Abstract: Today automobile and aerospace industries realise Laser Beam Welding for a clean and non contact source of heating and fusion for joining of sheets. The welding performance is mainly based on by the laser welding parameters. Some concepts related to Artificial Neural Networks and how can be applied to model weld bead geometry and mechanical properties in terms of equipment parameters are reported in order to evaluate the accuracy and compare it with traditional modeling schemes. This review reveals the output features of Titanium and Aluminium weld bead geometry and mechanical properties such as ultimate tensile strength, yield strength, elongation and reduction of the area of the weld using Artificial Neural Network.
Abstract: This paper To get the angle value with a MEMS rate
gyroscope in some specific field, the usual method is to make an
integral operation to the rate output, which will lead the error
cumulating effect. So the rate gyro is not suitable. MEMS rate
integrating gyroscope (MRIG) will solve this problem. A DSP system
has been developed to implement the control arithmetic. The system
can measure the angle of rotation directly by the control loops that
make the sensor work in whole-angle mode. Modeling the system with
MATLAB, desirable results of angle outputs are got, which prove the
feasibility of the control arithmetic.
Abstract: Occurrences of spurious crests on the troughs of large,
relatively steep second-order Stokes waves are anomalous and not an
inherent characteristic of real waves. Here, the effects of such
occurrences on the statistics described by the standard second-order
stochastic model are examined theoretically and by way of
simulations. Theoretical results and simulations indicate that when
spurious occurrences are sufficiently large, the standard model leads
to physically unrealistic surface features and inaccuracies in the
statistics of various surface features, in particular, the troughs and
thus zero-crossing heights of large waves. Whereas inaccuracies can
be fairly noticeable for long-crested waves in both deep and
shallower depths, they tend to become relatively insignificant in
directional waves.
Abstract: A registration framework for image-guided robotic
surgery is proposed for three emergency neurosurgical procedures,
namely Intracranial Pressure (ICP) Monitoring, External Ventricular
Drainage (EVD) and evacuation of a Chronic Subdural Haematoma
(CSDH). The registration paradigm uses CT and white light as
modalities. This paper presents two simulation studies for a
preliminary evaluation of the registration protocol: (1) The loci of the
Target Registration Error (TRE) in the patient-s axial, coronal and
sagittal views were simulated based on a Fiducial Localisation Error
(FLE) of 5 mm and (2) Simulation of the actual framework using
projected views from a surface rendered CT model to represent white
light images of the patient. Craniofacial features were employed as
the registration basis to map the CT space onto the simulated
intraoperative space. Photogrammetry experiments on an artificial
skull were also performed to benchmark the results obtained from the
second simulation. The results of both simulations show that the
proposed protocol can provide a 5mm accuracy for these
neurosurgical procedures.
Abstract: Financial forecasting using machine learning techniques has received great efforts in the last decide . In this ongoing work, we show how machine learning of graphical models will be able to infer a visualized causal interactions between different banks in the Saudi equities market. One important discovery from such learned causal graphs is how companies influence each other and to what extend. In this work, a set of graphical models named Gaussian graphical models with developed ensemble penalized feature selection methods that combine ; filtering method, wrapper method and a regularizer will be shown. A comparison between these different developed ensemble combinations will also be shown. The best ensemble method will be used to infer the causal relationships between banks in Saudi equities market.
Abstract: In rotating machinery one of the critical components
that is prone to premature failure is the rolling bearing.
Consequently, early warning of an imminent bearing failure is much
critical to the safety and reliability of any high speed rotating
machines. This study is concerned with the application of Recurrence
Quantification Analysis (RQA) in fault detection of rolling element
bearings in rotating machinery. Based on the results from this study it
is reported that the RQA variable, percent determinism, is sensitive
to the type of fault investigated and therefore can provide useful
information on bearing damage in rolling element bearings.
Abstract: The aim of this study was to establish the feasibility of a minute incremental exercise testing protocol in young asthma children. Twenty-two children with clinically diagnosed mild to moderate asthma volunteered to participate. The maximum incremental exercise test was performed using a cycle ergometer with an electromagnetic braking. A warm-up unloaded for 2 minutes then the workload was started at 40 watts for 2 minutes, and then stepwise increments of 8 watts per 2 minutes were applied. The pedaling frequency was set at 50 rpm. Ventilation and gas exchange were measured with a breath-by-breath automatic metabolic measurement system. Results showed that this test was well tolerated by all asthmatic children. Most of the children reached the VO2 plateau and satisfied the criteria for maximal respiratory exchange ratio of ≥ 1. This Study demonstrated that this testing protocol was suitable for young asthmatic children.
Abstract: Purpose: To explore the use of Curvelet transform to
extract texture features of pulmonary nodules in CT image and support
vector machine to establish prediction model of small solitary
pulmonary nodules in order to promote the ratio of detection and
diagnosis of early-stage lung cancer. Methods: 2461 benign or
malignant small solitary pulmonary nodules in CT image from 129
patients were collected. Fourteen Curvelet transform textural features
were as parameters to establish support vector machine prediction
model. Results: Compared with other methods, using 252 texture
features as parameters to establish prediction model is more proper.
And the classification consistency, sensitivity and specificity for the
model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based
on texture features extracted from Curvelet transform, support vector
machine prediction model is sensitive to lung cancer, which can
promote the rate of diagnosis for early-stage lung cancer to some
extent.
Abstract: A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n-dimensional fuzzy decision region is approximated by union of hyperellipsoids. By explicitly parameterizing these hyperellipsoids, the decision regions are determined by estimating the parameters of each hyperellipsoid.Genetic Algorithm is applied to estimate the parameters of each region component. With the global optimization ability of GA, the learned decision region can be arbitrarily complex.
Abstract: In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input matrix, are fed to an ANN. Simulation results are provided and show that the proposed system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions.
Abstract: A feature weighting and selection method is proposed
which uses the structure of a weightless neuron and exploits the
principles that govern the operation of Genetic Algorithms and
Evolution. Features are coded onto chromosomes in a novel way
which allows weighting information regarding the features to be
directly inferred from the gene values. The proposed method is
significant in that it addresses several problems concerned with
algorithms for feature selection and weighting as well as providing
significant advantages such as speed, simplicity and suitability for
real-time systems.
Abstract: One of the most important problems in production planning of flexible manufacturing system (FMS) is machine tool selection and operation allocation problem that directly influences the production costs and times .In this paper minimizing machining cost, set-up cost and material handling cost as a multi-objective problem in flexible manufacturing systems environment are considered. We present a 0-1 integer linear programming model for the multiobjective machine tool selection and operation allocation problem and due to the large scale nature of the problem, solving the problem to obtain optimal solution in a reasonable time is infeasible, Paretoant colony optimization (P-ACO) approach for solving the multiobjective problem in reasonable time is developed. Experimental results indicate effectiveness of the proposed algorithm for solving the problem.
Abstract: Acoustical properties of speech have been shown to
be related to mental states of speaker with symptoms: depression
and remission. This paper describes way to address the issue of
distinguishing depressed patients from remitted subjects based on
measureable acoustics change of their spoken sound. The vocal-tract
related frequency characteristics of speech samples from female
remitted and depressed patients were analyzed via speech
processing techniques and consequently, evaluated statistically by
cross-validation with Support Vector Machine. Our results
comparatively show the classifier's performance with effectively
correct separation of 93% determined from testing with the subjectbased
feature model and 88% from the frame-based model based on
the same speech samples collected from hospital visiting interview
sessions between patients and psychiatrists.
Abstract: Association rules are an important problem in data
mining. Massively increasing volume of data in real life databases
has motivated researchers to design novel and incremental algorithms
for association rules mining. In this paper, we propose an incremental
association rules mining algorithm that integrates shocking
interestingness criterion during the process of building the model. A
new interesting measure called shocking measure is introduced. One
of the main features of the proposed approach is to capture the user
background knowledge, which is monotonically augmented. The
incremental model that reflects the changing data and the user beliefs
is attractive in order to make the over all KDD process more
effective and efficient. We implemented the proposed approach and
experiment it with some public datasets and found the results quite
promising.
Abstract: An automated wood recognition system is designed to
classify tropical wood species.The wood features are extracted based
on two feature extractors: Basic Grey Level Aura Matrix (BGLAM)
technique and statistical properties of pores distribution (SPPD)
technique. Due to the nonlinearity of the tropical wood species
separation boundaries, a pre classification stage is proposed which
consists ofKmeans clusteringand kernel discriminant analysis (KDA).
Finally, Linear Discriminant Analysis (LDA) classifier and KNearest
Neighbour (KNN) are implemented for comparison purposes.
The study involves comparison of the system with and without pre
classification using KNN classifier and LDA classifier.The results
show that the inclusion of the pre classification stage has improved
the accuracy of both the LDA and KNN classifiers by more than
12%.
Abstract: A shaft-type activated sludge reactor has been
developed in order to study the feasibility of high-rate wastewater
treatment. The reactor having volume of about 14.5 L was operated
with the acclimated mixed activated sludge under batch and
continuous mode using a synthetic wastewater as feed. The batch
study was performed with varying chemical oxygen demand (COD)
concentrations of 1000–3500 mg·L-1 for a batch period up to 9 h. The
kinetic coefficients: Ks, k, Y and kd were obtained as 2040.2 mg·L-1
and 0.105 h-1, 0.878 and 0.0025 h-1 respectively from Monod-s
approach. The continuous study showed a stable and steady state
operation for a hydraulic retention time (HRT) of 8 h and influent
COD of about 1000 mg·L-1. A maximum COD removal efficiency of
about 80% was attained at a COD loading rate and food-tomicroorganism
(F/M) ratio (COD basis) of 3.42 kg·m-3d-1 and 1.0
kg·kg-1d-1 respectively under a HRT of 8 h. The reactor was also
found to handle COD loading rate and F/M ratio of 10.8 kg·m-3d-1
and 2.20 kg·kg-1d-1 respectively showing a COD removal efficiency
of about 46%.
Abstract: In this project cadmium ions were adsorbed from
aqueous solutions onto either date pits; a cheap agricultural and nontoxic
material, or chemically activated carbon prepared from date pits
using phosphoric acid. A series of experiments were conducted in a
batch adsorption technique to assess the feasibility of using the
prepared adsorbents. The effects of the process variables such as
initial cadmium ions concentration, contact time, solution pH and
adsorbent dose on the adsorption capacity of both adsorbents were
studied. The experimental data were tested using different isotherm
models such as Langmuir, Freundlich, Tempkin and Dubinin-
Radushkevich. The results showed that although the equilibrium data
could be described by all models used, Langmuir model gave slightly
better results when using activated carbon while Freundlich model,
gave better results with date pits.
Abstract: The ever increasing product diversity and competition on the market of goods and services has dictated the pace of growth in the number of advertisements. Despite their admittedly diminished effectiveness over the recent years, advertisements remain the favored method of sales promotion. Consequently, the challenge for an advertiser is to explore every possible avenue of making an advertisement more noticeable, attractive and impellent for consumers. One way to achieve this is through invoking celebrity endorsements. On the one hand, the use of a celebrity to endorse a product involves substantial costs, however, on the other hand, it does not immediately guarantee the success of an advertisement. The question of how celebrities can be used in advertising to the best advantage is therefore of utmost importance. Celebrity endorsements have become commonplace: empirical evidence indicates that approximately 20 to 25 per cent of advertisements feature some famous person as a product endorser. The popularity of celebrity endorsements demonstrates the relevance of the topic, especially in the context of the current global economic downturn, when companies are forced to save in order to survive, yet simultaneously to heavily invest in advertising and sales promotion. The issue of the effective use of celebrity endorsements also figures prominently in the academic discourse. The study presented below is thus aimed at exploring what qualities (characteristics) of a celebrity endorser have an impact on the ffectiveness of the advertisement in which he/she appears and how.
Abstract: An appropriate project delivery system (PDS) is crucial
to the success of a construction projects. Case-based Reasoning (CBR)
is a useful support for PDS selection. However, the traditional CBR
approach represents cases as attribute-value vectors without taking
relations among attributes into consideration, and could not calculate
the similarity when the structures of cases are not strictly same.
Therefore, this paper solves this problem by adopting the Relational
Case-based Reasoning (RCBR) approach for PDS selection,
considering both the structural similarity and feature similarity. To
develop the feature terms of the construction projects, the criteria and
factors governing PDS selection process are first identified. Then
feature terms for the construction projects are developed. Finally, the
mechanism of similarity calculation and a case study indicate how
RCBR works for PDS selection. The adoption of RCBR in PDS
selection expands the scope of application of traditional CBR method
and improves the accuracy of the PDS selection system.
Abstract: Feature selection has recently been the subject of intensive research in data mining, specially for datasets with a large number of attributes. Recent work has shown that feature selection can have a positive effect on the performance of machine learning algorithms. The success of many learning algorithms in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. In this paper, a novel feature search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results.