Abstract: We present and analyze reliable numerical techniques
for simulating complex flow and transport phenomena related to
natural gas transportation in pipelines. Such kind of problems
are of high interest in the field of petroleum and environmental
engineering. Modeling and understanding natural gas flow and
transformation processes during transportation is important for the
sake of physical realism and the design and operation of pipeline
systems. In our approach a two fluid flow model based on a system
of coupled hyperbolic conservation laws is considered for describing
natural gas flow undergoing hydratization. The accurate numerical
approximation of two-phase gas flow remains subject of strong
interest in the scientific community. Such hyperbolic problems are
characterized by solutions with steep gradients or discontinuities, and
their approximation by standard finite element techniques typically
gives rise to spurious oscillations and numerical artefacts. Recently,
stabilized and discontinuous Galerkin finite element techniques
have attracted researchers’ interest. They are highly adapted to the
hyperbolic nature of our two-phase flow model. In the presentation
a streamline upwind Petrov-Galerkin approach and a discontinuous
Galerkin finite element method for the numerical approximation of
our flow model of two coupled systems of Euler equations are
presented. Then the efficiency and reliability of stabilized continuous
and discontinous finite element methods for the approximation is
carefully analyzed and the potential of the either classes of numerical
schemes is investigated. In particular, standard benchmark problems
of two-phase flow like the shock tube problem are used for the
comparative numerical study.
Abstract: Given a fixed fund, purchasing fewer hosts of higher capability or inversely more of lower capability is a must-be-made trade-off in practices for building a Hadoop big data platform. An exploratory study is presented for a Housing Big Data Platform project (HBDP), where typical big data computing is with SQL queries of aggregate, join, and space-time condition selections executed upon massive data from more than 10 million housing units. In HBDP, an empirical formula was introduced to predict the performance of host clusters potential for the intended typical big data computing, and it was shaped via a regression approach. With this empirical formula, it is easy to suggest an optimal cluster configuration. The investigation was based on a typical Hadoop computing ecosystem HDFS+Hive+Spark. A proper metric was raised to measure the performance of Hadoop clusters in HBDP, which was tested and compared with its predicted counterpart, on executing three kinds of typical SQL query tasks. Tests were conducted with respect to factors of CPU benchmark, memory size, virtual host division, and the number of element physical host in cluster. The research has been applied to practical cluster procurement for housing big data computing.
Abstract: Emissions are a consequence of electricity generation. A major option for low carbon generation, local energy systems featuring Combined Heat and Power with solar PV (CHPV) has significant potential to increase energy performance, increase resilience, and offer greater control of local energy prices while complementing the UK’s emissions standards and targets. Recent advances in dynamic modelling and simulation of buildings and clusters of buildings using the IDEAS framework have successfully validated a novel multi-vector (simultaneous control of both heat and electricity) approach to integrating the wide range of primary and secondary plant typical of local energy systems designs including CHP, solar PV, gas boilers, absorption chillers and thermal energy storage, and associated electrical and hot water networks, all operating under a single unified control strategy. Results from this work indicate through simulation that integrated control of thermal storage can have a pivotal role in optimizing system performance well beyond the present expectations. Environmental impact analysis and reporting of all energy systems including CHPV LES presently employ a static annual average carbon emissions intensity for grid supplied electricity. This paper focuses on establishing and validating CHPV environmental performance against conventional emissions values and assessment benchmarks to analyze emissions performance without and with an active thermal store in a notional group of non-domestic buildings. Results of this analysis are presented and discussed in context of performance validation and quantifying the reduced environmental impact of CHPV systems with active energy storage in comparison with conventional LES designs.
Abstract: The growing cities of the developing country are characterized by rapid growth and poor infrastructure management inviting and accelerating relative environmental problems. Even though the movements of the sustainability had already been developed around the world, it is still increasing in the developing countries to plant sustainable practices. Aligned with the sustainable development actions, many sustainable assessment tools are also developed to rate and evaluate the sustainability performances through the building to community level. Among them, CASBEE is developed by Japanese organizations and is recognized as one of the international well-known assessment tools. The main purpose of the study is to find out the potential of CASBEE tool reflecting sustainability city level performances in developing countries. The research framework was designed with three major phases: Quantitative Approach, Qualitative Approach and Evaluation Reflection. The first two approaches were based on the investigation of tool’s contents and indicators by means of three sustainable dimensions and sustainability categories. To know the reality and reflection on developing country, Pathein City from Myanmar was selected and evaluated by 2012 version of CASBEE for Cities. The evaluation practices went through assigned indicators and the evaluation outcome presents the performances of Pathein city’s environmental efficiency as a very good in current conditions. The results of this study indicate that the indicators of this tool have balance coverage among three dimensions of sustainability but it has not yet counted enough for some indicators like location, infrastructure and institution which are relative to society dimension. In the developing countries’ cities, the most critical issues on development such as affordable housing and heritage preservation which are already planted in Pathein City but the tool does not account for those issues. Moreover, in some of the indicators, the benchmark and the weighting coefficient are strongly linked to the system birth region. By means of this study, it can be stated that CASBEE for Cities would be potential for delivering sustainable city level development in developing country especially in Myanmar along with further inclusion of the indicators.
Abstract: Unconstrained authentication is an important component for personal automated systems and human-computer interfaces. Existing solutions mostly use face as the primary object of analysis. The performance of face-based systems is largely determined by the extent of deformation caused in the facial region and amount of useful information available in occluded face images. Periocular region is a useful portion of face with discriminative ability coupled with resistance to deformation. A reliable portion of periocular area is available for occluded images. The present work demonstrates that joint representation of periocular texture and periocular structure provides an effective expression and poses invariant representation. The proposed methodology provides an effective and compact description of periocular texture and shape. The method is tested over four benchmark datasets exhibiting varied acquisition conditions.
Abstract: In recent years, the combined economic and emission power dispatch is one of the main problems of electrical power system. It aims to schedule the power generation of generators in order to minimize cost production and emission of harmful gases caused by fossil-fueled thermal units such as CO, CO2, NOx, and SO2. To solve this complicated multi-objective problem, an improved version of the particle swarm optimization technique that includes non-dominated sorting concept has been proposed. Valve point loading effects and system losses have been considered. The three-unit and ten-unit benchmark systems have been used to show the effectiveness of the suggested optimization technique for solving this kind of nonconvex problem. The simulation results have been compared with those obtained using genetic algorithm based method. Comparison results show that the proposed approach can provide a higher quality solution with better performance.
Abstract: Test data compression is an efficient method for reducing the test application cost. The problem of reducing test data has been addressed by researchers in three different aspects: Test Data Compression, Built-in-Self-Test (BIST) and Test set compaction. The latter two methods are capable of enhancing fault coverage with cost of hardware overhead. The drawback of the conventional methods is that they are capable of reducing the test storage and test power but when test data have redundant length of runs, no additional compression method is followed. This paper presents a modified Run Length Coding (RLC) technique with Multilevel Selective Huffman Coding (MLSHC) technique to reduce test data volume, test pattern delivery time and power dissipation in scan test applications where redundant length of runs is encountered then the preceding run symbol is replaced with tiny codeword. Experimental results show that the presented method not only improves the test data compression but also reduces the overall test data volume compared to recent schemes. Experiments for the six largest ISCAS-98 benchmarks show that our method outperforms most known techniques.
Abstract: The objective of this study is to examine the performance of three well-known multiobjective evolutionary algorithms for solving optimization problems. The first algorithm is the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the second one is the Strength Pareto Evolutionary Algorithm 2 (SPEA-2), and the third one is the Multiobjective Evolutionary Algorithms based on decomposition (MOEA/D). The examined multiobjective algorithms are analyzed and tested on the ZDT set of test functions by three performance metrics. The results indicate that the NSGA-II performs better than the other two algorithms based on three performance metrics.
Abstract: Several computationally challenging issues are
encountered while classifying complex natural scenes. In this
paper, we address the problems that are encountered in rotation
invariance with multi-intensity analysis for multi-scene overlapping.
In the present literature, various algorithms proposed techniques
for multi-intensity analysis, but there are several restrictions in
these algorithms while deploying them in multi-scene overlapping
classifications. In order to resolve the problem of multi-scenery
overlapping classifications, we present a framework that is based
on macro and micro basis functions. This algorithm conquers the
minimum classification false alarm while pigeonholing multi-scene
overlapping. Furthermore, a quadrangle multi-intensity decay is
invoked. Several parameters are utilized to analyze invariance
for multi-scenery classifications such as rotation, classification,
correlation, contrast, homogeneity, and energy. Benchmark datasets
were collected for complex natural scenes and experimented for
the framework. The results depict that the framework achieves
a significant improvement on gray-level matrix of co-occurrence
features for overlapping in diverse degree of orientations while
pigeonholing multi-scene overlapping.
Abstract: The benchmarking of tools for dynamic analysis of
vulnerabilities in web applications is something that is done
periodically, because these tools from time to time update their
knowledge base and search algorithms, in order to improve their
accuracy. Unfortunately, the vast majority of these evaluations are
made by software enthusiasts who publish their results on blogs
or on non-academic websites and always with the same evaluation
methodology. Similarly, academics who have carried out this type of
analysis from a scientific approach, the majority, make their analysis
within the same methodology as well the empirical authors. This
paper is based on the interest of finding answers to questions that
many users of this type of tools have been asking over the years,
such as, to know if the tool truly test and evaluate every vulnerability
that it ensures do, or if the tool, really, deliver a real report of all the
vulnerabilities tested and exploited. This kind of questions have also
motivated previous work but without real answers. The aim of this
paper is to show results that truly answer, at least on the tested tools,
all those unanswered questions. All the results have been obtained
by changing the common model of benchmarking used for all those
previous works.
Abstract: Housings in developing countries have often inadequate
seismic protection, particularly for masonry. People choose this type
of structure since the cost and application are relatively cheap.
Seismic protection of masonry remains an interesting issue among
researchers. In this study, we develop a low-cost seismic isolation
system for masonry using fiber reinforced elastomeric isolators. The
elastomer proposed consists of few layers of rubber pads and fiber
lamina, making it lower in cost comparing to the conventional
isolators. We present a finite element (FE) analysis to predict the
behavior of the low cost rubber isolators undergoing moderate
deformations. The FE model of the elastomer involves a hyperelastic
material property for the rubber pad. We adopt a Yeoh hyperelasticity
model and estimate its coefficients through the available experimental
data. Having the shear behavior of the elastomers, we apply that
isolation system onto small masonry housing. To attach the isolators
on the building, we model the shear behavior of the isolation system
by means of a damped nonlinear spring model. By this attempt, the
FE analysis becomes computationally inexpensive. Several ground
motion data are applied to observe its sensitivity. Roof acceleration
and tensile damage of walls become the parameters to evaluate
the performance of the isolators. In this study, a concrete damage
plasticity model is used to model masonry in the nonlinear range.
This tool is available in the standard package of Abaqus FE software.
Finally, the results show that the low-cost isolators proposed are
capable of reducing roof acceleration and damage level of masonry
housing. Through this study, we are also capable of monitoring the
shear deformation of isolators during seismic motion. It is useful to
determine whether the isolator is applicable. According to the results,
the deformations of isolators on the benchmark one story building are
relatively small.
Abstract: In this paper, the problem of robust model predictive control (MPC) for discrete-time linear systems in linear fractional transformation form with structured uncertainty and norm-bounded disturbance is investigated. The problem of minimization of the cost function for MPC design is converted to minimization of the worst case of the cost function. Then, this problem is reduced to minimization of an upper bound of the cost function subject to a terminal inequality satisfying the l2-norm of the closed loop system. The characteristic of the linear fractional transformation system is taken into account, and by using some mathematical tools, the robust predictive controller design problem is turned into a linear matrix inequality minimization problem. Afterwards, a formulation which includes an integrator to improve the performance of the proposed robust model predictive controller in steady state condition is studied. The validity of the approaches is illustrated through a robust control benchmark problem.
Abstract: An essential component of a finite volume method (FVM) is the advection scheme that estimates values on the cell faces based on the calculated values on the nodes or cell centers. The most widely used advection schemes are upwind schemes. These schemes have been developed in FVM on different kinds of structured and unstructured grids. In this research, the physical influence scheme (PIS) is developed for a cell-centered FVM that uses an implicit coupled solver. Results are compared with the exponential differencing scheme (EDS) and the skew upwind differencing scheme (SUDS). Accuracy of these schemes is evaluated for a lid-driven cavity flow at Re = 1000, 3200, and 5000 and a backward-facing step flow at Re = 800. Simulations show considerable differences between the results of EDS scheme with benchmarks, especially for the lid-driven cavity flow at high Reynolds numbers. These differences occur due to false diffusion. Comparing SUDS and PIS schemes shows relatively close results for the backward-facing step flow and different results in lid-driven cavity flow. The poor results of SUDS in the lid-driven cavity flow can be related to its lack of sensitivity to the pressure difference between cell face and upwind points, which is critical for the prediction of such vortex dominant flows.
Abstract: Wireless sensor networks (WSNs) can facilitate continuous monitoring of patients and increase early detection of emergency conditions and diseases. High density WSNs helps us to accurately monitor a remote environment by intelligently combining the data from the individual nodes. Due to energy capacity limitation of sensors, enhancing the lifetime and the reliability of WSNs are important factors in designing of these networks. The clustering strategies are verified as effective and practical algorithms for reducing energy consumption in WSNs and can tackle WSNs limitations. In this paper, an Energy-efficient weight-based Clustering Protocol (EWCP) is presented. Artificial retina is selected as a case study of WSNs applied in body sensors. Cluster heads’ (CHs) selection is equipped with energy efficient parameters. Moreover, cluster members are selected based on their distance to the selected CHs. Comparing with the other benchmark protocols, the lifetime of EWCP is improved significantly.
Abstract: This paper presents a numerical investigation on the seismic performance of a benchmark bridge with different optimal isolation systems under near fault ground motion. Usually, very large displacements make seismic isolation an unfeasible solution due to boundary conditions, especially in case of existing bridges or high risk seismic regions. Hence, near-fault ground motions are most likely to affect either structures with long natural period range like isolated structures or structures sensitive to velocity content such as viscously damped structures. The work is aimed at analyzing the seismic performance of a three-span continuous bridge designed with different isolation systems having different levels of damping. The case study was analyzed in different configurations including: (a) simply supported, (b) isolated with lead rubber bearings (LRBs), (c) isolated with rubber isolators and 10% classical damping (HDLRBs), and (d) isolated with rubber isolators and 70% supplemental damping ratio. Case (d) represents an alternative control strategy that combines the effect of seismic isolation with additional supplemental damping trying to take advantages from both solutions. The bridge is modeled in SAP2000 and solved by time history direct-integration analyses under a set of six recorded near-fault ground motions. In addition to this, a set of analysis under Italian code provided seismic action is also conducted, in order to evaluate the effectiveness of the suggested optimal control strategies under far field seismic action. Results of the analysis demonstrated that an isolated bridge equipped with HDLRBs and a total equivalent damping ratio of 70% represents a very effective design solution for both mitigation of displacement demand at the isolation level and base shear reduction in the piers also in case of near fault ground motion.
Abstract: This paper presents a genetic algorithm based permutation and non-permutation scheduling heuristics (GAPNP) to solve a multi-stage finite capacity material requirement planning (FCMRP) problem in automotive assembly flow shop with unrelated parallel machines. In the algorithm, the sequences of orders are iteratively improved by the GA characteristics, whereas the required operations are scheduled based on the presented permutation and non-permutation heuristics. Finally, a linear programming is applied to minimize the total cost. The presented GAPNP algorithm is evaluated by using real datasets from automotive companies. The required parameters for GAPNP are intently tuned to obtain a common parameter setting for all case studies. The results show that GAPNP significantly outperforms the benchmark algorithm about 30% on average.
Abstract: This paper presents modeling and control of a highly nonlinear system including, non-interacting two spherical tanks using iterative learning control (ILC). Consequently, the objective of the paper is to control the liquid levels in the nonlinear tanks. First, a proportional-integral-derivative (PID) controller is applied to the plant model as a suitable benchmark for comparison. Then, dynamic responses of the control system corresponding to different step inputs are investigated. It is found that the conventional PID control is not able to fulfill the design criteria such as desired time constant. Consequently, an iterative learning controller is proposed to accurately control the coupled nonlinear tanks system. The simulation results clearly demonstrate the superiority of the presented ILC approach over the conventional PID controller to cope with the nonlinearities presented in the dynamic system.
Abstract: The growing popularity of solid state thermoelectric
devices in cooling applications has sparked an increasing diversity of
thermoelectric coolers (TECs) on the market, commonly known as
“Peltier modules”. They can also be used as generators, converting
a temperature difference into electric power, and opportunities are
plentiful to make use of these devices as thermoelectric generators
(TEGs) to supply energy to low power, autonomous embedded
electronic applications. Their adoption as energy harvesters in this
new domain of usage is obstructed by the complex thermoelectric
models commonly associated with TEGs. Low cost TECs for the
consumer market lack the required parameters to use the models
because they are not intended for this mode of operation, thereby
urging an alternative method to obtain electric power estimations
in specific operating conditions. The design of the test setup
implemented in this paper is specifically targeted at benchmarking
commercial, off-the-shelf TECs for use as energy harvesters in
domestic environments: applications with limited temperature
differences and space available. The usefulness is demonstrated by
testing and comparing single and multi stage TECs with different
sizes. The effect of a boost converter stage on the thermoelectric
end-to-end efficiency is also discussed.
Abstract: For design optimization with high-dimensional expensive problems, an effective and efficient optimization methodology is desired. This work proposes a series of modification to the Differential Evolution (DE) algorithm for solving computation Intensive Black-Box Problems. The proposed methodology is called Radial Basis Meta-Model Algorithm Assisted Differential Evolutionary (RBF-DE), which is a global optimization algorithm based on the meta-modeling techniques. A meta-modeling assisted DE is proposed to solve computationally expensive optimization problems. The Radial Basis Function (RBF) model is used as a surrogate model to approximate the expensive objective function, while DE employs a mechanism to dynamically select the best performing combination of parameters such as differential rate, cross over probability, and population size. The proposed algorithm is tested on benchmark functions and real life practical applications and problems. The test results demonstrate that the proposed algorithm is promising and performs well compared to other optimization algorithms. The proposed algorithm is capable of converging to acceptable and good solutions in terms of accuracy, number of evaluations, and time needed to converge.
Abstract: In this paper, we present a method of applying
Independent Topic Analysis (ITA) to increasing the number of
document data. The number of document data has been increasing
since the spread of the Internet. ITA was presented as one method
to analyze the document data. ITA is a method for extracting the
independent topics from the document data by using the Independent
Component Analysis (ICA). ICA is a technique in the signal
processing; however, it is difficult to apply the ITA to increasing
number of document data. Because ITA must use the all document
data so temporal and spatial cost is very high. Therefore, we
present Incremental ITA which extracts the independent topics from
increasing number of document data. Incremental ITA is a method
of updating the independent topics when the document data is added
after extracted the independent topics from a just previous the data.
In addition, Incremental ITA updates the independent topics when the
document data is added. And we show the result applied Incremental
ITA to benchmark datasets.