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检索条件"主题词=Genetic Algorithm optimization"

共 228 条记录，以下是161-170 订阅

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Battery Characterization and State-of-Charge Prediction for Different Journey Conditions with the help of the "Journey Mapping" Concept 41

Battery Characterization and State-of-Charge Prediction for ...

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41st Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Divakarla, Kavya Prabha Nalakath, Shamsuddeen Drennan, Martin Ahmed, Ryan Emadi, Ali Razavi, Saiedeh McMaster Univ Elect & Comp Engn Dept Hamilton ON Canada Univ Ontario Inst Technol Automot Mech & Mfg Engn Dept Oshawa ON Canada McMaster Univ Mech Engn Dept Hamilton ON Canada McMaster Univ Civil Engn Dept Hamilton ON Canada

ISBN: (纸本)9781479917624

Electric vehicle battery modeling and state-of-charge prediction has gained a lot of importance with the growing range anxiety among electric vehicle users. The future of battery implementation in electric vehicles might be in their customized design according to specific journey conditions. As such, this paper highlights the application of a novel concept - Journey Mapping for a Ford Focus Electric 2012's battery characterization and SOC prediction with the help of genetic algorithm and the Recursive Least Squares techniques respectively. The Journey Mapping concept, which re-defines driving cycles in order to better capture the journey of a vehicle by including various external conditions such as weather, terrain, traffic, driver behavior, road, aerodynamic and vehicle proved to be a a more accurate testing bed for electric vehicle battery modeling.

关键词： Automotive drive cycles data acquisition genetic algorithm optimization modeling simulation and state-of-charge (SOC)

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Vector Median Filter with Directional Detector for Color Image Denoising

Vector Median Filter with Directional Detector for Color Ima...

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World Congress on Engineering (WCE 2011)

作者： Khryashchev, Vladimir V. Kuykin, Denis K. Studenova, Alina A. PG Demidov Yaroslavl State Univ Image Proc Lab 14 Sovetskaya Yaroslavl 150000 Russia PG Demidov Yaroslavl State Univ 14 Sovetskaya Yaroslavl 150000 Russia

ISBN: (纸本)9789881925145

In this paper a new approach to the problem of impulse noise removal from color images is presented. The proposed method is based on noise detection algorithm and weighted vector median filter. The results of its analysis compared to other known median-based vector filters are presented. The analysis shows that the proposed algorithm is more efficient in random-valued impulse noise removal from color digital images than other considered filters.

关键词： color image processing impulse noise removal vector median filter order statistic genetic algorithm optimization

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Solution of the Antenna Placement Problem by Means of Global optimization Techniques 20

Solution of the Antenna Placement Problem by Means of Global...

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20th International Conference on Microwaves, Radar, and Wireless Communication (MIKON)

作者： Ural, Mustafa Bayseferogullari, Can ASELSAN AS TR-06750 Ankara Turkey

ISBN: (纸本)9788393152520

In this work [1], minimization of platform-based coupling between the antennas of two VHF radios on an aircraft platform is aimed. For this purpose;an optimal antenna placement, which yields minimum average coupling between the antennas of the platform over the whole frequency band of operation is determined. Two important global optimization techniques, namely genetic algorithm optimization (GAO) and Particle Swarm optimization (PSO), are used in determination of the optimal antenna placement. Throughout this work, antenna placements and coupling results determined by two different optimization techniques and performances of these optimization techniques are compared with each other. At the end of the work, far-field radiation pattern performances of the antennas at their optimal places are analyzed in terms of directivity and coverage. Aircraft platform and antennas placed on it are modeled based on their real electrical and physical properties in Computer Simulation Technology - Microwave Studio (CST - MWS) simulation tool. Transient Solver of CST - MWS, which is based on Finite Integration Technique, is used for optimization process, determining the coupling results and far-field radiation patterns.

关键词： genetic algorithm optimization Particle Swarm optimization Antenna Placement Analysis

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optimization of High-Efficiency Region of EV Traction Motor through Innovative Motor Constant Considering the Current Angle and Iron Loss

Optimization of High-Efficiency Region of EV Traction Motor ...

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10th IEEE International Conference on Communications and Electronics (IEEE ICCE)

作者： Hsieh, Min-Fu Huynh, Anh Thanh Duong, Le-Minh-Hoc Le, Nguyen-Yen-Truc Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan Univ Nottingham Power Elect & Machines Ctr Nottingham NG7 2GT England

ISBN: (纸本)9798350379808;9798350379792

This paper presents a novel definition of motor constants that integrates iron loss, current angle, and copper loss, providing a comprehensive assessment of motor performance and efficiency. By incorporating this innovative motor constant into the optimization process, multi-objective optimization becomes more streamlined, leading to enhanced motor designs with expanded high-efficiency operational ranges. The genetic algorithm (GA) optimization approach employed in this study, utilizing the new motor constant with the current angle as the fitness function, enables the identification of optimal parameters for the motor stator to maximize the high-efficiency operational range in the field weakening (FW) region. The analyses uncover a significant correlation between the new motor constant and efficiency, considering the influence of the current angle to achieve broader high-efficiency operational ranges. The electromagnetic properties of the designed motors are thoroughly examined and validated using finite element analysis (FEA).

关键词： Motor constant iron loss current angle copper loss genetic algorithm optimization multi-objective optimization

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geneticSecOps: Harnessing Heuristic genetic algorithms for Automated Security Testing and Vulnerability Detection in DevSecOps 6

GeneticSecOps: Harnessing Heuristic Genetic Algorithms for A...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Thantharate, Pratik Anurag, T. IEEE Member United States

ISBN: (纸本)9798350304480

This paper addresses security testing and vulnerability detection within the DevSecOps framework, emphasizing the need for automated tools to streamline and enhance security practices. Current challenges are identified, and related work is reviewed to establish context. Our Python-based solution offers two main components: an automated security testing tool for rigorous testing throughout the software development lifecycle and an automated vulnerability detection tool to identify potential vulnerabilities in system logs and network traffic. To optimize feature selection in the vulnerability detection tool, we employ the genetic algorithm (GA). The GA efficiently explores potential feature combinations, selecting the most relevant features to maximize vulnerability detection accuracy while minimizing the number of features. Simulated results validate the effectiveness of our solution in real-world scenarios, with the optimal solution achieving a fitness score of 0.982. By automating security testing and vulnerability detection, our approach enhances the security and reliability of software development. The paper concludes by discussing the implications of our findings and proposes future research directions. Embracing automation in security testing fortifies software applications and safeguards critical data from threats. Our proposed solution offers a promising avenue for advancing automated security testing and vulnerability detection within the DevSecOps paradigm. © 2023 IEEE.

关键词： Automated Security Testing Cybersecurity in Software Development DevSecOps Automation genetic algorithm optimization Vulnerability Detection Tools

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Atomic clock frequency difference prediction algorithm based on genetic wavelet neural network 13

Atomic clock frequency difference prediction algorithm based...

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2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)

作者： Zhu Jiangmiao Chen Ye Gao Yuan Wang Yuzhuo Yan Di Faculty of Information Technology of Beijing University of Technology Beijing Institute of Metrology

ISBN: (纸本)9781509050369;9781509050352

Atomic clock frequency difference prediction is the key step in atomic clock time scale calculation and atomic clock control. Precise prediction algorithm can accurately predict the future operation state of atomic clock which can be used to improve the accuracy of atomic time. In order to further improve the prediction accuracy of atomic clock frequency difference,a genetic wavelet neural network(GAWNN) atomic clock frequency difference prediction algorithm is proposed in this paper, which is based on wavelet neural network(WNN) atomic clock frequency difference prediction algorithm. The genetic algorithm is used to optimize the wavelet neural network so as to select the appropriate number of hidden layers and the number of training points to construct the atomic clock frequency difference prediction model. In this paper, the algorithm is validated by the hydrogen clock and cesium clock actual frequency difference data of the National Institute of Metrology, and the results show that the algorithm improves the prediction accuracy of hydrogen clock and cesium clock frequency difference data.

关键词： atomic clock wavelet neural network genetic algorithm optimization frequency difference prediction algorithm

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optimization of interplanetary trajectories with deep space maneuvers - Model development and application to a Uranus orbiter mission:

Optimization of interplanetary trajectories with deep space ...

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作者： Molenaar, S Delft University of Technology

学位级别：master

Since the dawn of the space age many space missions have been carried out in order to explore the solar system. Only one spacecraft has ever had the privilege to travel to the distant planet Uranus, namely Voyager 2. However, Voyager 2 has only been the tip of the iceberg when it comes to uncovering the mysteries of Uranus, its rings and moons. An orbiter mission to Uranus is a logical step in the range of planetary explorers, as it could provide insight in for example the formation process of ice giants and in atmospheric phenomena. optimization of the interplanetary trajectory is a major part of the analysis of a mission to Uranus. Many different techniques can be applied to go from Earth to another planet. Of course a direct flight is possible, where one engine burn is applied at a parking orbit around Earth and another burn is performed to get into orbit around the target planet. It is also possible, however, to use the gravity of other planets to change the trajectory in such a way that it could save fuel with respect to the direct interplanetary transfer. It is also possible to perform an engine burn somewhere along the interplanetary orbit. Such a deep space maneuver (DSM) may also improve the total fuel consumption for the mission. The mission to Uranus and the addition of deep space maneuvers to the trajectory form the backbone of this thesis. Therefore the following thesis objectives can be formulated: I Develop a method to optimize interplanetary trajectories with deep space ma- neuvers and integrate this method in the software package galomusit. II Apply the developed method to optimize the trajectory for a Uranus orbiter mission. Based on the literature study prior to this thesis a number of assumptions are made regarding the mission to Uranus. First of all, in this thesis only high thrust trajectories are used. Secondly, planets can be used to perform either a powered or an unpowered gravity assist, but aerogravity assists are not applied. Finally, the

关键词： Deep Space Maneuvers Orbital Mechanics genetic algorithm optimization

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DESIGN OF A MODULAR, AXIAL-FLUX DIRECT DRIVE PERMANENT MAGNET GENERATOR FOR WIND TURBINES

DESIGN OF A MODULAR, AXIAL-FLUX DIRECT DRIVE PERMANENT MAGNE...

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作者： AYDIN BASKAYA MIDDLE EAST TECHNICAL UNIVERSITY

学位级别：硕士

Wind energy technology is becoming more important issue in terms of renewable energy applications. Theoretical maximum of wind energy utilization is known (which is predetermined by Betz as 59%) and generally imperfections in blade manufacture reduce the actual energy yield of the turbine less than the useable energy. Therefore, maximum energy yield from generator topology is desired. According to fault statistics of wind turbines, most of the cases are related to gear- box failures. Mechanical losses and heat losses are again result from these gear-box drive train systems. In this thesis, axial flux permanent magnet direct drive (AFPM DD) topology is investigated because of its high energy yield, lower maintenance periods due to its modular direct-drive concept and axial length advantages. Proposed generator has an electrical output power of 5 MW at 12 rpm. Outer stator axial flux concept will be used in the air-cored generator. genetic algorithm (GA) optimization is used in determining analytical parameters of the design. In this thesis, cost based optimization procedure is carried under 9 different wind speed conditions in order to get a more realistic design. Wind speed data are taken from real field based measurement values of a sample wind power plant (WPP) located in Canakkale, Turkey. The algorithm calculated the design parameters of the proposed AFPM generator based on a power generation reference of a commercial 5 MW Permanent Magnet Synchronous Generator (PMSG) wind turbine under aforementioned wind conditions. In addition, the algorithm also considers the wind speed time probabilistic and related generation incomes. In order to verify the validity of analytical design method, first a sample 50 kW AFPM design is simulated by using FEA (Finite Element Analysis) and then critical parameters such as air gap flux density, and induced emf values are compared with the analytical results. Comparison of the results of the designed generator supported the proposed

关键词： Wind energy conversion axial flux permanent magnet generator direct drive modular generator genetic algorithm optimization finite element analysis (FEA)

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A Novel Approach to Make an Efficient Thermal Management of a Solar Electric Four-Wheeler through Model-Based System Simulation 18

A Novel Approach to Make an Efficient Thermal Management of ...

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18th Symposium on International Automotive Technology, SIAT 2024

作者： Karthikeyan, Vikram Raj Gumma, Muralidhar Altair Engineering India Pvt. Ltd. India

The objective of this paper is to determine and design an optimized thermal management system for a solar electric four-wheeler while considering system influence. The major systems that will be analyzed and optimized include the HVAC, solar system, and battery. The HVAC system imposes a challenge to the designers to fulfill the passenger's comfort and to operate it efficiently under a wide range of external loads from solar radiation, ambient temperature & and humidity, human metabolic activity, and other loads like the propulsion system temperature on the cabin. From the literature, it is found that the air conditioning system reduces on average 14% of the total battery capacity whereas the heating system reduces it by 18% [1], which makes the HVAC system design a crucial aspect to consider for the system influence. The battery car voltage changes significantly to meet the power demand and because of this, the battery system produces a large amount of heat while discharging which will overheat the battery system and can lead to hazardous chemical reactions or even the efficiency will get seriously compromised if the thermal management system is not properly designed. The solar power system being an additional source of power must be effectively designed with all the required power electronic controllers such that the additional power produced from the solar compensates for the thermal losses at the system level and provides an additional range to the battery. To achieve this system-level optimization, a model-based system simulation approach with a genetic algorithm will be applied to optimize the thermal variables for the system. The variables will be selected considering the trade-offs between performance, efficiency, and comfort. The expected outcome of this project is an efficient and optimized thermal management system that will enhance the range of the solar electric four-wheeler. © 2024 SAE International. All rights reserved.

关键词： Battery thermal management system (BTMS) genetic algorithm optimization HVAC system Model-based system simulation Solar electric four-wheeler Solar power system

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Study on Power Loss Minimization for Distribution Network Reconfiguration Using genetic algorithm Case Study: Addis North 132/15 KV Substation

Study on Power Loss Minimization for Distribution Network Re...

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作者： Habtamu, Eshete Addis Ababa University

学位级别：硕士

Distribution system is a largest portion of network of electrical power system. Different actions have been taken to improve the efficiency of the distribution network. One of the most basic and the commonest way to improve the performance of the distribution network is the network reconfiguration. Electric distribution systems reconfiguration comprises tie and sectionalizing switches. Tie switches are normally open, and sectionalizing switches are normally closed. By opening and closing these switches, the distribution network can be reconfigured. This reconfiguration can be done for the objective of loss minimization. In order to get feasible results (loss minimization), the reconfiguration must meet some constraints, like Kirchhoff‟s voltage and current laws, other equality and inequality constraints. Distribution network reconfiguration is an optimization problem and needs a suitable algorithm (method).The method used for this optimization problem is a genetic algorithm optimization method. The genetic algorithm has been described in detail and then applied specifically to the network reconfiguration problem. In the optimization process, load flow of the distribution system was computed. Then, computer simulation was performed by using DIgSILENT PowerFactory software for analyzing the distribution network reconfiguration. In addition, optimized genetic algorithm was used as a tool for network reconfiguration. Addis North 132/15 kV substation feeders are used as test system for this particular study. Before the reconfiguration, the power loss is 3.983783 MW, after reconfiguration the power loss has decreased from 3.983783 MW to 1.594640 MW, which is 2.389143 MW (59.91716%) reduction. Besides, the maximum voltage drop before reconfiguration is 0.352639, and the reconfiguration increased it to 5.050102. The minimum voltage before reconfiguration is 0.992618 p.u., and the minimum voltage is found to be 0.947288 p.u. after reconfiguration. In addition, the maximum vo

关键词： Network Reconfiguration genetic algorithm optimization Power Loss Minimization

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