculture to drought using the WINISAREG model and seasonal
standard precipitation index SPI(2) for the period 1951-2004. This
model was previously validated for maize on soils of different water
holding capacity (TAW) in various locations. Simulations are
performed for Plovdiv, Stara Zagora and Sofia. Results relative to
Plovdiv show that in soils of large TAW (180 mm m-1) net irrigation
requirements (NIRs) range 0-40 mm in wet years and 350-380 mm in
dry years. In soils of small TAW (116 mm m-1), NIRs reach 440 mm
in the very dry year. NIRs in Sofia are about 80 mm smaller. Rainfed
maize is associated with great yield variability (29%
Abstract: In order to monitor the water table depth on soil profile
salinity buildup, a field study was carried out during 2006-07. Wheat
(Rabi) and Sorghum (Kharif) fodder were sown in with three
treatments. The results showed that watertable depth lowered from
1.15m to 2.89 m depth at the end of experiment. With lower of
watertable depth, pH, ECe and SAR decreased under crops both
without and with gypsum and increased in fallowing. Soil moisture
depletion was directly proportional to lowering of watertable. With the
application of irrigation water (58cm) pH, ECe and SAR were reduced
in cropped plots, reduction was higher in gypsum applied plots than
non-gypsum plots. In case of fallowing, there was increase in pH, EC,
while slight reduction occurred in SAR values. However, soil salinity
showed an increasing upward trend under fallowing and its value in
0-30 cm soil layer was the highest amongst the treatments.
Abstract: In this paper, we propose a novel approach for image
segmentation via fuzzification of Rènyi Entropy of Generalized
Distributions (REGD). The fuzzy REGD is used to precisely measure
the structural information of image and to locate the optimal
threshold desired by segmentation. The proposed approach draws
upon the postulation that the optimal threshold concurs with
maximum information content of the distribution. The contributions
in the paper are as follow: Initially, the fuzzy REGD as a measure of
the spatial structure of image is introduced. Then, we propose an
efficient entropic segmentation approach using fuzzy REGD.
However the proposed approach belongs to entropic segmentation
approaches (i.e. these approaches are commonly applied to grayscale
images), it is adapted to be viable for segmenting color images.
Lastly, diverse experiments on real images that show the superior
performance of the proposed method are carried out.
Abstract: The purpose of this paper is to investigate the
durability of cement mortar in presence of Rice Husk Ash (RHA).
The strength and durability of mortar with different replacement
level (0%, 10%, 15%, 20%, 25% and 30%) of Ordinary Portland
Cement (OPC) by RHA is investigated here. RHA was
manufactured from an uncontrolled burning process. Test samples
were prepared with river sand of FM 2.73. Samples were kept in
controlled environment up to test time. The results show that
addition of RHA was shown better results for 20% replacement
level than OPC at 90 days. In durability test all samples passed for
20 cycles except 25% and 30% replacement level.
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: Inter-symbol interference if not taken care off may cause severe error at the receiver and the detection of signal becomes difficult. An adaptive equalizer employing Recursive Least Squares algorithm can be a good compensation for the ISI problem. In this paper performance of communication link in presence of Least Mean Square and Recursive Least Squares equalizer algorithm is analyzed. A Model of communication system having Quadrature amplitude modulation and Rician fading channel is implemented using MATLAB communication block set. Bit error rate and number of errors is evaluated for RLS and LMS equalizer algorithm, due to change in Signal to Noise Ratio (SNR) and fading component gain in Rician fading Channel.
Abstract: The radio frequency identification (RFID) is a
technology for automatic identification of items, particularly in
supply chain, but it is becoming increasingly important for industrial
applications. Unlike barcode technology that detects the optical
signals reflected from barcode labels, RFID uses radio waves to
transmit the information from an RFID tag affixed to the physical
object. In contrast to today most often use of this technology in
warehouse inventory and supply chain, the focus of this paper is an
overview of the structure of RFID systems used by RFID technology
and it also presents a solution based on the application of RFID for
brand authentication, traceability and tracking, by implementing a
production management system and extending its use to traders.
Abstract: WLAN Positioning has been presented by many
approaches in literatures using the characteristics of Received Signal
Strength (RSS), Time of Arrival (TOA) or Time Difference of
Arrival (TDOA), Angle of Arrival (AOA) and cell ID. Among these,
RSS approach is the simplest method to implement because there is
no need of modification on both access points and client devices
whereas its accuracy is terrible due to physical environments. For
TOA or TDOA approach, the accuracy is quite acceptable but most
researches have to modify either software or hardware on existing
WLAN infrastructure. The scales of modifications are made on only
access card up to the changes in protocol of WLAN. Hence, it is an
unattractive approach to use TOA or TDOA for positioning system.
In this paper, the new concept of merging both RSS and TOA
positioning techniques is proposed. In addition, the method to
achieve TOA characteristic for positioning WLAN user without any
extra modification necessarily appended in the existing system is
presented. The measurement results confirm that the proposed
technique using both RSS and TOA characteristics provides better
accuracy than using only either RSS or TOA approach.
Abstract: Semiconductor detector arrays are widely used in
high-temperature plasma diagnostics. They have a fast response,
which allows observation of many processes and instabilities in
tokamaks. In this paper, there are reviewed several diagnostics based
on semiconductor arrays as cameras, AXUV photodiodes (referred
often as fast “bolometers") and detectors of both soft X-rays and
visible light installed on the COMPASS tokamak recently. Fresh
results from both spring and summer campaigns in 2012 are
introduced. Examples of the utilization of the detectors are shown on
the plasma shape determination, fast calculation of the radiation
center, two-dimensional plasma radiation tomography in different
spectral ranges, observation of impurity inflow, and also on
investigation of MHD activity in the COMPASS tokamak discharges.
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.