Abstract: Machine Translation, (hereafter in this document
referred to as the "MT") faces a lot of complex problems from its
origination. Extracting multiword expressions is also one of the
complex problems in MT. Finding multiword expressions during
translating a sentence from English into Urdu, through existing
solutions, takes a lot of time and occupies system resources. We have
designed a simple relational data approach, in which we simply set a
bit in dictionary (database) for multiword, to find and handle
multiword expression. This approach handles multiword efficiently.
Abstract: This paper introduces a measure of similarity between
two clusterings of the same dataset produced by two different
algorithms, or even the same algorithm (K-means, for instance, with
different initializations usually produce different results in clustering
the same dataset). We then apply the measure to calculate the
similarity between pairs of clusterings, with special interest directed
at comparing the similarity between various machine clusterings and
human clustering of datasets. The similarity measure thus can be used
to identify the best (in terms of most similar to human) clustering
algorithm for a specific problem at hand. Experimental results
pertaining to the text categorization problem of a Portuguese corpus
(wherein a translation-into-English approach is used) are presented, as well as results on the well-known benchmark IRIS dataset. The
significance and other potential applications of the proposed measure
are discussed.
Abstract: An emotional speech recognition system for the
applications on smart phones was proposed in this study to combine
with 3G mobile communications and social networks to provide users
and their groups with more interaction and care. This study developed
a mechanism using the support vector machines (SVM) to recognize
the emotions of speech such as happiness, anger, sadness and normal.
The mechanism uses a hierarchical classifier to adjust the weights of
acoustic features and divides various parameters into the categories of
energy and frequency for training. In this study, 28 commonly used
acoustic features including pitch and volume were proposed for
training. In addition, a time-frequency parameter obtained by
continuous wavelet transforms was also used to identify the accent and
intonation in a sentence during the recognition process. The Berlin
Database of Emotional Speech was used by dividing the speech into
male and female data sets for training. According to the experimental
results, the accuracies of male and female test sets were increased by
4.6% and 5.2% respectively after using the time-frequency parameter
for classifying happy and angry emotions. For the classification of all
emotions, the average accuracy, including male and female data, was
63.5% for the test set and 90.9% for the whole data set.
Abstract: Traditional parallel single string matching algorithms
are always based on PRAM computation model. Those algorithms
concentrate on the cost optimal design and the theoretical speed.
Based on the distributed string matching algorithm proposed by
CHEN, a practical distributed string matching algorithm architecture
is proposed in this paper. And also an improved single string matching
algorithm based on a variant Boyer-Moore algorithm is presented. We
implement our algorithm on the above architecture and the
experiments prove that it is really practical and efficient on distributed
memory machine. Its computation complexity is O(n/p + m), where n
is the length of the text, and m is the length of the pattern, and p is the
number of the processors.
Abstract: The manufacturing transmission line tower parts has
being generated hazardous waste which is required proper disposal
of waste for protection of land pollution. Manufacturing Process in
the manufacturing of steel angle, plates, pipes, channels are passes
through conventional, semi automatic and CNC machines for
cutting, marking, punching, drilling, notching, bending operations.
All fabricated material Coated with thin layer of Zinc in Galvanizing
plant where molten zinc is used for coating. Prior to Galvanizing,
chemical like 33% concentrated HCl Acid, ammonium chloride and
d-oil being used for pretreatment of iron. The bath of water with
sodium dichromate is used for cooling and protection of the
galvanized steel. For the heating purpose the furnace oil burners are
used. These above process the Zinc dross, Zinc ash, ETP sludge and
waste pickled acid generated as hazardous waste. The RPG has
made captive secured land fill site, since 1997 since then it was
using for disposal of hazardous waste after completion of SLF
(Secured land fill) site. The RPG has raised height from ground
level then now it is being used for disposal of waste as he designed
the SLF after in creasing height of from GL it is functional without
leach ate or adverse impacts in the environment.
Abstract: Inconel718 has been widely used as a super alloy in aerospace application due to the high strength at elevated temperatures, satisfactory oxidation resistance and heat corrosion resistance. In this study, the Inconel718 has been fabricated using high technology of Metal Injection Molding (MIM) process due to the cost effective technique for producing small, complex and precision parts in high volume compared with conventional method through machining. Through MIM, the binder system is one of the most important criteria in order to successfully fabricate the Inconel718. Even though, the binder system is a temporary, but failure in the selection and removal of the binder system will affect on the final properties of the sintered parts. Therefore, the binder system based on palm oil derivative which is palm stearin has been formulated and developed to replace the conventional binder system. The rheological studies of the mixture between the powder and binders system have been determined properly in order to be successful during injection into injection molding machine. After molding, the binder holds the particles in place. The binder system has to be removed completely through debinding step. During debinding step, solvent debinding and thermal pyrolysis has been used to remove completely of the binder system. The debound part is then sintered to give the required physical and mechanical properties. The results show that the properties of the final sintered parts fulfill the Standard Metal Powder Industries Federation (MPIF) 35 for MIM parts.
Abstract: This study focuses on bureau management
technologies and information systems in developing countries.
Developing countries use such systems which facilitate executive and
organizational functions through the utilization of bureau
management technologies and provide the executive staff with
necessary information.
The concepts of data and information differ from each other in
developing countries, and thus the concepts of data processing and
information processing are different. Symbols represent ideas,
objects, figures, letters and numbers. Data processing system is an
integrated system which deals with the processing of the data related
to the internal and external environment of the organization in order
to make decisions, create plans and develop strategies; it goes
without saying that this system is composed of both human beings
and machines. Information is obtained through the acquisition and
the processing of data. On the other hand, data are raw
communicative messages. Within this framework, data processing
equals to producing plausible information out of raw data.
Organizations in developing countries need to obtain information
relevant to them because rapid changes in the organizational arena
require rapid access to accurate information. The most significant
role of the directors and managers who work in the organizational
arena is to make decisions. Making a correct decision is possible only
when the directors and managers are equipped with sound ideas and
appropriate information. Therefore, acquisition, organization and
distribution of information gain significance. Today-s organizations
make use of computer-assisted “Management Information Systems"
in order to obtain and distribute information.
Decision Support System which is closely related to practice is an
information system that facilitates the director-s task of making
decisions. Decision Support System integrates human intelligence,
information technology and software in order to solve the complex
problems. With the support of the computer technology and software
systems, Decision Support System produces information relevant to
the decision to be made by the director and provides the executive
staff with supportive ideas about the decision.
Artificial Intelligence programs which transfer the studies and
experiences of the people to the computer are called expert systems.
An expert system stores expert information in a limited area and can
solve problems by deriving rational consequences.
Bureau management technologies and information systems in
developing countries create a kind of information society and
information economy which make those countries have their places
in the global socio-economic structure and which enable them to play
a reasonable and fruitful role; therefore it is of crucial importance to
make use of information and management technologies in order to
work together with innovative and enterprising individuals and it is
also significant to create “scientific policies" based on information
and technology in the fields of economy, politics, law and culture.
Abstract: We consider a typical problem in the assembly of
printed circuit boards (PCBs) in a two-machine flow shop system to
simultaneously minimize the weighted sum of weighted tardiness and
weighted flow time. The investigated problem is a group scheduling
problem in which PCBs are assembled in groups and the interest is to
find the best sequence of groups as well as the boards within each
group to minimize the objective function value. The type of setup
operation between any two board groups is characterized as carryover
sequence-dependent setup time, which exactly matches with the real
application of this problem. As a technical constraint, all of the
boards must be kitted before the assembly operation starts (kitting
operation) and by kitting staff. The main idea developed in this paper
is to completely eliminate the role of kitting staff by assigning the
task of kitting to the machine operator during the time he is idle
which is referred to as integration of internal (machine) and external
(kitting) setup times. Performing the kitting operation, which is a
preparation process of the next set of boards while the other boards
are currently being assembled, results in the boards to continuously
enter the system or have dynamic arrival times. Consequently, a
dynamic PCB assembly system is introduced for the first time in the
assembly of PCBs, which also has characteristics similar to that of
just-in-time manufacturing. The problem investigated is
computationally very complex, meaning that finding the optimal
solutions especially when the problem size gets larger is impossible.
Thus, a heuristic based on Genetic Algorithm (GA) is employed. An
example problem on the application of the GA developed is
demonstrated and also numerical results of applying the GA on
solving several instances are provided.
Abstract: These days wireless local area networks has become
very popular, when the initial IEEE802.11 is the standard for
providing wireless connectivity to automatic machinery, equipment
and stations that require rapid deployment, which may be portable,
handheld or which may be mounted on moving vehicles within a
local area. IEEE802.11 Wireless local area network is a sharedmedium
communication network that transmits information over
wireless links for all IEEE802.11 stations in its transmission range to
receive. When a user is moving from one location to another, how
the other user knows about the required station inside WLAN. For
that we designed and implemented a system to locate a mobile user
inside the wireless local area network based on RSSI with the help of
four specially designed architectures. These architectures are based
on statistical or we can say manual configuration of mapping and
radio map of indoor and outdoor location with the help of available
Sniffer based and cluster based techniques. We found a better
location of a mobile user in WLAN. We tested this work in indoor
and outdoor environments with different locations with the help of
Pamvotis, a simulator for WLAN.
Abstract: This paper aims to select the optimal location and
setting parameters of TCSC (Thyristor Controlled Series
Compensator) controller using Particle Swarm Optimization (PSO)
and Genetic Algorithm (GA) to mitigate small signal oscillations in a
multimachine power system. Though Power System Stabilizers
(PSSs) are prime choice in this issue, installation of FACTS device
has been suggested here in order to achieve appreciable damping of
system oscillations. However, performance of any FACTS devices
highly depends upon its parameters and suitable location in the
power network. In this paper PSO as well as GA based techniques are
used separately and compared their performances to investigate this
problem. The results of small signal stability analysis have been
represented employing eigenvalue as well as time domain response in
face of two common power system disturbances e.g., varying load
and transmission line outage. It has been revealed that the PSO based
TCSC controller is more effective than GA based controller even
during critical loading condition.
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: Misalignment and unbalance are the major concerns
in rotating machinery. When the power supply to any rotating system
is cutoff, the system begins to lose the momentum gained during
sustained operation and finally comes to rest. The exact time period
from when the power is cutoff until the rotor comes to rest is called
Coast Down Time. The CDTs for different shaft cutoff speeds were
recorded at various misalignment and unbalance conditions. The
CDT reduction percentages were calculated for each fault and there
is a specific correlation between the CDT reduction percentage and
the severity of the fault. In this paper, radial basis network, a new
generation of artificial neural networks, has been successfully
incorporated for the prediction of CDT for misalignment and
unbalance conditions. Radial basis network has been found to be
successful in the prediction of CDT for mechanical faults in rotating
machinery.
Abstract: Small signal stability causes small perturbations in the
generator that can cause instability in the power network. It is
generally known that small signal stability are directly related to the
generator and load properties. This paper examines the effects of
generator input variations on power system oscillations for a small
signal stability study. Eigenvaules and eigenvectors are used to
examine the stability of the power system. The dynamic power
system's mathematical model is constructed and thus calculated using
load flow and small signal stability toolbox on MATLAB. The power
system model is based on a 3-machine 9-bus system that was
modified to suit this study. In this paper, Participation Factors are a
means to gauge the effects of variation in generation with other
parameters on the network are also incorporated.
Abstract: In this paper, some common gearboxes vibration analysis methods and condition monitoring systems are explained. In addition, an experimental gearbox vibration analysis is discussed through a critical case history for a mixer gearbox related to Iran oil industry. The case history also consists of gear manufacturing (machining) recommendations, lubrication condition of gearbox and machinery maintenance activities that caused reduction in noise and vibration of the gearbox. Besides some of the recent patents and innovations in gearboxes, lubrication and vibration monitoring systems explained. Finally micro pitting and surface fatigue in pinion and bevel of mentioned horizontal to vertical gearbox discussed in details.
Abstract: In this paper, we propose a hybrid machine learning
system based on Genetic Algorithm (GA) and Support Vector
Machines (SVM) for stock market prediction. A variety of indicators
from the technical analysis field of study are used as input features.
We also make use of the correlation between stock prices of different
companies to forecast the price of a stock, making use of technical
indicators of highly correlated stocks, not only the stock to be
predicted. The genetic algorithm is used to select the set of most
informative input features from among all the technical indicators.
The results show that the hybrid GA-SVM system outperforms the
stand alone SVM system.
Abstract: In this paper, a model for an information retrieval
system is proposed which takes into account that knowledge about
documents and information need of users are dynamic. Two
methods are combined, one qualitative or symbolic and the other
quantitative or numeric, which are deemed suitable for many
clustering contexts, data analysis, concept exploring and
knowledge discovery. These two methods may be classified as
inductive learning techniques. In this model, they are introduced to
build “long term" knowledge about past queries and concepts in a
collection of documents. The “long term" knowledge can guide
and assist the user to formulate an initial query and can be
exploited in the process of retrieving relevant information. The
different kinds of knowledge are organized in different points of
view. This may be considered an enrichment of the exploration
level which is coherent with the concept of document/query
structure.
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: Protein residue contact map is a compact
representation of secondary structure of protein. Due to the
information hold in the contact map, attentions from researchers in
related field were drawn and plenty of works have been done
throughout the past decade. Artificial intelligence approaches have
been widely adapted in related works such as neural networks,
genetic programming, and Hidden Markov model as well as support
vector machine. However, the performance of the prediction was not
generalized which probably depends on the data used to train and
generate the prediction model. This situation shown the importance
of the features or information used in affecting the prediction
performance. In this research, support vector machine was used to
predict protein residue contact map on different combination of
features in order to show and analyze the effectiveness of the
features.
Abstract: Case-Based Reasoning (CBR) is one of machine
learning algorithms for problem solving and learning that caught a lot
of attention over the last few years. In general, CBR is composed of
four main phases: retrieve the most similar case or cases, reuse the
case to solve the problem, revise or adapt the proposed solution, and
retain the learned cases before returning them to the case base for
learning purpose. Unfortunately, in many cases, this retain process
causes the uncontrolled case base growth. The problem affects
competence and performance of CBR systems. This paper proposes
competence-based maintenance method based on deletion policy
strategy for CBR. There are three main steps in this method. Step 1,
formulate problems. Step 2, determine coverage and reachability set
based on coverage value. Step 3, reduce case base size. The results
obtained show that this proposed method performs better than the
existing methods currently discussed in literature.
Abstract: Among neural models the Support Vector Machine
(SVM) solutions are attracting increasing attention, mostly because
they eliminate certain crucial questions involved by neural network
construction. The main drawback of standard SVM is its high
computational complexity, therefore recently a new technique, the
Least Squares SVM (LS–SVM) has been introduced. In this paper we
present an extended view of the Least Squares Support Vector
Regression (LS–SVR), which enables us to develop new
formulations and algorithms to this regression technique. Based on
manipulating the linear equation set -which embodies all information
about the regression in the learning process- some new methods are
introduced to simplify the formulations, speed up the calculations
and/or provide better results.