The design optimization of synchronous reluctance (SyR) machine and its extension to internal permanent magnet (IPM) motors for wide speed ranges is considered in this paper by means of a Finite Element Analysis-based...
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ISBN:
(纸本)9781467308014
The design optimization of synchronous reluctance (SyR) machine and its extension to internal permanent magnet (IPM) motors for wide speed ranges is considered in this paper by means of a Finite Element Analysis-based multi-objective genetic algorithm (MOGA). The paper is focused on the rotor design, that is controversial key aspect of the design of high saliency SyR and IPM machines, due to the difficult modeling dominated by magnetic saturation. A three step procedure is presented, to obtain a starting SyR design with the optimal torque versus torque ripple compromise and then properly include PMs into the SyR geometry, given the desired constant power speed range of the final IPM machine. The designed rotors have been extensively analyzed by computer simulations and two SyR prototypes have been realized to demonstrate the feasibility of the design procedure.
Ferroelectric Random Access Memory (FRAM) by Texas Instruments (TI) is a non-volatile memory which allows lower power and faster data throughput compared to other nonvolatile solutions. These features have accelerated...
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ISBN:
(纸本)9781479968435
Ferroelectric Random Access Memory (FRAM) by Texas Instruments (TI) is a non-volatile memory which allows lower power and faster data throughput compared to other nonvolatile solutions. These features have accelerated the interest in this technology as the future of embedded unified memory, in particular in data logging, remote sensing and Wireless Sensor Network (WSN). The application of Model Predictive Control (MPC) in WSN has gained lot of attention in the last years and it requires solving convex optimization problems in real-time. In this paper several convex optimization algorithms have been implemented and compared on a FRAM-based MSP-EXP430FR5739 node by TI, to evaluate its suitability in extending the potentialities of onboard volatile Static Random Access Memory (SRAM) for embedded optimization-based control.
In spite of many years of work by scientists and specialists on various software qualities, testing stays one of the most broadly honed and concentrated on methodologies for evaluating and improving software quality. ...
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ISBN:
(纸本)9783319606187;9783319606170
In spite of many years of work by scientists and specialists on various software qualities, testing stays one of the most broadly honed and concentrated on methodologies for evaluating and improving software quality. Our objective, in this paper, is to present how optimization techniques provide solutions to different and difficult issues in different areas of software engineering. optimization algorithms are mathematical procedures, which intends to best optimal results for the defect, fault, failure to accomplish tractability, strength, and low arrangement cost. In this paper, a comprehensive overview of software testing and metrics based on soft computing and optimization techniques is presented. In this survey, we try to explain some major problems like defect prediction, software fault prediction and their solutions by soft computing and optimization algorithms. The paper presents an overview of the usage of Mathematical optimization algorithms and soft computing approaches.
Four optimization algorithms (genetic algorithm, simulated annealing, particle swarm optimization and random forest) were applied with an MLP based auto associative neural network on two classification datasets and on...
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ISBN:
(纸本)9781479938407
Four optimization algorithms (genetic algorithm, simulated annealing, particle swarm optimization and random forest) were applied with an MLP based auto associative neural network on two classification datasets and one prediction dataset. This work was undertaken to investigate the effectiveness of using auto associative neural networks and optimization algorithms in missing data prediction and classification tasks. If performed appropriately, computational intelligence and optimization algorithm systems could lead to consistent, accurate and trustworthy predictions and classifications resulting in more adequate decisions. The results reveal GA, SA and PSO to be more efficient when compared to RF in terms of predicting the forest area to be affected by fire. GA, SA, and PSO had the same accuracy of 93.3%, while RF showed 92.99% accuracy. For the classification problems, RF showed 93.66% and 92.11% accuracy on the German credit and Heart disease datasets respectively, outperforming GA, SA and PSO.
Three modifications of the ant colony optimization method for solving the base station location problem for creating a wireless network is introduced in the article. By means of computer simulation the influence of th...
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ISBN:
(纸本)9781467376983
Three modifications of the ant colony optimization method for solving the base station location problem for creating a wireless network is introduced in the article. By means of computer simulation the influence of the input parameters to the speed of finding a problem solution is determined. It also was detected the best kind of the proposed ant colony optimization algorithms applied to the task.
The biological world is an ideal place for seeking inspiration for developing mathematical optimization algorithms. In this paper we propose two hybrid stochastic optimization algorithms that bear resemblance to the s...
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ISBN:
(纸本)9781479954964
The biological world is an ideal place for seeking inspiration for developing mathematical optimization algorithms. In this paper we propose two hybrid stochastic optimization algorithms that bear resemblance to the sexual reproduction cycle of Jellyfish and asexual reproductive cycle of species of Hydra. The performance of these two algorithms are investigated against other common optimization algorithms on a set of benchmark optimization problems. The results show that the proposed algorithms perform well.
The test case construction is amongst the most labor-intensive tasks and has significant influence on the effectiveness and efficiency in software testing. Due to the market needed for diverse types of tests, recently...
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ISBN:
(纸本)9789814585422;9789814585415
The test case construction is amongst the most labor-intensive tasks and has significant influence on the effectiveness and efficiency in software testing. Due to the market needed for diverse types of tests, recently, several number of t-way testing strategies (where t indicates the interaction strengths) have been developed adopting different approaches Algebraic, Pure computational, and optimization algorithms (OpA). This paper presents an orchestrated survey of the existing OpA t-way strategies as Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Algorithm (ACA), Particle Swarm optimization based strategy (PSTG), and Harmony Search Strategy (HSS). The results demonstrate the strength and the limitations of each strategy, thereby highlighting possible research for future work in this area.
This paper surveys dimension reduction techniques in medical big data using optimization algorithms to address challenges like computational inefficiency, overfitting, and interpretability in high-dimensional datasets...
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ISBN:
(纸本)9798350367782;9798350367775
This paper surveys dimension reduction techniques in medical big data using optimization algorithms to address challenges like computational inefficiency, overfitting, and interpretability in high-dimensional datasets. As medical data from sources like electronic health records, genomics, and imaging grow, efficient processing is essential for personalized healthcare. The paper explores feature extraction (PCA, LDA) and feature selection methods, emphasizing metaheuristic algorithms like Genetic algorithms (GA), Particle Swarm optimization (PSO), and Ant Colony optimization (ACO). These algorithms enhance machine learning model accuracy by selecting relevant features, reducing computational costs, and handling nonlinear relationships in medical data. Applications in diagnosis, treatment prediction, and disease classification are discussed. Future research aims to integrate various optimization strategies and deep learning for more effective dimensionality reduction in healthcare.
Conventional silicon optical waveguide can be effectively coupled to plasmonic waveguide, but there is no structure of comparable coupling efficiency, wide optical bandwidth and polarization independence to convert li...
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ISBN:
(纸本)9781510637054
Conventional silicon optical waveguide can be effectively coupled to plasmonic waveguide, but there is no structure of comparable coupling efficiency, wide optical bandwidth and polarization independence to convert light from silicon waveguide to metal-dielectric-metal (MDM) waveguide. In this paper, we investigate a novel mode converter based on the embedded coding metamaterials to effectively convert the TE/TM mode in a silicon waveguide to the SPPs mode. We use some optimization methods (genetic algorithm, particle swarm optimization, multi-traversal direct-binary search and simulated annealing) in the design of coding metamaterials to improve the performance metrics. In order to obtain better results, we change the value of different parameters under the control of a single variable to study its influence on the structure of the design. The simulation results have been demonstrated numerically that high transmission efficiency is up to 93% and the bandwidth can cover from 1450 nm to 1650 nm, the converter can perform polarization-invariant conversion as well. Compared with the previous researches, we not only propose a high-performance mode converter but also introduce an efficient algorithm for the inverse design of coding metamaterials.
The aim of this work is to introduce an effective tool in order to help the EM designer to select the best optimization algorithm through an easy-to-manage classification of Evolutionary algorithms. In fact, choosing ...
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ISBN:
(纸本)9781479978069
The aim of this work is to introduce an effective tool in order to help the EM designer to select the best optimization algorithm through an easy-to-manage classification of Evolutionary algorithms. In fact, choosing the best tool for an application could be really difficult, especially for a user not aware of optimization theory. Here we propose a general analysis for EAs, highlighting their block-structure and classifying them through some objective (non-qualitative) parameters.
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