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The hydrothermal coordination can be defined as a problem to determine the optimum usage of the hydroelectric and thermoelectric resources available during a period. In hydrothermal generation systems with a predominance of hydroelectric power plants, like in the Brazilian system, the problem consists in replacing the thermal generation by hydropower generation to minimize the system operational costs...
This paper presents the results of the application of a Genetic Algorithm (GA) to predict path loss for urban areas for Long Term Evolution (LTE) and Long Term Evolution Advanced (LTE — A) networks at 879 MHz using empirical propagation models. We conducted a comparative test, where simulated Free Space and Ericsson 9999 models along with their optimized versions were compared with experimental data...
The nonlinear plants with significant time delays are difficult to be controlled by classical means. In the present paper intelligent approaches are applied for the design of a nonlinear Smith predictor for compensation of the plant time delay based on a Takagi-Sugeno-Kang plant model and a fuzzy logic parallel distributed compensation (PDC). The design and the advantages of the PDC-Smith are illustrated...
This paper focuses on the effective analysis of the mine gas emission monitoring data, so as to realize the accurate and reliable mine gas emission prediction. Firstly, a weighted multiple computing models based on parametric t—norm is constructed. And a new mine gas emission combination forecasting method is proposed. The BP neural network model and the support vector machine were used...
The process of modeling of a real system usually implies an iterative approach where an initial model is incrementally modified in order to increase its accuracy with regard to available experimental data. Many approaches are discussed in the literature, among which some are based on Artificial Intelligence techniques. Continuous improvement in the mathematical model adequacy is very similar with...
In this paper we examine the use of trust metrics for punishing dishonest (cheating) and malicious (griefing) behavior in peer-to-peer massively multiplayer online games (MMOG’s). In particular, we first replicate the work of Goodman and Verbrugge and then propose a metric of our own. Our approach uses a reinforcement learning trust metric that can more rapidly and accurately detect dishonest and...
Data Envelopment Analysis (DEA) is a nonparametric methodology for estimating technical efficiency of a set of Decision Making Units (DMUs) from a dataset of inputs and outputs. This paper is devoted to computational aspects of DEA models under the application of the Principle of Least Action. This principle guarantees that the efficient closest targets are determined as benchmarks for each assessed...
In our work we present a basic outline of information retrieval process using evolutionary computation and some of the basic models which are being employed. Different evolutionary techniques like particle swarm optimization, ant colony optimization, and genetic algorithm are used for optimizing the data using different sets of algorithms which are comparatively better than traditional computing techniques...
As an initial step towards developing a portable diagnostic system for skin cancer, an optimum fourth order Debye model for the human skin at millimetre-wave band is presented. The model is obtained using heuristic genetic algorithm, which enables the precise estimation of the dispersive nature of the human skin tissue when irradiated at a high frequency band of 10 GHz to 45 GHz. The precise derivation...
Several industrial chemical processes are found tobe FOPDT (first order plus dead time). The presence of deadtime makes the transfer function to be irrational and makesthe simulation and controller design very complex. In thepresent work an FOPDT model of the pH neutralizationprocess which is found in sugar mills is considered as a subjectof study and analysis. Having obtained its Padéapproximation,...
In software project management, software effort estimation is an important task. Software effort estimation will be helpful for finding the estimation of cost, duration of the project. In the recent time, many types of model are coming but among all, COCOMO model is widely used model. In past lots of Research have been done for correct and Accurate Effort Estimation. Many Algorithm has been applying...
To improve research efficiency of engineering problems, Surrogate model has gained its popularity in replacing real engineering model. This paper proposes a kind of Global Sequential Sampling Algorithm (GSSA) based on surrogate model. With the process of iteration, GSSA can sample both in unexplored region and large-error region, then iteratively update the samples. OLHS is used as initial sampling...
Generalized Memory Polynomial (GMP) models are widely used for the linearization of power amplifiers. They offer a good tradeoff between linearization performance and implementation complexity. Their structure is defined by 8 integer parameters representing different non-linearity orders and memory lengths. These 8 degrees of freedom allow achieving very good linearization performance with a small...
In the paper the novel feature selection method, using wrapper model and ensemble approach, is presented. In the proposed method features are selected dynamically, i.e. separately for each classified object. First, a set of identical one-feature classifiers using different single feature is created and next the ensemble of features (classifiers) is selected as a solution of optimization problem using...
Accurate modelling of photovoltaic (PV) modules is necessary to understand PV cell operation and to develop maximum power point tracking (MPPT) algorithm for efficient operation of the PV system. A variety of models are proposed in the literature that use a current source, diodes and resistors to represent a PV module. The parameter values involved in the model need to be accurately estimated to improve...
Vehicle identification is one of the frequently studied problems in video surveillance. Commonly, identifying an unknown vehicle object requires a large amount of training instances. Unfortunately, in the large parking scenario, the cost may be prohibitively expensive because of the finitely waiting time from the car owners. In this paper, we show that it is possible to identify a registered vehicle...
This study aims to optimize patient flow in emergency departments (ED) while minimizing associated costs. In order to compare the effects of the optimization, a simulation model for emergency departments has been implemented using District Event Simulation (DES) and queuing theory, while for the optimization, Genetic Algorithm is used to find the best arrangements. Principally, a discrete event based,...
The problem of estimating process derivatives in the on-line optimization case has been extensively addressed in last few decades. In fact, it has received such attention due to the fact that earlier methods have encountered major drawbacks in the past. These drawbacks have either caused the non-convergence of the optimization algorithm, or led to the decrease in its performance. This paper investigates...
In this paper, a mathematical model for vehicle-occupant frontal crash is developed. The developed model is represented as a double-spring-mass-damper system, whereby the front mass and the rear mass represent the vehicle chassis and the occupant, respectively. The springs and dampers in the model are nonlinear piecewise functions of displacements and velocities respectively. More specifically, a...
The computational power of nature is a mystery, although we have many computational models, but natural phenomena presents various challenges for them every day. NP-Complete and NP-Hard problems demand efficient solutions, but none of these problems are known to have a polynomial time solution. Nature inspired algorithms are playing major role in solving those problems with amazing efficiency. In...
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