Abstract: This paper presents a systematic approach for
designing Static Synchronous Series Compensator (SSSC) based
supplementary damping controllers for damping low frequency
oscillations in a single-machine infinite-bus power system. The
design problem of the proposed controller is formulated as an
optimization problem and RCGA is employed to search for optimal
controller parameters. By minimizing the time-domain based
objective function, in which the deviation in the oscillatory rotor
speed of the generator is involved; stability performance of the
system is improved. Simulation results are presented and compared
with a conventional method of tuning the damping controller
parameters to show the effectiveness and robustness of the proposed
design approach.
Abstract: In this paper, a new alignment method based on the particle swarm optimization (PSO) technique is presented. The PSO algorithm is used for locating the optimal coupling position with the highest optical power with three-degrees of freedom alignment. This algorithm gives an interesting results without a need to go thru the complex mathematical modeling of the alignment system. The proposed algorithm is validated considering practical tests considering the alignment of two Single Mode Fibers (SMF) and the alignment of SMF and PCF fibers.
Abstract: From a long time age, human beings have chosen their
place of residence and comfort so that those places would have
relatively ideal natural and climatic conditions. For this reason, from
the beginning, the civilizations have been formed in the susceptible
natural regions such as Mesopotamia in Iran and Nile coasts in
Egypt. Also, the core of human density has been made in the form of
an oasis in the deserts.
Regarding the formation and combination of the native
architecture in different regions of Iran, we find that different
properties of these climates have affected frequently the formation of
cities and the architectural combinations of these regions. Thus, the
precise determinations of climatic areas and attaining the climatic
properties of different regions have a great deal of importance in
presenting appropriate and compatible-with-climate designs.
Abstract: This study suggests how an order-receiving company
can avoid disclosing schedule information on unit tasks to the
order-placing company when carrying out a collaborative project on
the value chain in an order-oriented industry. Specifically, it suggests
methods for keeping schedule information confidential, and
categorizes potential situations by inter-task dependency. Lastly, an
approach to select the most optimal non-disclosure method is
discussed. With the methods for not disclosing work-related
information suggested in the study, order-receiving companies can
logically deal with political issues relating to the question of whether
or not to disclose information upon the execution of a collaborative
project in cooperation with an order-placing firm. Moreover,
order-placing companies can monitor undistorted information, while
respecting the legitimate rights of an order-receiving company.
Therefore, it is fair to say that the suggestions made in this study will
contribute to the smooth operation of collaborative intercompany
projects.
Abstract: The Resource-Constrained Project Scheduling
Problem (RCPSP) is concerned with single-item or small batch
production where limited resources have to be allocated to dependent
activities over time. Over the past few decades, a lot of work has
been made with the use of optimal solution procedures for this basic
problem type and its extensions. Brucker and Knust[1] discuss, how
timetabling problems can be modeled as a RCPSP. Authors discuss
high school timetabling and university course timetabling problem as
an example. We have formulated two mathematical formulations of
course timetabling problem in a new way which are the prototype of
single-mode RCPSP. Our focus is to show, how course timetabling
problem can be transformed into RCPSP. We solve this
transformation model with genetic algorithm.
Abstract: In intensity modulated radiation therapy (IMRT)
treatment planning, beam angles are usually preselected on the basis of
experience and intuition. Therefore, getting an appropriate beam
configuration needs a very long time. Based on the present situation,
the paper puts forward beam orientation optimization using ant colony
optimization (ACO). We use ant colony optimization to select the
beam configurations, after getting the beam configuration using
Conjugate Gradient (CG) algorithm to optimize the intensity profiles.
Combining with the information of the effect of pencil beam, we can
get the global optimal solution accelerating. In order to verify the
feasibility of the presented method, a simulated and clinical case was
tested, compared with dose-volume histogram and isodose line
between target area and organ at risk. The results showed that the
effect was improved after optimizing beam configurations. The
optimization approach could make treatment planning meet clinical
requirements more efficiently, so it had extensive application
perspective.
Abstract: Many organisations are nowadays interested to adopt
lean manufacturing strategy that would enable them to compete in
this competitive globalisation market. In this respect, it is necessary
to assess the implementation of lean manufacturing in different
organisations so that the important best practices can be identified.
This paper describes the development of key areas which will be
used to assess the adoption and implementation of lean
manufacturing practices. There are some key areas developed to
evaluate and reduce the most optimal projects so as to enhance their
production efficiency and increase the purpose of the economic
benefits of the manufacturing unit.
Lean manufacturing is becoming lean enterprise by treating its
customers and suppliers as partners. This gives the extra edge in
today-s cost and time competitive markets. The organisation is
becoming strong in all the conventional competition points. They are
Price, Quality and Delivery. Lean enterprise owners can deliver high
quality products quickly, with low price.
Abstract: In this paper, a Smart Home Service Robot, McBot II,
which performs mess-cleanup function etc. in house, is designed much
more optimally than other service robots. It is newly developed in
much more practical system than McBot I which we had developed
two years ago. One characteristic attribute of mobile platforms
equipped with a set of dependent wheels is their omni- directionality
and the ability to realize complex translational and rotational
trajectories for agile navigation in door. An accurate coordination of
steering angle and spinning rate of each wheel is necessary for a
consistent motion. This paper develops trajectory controller of
3-wheels omni-directional mobile robot using fuzzy azimuth estimator.
A specialized anthropomorphic robot manipulator which can be
attached to the housemaid robot McBot II, is developed in this paper.
This built-in type manipulator consists of both arms with 3 DOF
(Degree of Freedom) each and both hands with 3 DOF each. The
robotic arm is optimally designed to satisfy both the minimum
mechanical size and the maximum workspace. Minimum mass and
length are required for the built-in cooperated-arms system. But that
makes the workspace so small. This paper proposes optimal design
method to overcome the problem by using neck joint to move the arms
horizontally forward/backward and waist joint to move them
vertically up/down. The robotic hand, which has two fingers and a
thumb, is also optimally designed in task-based concept. Finally, the
good performance of the developed McBot II is confirmed through
live tests of the mess-cleanup task.
Abstract: Transmission network expansion planning (TNEP) is an important component of power system planning that its task is to minimize the network construction and operational cost while satisfying the demand increasing, imposed technical and economic conditions. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, the lines adequacy rate has not been studied after the planning horizon, i.e. when the expanded network misses its adequacy and needs to be expanded again. In this paper, in order to take transmission lines condition after expansion in to account from the line loading view point, the adequacy of transmission network is considered for solution of STNEP problem. To obtain optimal network arrangement, a decimal codification genetic algorithm (DCGA) is being used for minimizing the network construction and operational cost. The effectiveness of the proposed idea is tested on the Garver's six-bus network. The results evaluation reveals that the annual worth of network adequacy has a considerable effect on the network arrangement. In addition, the obtained network, based on the DCGA, has lower investment cost and higher adequacy rate. Thus, the network satisfies the requirements of delivering electric power more safely and reliably to load centers.
Abstract: This paper evaluates the dividend payments for general
claim size distributions in the presence of a dividend barrier. The
surplus of a company is modeled using the classical risk process
perturbed by diffusion, and in addition, it is assumed to accrue interest
at a constant rate. After presenting the integro-differential equation
with initial conditions that dividend payments satisfies, the paper
derives a useful expression of the dividend payments by employing
the theory of Volterra equation. Furthermore, the optimal value of
dividend barrier is found. Finally, numerical examples illustrate the
optimality of optimal dividend barrier and the effects of parameters
on dividend payments.
Abstract: In this study we survey the method for fast finding a minimum link path between two arbitrary points within a simple polygon, which can pass only through the vertices, with preprocessing.
Abstract: Due to the deregulation of the Electric Supply
Industry and the resulting emergence of electricity market, the
volumes of power purchases are on the rise all over the world. In a
bid to meet the customer-s demand in a reliable and yet economic
manner, utilities purchase power from the energy market over and
above its own production. This paper aims at developing an optimal
power purchase model with two objectives viz economy and
environment ,taking various functional operating constraints such as
branch flow limits, load bus voltage magnitudes limits, unit capacity
constraints and security constraints into consideration.The price of
purchased power being an uncertain variable is modeled using fuzzy
logic. DEMO (Differential Evolution For Multi-objective
Optimization) is used to obtain the pareto-optimal solution set of the
multi-objective problem formulated. Fuzzy set theory has been
employed to extract the best compromise non-dominated solution.
The results obtained on IEEE 30 bus system are presented and
compared with that of NSGAII.
Abstract: In this paper, the C1-conforming finite element method is analyzed for a class of nonlinear fourth-order hyperbolic partial differential equation. Some a priori bounds are derived using Lyapunov functional, and existence, uniqueness and regularity for the weak solutions are proved. Optimal error estimates are derived for both semidiscrete and fully discrete schemes.
Abstract: This work concerns the topological optimization
problem for determining the optimal petroleum refinery
configuration. We are interested in further investigating and
hopefully advancing the existing optimization approaches and
strategies employing logic propositions to conceptual process
synthesis problems. In particular, we seek to contribute to this
increasingly exciting area of chemical process modeling by
addressing the following potentially important issues: (a) how the
formulation of design specifications in a mixed-logical-and-integer
optimization model can be employed in a synthesis problem to enrich
the problem representation by incorporating past design experience,
engineering knowledge, and heuristics; and (b) how structural
specifications on the interconnectivity relationships by space (states)
and by function (tasks) in a superstructure should be properly
formulated within a mixed-integer linear programming (MILP)
model. The proposed modeling technique is illustrated on a case
study involving the alternative processing routes of naphtha, in which
significant improvement in the solution quality is obtained.
Abstract: The increasing competitiveness in manufacturing
industry is forcing manufacturers to seek effective processing
schedules. The paper presents an optimization manufacture
scheduling approach for dependent details processing with given
processing sequences and times on multiple machines. By defining
decision variables as start and end moments of details processing it is
possible to use straightforward variables restrictions to satisfy
different technological requirements and to formulate easy to
understand and solve optimization tasks for multiple numbers of
details and machines. A case study example is solved for seven base
moldings for CNC metalworking machines processed on five
different machines with given processing order among details and
machines and known processing time-s duration. As a result of linear
optimization task solution the optimal manufacturing schedule
minimizing the overall processing time is obtained. The
manufacturing schedule defines the moments of moldings delivery
thus minimizing storage costs and provides mounting due-time
satisfaction. The proposed optimization approach is based on real
manufacturing plant problem. Different processing schedules variants
for different technological restrictions were defined and implemented
in the practice of Bulgarian company RAIS Ltd. The proposed
approach could be generalized for other job shop scheduling
problems for different applications.
Abstract: In this paper, an artificial intelligent technique for
robust digital image watermarking in multiwavelet domain is
proposed. The embedding technique is based on the quantization
index modulation technique and the watermark extraction process
does not require the original image. We have developed an
optimization technique using the genetic algorithms to search for
optimal quantization steps to improve the quality of watermarked
image and robustness of the watermark. In addition, we construct a
prediction model based on image moments and back propagation
neural network to correct an attacked image geometrically before the
watermark extraction process begins. The experimental results show
that the proposed watermarking algorithm yields watermarked image
with good imperceptibility and very robust watermark against various
image processing attacks.
Abstract: As global industry developed rapidly, the energy
demand also rises simultaneously. In the production process, there’s a
lot of energy consumed in the process. Formally, the energy used in
generating the heat in the production process. In the total energy
consumption, 40% of the heat was used in process heat, mechanical
work, chemical energy and electricity. The remaining 50% were
released into the environment. It will cause energy waste and
environment pollution. There are many ways for recovering the waste
heat in factory. Organic Rankine Cycle (ORC) system can produce
electricity and reduce energy costs by recovering the waste of low
temperature heat in the factory. In addition, ORC is the technology
with the highest power generating efficiency in low-temperature heat
recycling. However, most of factories executives are still hesitated
because of the high implementation cost of the ORC system, even a lot
of heat are wasted. Therefore, this study constructs a nonlinear
mathematical model of waste heat recovery equipment configuration
to maximize profits. A particle swarm optimization algorithm is
developed to generate the optimal facility installation plan for the ORC
system.
Abstract: Testing accounts for the major percentage of technical
contribution in the software development process. Typically, it
consumes more than 50 percent of the total cost of developing a
piece of software. The selection of software tests is a very important
activity within this process to ensure the software reliability
requirements are met. Generally tests are run to achieve maximum
coverage of the software code and very little attention is given to the
achieved reliability of the software. Using an existing methodology,
this paper describes how to use Bayesian Belief Networks (BBNs) to
select unit tests based on their contribution to the reliability of the
module under consideration. In particular the work examines how the
approach can enhance test-first development by assessing the quality
of test suites resulting from this development methodology and
providing insight into additional tests that can significantly reduce
the achieved reliability. In this way the method can produce an
optimal selection of inputs and the order in which the tests are
executed to maximize the software reliability. To illustrate this
approach, a belief network is constructed for a modern software
system incorporating the expert opinion, expressed through
probabilities of the relative quality of the elements of the software,
and the potential effectiveness of the software tests. The steps
involved in constructing the Bayesian Network are explained as is a
method to allow for the test suite resulting from test-driven
development.
Abstract: This paper presents a heuristic to solve large size 0-1 Multi constrained Knapsack problem (01MKP) which is NP-hard. Many researchers are used heuristic operator to identify the redundant constraints of Linear Programming Problem before applying the regular procedure to solve it. We use the intercept matrix to identify the zero valued variables of 01MKP which is known as redundant variables. In this heuristic, first the dominance property of the intercept matrix of constraints is exploited to reduce the search space to find the optimal or near optimal solutions of 01MKP, second, we improve the solution by using the pseudo-utility ratio based on surrogate constraint of 01MKP. This heuristic is tested for benchmark problems of sizes upto 2500, taken from literature and the results are compared with optimum solutions. Space and computational complexity of solving 01MKP using this approach are also presented. The encouraging results especially for relatively large size test problems indicate that this heuristic can successfully be used for finding good solutions for highly constrained NP-hard problems.
Abstract: Concrete performance is strongly affected by the
particle packing degree since it determines the distribution of the
cementitious component and the interaction of mineral particles. By
using packing theory designers will be able to select optimal
aggregate materials for preparing concrete with low cement content,
which is beneficial from the point of cost. Optimum particle packing
implies minimizing porosity and thereby reducing the amount of
cement paste needed to fill the voids between the aggregate particles,
taking also the rheology of the concrete into consideration. For
reaching good fluidity superplasticizers are required. The results from
pilot tests at Luleå University of Technology (LTU) show various
forms of the proposed theoretical models, and the empirical approach
taken in the study seems to provide a safer basis for developing new,
improved packing models.