Abstract: A self tuning PID control strategy using reinforcement
learning is proposed in this paper to deal with the control of wind
energy conversion systems (WECS). Actor-Critic learning is used to
tune PID parameters in an adaptive way by taking advantage of the
model-free and on-line learning properties of reinforcement learning
effectively. In order to reduce the demand of storage space and to
improve the learning efficiency, a single RBF neural network is used
to approximate the policy function of Actor and the value function of
Critic simultaneously. The inputs of RBF network are the system
error, as well as the first and the second-order differences of error.
The Actor can realize the mapping from the system state to PID
parameters, while the Critic evaluates the outputs of the Actor and
produces TD error. Based on TD error performance index and
gradient descent method, the updating rules of RBF kernel function
and network weights were given. Simulation results show that the
proposed controller is efficient for WECS and it is perfectly
adaptable and strongly robust, which is better than that of a
conventional PID controller.
Abstract: As the world move to the accomplishment of Performance Based Engineering philosophies in seismic design of Civil Engineering structures, new seismic design provisions require Structural Engineers to perform both static and dynamic analysis for the design of structures. While Linear Equivalent Static Analysis is performed for regular buildings up to 90m height in zone I and II, Dynamic Analysis should be performed for regular and irregular buildings in zone IV and V. Dynamic Analysis can take the form of a dynamic Time History Analysis or a linear Response Spectrum Analysis. In present study, Multi-storey irregular buildings with 20 stories have been modeled using software packages ETABS and SAP 2000 v.15 for seismic zone V in India. This paper also deals with the effect of the variation of the building height on the structural response of the shear wall building. Dynamic responses of building under actual earthquakes, EL-CENTRO 1949 and CHI-CHI Taiwan 1999 have been investigated. This paper highlights the accuracy and exactness of Time History analysis in comparison with the most commonly adopted Response Spectrum Analysis and Equivalent Static Analysis.
Abstract: This paper is a description approach to predict
incoming and outgoing data rate in network system by using
association rule discover, which is one of the data mining
techniques. Information of incoming and outgoing data in each
times and network bandwidth are network performance
parameters, which needed to solve in the traffic problem. Since
congestion and data loss are important network problems. The result
of this technique can predicted future network traffic. In addition,
this research is useful for network routing selection and network
performance improvement.
Abstract: The importance of low power consumption is widely
acknowledged due to the increasing use of portable devices, which
require minimizing the consumption of energy. Energy dissipation is
heavily dependent on the software used in the system. Applying
design patterns in object-oriented designs is a common practice
nowadays. In this paper we analyze six design patterns and explore
the effect of them on energy consumption and performance.
Abstract: In recent years, tuned mass damper (TMD) control systems for civil engineering structures have attracted considerable attention. This paper emphasizes on the application of particle swarm application (PSO) to design and optimize the parameters of the TMD control scheme for achieving the best results in the reduction of the building response under earthquake excitations. The Integral of the Time multiplied Absolute value of the Error (ITAE) based on relative displacement of all floors in the building is taken as a performance index of the optimization criterion. The problem of robustly TMD controller design is formatted as an optimization problem based on the ITAE performance index to be solved using the PSO technique which has a story ability to find the most optimistic results. An 11- story realistic building, located in the city of Rasht, Iran is considered as a test system to demonstrate effectiveness of the proposed method. The results analysis through the time-domain simulation and some performance indices reveals that the designed PSO based TMD controller has an excellent capability in reduction of the seismically excited example building.
Abstract: A hybrid feature based adaptive particle filter algorithm is presented for object tracking in real scenarios with static camera.
The hybrid feature is combined by two effective features: the Grayscale Arranging Pairs (GAP) feature and the color histogram feature. The GAP feature has high discriminative ability even under conditions of severe illumination variation and dynamic background
elements, while the color histogram feature has high reliability to identify the detected objects. The combination of two features covers the shortage of single feature. Furthermore, we adopt an updating
target model so that some external problems such as visual angles can be overcame well. An automatic initialization algorithm is introduced which provides precise initial positions of objects. The experimental
results show the good performance of the proposed method.
Abstract: With data centers, end-users can realize the pervasiveness of services that will be one day the cornerstone of our lives. However, data centers are often classified as computing systems that consume the most amounts of power. To circumvent such a problem, we propose a self-adaptive weighted sum methodology that jointly optimizes the performance and power consumption of any given data center. Compared to traditional methodologies for multi-objective optimization problems, the proposed self-adaptive weighted sum technique does not rely on a systematical change of weights during the optimization procedure. The proposed technique is compared with the greedy and LR heuristics for large-scale problems, and the optimal solution for small-scale problems implemented in LINDO. the experimental results revealed that the proposed selfadaptive weighted sum technique outperforms both of the heuristics and projects a competitive performance compared to the optimal solution.
Abstract: Literature reveals that many investors rely on technical trading rules when making investment decisions. If stock markets are efficient, one cannot achieve superior results by using these trading rules. However, if market inefficiencies are present, profitable opportunities may arise. The aim of this study is to investigate the effectiveness of technical trading rules in 34 emerging stock markets. The performance of the rules is evaluated by utilizing White-s Reality Check and the Superior Predictive Ability test of Hansen, along with an adjustment for transaction costs. These tests are able to evaluate whether the best model performs better than a buy-and-hold benchmark. Further, they provide an answer to data snooping problems, which is essential to obtain unbiased outcomes. Based on our results we conclude that technical trading rules are not able to outperform a naïve buy-and-hold benchmark on a consistent basis. However, we do find significant trading rule profits in 4 of the 34 investigated markets. We also present evidence that technical analysis is more profitable in crisis situations. Nevertheless, this result is relatively weak.
Abstract: One of the major, difficult tasks in automated video
surveillance is the segmentation of relevant objects in the scene.
Current implementations often yield inconsistent results on average
from frame to frame when trying to differentiate partly occluding
objects. This paper presents an efficient block-based segmentation
algorithm which is capable of separating partly occluding objects and
detecting shadows. It has been proven to perform in real time with a
maximum duration of 47.48 ms per frame (for 8x8 blocks on a
720x576 image) with a true positive rate of 89.2%. The flexible
structure of the algorithm enables adaptations and improvements with
little effort. Most of the parameters correspond to relative differences
between quantities extracted from the image and should therefore not
depend on scene and lighting conditions. Thus presenting a
performance oriented segmentation algorithm which is applicable in
all critical real time scenarios.
Abstract: None of the processing models in the software
development has explained the software systems performance
evaluation and modeling; likewise, there exist uncertainty in the
information systems because of the natural essence of requirements,
and this may cause other challenges in the processing of software
development. By definition an extended version of UML (Fuzzy-
UML), the functional requirements of the software defined
uncertainly would be supported. In this study, the behavioral
description of uncertain information systems by the aid of fuzzy-state
diagram is crucial; moreover, the introduction of behavioral diagrams
role in F-UML is investigated in software performance modeling
process. To get the aim, a fuzzy sub-profile is used.
Abstract: Power loss reduction is one of the main targets in power industry and so in this paper, the problem of finding the optimal configuration of a radial distribution system for loss reduction is considered. Optimal reconfiguration involves the selection of the best set of branches to be opened ,one each from each loop, for reducing resistive line losses , and reliving overloads on feeders by shifting the load to adjacent feeders. However ,since there are many candidate switching combinations in the system ,the feeder reconfiguration is a complicated problem. In this paper a new approach is proposed based on a simple optimum loss calculation by determining optimal trees of the given network. From graph theory a distribution network can be represented with a graph that consists a set of nodes and branches. In fact this problem can be viewed as a problem of determining an optimal tree of the graph which simultaneously ensure radial structure of each candidate topology .In this method the refined genetic algorithm is also set up and some improvements of algorithm are made on chromosome coding. In this paper an implementation of the algorithm presented by [7] is applied by modifying in load flow program and a comparison of this method with the proposed method is employed. In [7] an algorithm is proposed that the choice of the switches to be opened is based on simple heuristic rules. This algorithm reduce the number of load flow runs and also reduce the switching combinations to a fewer number and gives the optimum solution. To demonstrate the validity of these methods computer simulations with PSAT and MATLAB programs are carried out on 33-bus test system. The results show that the performance of the proposed method is better than [7] method and also other methods.
Abstract: Demolitions of buildings have created a lot of waste
and one of it is clay bricks. The waste clay bricks were ground to
roughly cement fineness and used to partially replaced cement at
10%, 20% and 30% with w/b ratio of 0.6 and tested at 7, 28, 60, 90
and 120 days. The result shows that the compressive strength of GCB
concrete increases over age however, decreases as the level of
replacements increases. It was also found that 10% replacement of
GCB gave the highest compressive strength, however for optimum
replacement, 30% was chosen as it still attained strength of grade 30
concrete. In terms of durability performances, results show that GCB
replacement up to 30% was found to be efficient in reducing water
absorption as well as water permeability. These studies show that
GCB has the potential to be used as partial cement replacement in
making concrete.
Abstract: The use of power system stabilizers (PSSs) to damp
power system swing mode of oscillations is practical important. Our
purpose is to retune the power system stabilizer (PSS1A) parameters
in Unitrol D produced by ABB– was installed in 1995in Benghazi
North Power Plants (BNPPs) at General Electricity Company of
Libya (GECOL). The optimal values of the power system stabilizer
(PSS1A) parameters are determined off-line by a particle swarm
optimization technique (PSO). The objective is to damp the local and
inter-area modes of oscillations that occur following power system
disturbances. The retuned power system stabilizer (PSS1A) can cope
with large disturbance at different operating points and has enhanced
power system stability.
Abstract: EDF (Early Deadline First) algorithm is a very important scheduling algorithm for real- time systems . The EDF algorithm assigns priorities to each job according to their absolute deadlines and has good performance when the real-time system is not overloaded. When the real-time system is overloaded, many misdeadlines will be produced. But these misdeadlines are not uniformly distributed, which usually focus on some tasks. In this paper, we present an adaptive fuzzy control scheduling based on EDF algorithm. The improved algorithm can have a rectangular distribution of misdeadline ratios among all real-time tasks when the system is overloaded. To evaluate the effectiveness of the improved algorithm, we have done extensive simulation studies. The simulation results show that the new algorithm is superior to the old algorithm.
Abstract: In the recent past Learning Classifier Systems have
been successfully used for data mining. Learning Classifier System
(LCS) is basically a machine learning technique which combines
evolutionary computing, reinforcement learning, supervised or
unsupervised learning and heuristics to produce adaptive systems. A
LCS learns by interacting with an environment from which it
receives feedback in the form of numerical reward. Learning is
achieved by trying to maximize the amount of reward received. All
LCSs models more or less, comprise four main components; a finite
population of condition–action rules, called classifiers; the
performance component, which governs the interaction with the
environment; the credit assignment component, which distributes the
reward received from the environment to the classifiers accountable
for the rewards obtained; the discovery component, which is
responsible for discovering better rules and improving existing ones
through a genetic algorithm. The concatenate of the production rules
in the LCS form the genotype, and therefore the GA should operate
on a population of classifier systems. This approach is known as the
'Pittsburgh' Classifier Systems. Other LCS that perform their GA at
the rule level within a population are known as 'Mitchigan' Classifier
Systems. The most predominant representation of the discovered
knowledge is the standard production rules (PRs) in the form of IF P
THEN D. The PRs, however, are unable to handle exceptions and do
not exhibit variable precision. The Censored Production Rules
(CPRs), an extension of PRs, were proposed by Michalski and
Winston that exhibit variable precision and supports an efficient
mechanism for handling exceptions. A CPR is an augmented
production rule of the form: IF P THEN D UNLESS C, where
Censor C is an exception to the rule. Such rules are employed in
situations, in which conditional statement IF P THEN D holds
frequently and the assertion C holds rarely. By using a rule of this
type we are free to ignore the exception conditions, when the
resources needed to establish its presence are tight or there is simply
no information available as to whether it holds or not. Thus, the IF P
THEN D part of CPR expresses important information, while the
UNLESS C part acts only as a switch and changes the polarity of D
to ~D. In this paper Pittsburgh style LCSs approach is used for
automated discovery of CPRs. An appropriate encoding scheme is
suggested to represent a chromosome consisting of fixed size set of
CPRs. Suitable genetic operators are designed for the set of CPRs
and individual CPRs and also appropriate fitness function is proposed
that incorporates basic constraints on CPR. Experimental results are
presented to demonstrate the performance of the proposed learning
classifier system.
Abstract: In this paper, the effect of atmospheric turbulence on
bit error probability in free-space optical CDMA scheme with
Sequence Inverse Keyed (SIK) optical correlator receiver is analyzed.
Here Intensity Modulation scheme is considered for transmission.
The turbulence induced fading is described by the newly introduced
gamma-gamma pdf[1] as a tractable mathematical model for
atmospheric turbulence. Results are evaluated with Gold and Kasami
code & it is shown that Gold sequence can be used for more
efficient transmission than Kasami sequence in an atmospheric
turbulence channel.
Abstract: This paper presents the results of the experimental
tests of the cooling performance of a 12,000-Btu/h modified air
conditioner (referred to as M-AC) that use the ground as a heat sink
of a condenser. In the tests, cooling capacity of M-AC with an
optimal length of a condensing coil as well as life expectancy of
copper coil buried underground were investigated. The lengths of
copper coil fabricated and used as condenser coil of M-AC were set
at 67, 50, 40 and 30 m whereas that of a 12,000-Btu/h conventional
split-type air conditioner (referred to as C-AC) was about 22 m. The
results showed that the ground can absorb heat rejected from a
condenser of M-AC. The coefficient of performance (COP) of C-AC
was about 2.5 whereas those of M-AC were found to be higher. It
was found that the values of COP of M-AC with condensing coils of
67, 50 and 40 m long were about 6.9, 5.5 and 3.3, respectively, while
that of 30-m-long one was found to be about 2.1. The electrical
consumptions of M-AC were found lower than that of C-AC in the
range of 11.5 – 15.5%. Additionally, life expectancy of underground
condensing coil of M-AC was found to be over 7 years.
Abstract: A Comparison and evaluation of the different
condition monitoring (CM) techniques was applied experimentally
on RC e.g. Dynamic cylinder pressure and crankshaft Instantaneous
Angular Speed (IAS), for the detection and diagnosis of valve faults
in a two - stage reciprocating compressor for a programme of
condition monitoring which can successfully detect and diagnose a
fault in machine. Leakage in the valve plate was introduced
experimentally into a two-stage reciprocating compressor. The effect
of the faults on compressor performance was monitored and the
differences with the normal, healthy performance noted as a fault
signature been used for the detection and diagnosis of faults.
The paper concludes with what is considered to be a unique
approach to condition monitoring. First, each of the two most useful
techniques is used to produce a Truth Table which details the
circumstances in which each method can be used to detect and
diagnose a fault. The two Truth Tables are then combined into a
single Decision Table to provide a unique and reliable method of
detection and diagnosis of each of the individual faults introduced
into the compressor. This gives accurate diagnosis of compressor
faults.
Abstract: In the past few years there is a change in the view of high performance applications and parallel computing. Initially such applications were targeted towards dedicated parallel machines. Recently trend is changing towards building meta-applications composed of several modules that exploit heterogeneous platforms and employ hybrid forms of parallelism. The aim of this paper is to propose a model of virtual parallel computing. Virtual parallel computing system provides a flexible object oriented software framework that makes it easy for programmers to write various parallel applications.
Abstract: Mining sequential patterns from large customer transaction databases has been recognized as a key research topic in database systems. However, the previous works more focused on mining sequential patterns at a single concept level. In this study, we introduced concept hierarchies into this problem and present several algorithms for discovering multiple-level sequential patterns based on the hierarchies. An experiment was conducted to assess the performance of the proposed algorithms. The performances of the algorithms were measured by the relative time spent on completing the mining tasks on two different datasets. The experimental results showed that the performance depends on the characteristics of the datasets and the pre-defined threshold of minimal support for each level of the concept hierarchy. Based on the experimental results, some suggestions were also given for how to select appropriate algorithm for a certain datasets.