Abstract: PCMs have always been viewed as a suitable
candidate for off peak thermal storage, particularly for refrigeration
systems, due to the high latent energy densities of these materials.
However, due to the need to have them encapsulated within a
container this density is reduced. Furthermore, PCMs have a low
thermal conductivity which reduces the useful amount of energy
which can be stored. To consider these factors, the true energy
storage density of a PCM system was proposed and optimised for
PCMs encapsulated in slabs. Using a validated numerical model of
the system, a parametric study was undertaken to investigate the
impact of the slab thickness, gap between slabs and the mass flow
rate. The study showed that, when optimised, a PCM system can
deliver a true energy storage density between 53% and 83% of the
latent energy density of the PCM.
Abstract: In many industries, control charts is one of the most
frequently used tools for quality management. Hotelling-s T2 is used
widely in multivariate control chart. However, it has little defect when
detecting small or medium process shifts. The use of supplementary
sensitizing rules can improve the performance of detection. This study
applied sensitizing rules for Hotelling-s T2 control chart to improve the
performance of detection. Support vector machines (SVM) classifier
to identify the characteristic or group of characteristics that are
responsible for the signal and to classify the magnitude of the mean
shifts. The experimental results demonstrate that the support vector
machines (SVM) classifier can effectively identify the characteristic
or group of characteristics that caused the process mean shifts and the
magnitude of the shifts.
Abstract: Corporate social responsibility (CSR) can be defined as the management of social, environmental, economical and ethical concepts and firms sensivities to the expectations of the social stakeholders. CSR is seen as an important competitive advantage in the textile sector because this sector has an important impact on the environment and it is labor extensive. Textile sector has a strong advantage when compared with other sectors in Turkey due to its low labor costs and abundancy of raw materials. Turkey was a producer and an exporter of cotton, and an importer of fiber, clothes and dresses until 1950s. After 1950s, Turkey has begun to export fiber, ready-made clothes and become one of the most important textile producers in the world recently. CSR practices of the textile firms that are quoted in Istanbul Stock Exchange and these firms sensivities to their internal and external stakeholders and environment will be presented in this study.
Abstract: Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM is applied to an infrared (IR) binary communication system with different types of channel models including Ricean multipath fading and partially developed scattering channel with additive white Gaussian noise (AWGN) at the receiver. The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these channel stochastic models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to classical binary signal maximum likelihood detection using a matched filter driven by On-Off keying (OOK) modulation. We found that the performance of SVM is superior to that of the traditional optimal detection schemes used in statistical communication, especially for very low signal-to-noise ratio (SNR) ranges. For large SNR, the performance of the SVM is similar to that of the classical detectors. The implication of these results is that SVM can prove very beneficial to IR communication systems that notoriously suffer from low SNR at the cost of increased computational complexity.
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: In this paper, we have proposed a low cost optimized solution for the movement of a three-arm manipulator using Genetic Algorithm (GA) and Analytical Hierarchy Process (AHP). A scheme is given for optimizing the movement of robotic arm with the help of Genetic Algorithm so that the minimum energy consumption criteria can be achieved. As compared to Direct Kinematics, Inverse Kinematics evolved two solutions out of which the best-fit solution is selected with the help of Genetic Algorithm and is kept in search space for future use. The Inverse Kinematics, Fitness Value evaluation and Binary Encoding like tasks are simulated and tested. Although, three factors viz. Movement, Friction and Least Settling Time (or Min. Vibration) are used for finding the Fitness Function / Fitness Values, however some more factors can also be considered.
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: In this paper, the decomposition-aggregation method
is used to carry out connective stability criteria for general linear
composite system via aggregation. The large scale system is
decomposed into a number of subsystems. By associating directed
graphs with dynamic systems in an essential way, we define the
relation between system structure and stability in the sense of
Lyapunov. The stability criteria is then associated with the stability
and system matrices of subsystems as well as those interconnected
terms among subsystems using the concepts of vector differential
inequalities and vector Lyapunov functions. Then, we show that the
stability of each subsystem and stability of the aggregate model
imply connective stability of the overall system. An example is
reported, showing the efficiency of the proposed technique.
Abstract: Society has grown to rely on Internet services, and the
number of Internet users increases every day. As more and more
users become connected to the network, the window of opportunity
for malicious users to do their damage becomes very great and
lucrative. The objective of this paper is to incorporate different
techniques into classier system to detect and classify intrusion from
normal network packet. Among several techniques, Steady State
Genetic-based Machine Leaning Algorithm (SSGBML) will be used
to detect intrusions. Where Steady State Genetic Algorithm (SSGA),
Simple Genetic Algorithm (SGA), Modified Genetic Algorithm and
Zeroth Level Classifier system are investigated in this research.
SSGA is used as a discovery mechanism instead of SGA. SGA
replaces all old rules with new produced rule preventing old good
rules from participating in the next rule generation. Zeroth Level
Classifier System is used to play the role of detector by matching
incoming environment message with classifiers to determine whether
the current message is normal or intrusion and receiving feedback
from environment. Finally, in order to attain the best results,
Modified SSGA will enhance our discovery engine by using Fuzzy
Logic to optimize crossover and mutation probability. The
experiments and evaluations of the proposed method were performed
with the KDD 99 intrusion detection dataset.
Abstract: The rapid urbanization of cities has a bane in the form
road accidents that cause extensive damage to life and limbs. A
number of location based factors are enablers of road accidents in the
city. The speed of travel of vehicles is non-uniform among locations
within a city. In this study, the perception of vehicle users is captured
on a 10-point rating scale regarding the degree of variation in speed
of travel at chosen locations in the city. The average rating is used to
cluster locations using fuzzy c-means clustering and classify them as
low, moderate and high speed of travel locations. The high speed of
travel locations can be classified proactively to ensure that accidents
do not occur due to the speeding of vehicles at such locations. The
advantage of fuzzy c-means clustering is that a location may be a
part of more than one cluster to a varying degree and this gives a
better picture about the location with respect to the characteristic
(speed of travel) being studied.
Abstract: This paper proposes a method of adaptively generating a gait pattern of biped robot. The gait synthesis is based on human's gait pattern analysis. The proposed method can easily be applied to generate the natural and stable gait pattern of any biped robot. To analyze the human's gait pattern, sequential images of the human's gait on the sagittal plane are acquired from which the gait control values are extracted. The gait pattern of biped robot on the sagittal plane is adaptively generated by a genetic algorithm using the human's gait control values. However, gait trajectories of the biped robot on the sagittal plane are not enough to construct the complete gait pattern because the biped robot moves on 3-dimension space. Therefore, the gait pattern on the frontal plane, generated from Zero Moment Point (ZMP), is added to the gait one acquired on the sagittal plane. Consequently, the natural and stable walking pattern for the biped robot is obtained.
Abstract: Underpricing is one anomaly in initial public offerings
(IPO) literature that has been widely observed across different stock
markets with different trends emerging over different time periods.
This study seeks to determine how IPOs on the JSE performed on the
first day, first week and first month over the period of 1996-2011.
Underpricing trends are documented for both hot and cold market
periods in terms of four main sectors (cyclical, defensive, growth
stock and interest rate sensitive stocks). Using a sample of 360 listed
companies on the JSE, the empirical findings established that IPOs
on the JSE are significantly underpriced with an average market
adjusted first day return of 62.9%. It is also established that hot
market IPOs on the JSE are more underpriced than the cold market
IPOs. Also observed is the fact that as the offer price per share
increases above the median price for any given period, the level of
underpricing decreases substantially. While significant differences
exist in the level of underpricing of IPOs in the four different sectors
in the hot and cold market periods, interest rates sensitive stocks
showed a different trend from the other sectors and thus require
further investigation to uncover this pattern.
Abstract: The Bangalore City is facing the acute problem of
pollution in the atmosphere due to the heavy increase in the traffic
and developmental activities in recent years. The present study is an
attempt in the direction to assess trend of the ambient air quality
status of three stations, viz., AMCO Batteries Factory, Mysore Road,
GRAPHITE INDIA FACTORY, KHB Industrial Area, Whitefield
and Ananda Rao Circle, Gandhinagar with respect to some of the
major criteria pollutants such as Total Suspended particular matter
(SPM), Oxides of nitrogen (NOx), and Oxides of sulphur (SO2). The
sites are representative of various kinds of growths viz., commercial,
residential and industrial, prevailing in Bangalore, which are
contributing to air pollution. The concentration of Sulphur Dioxide
(SO2) at all locations showed a falling trend due to use of refined
petrol and diesel in the recent years. The concentration of Oxides of
nitrogen (NOx) showed an increasing trend but was within the
permissible limits. The concentration of the Suspended particular
matter (SPM) showed the mixed trend. The correlation between
model and observed values is found to vary from 0.4 to 0.7 for SO2,
0.45 to 0.65 for NOx and 0.4 to 0.6 for SPM. About 80% of data is
observed to fall within the error band of ±50%. Forecast test for the
best fit models showed the same trend as actual values in most of the
cases. However, the deviation observed in few cases could be
attributed to change in quality of petro products, increase in the
volume of traffic, introduction of LPG as fuel in many types of
automobiles, poor condition of roads, prevailing meteorological
conditions, etc.
Abstract: This is a survey research using quantitative and qualitative methodology. There were three objectives: 1) To study participatory level of community in water and waste environment management. 2) To study the affecting factors for community participation in water and waste environment management in Ampawa District, Samut Songkram Province. 3) To search for the participatory patterns in water and waste management. The population sample for the quantitative research was 1,364 people living in Ampawa District. The methodology was simple random sampling. Research instrument was a questionnaire and the qualitative research used purposive sampling in 6 Sub Districts which are Ta Ka, Suanluang, Bangkae, Muangmai, Kwae-om, and Bangnanglee Sub District Administration Organization. Total population is 63. For data analysis, the study used content analysis from quantitative research to synthesize and build question frame from the content for interview and conducting focus group interview. The study found that the community participatory in the issue of level in water and waste management are moderate of planning, operation, and evaluation. The issue of being beneficial is at low level. Therefore, the overall participatory level of community in water and waste environment management is at a medium level. The factors affecting the participatory of community in water and waste management are age, the period dwelling in the community and membership in which the mean difference is statistic significant at 0.05 in area of operation, being beneficial, and evaluation. For patterns of community participation, there is the correlation with water and waste management in 4 concerns which are 1) Participation in planning 2) Participation in operation 3) Participation in being beneficial both directly and indirectly benefited 4) Participation in evaluation and monitoring. The recommendation from this study is the need to create conscious awareness in order to increase participation level of people by organizing activities that promote participation with volunteer spirit. Government should open opportunities for people to participate in sharing ideas and create the culture of living together with equality which would build more concrete participation.
Abstract: An electrocardiogram (ECG) feature extraction system
based on the calculation of the complex resonance frequency
employing Prony-s method is developed. Prony-s method is applied
on five different classes of ECG signals- arrhythmia as a finite sum
of exponentials depending on the signal-s poles and the resonant
complex frequencies. Those poles and resonance frequencies of the
ECG signals- arrhythmia are evaluated for a large number of each
arrhythmia. The ECG signals of lead II (ML II) were taken from
MIT-BIH database for five different types. These are the ventricular
couplet (VC), ventricular tachycardia (VT), ventricular bigeminy
(VB), and ventricular fibrillation (VF) and the normal (NR). This
novel method can be extended to any number of arrhythmias.
Different classification techniques were tried using neural networks
(NN), K nearest neighbor (KNN), linear discriminant analysis (LDA)
and multi-class support vector machine (MC-SVM).
Abstract: This paper presents the study of parameters affecting
the environment protection in the printing industry. The paper has
also compared LCA studies performed within the printing industry in
order to identify common practices, limitations, areas for
improvement, and opportunities for standardization. This comparison
is focused on the data sources and methodologies used in the printing
pollutants register. The presented concepts, methodology and results
represent the contribution to the sustainable development
management. Furthermore, the paper analyzes the result of the
quantitative identification of hazardous substances emitted in printing
industry of Novi Sad.
Abstract: The segmentation of endovascular tools in fluoroscopy images can be accurately performed automatically or by minimum user intervention, using known modern techniques. It has been proven in literature, but no clinical implementation exists so far because the computational time requirements of such technology have not yet been met. A classical segmentation scheme is composed of edge enhancement filtering, line detection, and segmentation. A new method is presented that consists of a vector that propagates in the image to track an edge as it advances. The filtering is performed progressively in the projected path of the vector, whose orientation allows for oriented edge detection, and a minimal image area is globally filtered. Such an algorithm is rapidly computed and can be implemented in real-time applications. It was tested on medical fluoroscopy images from an endovascular cerebral intervention. Ex- periments showed that the 2D tracking was limited to guidewires without intersection crosspoints, while the 3D implementation was able to cope with such planar difficulties.
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.
Abstract: In this paper, we propose an energy efficient cluster
based communication protocol for wireless sensor network. Our
protocol considers both the residual energy of sensor nodes and the
distance of each node from the BS when selecting cluster-head. This
protocol can successfully prolong the network-s lifetime by 1)
reducing the total energy dissipation on the network and 2) evenly
distributing energy consumption over all sensor nodes. In this
protocol, the nodes with more energy and less distance from the BS
are probable to be selected as cluster-head. Simulation results with
MATLAB show that proposed protocol could increase the lifetime of
network more than 94% for first node die (FND), and more than 6%
for the half of the nodes alive (HNA) factor as compared with
conventional protocols.
Abstract: The goal of this project is to design a system to
recognition voice commands. Most of voice recognition systems
contain two main modules as follow “feature extraction" and “feature
matching". In this project, MFCC algorithm is used to simulate
feature extraction module. Using this algorithm, the cepstral
coefficients are calculated on mel frequency scale. VQ (vector
quantization) method will be used for reduction of amount of data to
decrease computation time. In the feature matching stage Euclidean
distance is applied as similarity criterion. Because of high accuracy
of used algorithms, the accuracy of this voice command system is
high. Using these algorithms, by at least 5 times repetition for each
command, in a single training session, and then twice in each testing
session zero error rate in recognition of commands is achieved.