Abstract: This paper presents the solution of power economic dispatch (PED) problem of generating units with valve point effects and multiple fuel options using Self-Adaptive Differential Evolution (SDE) algorithm. The global optimal solution by mathematical approaches becomes difficult for the realistic PED problem in power systems. The Differential Evolution (DE) algorithm is found to be a powerful evolutionary algorithm for global optimization in many real problems. In this paper the key parameters of control in DE algorithm such as the crossover constant CR and weight applied to random differential F are self-adapted. The PED problem formulation takes into consideration of nonsmooth fuel cost function due to valve point effects and multi fuel options of generator. The proposed approach has been examined and tested with the numerical results of PED problems with thirteen-generation units including valve-point effects, ten-generation units with multiple fuel options neglecting valve-point effects and ten-generation units including valve-point effects and multiple fuel options. The test results are promising and show the effectiveness of proposed approach for solving PED problems.
Abstract: This paper presents an indirect adaptive stabilization
scheme for first-order continuous-time systems under saturated input
which is described by a sigmoidal function. The singularities are
avoided through a modification scheme for the estimated plant
parameter vector so that its associated Sylvester matrix is guaranteed
to be non-singular and then the estimated plant model is controllable.
The modification mechanism involves the use of a hysteresis
switching function. An alternative hybrid scheme, whose estimated
parameters are updated at sampling instants is also given to solve a
similar adaptive stabilization problem. Such a scheme also uses
hysteresis switching for modification of the parameter estimates so as
to ensure the controllability of the estimated plant model.
Abstract: The goals of the present research are to estimate Six Sigma implementation in Latvian commercial banks and to identify the perceived benefits of its implementation. To achieve the goals, the authors used sequential explanatory method. To obtain empirical data, the authors have developed the questionnaire and adapted it for the employees of Latvian commercial banks. The questions are related to Six Sigma implementation and its perceived benefits. The questionnaire mainly consists of closed questions, the evaluation of which is based on 5 point Likert scale. The obtained empirical data has shown that of the two hypotheses put forward in the present research – Hypothesis 1 – has to be rejected, while Hypothesis 2 has been partially confirmed. The authors have also faced some research limitations related to the fact that the participants in the questionnaire belong to different rank of the organization hierarchy.
Abstract: Project managers are the ultimate responsible for the
overall characteristics of a project, i.e. they should deliver the project
on time with minimum cost and with maximum quality. It is vital for
any manager to decide a trade-off between these conflicting
objectives and they will be benefited of any scientific decision
support tool. Our work will try to determine optimal solutions (rather
than a single optimal solution) from which the project manager will
select his desirable choice to run the project. In this paper, the
problem in project scheduling notated as
(1,T|cpm,disc,mu|curve:quality,time,cost) will be studied. The
problem is multi-objective and the purpose is finding the Pareto
optimal front of time, cost and quality of a project
(curve:quality,time,cost), whose activities belong to a start to finish
activity relationship network (cpm) and they can be done in different
possible modes (mu) which are non-continuous or discrete (disc), and
each mode has a different cost, time and quality . The project is
constrained to a non-renewable resource i.e. money (1,T). Because
the problem is NP-Hard, to solve the problem, a meta-heuristic is
developed based on a version of genetic algorithm specially adapted
to solve multi-objective problems namely FastPGA. A sample project
with 30 activities is generated and then solved by the proposed
method.
Abstract: In this paper we present a modification to existed model of threshold for shot cut detection, which is able to adapt itself to the sequence statistics and operate in real time, because it use for calculation only previously evaluated frames. The efficiency of proposed modified adaptive threshold scheme was verified through extensive test experiment with several similarity metrics and achieved results were compared to the results reached by the original model. According to results proposed threshold scheme reached higher accuracy than existed original model.
Abstract: It is not easy to imagine how the existing city can be
converted to the principles of sustainability, however, the need for
innovation, requires a pioneering phase which must address the main
problems of rehabilitation of the operating models of the city. Today,
however, there is a growing awareness that the identification and
implementation of policies and measures to promote the adaptation,
resilience and reversibility of the city, require the contribution of our
discipline. This breakthrough is present in some recent international
experiences of Climate Plans, in which the envisaged measures are
closely interwoven with those of urban planning. These experiences,
provide some answers principle questions, such as: how the strategies
to combat climate can be integrated in the instruments of the local
government; what new and specific analysis must be introduced in
urban planning in order to understand the issues of urban
sustainability, and how the project compares with different spatial
scales.
Abstract: There are two common types of operational research techniques, optimisation and metaheuristic methods. The latter may be defined as a sequential process that intelligently performs the exploration and exploitation adopted by natural intelligence and strong inspiration to form several iterative searches. An aim is to effectively determine near optimal solutions in a solution space. In this work, a type of metaheuristics called Ant Colonies Optimisation, ACO, inspired by a foraging behaviour of ants was adapted to find optimal solutions of eight non-linear continuous mathematical models. Under a consideration of a solution space in a specified region on each model, sub-solutions may contain global or multiple local optimum. Moreover, the algorithm has several common parameters; number of ants, moves, and iterations, which act as the algorithm-s driver. A series of computational experiments for initialising parameters were conducted through methods of Rigid Simplex, RS, and Modified Simplex, MSM. Experimental results were analysed in terms of the best so far solutions, mean and standard deviation. Finally, they stated a recommendation of proper level settings of ACO parameters for all eight functions. These parameter settings can be applied as a guideline for future uses of ACO. This is to promote an ease of use of ACO in real industrial processes. It was found that the results obtained from MSM were pretty similar to those gained from RS. However, if these results with noise standard deviations of 1 and 3 are compared, MSM will reach optimal solutions more efficiently than RS, in terms of speed of convergence.
Abstract: Ten lactating multiparous Holstein cows were used in
a cross-over design with two dietary treatments and 28-d periods
(with 14 d as an adaptation) to study the effect of restaurant fat on
milk production and composition. Each cow was offered 14.7 kg
DM /d of the basal concentrate diet based on barley and corn (crude
protein = 17.7%, neutral detergent fiber = 23.5%, and acid detergent
fiber = 5.8% of dry matter) with free access to alfalfa. Dietary
treatments were arranged as supplying each cow with 0 (CONTROL)
or 150 g/day (RF) of restaurant fat. Supplemental RF did not
significantly (P > 0.25) affect milk yield, composition, and
composition yields, except for milk fat contents. Milk fat contents
were depressed (P < 0.05) with supplemental RF. Our results indicate
that RF could depress milk fat without affecting milk yield and that
the depression in milk fat in response to RF precedes the depression
in milk yield.
Abstract: One of the purposes of the robust method of
estimation is to reduce the influence of outliers in the data, on the
estimates. The outliers arise from gross errors or contamination from
distributions with long tails. The trimmed mean is a robust estimate.
This means that it is not sensitive to violation of distributional
assumptions of the data. It is called an adaptive estimate when the
trimming proportion is determined from the data rather than being
fixed a “priori-.
The main objective of this study is to find out the robustness
properties of the adaptive trimmed means in terms of efficiency, high
breakdown point and influence function. Specifically, it seeks to find
out the magnitude of the trimming proportion of the adaptive
trimmed mean which will yield efficient and robust estimates of the
parameter for data which follow a modified Weibull distribution with
parameter λ = 1/2 , where the trimming proportion is determined by a
ratio of two trimmed means defined as the tail length. Secondly, the
asymptotic properties of the tail length and the trimmed means are
also investigated. Finally, a comparison is made on the efficiency of
the adaptive trimmed means in terms of the standard deviation for the
trimming proportions and when these were fixed a “priori".
The asymptotic tail lengths defined as the ratio of two trimmed
means and the asymptotic variances were computed by using the
formulas derived. While the values of the standard deviations for the
derived tail lengths for data of size 40 simulated from a Weibull
distribution were computed for 100 iterations using a computer
program written in Pascal language.
The findings of the study revealed that the tail lengths of the
Weibull distribution increase in magnitudes as the trimming
proportions increase, the measure of the tail length and the adaptive
trimmed mean are asymptotically independent as the number of
observations n becomes very large or approaching infinity, the tail
length is asymptotically distributed as the ratio of two independent
normal random variables, and the asymptotic variances decrease as
the trimming proportions increase. The simulation study revealed
empirically that the standard error of the adaptive trimmed mean
using the ratio of tail lengths is relatively smaller for different values
of trimming proportions than its counterpart when the trimming
proportions were fixed a 'priori'.
Abstract: Pattern matching based on regular tree grammars have been widely used in many areas of computer science. In this paper, we propose a pattern matcher within the framework of code generation, based on a generic and a formalized approach. According to this approach, parsers for regular tree grammars are adapted to a general pattern matching solution, rather than adapting the pattern matching according to their parsing behavior. Hence, we first formalize the construction of the pattern matches respective to input trees drawn from a regular tree grammar in a form of the so-called match trees. Then, we adopt a recently developed generic parser and tightly couple its parsing behavior with such construction. In addition to its generality, the resulting pattern matcher is characterized by its soundness and efficient implementation. This is demonstrated by the proposed theory and by the derived algorithms for its implementation. A comparison with similar and well-known approaches, such as the ones based on tree automata and LR parsers, has shown that our pattern matcher can be applied to a broader class of grammars, and achieves better approximation of pattern matches in one pass. Furthermore, its use as a machine code selector is characterized by a minimized overhead, due to the balanced distribution of the cost computations into static ones, during parser generation time, and into dynamic ones, during parsing time.
Abstract: Keystroke authentication is a new access control system
to identify legitimate users via their typing behavior. In this paper,
machine learning techniques are adapted for keystroke authentication.
Seven learning methods are used to build models to differentiate user
keystroke patterns. The selected classification methods are Decision
Tree, Naive Bayesian, Instance Based Learning, Decision Table, One
Rule, Random Tree and K-star. Among these methods, three of them
are studied in more details. The results show that machine learning
is a feasible alternative for keystroke authentication. Compared to
the conventional Nearest Neighbour method in the recent research,
learning methods especially Decision Tree can be more accurate. In
addition, the experiment results reveal that 3-Grams is more accurate
than 2-Grams and 4-Grams for feature extraction. Also, combination
of attributes tend to result higher accuracy.
Abstract: Human activities are increasingly based on the use of remote resources and services, and on the interaction between
remotely located parties that may know little about each other. Mobile agents must be prepared to execute on different hosts with
various environmental security conditions. The aim of this paper is to
propose a trust based mechanism to improve the security of mobile
agents and allow their execution in various environments. Thus, an
adaptive trust mechanism is proposed. It is based on the dynamic interaction between the agent and the environment. Information
collected during the interaction enables generation of an environment
key. This key informs on the host-s trust degree and permits the mobile agent to adapt its execution. Trust estimation is based on
concrete parameters values. Thus, in case of distrust, the source of problem can be located and a mobile agent appropriate behavior can
be selected.
Abstract: It has formed an essential issue that Climate Change, composed of highly knowledge complexity, reveals its significant impact on human existence. Therefore, specific national policies, some of which present the educational aspects, have been published for overcoming the imperative problem. Accordingly, the study aims to analyze as well as integrate the relationship between Climate Change and environmental education and apply the perspective of concept map to represent the knowledge contents and structures of Climate Change; by doing so, knowledge contents of Climate Change could be represented in an even more comprehensive way and manipulated as the tool for environmental education. The method adapted for this study is knowledge conversion model compounded of the platform for experts and teachers, who were the participants for this study, to cooperate and combine each participant-s standpoints into a complete knowledge framework that is the foundation for structuring the concept map. The result of this research contains the important concepts, the precise propositions and the entire concept map for representing the robust concepts of Climate Change.
Abstract: In diversity rich environments, such as in Ultra-
Wideband (UWB) applications, the a priori determination of the
number of strong diversity branches is difficult, because of the considerably large number of diversity paths, which are characterized
by a variety of power delay profiles (PDPs). Several
Rake implementations have been proposed in the past, in order to reduce the number of the estimated and combined paths. To this
aim, we introduce two adaptive Rake receivers, which combine
a subset of the resolvable paths considering simultaneously the
quality of both the total combining output signal-to-noise ratio (SNR) and the individual SNR of each path. These schemes achieve
better adaptation to channel conditions compared to other known receivers, without further increasing the complexity. Their performance
is evaluated in different practical UWB channels, whose models are based on extensive propagation measurements. The
proposed receivers compromise between the power consumption,
complexity and performance gain for the additional paths, resulting in important savings in power and computational resources.
Abstract: This paper presents an adaptive technique for generation
of data required for construction of artificial neural network-based
performance model of nano-scale CMOS inverter circuit. The training
data are generated from the samples through SPICE simulation. The
proposed algorithm has been compared to standard progressive sampling
algorithms like arithmetic sampling and geometric sampling.
The advantages of the present approach over the others have been
demonstrated. The ANN predicted results have been compared with
actual SPICE results. A very good accuracy has been obtained.
Abstract: Many Wireless Sensor Network (WSN) applications necessitate secure multicast services for the purpose of broadcasting delay sensitive data like video files and live telecast at fixed time-slot. This work provides a novel method to deal with end-to-end delay and drop rate of packets. Opportunistic Routing chooses a link based on the maximum probability of packet delivery ratio. Null Key Generation helps in authenticating packets to the receiver. Markov Decision Process based Adaptive Scheduling algorithm determines the time slot for packet transmission. Both theoretical analysis and simulation results show that the proposed protocol ensures better performance in terms of packet delivery ratio, average end-to-end delay and normalized routing overhead.
Abstract: This research adapts experimental design to investigate
the effect of conditioning or not and pre-exposure or not on brand
attitude, so it is a 2×2=4 factorial design. The results show that the
brand attitude of conditioning group is significantly higher than that of
unconditioning group. The brand attitude with pre-exposure is
significantly higher than that without pre-exposure. Conditioning or
not and pre-exposure or not have significant interaction. No matter the
celebrity is pre-exposure or not, the brand attitude is higher under
conditioning process.
Abstract: Adaptive control involves modifying the control law
used by the controller to cope with the fact that the parameters of the
system being controlled change drastically due to change in
environmental conditions or in system itself. This technique is based
on the fundamental characteristic of adaptation of living organism.
The adaptive control process is one that continuously and
automatically measures the dynamic behavior of plant, compares it
with the desired output and uses the difference to vary adjustable
system parameters or to generate an actuating signal in such a way so
that optimal performance can be maintained regardless of system
changes. This paper deals with application of model reference
adaptive control scheme in first order system. The rule which is used
for this application is MIT rule. This paper also shows the effect of
adaptation gain on the system performance. Simulation is done in
MATLAB and results are discussed in detail.
Abstract: According to the interaction of inflation and
unemployment, expectation of the rate of inflation in Croatia is
estimated. The interaction between inflation and unemployment is
shown by model based on three first-order differential i.e. difference
equations: Phillips relation, adaptive expectations equation and
monetary-policy equation. The resulting equation is second order
differential i.e. difference equation which describes the time path of
inflation. The data of the rate of inflation and the rate of
unemployment are used for parameters estimation. On the basis of
the estimated time paths, the stability and convergence analysis is
done for the rate of inflation.
Abstract: The aeration process via injectors is used to combat
the lack of oxygen in lakes due to eutrophication. A 3D numerical
simulation of the resulting flow using a simplified model is presented.
In order to generate the best dynamic in the fluid with respect to
the aeration purpose, the optimization of the injectors location is
considered. We propose to adapt to this problem the topological
sensitivity analysis method which gives the variation of a criterion
with respect to the creation of a small hole in the domain. The main
idea is to derive the topological sensitivity analysis of the physical
model with respect to the insertion of an injector in the fluid flow
domain. We propose in this work a topological optimization algorithm
based on the studied asymptotic expansion. Finally we present some
numerical results, showing the efficiency of our approach