Abstract: We present a non standard Euclidean vehicle
routing problem adding a level of clustering, and we revisit the use
of self-organizing maps as a tool which naturally handles such
problems. We present how they can be used as a main operator
into an evolutionary algorithm to address two conflicting
objectives of route length and distance from customers to bus stops
minimization and to deal with capacity constraints. We apply the
approach to a real-life case of combined clustering and vehicle
routing for the transportation of the 780 employees of an
enterprise. Basing upon a geographic information system we
discuss the influence of road infrastructures on the solutions
generated.
Abstract: Shape optimization of the airfoil with high aspect ratio
of long endurance unmanned aerial vehicle (UAV) is performed by the
multi-objective optimization technology coupled with computational
fluid dynamics (CFD). For predicting the aerodynamic characteristics
around the airfoil the high-fidelity Navier-Stokes solver is employed
and SMOGA (Simple Multi-Objective Genetic Algorithm), which is
developed by authors, is used for solving the multi-objective
optimization problem. To obtain the optimal solutions of the design
variable (i.e., sectional airfoil profile, wing taper ratio and sweep) for
high performance of UAVs, both the lift and lift-to-drag ratio are
maximized whereas the pitching moment should be minimized,
simultaneously. It is found that the lift force and lift-to-drag ratio are
linearly dependent and a unique and dominant solution are existed.
However, a trade-off phenomenon is observed between the lift-to-drag
ratio and pitching moment. As the result of optimization, sixty-five
(65) non-dominated Pareto individuals at the cutting edge of design
spaces that is decided by airfoil shapes can be obtained.
Abstract: This paper presents a supervised clustering algorithm,
namely Grid-Based Supervised Clustering (GBSC), which is able to
identify clusters of any shapes and sizes without presuming any
canonical form for data distribution. The GBSC needs no prespecified
number of clusters, is insensitive to the order of the input
data objects, and is capable of handling outliers. Built on the
combination of grid-based clustering and density-based clustering,
under the assistance of the downward closure property of density
used in bottom-up subspace clustering, the GBSC can notably reduce
its search space to avoid the memory confinement situation during its
execution. On two-dimension synthetic datasets, the GBSC can
identify clusters with different shapes and sizes correctly. The GBSC
also outperforms other five supervised clustering algorithms when
the experiments are performed on some UCI datasets.
Abstract: An important step in three-dimensional reconstruction
and computer vision is camera calibration, whose objective is to
estimate the intrinsic and extrinsic parameters of each camera. In this
paper, two linear methods based on the different planes are given. In
both methods, the general plane is used to replace the calibration
object with very good precision. In the first method, after controlling
the camera to undergo five times- translation movements and taking
pictures of the orthogonal planes, a set of linear constraints of the
camera intrinsic parameters is then derived by means of homography
matrix. The second method is to get all camera parameters by taking
only one picture of a given radius circle. experiments on simulated
data and real images,indicate that our method is reasonable and is a
good supplement to camera calibration.
Abstract: Safety Health and Environment Code of Practice (SHE
COP) was developed to help road transportation operators to manage
its operation in a systematic and safe manner. A study was conducted
to determine the effectiveness of SHE COP implementation during
non-OPS period. The objective of the study is to evaluate the
implementations of SHE COP among bus operators during wee hour
operations. The data was collected by completing a set of checklist
after observing the activities during pre departure, during the trip, and
upon arrival. The results show that there are seven widely practiced
SHE COP elements. 22% of the buses have average speed exceeding
the maximum permissible speed on the highways (90 km/h), with
13% of the buses were travelling at the speed of more than 100 km/h.
The statistical analysis shows that there is only one significant
association which relates speeding with prior presence of
enforcement officers.
Abstract: This paper is focused on issues of nonlinear dynamic process modeling and model-based predictive control of a fed-batch sugar crystallization process applying the concept of artificial neural networks as computational tools. The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. A feed forward neural network (FFNN) model of the process is first built as part of the controller structure to predict the process response over a specified (prediction) horizon. The predictions are supplied to an optimization procedure to determine the values of the control action over a specified (control) horizon that minimizes a predefined performance index. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. However, the simulation results demonstrated smooth behavior of the control actions and satisfactory reference tracking.
Abstract: Nowadays, the pace of business change is such that,
increasingly, new functionality has to be realized and reliably
installed in a matter of days, or even hours. Consequently, more and
more business processes are prone to a continuous change. The
objective of the research in progress is to use the MAP model, in a
conceptual modeling method for flexible and adaptive business
process. This method can be used to capture the flexibility
dimensions of a business process; it takes inspiration from
modularity concept in the object oriented paradigm to establish a
hierarchical construction of the BP modeling. Its intent is to provide
a flexible modeling that allows companies to quickly adapt their
business processes.
Abstract: The objective of this paper, is to apply support vector machine (SVM) approach for the classification of cancerous and normal regions of prostate images. Three kinds of textural features are extracted and used for the analysis: parameters of the Gauss- Markov random field (GMRF), correlation function and relative entropy. Prostate images are acquired by the system consisting of a microscope, video camera and a digitizing board. Cross-validated classification over a database of 46 images is implemented to evaluate the performance. In SVM classification, sensitivity and specificity of 96.2% and 97.0% are achieved for the 32x32 pixel block sized data, respectively, with an overall accuracy of 96.6%. Classification performance is compared with artificial neural network and k-nearest neighbor classifiers. Experimental results demonstrate that the SVM approach gives the best performance.
Abstract: The evolution of current modeling specifications gives rise to the problem of generating automated test cases from a variety of application tools. Past endeavours on behavioural testing of UML statecharts have not systematically leveraged the potential of existing graph theory for testing of objects. Therefore there exists a need for a simple, tool-independent, and effective method for automatic test generation. An architecture, codenamed ACUTE-J (Automated stateChart Unit Testing Engine for Java), for automating the unit test generation process is presented. A sequential approach for converting UML statechart diagrams to JUnit test classes is described, with the application of existing graph theory. Research byproducts such as a universal XML Schema and API for statechart-driven testing are also proposed. The result from a Java implementation of ACUTE-J is discussed in brief. The Chinese Postman algorithm is utilised as an illustration for a run-through of the ACUTE-J architecture.
Abstract: Restructured electricity markets may provide
opportunities for producers to exercise market power maintaining
prices in excess of competitive levels. In this paper an oligopolistic
market is presented that all Generation Companies (GenCos) bid in a
Cournot model. Genetic algorithm (GA) is applied to obtain
generation scheduling of each GenCo as well as hourly market
clearing prices (MCP). In order to consider network constraints a
multiperiod framework is presented to simulate market clearing
mechanism in which the behaviors of market participants are
modelled through piecewise block curves. A mixed integer linear
programming (MILP) is employed to solve the problem. Impacts of
market clearing process on participants- characteristic and final
market prices are presented. Consequently, a novel multi-objective
model is addressed for security constrained optimal bidding strategy
of GenCos. The capability of price-maker GenCos to alter MCP is
evaluated through introducing an effective-supply curve. In addition,
the impact of exercising market power on the variation of market
characteristics as well as GenCos scheduling is studied.
Abstract: Polyurethane foams (PUF) has been prepared from
vegetable; soybean based polyols. They were characterized into
flexible and semi rigid polyurethane foam. This work is directed to
production of flexible polyurethane foams by a process involving the
reaction of mixture of 2,4- and 2,6-Toluene di Isocyanate isomers,
with portion of to blends of soy polyols with petroleum polyol in the
presence of other ingredients such as blowing agents, silicone
surfactants and accelerating agents. Additon of chain extender
improves the property then further decreases the properties on further
addition of the same. The objective of this work was to study the
effect of chain extender and role of phosphoric acid catalyst to the
final properties and correlate the morphology image with mechanical
properties of these foams.
Abstract: The objective of the research is to study and compare
response surface designs: Central composite designs (CCD), Box-
Behnken designs (BBD), Small composite designs (SCD), Hybrid
designs, and Uniform shell designs (USD) over sets of reduced models
when the design is in a spherical region for 3 and 4 design variables.
The two optimality criteria ( D and G ) are considered which larger
values imply a better design. The comparison of design optimality
criteria of the response surface designs across the full second order
model and sets of reduced models for 3 and 4 factors based on the
two criteria are presented.
Abstract: In this paper we present the algorithm which allows
us to have an object tracking close to real time in Full HD videos.
The frame rate (FR) of a video stream is considered to be between
5 and 30 frames per second. The real time track building will be
achieved if the algorithm can follow 5 or more frames per second. The
principle idea is to use fast algorithms when doing preprocessing to
obtain the key points and track them after. The procedure of matching
points during assignment is hardly dependent on the number of points.
Because of this we have to limit pointed number of points using the
most informative of them.
Abstract: This paper presents a new method which applies an
artificial bee colony algorithm (ABC) for capacitor placement in
distribution systems with an objective of improving the voltage profile
and reduction of power loss. The ABC algorithm is a new population
based meta heuristic approach inspired by intelligent foraging behavior
of honeybee swarm. The advantage of ABC algorithm is that
it does not require external parameters such as cross over rate and
mutation rate as in case of genetic algorithm and differential evolution
and it is hard to determine these parameters in prior. The other
advantage is that the global search ability in the algorithm is implemented
by introducing neighborhood source production mechanism
which is a similar to mutation process. To demonstrate the validity
of the proposed algorithm, computer simulations are carried out on
69-bus system and compared the results with the other approach
available in the literature. The proposed method has outperformed the
other methods in terms of the quality of solution and computational
efficiency.
Abstract: In this paper, multi-processors job shop scheduling problems are solved by a heuristic algorithm based on the hybrid of priority dispatching rules according to an ant colony optimization algorithm. The objective function is to minimize the makespan, i.e. total completion time, in which a simultanous presence of various kinds of ferons is allowed. By using the suitable hybrid of priority dispatching rules, the process of finding the best solution will be improved. Ant colony optimization algorithm, not only promote the ability of this proposed algorithm, but also decreases the total working time because of decreasing in setup times and modifying the working production line. Thus, the similar work has the same production lines. Other advantage of this algorithm is that the similar machines (not the same) can be considered. So, these machines are able to process a job with different processing and setup times. According to this capability and from this algorithm evaluation point of view, a number of test problems are solved and the associated results are analyzed. The results show a significant decrease in throughput time. It also shows that, this algorithm is able to recognize the bottleneck machine and to schedule jobs in an efficient way.
Abstract: Economic Load Dispatch (ELD) is a method of determining
the most efficient, low-cost and reliable operation of a power
system by dispatching available electricity generation resources to
supply load on the system. The primary objective of economic
dispatch is to minimize total cost of generation while honoring
operational constraints of available generation resources. In this paper
an intelligent water drop (IWD) algorithm has been proposed to
solve ELD problem with an objective of minimizing the total cost of
generation. Intelligent water drop algorithm is a swarm-based natureinspired
optimization algorithm, which has been inspired from natural
rivers. A natural river often finds good paths among lots of possible
paths in its ways from source to destination and finally find almost
optimal path to their destination. These ideas are embedded into
the proposed algorithm for solving economic load dispatch problem.
The main advantage of the proposed technique is easy is implement
and capable of finding feasible near global optimal solution with
less computational effort. In order to illustrate the effectiveness of
the proposed method, it has been tested on 6-unit and 20-unit test
systems with incremental fuel cost functions taking into account the
valve point-point loading effects. Numerical results shows that the
proposed method has good convergence property and better in quality
of solution than other algorithms reported in recent literature.
Abstract: The only relevant basis for the design of an educational application are objectives of learning for the content area. This study analyses the process in which the real – not only the formal – objectives could work as the starting point for the construction of an educational game. The application context is the education of perioperative nursing. The process is based on the panel discussions of nursing teachers. In the panels, the teachers elaborated the objectives. The transcribed discussions were analysed in terms of the conceptions of learning and teaching of perioperative nursing. The outcome of the study is first the elaborated objectives, which will be used in the implementation of an educational game for the needs of pre-, intra and post-operative nursing skills learning. Second, the study shows that different views of learning are necessary to be understood in order to design an appropriate educational application.
Abstract: Grid computing provides a virtual framework for
controlled sharing of resources across institutional boundaries.
Recently, trust has been recognised as an important factor for
selection of optimal resources in a grid. We introduce a new method
that provides a quantitative trust value, based on the past interactions
and present environment characteristics. This quantitative trust value
is used to select a suitable resource for a job and eliminates run time
failures arising from incompatible user-resource pairs. The proposed
work will act as a tool to calculate the trust values of the various
components of the grid and there by improves the success rate of the
jobs submitted to the resource on the grid. The access to a resource
not only depend on the identity and behaviour of the resource but
also upon its context of transaction, time of transaction, connectivity
bandwidth, availability of the resource and load on the resource. The
quality of the recommender is also evaluated based on the accuracy
of the feedback provided about a resource. The jobs are submitted for
execution to the selected resource after finding the overall trust value
of the resource. The overall trust value is computed with respect to
the subjective and objective parameters.
Abstract: In this paper an ant colony optimization algorithm is
developed to solve the permutation flow shop scheduling problem. In
the permutation flow shop scheduling problem which has been vastly
studied in the literature, there are a set of m machines and a set of n
jobs. All the jobs are processed on all the machines and the sequence
of jobs being processed is the same on all the machines. Here this
problem is optimized considering two criteria, makespan and total
flow time. Then the results are compared with the ones obtained by
previously developed algorithms. Finally it is visible that our
proposed approach performs best among all other algorithms in the
literature.
Abstract: Magetan area is going to be the object of this research
which is located in East Java, Indonesia. The data were obtained
from 270 civil servants working at the Magetan District government.
The data were analyzed using the Structural Equation Modeling with
Partial Least Square program. The research showed the following
findings: (1) job motivation variable has a positive and significant
effect on organizational citizenship behavior (OCB); (2) work
environment has positive and significant effect on OCB; (3)
leadership variable has positive and significant effect on OCB; (4)
job motivation variable has no significant effect on job satisfaction;
(5) work environment variable has no significant effect on job
satisfaction; (6) leadership variable has no significant effect on job
satisfaction; (7) OCB is positively and significantly associated with
job satisfaction; (8) job satisfaction variable is positively and
significantly correlated with quality of public service at the Magetan
District government.