Abstract: This paper presents the development of a hybrid
thermal model for the EVO Electric AFM 140 Axial Flux Permanent
Magnet (AFPM) machine as used in hybrid and electric vehicles. The
adopted approach is based on a hybrid lumped parameter and finite
difference method. The proposed method divides each motor
component into regular elements which are connected together in a
thermal resistance network representing all the physical connections
in all three dimensions. The element shape and size are chosen
according to the component geometry to ensure consistency. The
fluid domain is lumped into one region with averaged heat transfer
parameters connecting it to the solid domain. Some model parameters
are obtained from Computation Fluid Dynamic (CFD) simulation and
empirical data. The hybrid thermal model is described by a set of
coupled linear first order differential equations which is discretised
and solved iteratively to obtain the temperature profile. The
computation involved is low and thus the model is suitable for
transient temperature predictions. The maximum error in temperature
prediction is 3.4% and the mean error is consistently lower than the
mean error due to uncertainty in measurements. The details of the
model development, temperature predictions and suggestions for
design improvements are presented in this paper.
Abstract: Computer technology and the Internet have made a
breakthrough in the existence of data communication. This has
opened a whole new way of implementing steganography to ensure
secure data transfer. Steganography is the fine art of hiding the
information. Hiding the message in the carrier file enables the
deniability of the existence of any message at all. This paper designs
a stego machine to develop a steganographic application to hide data
containing text in a computer video file and to retrieve the hidden
information. This can be designed by embedding text file in a video
file in such away that the video does not loose its functionality using
Least Significant Bit (LSB) modification method. This method
applies imperceptible modifications. This proposed method strives
for high security to an eavesdropper-s inability to detect hidden
information.
Abstract: Traditionally, wind tunnel models are made of metal
and are very expensive. In these years, everyone is looking for ways
to do more with less. Under the right test conditions, a rapid
prototype part could be tested in a wind tunnel. Using rapid prototype
manufacturing techniques and materials in this way significantly
reduces time and cost of production of wind tunnel models. This
study was done of fused deposition modeling (FDM) and their ability
to make components for wind tunnel models in a timely and cost
effective manner. This paper discusses the application of wind tunnel
model configuration constructed using FDM for transonic wind
tunnel testing. A study was undertaken comparing a rapid
prototyping model constructed of FDM Technologies using
polycarbonate to that of a standard machined steel model. Testing
covered the Mach range of Mach 0.3 to Mach 0.75 at an angle-ofattack
range of - 2° to +12°. Results from this study show relatively
good agreement between the two models and rapid prototyping
Method reduces time and cost of production of wind tunnel models.
It can be concluded from this study that wind tunnel models
constructed using rapid prototyping method and materials can be
used in wind tunnel testing for initial baseline aerodynamic database
development.
Abstract: Frequent machine breakdowns, low plant availability and increased overtime are a great threat to a manufacturing plant as they increase operating costs of an industry. The main aim of this study was to improve Overall Equipment Effectiveness (OEE) at a manufacturing company through the implementation of innovative maintenance strategies. A case study approach was used. The paper focuses on improving the maintenance in a manufacturing set up using an innovative maintenance regime mix to improve overall equipment effectiveness. Interviews, reviewing documentation and historical records, direct and participatory observation were used as data collection methods during the research. Usually production is based on the total kilowatt of motors produced per day. The target kilowatt at 91% availability is 75 Kilowatts a day. Reduced demand and lack of raw materials particularly imported items are adversely affecting the manufacturing operations. The company had to reset its targets from the usual figure of 250 Kilowatt per day to mere 75 per day due to lower availability of machines as result of breakdowns as well as lack of raw materials. The price reductions and uncertainties as well as general machine breakdowns further lowered production. Some recommendations were given. For instance, employee empowerment in the company will enhance responsibility and authority to improve and totally eliminate the six big losses. If the maintenance department is to realise its proper function in a progressive, innovative industrial society, then its personnel must be continuously trained to meet current needs as well as future requirements. To make the maintenance planning system effective, it is essential to keep track of all the corrective maintenance jobs and preventive maintenance inspections. For large processing plants these cannot be handled manually. It was therefore recommended that the company implement (Computerised Maintenance Management System) CMMS.
Abstract: Planning capacities when regenerating complex investment goods involves particular challenges in that the planning is subject to a large degree of uncertainty regarding load information. Using information fusion – by applying Bayesian Networks – a method is being developed for forecasting the anticipated expenditures (human labor, tool and machinery utilization, time etc.) for regenerating a good. The generated forecasts then later serve as a tool for planning capacities and ensure a greater stability in the planning processes.
Abstract: Noise level has critical effects on the diagnostic
performance of signal-averaged electrocardiogram (SAECG), because
the true starting and end points of QRS complex would be masked by
the residual noise and sensitive to the noise level. Several studies and
commercial machines have used a fixed number of heart beats
(typically between 200 to 600 beats) or set a predefined noise level
(typically between 0.3 to 1.0 μV) in each X, Y and Z lead to perform
SAECG analysis. However different criteria or methods used to
perform SAECG would cause the discrepancies of the noise levels
among study subjects. According to the recommendations of 1991
ESC, AHA and ACC Task Force Consensus Document for the use of
SAECG, the determinations of onset and offset are related closely to
the mean and standard deviation of noise sample. Hence this study
would try to perform SAECG using consistent root-mean-square
(RMS) noise levels among study subjects and analyze the noise level
effects on SAECG. This study would also evaluate the differences
between normal subjects and chronic renal failure (CRF) patients in
the time-domain SAECG parameters.
The study subjects were composed of 50 normal Taiwanese and 20
CRF patients. During the signal-averaged processing, different RMS
noise levels were adjusted to evaluate their effects on three time
domain parameters (1) filtered total QRS duration (fQRSD), (2) RMS
voltage of the last QRS 40 ms (RMS40), and (3) duration of the low
amplitude signals below 40 μV (LAS40). The study results
demonstrated that the reduction of RMS noise level can increase
fQRSD and LAS40 and decrease the RMS40, and can further increase
the differences of fQRSD and RMS40 between normal subjects and
CRF patients. The SAECG may also become abnormal due to the
reduction of RMS noise level. In conclusion, it is essential to establish
diagnostic criteria of SAECG using consistent RMS noise levels for
the reduction of the noise level effects.
Abstract: Induction machine models used for steady-state and
transient analysis require machine parameters that are usually
considered design parameters or data. The knowledge of induction
machine parameters is very important for Indirect Field Oriented
Control (IFOC). A mismatched set of parameters will degrade the
response of speed and torque control. This paper presents an
improvement approach on rotor time constant adaptation in IFOC for
Induction Machines (IM). Our approach tends to improve the
estimation accuracy of the fundamental model for flux estimation.
Based on the reduced order of the IM model, the rotor fluxes and
rotor time constant are estimated using only the stator currents and
voltages. This reduced order model offers many advantages for real
time identification parameters of the IM.
Abstract: This article concerned with the translation of Quranic
verses to Braille symbols, by using Visual basic program. The
system has the ability to translate the special vibration for the Quran.
This study limited for the (Noun + Scoon) vibrations. It builds on an
existing translation system that combines a finite state machine with
left and right context matching and a set of translation rules. This
allows to translate the Arabic language from text to Braille symbols
after detect the vibration for the Quran verses.
Abstract: In this paper we propose a robust environmental sound classification approach, based on spectrograms features driven from log-Gabor filters. This approach includes two methods. In the first methods, the spectrograms are passed through an appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criteria. The second method uses the same steps but applied only to three patches extracted from each spectrogram.
To investigate the accuracy of the proposed methods, we conduct experiments using a large database containing 10 environmental sound classes. The classification results based on Multiclass Support Vector Machines show that the second method is the most efficient with an average classification accuracy of 89.62 %.
Abstract: The nature of consumer products causes the difficulty
in forecasting the future demands and the accuracy of the forecasts
significantly affects the overall performance of the supply chain
system. In this study, two data mining methods, artificial neural
network (ANN) and support vector machine (SVM), were utilized to
predict the demand of consumer products. The training data used was
the actual demand of six different products from a consumer product
company in Thailand. The results indicated that SVM had a better
forecast quality (in term of MAPE) than ANN in every category of
products. Moreover, another important finding was the margin
difference of MAPE from these two methods was significantly high
when the data was highly correlated.
Abstract: The standard investigational method for obstructive
sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG),
which consists of a simultaneous, usually overnight recording of
multiple electro-physiological signals related to sleep and
wakefulness. This is an expensive, encumbering and not a readily
repeated protocol, and therefore there is need for simpler and easily
implemented screening and detection techniques. Identification of
apnea/hypopnea events in the screening recordings is the key factor
for the diagnosis of OSAS. The analysis of a solely single-lead
electrocardiographic (ECG) signal for OSAS diagnosis, which may
be done with portable devices, at patient-s home, is the challenge of
the last years. A novel artificial neural network (ANN) based
approach for feature extraction and automatic identification of
respiratory events in ECG signals is presented in this paper. A
nonlinear principal component analysis (NLPCA) method was
considered for feature extraction and support vector machine for
classification/recognition. An alternative representation of the
respiratory events by means of Kohonen type neural network is
discussed. Our prospective study was based on OSAS patients of the
Clinical Hospital of Pneumology from Iaşi, Romania, males and
females, as well as on non-OSAS investigated human subjects. Our
computed analysis includes a learning phase based on cross signal
PSG annotation.
Abstract: As more people from non-technical backgrounds
are becoming directly involved with large-scale ontology
development, the focal point of ontology research has shifted
from the more theoretical ontology issues to problems
associated with the actual use of ontologies in real-world,
large-scale collaborative applications. Recently the National
Science Foundation funded a large collaborative ontology
development project for which a new formal ontology model,
the Ontology Abstract Machine (OAM), was developed to
satisfy some unique functional and data representation
requirements. This paper introduces the OAM model and the
related algorithms that enable maintenance of an ontology that
supports node-based user access. The successful software
implementation of the OAM model and its subsequent
acceptance by a large research community proves its validity
and its real-world application value.
Abstract: This paper proposes an innovative methodology for
Acceptance Sampling by Variables, which is a particular category of
Statistical Quality Control dealing with the assurance of products
quality. Our contribution lies in the exploitation of machine learning
techniques to address the complexity and remedy the drawbacks of
existing approaches. More specifically, the proposed methodology
exploits Artificial Neural Networks (ANNs) to aid decision making
about the acceptance or rejection of an inspected sample. For any
type of inspection, ANNs are trained by data from corresponding
tables of a standard-s sampling plan schemes. Once trained, ANNs
can give closed-form solutions for any acceptance quality level and
sample size, thus leading to an automation of the reading of the
sampling plan tables, without any need of compromise with the
values of the specific standard chosen each time. The proposed
methodology provides enough flexibility to quality control engineers
during the inspection of their samples, allowing the consideration of
specific needs, while it also reduces the time and the cost required for
these inspections. Its applicability and advantages are demonstrated
through two numerical examples.
Abstract: The interline power flow controller (IPFC) is one of
the latest generation flexible AC transmission systems (FACTS)
controller used to control power flows of multiple transmission lines.
This paper presents a mathematical model of IPFC, termed as power
injection model (PIM). This model is incorporated in Newton-
Raphson (NR) power flow algorithm to study the power flow control
in transmission lines in which IPFC is placed. A program in
MATLAB has been written in order to extend conventional NR
algorithm based on this model. Numerical results are carried out on a
standard 2 machine 5 bus system. The results without and with IPFC
are compared in terms of voltages, active and reactive power flows to
demonstrate the performance of the IPFC model.
Abstract: One of the main concerns about parallel mechanisms
is the presence of singular points within their workspaces. In singular
positions the mechanism gains or loses one or several degrees of
freedom. It is impossible to control the mechanism in singular
positions. Therefore, these positions have to be avoided. This is a
vital need especially in computer controlled machine tools designed
and manufactured on the basis of parallel mechanisms. This need has
to be taken into consideration when selecting design parameters. A
prerequisite to this is a thorough knowledge about the effect of
design parameters and constraints on singularity. In this paper,
quality condition index was introduced as a criterion for evaluating
singularities of different configurations of a hexapod mechanism
obtainable by different design parameters. It was illustrated that this
method can effectively be employed to obtain the optimum
configuration of hexapod mechanism with the aim of avoiding
singularity within the workspace. This method was then employed to
design the hexapod table of a CNC milling machine.
Abstract: Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification
Abstract: Face recognition is a technique to automatically
identify or verify individuals. It receives great attention in
identification, authentication, security and many more applications.
Diverse methods had been proposed for this purpose and also a lot of
comparative studies were performed. However, researchers could not
reach unified conclusion. In this paper, we are reporting an extensive
quantitative accuracy analysis of four most widely used face
recognition algorithms: Principal Component Analysis (PCA),
Independent Component Analysis (ICA), Linear Discriminant
Analysis (LDA) and Support Vector Machine (SVM) using AT&T,
Sheffield and Bangladeshi people face databases under diverse
situations such as illumination, alignment and pose variations.
Abstract: In this work, bending fatigue life of notched
specimens with various notch geometries and dimensions is
investigated by experiment and Manson-Caffin theoretical method. In
this theoretical method, fatigue life of notched specimens is
calculated using the fatigue life obtained from the experiments for
plain specimens (without notch). Three notch geometries including
∪-shape, ∨-shape and C -shape notches are considered in this
investigation. The experiments are conducted on a rotary bending
Moore machine. The specimens are made of a low carbon steel alloy,
which has wide application in industry. The stress- life curves are
captured for all notched specimen by experiment. The results indicate
that Manson-Caffin analytical method cannot adequately predict
the fatigue life of notched specimen. However, it seems that the
difference between the experiments and Manson-Caffin predictions
can be compensated by a proportional factor.
Abstract: Cognitive models allow predicting some aspects of utility
and usability of human machine interfaces (HMI), and simulating
the interaction with these interfaces. The action of predicting is based
on a task analysis, which investigates what a user is required to do
in terms of actions and cognitive processes to achieve a task. Task
analysis facilitates the understanding of the system-s functionalities.
Cognitive models are part of the analytical approaches, that do not
associate the users during the development process of the interface.
This article presents a study about the evaluation of a human
machine interaction with a contextual assistant-s interface using ACTR
and GOMS cognitive models. The present work shows how these
techniques may be applied in the evaluation of HMI, design and
research by emphasizing firstly the task analysis and secondly the
time execution of the task. In order to validate and support our
results, an experimental study of user performance is conducted at
the DOMUS laboratory, during the interaction with the contextual
assistant-s interface. The results of our models show that the GOMS
and ACT-R models give good and excellent predictions respectively
of users performance at the task level, as well as the object level.
Therefore, the simulated results are very close to the results obtained
in the experimental study.
Abstract: This paper aims to develop a NOx emission model of
an acid gas incinerator using Nelder-Mead least squares support
vector regression (LS-SVR). Malaysia DOE is actively imposing the
Clean Air Regulation to mandate the installation of analytical
instrumentation known as Continuous Emission Monitoring System
(CEMS) to report emission level online to DOE . As a hardware
based analyzer, CEMS is expensive, maintenance intensive and often
unreliable. Therefore, software predictive technique is often
preferred and considered as a feasible alternative to replace the
CEMS for regulatory compliance. The LS-SVR model is built based
on the emissions from an acid gas incinerator that operates in a LNG
Complex. Simulated Annealing (SA) is first used to determine the
initial hyperparameters which are then further optimized based on the
performance of the model using Nelder-Mead simplex algorithm.
The LS-SVR model is shown to outperform a benchmark model
based on backpropagation neural networks (BPNN) in both training
and testing data.