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检索条件"主题词=Genetic Algorithm optimization"

共 228 条记录，以下是151-160 订阅

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Lyapunov-based Nonlinear Controller for Quadrotor Position and Attitude Tracking with GA optimization

Lyapunov-based Nonlinear Controller for Quadrotor Position a...

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IEEE Conference on Industrial Electronics and Applications

作者： Safaei, Ali Mahyuddin, Muhammad Nasiruddin Univ Tehran Tehran Iran Univ Sains Malaysia Sch Elect & Elect Engn George Town Malaysia

ISBN: (纸本)9781509009251

In this paper, a nonlinear controller using the lyapunov stability theorem is proposed for quadrotor position and attitude tracking. The quadrotor dynamics is defined for controller design and simulations. Moreover, a simple optimization based on genetic algorithm is presented for determining the elements of the controller gain matrices. The simulation results show the appropriate tracking of the controlled quadrotor model. The generated model and the designed nonlinear controller could be utilized as a basis for further investigations on autonomous control of whether a single or a team of quadrotors.

关键词： Quad rotor dynamics Nonlinear control Lyapunov Stability Theorem genetic algorithm optimization

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Optimal design of a hybrid energy plant by accounting for the cumulative energy demand 10

Optimal design of a hybrid energy plant by accounting for th...

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10th International Conference on Applied Energy (ICAE)

作者： Bahlawan, Hilal Morini, Mirko Pinelli, Michele Poganietz, Witold-Roger Spina, Pier Ruggero Venturini, Mauro Univ Ferrara Dipartimento Ingn Via Saragat 1 I-44122 Ferrara Italy Univ Parma Dipartimento Ingn & Architettura Parco Area Sci 181-A I-43124 Parma Italy Karlsruhe Inst Technol Karlsruhe Germany

In this paper, the optimal design of a hybrid energy plant composed of a solar thermal collector, a photovoltaic panel, a combined heat and power system, an absorption chiller, an air source heat pump, a ground source heat pump and a thermal energy storage is studied. The size of each technology is optimized by applying a model implemented in Matlab environment. The optimization goal is the minimization of the primary energy consumed throughout the life cycle of the hybrid energy plant by using a genetic algorithm. The primary energy consumed during the manufacturing phase of the hybrid energy plant is represented by the cumulative energy demand and is calculated by carrying out a cradle to gate life cycle assessment. The primary energy consumed during the operation phase is evaluated by simulating the system throughout one year. The cumulative energy demand of each system composing the hybrid energy plant is calculated as a function of the technology size. Therefore, the problem of life cycle assessment scaling of renewable and non-renewable energy systems is also taken into account in this paper. A tower located in the north of Italy is selected as a case study and two different approaches are evaluated. The first approach consists of solving the sizing optimization problem by minimizing the primary energy consumption only during the operation phase, while in the second approach the primary energy consumption is minimized throughout the life cycle of the plant by integrating the life cycle assessment into the optimization process. The results show that, if life cycle assessment is accounted for, the optimal hybrid energy plant configuration is different and a higher primary energy saving (approximately 12%) is achieved. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： Hybrid energy plant genetic algorithm optimization Life cycle assessment Primary energy saving

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A SOI waveguide grating coupler enhanced by additional silicon nitride layer 8

A SOI waveguide grating coupler enhanced by additional silic...

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Conference on Optoelectronic Devices and Integration VIII

作者： Shan, Xiaotao Zhang, Zan Huang, Beiju Zhang, Zanyun Cheng, Chuantong Bai, Bing Gao, Tianxi Xu, Xiaobo Chen, Hongda Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China Chinese Acad Sci Inst Semicond State Key Lab Integrated Optoelect Beijing 100083 Peoples R China Tianjin Polytech Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

ISBN: (数字)9781510630864

ISBN: (纸本)9781510630864

In this work, a bidirectional grating coupler for perfectly vertical coupling with enhanced coupling efficiency is proposed. A silicon nitride layer above the grating regain is utilized to enhance the coupling efficiency. With the help of the silicon nitride layer, reflection back into the fiber is diminished and maximal coupling into the guided mode is achieved. In addition, this grating coupler shows strong fiber misalignment tolerance. genetic algorithm (GA) is used to simultaneously optimize the grating and silicon nitride layer. The optimal design obtained from GA shows that the total in-plane optical coupling in C-band is enhanced from about 56.3% to 67%;meanwhile the back-reflection is reduced from 17.6% to 5.3%. What's more, the device proposed here shows a wide-band character with a 1-dB-bandwidth of 54 nm. Such a design can provide an efficient and cost-effective solution for optical vertical coupling of a WDM application and low-cost fiber packaging for silicon PIC.

关键词： Silicon Photonics grating coupler genetic algorithm optimization

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Development of the modified cellular automata sphere growth model for creation of the digital material representations

Development of the modified cellular automata sphere growth ...

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17th Conference of the European-Scientific-Association-on-Material-Forming (ESAFORM)

作者： Madej, Lukasz Pasternak, Kamil Szyndler, Joanna Wajda, Wojciech AGH Univ Sci & Technol Dept Modelling & Informat Technol Al Mickiewicza 30 PL-30059 Krakow Poland Inst Met & Mat Sci Polish Acad Sci Krakow Poland

ISBN: (纸本)9783038351061

Development of reliable algorithm for generation of statistical representations of microstructure morphologies is the subject of the present work. The implemented cellular automata sphere growth model is presented first. Obtained grain size distribution of digital microstructure is compared with experimental measurements to prove efficiency of the proposed algorithm. Then, the grain growth model was additionally combined with the genetic algorithm optimization to extend its microstructure generation capabilities. Finally, possibilities of practical applications of generated digital material representation for modelling texture evolution during channel die test is presented.

关键词： digital material representation cellular automata genetic algorithm optimization

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Comparison of Typical PV Module Performances Based on the Circuit Models 10

Comparison of Typical PV Module Performances Based on the Ci...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Tutkun, N. Gegin, K. Sarma, N. Salam, Z. Duzce Univ Fac Engn Dept Elect & Elect Engn Duzce Turkey Univ Teknol Malaysia Fac Elect Engn Ctr Elect Energy Syst Skudai Malaysia

ISBN: (纸本)9781538656860

In PV systems, it is usually expected that power generation under varying conditions at any time should be maximum as much as possible. Therefore, it is essential to build a circuit model based on a widely used single or double diode model under instantly varying conditions with minimum estimation error through the measured values in the manufacturers' data sheet. It is fact that these approximate circuits models ease the performance analysis of PV systems if the unknown parameters are accurately estimated at various temperatures and unchanged irradiance levels or vice versa. This paper comparatively studies the performances of the two approximate circuit models for the four types of PV modules using the real-coded genetic algorithms. The results indicated that I-V characteristics obtained from the identified parameters exhibit almost similar trend with those given in manufacturers' data sheets under the same conditions. Besides, the estimated parameters obtained from using both models slightly show difference from each other and it can be said that the single diode model is solely satisfactory to model a typical PV model as long as they are accurately estimated.

关键词： double diode circuit model genetic algorithm optimization PV-Battery system single diode circuit model

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MODELING AND STATE OF CHARGE ESTIMATION OF ELECTRIC VEHICLE BATTERIES

MODELING AND STATE OF CHARGE ESTIMATION OF ELECTRIC VEHICLE ...

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作者： Ryan Ahmed McMaster University

学位级别：博士

Electric vehicles have received substantial attention in the past few years since they provide a more sustainable, efficient, and greener transportation alternative in comparison to conventional fossil-fuel powered vehicles. Lithium-Ion batteries represent the most important component in the electric vehicle powertrain and thus require accurate monitoring and control. Many challenges are still facing the mass market production of electric vehicles; these challenges include battery cost, range anxiety, safety, and reliability. These challenges can be significantly mitigated by incorporating an efficient battery management system. The battery management system is responsible for estimating, in real- time, the battery state of charge, state of health, and remaining useful life in addition to communicating with other vehicle components and subsystems. In order for the battery management system to effectively perform these tasks, a high-fidelity battery model along with an accurate, robust estimation strategy must work collaboratively at various power demands, temperatures, and states of life. Lithium ion batteries are considered in this research. For these batteries, electrochemical models represent an attractive approach since they are capable of modeling lithium diffusion processes and track changes in lithium concentrations and potentials inside the electrodes and the electrolyte. Therefore, electrochemical models provide a connection to the physical reactions that occur in the battery thus favoured in state of charge and state of health estimation in comparison to other modeling techniques. The research presented in this thesis focuses on advancing the development and implementation of battery models, state of charge, and state of health estimation strategies. Most electrochemical battery models have been verified using simulation data and have rarely been experimentally applied. This is because most electrochemical battery model parameters are considered proprietar

关键词： Lithium-Ion Batteries genetic algorithm optimization electrochemical battery model parameter identification state of charge estimation Battery management systems (BMS) interacting multiple models (IMM) state of charge (SOC) state of health (SOH) filtration estimation

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An Efficient Partially Parallel Encoder Suitable for 5G-LDPC Codes 9

An Efficient Partially Parallel Encoder Suitable for 5G-LDPC...

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9th International Conference on Computer and Communication Systems (ICCCS)

作者： Jia, Ke Zhang, GuoHua Yin, RuiJiang Ni, MengDi Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian Peoples R China

ISBN: (纸本)9798350350227;9798350350210

In order to serve as a physical channel encoding solution for the 5G new radio (NR) data channel, and to meet the demand for quasi-cyclic low-density parity-check (QC-LDPC) codes, this paper proposes an efficient parallel encoder with a compatible structure. The design of this encoder is based on a forward substitution efficient encoding algorithm, which is integrated into a configurable circuit structure, allowing it to adapt flexibly to different base graphs of 5G-LDPC codes. Furthermore, the performance of this encoder can be enhanced through the utilization of a genetic algorithm (GA) optimization approach, aimed at achieving higher throughput or improved error correction (EC) capability. The optimized encoder not only provides high throughput, but also achieves optimal HUE(hardware usage efficiency).

关键词： 5G new radio (NR) encoder genetic algorithm optimization low-density parity-check (LDPC) codes

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Missing Data Imputation using Evolutionary k- Nearest Neighbor algorithm for Gene Expression Data 16

Missing Data Imputation using Evolutionary k- Nearest Neighb...

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16th International Conference on Advances in ICT for Emerging Regions (ICTer)

作者： de Silva, Hiroshi Perera, A. Shehan Univ Moratuwa Dept Comp Sci & Engn Moratuwa Sri Lanka

ISBN: (纸本)9781509060788

Gene expression data are recognized as a common data source which contains missing expression values. In this paper, we present a genetic algorithm optimized k-Nearest neighbor algorithm (Evolutionary kNNImputation) for missing data imputation. Despite the common imputation methods this paper addresses the effectiveness of using supervised learning algorithms for missing data imputation. We have compared the k-Nearest Neighbor Imputation algorithm with the proposed Evolutionary k-Nearest Neighbor Imputation algorithm. The two algorithms were tested using gene expression datasets. Certain percentages of values are randomly deleted in the datasets and recovered the missing values using the two algorithms. Results show that Evolutionary kNNImputation outperforms kNNImputation.

关键词： Missing data imputation kNNImputation EvlkNNImputation genetic algorithm optimization

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Design of a PV Powered Charging Station for PHEVs 6

Design of a PV Powered Charging Station for PHEVs

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6th International Renewable and Sustainable Energy Conference (IRSEC)

作者： Tutkun, N. Afandi, A. N. Istanbul Aydin Univ Fac Engn Dept Elect & Elect Engn Istanbul Turkey Univ Negeri Malang Elect Engn Dept Malang Indonesia

ISBN: (纸本)9781728111827

Recently the plug-in hybrid electric vehicles (PHEVs) have increasingly been used for transportation due to less petrol consumption and low carbon emission. The wellknown disadvantages of this vehicle are charging time and mile range compared to gas-powered vehicles. Moreover, charging a vast number of vehicles directly from the grid may cause damage to the local grid due to due to high current draws. Thus off-grid sources may be employed to share amount of power for charging PHEVs. This paper focuses on the design of a grid connected 30 kW photovoltaic powered PHEV charging station with optional battery storage units it is indispensable to design such system with low cost operation with respect to grid utility price. This is achieved by optimizing charging time of PHEVs through demand side management strategies such as load shifting, valley filling etc. using the real-coded genetic algorithms.

关键词： plug-in hybrid electric vehicles genetic algorithm optimization PV charging station renewable sources

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Gain and Bandwidth optimization of a Novel Microstrip Patch Antenna 4

Gain and Bandwidth Optimization of a Novel Microstrip Patch ...

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4th International Conference on Advances in Computing and Communications (ICACC)

作者： Chandran, Parvathy P. Viswasom, Sanoj Coll Engn Dept Elect & Commun Thiruvananthapuram Kerala India

ISBN: (纸本)9781479943630

A new slotted microstrip patch antenna with improved bandwidth and gain is proposed. As a method to enhance bandwidth two types of slots, namely a rectangular and a fractal U shaped, are imposed on the patch surface. By cutting a cylindrical slot on the ground plane gain is also improved. genetic algorithm optimization is used as a means for optimizing gain and bandwidth of the patch antenna. The work is carried for a frequency of 1.6 GHz. High frequency structure simulation (HFSS) Software has been used to simulate and find the optimum design and results. A gain improvement of almost 18.6% and a bandwidth enhancement of about 100% has obtained.

关键词： Microstrip patch antenna Inset feed Bandwidth optimization Gain optimization genetic algorithm optimization

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