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An optimization iterative algorithm based on nonnegative constraint with application to Allan variance analysis technique

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ADVANCES IN SPACE RESEARCH 2014年第5期53卷 836-844页

作者： Lv, Hanfeng Zhang, Liang Wang, Dingjie Wu, Jie Natl Univ Def Technol Coll Aerosp Sci & Engn Staff Room Flight Dynam & Control Changsha 410073 Hunan Peoples R China

It is well known that inertial integrated navigation systems can provide accurate navigation information. In these systems, inertial sensor random error often becomes the limiting factor to get a better performance. So it is imperative to have accurate characterization of the random error. Allan variance analysis technique has a good performance in analyzing inertial sensor random error, and it is always used to characterize various types of the random error terms. This paper proposes a new method named optimization iterative algorithm based on nonnegative constraint applied to Allan variance analysis technique to estimate parameters of the random error terms. The parameter estimates by this method are nonnegative and optimal, and the estimation process does not have matrix nearly singular issues. Testing with simulation data and the experimental data of a fiber optical gyro, the parameters estimated by the presented method are compared against other excellent methods with good agreement;moreover, the objective function has the minimum value. (C) 2014 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Allan variance Parameter estimation optimization algorithm Inertial navigation Random error model

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Meta-Importance Sampling Fuzzy Simulation Combined with Kriging Model for Fuzzy Reliability Analysis

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AIAA JOURNAL 2022年第8期60卷 5018-5022页

作者： Wang, Jiaqi Lu, Zhenzhou Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China

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Optimal Selection of Switch Model Parameters for ADC-Based Full-Bridge Converter

Optimal Selection of Switch Model Parameters for ADC-Based F...

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Alsarayreh, Saif Süto, Zoltán University of Technology and Economics Department of Automation and Applied Informatics Budapest Budapest Hungary

ISBN: (纸本)9798350327816

The incorporation of real-time Hardware-In-the-Loop (HIL) simulators has become one of the pillars of the power electronics control design cycle. This integration is necessary to verify the effectiveness of controller implementations. Despite the rapid evolution in FPGA devices' computational capacity employed by Hardware-in-the-Loop (HIL) tools, developers are consistently faced with the need to strike a balance between acceptable accuracy and the practicality of implementing models and techniques. The methodology presented in this paper is for implementing a full-bridge converter using the Associate Discrete Circuit (ADC) model, specifically designed for real-time simulator applications. Furthermore, this study presents a novel methodology for parameter selection of (ADC) parameters utilizing the Artificial Bee Colony algorithm (ABC). The utilization of the ADC-based model facilitates the establishment of a uniform converter topology during simulation, irrespective of the switch states. The simulation results demonstrate the effectiveness of the introduced methodology in selecting optimal parameters for an ADC switch-based full-bridge converter, reducing overshoot in the converter's output voltage and current. © 2023 IEEE.

关键词： Artificial bee colony field-programmable gate array Full-bridge converter optimization algorithm Real-time Simulation Terms-Associate Discrete Circuit

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Adaptive Control Method for Driverless Cars Considering Probabilistic Graph Path Planning for Intelligent Vehicles 3

Adaptive Control Method for Driverless Cars Considering Prob...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Zhou, Zhen School of Intelligent Manufacturing Panzhihua University Panzhihua China

ISBN: (纸本)9798350317022

Traditional path planning methods usually aim to optimize the shortest path length or time, which makes it difficult to accurately plan safe and feasible vehicle driving paths in complex environments. To solve this problem, the algorithm combines a potential field model and a probability graph algorithm. Under the premise of comprehensive evaluation of vehicle driving risks, an artificial potential field algorithm is used to construct a potential field model for the environment. This model mainly considers the coupling effects of multiple obstacles, environmental threats, and road conditions in the environment, and can accurately represent the interaction between vehicles and the environment. At the same time, probability graph algorithm is used for path planning to optimize the multi-dimensional transportation costs between nodes. This method establishes a multidimensional access cost evaluation matrix between sampling points, integrating multiple aspects of vehicle driving, such as road conditions, traffic flow, and turning radius. Finally, the simulation results indicate that the algorithm comprehensively considers the coupling effects of obstacles, environmental threats, and road conditions in the path planning process. The probability graph algorithm has been used to optimize multidimensional travel costs, which can generate feasible, safe, and efficient travel paths, providing a multi-objective optimization path planning algorithm for intelligent vehicles. The application of this algorithm can reduce the load of intelligent vehicle path planning, effectively solve path planning problems in complex environments, and further promote the development of unmanned intelligent vehicle technology. © 2023 IEEE.

关键词： Adaptive Control Driverless Car optimization algorithm Probabilistic Graph Path Planning algorithm

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Efficient and optimized approximate GDI full adders based on dynamic threshold CNTFETs for specific least significant bits

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Frontiers of Information Technology & Electronic Engineering 2023年第4期24卷 599-616页

作者： Ayoub SADEGHI Razieh GHASEMI Hossein GHASEMIAN Nabiollah SHIRI Department of Electrical Engineering Shiraz BranchIslamic Azad UniversityShiraz ***Iran School of Electrical Engineering Iran University of Science and TechnologyTehran ***Iran Department ofElectrical and Electronic Engineering Shiraz University of TechnologyShiraz ***Iran

Carbon nanotube field-effect transistors(CNTFETs) are reliable alternatives for conventional transistors, especially for use in approximate computing(AC) based error-resilient digital circuits. In this paper, CNTFET technology and the gate diffusion input(GDI) technique are merged, and three new AC-based full adders(FAs) are presented with 6, 6, and 8 transistors, separately. The nondominated sorting based genetic algorithm II(NSGA-II) is used to attain the optimal performance of the proposed cells by considering the number of tubes and chirality vectors as its variables. The results confirm the circuits' improvement by about 50% in terms of power-delay-product(PDP) at the cost of area occupation. The Monte Carlo method(MCM) and 32-nm CNTFET technology are used to evaluate the lithographic variations and the stability of the proposed circuits during the fabrication process, in which the higher stability of the proposed circuits compared to those in the literature is observed. The dynamic threshold(DT) technique in the transistors of the proposed circuits amends the possible voltage drop at the outputs. Circuitry performance and error metrics of the proposed circuits nominate them for the least significant bit(LSB) parts of more complex arithmetic circuits such as multipliers.

关键词： Carbon nanotube field-effect transistor(CNTFET) optimization algorithm Nondominated sorting based genetic algorithm II(NSGA-II) Gate diffusion input(GDI) Approximate computing

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Application of Decision Making algorithm for Green Electromechanical Product Design optimization

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2019年第2期23卷 293-299页

作者： Zhang, Yi Meng, Qinghui Henan Polytech Inst Sch Mech & Elect Engn 666 Kongming Rd Nanyang 473000 Henan Peoples R China Nanjing Univ Sci & Technol Sch Elect Engn & Optoelect Technol 200 Xiaolingwei Nanjing 210094 Jiangsu Peoples R China Henan Univ Sci & Technol Sch Control Engn 666 Kongming Rd Nanyang 473000 Henan Peoples R China

Green products are considered to be the only way for human beings to follow the strategy of sustainable development. They have become one of the hotspots of modern design, manufacture and consumption. A green design method of electromechanical products based on case-based reasoning is presented in this paper. This paper puts forward and uses a "EW&AHP fusion technology" to scientifically determine the index weight, and uses multi target decision-making method to design the index system, establish the evaluationa* optimization algorithm model of green electromechanical product design scheme, and comprehensively evaluate and optimize the green product design scheme from the aspects of economy, technology and green. Sort, provide decision support for production and operation of related enterprises. The results show that the algorithm can not only give full play to the role of the data itself, but also fully reflect the green requirements of the green electromechanical products, and also give consideration to the profit goal of the enterprise.

关键词： green electromechanical products optimization algorithm entropy weight method analytic hierarchy process EW & AHP fusion technology

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Artificial intelligence algorithms for cyberspace security applications:a technological and status review

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Frontiers of Information Technology & Electronic Engineering 2023年第8期24卷 1117-1142页

作者： Jie CHEN Dandan WU Ruiyun XIE School of Cybersecurity Northwestern Polytechnical UniversityXi’an710000China China Electronics Technology Network Information Security Co. Ltd.Chengdu610000China

Three technical problems should be solved urgently in cyberspace security:the timeliness and accuracy of network attack detection,the credibility assessment and prediction of the security situation,and the effectiveness of security defense strategy *** intelligence(AI)algorithms have become the core means to increase the chance of security and improve the network attack and defense ability in the application of cyberspace ***,the breakthrough and application of AI technology have provided a series of advanced approaches for further enhancing network defense *** work presents a comprehensive review of AI technology articles for cyberspace security applications,mainly from 2017 to *** papers are selected from a variety of journals and conferences:52.68%are from Elsevier,Springer,and IEEE journals and 25%are from international *** a specific focus on the latest approaches in machine learning(ML),deep learning(DL),and some populara* optimization algorithms,the characteristics of the algorithmic models,performance results,datasets,potential benefits,and limitations are analyzed,and some of the existing challenges are *** work is intended to provide technical guidance for researchers who would like to obtain the potential of AI technical methods for cyberspace security and to provide tips for the later resolution of specific cyberspace security issues,and a mastery of the current development trends of technology and application and hot issues in the field of network *** also indicates certain existing challenges and gives directions for addressing them effectively.

关键词： Artificial intelligence(AI) Machine learning(ML) Deep learning(DL) optimization algorithm Hybrid algorithm Cyberspace security

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Line start synchronous reluctance motor with improved power factor for agriculture electric pump applications

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JOURNAL OF ENGINEERING-JOE 2022年第3期2022卷 295-310页

作者： Uvaraj, G. Aswin Lenin, N. C. Padmanaban, Sanjeevikumar Khan, Baseem Vellore Inst Technol Sch Elect Engn Chennai Tamil Nadu India Vellore Inst Technol Testing & Res Ctr Deputy Director Elect Vehicles Incubat Chennai Tamil Nadu India Aarhus Univ Dept Business Dev & Technol CTiF Global Capsule Birk Centerpk 15 Herning Denmark Hawassa Univ Dept Elect & Comp Engn Hawassa 05 Ethiopia

Line start synchronous reluctance motor (LSRM) is more efficient than the existing induction motor (IM). However, the power factor is a major concern in LSRM. The power factor of the LSRM depends on the saliency ratio. To increase the saliency ratio, either d-axis inductance (L-d) has to be improved, or q-axis inductance (L-q) reduced. Here, q-axis inductance is decreased by making the minimal or negligible flux variation in the q-axis. Initially, the rotor geometry has been optimized employing ana* optimization algorithm. Then, finite element analysis is adopted to predict the capability of the designed rotors. Stress analysis has been carried out for both axial and radial rotor structures to verify the mechanical robustness. The fabrication steps such as folding, stacking and screwing, wire cut, die-casting and milling process of both the rotor structures are explained in detail. Further, both the machines are tested with the centrifugal pump. From the test results, it is observed that the proposed rotor structure provides a higher power factor.

关键词： screwing wire cut optimization algorithm folding wire cut negligible flux variation finite element analysis rotors reluctance motors pumps mechanical robustness higher power factor d-axis inductance induction motors improved power factor minimal flux variation stacking wire cut designed rotors LSRM q-axis inductance centrifugal pump power factor axial rotor structures rotor structure agriculture induction motor radial rotor structures line start synchronous reluctance motor stress analysis saliency ratio agriculture electric pump applications rotor geometry

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Stochastic Gradient optimization of Transonic Airfoils

Stochastic Gradient Optimization of Transonic Airfoils

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Ulker, F. Demet Drela, Mark Univ Colorado Ann & HJ Smead Dept Aerosp Engn Sci Boulder CO 80303 USA MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

Stochastic gradient-based optimization is used to overcome the combinatorial scaling problem of deterministic multi-point optimization over potentially many design parameters. The specific application is transonic airfoil design to minimize the mission fuel burn of a transport aircraft flying over some ranges of the operating (design) parameters, specifically the Mach number, Reynolds number, and lift coefficient. At each optimization descent step, mini-batch samples are used to approximate the objective function and its design gradient over the design variables, which here are Chebyshev-mode geometric design variables and the angle of attack. Robustness of optimal solution to initial seed airfoil and on the chosen operating ranges is examined. Adaptive variation of the Mach range is implemented to address the fact that the appropriate range is not known a priori, and also to suppress problems arising from solution non-convergence at unrealistically high sampled Mach numbers. The transonic airfoil analysis code MSES is used as the flow solver, with flow parameters chosen in each design step by mini-batch random sampling. The stochastic present optimization method improves performance over the multi-dimensional parameter space, and prevents the appearance of irregular geometries frequently seen in single-point or sparse-sampled deterministic airfoil optimization.

关键词： Drag Divergence Mach Number Airfoil Databases Lift Coefficient Multi Point optimization Transport Aircraft Thrust Specific Fuel Consumption Stochastic Gradient Descent Stochastic optimization Swept Wing optimization algorithm

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Multi-Material Topology optimization for Conceptual-Level Aircraft Seat Design

Multi-Material Topology Optimization for Conceptual-Level Ai...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Roper, Stephen W. K. Kupchanko, Logan Lee, Haksung Huh, Mongyoung Kim, Il Yong Queens Univ Dept Mech & Mat Engn Kingston ON K7L 3N6 Canada Korea Inst Carbon Convergence Technol Design & Anal Ctr Jeonju 54853 South Korea

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

As airlines seek to increase flight capacity and reduce operational costs, there is continued demand to reduce mass of conventional structures while maintaining strict safety standards and certification requirements. While the industry looks to adopt advanced composite materials and manufacturing methods to achieve these goals, new design tools are required to further optimize for component shape, sizing, and material selection. This paper presents multi-material topology optimization as the ideal numerical design tool for these complex problems. First, an overview of classical topology optimization is provided, followed by the extensions to multi-material. Following this review is a practical case study for the conceptual design of an aircraft passenger seat. This problem focuses on the study of single and multi-material designs and investigates the potential use of fiber reinforced polymers alongside conventional aluminum structures. Designs are discussed and compared with preliminary considerations of performance, cost, and design interpretation.

关键词： Topology optimization Aircrafts Composite Materials Finite Element Analysis Material Properties Elastic Modulus Structural Response Compression Molding Landing Gear optimization algorithm

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