Abstract: This paper presents a simple three phase power flow
method for solution of three-phase unbalanced radial distribution
system (RDN) with voltage dependent loads. It solves a simple
algebraic recursive expression of voltage magnitude, and all the data
are stored in vector form. The algorithm uses basic principles of
circuit theory and can be easily understood. Mutual coupling between
the phases has been included in the mathematical model. The
proposed algorithm has been tested with several unbalanced radial
distribution networks and the results are presented in the article. 8-
bus and IEEE 13 bus unbalanced radial distribution system results
are in agreements with the literature and show that the proposed
model is valid and reliable.
Abstract: Fault detection determines faultexistence and detecting
time. This paper discusses two layered fault detection methods to
enhance the reliability and safety. Two layered fault detection methods
consist of fault detection methods of component level controllers and
system level controllers. Component level controllers detect faults by
using limit checking, model-based detection, and data-driven
detection and system level controllers execute detection by stability
analysis which can detect unknown changes. System level controllers
compare detection results via stability with fault signals from lower
level controllers. This paper addresses fault detection methods via
stability and suggests fault detection criteria in nonlinear systems. The
fault detection method applies tothe hybrid control unit of a military
hybrid electric vehicleso that the hybrid control unit can detect faults
of the traction motor.
Abstract: 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.
Abstract: Microscopic emission and fuel consumption models
have been widely recognized as an effective method to quantify real
traffic emission and energy consumption when they are applied with
microscopic traffic simulation models. This paper presents a
framework for developing the Microscopic Emission (HC, CO, NOx,
and CO2) and Fuel consumption (MEF) models for light-duty
vehicles. The variable of composite acceleration is introduced into
the MEF model with the purpose of capturing the effects of historical
accelerations interacting with current speed on emission and fuel
consumption. The MEF model is calibrated by multivariate
least-squares method for two types of light-duty vehicle using
on-board data collected in Beijing, China by a Portable Emission
Measurement System (PEMS). The instantaneous validation results
shows the MEF model performs better with lower Mean Absolute
Percentage Error (MAPE) compared to other two models. Moreover,
the aggregate validation results tells the MEF model produces
reasonable estimations compared to actual measurements with
prediction errors within 12%, 10%, 19%, and 9% for HC, CO, NOx
emissions and fuel consumption, respectively.
Abstract: An adaptive Chinese hand-talking system is presented
in this paper. By analyzing the 3 data collecting strategies for new
users, the adaptation framework including supervised and unsupervised
adaptation methods is proposed. For supervised adaptation,
affinity propagation (AP) is used to extract exemplar subsets, and enhanced
maximum a posteriori / vector field smoothing (eMAP/VFS)
is proposed to pool the adaptation data among different models. For
unsupervised adaptation, polynomial segment models (PSMs) are
used to help hidden Markov models (HMMs) to accurately label
the unlabeled data, then the "labeled" data together with signerindependent
models are inputted to MAP algorithm to generate
signer-adapted models. Experimental results show that the proposed
framework can execute both supervised adaptation with small amount
of labeled data and unsupervised adaptation with large amount
of unlabeled data to tailor the original models, and both achieve
improvements on the performance of recognition rate.
Abstract: This paper proposes a stroke extraction method for use in off-line signature verification. After giving a brief overview of the current ongoing researches an algorithm is introduced for detecting and following strokes in static images of signatures. Problems like the handling of junctions and variations in line width and line intensity are discussed in detail. Results are validated by both using an existing on-line signature database and by employing image registration methods.
Abstract: In this paper, we investigated the characteristic of a
clinical dataseton the feature selection and classification
measurements which deal with missing values problem.And also
posed the appropriated techniques to achieve the aim of the activity;
in this research aims to find features that have high effect to mortality
and mortality time frame. We quantify the complexity of a clinical
dataset. According to the complexity of the dataset, we proposed the
data mining processto cope their complexity; missing values, high
dimensionality, and the prediction problem by using the methods of
missing value replacement, feature selection, and classification.The
experimental results will extend to develop the prediction model for
cardiology.
Abstract: Experimental investigation of heat transfer and
friction factor characteristics of circular tube fitted with 300 right-left helical screw inserts with 100 mm spacer of different twist ratio has
been presented for laminar and turbulent flow.. The experimental data obtained were compared with those obtained from plain tube
published data. The heat transfer coefficient enhancement for 300 RL
inserts with 100 mm spacer is quite comparable with for 300 R-L
inserts. Performance evaluation analysis has been made and found
that the performance ratio increases with increasing Reynolds number
and decreasing twist ration with the maximum for the twist ratio 2.93.
Also, the performance ratio of more than one indicates that the type
of twist inserts can be used effectively for heat transfer augmentation.
Abstract: A novel behavioral detection framework is proposed
to detect zero day buffer overflow vulnerabilities (based on network
behavioral signatures) using zero-day exploits, instead of the
signature-based or anomaly-based detection solutions currently
available for IDPS techniques. At first we present the detection
model that uses shadow honeypot. Our system is used for the online
processing of network attacks and generating a behavior detection
profile. The detection profile represents the dataset of 112 types of
metrics describing the exact behavior of malware in the network. In
this paper we present the examples of generating behavioral
signatures for two attacks – a buffer overflow exploit on FTP server
and well known Conficker worm. We demonstrated the visualization
of important aspects by showing the differences between valid
behavior and the attacks. Based on these metrics we can detect
attacks with a very high probability of success, the process of
detection is however very expensive.
Abstract: Defect prevention is the most vital but habitually
neglected facet of software quality assurance in any project. If
functional at all stages of software development, it can condense the
time, overheads and wherewithal entailed to engineer a high quality
product. The key challenge of an IT industry is to engineer a
software product with minimum post deployment defects.
This effort is an analysis based on data obtained for five selected
projects from leading software companies of varying software
production competence. The main aim of this paper is to provide
information on various methods and practices supporting defect
detection and prevention leading to thriving software generation. The
defect prevention technique unearths 99% of defects. Inspection is
found to be an essential technique in generating ideal software
generation in factories through enhanced methodologies of abetted
and unaided inspection schedules. On an average 13 % to 15% of
inspection and 25% - 30% of testing out of whole project effort time
is required for 99% - 99.75% of defect elimination.
A comparison of the end results for the five selected projects
between the companies is also brought about throwing light on the
possibility of a particular company to position itself with an
appropriate complementary ratio of inspection testing.
Abstract: Particle damping is a technique to reduce the
structural vibrations by means of placing small metallic particles
inside a cavity that is attached to the structure at location of high
vibration amplitudes. In this paper, we have presented an analytical
model to simulate the particle damping of two dimensional transient
vibrations in structure operating under high centrifugal loads. The
simulation results show that this technique remains effective as long
as the ratio of the dynamic acceleration of the structure to the applied
centrifugal load is more than 0.1. Particle damping increases with the
increase of particle to structure mass ratio. However, unlike to the
case of particle damping in the absence of centrifugal loads where
the damping efficiency strongly depends upon the size of the cavity,
here this dependence becomes very weak. Despite the simplicity of
the model, the simulation results are considerably in good agreement
with the very scarce experimental data available in the literature for
particle damping under centrifugal loads.
Abstract: Fine-grained data replication over the Internet allows duplication of frequently accessed data objects, as opposed to entire sites, to certain locations so as to improve the performance of largescale content distribution systems. In a distributed system, agents representing their sites try to maximize their own benefit since they are driven by different goals such as to minimize their communication costs, latency, etc. In this paper, we will use game theoretical techniques and in particular auctions to identify a bidding mechanism that encapsulates the selfishness of the agents, while having a controlling hand over them. In essence, the proposed game theory based mechanism is the study of what happens when independent agents act selfishly and how to control them to maximize the overall performance. A bidding mechanism asks how one can design systems so that agents- selfish behavior results in the desired system-wide goals. Experimental results reveal that this mechanism provides excellent solution quality, while maintaining fast execution time. The comparisons are recorded against some well known techniques such as greedy, branch and bound, game theoretical auctions and genetic algorithms.
Abstract: This paper presents an algorithm for reconstructing phase and magnitude responses of the impulse response when only the output data are available. The system is driven by a zero-mean independent identically distributed (i.i.d) non-Gaussian sequence that is not observed. The additive noise is assumed to be Gaussian. This is an important and essential problem in many practical applications of various science and engineering areas such as biomedical, seismic, and speech processing signals. The method is based on evaluating the bicepstrum of the third-order statistics of the observed output data. Simulations results are presented that demonstrate the performance of this method.
Abstract: An electric utility-s main concern is to plan, design, operate and maintain its power supply to provide an acceptable level of reliability to its users. This clearly requires that standards of reliability be specified and used in all three sectors of the power system, i.e., generation, transmission and distribution. That is why reliability of a power system is always a major concern to power system planners. This paper presents the reliability analysis of Bangladesh Power System (BPS). Reliability index, loss of load probability (LOLP) of BPS is evaluated using recursive algorithm and considering no de-rated states of generators. BPS has sixty one generators and a total installed capacity of 5275 MW. The maximum demand of BPS is about 5000 MW. The relevant data of the generators and hourly load profiles are collected from the National Load Dispatch Center (NLDC) of Bangladesh and reliability index 'LOLP' is assessed for the period of last ten years.
Abstract: Spatial outliers in remotely sensed imageries represent
observed quantities showing unusual values compared to their
neighbor pixel values. There have been various methods to detect the
spatial outliers based on spatial autocorrelations in statistics and data
mining. These methods may be applied in detecting forest fire pixels
in the MODIS imageries from NASA-s AQUA satellite. This is
because the forest fire detection can be referred to as finding spatial
outliers using spatial variation of brightness temperature. This point is
what distinguishes our approach from the traditional fire detection
methods. In this paper, we propose a graph-based forest fire detection
algorithm which is based on spatial outlier detection methods, and test
the proposed algorithm to evaluate its applicability. For this the
ordinary scatter plot and Moran-s scatter plot were used. In order to
evaluate the proposed algorithm, the results were compared with the
MODIS fire product provided by the NASA MODIS Science Team,
which showed the possibility of the proposed algorithm in detecting
the fire pixels.
Abstract: The purpose of the study is to determine secondary prospective mathematics teachers- views related to using flash animations in mathematics lessons and to reveal how the sample presentations towards different mathematical concepts altered their views. This is a case study involving three secondary prospective mathematics teachers from a state university in Turkey. The data gathered from two semi-structural interviews. Findings revealed that these animations help understand mathematics meaningfully, relate mathematics and real world, visualization, and comprehend the importance of mathematics. The analysis of the data indicated that the sample presentations enhanced participants- views about using flash animations in mathematics lessons.
Abstract: Reliable water level forecasts are particularly
important for warning against dangerous flood and inundation. The
current study aims at investigating the suitability of the adaptive
network based fuzzy inference system for continuous water level
modeling. A hybrid learning algorithm, which combines the least
square method and the back propagation algorithm, is used to
identify the parameters of the network. For this study, water levels
data are available for a hydrological year of 2002 with a sampling
interval of 1-hour. The number of antecedent water level that should
be included in the input variables is determined by two statistical
methods, i.e. autocorrelation function and partial autocorrelation
function between the variables. Forecasting was done for 1-hour until
12-hour ahead in order to compare the models generalization at
higher horizons. The results demonstrate that the adaptive networkbased
fuzzy inference system model can be applied successfully and
provide high accuracy and reliability for river water level estimation.
In general, the adaptive network-based fuzzy inference system
provides accurate and reliable water level prediction for 1-hour ahead
where the MAPE=1.15% and correlation=0.98 was achieved. Up to
12-hour ahead prediction, the model still shows relatively good
performance where the error of prediction resulted was less than
9.65%. The information gathered from the preliminary results
provide a useful guidance or reference for flood early warning
system design in which the magnitude and the timing of a potential
extreme flood are indicated.
Abstract: IP networks are evolving from data communication
infrastructure into many real-time applications such as video
conferencing, IP telephony and require stringent Quality of Service
(QoS) requirements. A rudimentary issue in QoS routing is to find a
path between a source-destination pair that satisfies two or more endto-
end constraints and termed to be NP hard or complete. In this
context, we present an algorithm Multi Constraint Path Problem
Version 3 (MCPv3), where all constraints are approximated and
return a feasible path in much quicker time. We present another
algorithm namely Delay Coerced Multi Constrained Routing
(DCMCR) where coerce one constraint and approximate the
remaining constraints. Our algorithm returns a feasible path, if exists,
in polynomial time between a source-destination pair whose first
weight satisfied by the first constraint and every other weight is
bounded by remaining constraints by a predefined approximation
factor (a). We present our experimental results with different
topologies and network conditions.
Abstract: In the 1980s, companies began to feel the effect of three major influences on their product development: newer and innovative technologies, increasing product complexity and larger organizations. And therefore companies were forced to look for new product development methods. This paper tries to focus on the two of new product development methods (DFM and CE). The aim of this paper is to see and analyze different product development methods specifically on Design for Manufacturability and Concurrent Engineering. Companies can achieve and be benefited by minimizing product life cycle, cost and meeting delivery schedule. This paper also presents simplified models that can be modified and used by different companies based on the companies- objective and requirements. Methodologies that are followed to do this research are case studies. Two companies were taken and analysed on the product development process. Historical data, interview were conducted on these companies in addition to that, Survey of literatures and previous research works on similar topics has been done during this research. This paper also tries to show the implementation cost benefit analysis and tries to calculate the implementation time. From this research, it has been found that the two companies did not achieve the delivery time to the customer. Some of most frequently coming products are analyzed and 50% to 80 % of their products are not delivered on time to the customers. The companies are following the traditional way of product development that is sequentially design and production method, which highly affect time to market. In the case study it is found that by implementing these new methods and by forming multi disciplinary team in designing and quality inspection; the company can reduce the workflow steps from 40 to 30.
Abstract: The goal of a network-based intrusion detection
system is to classify activities of network traffics into two major
categories: normal and attack (intrusive) activities. Nowadays, data
mining and machine learning plays an important role in many
sciences; including intrusion detection system (IDS) using both
supervised and unsupervised techniques. However, one of the
essential steps of data mining is feature selection that helps in
improving the efficiency, performance and prediction rate of
proposed approach. This paper applies unsupervised K-means
clustering algorithm with information gain (IG) for feature selection
and reduction to build a network intrusion detection system. For our
experimental analysis, we have used the new NSL-KDD dataset,
which is a modified dataset for KDDCup 1999 intrusion detection
benchmark dataset. With a split of 60.0% for the training set and the
remainder for the testing set, a 2 class classifications have been
implemented (Normal, Attack). Weka framework which is a java
based open source software consists of a collection of machine
learning algorithms for data mining tasks has been used in the testing
process. The experimental results show that the proposed approach is
very accurate with low false positive rate and high true positive rate
and it takes less learning time in comparison with using the full
features of the dataset with the same algorithm.