Abstract: This paper presents a general approach to implement
efficient queries’ interpreters in a functional programming language.
Indeed, most of the standard tools actually available use an imperative
and/or object-oriented language for the implementation (e.g. Java for
Jena-Fuseki) but other paradigms are possible with, maybe, better
performances. To proceed, the paper first explains how to model
data structures and queries in a functional point of view. Then, it
proposes a general methodology to get performances (i.e. number of
computation steps to answer a query) then it explains how to integrate
some optimization techniques (short-cut fusion and, more important,
data transformations). It then compares the functional server proposed
to a standard tool (Fuseki) demonstrating that the first one can be
twice to ten times faster to answer queries.
Abstract: Computer-based optimization techniques can be employed to improve the efficiency of energy conversions processes, including reducing the aerodynamic loss in a thermal power plant turbomachine. In this paper, towards mitigating secondary flow losses, a design optimization workflow is implemented for the casing geometry of a 1.5 stage axial flow turbine that improves the turbine isentropic efficiency. The improved turbine is used in an open thermodynamic gas cycle with regeneration and cogeneration. Performance estimates are obtained by the commercial software Cycle – Tempo. Design and off design conditions are considered as well as variations in inlet air temperature. Reductions in both the natural gas specific fuel consumption and in CO2 emissions are predicted by using the gas turbine cycle fitted with the new casing design. These gains are attractive towards enhancing the competitiveness and reducing the environmental impact of thermal power plant.
Abstract: In Wireless Sensor Network (WSN), the sensor containing motes (nodes) incorporate batteries that can lament at some extent. To upgrade the energy utilization, clustering is one of the prototypical approaches for split sensor motes into a number of clusters where one mote (also called as node) proceeds as a Cluster Head (CH). CH selection is one of the optimization techniques for enlarging stability and network lifespan. Deterministic Stable Election Protocol (DSEP) is an effectual clustering protocol that makes use of three kinds of nodes with dissimilar residual energy for CH election. Fuzzy Logic technology is used to expand energy level of DSEP protocol by using fuzzy inference system. This paper presents protocol DSEP using Fuzzy Logic (DSEP-FL) CH by taking into account four linguistic variables such as energy, concentration, centrality and distance to base station. Simulation results show that our proposed method gives more effective results in term of a lifespan of network and stability as compared to the performance of other clustering protocols.
Abstract: Many optimization techniques available in the literature have been developed in order to solve the problem of voltage stability enhancement in power systems. However, there are a number of drawbacks in the use of previous techniques aimed at determining the optimal location and size of reactive compensators in a network. In this paper, an Improved Cuckoo Search algorithm is applied as an appropriate optimization algorithm to determine the optimum location and size of a Static Var Compensator (SVC) in a transmission network. The main objectives are voltage stability improvement and total cost minimization. The results of the presented technique are then compared with other available optimization techniques.
Abstract: A Mobile Adhoc Network (MANET) is a collection of mobile nodes that communicate with each other with wireless links and without pre-existing communication infrastructure. Routing is an important issue which impacts network performance. As MANETs lack central administration and prior organization, their security concerns are different from those of conventional networks. Wireless links make MANETs susceptible to attacks. This study proposes a new trust mechanism to mitigate wormhole attack in MANETs. Different optimization techniques find available optimal path from source to destination. This study extends trust and reputation to an improved link quality and channel utilization based Adhoc Ondemand Multipath Distance Vector (AOMDV). Differential Evolution (DE) is used for optimization.
Abstract: In this paper we present a classification of the various technologies applied for the solution of the portfolio selection problem according to the discipline and the methodological framework followed. We provide a concise presentation of the emerged categories and we are trying to identify which methods considered obsolete and which lie at the heart of the debate. On top of that, we provide a comparative study of the different technologies applied for efficient portfolio construction and we suggest potential paths for future work that lie at the intersection of the presented techniques.
Abstract: Floorplanning plays a vital role in the physical design
process of Very Large Scale Integrated (VLSI) chips. It is an
essential design step to estimate the chip area prior to the optimized
placement of digital blocks and their interconnections. Since VLSI
floorplanning is an NP-hard problem, many optimization techniques
were adopted in the literature. In this work, a music-inspired
Harmony Search (HS) algorithm is used for the fixed die outline
constrained floorplanning, with the aim of reducing the total chip
area. HS draws inspiration from the musical improvisation process of
searching for a perfect state of harmony. Initially, B*-tree is used to
generate the primary floorplan for the given rectangular hard
modules and then HS algorithm is applied to obtain an optimal
solution for the efficient floorplan. The experimental results of the
HS algorithm are obtained for the MCNC benchmark circuits.
Abstract: High density electrical prospecting has been widely
used in groundwater investigation, civil engineering and
environmental survey. For efficient inversion, the forward modeling
routine, sensitivity calculation, and inversion algorithm must be
efficient. This paper attempts to provide a brief summary of the past
and ongoing developments of the method. It includes reviews of the
procedures used for data acquisition, processing and inversion of
electrical resistivity data based on compilation of academic literature.
In recent times there had been a significant evolution in field survey
designs and data inversion techniques for the resistivity method. In
general 2-D inversion for resistivity data is carried out using the
linearized least-square method with the local optimization technique
.Multi-electrode and multi-channel systems have made it possible to
conduct large 2-D, 3-D and even 4-D surveys efficiently to resolve
complex geological structures that were not possible with traditional
1-D surveys. 3-D surveys play an increasingly important role in very
complex areas where 2-D models suffer from artifacts due to off-line
structures. Continued developments in computation technology, as
well as fast data inversion techniques and software, have made it
possible to use optimization techniques to obtain model parameters to
a higher accuracy. A brief discussion on the limitations of the
electrical resistivity method has also been presented.
Abstract: Characterization of the engineering behavior of
unsaturated soil is dependent on the soil-water characteristic curve
(SWCC), a graphical representation of the relationship between water
content or degree of saturation and soil suction. A reasonable
description of the SWCC is thus important for the accurate prediction
of unsaturated soil parameters. The measurement procedures for
determining the SWCC, however, are difficult, expensive, and timeconsuming.
During the past few decades, researchers have laid a
major focus on developing empirical equations for predicting the
SWCC, with a large number of empirical models suggested. One of
the most crucial questions is how precisely existing equations can
represent the SWCC. As different models have different ranges of
capability, it is essential to evaluate the precision of the SWCC
models used for each particular soil type for better SWCC estimation.
It is expected that better estimation of SWCC would be achieved via
a thorough statistical analysis of its distribution within a particular
soil class. With this in view, a statistical analysis was conducted in
order to evaluate the reliability of the SWCC prediction models
against laboratory measurement. Optimization techniques were used
to obtain the best-fit of the model parameters in four forms of SWCC
equation, using laboratory data for relatively coarse-textured (i.e.,
sandy) soil. The four most prominent SWCCs were evaluated and
computed for each sample. The result shows that the Brooks and
Corey model is the most consistent in describing the SWCC for sand
soil type. The Brooks and Corey model prediction also exhibit
compatibility with samples ranging from low to high soil water
content in which subjected to the samples that evaluated in this study.
Abstract: The emergence of the Semantic Web technology
increases day by day due to the rapid growth of multiple web pages.
Many standard formats are available to store the semantic web data.
The most popular format is the Resource Description Framework
(RDF). Querying large RDF graphs becomes a tedious procedure
with a vast increase in the amount of data. The problem of query
optimization becomes an issue in querying large RDF graphs.
Choosing the best query plan reduces the amount of query execution
time. To address this problem, nature inspired algorithms can be used
as an alternative to the traditional query optimization techniques. In
this research, the optimal query plan is generated by the proposed
SAPSO algorithm which is a hybrid of Simulated Annealing (SA)
and Particle Swarm Optimization (PSO) algorithms. The proposed
SAPSO algorithm has the ability to find the local optimistic result
and it avoids the problem of local minimum. Experiments were
performed on different datasets by changing the number of predicates
and the amount of data. The proposed algorithm gives improved
results compared to existing algorithms in terms of query execution
time.
Abstract: Seismic design criteria based on performance of
structures have recently been adopted by practicing engineers in
response to destructive earthquakes. A simple but efficient
structural-analysis tool capable of predicting both the strength and
ductility is needed to analyze reinforced concrete (RC) structures
under such event. A three-dimensional lattice model is developed in
this study to analyze torsions in high-strength RC members.
Optimization techniques for determining optimal variables in each
lattice model are introduced. Pure torsion tests of RC members are
performed to validate the proposed model. Correlation studies
between the numerical and experimental results confirm that the
proposed model is well capable of representing salient features of the
experimental results.
Abstract: The amount of energy the world uses doubles every 20 years. Green homes play an important role in reducing the residential energy demand. This paper presents a platform that is intended to learn the behavior of home residents and build a profile about their habits and actions. The proposed resident aware home controller intervenes in the operation of home appliances in order to save energy without compromising the convenience of the residents. The presented platform can be used to simulate the actions and movements happening inside a home. The paper includes several optimization techniques that are meant to save energy in the home. In addition, several test scenarios are presented that show how the controller works. Moreover, this paper shows the computed actual savings when each of the presented techniques is implemented in a typical home. The test scenarios have validated that the techniques developed are capable of effectively saving energy at homes.
Abstract: Genetic Algorithm (GA) is a powerful technique for solving optimization problems. It follows the idea of survival of the fittest - Better and better solutions evolve from previous generations until a near optimal solution is obtained. GA uses the main three operations, the selection, crossover and mutation to produce new generations from the old ones. GA has been widely used to solve optimization problems in many applications such as traveling salesman problem, airport traffic control, information retrieval (IR), reactive power optimization, job shop scheduling, and hydraulics systems such as water pipeline systems. In water pipeline systems we need to achieve some goals optimally such as minimum cost of construction, minimum length of pipes and diameters, and the place of protection devices. GA shows high performance over the other optimization techniques, moreover, it is easy to implement and use. Also, it searches a limited number of solutions.
Abstract: Mobile application development is different from regular application development due to the hardware resource limitations existed in the mobile platforms. In the mobile environment, the application needs to be optimized by the developer to produce optimal software with least overhead. This study discussed about performance optimization techniques that are employed in general application development, and how such techniques are performing on mobile platforms through some empirical evaluations on a mobile emulator, Nokia X3-02 and Nokia C5-03devices. The scope of the work is only confined to mobile platform based on Java Mobile edition architecture. The empirical results showed that techniques such as loop unrolling, dependency chain, and linearized getter and setter performed better by a factor of 3 to 7. Whereas declaration and initialization on the same line or separate line did not improve the performance.
Abstract: Economic Dispatch is one of the most important power system management tools. It is used to allocate an amount of power generation to the generating units to meet the load demand. The Economic Dispatch problem is a large scale nonlinear constrained optimization problem. In general, heuristic optimization techniques are used to solve non-convex Economic Dispatch problem. In this paper, ideas from Reinforcement Learning are proposed to solve the non-convex Economic Dispatch problem. Q-Learning is a reinforcement learning techniques where each generating unit learn the optimal schedule of the generated power that minimizes the generation cost function. The eligibility traces are used to speed up the Q-Learning process. Q-Learning with eligibility traces is used to solve Economic Dispatch problems with valve point loading effect, multiple fuel options, and power transmission losses.
Abstract: In this paper, the shape design process is briefly discussed emphasizing the use of topology optimization in the conceptual design stage. The basic idea is to view feasible domains for sensitivity region concepts. In this method, the main process consists of two steps: as the design moves further inside the feasible domain using Taguchi method, and thus becoming more successful topology optimization, the sensitivity region becomes larger. In designing a double-eccentric butterfly valve, related to hydrodynamic performance and disc structure, are discussed where the use of topology optimization has proven to dramatically improve an existing design and significantly decrease the development time of a shape design. Computational Fluid Dynamics (CFD) analysis results demonstrate the validity of this approach.
Abstract: Bloom filter is a probabilistic and memory efficient
data structure designed to answer rapidly whether an element is
present in a set. It tells that the element is definitely not in the set but
its presence is with certain probability. The trade-off to use Bloom
filter is a certain configurable risk of false positives. The odds of a
false positive can be made very low if the number of hash function is
sufficiently large. For spam detection, weight is attached to each set
of elements. The spam weight for a word is a measure used to rate the
e-mail. Each word is assigned to a Bloom filter based on its weight.
The proposed work introduces an enhanced concept in Bloom filter
called Bin Bloom Filter (BBF). The performance of BBF over
conventional Bloom filter is evaluated under various optimization
techniques. Real time data set and synthetic data sets are used for
experimental analysis and the results are demonstrated for bin sizes 4,
5, 6 and 7. Finally analyzing the results, it is found that the BBF
which uses heuristic techniques performs better than the traditional
Bloom filter in spam detection.
Abstract: Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA optimization techniques, for Thyristor Controlled Series Compensator (TCSC)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both the PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques in terms of computational time and convergence rate is compared. Further, the optimized controllers are tested on a weakly connected power system subjected to different disturbances, and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a TCSC-based controller, to enhance power system stability.
Abstract: Over Current Relays (OCRs) and Directional Over Current Relays (DOCRs) are widely used for the radial protection and ring sub transmission protection systems and for distribution systems. All previous work formulates the DOCR coordination problem either as a Non-Linear Programming (NLP) for TDS and Ip or as a Linear Programming (LP) for TDS using recently a social behavior (Particle Swarm Optimization techniques) introduced to the work. In this paper, a Modified Particle Swarm Optimization (MPSO) technique is discussed for the optimal settings of DOCRs in power systems as a Non-Linear Programming problem for finding Ip values of the relays and for finding the TDS setting as a linear programming problem. The calculation of the Time Dial Setting (TDS) and the pickup current (Ip) setting of the relays is the core of the coordination study. PSO technique is considered as realistic and powerful solution schemes to obtain the global or quasi global optimum in optimization problem.
Abstract: Network reconfiguration is an operation to modify the
network topology. The implementation of network reconfiguration
has many advantages such as loss minimization, increasing system
security and others. In this paper, two topics about the network
reconfiguration in distribution system are briefly described. The first
topic summarizes its impacts while the second explains some
heuristic optimization techniques for solving the network
reconfiguration problem.