Abstract: The conjugate gradient optimization algorithm is combined with the modified back propagation algorithm to yield a computationally efficient algorithm for training multilayer perceptron (MLP) networks (CGFR/AG). The computational efficiency is enhanced by adaptively modifying initial search direction as described in the following steps: (1) Modification on standard back propagation algorithm by introducing a gain variation term in the activation function, (2) Calculation of the gradient descent of error with respect to the weights and gains values and (3) the determination of a new search direction by using information calculated in step (2). The performance of the proposed method is demonstrated by comparing accuracy and computation time with the conjugate gradient algorithm used in MATLAB neural network toolbox. The results show that the computational efficiency of the proposed method was better than the standard conjugate gradient algorithm.
Abstract: A Novel fuzzy neural network combining with support vector learning mechanism called support-vector-based fuzzy neural networks (SVBFNN) is proposed. The SVBFNN combine the capability of minimizing the empirical risk (training error) and expected risk (testing error) of support vector learning in high dimensional data spaces and the efficient human-like reasoning of FNN.
Abstract: An alternative approach to the use of Discrete Fourier
Transform (DFT) for Magnetic Resonance Imaging (MRI) reconstruction
is the use of parametric modeling technique. This method
is suitable for problems in which the image can be modeled by
explicit known source functions with a few adjustable parameters.
Despite the success reported in the use of modeling technique as an
alternative MRI reconstruction technique, two important problems
constitutes challenges to the applicability of this method, these are
estimation of Model order and model coefficient determination. In
this paper, five of the suggested method of evaluating the model
order have been evaluated, these are: The Final Prediction Error
(FPE), Akaike Information Criterion (AIC), Residual Variance (RV),
Minimum Description Length (MDL) and Hannan and Quinn (HNQ)
criterion. These criteria were evaluated on MRI data sets based on the
method of Transient Error Reconstruction Algorithm (TERA). The
result for each criterion is compared to result obtained by the use of a
fixed order technique and three measures of similarity were evaluated.
Result obtained shows that the use of MDL gives the highest measure
of similarity to that use by a fixed order technique.
Abstract: Electromagnetic flowmeters with DC excitation are used for a wide range of fluid measurement tasks, but are rarely found in dosing applications with short measurement cycles due to the achievable accuracy. This paper will identify a number of factors that influence the accuracy of this sensor type when used for short-term measurements. Based on these results a new signal-processing algorithm will be described that overcomes the identified problems to some extend. This new method allows principally a higher accuracy of electromagnetic flowmeters with DC excitation than traditional methods.
Abstract: Electricity market activities and a growing demand for electricity have led to heavily stressed power systems. This requires operation of the networks closer to their stability limits. Power system operation is affected by stability related problems, leading to unpredictable system behavior. Voltage stability refers to the ability of a power system to sustain appropriate voltage levels through large and small disturbances. Steady-state voltage stability is concerned with limits on the existence of steady-state operating points for the network. FACTS devices can be utilized to increase the transmission capacity, the stability margin and dynamic behavior or serve to ensure improved power quality. Their main capabilities are reactive power compensation, voltage control and power flow control. Among the FACTS controllers, Static Var Compensator (SVC) provides fast acting dynamic reactive compensation for voltage support during contingency events. In this paper, voltage stability assessment with appropriate representations of tap-changer transformers and SVC is investigated. Integrating both of these devices is the main topic of this paper. Effect of the presence of tap-changing transformers on static VAR compensator controller parameters and ratings necessary to stabilize load voltages at certain values are highlighted. The interrelation between transformer off nominal tap ratios and the SVC controller gains and droop slopes and the SVC rating are found. P-V curves are constructed to calculate loadability margins.
Abstract: An HPLC-UV analytical method was developed to
determine ethylenediaminetetraacetic acid (EDTA) in dairy
wastewater and surface water. The optimizing separation was achieved
by reversed–phase ion-pair liquid chromatography on a C18 column
using methanol as mobile phase solvent, tetrabutylammonium bromide
as the ion-pair reagent in pH 3.3 formate buffer solution at a flow rate
of 0.9 mL min-1 with a UV detector at 265 nm. No interference of Ca,
Mg or NO3
- was detected. Method performance was evaluated in terms
of linearity, repeatability and reproducibility. The method detection
limit was 5 μg L-1. The contents of EDTA in dairy effluents were 72 ~
261 μg L-1 at a large dairy site. A change of EDTA concentration was
observed downstream of the dairy effluent discharge, but this was well
under the predicted no effect concentration for aquatic ecosystem.
Abstract: Network on a chip (NoC) has been proposed as a viable solution to counter the inefficiency of buses in the current VLSI on-chip interconnects. However, as the silicon chip accommodates more transistors, the probability of transient faults is increasing, making fault tolerance a key concern in scaling chips. In packet based communication on a chip, transient failures can corrupt the data packet and hence, undermine the accuracy of data communication. In this paper, we present a comparative analysis of transient fault tolerant techniques including end-to-end, node-by-node, and stochastic communication based on flooding principle.
Abstract: Wireless Sensor Networks consist of small battery
powered devices with limited energy resources. once deployed, the
small sensor nodes are usually inaccessible to the user, and thus
replacement of the energy source is not feasible. Hence, One of the
most important issues that needs to be enhanced in order to improve
the life span of the network is energy efficiency. to overcome this
demerit many research have been done. The clustering is the one of
the representative approaches. in the clustering, the cluster heads
gather data from nodes and sending them to the base station. In this
paper, we introduce a dynamic clustering algorithm using genetic
algorithm. This algorithm takes different parameters into
consideration to increase the network lifetime. To prove efficiency of
proposed algorithm, we simulated the proposed algorithm compared
with LEACH algorithm using the matlab
Abstract: Aiming the application of localized hyperthermia, a
magnetic induction system with new approaches is proposed. The techniques in this system for improving the effectiveness of localized hyperthermia are that using magnetic circuit and the multiple-coil array instead of a giant coil for generating magnetic field. Specially, amorphous metal is adopted as the material of magnetic circuit. Detail
design parameters of hardware are well described. Simulation tool is
employed for this work and experiment result is reported as well.
Abstract: This paper presents a general trainable framework
for fast and robust upright human face and non-human object
detection and verification in static images. To enhance the
performance of the detection process, the technique we develop is
based on the combination of fast neural network (FNN) and
classical neural network (CNN). In FNN, a useful correlation is
exploited to sustain high level of detection accuracy between input
image and the weight of the hidden neurons. This is to enable the
use of Fourier transform that significantly speed up the time
detection. The combination of CNN is responsible to verify the
face region. A bootstrap algorithm is used to collect non human
object, which adds the false detection to the training process of the
human and non-human object. Experimental results on test images
with both simple and complex background demonstrate that the
proposed method has obtained high detection rate and low false
positive rate in detecting both human face and non-human object.
Abstract: Mendelian Disease Genes represent a collection of single points of failure for the various systems they constitute. Such genes have been shown, on average, to encode longer proteins than 'non-disease' proteins. Existing models suggest that this results from the increased likeli-hood of longer genes undergoing mutations. Here, we show that in saturated mutagenesis experiments performed on model organisms, where the likelihood of each gene mutating is one, a similar relationship between length and the probability of a gene being lethal was observed. We thus suggest an extended model demonstrating that the likelihood of a mutated gene to produce a severe phenotype is length-dependent. Using the occurrence of conserved domains, we bring evidence that this dependency results from a correlation between protein length and the number of functions it performs. We propose that protein length thus serves as a proxy for protein cardinality in different networks required for the organism's survival and well-being. We use this example to argue that the collection of Mendelian Disease Genes can, and should, be used to study the rules governing systems vulnerability in living organisms.
Abstract: This paper proposes a fast tree join scheme to provide
seamless multicast handover in the mobile networks based on the Fast
Mobile IPv6 (FMIPv6). In the existing FMIPv6-based multicast
handover scheme, the bi-directional tunnelling or the remote
subscription is employed with the packet forwarding from the previous
access router (AR) to the new AR. In general, the remote subscription
approach is preferred to the bi-directional tunnelling one, since in the
remote subscription scheme we can exploit an optimized multicast
path from a multicast source to many mobile receivers. However, in
the remote subscription scheme, if the tree joining operation takes a
long time, the amount of data packets to be forwarded and buffered for
multicast handover will increase, and thus the corresponding buffer
may overflow, which results in severe packet losses. In order to reduce
these costs associated with packet forwarding and buffering, this paper
proposes the fast join to multicast tree, in which the new AR will join
the multicast tree as fast as possible, so that the new multicast data
packets can also arrive at the new AR, by which the packet forwarding
and buffering costs can be reduced. From numerical analysis, it is
shown that the proposed scheme can give better performance than the
existing FMIPv6-based multicast handover schemes in terms of the
multicast packet delivery costs.
Abstract: Coloured Petri net (CPN) has been widely adopted in various areas in Computer Science, including protocol specification, performance evaluation, distributed systems and coordination in multi-agent systems. It provides a graphical representation of a system and has a strong mathematical foundation for proving various properties. This paper proposes a novel representation of a coloured Petri net using an extension of logic programming called abductive logic programming (ALP), which is purely based on classical logic. Under such a representation, an implementation of a CPN could be directly obtained, in which every inference step could be treated as a kind of equivalence preserved transformation. We would describe how to implement a CPN under such a representation using common meta-programming techniques in Prolog. We call our framework CPN-LP and illustrate its applications in modeling an intelligent agent.
Abstract: A model to identify the lifetime of target tracking
wireless sensor network is proposed. The model is a static clusterbased
architecture and aims to provide two factors. First, it is to
increase the lifetime of target tracking wireless sensor network.
Secondly, it is to enable good localization result with low energy
consumption for each sensor in the network. The model consists of
heterogeneous sensors and each sensing member node in a cluster
uses two operation modes–active mode and sleep mode. The
performance results illustrate that the proposed architecture consumes
less energy and increases lifetime than centralized and dynamic
clustering architectures, for target tracking sensor network.
Abstract: Resource discovery is one of the chief services of a grid. A new approach to discover the provenances in grid through learning automata has been propounded in this article. The objective of the aforementioned resource-discovery service is to select the resource based upon the user-s applications and the mercantile yardsticks that is to say opting for an originator which can accomplish the user-s tasks in the most economic manner. This novel service is submitted in two phases. We proffered an applicationbased categorization by means of an intelligent nerve-prone plexus. The user in question sets his or her application as the input vector of the nerve-prone nexus. The output vector of the aforesaid network limns the appropriateness of any one of the resource for the presented executive procedure. The most scrimping option out of those put forward in the previous stage which can be coped with to fulfill the task in question is picked out. Te resource choice is carried out by means of the presented algorithm based upon the learning automata.
Abstract: In this paper, an efficient wave concept iterative
process (WCIP) with auxiliary Sources is presented for full wave
investigation of an active microwave structure on micro strip
technology. Good agreement between the experimental and
simulation results is observed.
Abstract: In automotive systems almost all steps concerning the
calibration of several control systems, e.g., low idle governor or
boost pressure governor, are made with the vehicle because the timeto-
production and cost requirements on the projects do not allow for
the vehicle analysis necessary to build reliable models. Here is
presented a procedure using parametric and NN (neural network)
models that enables the generation of vehicle system models based
on normal ECU engine control unit) vehicle measurements. These
models are locally valid and permit pre and follow-up calibrations so
that, only the final calibrations have to be done with the vehicle.
Abstract: The learning society has currently transformed from 'wired society' to become 'mobile society' which is facilitated by wireless network. To suit to this new paradigm, m-learning was given birth and rapidly building its prospect to be included in the future curriculum. Research and studies on m-learning spruced up in numerous aspects but there is still scarcity in studies on curriculum design of m-learning. This study is a part of an ongoing bigger study probing into the m-learning curriculum for secondary schools. The paper reports on the first phase of the study which aims to probe into the needs of curriculum design for m-learning at the secondary school level and the researcher adopted the needs analysis method. Data accrued from responses on survey questionnaires based on Lickert-point scale were analyzed statistically. The findings from this preliminary study serve as a basis for m-learning curriculum development for secondary schools.
Abstract: In today-s competitive global business environment,
the concept of supply chain management (SCM) continues to become
increasingly market-oriented, shifting the primary driver of the value
chain from supply to demand. Recent recommendations encourage
researchers to focus investigations on the supply chain process
integration (SCPI) capabilities that integrate a focal firm with its
network of suppliers and business customers to create value for it.
However, theoretical and empirical researches pertaining to the
antecedents and consequences of a focal firm-s SCPI capabilities have
been limited and piecemeal. The purpose of this study is to investigate
the critical determinants and consequences of a focal firm-s SCPI
capabilities. We test our proposed research framework using a sample
of 139 sales managers of manufacturing industries in Taiwan, our
research findings show that (1) both perceived business customer-s
power and focal firm-s market-oriented culture positively influences a
focal firm-s SCPI capabilities, and (2) SCPI capabilities positively
influence a focal firm-s SCM performance, both operational and
strategic benefits. Implications for practitioners and researchers and
suggestions for future research are also addressed in this study.
Abstract: Dehydration of methanol to dimethyl ether (DME)
over a commercial Al2O3 catalyst was studied in an isothermal integral
fixed bed reactor. The experiments were performed on the temperature
interval 513-613 K, liquid hourly space velocity (LHSV) of 0.9-2.1h-1,
pressures between 0.1 and 1.0 MPa. The effect of different operation
conditions on the dehydration of methanol was investigated in a
laboratory scale experiment. A new intrinsic kinetics equation based
on the mechanism of Langmuir-Hinshelwood dissociation adsorption
was developed for the dehydration reaction by fitting the expressions
to the experimental data. An activation energy of 67.21 kJ/mol was
obtained for the catalyst with the best performance. Statistic test
showed that this new intrinsic kinetics equation was acceptable.