Abstract: One-way functions are functions that are easy to
compute but hard to invert. Their existence is an open conjecture; it
would imply the existence of intractable problems (i.e. NP-problems
which are not in the P complexity class).
If true, the existence of one-way functions would have an impact
on the theoretical framework of physics, in particularly, quantum
mechanics. Such aspect of one-way functions has never been shown
before.
In the present work, we put forward the following.
We can calculate the microscopic state (say, the particle spin in the
z direction) of a macroscopic system (a measuring apparatus
registering the particle z-spin) by the system macroscopic state (the
apparatus output); let us call this association the function F. The
question is: can we compute the function F in the inverse direction?
In other words, can we compute the macroscopic state of the system
through its microscopic state (the preimage F -1)?
In the paper, we assume that the function F is a one-way function.
The assumption implies that at the macroscopic level the Schrödinger
equation becomes unfeasible to compute. This unfeasibility plays a
role of limit of the validity of the linear Schrödinger equation.
Abstract: The study describes chitosan membrane platform
modified with nanostructure pattern which using nanotechnology to
fabricate. The cell-substrate interaction between neuro-2a neuroblasts
cell lines and chitosan membrane (flat, nanostructure and
nanostructure pattern types) was investigated. The adhered
morphology of neuro-2a cells depends on the topography of chitosan
surface. We have found that neuro-2a showed different morphogenesis
when cells adhered on flat and nanostructure chitosan membrane. The
cell projected area of neuro-2a on flat chitosan membrane is larger
than on nanostructure chitosan membrane. In addition, neuro-2a cells
preferred to adhere on flat chitosan surface region than on
nanostructure chitosan membrane to immobilize and differentiation.
The experiment suggests surface topography can be used as a critical
mechanism to isolate group of neuro-2a to a particular rectangle area
on chitosan membrane. Our finding will provide a platform to take
patch clamp to record electrophysiological behavior about neurons in
vitro in the future.
Abstract: This paper develops driver reaction-time models for
car-following analysis based on human factors. The reaction time
was classified as brake-reaction time (BRT) and
acceleration/deceleration reaction time (ADRT). The BRT occurs
when the lead vehicle is barking and its brake light is on, while the
ADRT occurs when the driver reacts to adjust his/her speed using the
gas pedal only. The study evaluates the effect of driver
characteristics and traffic kinematic conditions on the driver reaction
time in a car-following environment. The kinematic conditions
introduced urgency and expectancy based on the braking behaviour
of the lead vehicle at different speeds and spacing. The kinematic
conditions were used for evaluating the BRT and are classified as
normal, surprised, and stationary. Data were collected on a driving
simulator integrated into a real car and included the BRT and ADRT
(as dependent variables) and driver-s age, gender, driving experience,
driving intensity (driving hours per week), vehicle speed, and
spacing (as independent variables). The results showed that there was
a significant difference in the BRT at normal, surprised, and
stationary scenarios and supported the hypothesis that both urgency
and expectancy had significant effects on BRT. Driver-s age, gender,
speed, and spacing were found to be significant variables for the
BRT in all scenarios. The results also showed that driver-s age and
gender were significant variables for the ADRT. The research
presented in this paper is part of a larger project to develop a driversensitive
in-vehicle rear-end collision warning system.
Abstract: In this paper, a clustering algorithm named KHarmonic
means (KHM) was employed in the training of Radial
Basis Function Networks (RBFNs). KHM organized the data in
clusters and determined the centres of the basis function. The popular
clustering algorithms, namely K-means (KM) and Fuzzy c-means
(FCM), are highly dependent on the initial identification of elements
that represent the cluster well. In KHM, the problem can be avoided.
This leads to improvement in the classification performance when
compared to other clustering algorithms. A comparison of the
classification accuracy was performed between KM, FCM and KHM.
The classification performance is based on the benchmark data sets:
Iris Plant, Diabetes and Breast Cancer. RBFN training with the KHM
algorithm shows better accuracy in classification problem.
Abstract: This paper presents a semi-supervised learning algorithm called Iterative-Cross Training (ICT) to solve the Web pages classification problems. We apply Inductive logic programming (ILP) as a strong learner in ICT. The objective of this research is to evaluate the potential of the strong learner in order to boost the performance of the weak learner of ICT. We compare the result with the supervised Naive Bayes, which is the well-known algorithm for the text classification problem. The performance of our learning algorithm is also compare with other semi-supervised learning algorithms which are Co-Training and EM. The experimental results show that ICT algorithm outperforms those algorithms and the performance of the weak learner can be enhanced by ILP system.
Abstract: In the traditional theory of non-uniform torsion the
axial displacement field is expressed as the product of the unit twist
angle and the warping function. The first one, variable along the
beam axis, is obtained by a global congruence condition; the second
one, instead, defined over the cross-section, is determined by solving
a Neumann problem associated to the Laplace equation, as well as for
the uniform torsion problem.
So, as in the classical theory the warping function doesn-t punctually
satisfy the first indefinite equilibrium equation, the principal aim of
this work is to develop a new theory for non-uniform torsion of
beams with axial symmetric cross-section, fully restrained on both
ends and loaded by a constant torque, that permits to punctually
satisfy the previous equation, by means of a trigonometric expansion
of the axial displacement and unit twist angle functions.
Furthermore, as the classical theory is generally applied with good
results to the global and local analysis of ship structures, two beams
having the first one an open profile, the second one a closed section,
have been analyzed, in order to compare the two theories.
Abstract: In the past years a lot of effort has been made in the
field of face detection. The human face contains important features
that can be used by vision-based automated systems in order to
identify and recognize individuals. Face location, the primary step of
the vision-based automated systems, finds the face area in the input
image. An accurate location of the face is still a challenging task.
Viola-Jones framework has been widely used by researchers in order
to detect the location of faces and objects in a given image. Face
detection classifiers are shared by public communities, such as
OpenCV. An evaluation of these classifiers will help researchers to
choose the best classifier for their particular need. This work focuses
of the evaluation of face detection classifiers minding facial
landmarks.
Abstract: In this paper, a mathematical model is proposed to
estimate the dropping probabilities of cellular wireless networks by
queuing handoff instead of reserving guard channels. Usually, prioritized
handling of handoff calls is done with the help of guard channel
reservation. To evaluate the proposed model, gamma inter-arrival and
general service time distributions have been considered. Prevention of
some of the attempted calls from reaching to the switching center due
to electromagnetic propagation failure or whimsical user behaviour
(missed call, prepaid balance etc.), make the inter-arrival time of
the input traffic to follow gamma distribution. The performance is
evaluated and compared with that of guard channel scheme.
Abstract: High voltage generators are being subject to higher
voltage rating and are being designed to operate in harsh conditions.
Stator windings are the main component of generators in which
Electrical, magnetically and thermal stresses remain major failures
for insulation degradation accelerated aging. A large number of
generators failed due to stator winding problems, mainly insulation
deterioration. Insulation degradation assessment plays vital role in the
asset life management. Mostly the stator failure is catastrophic
causing significant damage to the plant. Other than generation loss,
stator failure involves heavy repair or replacement cost. Electro
thermal analysis is the main characteristic for improvement design of
stator slot-s insulation. Dielectric parameters such as insulation
thickness, spacing, material types, geometry of winding and slot are
major design consideration. A very powerful method available to
analyze electro thermal performance is Finite Element Method
(FEM) which is used in this paper. The analysis of various stator coil
and slot configurations are used to design the better dielectric system
to reduce electrical and thermal stresses in order to increase the
power of generator in the same volume of core. This paper describes
the process used to perform classical design and improvement
analysis of stator slot-s insulation.
Abstract: We demonstrate through a sample application, Ebanking,
that the Web Service Modelling Language Ontology component
can be used as a very powerful object-oriented database design
language with logic capabilities. Its conceptual syntax allows the
definition of class hierarchies, and logic syntax allows the definition
of constraints in the database. Relations, which are available for
modelling relations of three or more concepts, can be connected to
logical expressions, allowing the implicit specification of database
content. Using a reasoning tool, logic queries can also be made
against the database in simulation mode.
Abstract: In general, class complexity is measured based on any
one of these factors such as Line of Codes (LOC), Functional points
(FP), Number of Methods (NOM), Number of Attributes (NOA) and so on. There are several new techniques, methods and metrics with
the different factors that are to be developed by the researchers for calculating the complexity of the class in Object Oriented (OO)
software. Earlier, Arockiam et.al has proposed a new complexity measure namely Extended Weighted Class Complexity (EWCC)
which is an extension of Weighted Class Complexity which is proposed by Mishra et.al. EWCC is the sum of cognitive weights of
attributes and methods of the class and that of the classes derived. In EWCC, a cognitive weight of each attribute is considered to be 1.
The main problem in EWCC metric is that, every attribute holds the
same value but in general, cognitive load in understanding the
different types of attributes cannot be the same. So here, we are proposing a new metric namely Attribute Weighted Class Complexity
(AWCC). In AWCC, the cognitive weights have to be assigned for the attributes which are derived from the effort needed to understand
their data types. The proposed metric has been proved to be a better
measure of complexity of class with attributes through the case studies and experiments
Abstract: Discrimination in employment has its wider social and
economic consequences other than mere violating a basic human
right. Discrimination involves treating people differently because of
certain grounds such as race, color, or sex, which results in the
impairment of equality of opportunity and treatment. As an essential
part of promoting decent work, combating discrimination through the
principle of non-discrimination has been established by the
International Labor Organization (ILO) through the Declaration on
Fundamental Principles and Rights at Work 1998. Considering
elimination of discrimination in employment as a core labor standard,
member states are expected to respect, promote and implement it to
their national laws and policies. Being a member state, Malaysia has
to position herself align with this international requirement. The
author discusses the related convention together with Malaysia-s
responses on the matter. At the closing stage, the prospect of
Malaysia is presumed taking into account of the current positions and
reports submitted to the ILO.
Abstract: This study applies the sequential panel selection
method (SPSM) procedure proposed by Chortareas and Kapetanios
(2009) to investigate the time-series properties of energy
consumption in 50 US states from 1963 to 2009. SPSM involves the
classification of the entire panel into a group of stationary series and
a group of non-stationary series to identify how many and which
series in the panel are stationary processes. Empirical results obtained
through SPSM with the panel KSS unit root test developed by Ucar
and Omay (2009) combined with a Fourier function indicate that
energy consumption in all the 50 US states are stationary. The results
of this study have important policy implications for the 50 US states.
Abstract: This paper describes a method to improve the robustness of a face recognition system based on the combination of two compensating classifiers. The face images are preprocessed by the appearance-based statistical approaches such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). LDA features of the face image are taken as the input of the Radial Basis Function Network (RBFN). The proposed approach has been tested on the ORL database. The experimental results show that the LDA+RBFN algorithm has achieved a recognition rate of 93.5%
Abstract: In this study the effect of incorporation of recycled
glass-fibre reinforced polymer (GFRP) waste materials, obtained by
means of milling processes, on mechanical behaviour of polyester
polymer mortars was assessed. For this purpose, different contents of
recycled GFRP waste powder and fibres, with distinct size gradings,
were incorporated into polyester based mortars as sand aggregates
and filler replacements. Flexural and compressive loading capacities
were evaluated and found better than unmodified polymer mortars.
GFRP modified polyester based mortars also show a less brittle
behaviour, with retention of some loading capacity after peak load.
Obtained results highlight the high potential of recycled GFRP waste
materials as efficient and sustainable reinforcement and admixture for
polymer concrete and mortars composites, constituting an emergent
waste management solution.
Abstract: In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (δ, θ, α, β and γ) and the Parseval-s theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.
Abstract: Exploding concentrated underwater charges to
damage underwater structures such as ship hulls is a part of naval
warfare strategies. Adding small amounts of foreign particles (like
clay or silica) of nanosize significantly improves the engineering
properties of the polymers. In the present work the clay in terms 1, 2
and 3 percent by weight was surface treated with a suitable silane
agent. The hybrid nanocomposite was prepared by the hand lay-up
technique. Mathematical regression models have been employed for
theoretical prediction. This will result in considerable savings in terms of project time, effort and cost.
Abstract: The use of High Order Statistics (HOS) analysis is
expected to provide so many candidates of features that can be selected for pattern recognition. More candidates of the feature can
be extracted using simple manipulation through a specific mathematical function prior to the HOS analysis. Feature extraction
method using HOS analysis combined with Difference to the Nth-Power manipulation has been examined in application for Automatic
Modulation Recognition (AMR) to perform scheme recognition of three digital modulation signal, i.e. QPSK-16QAM-64QAM in the
AWGN transmission channel. The simulation results is reported
when the analysis of HOS up to order-12 and the manipulation of Difference to the Nth-Power up to N = 4. The obtained accuracy rate
of AMR using the method of Simple Decision obtained 90% in SNR > 10 dB in its classifier, while using the method of Voted Decision is
96% in SNR > 2 dB.
Abstract: This paper addresses the problems encountered by conventional distance relays when protecting double-circuit transmission lines. The problems arise principally as a result of the mutual coupling between the two circuits under different fault conditions; this mutual coupling is highly nonlinear in nature. An adaptive protection scheme is proposed for such lines based on application of artificial neural network (ANN). ANN has the ability to classify the nonlinear relationship between measured signals by identifying different patterns of the associated signals. One of the key points of the present work is that only current signals measured at local end have been used to detect and classify the faults in the double circuit transmission line with double end infeed. The adaptive protection scheme is tested under a specific fault type, but varying fault location, fault resistance, fault inception angle and with remote end infeed. An improved performance is experienced once the neural network is trained adequately, which performs precisely when faced with different system parameters and conditions. The entire test results clearly show that the fault is detected and classified within a quarter cycle; thus the proposed adaptive protection technique is well suited for double circuit transmission line fault detection & classification. Results of performance studies show that the proposed neural network-based module can improve the performance of conventional fault selection algorithms.
Abstract: The C3 plants are frequently suffering from exposure
to high temperature stress which limits the growth and yield of these
plants. This study seeks to clarify the physiological mechanisms of
heat tolerance in relation to oxidative stress in C3 species. Fifteen C3
species were exposed to prolonged moderately high temperature
stress 36/30°C for 40 days in a growth chamber. Chlorophyll
fluorescence (Fv/Fm) showed great difference among species at 40
days of the stress. The species showed decreases in Fv/Fm and
increases in malondialdehyde (MDA) content under stress condition
as well as negative correlation between Fv/Fm and MDA (r = -0.61*)
at 40 days of the stress. Hydrogen peroxide (H2O2) content before
and after stress in addition to its response under stress showed great
differences among species. The results suggest that the difference in
heat tolerance among C3 species is closely associated with the ability
to suppress oxidative damage but not with the content of reactive
oxygen species (ROS) which is regulated by complex network.