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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 evaluation 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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optimization of a double-gap output cavity for a high efficiency klystron operating in the X-band 22

Optimization of a double-gap output cavity for a high effici...

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22nd International Vacuum Electronics Conference (IVEC)

作者： Hamel, Pierrick Arsenyev, Sergey Plouin, Juliette Marchand, Claude CEA IRFU Saclay France

ISBN: (纸本)9781665441056

This paper presents the design of a double-gap output cavity for a klystron operating in the X band. First, the bunching circuit is presented. This bunching circuit provides a bunched electron beam used as the input for the final double-gap cavity design. Then two methods are presented to optimize this design, the first one is based on a clustering algorithm using a disk model simulation tool while the second one is based on a gradient descent using Particle In Cell (PIC) simulations. Both geometries provide about 65% efficiency.

关键词： Klystron optimization algorithm double-gap output cavity

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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 popular 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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An optimization Technique for Voltage Regulation in Electrical Power Systems 18

An Optimization Technique for Voltage Regulation in Electric...

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18th International Multi-Conference on Systems, Signals & Devices (SSD)

作者： Ghaeb, Jasim A. Alkayyali, Malek Philadelphia Univ Fac Engn Mechatron Engn Dept Amman 19392 Jordan Clemson Univ Dept Mech Engn Clemson SC 29631 USA

ISBN: (纸本)9781665414937

In this work, a Particle Swarm optimization (PSO) technique is proposed to determine the optimal firing angles of the Thyristor-Controlled Reactor (TCR) to regulate the voltage of the electrical power system. A 500 km-length electrical power system is considered. The transmission line is modeled by three pi-section networks each represents a distance of 500/3 km. The mathematical model of the electrical power system is derived and used by the PSO algorithm to find the required TCR firing angles for voltage regulation. Different test cases have been conducted to assess and validate the proposed PSO technique capabilities. The results have revealed the ability of the proposed PSO technique to regulate the load voltage efficiently to average load voltage change equals 0.271%.

关键词： Voltage regulation Static VAR Compensator Electrical Power System optimization algorithm

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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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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 an 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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Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning algorithms

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BUILDINGS 2024年第1期14卷 190页

作者： Yang, Yanhua Liu, Guiyong Zhang, Haihong Zhang, Yan Yang, Xiaolong Gansu Prov Transportat Res Inst Grp Co Ltd Gansu Prov Rd Mat Engn Lab Lanzhou 730001 Peoples R China Guangxi Univ Sch Civil Engn & Architecture Nanning 530004 Peoples R China

Machine learning (ML) algorithms have been widely used in big data prediction and analysis in terms of their excellent data regression ability. However, the prediction accuracy of different ML algorithms varies between different regression problems and data sets. In order to construct a prediction model with optimal accuracy for fly ash concrete (FAC), ML algorithms such as genetic programming (GP), support vector regression (SVR), random forest (RF), extremely gradient boost (XGBoost), backpropagation artificial neural network (BP-ANN) and adaptive network-based fuzzy inference system (ANFIS) were selected as regression and prediction algorithms in this study;the particle swarm optimization (PSO) algorithm was also used to optimize the structure and hyperparameters of each algorithm. The statistical results show that the performance of the assembled algorithms is better than that of an NN-based algorithm. In addition, PSO can effectively improve the prediction accuracy of the ML algorithms. The comprehensive performance of each model is analyzed using a Taylor diagram, and the PSO-XGBoost model has the best comprehensive performance, with R2 and MSE equal to 0.9072 and 11.4546, respectively.

关键词： fly ash concrete machine learning optimization algorithm compressive strength

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Numerical optimization of Warp Drive Geometries

Numerical Optimization of Warp Drive Geometries

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AIAA Propulsion and Energy Forum / IEEE Electric Aircraft Technologies Symposium (EATS)

作者： Helmerich, Christopher Fuchs, Jared Agnew, Joseph Univ Alabama Huntsville Dept Phys & Astron Huntsville AL 35899 USA Univ Alabama Huntsville Dept Mech & Aerosp Engn Huntsville AL 35899 USA

ISBN: (数字)9781624106118

ISBN: (纸本)9781624106118

Warp drive research has primarily focused on the evaluation of analytic solutions to Einstein's field equations. In this paper, we take a different approach and compute the stress-energy-momentum tensor numerically using Einstein's field equations for a given space-time metric. Once the stress-energy-momentum tensor is computed, a fitness parameter can be calculated based on how efficiently it achieves a certain warp velocity. The initial space-time metric is then updated based on the fitness parameter. This process allows for a much broader range of optimization techniques to be applied since no analytic formula is needed to describe the metric as it is being optimized. In addition, the numerical techniques provide insight into infrequently-discussed parts of warp drive theory, namely, properties of not only the total energy densities needed but also the distribution of momentum, shear stress, and pressure density throughout the warp bubble. These investigations, therefore, provide new insight into the dynamics of warp bubbles.

关键词： Numerical optimization Energy Distribution Shear Stress Greedy algorithm Annealing Streamlines Toroidal Vortex Planets optimization algorithm Reinforcement Learning

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