Particle Swarm optimization (PSO) algorithms have been proposed to solve engineering problems that require to find an optimal point of operation. There are several embedded applications which requires to solve online ...
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Particle Swarm optimization (PSO) algorithms have been proposed to solve engineering problems that require to find an optimal point of operation. There are several embedded applications which requires to solve online optimization problems with a highperformance. However, the PSO suffers on large execution times, and this fact becomes evident when using Reduced Instruction Set Computer (RISC) microprocessors in which the operational frequencies are low in comparison with the high operational frequencies of traditional personal computers (PCs). This paper compares two hardware implementations of the parallel PSO algorithm using an efficient floating-point arithmetic which perform computations with large dynamic range and high precision. The full-parallel and the partially-parallel PSO architectures allow the parallel capabilities of the PSO to be exploited in order to decrease the running time. Three well-known benchmark test functions have been used to validate the hardware architectures and a comparison in terms of cost in logic area, quality of the solution and performance is reported. In addition, a comparison of the execution time between the hardware and two C-code software implementations, based on a Intel Core Duo at 1.6GHz and a embedded Microblaze microprocessor at 50MHz, are presented.
For high-power traveling wave tubes, in particular, continuous-wave traveling wave tubes. we often will use the focusing magnetic field of the solenoid. Because the traveling wave tube which use of the focusing magnet...
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For high-power traveling wave tubes, in particular, continuous-wave traveling wave tubes. we often will use the focusing magnetic field of the solenoid. Because the traveling wave tube which use of the focusing magnetic field of the solenoid have good electron beam with laminar flow, good flow rate and small dynamic debunching. At present for the design of the magnetic field of the solenoid there are a variety of software. Such as CST, ANSYS, TAU and so on. Prior to conducting a computer simulation, we will take a preliminary design and calculate for the the magnetic field of the solenoid. And then use the software for a more precise calculation and optimization. To get to meet the requirements of the structure and performance, improve design efficiency and accuracy.
In this study we develop a feedback controller for a four wheeled autonomous mobile robot. The purpose of the controller is to guarantee robust performance of an aggressive maneuver (90 degrees turn) at high velocity ...
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In this study we develop a feedback controller for a four wheeled autonomous mobile robot. The purpose of the controller is to guarantee robust performance of an aggressive maneuver (90 degrees turn) at high velocity (about 10 m/s) on a loose surface (dirty road). To tackle this highly nonlinear control problem, we employ multi-objective evolutionary algorithms to explore and optimize the parameters of a neural network-based controller. The obtained controller is shown to be robust with respect to uncertainties of the robot parameters, speed of the maneuver and properties of the ground. The controller is tested using two mathematical models of significantly different complexity and accuracy.
Removing noise from data is often the first step in data analysis. highperformance image denoising algorithms have no blurring effect on the image and no changes or relocation on the image edges. This paper presents ...
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Removing noise from data is often the first step in data analysis. highperformance image denoising algorithms have no blurring effect on the image and no changes or relocation on the image edges. This paper presents a new approach for image denoising based on Partial Differential Equations (PDE) using Artificial Intelligence (AI) techniques. The nonlinear Diffusion techniques and PDE-based variational models are very popular in image restoring and processing but in this proposed heuristic method, Particle Swarm optimization (PSO) is used for Complex PDE parameter tuning by minimizing the Structural SIMilarity (SSIM) measure. Complex diffusion is a generalization of diffusion and free Schrodinger equations which has properties of both forward and inverse diffusion. The proposed method is confirmed by obtained simulation results of standard images.
In modern parallel and distributed systems, inter-processor communications are a crucial factor of performance. The time for exchanging data is usually larger than that for computing elementary operations. Consequentl...
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In modern parallel and distributed systems, inter-processor communications are a crucial factor of performance. The time for exchanging data is usually larger than that for computing elementary operations. Consequently, these communications slow down the execution of the application scheduled on the computing platform. Accounting for these communications is essential for attaining efficient hardware and software utilization. Moreover, energy dissipation due to the transfer of data between processing elements has become a major concern. Therefore, in this paper we develop an energy-aware static algorithm, which intrinsically optimizes the energy consumption due to the transfer of data in a distributed system. This is achieved by properly allocating and scheduling the tasks that constitute the applications on the processing elements, minimizing inter-processor communications. The proposed algorithm is a new Cellular Genetic Algorithm based on task clustering techniques. That is, the genetic operators work considering groups of tasks instead of applying them directly on the tasks. Simulation results showed that this algorithm is very compelling in terms of application completion time, inter-processor communication and energy communication dissipation.
The femtocell networks that use Home eNodeB and existing networks as backhaul connectivity can fulfill the upcoming demand of high data rate for wireless communication system as well as can extend the coverage area. I...
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ISBN:
(纸本)9781424457267;9780769539614
The femtocell networks that use Home eNodeB and existing networks as backhaul connectivity can fulfill the upcoming demand of high data rate for wireless communication system as well as can extend the coverage area. It is also of interest to minimize operational effort by introducing self-optimizing mechanisms, and the optimization of the Home eNodeB involved handover is an important goal of LTE-Advanced. Since the different network architecture and functionality between Home eNodeB and LTE eNodeB, the handover procedure between the femtocell and macrocell should be modified in LTE network. In this paper, modified signaling procedure of handover is presented in the Home eNodeB gateway based femtocell network architecture. A new handover algorithm based on the UE's speed and QoS is proposed. The comparison between the proposed algorithm and the traditional handover algorithm shows that the algorithms proposed in this paper have a better performance in the reducing of unnecessary handovers and the number of handovers.
A portfolio selection problem is about finding an optimal scheme to allocate a fixed amount of capital to a set of available assets. The optimal scheme is very helpful for investors in making decisions. However, findi...
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A portfolio selection problem is about finding an optimal scheme to allocate a fixed amount of capital to a set of available assets. The optimal scheme is very helpful for investors in making decisions. However, finding the optimal scheme is difficult and time-consuming especially when the number of assets is large and some actual investment constraints are considered. This paper proposes a new approach based on estimation of distribution algorithms (EDAs) for solving a cardinality constrained portfolio selection (CCPS) problem. The proposed algorithm, termed PBIL-CCPS, hybridizes an EDA called population-based incremental learning (PBIL) algorithm and a continuous PBIL (PBILc) algorithm, to optimize the selection of assets and the allocation of capital respectively. The proposed algorithm adopts an adaptive parameter control strategy and an elitist strategy. The performance of the proposed algorithm is compared with a genetic algorithm and a particle swarm optimization algorithm. The results demonstrate that the proposed algorithm can achieve a satisfactory result for portfolio selection and perform well in searching nondominated portfolios with high expected returns.
Fuzzy control is a nonlinear control method;its performance depends on the fuzzy control rules, quantification factor and the scale factor. Because these multiple objectives are influence with each others, therefore i...
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This paper presents the design, development and implementation of an automation system of batch process suitable for vulcanization accelerator production applications. The unique feature of the accelerator production ...
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This paper presents the design, development and implementation of an automation system of batch process suitable for vulcanization accelerator production applications. The unique feature of the accelerator production process is that it has complex nonlinear, time-varying and large time-delay characteristics. Based on its kinetics characteristics and technical analysis of the batch process, adaptive fuzzy PID controller is adopted to optimize the production performance. The hardware and software platform built on the multifunction process control equipment and SIMATIC PCS7 allows the real-time execution of the advanced process control techniques. Results obtained by the simulation execution of the proposed algorithms are also presented. The final experimental results show that it meets the control requirements of batch process for vulcanization accelerator production well.
A promising way to guarantee dependability of service-based systems (SBSs) is replanning service bindings dynamically with the environment changing. As embedded into system execution, such replanning process will dire...
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A promising way to guarantee dependability of service-based systems (SBSs) is replanning service bindings dynamically with the environment changing. As embedded into system execution, such replanning process will directly affect the overall performance of SBSs. While various replanning techniques have been proposed up to now, it is still challenging that how to provide cost-effective replanning methods for high-quality SBSs. In this paper, we propose a practical solution by improving the flexibility of replanning and establish an adaptive replanning mechanism for that. This new mechanism places more emphasis on the cause-effect relationship among system execution states, solution space changes, replanning strategies and their potential effects in system adaptation, and focuses on improving the following three leading aspects, i.e. replanning trigger, search scope determination and service selection, which affect the actual cost and effect of replanning. The main idea and key algorithms for implementing this adaptive replanning mechanism are presented. Besides this, empirical study based on a load rate querying service is used to illustrate and evaluate our approach.
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