Abstract: In the context of large volume Big Divisor (nearly)
SLagy D3/D7 μ-Split SUSY [1], after an explicit identification
of first generation of SM leptons and quarks with fermionic superpartners
of four Wilson line moduli, we discuss the identification of
gravitino as a potential dark matter candidate by explicitly calculating
the decay life times of gravitino (LSP) to be greater than age of
universe and lifetimes of decays of the co-NLSPs (the first generation
squark/slepton and a neutralino) to the LSP (the gravitino) to be
very small to respect BBN constraints. Interested in non-thermal
production mechanism of gravitino, we evaluate the relic abundance
of gravitino LSP in terms of that of the co-NLSP-s by evaluating
their (co-)annihilation cross sections and hence show that the former
satisfies the requirement for a potential Dark Matter candidate. We
also show that it is possible to obtain a 125 GeV light Higgs in our
setup.
Abstract: This work addresses the problem of optimizing
completely batch water-using network with multiple contaminants
where the flow change caused by mass transfer is taken into
consideration for the first time. A mathematical technique for
optimizing water-using network is proposed based on
source-tank-sink superstructure. The task is to obtain the freshwater
usage, recycle assignments among water-using units, wastewater
discharge and a steady water-using network configuration by
following steps. Firstly, operating sequences of water-using units are
determined by time constraints. Next, superstructure is simplified by
eliminating the reuse and recycle from water-using units with
maximum concentration of key contaminants. Then, the non-linear
programming model is solved by GAMS (General Algebra Model
System) for minimum freshwater usage, maximum water recycle and
minimum wastewater discharge. Finally, numbers of operating periods
are calculated to acquire the steady network configuration. A case
study is solved to illustrate the applicability of the proposed approach.
Abstract: Mobile ad hoc network is a collection of mobile
nodes communicating through wireless channels without any
existing network infrastructure or centralized administration.
Because of the limited transmission range of wireless network
interfaces, multiple "hops" may be needed to exchange data
across the network. Consequently, many routing algorithms
have come into existence to satisfy the needs of
communications in such networks. Researchers have
conducted many simulations comparing the performance of
these routing protocols under various conditions and
constraints. One question that arises is whether speed of nodes
affects the relative performance of routing protocols being
studied. This paper addresses the question by simulating two
routing protocols AODV and DSDV. Protocols were
simulated using the ns-2 and were compared in terms of
packet delivery fraction, normalized routing load and average
delay, while varying number of nodes, and speed.
Abstract: Securing instream flows for aquatic ecosystems is
critical for sustainable water management and the promotion of
human and environmental health. Using a case study from the semiarid
region of southern Alberta (Canada) this paper considers how
the determination of instream flow standards requires judgments with
respect to: (1) The relationship between instream flow indicators and
assessments of overall environmental health; (2) The indicators used
to determine adequate instream flows, and; (3) The assumptions
underlying efforts to model instream flows given data constraints. It
argues that judgments in each of these areas have an inherently
ethical component because instream flows have direct effects on the
water(s) available to meet obligations to humans and non-humans.
The conclusion expands from the case study to generic issues
regarding instream flows, the growing water ethics literature and
prospects for linking science to policy.
Abstract: This paper introduces a mixed integer programming model to find the optimum development plan for port Anzali. The model minimizes total system costs taking into account both port infrastructure costs and shipping costs. Due to the multipurpose function of the port, the model consists of 1020 decision variables and 2490 constraints. Results of the model determine the optimum number of berths that should be constructed in each period and for each type of cargo. In addition to, the results of sensitivity analysis on port operation quantity provide useful information for managers to choose the best scenario for port planning with the lowest investment risks. Despite all limitations-due to data availability-the model offers a straightforward decision tools to port planners aspiring to achieve optimum port planning steps.
Abstract: This paper presents Qmulus- a Cloud Based GPS
Model. Qmulus is designed to compute the best possible route which
would lead the driver to the specified destination in the shortest time
while taking into account real-time constraints. Intelligence
incorporated to Qmulus-s design makes it capable of generating and
assigning priorities to a list of optimal routes through customizable
dynamic updates. The goal of this design is to minimize travel and
cost overheads, maintain reliability and consistency, and implement
scalability and flexibility. The model proposed focuses on
reducing the bridge between a Client Application and a Cloud
service so as to render seamless operations. Qmulus-s system
model is closely integrated and its concept has the potential to be
extended into several other integrated applications making it capable
of adapting to different media and resources.
Abstract: Over the past several years, there has been a
considerable amount of research within the field of Quality of
Service (QoS) support for distributed multimedia systems. One of the
key issues in providing end-to-end QoS guarantees in packet
networks is determining a feasible path that satisfies a number of
QoS constraints. The problem of finding a feasible path is NPComplete
if number of constraints is more than two and cannot be
exactly solved in polynomial time. We proposed Feasible Path
Selection Algorithm (FPSA) that addresses issues with pertain to
finding a feasible path subject to delay and cost constraints and it
offers higher success rate in finding feasible paths.
Abstract: The network of delivering commodities has been an important design problem in our daily lives and many transportation applications. The delivery performance is evaluated based on the system reliability of delivering commodities from a source node to a sink node in the network. The system reliability is thus maximized to find the optimal routing. However, the design problem is not simple because (1) each path segment has randomly distributed attributes; (2) there are multiple commodities that consume various path capacities; (3) the optimal routing must successfully complete the delivery process within the allowable time constraints. In this paper, we want to focus on the design optimization of the Multi-State Flow Network (MSFN) for multiple commodities. We propose an efficient approach to evaluate the system reliability in the MSFN with respect to randomly distributed path attributes and find the optimal routing subject to the allowable time constraints. The delivery rates, also known as delivery currents, of the path segments are evaluated and the minimal-current arcs are eliminated to reduce the complexity of the MSFN. Accordingly, the correct optimal routing is found and the worst-case reliability is evaluated. It has been shown that the reliability of the optimal routing is at least higher than worst-case measure. Two benchmark examples are utilized to demonstrate the proposed method. The comparisons between the original and the reduced networks show that the proposed method is very efficient.
Abstract: In this paper, the problem of finding the optimal
topological configuration of a deregulated distribution network is
considered. The new features of this paper are proposing a multiobjective
function and its application on deregulated distribution
networks for finding the optimal configuration. The multi-objective
function will be defined for minimizing total Energy Supply Costs
(ESC) and energy losses subject to load flow constraints. The
optimal configuration will be obtained by using Binary Genetic
Algorithm (BGA).The proposed method has been tested to analyze a
sample and a practical distribution networks.
Abstract: The noticeable advance in the area of computer technology has paved the way for the invention of powerful mobile devices. However, limited storage, short battery life, and relatively low computational power define the major problems of such devices. Due to the ever increasing computational requirements, such devices may fail to process needed tasks under certain constraints. One of the proposed solutions to this drawback is the introduction of Collaborative Computing, a new concept dealing with the distribution of computational tasks amongst several handhelds. This paper introduces the basics of Collaborative Computing, and proposes a new protocol that aims at managing and optimizing computing tasks in Ad-Hoc Collaborative Computing Environments.
Abstract: The proper design of RF pulses in magnetic resonance imaging (MRI) has a direct impact on the quality of acquired images, and is needed for many applications. Several techniques have been proposed to obtain the RF pulse envelope given the desired slice profile. Unfortunately, these techniques do not take into account the limitations of practical implementation such as limited amplitude resolution. Moreover, implementing constraints for special RF pulses on most techniques is not possible. In this work, we propose to develop an approach for designing optimal RF pulses under theoretically any constraints. The new technique will pose the RF pulse design problem as a combinatorial optimization problem and uses efficient techniques from this area such as genetic algorithms (GA) to solve this problem. In particular, an objective function will be proposed as the norm of the difference between the desired profile and the one obtained from solving the Bloch equations for the current RF pulse design values. The proposed approach will be verified using analytical solution based RF simulations and compared to previous methods such as Shinnar-Le Roux (SLR) method, and analysis, selected, and tested the options and parameters that control the Genetic Algorithm (GA) can significantly affect its performance to get the best improved results and compared to previous works in this field. The results show a significant improvement over conventional design techniques, select the best options and parameters for GA to get most improvement over the previous works, and suggest the practicality of using of the new technique for most important applications as slice selection for large flip angles, in the area of unconventional spatial encoding, and another clinical use.
Abstract: The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the next generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which Genetic Algorithm (GA) was applied.
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: The most common type of controller being used in
the industry is PI(D) controller which has been used since 1945 and
is still being widely used due to its efficiency and simplicity. In
most cases, the PI(D) controller was tuned without taking into
consideration of the effect of actuator saturation. In real processes,
the most common actuator which is valve will act as constraint and
restrict the controller output. Since the controller is not designed to
encounter saturation, the process may windup and consequently
resulted in large oscillation or may become unstable. Usually, an
antiwindup compensator is added to the feedback control loop to
reduce the deterioration effect of integral windup. This research
aims to specifically control processes with constraints. The
proposed method was applied to two different types of food
processes, which are blending and spray drying. Simulations were
done using MATLAB and the performances of the proposed
method were compared with other conventional methods. The
proposed technique was able to control the processes and avoid
saturation such that no anti windup compensator is needed.
Abstract: This paper presents an efficient algorithm for
optimization of radial distribution systems by a network
reconfiguration to balance feeder loads and eliminate overload
conditions. The system load-balancing index is used to determine the
loading conditions of the system and maximum system loading
capacity. The index value has to be minimum in the optimal network
reconfiguration of load balancing. A method based on Tabu search
algorithm, The Tabu search algorithm is employed to search for the
optimal network reconfiguration. The basic idea behind the search is
a move from a current solution to its neighborhood by effectively
utilizing a memory to provide an efficient search for optimality. It
presents low computational effort and is able to find good quality
configurations. Simulation results for a radial 69-bus system with
distributed generations and capacitors placement. The study results
show that the optimal on/off patterns of the switches can be identified
to give the best network reconfiguration involving balancing of
feeder loads while respecting all the constraints.
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: In this paper, the phase control antenna array synthesis
is presented. The problem is formulated as a constrained optimization
problem that imposes nulls with prescribed level while maintaining
the sidelobe at a prescribed level. For efficient use of the algorithm
memory, compared to the well known Particle Swarm Optimization
(PSO), the Accelerated Particle Swarm Optimization (APSO) is used
to estimate the phase parameters of the synthesized array. The
objective function is formed using a main objective and set of
constraints with penalty factors that measure the violation of each
feasible solution in the search space to each constraint. In this case
the obtained feasible solution is guaranteed to satisfy all the
constraints. Simulation results have shown significant performance
increases and a decreased randomness in the parameter search space
compared to a single objective conventional particle swarm
optimization.
Abstract: QoS Routing aims to find paths between senders and
receivers satisfying the QoS requirements of the application which
efficiently using the network resources and underlying routing
algorithm to be able to find low-cost paths that satisfy given QoS
constraints. The problem of finding least-cost routing is known to be
NP-hard or complete and some algorithms have been proposed to
find a near optimal solution. But these heuristics or algorithms either
impose relationships among the link metrics to reduce the complexity
of the problem which may limit the general applicability of the
heuristic, or are too costly in terms of execution time to be applicable
to large networks. In this paper, we concentrate an algorithm that
finds a near-optimal solution fast and we named this algorithm as
optimized Delay Constrained Routing (ODCR), which uses an
adaptive path weight function together with an additional constraint
imposed on the path cost, to restrict search space and hence ODCR
finds near optimal solution in much quicker time.
Abstract: Starting from a biologically inspired framework, Gabor filters were built up from retinal filters via LMSE algorithms. Asubset of retinal filter kernels was chosen to form a particular Gabor filter by using a weighted sum. One-dimensional optimization approaches were shown to be inappropriate for the problem. All model parameters were fixed with biological or image processing constraints. Detailed analysis of the optimization procedure led to the introduction of a minimization constraint. Finally, quantization of weighting factors was investigated. This resulted in an optimized cascaded structure of a Gabor filter bank implementation with lower computational cost.
Abstract: Topology Optimization is a defined as the method of
determining optimal distribution of material for the assumed design
space with functionality, loads and boundary conditions [1].
Topology optimization can be used to optimize shape for the
purposes of weight reduction, minimizing material requirements or
selecting cost effective materials [2]. Topology optimization has been
implemented through the use of finite element methods for the
analysis, and optimization techniques based on the method of moving
asymptotes, genetic algorithms, optimality criteria method, level sets
and topological derivatives. Case study of Typical “Fuselage design"
is considered for this paper to explain the benefits of Topology
Optimization in the design cycle. A cylindrical shell is assumed as
the design space and aerospace standard pay loads were applied on
the fuselage with wing attachments as constraints. Then topological
optimization is done using Finite Element (FE) based software. This
optimization results in the structural concept design which satisfies
all the design constraints using minimum material.