Abstract: This paper presents modeling and optimization of two NP-hard problems in flexible manufacturing system (FMS), part type selection problem and loading problem. Due to the complexity and extent of the problems, the paper was split into two parts. The first part of the papers has discussed the modeling of the problems and showed how the real coded genetic algorithms (RCGA) can be applied to solve the problems. This second part discusses the effectiveness of the RCGA which uses an array of real numbers as chromosome representation. The novel proposed chromosome representation produces only feasible solutions which minimize a computational time needed by GA to push its population toward feasible search space or repair infeasible chromosomes. The proposed RCGA improves the FMS performance by considering two objectives, maximizing system throughput and maintaining the balance of the system (minimizing system unbalance). The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could reach near optimum solutions in a reasonable amount of time.
Abstract: The ability of UML to handle the modeling process of complex industrial software applications has increased its popularity to the extent of becoming the de-facto language in serving the design purpose. Although, its rich graphical notation naturally oriented towards the object-oriented concept, facilitates the understandability, it hardly successes to report all domainspecific aspects in a satisfactory way. OCL, as the standard language for expressing additional constraints on UML models, has great potential to help improve expressiveness. Unfortunately, it suffers from a weak formalism due to its poor semantic resulting in many obstacles towards the build of tools support and thus its application in the industry field. For this reason, many researches were established to formalize OCL expressions using a more rigorous approach. Our contribution join this work in a complementary way since it focuses specifically on OCL predefined properties which constitute an important part in the construction of OCL expressions. Using formal methods, we mainly succeed in expressing rigorously OCL predefined functions.
Abstract: Interpretation of aerial images is an important task in
various applications. Image segmentation can be viewed as the essential
step for extracting information from aerial images. Among many
developed segmentation methods, the technique of clustering has been
extensively investigated and used. However, determining the number
of clusters in an image is inherently a difficult problem, especially
when a priori information on the aerial image is unavailable. This
study proposes a support vector machine approach for clustering
aerial images. Three cluster validity indices, distance-based index,
Davies-Bouldin index, and Xie-Beni index, are utilized as quantitative
measures of the quality of clustering results. Comparisons on the
effectiveness of these indices and various parameters settings on the
proposed methods are conducted. Experimental results are provided
to illustrate the feasibility of the proposed approach.
Abstract: The number of features required to represent an image
can be very huge. Using all available features to recognize objects
can suffer from curse dimensionality. Feature selection and
extraction is the pre-processing step of image mining. Main issues in
analyzing images is the effective identification of features and
another one is extracting them. The mining problem that has been
focused is the grouping of features for different shapes. Experiments
have been conducted by using shape outline as the features. Shape
outline readings are put through normalization and dimensionality
reduction process using an eigenvector based method to produce a
new set of readings. After this pre-processing step data will be
grouped through their shapes. Through statistical analysis, these
readings together with peak measures a robust classification and
recognition process is achieved. Tests showed that the suggested
methods are able to automatically recognize objects through their
shapes. Finally, experiments also demonstrate the system invariance
to rotation, translation, scale, reflection and to a small degree of
distortion.
Abstract: Active Vibration Control (AVC) is an important
problem in structures. One of the ways to tackle this problem is to
make the structure smart, adaptive and self-controlling. The objective
of active vibration control is to reduce the vibration of a system by
automatic modification of the system-s structural response. This
paper features the modeling and design of a Periodic Output
Feedback (POF) control technique for the active vibration control of
a flexible Timoshenko cantilever beam for a multivariable case with
2 inputs and 2 outputs by retaining the first 2 dominant vibratory
modes using the smart structure concept. The entire structure is
modeled in state space form using the concept of piezoelectric
theory, Timoshenko beam theory, Finite Element Method (FEM) and
the state space techniques. Simulations are performed in MATLAB.
The effect of placing the sensor / actuator at 2 finite element
locations along the length of the beam is observed. The open loop
responses, closed loop responses and the tip displacements with and
without the controller are obtained and the performance of the smart
system is evaluated for active vibration control.
Abstract: The Comparison analysis of the Wald-s and Bayestype sequential methods for testing hypotheses is offered. The merits of the new sequential test are: universality which consists in optimality (with given criteria) and uniformity of decision-making regions for any number of hypotheses; simplicity, convenience and uniformity of the algorithms of their realization; reliability of the obtained results and an opportunity of providing the errors probabilities of desirable values. There are given the Computation results of concrete examples which confirm the above-stated characteristics of the new method and characterize the considered methods in regard to each other.
Abstract: Computerized alarm systems have been applied
increasingly to nuclear power plants. For existing plants, an add-on
computer alarm system is often installed to the control rooms. Alarm
avalanches during the plant transients are major problems with the
alarm systems in nuclear power plants. Computerized alarm systems
can process alarms to reduce the number of alarms during the plant
transients. This paper describes various alarm processing methods, an
alarm cause tracking function, and various alarm presentation schemes
to show alarm information to the operators effectively which are
considered during the development of several computerized alarm
systems for Korean nuclear power plants and are found to be helpful to
the operators.
Abstract: A model of vortex wake is suggested to determine the
induced power during animal hovering flight. The wake is modeled
by a series of equi-spaced rigid rectangular vortex plates, positioned
horizontally and moving vertically downwards with identical speeds;
each plate is generated during powering of the functionally wing
stroke. The vortex representation of the wake considered in the
current theory allows a considerable loss of momentum to occur. The
current approach accords well with the nature of the wingbeat since it
considers the unsteadiness in the wake as an important fluid
dynamical characteristic. Induced power in hovering is calculated as
the aerodynamic power required to generate the vortex wake system.
Specific mean induced power to mean wing tip velocity ratio is
determined by solely the normal spacing parameter (f) for a given
wing stroke amplitude. The current theory gives much higher specific
induced power estimate than anticipated by classical methods.
Abstract: In this paper, we have proposed a Haar wavelet quasilinearization
method to solve the well known Blasius equation. The
method is based on the uniform Haar wavelet operational matrix
defined over the interval [0, 1]. In this method, we have proposed the
transformation for converting the problem on a fixed computational
domain. The Blasius equation arises in the various boundary layer
problems of hydrodynamics and in fluid mechanics of laminar
viscous flows. Quasi-linearization is iterative process but our
proposed technique gives excellent numerical results with quasilinearization
for solving nonlinear differential equations without any
iteration on selecting collocation points by Haar wavelets. We have
solved Blasius equation for 1≤α ≤ 2 and the numerical results are
compared with the available results in literature. Finally, we
conclude that proposed method is a promising tool for solving the
well known nonlinear Blasius equation.
Abstract: The complex hybrid and nonlinear nature of many processes that are met in practice causes problems with both structure modelling and parameter identification; therefore, obtaining a model that is suitable for MPC is often a difficult task. The basic idea of this paper is to present an identification method for a piecewise affine (PWA) model based on a fuzzy clustering algorithm. First we introduce the PWA model. Next, we tackle the identification method. We treat the fuzzy clustering algorithm, deal with the projections of the fuzzy clusters into the input space of the PWA model and explain the estimation of the parameters of the PWA model by means of a modified least-squares method. Furthermore, we verify the usability of the proposed identification approach on a hybrid nonlinear batch reactor example. The result suggest that the batch reactor can be efficiently identified and thus formulated as a PWA model, which can eventually be used for model predictive control purposes.
Abstract: This paper presents kinematic and dynamic analysis of a novel 8-DOF hybrid robot manipulator. The hybrid robot manipulator under consideration consists of a parallel robot which
is followed by a serial mechanism. The parallel mechanism has three translational DOF, and the serial mechanism has five DOF so that the overall degree of freedom is eight. The introduced
manipulator has a wide workspace and a high capability to reduce
the actuating energy. The inverse and forward kinematic solutions are described in closed form. The theoretical results are verified by
a numerical example. Inverse dynamic analysis of the robot is presented by utilizing the Iterative Newton-Euler and Lagrange dynamic formulation methods. Finally, for performing a multi-step
arc welding process, results have indicated that the introduced manipulator is highly capable of reducing the actuating energy.
Abstract: In this paper, we seek to determine one reasonable
local hub port and optimal routes for a containership fleet,
performing pick-ups and deliveries, between the hub and spoke ports
in a same region. The relationship between a hub port, and traffic in
feeder lines is analyzed. A new network planning method is proposed,
an integrated hub port location and route design, a capacitated vehicle
routing problem with pick-ups, deliveries and time deadlines are
formulated and solved using an improved genetic algorithm for
positioning the hub port and establishing routes for a containership
fleet. Results on the performance of the algorithm and the feasibility
of the approach show that a relatively small fleet of containerships
could provide efficient services within deadlines.
Abstract: In this paper, fluid flow patterns of steady incompressible flow inside shear driven cavity are studied. The numerical simulations are conducted by using lattice Boltzmann method (LBM) for different Reynolds numbers. In order to simulate the flow, derivation of macroscopic hydrodynamics equations from the continuous Boltzmann equation need to be performed. Then, the numerical results of shear-driven flow inside square and triangular cavity are compared with results found in literature review. Present study found that flow patterns are affected by the geometry of the cavity and the Reynolds numbers used.
Abstract: The aim of our work is to study phase composition,
particle size and magnetic response of Fe2O3/TiO2 nanocomposites
with respect to the final annealing temperature. Those nanomaterials
are considered as smart catalysts, separable from a liquid/gaseous
phase by applied magnetic field. The starting product was obtained
by an ecologically acceptable route, based on heterogeneous
precipitation of the TiO2 on modified g-Fe2O3 nanocrystals dispersed
in water. The precursor was subsequently annealed on air at
temperatures ranging from 200 oC to 900 oC. The samples were
investigated by synchrotron X-ray powder diffraction (S-PXRD),
magnetic measurements and Mössbauer spectroscopy. As evidenced
by S-PXRD and Mössbauer spectroscopy, increasing the annealing
temperature causes evolution of the phase composition from
anatase/maghemite to rutile/hematite, finally above 700 oC the
pseudobrookite (Fe2TiO5) also forms. The apparent particle size of
the various Fe2O3/TiO2 phases has been determined from the highquality
S-PXRD data by using two different approaches: the Rietveld
refinement and the Debye method. Magnetic response of the samples
is discussed in considering the phase composition and the particle
size.
Abstract: As the increase of intraoral acidity due to ingestion of sweet foods and acidic beverages usually bring forth a dental caries and a erosion, the measurement of intraoral pH is essential in the study of oral environment. The indwelling intraoral pH telemetry for lasting longer than 24 hours in the mouth was developed to overcome the limits of conventional wire electrode method previously used for salivary and plaque pH measurement, and to assess its effectiveness.
Abstract: Fourier transform infrared (FT-IR) spectroscopic imaging
is an emerging technique that provides both chemically and
spatially resolved information. The rich chemical content of data
may be utilized for computer-aided determinations of structure and
pathologic state (cancer diagnosis) in histological tissue sections for
prostate cancer. FT-IR spectroscopic imaging of prostate tissue has
shown that tissue type (histological) classification can be performed to
a high degree of accuracy [1] and cancer diagnosis can be performed
with an accuracy of about 80% [2] on a microscopic (≈ 6μm)
length scale. In performing these analyses, it has been observed
that there is large variability (more than 60%) between spectra from
different points on tissue that is expected to consist of the same
essential chemical constituents. Spectra at the edges of tissues are
characteristically and consistently different from chemically similar
tissue in the middle of the same sample. Here, we explain these
differences using a rigorous electromagnetic model for light-sample
interaction. Spectra from FT-IR spectroscopic imaging of chemically
heterogeneous samples are different from bulk spectra of individual
chemical constituents of the sample. This is because spectra not
only depend on chemistry, but also on the shape of the sample.
Using coupled wave analysis, we characterize and quantify the nature
of spectral distortions at the edges of tissues. Furthermore, we
present a method of performing histological classification of tissue
samples. Since the mid-infrared spectrum is typically assumed to
be a quantitative measure of chemical composition, classification
results can vary widely due to spectral distortions. However, we
demonstrate that the selection of localized metrics based on chemical
information can make our data robust to the spectral distortions
caused by scattering at the tissue boundary.
Abstract: A data cutting and sorting method (DCSM) is proposed
to optimize the performance of data mining. DCSM reduces the
calculation time by getting rid of redundant data during the data
mining process. In addition, DCSM minimizes the computational units
by splitting the database and by sorting data with support counts. In the
process of searching for the relationship between metabolic syndrome
and lifestyles with the health examination database of an electronics
manufacturing company, DCSM demonstrates higher search
efficiency than the traditional Apriori algorithm in tests with different
support counts.
Abstract: In this paper, we present a comparative study between two computer vision systems for objects recognition and tracking, these algorithms describe two different approach based on regions constituted by a set of pixels which parameterized objects in shot sequences. For the image segmentation and objects detection, the FCM technique is used, the overlapping between cluster's distribution is minimized by the use of suitable color space (other that the RGB one). The first technique takes into account a priori probabilities governing the computation of various clusters to track objects. A Parzen kernel method is described and allows identifying the players in each frame, we also show the importance of standard deviation value research of the Gaussian probability density function. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion.
Abstract: When reconstructing a scenario, it is necessary to
know the structure of the elements present on the scene to have an
interpretation. In this work we link 3D scenes reconstruction to
evolutionary algorithms through the vision stereo theory. We
consider vision stereo as a method that provides the reconstruction of
a scene using only a couple of images of the scene and performing
some computation. Through several images of a scene, captured from
different positions, vision stereo can give us an idea about the threedimensional
characteristics of the world. Vision stereo usually
requires of two cameras, making an analogy to the mammalian vision
system. In this work we employ only a camera, which is translated
along a path, capturing images every certain distance. As we can not
perform all computations required for an exhaustive reconstruction,
we employ an evolutionary algorithm to partially reconstruct the
scene in real time. The algorithm employed is the fly algorithm,
which employ “flies" to reconstruct the principal characteristics of
the world following certain evolutionary rules.
Abstract: This paper reviews the objectives, methods and results of previous studies on biodrying of solid waste in several countries. Biodrying of solid waste is a novel technology in developing countries such as in Malaysia where high moisture content in organic waste makes the segregation process for recycling purposes complicated and diminishes the calorific value for the use of fuel source. In addition, the high moisture content also encourages the breeding of vectors and disease-bearing animals. From the laboratory results, the average moisture content of organic waste, paper, plastics and metals are 58.17%, 37.93%, 29.79% and 1.03% respectively for UKM campus. Biodrying of solid waste is a simple method of waste treatment as well as a cost-efficient technology to dry the solid waste. The process depends on temperature monitoring and air flow control along with the natural biodegradable process of organic waste. This review shows that the biodrying of solid waste method has high potential in treatment and recycling of solid waste, be useful for biodrying study and implementation in Malaysia.