Abstract: The purpose of this research is to develop and apply the
RSCMAC to enhance the dynamic accuracy of Global Positioning
System (GPS). GPS devices provide services of accurate positioning,
speed detection and highly precise time standard for over 98% area on
the earth. The overall operation of Global Positioning System includes
24 GPS satellites in space; signal transmission that includes 2
frequency carrier waves (Link 1 and Link 2) and 2 sets random
telegraphic codes (C/A code and P code), on-earth monitoring stations
or client GPS receivers. Only 4 satellites utilization, the client position
and its elevation can be detected rapidly. The more receivable
satellites, the more accurate position can be decoded. Currently, the
standard positioning accuracy of the simplified GPS receiver is greatly
increased, but due to affected by the error of satellite clock, the
troposphere delay and the ionosphere delay, current measurement
accuracy is in the level of 5~15m. In increasing the dynamic GPS
positioning accuracy, most researchers mainly use inertial navigation
system (INS) and installation of other sensors or maps for the
assistance. This research utilizes the RSCMAC advantages of fast
learning, learning convergence assurance, solving capability of
time-related dynamic system problems with the static positioning
calibration structure to improve and increase the GPS dynamic
accuracy. The increasing of GPS dynamic positioning accuracy can be
achieved by using RSCMAC system with GPS receivers collecting
dynamic error data for the error prediction and follows by using the
predicted error to correct the GPS dynamic positioning data. The
ultimate purpose of this research is to improve the dynamic positioning
error of cheap GPS receivers and the economic benefits will be
enhanced while the accuracy is increased.
Abstract: A method is presented for using thermo-mechanical fatigue analysis as a tool in the design of automotive heat exchangers. Use of infra-red thermography to measure the real thermal history in the heat exchanger reduces the time necessary for calculating design parameters and improves prediction accuracy. Thermal shocks are the primary cause of heat exchanger damage. Thermo-mechanical simulation is based on the mean behavior of the aluminum tubes used in the heat exchanger. An energetic fatigue criterion is used to detect critical zones.
Abstract: Water quality is a subject of ongoing concern.
Deterioration of water quality has initiated serious management
efforts in many countries. This study endeavors to automatically
classify water quality. The water quality classes are evaluated using 6
factor indices. These factors are pH value (pH), Dissolved Oxygen
(DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen
(NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (TColiform).
The methodology involves applying data mining
techniques using multilayer perceptron (MLP) neural network
models. The data consisted of 11 sites of canals in Dusit district in
Bangkok, Thailand. The data is obtained from the Department of
Drainage and Sewerage Bangkok Metropolitan Administration
during 2007-2011. The results of multilayer perceptron neural
network exhibit a high accuracy multilayer perception rate at 96.52%
in classifying the water quality of Dusit district canal in Bangkok
Subsequently, this encouraging result could be applied with plan and
management source of water quality.
Abstract: A new analytical model is developed which provides
close-formed solutions for both transient indoor and envelope
temperature changes in buildings. Time-dependent boundary
temperature is presented as Fourier series which can approximate real
weather conditions. The final close-formed solutions are simple,
concise, and comprehensive. The model was compared with
numerical results and good accuracy was obtained. The model can
be used as design and control guidelines in engineering applications
for analysing mechanical heat transfer properties for buildings.
Abstract: Avoiding learning failures in mathematics e-learning environments caused by emotional problems in students with autism has become an important topic for combining of special education with information and communications technology. This study presents an adaptive emotional adjustment model in mathematics e-learning for students with autism, emphasizing the lack of emotional perception in mathematics e-learning systems. In addition, an emotion classification for students with autism was developed by inducing emotions in mathematical learning environments to record changes in the physiological signals and facial expressions of students. Using these methods, 58 emotional features were obtained. These features were then processed using one-way ANOVA and information gain (IG). After reducing the feature dimension, methods of support vector machines (SVM), k-nearest neighbors (KNN), and classification and regression trees (CART) were used to classify four emotional categories: baseline, happy, angry, and anxious. After testing and comparisons, in a situation without feature selection, the accuracy rate of the SVM classification can reach as high as 79.3-%. After using IG to reduce the feature dimension, with only 28 features remaining, SVM still has a classification accuracy of 78.2-%. The results of this research could enhance the effectiveness of eLearning in special education.
Abstract: Annotation of a protein sequence is pivotal for the understanding of its function. Accuracy of manual annotation provided by curators is still questionable by having lesser evidence strength and yet a hard task and time consuming. A number of computational methods including tools have been developed to tackle this challenging task. However, they require high-cost hardware, are difficult to be setup by the bioscientists, or depend on time intensive and blind sequence similarity search like Basic Local Alignment Search Tool. This paper introduces a new method of assigning highly correlated Gene Ontology terms of annotated protein sequences to partially annotated or newly discovered protein sequences. This method is fully based on Gene Ontology data and annotations. Two problems had been identified to achieve this method. The first problem relates to splitting the single monolithic Gene Ontology RDF/XML file into a set of smaller files that can be easy to assess and process. Thus, these files can be enriched with protein sequences and Inferred from Electronic Annotation evidence associations. The second problem involves searching for a set of semantically similar Gene Ontology terms to a given query. The details of macro and micro problems involved and their solutions including objective of this study are described. This paper also describes the protein sequence annotation and the Gene Ontology. The methodology of this study and Gene Ontology based protein sequence annotation tool namely extended UTMGO is presented. Furthermore, its basic version which is a Gene Ontology browser that is based on semantic similarity search is also introduced.
Abstract: Text data mining is a process of exploratory data
analysis. Classification maps data into predefined groups or classes.
It is often referred to as supervised learning because the classes are
determined before examining the data. This paper describes proposed
radial basis function Classifier that performs comparative crossvalidation
for existing radial basis function Classifier. The feasibility
and the benefits of the proposed approach are demonstrated by means
of data mining problem: direct Marketing. Direct marketing has
become an important application field of data mining. Comparative
Cross-validation involves estimation of accuracy by either stratified
k-fold cross-validation or equivalent repeated random subsampling.
While the proposed method may have high bias; its performance
(accuracy estimation in our case) may be poor due to high variance.
Thus the accuracy with proposed radial basis function Classifier was
less than with the existing radial basis function Classifier. However
there is smaller the improvement in runtime and larger improvement
in precision and recall. In the proposed method Classification
accuracy and prediction accuracy are determined where the
prediction accuracy is comparatively high.
Abstract: This paper presents an improvement method of
the multiple pitch estimation algorithm using comb filters.
Conventionally the pitch was estimated by using parallel
-connected comb filters method (PCF). However, PCF has
problems which often fail in the pitch estimation when there is
the fundamental frequency of higher tone near harmonics of
lower tone. Therefore the estimation is assigned to a wrong
note when shared frequencies happen. This issue often occurs
in estimating octave 3 or more. Proposed method, for solving
the problem, estimates the pitch with every harmonic instead of
every octave. As a result, our method reaches the accuracy of
more than 80%.
Abstract: In this paper, we present a method named Signal Level
Matrix (SLM) which can improve the accuracy and stability of active
RFID indoor positioning system. Considering the accuracy and cost,
we use uniform distribution mode to set up and separate the
overlapped signal covering areas, in order to achieve preliminary
location setting. Then, based on the proposed SLM concept and the
characteristic of the signal strength value that attenuates as the
distance increases, this system cross-examines the distribution of
adjacent signals to locate the users more accurately. The experimental
results indicate that the adaptive positioning method proposed in this
paper could improve the accuracy and stability of the positioning
system effectively and satisfyingly.
Abstract: Ground-level tropospheric ozone is one of the air
pollutants of most concern. It is mainly produced by photochemical
processes involving nitrogen oxides and volatile organic compounds
in the lower parts of the atmosphere. Ozone levels become
particularly high in regions close to high ozone precursor emissions
and during summer, when stagnant meteorological conditions with
high insolation and high temperatures are common.
In this work, some results of a study about urban ozone
distribution patterns in the city of Badajoz, which is the largest and
most industrialized city in Extremadura region (southwest Spain) are
shown. Fourteen sampling campaigns, at least one per month, were
carried out to measure ambient air ozone concentrations, during
periods that were selected according to favourable conditions to
ozone production, using an automatic portable analyzer.
Later, to evaluate the ozone distribution at the city, the measured
ozone data were analyzed using geostatistical techniques. Thus, first,
during the exploratory analysis of data, it was revealed that they were
distributed normally, which is a desirable property for the subsequent
stages of the geostatistical study. Secondly, during the structural
analysis of data, theoretical spherical models provided the best fit for
all monthly experimental variograms. The parameters of these
variograms (sill, range and nugget) revealed that the maximum
distance of spatial dependence is between 302-790 m and the
variable, air ozone concentration, is not evenly distributed in reduced
distances. Finally, predictive ozone maps were derived for all points
of the experimental study area, by use of geostatistical algorithms
(kriging). High prediction accuracy was obtained in all cases as
cross-validation showed. Useful information for hazard assessment
was also provided when probability maps, based on kriging
interpolation and kriging standard deviation, were produced.
Abstract: This paper presents a studyof the impact of reference
node locations on the accuracy of the indoor positioning systems. In
particular, we analyze the localization accuracy of the RSSI database
mapping techniques, deploying on the IEEE 802.15.4 wireless
networks. The results show that the locations of the reference nodes
used in the positioning systems affect the signal propagation
characteristics in the service area. Thisin turn affects the accuracy of the wireless indoor positioning system. We found that suitable
location of reference nodes could reduce the positioning error upto 35 %.
Abstract: In this paper a novel algorithm is proposed to merit
the accuracy of finger vein recognition. The performances of
Principal Component Analysis (PCA), Kernel Principal Component
Analysis (KPCA), and Kernel Entropy Component Analysis (KECA)
in this algorithm are validated and compared with each other in order
to determine which one is the most appropriate one in terms of finger
vein recognition.
Abstract: The accuracy of estimated stability and control
derivatives of a light aircraft from flight test data were evaluated. The light aircraft, named ChangGong-91, is the first certified aircraft from
the Korean government. The output error method, which is a maximum likelihood estimation technique and considers measurement
noise only, was used to analyze the aircraft responses measures. The
multi-step control inputs were applied in order to excite the short period mode for the longitudinal and Dutch-roll mode for the lateral-directional motion. The estimated stability/control derivatives of Chan Gong-91 were analyzed for the assessment of handling
qualities comparing them with those of similar aircraft. The accuracy of the flight derivative estimates derived from flight test measurement
was examined in engineering judgment, scatter and Cramer-Rao bound, which turned out to be satisfactory with minor defects..
Abstract: A number of competing methodologies have been developed
to identify genes and classify DNA sequences into coding
and non-coding sequences. This classification process is fundamental
in gene finding and gene annotation tools and is one of the most
challenging tasks in bioinformatics and computational biology. An
information theory measure based on mutual information has shown
good accuracy in classifying DNA sequences into coding and noncoding.
In this paper we describe a species independent iterative
approach that distinguishes coding from non-coding sequences using
the mutual information measure (MIM). A set of sixty prokaryotes is
used to extract universal training data. To facilitate comparisons with
the published results of other researchers, a test set of 51 bacterial
and archaeal genomes was used to evaluate MIM. These results
demonstrate that MIM produces superior results while remaining
species independent.
Abstract: Optical character recognition of cursive scripts
presents a number of challenging problems in both segmentation and
recognition processes in different languages, including Persian. In
order to overcome these problems, we use a newly developed Persian
word segmentation method and a recognition-based segmentation
technique to overcome its segmentation problems. This method is
robust as well as flexible. It also increases the system-s tolerances to
font variations. The implementation results of this method on a
comprehensive database show a high degree of accuracy which meets
the requirements for commercial use. Extended with a suitable pre
and post-processing, the method offers a simple and fast framework
to develop a full OCR system.
Abstract: Fuzzy Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here (long term load forecasting). Among the various techniques used in forecasting load, artificial intelligence techniques provide greater accuracy to the forecasts as compared to conventional techniques. Fuzzy Logic, a very robust artificial intelligent technique, is described in this paper to forecast load on long term basis. The paper gives a general algorithm to forecast long term load. The algorithm is an Extension of Short term load forecasting method to Long term load forecasting and concentrates not only on the forecast values of load but also on the errors incorporated into the forecast. Hence, by correcting the errors in the forecast, forecasts with very high accuracy have been achieved. The algorithm, in the paper, is demonstrated with the help of data collected for residential sector (LT2 (a) type load: Domestic consumers). Load, is determined for three consecutive years (from April-06 to March-09) in order to demonstrate the efficiency of the algorithm and to forecast for the next two years (from April-09 to March-11).
Abstract: In this present work, the development of an avionics
system for flight data collection of a Raptor 30 V2 is carried out. For the data acquisition both onground and onboard avionics systems are developed for testing of a small-scale Unmanned Aerial Vehicle
(UAV) helicopter. The onboard avionics record the helicopter state
outputs namely accelerations, angular rates and Euler angles, in real time, and the on ground avionics system record the inputs given to
the radio controlled helicopter through a transmitter, in real time. The avionic systems are designed and developed taking into consideration
low weight, small size, anti-vibration, low power consumption, and easy interfacing. To mitigate the medium frequency vibrations
embedded on the UAV helicopter during flight, a damper is designed
and its performance is evaluated. A number of flight tests are carried
out and the data obtained is then analyzed for accuracy and repeatability and conclusions are inferred.
Abstract: In this study, the density dependent nonlinear reactiondiffusion
equation, which arises in the insect dispersal models, is
solved using the combined application of differential quadrature
method(DQM) and implicit Euler method. The polynomial based
DQM is used to discretize the spatial derivatives of the problem. The
resulting time-dependent nonlinear system of ordinary differential
equations(ODE-s) is solved by using implicit Euler method. The
computations are carried out for a Cauchy problem defined by a onedimensional
density dependent nonlinear reaction-diffusion equation
which has an exact solution. The DQM solution is found to be in a
very good agreement with the exact solution in terms of maximum
absolute error. The DQM solution exhibits superior accuracy at large
time levels tending to steady-state. Furthermore, using an implicit
method in the solution procedure leads to stable solutions and larger
time steps could be used.
Abstract: In a handwriting recognition problem, characters can
be represented using chain codes. The main problem in representing
characters using chain code is optimizing the length of the chain
code. This paper proposes to use randomized algorithm to minimize
the length of Freeman Chain Codes (FCC) generated from isolated
handwritten characters. Feedforward neural network is used in the
classification stage to recognize the image characters. Our test results
show that by applying the proposed model, we reached a relatively
high accuracy for the problem of isolated handwritten when tested on
NIST database.
Abstract: Renewable energy resources are inexhaustible, clean as compared with conventional resources. Also, it is used to supply regions with no grid, no telephone lines, and often with difficult accessibility by common transport. Satellite earth stations which located in remote areas are the most important application of renewable energy. Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This paper presents the mathematical modeling of satellite earth station power system which is required for simulating the system.Aswan is selected to be the site under consideration because it is a rich region with solar energy. The complete power system is simulated using MATLAB–SIMULINK.An artificial neural network (ANN) based model has been developed for the optimum operation of earth station power system. An ANN is trained using a back propagation with Levenberg–Marquardt algorithm. The best validation performance is obtained for minimum mean square error. The regression between the network output and the corresponding target is equal to 96% which means a high accuracy. Neural network controller architecture gives satisfactory results with small number of neurons, hence better in terms of memory and time are required for NNC implementation. The results indicate that the proposed control unit using ANN can be successfully used for controlling the satellite earth station power system.