Abstract: This research was conducted for the first time at the
southeastern coasts of the Caspian Sea in order to evaluate the
performance of osteichthyes cooperatives through production (catch)
function. Using one of the indirect valuation methods in this research,
contributory factors in catch were identified and were inserted into
the function as independent variables. In order to carry out this
research, the performance of 25 Osteichthyes catching cooperatives
in the utilization year of 2009 which were involved in fishing in
Miankale wildlife refuge region. The contributory factors in catch
were divided into groups of economic, ecological and biological
factors. In the mentioned function, catch rate of the cooperative were
inserted into as the dependant variable and fourteen partial variables
in terms of nine general variables as independent variables. Finally,
after function estimation, seven variables were rendered significant at
99 percent reliably level. The results of the function estimation
indicated that human resource (fisherman quantity) had the greatest
positive effect on catch rate with an influence coefficient of 1.7 while
weather conditions had the greatest negative effect on the catch rate
of cooperatives with an influence coefficient of -2.07. Moreover,
factors like member's share, experience and fisherman training and
fishing effort played the main roles in the catch rate of cooperative
with influence coefficients of 0.81, 0.5 and 0.21, respectively.
Abstract: In-core memory requirement is a bottleneck in solving
large three dimensional Navier-Stokes finite element problem
formulations using sparse direct solvers. Out-of-core solution
strategy is a viable alternative to reduce the in-core memory
requirements while solving large scale problems. This study
evaluates the performance of various out-of-core sequential solvers
based on multifrontal or supernodal techniques in the context of
finite element formulations for three dimensional problems on a
Windows platform. Here three different solvers, HSL_MA78,
MUMPS and PARDISO are compared. The performance of these
solvers is evaluated on a 64-bit machine with 16GB RAM for finite
element formulation of flow through a rectangular channel. It is
observed that using out-of-core PARDISO solver, relatively large
problems can be solved. The implementation of Newton and
modified Newton's iteration is also discussed.
Abstract: Nowadays, engineering ceramics have significant
applications in different industries such as; automotive, aerospace,
electrical, electronics and even martial industries due to their
attractive physical and mechanical properties like very high hardness
and strength at elevated temperatures, chemical stability, low friction
and high wear resistance. However, these interesting properties plus
low heat conductivity make their machining processes too hard,
costly and time consuming. Many attempts have been made in order
to make the grinding process of engineering ceramics easier and
many scientists have tried to find proper techniques to economize
ceramics' machining processes. This paper proposes a new diamond
plunge grinding technique using ultrasonic vibration for grinding
Alumina ceramic (Al2O3). For this purpose, a set of laboratory
equipments have been designed and simulated using Finite Element
Method (FEM) and constructed in order to be used in various
measurements. The results obtained have been compared with the
conventional plunge grinding process without ultrasonic vibration
and indicated that the surface roughness and fracture strength
improved and the grinding forces decreased.
Abstract: The dynamics of User Datagram Protocol (UDP) traffic
over Ethernet between two computers are analyzed using nonlinear
dynamics which shows that there are two clear regimes in the data
flow: free flow and saturated. The two most important variables
affecting this are the packet size and packet flow rate. However,
this transition is due to a transcritical bifurcation rather than phase
transition in models such as in vehicle traffic or theorized large-scale
computer network congestion. It is hoped this model will help lay
the groundwork for further research on the dynamics of networks,
especially computer networks.
Abstract: High speed networks provide realtime variable bit rate
service with diversified traffic flow characteristics and quality
requirements. The variable bit rate traffic has stringent delay and
packet loss requirements. The burstiness of the correlated traffic
makes dynamic buffer management highly desirable to satisfy the
Quality of Service (QoS) requirements. This paper presents an
algorithm for optimization of adaptive buffer allocation scheme for
traffic based on loss of consecutive packets in data-stream and buffer
occupancy level. Buffer is designed to allow the input traffic to be
partitioned into different priority classes and based on the input
traffic behavior it controls the threshold dynamically. This algorithm
allows input packets to enter into buffer if its occupancy level is less
than the threshold value for priority of that packet. The threshold is
dynamically varied in runtime based on packet loss behavior. The
simulation is run for two priority classes of the input traffic –
realtime and non-realtime classes. The simulation results show that
Adaptive Partial Buffer Sharing (ADPBS) has better performance
than Static Partial Buffer Sharing (SPBS) and First In First Out
(FIFO) queue under the same traffic conditions.
Abstract: In this paper, we propose a Perceptually Optimized Foveation based Embedded ZeroTree Image Coder (POEFIC) that introduces a perceptual weighting to wavelet coefficients prior to control SPIHT encoding algorithm in order to reach a targeted bit rate with a perceptual quality improvement with respect to a given bit rate a fixation point which determines the region of interest ROI. The paper also, introduces a new objective quality metric based on a Psychovisual model that integrates the properties of the HVS that plays an important role in our POEFIC quality assessment. Our POEFIC coder is based on a vision model that incorporates various masking effects of human visual system HVS perception. Thus, our coder weights the wavelet coefficients based on that model and attempts to increase the perceptual quality for a given bit rate and observation distance. The perceptual weights for all wavelet subbands are computed based on 1) foveation masking to remove or reduce considerable high frequencies from peripheral regions 2) luminance and Contrast masking, 3) the contrast sensitivity function CSF to achieve the perceptual decomposition weighting. The new perceptually optimized codec has the same complexity as the original SPIHT techniques. However, the experiments results show that our coder demonstrates very good performance in terms of quality measurement.
Abstract: The increased number of automobiles in recent years
has resulted in great demand for fossil fuel. This has led to the
development of automobile by using alternative fuels which include
gaseous fuels, biofuels and vegetables oils as fuel. Energy from
biomass and more specific bio-diesel is one of the opportunities that
could cover the future demand of fossil fuel shortage. Biomass in the
form of cashew nut shell represents a new energy source and
abundant source of energy in India. The bio-fuel is derived from
cashew nut shell oil and its blend with diesel are promising
alternative fuel for diesel engine. In this work the pyrolysis Cashew
Nut Shell Liquid (CNSL)-Diesel Blends (CDB) was used to run the
Direct Injection (DI) diesel engine. The experiments were conducted
with various blends of CNSL and Diesel namely B20, B40, B60, B80
and B100. The results are compared with neat diesel operation. The
brake thermal efficiency was decreased for blends of CNSL and
Diesel except the lower blends of B20. The brake thermal efficiency
of B20 is nearly closer to that of diesel fuel. Also the emission level
of the all CNSL and Diesel blends was increased compared to neat
diesel. The higher viscosity and lower volatility of CNSL leads to
poor mixture formation and hence lower brake thermal efficiency and
higher emission levels. The higher emission level can be reduced by
adding suitable additives and oxygenates with CNSL and Diesel
blends.
Abstract: This paper presents the significant factor and give
some suggestion that should know before design. The main objective of this paper is guide the first step for someone who attends to design of grounding system before study in details later. The overview of
grounding system can protect damage from fault such as can save a human life and power system equipment. The unsafe conditions have
three cases. Case 1) maximum touch voltage exceeds the safety
criteria. In this case, the conductor compression ratio of the ground gird should be first adjusted to have optimal spacing of ground grid
conductors. If it still over limit, earth resistivity should be consider afterward. Case 2) maximum step voltage exceeds the safety criteria.
In this case, increasing the number of ground grid conductors around
the boundary can solve this problem. Case 3) both of maximum touch
and step voltage exceed the safety criteria. In this case, follow the solutions explained in case 1 and case 2. Another suggestion, vary depth of ground grid until maximum step and touch voltage do not
exceed the safety criteria.
Abstract: A model predictive controller based on recursive learning is proposed. In this SISO adaptive controller, a model is automatically updated using simple recursive equations. The identified models are then stored in the memory to be re-used in the future. The decision for model update is taken based on a new control performance index. The new controller allows the use of simple linear model predictive controllers in the control of nonlinear time varying processes.
Abstract: In this paper, a tooth shape optimization method for
cogging torque reduction in Permanent Magnet (PM) motors is
developed by using the Reduced Basis Technique (RBT) coupled by
Finite Element Analysis (FEA) and Design of Experiments (DOE)
methods. The primary objective of the method is to reduce the
enormous number of design variables required to define the tooth
shape. RBT is a weighted combination of several basis shapes. The
aim of the method is to find the best combination using the weights
for each tooth shape as the design variables. A multi-level design
process is developed to find suitable basis shapes or trial shapes at
each level that can be used in the reduced basis technique. Each level
is treated as a separated optimization problem until the required
objective – minimum cogging torque – is achieved. The process is
started with geometrically simple basis shapes that are defined by
their shape co-ordinates. The experimental design of Taguchi method
is used to build the approximation model and to perform
optimization. This method is demonstrated on the tooth shape
optimization of a 8-poles/12-slots PM motor.
Abstract: Increasing growth of information volume in the
internet causes an increasing need to develop new (semi)automatic
methods for retrieval of documents and ranking them according to
their relevance to the user query. In this paper, after a brief review
on ranking models, a new ontology based approach for ranking
HTML documents is proposed and evaluated in various
circumstances. Our approach is a combination of conceptual,
statistical and linguistic methods. This combination reserves the
precision of ranking without loosing the speed. Our approach
exploits natural language processing techniques to extract phrases
from documents and the query and doing stemming on words. Then
an ontology based conceptual method will be used to annotate
documents and expand the query. To expand a query the spread
activation algorithm is improved so that the expansion can be done
flexible and in various aspects. The annotated documents and the
expanded query will be processed to compute the relevance degree
exploiting statistical methods. The outstanding features of our
approach are (1) combining conceptual, statistical and linguistic
features of documents, (2) expanding the query with its related
concepts before comparing to documents, (3) extracting and using
both words and phrases to compute relevance degree, (4) improving
the spread activation algorithm to do the expansion based on
weighted combination of different conceptual relationships and (5)
allowing variable document vector dimensions. A ranking system
called ORank is developed to implement and test the proposed
model. The test results will be included at the end of the paper.
Abstract: Due to the ever growing amount of publications about
protein-protein interactions, information extraction from text is
increasingly recognized as one of crucial technologies in
bioinformatics. This paper presents a Protein Interaction Extraction
System using a Link Grammar Parser from biomedical abstracts
(PIELG). PIELG uses linkage given by the Link Grammar Parser to
start a case based analysis of contents of various syntactic roles as
well as their linguistically significant and meaningful combinations.
The system uses phrasal-prepositional verbs patterns to overcome
preposition combinations problems. The recall and precision are
74.4% and 62.65%, respectively. Experimental evaluations with two
other state-of-the-art extraction systems indicate that PIELG system
achieves better performance. For further evaluation, the system is
augmented with a graphical package (Cytoscape) for extracting
protein interaction information from sequence databases. The result
shows that the performance is remarkably promising.
Abstract: We propose that Virtual Learning Environments (VLEs) should be designed by taking into account the characteristics, the special needs and the specific operating rules of the academic institutions in which they are employed. In this context, we describe a VLE module that extends the support of the organization and delivery of course material by including administration activities related to the various stages of teaching. These include the co-ordination, collaboration and monitoring of the course material development process and institution-specific course material delivery modes. Our specialized module, which enhances VLE capabilities by Helping Educators and Learners through a Laboratory Assistance System, is willing to assist the Greek tertiary technological sector, which includes Technological Educational Institutes (T.E.I.).
Abstract: In this paper a new approach to face recognition is
presented that achieves double dimension reduction, making the
system computationally efficient with better recognition results and
out perform common DCT technique of face recognition. In pattern
recognition techniques, discriminative information of image
increases with increase in resolution to a certain extent, consequently
face recognition results change with change in face image resolution
and provide optimal results when arriving at a certain resolution
level. In the proposed model of face recognition, initially image
decimation algorithm is applied on face image for dimension
reduction to a certain resolution level which provides best
recognition results. Due to increased computational speed and feature
extraction potential of Discrete Cosine Transform (DCT), it is
applied on face image. A subset of coefficients of DCT from low to
mid frequencies that represent the face adequately and provides best
recognition results is retained. A tradeoff between decimation factor,
number of DCT coefficients retained and recognition rate with
minimum computation is obtained. Preprocessing of the image is
carried out to increase its robustness against variations in poses and
illumination level. This new model has been tested on different
databases which include ORL , Yale and EME color database.
Abstract: In the current research, neuro-fuzzy model and regression model was developed to predict Material Removal Rate in Electrical Discharge Machining process for AISI D2 tool steel with copper electrode. Extensive experiments were conducted with various levels of discharge current, pulse duration and duty cycle. The experimental data are split into two sets, one for training and the other for validation of the model. The training data were used to develop the above models and the test data, which was not used earlier to develop these models were used for validation the models. Subsequently, the models are compared. It was found that the predicted and experimental results were in good agreement and the coefficients of correlation were found to be 0.999 and 0.974 for neuro fuzzy and regression model respectively
Abstract: In this paper, some practical solid transportation models are formulated considering per trip capacity of each type of conveyances with crisp and rough unit transportation costs. This is applicable for the system in which full vehicles, e.g. trucks, rail coaches are to be booked for transportation of products so that transportation cost is determined on the full of the conveyances. The models with unit transportation costs as rough variables are transformed into deterministic forms using rough chance constrained programming with the help of trust measure. Numerical examples are provided to illustrate the proposed models in crisp environment as well as with unit transportation costs as rough variables.
Abstract: Temperature rise in a transformer depends on variety
of parameters such as ambient temperature, output current and type
of the core. Considering these parameters, temperature rise estimation
is still complicated procedure. In this paper, we present a new model
based on simple electrical equivalent circuit. This method avoids the
complication associated to accurate estimation and is in very good
agreement with practice.
Abstract: We have developed a microfluidic device system for the continuous producting of nanoparticles, and we have clarified the relationship between the mixing performance of reactors and the particle size. First, we evaluated the mixing performance of reactors by carring out the Villermaux–Dushman reaction and determined the experimental conditions for producing AgCl nanoparticles. Next, we produced AgCl nanoparticles and evaluated the mixing performance and the particle size. We found that as the mixing performance improves the size of produced particles decreases and the particle size distribution becomes sharper. We produced AgCl nanoparticles with a size of 86 nm using the microfluidic device that had the best mixing performance among the three reactors we tested in this study; the coefficient of variation (Cv) of the size distribution of the produced nanoparticles was 26.1%.
Abstract: If price and quantity are the fundamental building
blocks of any theory of market interactions, the importance of trading
volume in understanding the behavior of financial markets is clear.
However, while many economic models of financial markets have
been developed to explain the behavior of prices -predictability,
variability, and information content- far less attention has been
devoted to explaining the behavior of trading volume. In this article,
we hope to expand our understanding of trading volume by
developing a new measure of herding behavior based on a cross
sectional dispersion of volumes betas. We apply our measure to the
Toronto stock exchange using monthly data from January 2000 to
December 2002. Our findings show that the herd phenomenon
consists of three essential components: stationary herding, intentional
herding and the feedback herding.
Abstract: In this paper, we propose an algorithm to compute
initial cluster centers for K-means clustering. Data in a cell is
partitioned using a cutting plane that divides cell in two smaller cells.
The plane is perpendicular to the data axis with the highest variance
and is designed to reduce the sum squared errors of the two cells as
much as possible, while at the same time keep the two cells far apart
as possible. Cells are partitioned one at a time until the number of
cells equals to the predefined number of clusters, K. The centers of
the K cells become the initial cluster centers for K-means. The
experimental results suggest that the proposed algorithm is effective,
converge to better clustering results than those of the random
initialization method. The research also indicated the proposed
algorithm would greatly improve the likelihood of every cluster
containing some data in it.