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Quasi-Chaotic Microwave Oscillations: Formation, Application to Test Vector Network Analyzers 16

Quasi-Chaotic Microwave Oscillations: Formation, Application...

16th IEEE international Scientific and Technical conference Actual problems of Electronic Instrument Engineering, APEIE 2023

作者： Arutyunyan, Artush A. Minenko, Dmitriy E. Malyutin, George A. Artamonov, Aleksndr A. Malyutin, Nickolay D. Tomashevich, Aleksandr A. Dept. of Design of Units and Components for Radioelectronic Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Microelectronics The Research Institute of Microelectronic Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

ISBN: (纸本)9798350330885

The design of quasi-chaotic oscillation formation modules based on strip transmission lines with partial dielectric lithium niobate crystals is described. The problem of considering the peculiarities of measuring the parameters of quasi-chaotic signal formation modules and testing vector network analyzers using them in measuring frequency and dynamic characteristics is stated and solved. Proposition to use the comparison of experimental results of measurements of the quasi-chaotic signal formation module on the certified vector network analyzer (VNA 1) and on the tested virtual device (VNA 2) to estimate the insertion errors was made. Experimental frequency characteristics of the scattering parameters in the frequency range from 0.03 to 20 GHz are presented. The effect of formation of multiple resonances distorting the transmission coefficient and reflection coefficient is demonstrated. It is being proposed to apply the shaping modules for broadband dynamic calibration of panoramic network analyzers. The calibration quality is assessed by calculating the difference between the obtained modules of transmission coefficients during measurements on metrological devices and tested serial devices. modeling of insertion distortions of five types has been carried out: systematic additive independent of frequency;linear dependence;nonlinear distortions of the second order;nonlinear distortions of the third order;polynomial dependence of distortions. After determining the distortions introduced into the frequency dependence of the transmission coefficient, the VNA 2 is calibrated. © 2023 IEEE.

关键词： Electric network analyzers

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Moving target perception and 4D attitude calculation in flight area with vehicle-road coordination 6

Moving target perception and 4D attitude calculation in flig...

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6th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Wu, Guoyan Qiao, Yi Rao, Lingna Yang, Zhou Sui, Yunfeng Sichuan Airport Group Information Department Chengdu China Second Research Institute of Caac Research Center Chengdu China Chengdu Tianfu International Airport Information Department Chengdu China

ISBN: (纸本)9798350389418

To overcome the limitations of current remote control systems that depend heavily on manual intervention and limited image detection, particularly in managing intricate aircraft coupling and addressing vehicle drivers' imprecise aircraft position judgments, this paper presents a novel vehicle-road collaborative activity target perception and 4D pose estimation algorithm specifically tailored for traffic management and control in flight zones, such as airport runways and aprons. By leveraging the YOLOv6 framework, fused with the innovative EfficientRep Backbone and Rep-PAN Neck, the algorithm achieves direct regression of target poses, 3D spatial coordinates, and confidence scores, all without relying on prior aircraft or skeleton features. To address the scarcity of aircraft samples from vehicle drivers' viewpoints, 3D modeling is utilized to construct an airport model and simulate a diverse multi-view aircraft dataset, enabling transfer learning from the remote control tower's perspective to the vehicle's. Experimental outcomes validate the algorithm's precision in accurately computing aircraft 4D poses in both real-world and simulated environments, thereby providing robust support for vehicle-road collaboration in flight zone management, significantly enhancing safety and efficiency in these complex environments. © 2024 IEEE.

关键词： Airport runways

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Domain-Wall/Unary Encoding in QUBO for Permutation problems 3

Domain-Wall/Unary Encoding in QUBO for Permutation Problems

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3rd IEEE international conference on Quantum Computing and Engineering (QCE)

作者： Codognet, Philippe Univ Tokyo Sorbonne Univ CNRS JFLI Tokyo Japan

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

QUBO is the input language of quantum computers based on quantum annealing such as the D-Wave systems and of the "quantum-inspired" dedicated hardware such as Fujitsu's Digital Annealing Unit. We propose a new way to represent integer variables in QUBO together with constraints on those variables by using unary/domain-wall encoding in order to solve constrained optimization problem by quantum annealing. We detail in particular how to encode permutation constraints on integers within the unary/domain-wall encoding scheme, as these constraints are omnipresent in complex combinatorial problems. This new encoding in QUBO is interesting in the context of quantum annealing because it minimizes interactions between variables, and therefore connections needed between qubits in hardware. When compared to the usual one-hot encoding of integers in QUBO, we show than the new encoding scheme divides by nearly a factor 2 the number of connections between qubits for complex problems involving permutation constraints and makes it possible to cope with larger problem instances on current quantum and quantum-inspired hardware.

关键词： Computers Performance evaluation Annealing Computational modeling Qubit Quantum annealing Encoding

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Improved Two-Vector-Based Model Predictive Current control with Online Parameter Identification for Doubly Salient Electromagnetic Machine 26

Improved Two-Vector-Based Model Predictive Current Control w...

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26th international conference on Electrical Machines and systems, ICEMS 2023

作者： Zhou, Xingwei Zhan, Minhui Guo, Yaowu Niu, Shuangxia Dai, Shangjian Zhang, Li Hohai University Department of Electrical Engineering Nanjing China The Hong Kong Polytechnic University Department of Electrical Engineering Hong Kong Southeast University Department of Electrical Engineering Nanjing China

ISBN: (纸本)9798350317589

The traditional finite control set model predictive control (MPC) suffers from the problems of large current ripple and high system parameter dependence. To overcome these, an improved two-vector-based model predictive current control (TV-MPCC) with online parameters identification is proposed for doubly salient electromagnetic machine (DSEM). At first, the voltage vector combinations are localized and shortlisted based on the principle of differential-free beat current control, thus avoiding the complex computation of traditional traversal algorithms. Then, the impact of parameters mismatch is studied in depth, and the sensitivity of different parameters is obtained. Afterwards, an online identification based on MRAS is put forward to eliminate the influence of key parameters mismatch of self-inductance as well as the mutual inductance between armature and excitation windings. Finally, the validity and feasibility of the proposed strategies are verified by simulations under multiple operating conditions. © 2023 IEEE.

关键词： Model predictive control

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Review of the Mean Field Models for Predicting the Spread of Viral Infections

Review of the Mean Field Models for Predicting the Spread of...

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2023 IEEE Ural-Siberian conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2023

作者： Petrakova, Viktoriya Krivorotko, Olga Neverov, Andrei Institute of Computational Modeling Sb Ras Department of Computational Mathematics Krasnoyarsk Russia Sobolev Institute of Mathematics Sb Ras Department of Applied Inverse Problems Novosibirsk Russia Sobolev Institute of Mathematics Sb Ras International Mathematical Center Novosibirsk Russia

ISBN: (纸本)9798350307979

Models for the dynamics and control of disease spread are now developing rapidly and play a central role in making decisions about the introduction of various antiviral measures. One of the approaches to solving such problems may be the use of mean field models, which, on the one hand, allow to take into account some stochastic processes occurring in the population and, on the other hand, are expressed in the form of systems with a small number of equations, the solution of which does not require large computing power. This work is devoted to a review of mean field models now used to solve epidemiological problems, as well as a comparison of four specific approaches that can be formulated within the framework of mean field theory. © 2023 IEEE.

关键词： Computing power

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Alzheimer's Disease Imaging Recognition Based on Improved 3D-ShufflenetV2 Network 1

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8th international conference on Life System modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Tao, Xinjie Wei, Lisheng Wang, Qin Anhui Key Laboratory of Electric Drive and Control Anhui Polytechnic University Wuhu241000 China School of Electrical Engineering Anhui Polytechnic University Wuhu241000 China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

To solve the problems of low accuracy, data leakage, and low efficiency in current deep learning based two dimensional Alzheimer's disease (AD) imaging recognition, an improved 3D-ShufflenetV2 network has been proposed. Firstly, the network utilizes 3D convolution to extract spatial continuity information from three dimensional images, enabling the extraction of more brain lesion information. Secondly, a Squeeze-and-Excitation attention mechanism is introduced into the backbone network, recalibrating channel-level feature responses based on global information to enhance the model's focus on important features, thereby enhancing the model's classification performance. Finally, to increase the model's ability to extract global features, a global feature fusion branch has been added to the network structure. Experiments have shown that the improved network is capable of recognizing AD, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN). The classification performance of the modified network model shows significant improvement over the original network, demonstrating its practical value. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neurodegenerative diseases

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Data Enhanced Model Predictive control of a Coupled Tank System

Data Enhanced Model Predictive Control of a Coupled Tank Sys...

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IEEE/ASME international conference on Advanced Intelligent Mechatronics (AIM)

作者： Scheurenberg, Dominik Schmerling, Kaspar Abel, Dirk Rhein Westfal TH Aachen Inst Automat Control Aachen Germany

ISBN: (数字)9781665413084

ISBN: (纸本)9781665413084

Many advanced methods of process control are based on underlying process models. Therefore, the control performance usually depends strongly on the model accuracy, e.g. the extent of deviations between the real plant behavior and its mathematical representation. Such an advanced method is model predictive control, which has been successfully established in various fields. A model predictive control is based on a sufficiently accurate model of the system to optimize performance and ensure constraints are met. In practice, model descriptions can be subject to significant inaccuracies due to, e.g. insufficient data, unknown dynamics, or system changes. In addition, most processes in industry show time variant dynamics, e.g. due to wear and tear. modeling such varying characteristics is usually impractical or requires a disproportional amount of modeling effort. Furthermore, the resulting models are usually too complex for real-time process control. In this work, a model predictive control based on a grey-box model is presented. While the model's basic structure is given by a linearized physical model it is enhanced by a data-driven part using a long short-term memory network. An approach to formulate the linear, timevarying, model predictive control problem that takes into account the model uncertainties of the nominal model predicted by the long short-term memory network model is described. The improvements in control performance are demonstrated on a real demonstrator, which is a coupled tank system. The approach presented reduces the average absolute error by 69.16%, and overshooting by 11.7% on average.

关键词： Predictive control Data models control systems neural networks Grey-box model

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Two-level VSC real-time simulation modeling method based on controlled source compensation 2

Two-level VSC real-time simulation modeling method based on ...

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2nd international conference on Energy, Power, and Electrical Technology, ICEPET 2023

作者： Hu, Binjiang Xi, Xinze Zhu, Yihua Xing, Chao Li, Shuyong Ma, Zun Huang, Libin State Key Laboratory of HVDC Electric Power Research Institute CSG 11 of Kexiang Road Huangpu District Guangzhou510663 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System 11 of Kexiang Road Huangpu District Guangzhou510663 China CSG Key Laboratory of Power System Simulation 11 of Kexiang Road Huangpu District Guangzhou510663 China Electric Power Research Institute of Yunnan Power Grid Co. Ltd. Kunming650217 China

ISBN: (纸本)9781510668270

The accurate modeling of two-level VSC is of great significance for the real-time simulation of new power systems, and the modeling of its core component switch group has attracted more and more attention. Since traditional binary resistance models require time to "backtrack" to accurately locate the switching moment, interpolation is difficult to use for real-time simulation. In this paper, a two-level VSC real-time simulation modeling method based on controlled source compensation is proposed. This method combines the controlled source model with the binary resistance model, uses the binary resistance model to ensure the simulation accuracy when the switching state is constant, and introduces the controlled source model to compensate for the error caused by the delay of the trigger signal when the switching state is switched. According to the two-level VSC real-time simulation model realized by this method, while reducing the non-characteristic subharmonics, the admittance matrix can be pre-stored, which effectively reduces the model computing resource occupation, and is suitable for a variety of complex power electronic system modeling methods. © 2023 SPIE.

关键词： Electric switches

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DeSci Based on Web3 and DAO: A Comprehensive Overview and Reference Model

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2022年第5期9卷 1563-1573页

作者： Ding, Wenwen Hou, Jiachen Li, Juanjuan Guo, Chao Qin, Jirong Kozma, Robert Wang, Fei-Yue Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China North Automat Control Technol Inst Taiyuan 030006 Shanxi Peoples R China Chinese Inst Command & Control CICC Beijing 100089 Peoples R China Univ Memphis Dept Comp Sci & Math Memphis TN 38152 USA

Decentralized science (DeSci) is a hot topic emerging with the development of Web3 or Web3.0 and decentralized autonomous organizations (DAOs) and operations. DeSci fundamentally differs from the centralized science (CeSci) and Open Science (OS) movement built in the centralized way with centralized protocols. It changes the basic structure and legacy norms of current scientific systems via reshaping the cooperation mode, value system, and incentive mechanism. As such, it can provide a viable path for solving bottleneck problems in the development of science, such as oligarchy, silos, and so on, and make science more fair, free, responsible, and sensitive. However, DeSci itself still faces many challenges, including scaling, balancing the quality of participants, system suboptimal loops, lack of accountability mechanism, and so on. Taking these into consideration, this article presents a systematic introduction of DeSci, proposes a novel reference model with a six-layer architecture, addresses the potential applications, and also outlines the key research directions in this emerging field. This article is committed to providing helpful guidance and reference for future research efforts on DeSci.

关键词： Analytical models Systematics Protocols Decentralized autonomous organization Computational modeling Education Computer architecture CPSS DAO decentralized autonomous operations decentralized autonomous organizations decentralized funding decentralized science DeSci metaverses parallel DeSci parallel intelligence Web3

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COMPARISON OF REINFORCEMENT LEARNING METHODS FOR PRODUCTION control IN DISCRETE MANUFACTURING systems 18

COMPARISON OF REINFORCEMENT LEARNING METHODS FOR PRODUCTION ...

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ASME 18th Int Mfg Sci and Engn Conf held jointly with the 51st N Amer Res Conf / Japan-Soc-of-Mech-Engineers Int Conf on Leading Edge Mfg/Mat and Proc

作者： Yun, Lingxiang Wang, Jingwen Xiao, Minkun Li, Lin Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA

ISBN: (纸本)9780791887240

The widespread deployment of smart meters and communication technologies brings opportunities to improve the adaptability and flexibility of future manufacturing systems under growing complexity and uncertainty. As a result, data-driven approaches, especially Reinforcement Learning (RL) methods, are gaining wide attention in recent years. Although in the current literature, RL approaches show superiority over traditional methods in many applications, a comprehensive review and comparison of different RL methods and their use in discrete manufacturing system control under changing environments have not yet been established. This paper first provides a literature review of RL algorithms for decision-making in discrete manufacturing systems, and then systematically discusses the underlying mechanisms of four most commonly used RL algorithms. In addition, the performance of these RL algorithms is compared by solving a production control problem, which aims to maximize the profit under time-varying production costs. The comparison results show that the single-agent RL methods with discrete action space can provide satisfactory results in small-scale systems, while multi-agent RL algorithms are more suitable to solve problems in complex, large-scale systems to remit the curse of dimensionality. This research sheds light on the impacts of RL method selection on the search for high-quality solutions in manufacturing systems, which is envisioned to drive future research on RL-supported manufacturing systems.

关键词： machine learning reinforcement learning discrete manufacturing systems production control

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