In this paper, a procedure for system decompositon is developed for decentralized multivariable systems. Optimal input-output pairing techniques are used to rearrange a large multivariable system into a structure that...
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A high call blocking rate is a consequence of an inefficient utilization of system resources, which is often caused by a load imbalance in the network. Load imbalances are common in wireless networks with a large numb...
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A high call blocking rate is a consequence of an inefficient utilization of system resources, which is often caused by a load imbalance in the network. Load imbalances are common in wireless networks with a large number of cellular users. This paper investigates a load-balancing scheme for mobile networks that optimizes cellular performance with constraints of physical resource limits and users quality of service demands. In order to efficiently utilize the system resources, an intelligent distributed antenna system (IDAS) fed by a multi base transceiver station (BTS) has the ability to distribute the system resources over a given geographic area. To enable load balancing among distributed antenna modules we dynamically allocate the remote antenna modules to the BTSs using an intelligent algorithm. A self-optimizing network for an IDAS is formulated as an integer based linear constrained optimization problem, which tries to balance the load among the BTSs. A discrete particle swarm optimization (DPSO) algorithm as an evolutionary algorithm is proposed to solve the optimization problem. The computational results of the DPSO algorithm demonstrate optimum performance for small-scale networks and near-optimum performance for large-scale networks. The DPSO algorithm is faster with marginally less complexity than an exhaustive search algorithm.
Presented herein is a methodology for the multi-objective optimization of material distribution of functionally graded cylindrical shells for steady thermomechanical processes. The proposed approach focuses on isotrop...
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Presented herein is a methodology for the multi-objective optimization of material distribution of functionally graded cylindrical shells for steady thermomechanical processes. The proposed approach focuses on isotropic metal/ceramic and metal/metal functionally graded materials, which offer great promise in high temperature and high heat flux applications. The material composition is assumed to vary only in the thickness direction. The volume fractions of the constituent material phases at a point are obtained through piecewise cubic interpolation of volume fractions defined at a finite number of evenly spaced control points. The effective material properties are estimated using the self-consistent homogenization scheme. The volume fractions at the control points, which are chosen as the design variables, are optimized using an elitist, non-dominated sorting multi-objective genetic algorithm. Candidate designs are evaluated using an exact power-series solution to the two-dimensional quasi-static heat conduction and plane strain thermoelasticity problems. The formulation, which is applicable to both thin and thick functionally graded shells, can also be used to analyze and optimize functionally graded plates in the limit that the midsurface radius of the shell approaches infinity. The proposed methodology is illustrated by optimizing the material composition profile for two model problems. In the first model problem, both the mass and the peak hoop stress of Zirconia/Titanium alloy plates and shells are simultaneously minimized for a prescribed temperature load with a constraint on the maximum temperature experienced by the metal. The goal of the second model problem is to simultaneously minimize the mass and maximize the factor of safety of Tungsten/Copper alloy functionally graded plates and shells under an applied heat flux, subject to a constraint on the factor of safety. (c) 2006 Elsevier Ltd. All rights reserved.
In this paper, firstly, we point out and correct a common mistake, in the literature, in the formulation of the array factor of an elliptical antenna array (EAA). Secondly, this paper deals with the optimal design of ...
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In this paper, firstly, we point out and correct a common mistake, in the literature, in the formulation of the array factor of an elliptical antenna array (EAA). Secondly, this paper deals with the optimal design of EAAs with low side lobes level using two recently proposed optimisation methods, namely, the Symbiotic Organisms Search (SOS) algorithm and the Antlion Optimisation (ALO) method. Two cases are considered. Firstly, the antennas are assumed to be evenly distributed over the ellipse contour, i.e. their positions are fixed, and the optimisation problem is to search for the current amplitudes ('s) that minimise the maximum side lobe level (SLL). Secondly, the antennas are assumed to be uniformly fed, i.e. I-n's are assumed to be unity, and the optimisation problem is to search for the elements positions (along the ellipse) that minimise the maximum SLL. In both cases, SOS and ALO are used and their results are compared together. It is found that SOS is statistically better than ALO, though ALO consumes less CPU time to converge to the solution.
In recent years, analysis and interpretation of video sequences to detect and track objects of interest had become an active research field in computer vision and image processing. Detection and tracking includes extr...
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In recent years, analysis and interpretation of video sequences to detect and track objects of interest had become an active research field in computer vision and image processing. Detection and tracking includes extraction of moving object from frames and continuous tracking it thereafter forming persistent object trajectories over time. There are some really smart techniques proposed by researchers for efficient and robust detection or tracking of objects in videos. A comprehensive coverage of such innovative techniques for which solutions have been motivated by theories of soft computing approaches is proposed. The main objective of this research investigation is to study and highlight efforts of researchers who had conducted some brilliant work on soft computing based detection and tracking approaches in video sequence. The study is novel as it traces rise of soft computing methods in field of object detection and tracking in videos which has been neglected over the years. The survey is compilation of studies on neural network, deep learning, fuzzy logic, evolutionary algorithms, hybrid and recent innovative approaches that have been applied to field of detection and tracking. The paper also highlights benchmark datasets available to researchers for experimentation and validation of their own algorithms. Major research challenges in the field of detection and tracking along with some recommendations are also provided. The paper provides number of analyses to guide future directions of research and advocates for more applications of soft computing approaches for object detection and tracking approaches in videos. The paper is targeted at young researchers who will like to see it as platform for introduction to a mature and relatively complex field. The study will be helpful in appropriate use of an existing method for systematically designing a new approach or improving performance of existing approaches. (C) 2018 Elsevier B.V. All rights reserved.
We develop techniques for the implementation of motion estimation. Optical flow estimation has been proposed as a preprocessing step for many high-level vision algorithms. Gradient-based approaches compute the spatio-...
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We develop techniques for the implementation of motion estimation. Optical flow estimation has been proposed as a preprocessing step for many high-level vision algorithms. Gradient-based approaches compute the spatio-temporal derivatives, differentiating the image with respect to time and thus computing the optical flow field. Horn and Schunck's method in particular is considered a benchmarking algorithm of gradient-based differential methods, useful and powerful, yet simple and fast. They formulated an optical flow constraint equation from which to compute optical flow, which cannot fully determine the flow but can give the component of the flow in the direction of the intensity gradient. An additional constraint must be imposed, introducing a supplementary assumption to ensure a smooth variation in the flow across the image. The brightness derivatives involved in the equation system were estimated by Horn and Schunck using first differences averaging. Gradient-based methods for optical flow computation can suffer from unreliability of the image flow constraint equation in areas of an image where local brightness function is nonlinear or where there are rapid spatial or temporal changes in the intensity function. Little and Verri suggested regularization to help the numerical stability of the solution. Usually this takes the form of smoothing of the function or surface by convolving before the derivative is taken. Smoothing has the effects of suppressing noise and ensuring differentiability of discontinuities. The method proposed is a finite element method, based on a triangular mesh, in which diffusion is added into the system of equations. Thus the algorithm performs a type of smoothing while also retrieving the velocity. So the process involves diffusion with movement as opposed to the original Horn and Schunck process of movement only. In this proposed algorithm, the derivatives of image intensity are approximated using a finite element approach. Quantitative a
Increasing sediment yield is one of the important environmental challenges in river basins resulting from changing land *** current study develops an adaptive neuro fuzzy inference system(ANFIS)hy-bridized with evolut...
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Increasing sediment yield is one of the important environmental challenges in river basins resulting from changing land *** current study develops an adaptive neuro fuzzy inference system(ANFIS)hy-bridized with evolutionary algorithms to predict annual sediment yield at the catchment scale consid-ering some key factors affecting the alteration of the sediment *** key factors consist of the area of the sub-catchments,average slope of the sub-catchments,rainfall,and forest index,and the output of the model is sediment *** indices such as the Nash-Sutcliffe efficiency(NSE),root mean square error and vulnerability index(Ⅵ)were applied to evaluate the performance of the ***,hybrid models were compared in terms of complexities to select the best *** on the results in Talar River basin in Iran,several hybrid models in which particle swarm optimization(PSO),genetic algorithm,invasive weed optimization,biogeography-based optimization,and shuffled complex evolu-tion used to train the neuro fuzzy network are able to generate reliable sediment yield *** NSE of all previously listed models is more than 0.8 which means they are robust for assessing sediment yield resulting from land use change with a focus on *** proposed models are fairly similar in terms of computational complexities which implies no priority for selecting the best ***,PSO-ANFIS performed slightly better than the other models especially in terms of accuracy of the outputs due to a high NSE(0.92)and a low Ⅵ(1.9 Mg/ha).Using the proposed models is recommended due to the lower required time and data compared to a physically based models such as the The Soil and Water Assessment ***,some drawbacks restrict the application of the proposed *** example,the proposed models cannot be used for small temporal scales.
We consider the problem of finding small Golomb rulers, a hard combinatorial optimization task. This problem is here tackled by means of a hybrid evolutionary algorithm (EA). This EA incorporates ideas from greedy ran...
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We describe and critique the convergence properties of filterbased evolutionary pattern search algorithms (F-EPSAs). F-EPSAs implicitly use a filter to perform a multi-objective search for constrained problems such th...
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Nowadays, the growth of available data, known as big data, and machine learning techniques are changing our lives. The extraction of insights related to the underlying phenomena in data is key in order to improve deci...
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Nowadays, the growth of available data, known as big data, and machine learning techniques are changing our lives. The extraction of insights related to the underlying phenomena in data is key in order to improve decision-making processes. These underlying phenomena are described in emerging pattern mining by means of the description of the discriminative characteristics between the outputs of interest, which is a very important characteristic in machine learning. However, emerging pattern mining algorithms for big data environments have not been widely developed yet. This paper presents the first multi-objective evolutionary algorithm for emerging pattern mining in big data environments called BD-EFEP. BD-EFEP implements novelties for emerging pattern mining such as the MapReduce approach to improve the efficiency of the evaluation of the individuals, or the use of a token-competition-based procedure in order to boost the extraction of simple, general and reliable emerging pattern models. The experimental study performed using datasets with high number of examples shows the advantages of the algorithm proposed for the emerging pattern mining task in big data problems. Results show that the approach used by BD-EFEP opens new research lines for the extraction of high descriptive emerging patterns in big data environments.
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