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4th International Symposium on Power Electronics and control engineering, ISPECE 2021

作者： Zheng, Lidong Dong, Haobin Luo, Wang Ge, Jian Liu, Huan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advance Control and Intelligent Automation for Complex China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (数字)9781510650374

ISBN: (纸本)9781510650367

Overhauser magnetometer reaches an ultra-high accuracy benefit from the outputted frequency of free induction decay transversal signal is proportional to the magnetization on measuring the real scalar geomagnetic field. Nevertheless, the magnitude of the accuracy is an important factor that severely affects the validity of geomagnetic field data. Since the real value of the geomagnetic field is unknown, we propose a measurement quality estimation method through analyzing the envelope of the free induction decay transversal signal from the Overhauser magnetometer. The estimation model includes the SNR and the decay slope characteristics of the signal is indicated in an two-digit number changes from 00∼99. The results demonstrated that the proposed estimation method can effective performance the reliability of magnetometer measurement data in our experiments. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Magnetometers

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Experimental Study on the Pressure Drop and Thickness Variation of Carbon Black Particle Layer During Oxidation on Dpf Channels

SSRN

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SSRN 2023年

作者： Deng, Meng Meng, Zhongwei Ou, Juan Wu, Daigeng Bao, Zhongqiang Key Laboratory of Fluid and Power Machinery Ministry of Education School of Energy and Power Engineering Xihua University Chengdu610039 China Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province School of Automobile and Transportation Xihua University Chengdu610039 China Power Machinery and Vehicular Engineering Institute Zhejiang University Hangzhou310027 China

Studying the changes in pressure drop and thickness of particle layers during oxidation is crucial to understanding the regeneration performance of diesel particulate filters (DPFs) and optimizing the regeneration process. In this study, carbon black particle layers on the channels of a DPF were regenerated actively using a visualized single-channel test bench. Online measurements and analysis of pressure drop and particle layer thickness were performed to examine their dynamic behavior. The results showed that the regeneration process of the particle layer can be categorized into three stages. In the first stage, the pressure drop slowly decreased while the thickness continued to increase. In the second stage, the decrease rate of the pressure drop increased as the deposition thickness grew, which was most significant in the range of 112 μm to 147 μm. Simultaneously, the thickness of the particle layer is still increasing due to thermal expansion. When the thickness is no more than 193 μm, it peaked and then started to decrease. When the thickness exceeded 273 μm, the phenomenon of continuous thickening occurred. In the third stage, the pressure drop reached its lowest point, while the decreased rate of thickness was relatively high. Consequently, a time delay was observed between the rapid decline of thickness and the subsequent steep decrease in pressure drop, and this delay increased with the growth of the particle layer thickness. Once the deposition thickness reached 404 μm, the particle layer would suspend on the filter wall and form a gap during the heating process. © 2023, The Authors. All rights reserved.

关键词： Carbon black

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Surface Defect Detection Based on Deep Learning and Collaborative Cloud-edge Computation 7

Surface Defect Detection Based on Deep Learning and Collabor...

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7th International Conference on Computer and Communications, ICCC 2021

作者： He, Shunjie Yang, Bo Zhu, Dafeng Li, Cheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9781665409506

Surface defect detection is a fundamental step of quality management. Nowadays, surface defect detection system based on machine vision technology has made great progress which can gradually replace the human inspector. With the help of convolutional neural networks, the accuracy and stability of the surface defect detection algorithm are improving rapidly. However, it is difficult to process the images which have small defect areas and complex backgrounds. Furthermore, lacking defective samples and limited computation resources are also tough problems. To deal with these problems, a two-stage algorithm combined with object detection and classification networks is proposed. Transform learning and data augmentation are used to deal with the problem of lacking defect samples. Besides, a detection system based on collaborative cloud-edge computation is designed to improve the performance of the algorithm. The result shows that the proposed method has high performance and low requirements of computation resources. © 2021 IEEE.

关键词： Computer vision

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Loss-induced quantum nonreciprocity

arXiv

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arXiv 2024年

作者： Li, Baijun Zuo, Yunlan Kuang, Le-Man Jing, Hui Lee, Chaohong Institute of Quantum Precision Measurement State Key Laboratory of Radio Frequency Heterogeneous Integration College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Shenzhen518045 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China

Attribute to their robustness against loss and external noise, nonreciprocal photonic devices hold great promise for applications in quantum information processing. Recent advancements have demonstrated that nonreciprocal optical transmission in linear systems can be achieved through the strategic introduction of loss. However, a crucial question remains unanswered: can loss be harnessed as a resource for generating nonreciprocal quantum correlations? Here, we take a counterintuitive stance by engineering loss to generate a novel form of nonreciprocal quantum correlations, termed nonreciprocal photon blockade. We examine a dissipative three-cavity system comprising two nonlinear cavities and a linear cavity. The interplay of loss and nonlinearity leads to a robust nonreciprocal single- and two-photon blockade, facilitated by destructive quantum interference. Furthermore, we demonstrate the tunability of this nonreciprocal photon blockade by manipulating the relative phase between the two nonlinear cavities. Remarkably, this allows for the reversal of the direction of nonreciprocity. Our study not only sheds new light on the concept of loss-engineered quantum nonreciprocity but also opens up a unique pathway for the design of quantum nonreciprocal photonic devices. © 2024, CC BY-NC-SA.

关键词： Photons

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Cross-Modal De-Deviation for Enhancing Few-Shot Classification

SSRN

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SSRN 2023年

作者： Pan, Mei-Hong Shen, Hong-Bin School of Electronic Information and Electrical Engineering Shanghai Jiaotong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Few-shot learning poses a critical challenge due to the deviation problem caused by the scarcity of available samples. In this work, we aim to address deviations in both feature representations and prototypes. To achieve this, we propose a cross-modal de-deviation framework that leverages class semantic information to provide robust prior knowledge for the samples. This framework begins with a visual-to-semantic autoencoder trained on the labeled samples to predict semantic features for the unlabeled samples. Then, we devise a binary linear programming model to incorporate the initial prototypes with the cluster centers of the unlabeled samples. To avoid mismatch between the cluster centers and the initial prototypes, we conduct the label assignment process in the semantic space using the class ground truth semantic features as the reference points, with the cluster centers transformed into semantic representations. Moreover, we model a linear classifier with the concatenation of the refined prototypes and the class ground truth semantic features serving as the initial weights. Then we propose a novel optimization strategy based on the alternating least squares (ALS) model. From the ALS model, we can derive two closed-form solutions regarding to the features and weights, facilitating alternative optimization of them. Extensive experiments conducted on three standard benchmarks demonstrate the competitive advantages of our CMDD method over the state-of-the-art few-shot classification methods, confirming its effectiveness in reducing deviation. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Initialization-Free Distributed Constrained Optimization Algorithms with a Pre-Specified Time

Initialization-Free Distributed Constrained Optimization Alg...

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IEEE Conference on Decision and control

作者： Xiasheng Shi Chaoxu Mu Changyin Sun Lu Ren Yanxu Su School of Artificial Intelligence Anhui University Hefei China Ministry of Education Engineering Research Center of Autonomous Unmanned System Technology Anhui University Hefei China Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology Anhui University Heifei China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology School of Electrical and Information Engineering Tianjin University Tianjin China

The distributed constrained optimization problem over an undirected communication topology is investigated in this study. It focuses on addressing a global coupled equality constraint that applies to all agents. To tackle this problem, a distributed approach with arbitrary initialization is developed by virtue of the aperiodic sampling control idea and the consensus-based multi-agent system(MAS) technology. This approach is developed to address constrained optimization problems within a pre-specified time. In addition, this predefined time is freely defined by users and irrelevant to the initial states, control coefficients, and network structure of systems. The Lyapunov stability theory completes the convergence proof of the developed method. Then, the developed method is extended to handle distributed nonlinear constrained optimization problems. Finally, The availability of two developed methods is demonstrated through two simulation examples.

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Dynamics response analysis of airborne external storage system with clearance between missile-frame

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Chinese Journal of Aeronautics 2020年第12期33卷 3278-3287页

作者： Yongfeng YANG Qingyang ZHENG Jianjun WANG Zhongfu MA Xiangqiu LIU Department of Engineering Mechanics Northwestern Polytechnical UniversityXi'an 710072China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern UniversityShenyang 110819China China Airborne Missile Academy Luoyang 471099China

Considering the aircraft and its external components are subjected to complex and variable aerodynamic loads during the working process,the missile-frame clearance system of the airborne external missile is *** random vibration characteristics of the airborne external components are analyzed by finite element *** finite element model is optimized with reference to the test results,and the effects of different clearance on the dynamic response of the missile-frame system are *** result shows that the frequency response curves of the same position and the resonant peak frequencies are consistent under different *** acceleration response at both ends of the missile is large and the amplitude near the center of mass is *** results can be used to predict reasonable missile-frame clearance and make guidance to the structural design and reliability analysis of the missile-frame system.

关键词： Airborne Dynamic response Finite element method(FEM) Missile-frame Random vibration

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Adaptive Cohen's Class Time-Frequency Distribution

arXiv

引用

arXiv 2024年

作者： Cui, Manjun Zhang, Zhichao Han, Jie Chen, Yunjie Cao, Chunzheng The School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing210044 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Key Laboratory of Computational Science and Application of Hainan Province Haikou571158 China The School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing210044 China

The fixed kernel function-based Cohen's class time-frequency distributions (CCTFDs) allow flexibility in denoising for some specific polluted signals. Due to the limitation of fixed kernel functions, however, from the view point of filtering they fail to automatically adjust the response according to the change of signal to adapt to different signal characteristics. In this letter, we integrate Wiener filter principle and the time-frequency filtering mechanism of CCTFD to design the least-squares adaptive filter method in the Wigner-Ville distribution (WVD) domain, giving birth to the least-squares adaptive filter-based CCTFD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error denoising in the WVD domain. Some examples are also carried out to demonstrate that the proposed adaptive CCTFD outperforms some state-of-the-arts in noise suppression. Copyright © 2024, The Authors. All rights reserved.

关键词： Adaptive filters

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Inferring Topology of Networked Dynamical systems by Active Excitations

arXiv

引用

arXiv 2022年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Topology inference for networked dynamical systems (NDSs) has received considerable attention in recent years. The majority of pioneering works have dealt with inferring the topology from abundant observations of NDSs, so as to approximate the real one asymptotically. Leveraging the characteristic that NDSs will react to various disturbances and the disturbance’s influence will consistently spread, this paper focuses on inferring the topology by a few active excitations. The key challenge is to distinguish different influences of system noises and excitations from the exhibited state deviations, where the influences will decay with time and the exciatation cannot be arbitrarily large. To practice, we propose a one-shot excitation based inference method to infer h-hop neighbors of a node. The excitation conditions for accurate one-hop neighbor inference are first derived with probability guarantees. Then, we extend the results to h-hop neighbor inference and multiple excitations cases, providing the explicit relationships between the inference accuracy and excitation magnitude. Specifically, the excitation based inference method is not only suitable for scenarios where abundant observations are unavailable, but also can be leveraged as auxiliary means to improve the accuracy of existing methods. Simulations are conducted to verify the analytical results. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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Multi-adversarial Testing and Retraining for Improving the Robustness of Autonomous Driving Policies

Multi-adversarial Testing and Retraining for Improving the R...

引用

Chinese control Conference (CCC)

作者： keyu Liu Junjie Zhou Lin Wang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Process Ministry of Education of China Shanghai P. R. China

Autonomous vehicles, especially those based on deep reinforcement learning, are known for their susceptibility to the adversarial perturbations. To enhance their robustness, it is imperative to not only detect their decision errors through testing, but also fortify their robustness against these errors. This paper proposes an iterative optimization method that trains multiple adversarial agents with varying adversarial intensities to identify decision errors in the driving vehicle and enhance its robustness by retraining to counteract these adversarial agents. The effectiveness of the method is evaluated in a lane-changing scenario and the results demonstrate improved robustness of deep reinforcement learning based autonomous driving strategies compared to the adversarial reinforcement learning with a single adversary.

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