Abstract: This paper presents how the real-time chatter
prevention can be realized by feedback of acoustic cutting signal, and
the efficacy of the proposed adaptive spindle speed tuning algorithm is
verified by intensive experimental simulations. A pair of
microphones, perpendicular to each other, is used to acquire the
acoustic cutting signal resulting from milling chatter. A real-time
feedback control loop is constructed for spindle speed compensation
so that the milling process can be ensured to be within the stability
zone of stability lobe diagram. Acoustic Chatter Signal Index (ACSI)
and Spindle Speed Compensation Strategy (SSCS) are proposed to
quantify the acoustic signal and actively tune the spindle speed
respectively. By converting the acoustic feedback signal into ACSI,
an appropriate Spindle Speed Compensation Rate (SSCR) can be
determined by SSCS based on real-time chatter level or ACSI.
Accordingly, the compensation command, referred to as Added-On
Voltage (AOV), is applied to increase/decrease the spindle motor
speed. By inspection on the precision and quality of the workpiece
surface after milling, the efficacy of the real-time chatter prevention
strategy via acoustic signal feedback is further assured.
Abstract: In this contribution an innovative platform is being
presented that integrates intelligent agents in legacy e-learning environments. It introduces the design and development of a scalable
and interoperable integration platform supporting various assessment agents for e-learning environments. The agents are implemented in
order to provide intelligent assessment services to computational intelligent techniques such as Bayesian Networks and Genetic
Algorithms. The utilization of new and emerging technologies like web services allows integrating the provided services to any web
based legacy e-learning environment.
Abstract: The application of the synchronous dynamic random
access memory (SDRAM) has gone beyond the scope of personal
computers for quite a long time. It comes into hand whenever a big
amount of low price and still high speed memory is needed. Most of
the newly developed stand alone embedded devices in the field of
image, video and sound processing take more and more use of it. The
big amount of low price memory has its trade off – the speed. In
order to take use of the full potential of the memory, an efficient
controller is needed. Efficient stands for maximum random accesses
to the memory both for reading and writing and less area after
implementation. This paper proposes a target device independent
DDR SDRAM pipelined controller and provides performance
comparison with available solutions.
Abstract: Recently, a great number of theoretical frameworks
have been proposed to develop the linkages between knowledge
management (KM) and organizational strategies. However, while
there has been much theorizing and case study in the area, validated
research models integrating KM and information technology
strategies for empirical testing of these theories have been scarce. In
this research, we try to develop a research model for explaining the
relationship between KM strategy and IT strategy and their effects on
performance. Finally, meaningful propositions and conclusions are
derived, and suggestions for future research are proposed and
discussed.
Abstract: Clustering techniques have received attention in many areas including engineering, medicine, biology and data mining. The purpose of clustering is to group together data points, which are close to one another. The K-means algorithm is one of the most widely used techniques for clustering. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called PSO-ACO, for optimally clustering N object into K clusters. The new PSO-ACO algorithm is tested on several data sets, and its performance is compared with those of ACO, PSO and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handing data clustering.
Abstract: An iterative algorithm is proposed and tested in Cournot Game models, which is based on the convergence of sequential best responses and the utilization of a genetic algorithm for determining each player-s best response to a given strategy profile of its opponents. An extra outer loop is used, to address the problem of finite accuracy, which is inherent in genetic algorithms, since the set of feasible values in such an algorithm is finite. The algorithm is tested in five Cournot models, three of which have convergent best replies sequence, one with divergent sequential best replies and one with “local NE traps"[14], where classical local search algorithms fail to identify the Nash Equilibrium. After a series of simulations, we conclude that the algorithm proposed converges to the Nash Equilibrium, with any level of accuracy needed, in all but the case where the sequential best replies process diverges.
Abstract: In this paper, we propose a fuzzy aggregate
production planning (APP) model for blending problem in a brass
factory which is the problem of computing optimal amounts of raw
materials for the total production of several types of brass in a
period. The model has deterministic and imprecise parameters
which follows triangular possibility distributions. The brass casting
APP model can not always be solved by using common approaches
used in the literature. Therefore a mathematical model is presented
for solving this problem. In the proposed model, the Lai and
Hwang-s fuzzy ranking concept is relaxed by using one constraint
instead of three constraints. An application of the brass casting
APP model in a brass factory shows that the proposed model
successfully solves the multi-blend problem in casting process and
determines the optimal raw material purchasing policies.
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: Software reuse can be considered as the most realistic
and promising way to improve software engineering productivity and
quality. Automated assistance for software reuse involves the
representation, classification, retrieval and adaptation of components.
The representation and retrieval of components are important to
software reuse in Component-Based on Software Development
(CBSD). However, current industrial component models mainly focus
on the implement techniques and ignore the semantic information
about component, so it is difficult to retrieve the components that
satisfy user-s requirements. This paper presents a method of business
component retrieval based on specification matching to solve the
software reuse of enterprise information system. First, a business
component model oriented reuse is proposed. In our model, the
business data type is represented as sign data type based on XML,
which can express the variable business data type that can describe the
variety of business operations. Based on this model, we propose
specification match relationships in two levels: business operation
level and business component level. In business operation level, we
use input business data types, output business data types and the
taxonomy of business operations evaluate the similarity between
business operations. In the business component level, we propose five
specification matches between business components. To retrieval
reusable business components, we propose the measure of similarity
degrees to calculate the similarities between business components.
Finally, a business component retrieval command like SQL is
proposed to help user to retrieve approximate business components
from component repository.
Abstract: Lacking an inherent “natural" dissimilarity measure
between objects in categorical dataset presents special difficulties in
clustering analysis. However, each categorical attributes from a given
dataset provides natural probability and information in the sense of
Shannon. In this paper, we proposed a novel method which
heuristically converts categorical attributes to numerical values by
exploiting such associated information. We conduct an experimental
study with real-life categorical dataset. The experiment demonstrates
the effectiveness of our approach.
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: A numerical solution of the initial boundary value
problem of the suspended string vibrating equation with the
particular nonlinear damping term based on the finite difference
scheme is presented in this paper. The investigation of how the
second and third power terms of the nonlinear term affect the
vibration characteristic. We compare the vibration amplitude as a
result of the third power nonlinear damping with the second power
obtained from previous report provided that the same initial shape
and initial velocities are assumed. The comparison results show that
the vibration amplitude is inversely proportional to the coefficient of
the damping term for the third power nonlinear damping case, while
the vibration amplitude is proportional to the coefficient of the
damping term in the second power nonlinear damping case.
Abstract: We proposed the use of a Toda-Rayleigh ring as a
central pattern generator (CPG) for controlling hexapodal robots. We
show that the ring composed of six Toda-Rayleigh units coupled to
the limb actuators reproduces the most common hexapodal gaits. We
provide an electrical circuit implementation of the CPG and test our
theoretical results obtaining fixed gaits. Then we propose a method
of incorporation of the actuator (motor) dynamics in the CPG. With
this approach we close the loop CPG – environment – CPG, thus
obtaining a decentralized model for the leg control that does not
require higher level intervention to the CPG during locomotion in
a nonhomogeneous environments. The gaits generated by the novel
CPG are not fixed, but adapt to the current robot bahvior.
Abstract: The results of an experimental study of the process of
convective and boiling heat transfer in the vessel with stirrer for
smooth and rough ring-shaped pipes are presented. It is established
that creation of two-dimensional artificial roughness on the heated
surface causes the essential (~100%) intensification of convective
heat transfer. In case of boiling the influence of roughness appears on
the initial stage of boiling and in case of fully developed nucleate
boiling there was no intensification of heat transfer. The similitude
equation for calculating convective heat transfer coefficient, which
generalizes well experimental data both for the smooth and the rough
surfaces is proposed.
Abstract: This paper solves the Non Linear Schrodinger
Equation using the Split Step Fourier method for modeling an optical
fiber. The model generates a complex wave of optical pulses and
using the results obtained two graphs namely Loss versus
Wavelength and Dispersion versus Wavelength are generated. Taking
Chromatic Dispersion and Polarization Mode Dispersion losses into
account, the graphs generated are compared with the graphs
formulated by JDS Uniphase Corporation which uses standard values
of dispersion for optical fibers. The graphs generated when compared
with the JDS Uniphase Corporation plots were found to be more or
less similar thus verifying that the model proposed is right.
MATLAB software was used for doing the modeling.
Abstract: In this paper we present an energy efficient match-line
(ML) sensing scheme for high-speed ternary content-addressable
memory (TCAM). The proposed scheme isolates the sensing unit of
the sense amplifier from the large and variable ML capacitance. It
employs feedback in the sense amplifier to successfully detect a
match while keeping the ML voltage swing low. This reduced voltage
swing results in large energy saving. Simulation performed using
130nm 1.2V CMOS logic shows at least 30% total energy saving in
our scheme compared to popular current race (CR) scheme for
similar search speed. In terms of speed, dynamic energy, peak power
consumption and transistor count our scheme also shows better
performance than mismatch-dependant (MD) power allocation
technique which also employs feedback in the sense amplifier.
Additionally, the implementation of our scheme is simpler than CR
or MD scheme because of absence of analog control voltage and
programmable delay circuit as have been used in those schemes.
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.