Abstract: An alternative iterative computational procedure is
proposed for internal normal ball loads calculation in statically
loaded single-row, angular-contact ball bearings, subjected to a
known thrust load, which is applied in the inner ring at the geometric
bearing center line. An accurate method for curvature radii at
contacts with inner and outer raceways in the direction of the motion
is used. Numerical aspects of the iterative procedure are discussed.
Numerical examples results for a 218 angular-contact ball bearing
have been compared with those from the literature. Twenty figures
are presented showing the geometrical features, the behavior of the
convergence variables and the following parameters as functions of
the thrust load: normal ball loads, contact angle, distance between
curvature centers, and normal ball and axial deflections.
Abstract: Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In the light of the clustering data, we have verified some interactions which were not identified as core interactions in DIP and also, we have characterized some functionally unknown proteins according to the interaction data and functional correlation. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins, also to predict new interactions and to characterize functions of unknown proteins.
Abstract: The Model for Knowledge Base of Computational Objects
(KBCO model) has been successfully applied to represent the
knowledge of human like Plane Geometry, Physical, Calculus. However,
the original model cannot easyly apply in inorganic chemistry
field because of the knowledge specific problems. So, the aim of
this article is to introduce how we extend the Computional Object
(Com-Object) in KBCO model, kinds of fact, problems model, and
inference algorithms to develop a program for solving problems
in inorganic chemistry. Our purpose is to develop the application
that can help students in their study inorganic chemistry at schools.
This application was built successful by using Maple, C# and WPF
technology. It can solve automatically problems and give human
readable solution agree with those writting by students and teachers.
Abstract: Symbolic Circuit Analysis (SCA) is a technique used
to generate the symbolic expression of a network. It has become a
well-established technique in circuit analysis and design. The
symbolic expression of networks offers excellent way to perform
frequency response analysis, sensitivity computation, stability
measurements, performance optimization, and fault diagnosis. Many
approaches have been proposed in the area of SCA offering different
features and capabilities. Numerical Interpolation methods are very
common in this context, especially by using the Fast Fourier
Transform (FFT). The aim of this paper is to present a method for
SCA that depends on the use of Wavelet Transform (WT) as a
mathematical tool to generate the symbolic expression for large
circuits with minimizing the analysis time by reducing the number of
computations.
Abstract: Robustness is one of the primary performance criteria for an Intelligent Video Surveillance (IVS) system. One of the key factors in enhancing the robustness of dynamic video analysis is,providing accurate and reliable means for shadow detection. If left undetected, shadow pixels may result in incorrect object tracking and classification, as it tends to distort localization and measurement information. Most of the algorithms proposed in literature are computationally expensive; some to the extent of equalling computational requirement of motion detection. In this paper, the homogeneity property of shadows is explored in a novel way for shadow detection. An adaptive division image (which highlights homogeneity property of shadows) analysis followed by a relatively simpler projection histogram analysis for penumbra suppression is the key novelty in our approach.
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: The objective of the present work is to conduct
investigations leading to a more complete explanation of single phase
natural convective heat transfer in an enclosure with fin utilizing
nano fluids. The nano fluid used, which is composed of Aluminum
oxide nano particles in suspension of Ethylene glycol, is provided at
various volume fractions. The study is carried out numerically for a
range of Rayleigh numbers, fin heights and aspect ratio. The flow and
temperature distributions are taken to be two-dimensional. Regions
with the same velocity and temperature distributions are identified as
symmetry of sections. One half of such a rectangular region is chosen
as the computational domain taking into account the symmetry about
the fin. Transport equations are modeled by a stream functionvorticity
formulation and are solved numerically by finite-difference
schemes. Comparisons with previously published works on the basis
of special cases are done. Results are presented in the form of
streamline, vector and isotherm plots as well as the variation of local
Nusselt number along the fin under different conditions.
Abstract: Recent articles have addressed the problem to construct the confidence intervals for the mean of a normal distribution where the parameter space is restricted, see for example Wang [Confidence intervals for the mean of a normal distribution with restricted parameter space. Journal of Statistical Computation and Simulation, Vol. 78, No. 9, 2008, 829–841.], we derived, in this paper, analytic expressions of the coverage probability and the expected length of confidence interval for the normal mean when the whole parameter space is bounded. We also construct the confidence interval for the normal variance with restricted parameter for the first time and its coverage probability and expected length are also mathematically derived. As a result, one can use these criteria to assess the confidence interval for the normal mean and variance when the parameter space is restricted without the back up from simulation experiments.
Abstract: The mixing behaviors of dry and wet granular
materials in gas fluidized bed systems were investigated
computationally using the combined Computational Fluid Dynamics
and Discrete Element Method (CFD-DEM). Dry particles were
observed to mix fairly rapidly during the fluidization process due to
vigorous relative motions between particles induced by the flow of
gas. In contrast, due to the presence of strong cohesive forces arising
from capillary liquid bridges between wet particles, the mixing
efficiencies of wet granular materials under similar operating
conditions were observed to be reduced significantly.
Abstract: In this paper, the use of beam search and look-ahead strategies for solving the strip packing problem (SPP) is investigated. Given a strip of fixed width W, unlimited length L, and a set of n circular pieces of known radii, the objective is to determine the minimum length of the initial strip that packs all the pieces. An augmented algorithm which combines beam search and a look-ahead strategies is proposed. The look-ahead is used in order to evaluate the nodes at each level of the tree search. The best nodes are then retained for branching. The computational investigation showed that the proposed augmented algorithm is able to improve the best known solutions of the literature on most instances used.
Abstract: In hypersonic environments, the aerothermal effect
makes it difficult for the optical side windows of optical guided
missiles to withstand high heat. This produces cracking or breaking,
resulting in an inability to function. This study used computational
fluid mechanics to investigate the external cooling jet conditions of
optical side windows. The turbulent models k-ε and k-ω were
simulated. To be in better accord with actual aerothermal
environments, a thermal radiation model was added to examine
suitable amounts of external coolants and the optical window
problems of aero-thermodynamics. The simulation results indicate that
when there are no external cooling jets, because airflow on the optical
window and the tail groove produce vortices, the temperatures in these
two locations reach a peak of approximately 1600 K. When the
external cooling jets worked at 0.15 kg/s, the surface temperature of
the optical windows dropped to approximately 280 K. When adding
thermal radiation conditions, because heat flux dissipation was faster,
the surface temperature of the optical windows fell from 280 K to
approximately 260 K. The difference in influence of the different
turbulence models k-ε and k-ω on optical window surface temperature
was not significant.
Abstract: The effect of the blade tip geometry of a high pressure
gas turbine is studied experimentally and computationally for high
speed leakage flows. For this purpose two simplified models are
constructed, one models a flat tip of the blade and the second models
a cavity tip of the blade. Experimental results are obtained from a
transonic wind tunnel to show the static pressure distribution along
the tip wall and provide flow visualization. RANS computations
were carried to provide further insight into the mean flow behavior
and to calculate the discharge coefficient which is a measure of the
flow leaking over the tip. It is shown that in both geometries of tip
the flow separates over the tip to form a separation bubble. The
bubble is higher for the cavity tip while a complete shock wave
system of oblique waves ending with a normal wave can be seen for
the flat tip. The discharge coefficient for the flat tip shows less
dependence on the pressure ratio over the blade tip than the cavity
tip. However, the discharge coefficient for the cavity tip is lower than
that of the flat tip, showing a better ability to reduce the leakage flow
and thus increase the turbine efficiency.
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: The mobile systems are powered by batteries.
Reducing the system power consumption is a key to increase its
autonomy. It is known that mostly the systems are dealing with time
varying signals. Thus, we aim to achieve power efficiency by smartly
adapting the system processing activity in accordance with the input
signal local characteristics. It is done by completely rethinking the
processing chain, by adopting signal driven sampling and processing.
In this context, a signal driven filtering technique, based on the level
crossing sampling is devised. It adapts the sampling frequency and
the filter order by analysing the input signal local variations. Thus, it
correlates the processing activity with the signal variations. It leads
towards a drastic computational gain of the proposed technique
compared to the classical one.
Abstract: In this paper we have proposed three and two
stage still gray scale image compressor based on BTC. In our
schemes, we have employed a combination of four techniques
to reduce the bit rate. They are quad tree segmentation, bit
plane omission, bit plane coding using 32 visual patterns and
interpolative bit plane coding. The experimental results show
that the proposed schemes achieve an average bit rate of 0.46
bits per pixel (bpp) for standard gray scale images with an
average PSNR value of 30.25, which is better than the results
from the exiting similar methods based on BTC.
Abstract: This paper includes two novel techniques for skew
estimation of binary document images. These algorithms are based on
connected component analysis and Hough transform. Both these
methods focus on reducing the amount of input data provided to
Hough transform. In the first method, referred as word centroid
approach, the centroids of selected words are used for skew detection.
In the second method, referred as dilate & thin approach, the selected
characters are blocked and dilated to get word blocks and later
thinning is applied. The final image fed to Hough transform has the
thinned coordinates of word blocks in the image. The methods have
been successful in reducing the computational complexity of Hough
transform based skew estimation algorithms. Promising experimental
results are also provided to prove the effectiveness of the proposed
methods.
Abstract: This paper presents an effective traffic lights detection
method at the night-time. First, candidate blobs of traffic lights are
extracted from RGB color image. Input image is represented on the
dominant color domain by using color transform proposed by Ruta,
then red and green color dominant regions are selected as candidates.
After candidate blob selection, we carry out shape filter for noise
reduction using information of blobs such as length, area, area of
boundary box, etc. A multi-class classifier based on SVM (Support
Vector Machine) applies into the candidates. Three kinds of features
are used. We use basic features such as blob width, height, center
coordinate, area, area of blob. Bright based stochastic features are also
used. In particular, geometric based moment-s values between
candidate region and adjacent region are proposed and used to improve
the detection performance. The proposed system is implemented on
Intel Core CPU with 2.80 GHz and 4 GB RAM and tested with the
urban and rural road videos. Through the test, we show that the
proposed method using PF, BMF, and GMF reaches up to 93 % of
detection rate with computation time of in average 15 ms/frame.
Abstract: Fuzzy random variables have been introduced as an imprecise concept of numeric values for characterizing the imprecise knowledge. The descriptive parameters can be used to describe the primary features of a set of fuzzy random observations. In fuzzy environments, the expected values are usually represented as fuzzy-valued, interval-valued or numeric-valued descriptive parameters using various metrics. Instead of the concept of area metric that is usually adopted in the relevant studies, the numeric expected value is proposed by the concept of distance metric in this study based on two characters (fuzziness and randomness) of FRVs. Comparing with the existing measures, although the results show that the proposed numeric expected value is same with those using the different metric, if only triangular membership functions are used. However, the proposed approach has the advantages of intuitiveness and computational efficiency, when the membership functions are not triangular types. An example with three datasets is provided for verifying the proposed approach.
Abstract: Predictions of flow and heat transfer characteristics and shape optimization in internally finned circular tubes have been performed on three-dimensional periodically fully developed turbulent flow and thermal fields. For a trapezoidal fin profile, the effects of fin height h, upper fin widths d1, lower fin widths d2, and helix angle of fin ? on transport phenomena are investigated for the condition of fin number of N = 30. The CFD and mathematical optimization technique are coupled in order to optimize the shape of internally finned tube. The optimal solutions of the design variables (i.e., upper and lower fin widths, fin height and helix angle) are numerically obtained by minimizing the pressure loss and maximizing the heat transfer rate, simultaneously, for the limiting conditions of d1 = 0.5~1.5 mm, d2 = 0.5~1.5 mm, h= 0.5~1.5mm, ? = 10~30 degrees. The fully developed flow and thermal fields are predicted using the finite volume method and the optimization is carried out by means of the multi-objective genetic algorithm that is widely used in the constrained nonlinear optimization problem.
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.