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Arbitrary State Transition of Open Qubit System Based on Switching control

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arXiv 2024年

作者： Wu, Guangpu Xue, Shibei Ma, Shan Kuang, Sen Dong, Daoyi Petersen, Ian R. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Automation Central South University Changsha410083 China Department of Automation University of Science and Technology of China Hefei230027 China School of Engineering Australian National University CanberraACT2601 Australia

We present a switching control strategy based on Lyapunov control for arbitrary state transitions in open qubit systems. With coherent vector representation, we propose a switching control strategy, which can prevent the state of the qubit from entering invariant sets and singular value sets, effectively driving the system ultimately to a sufficiently small neighborhood of target states. In comparison to existing works, this control strategy relaxes the strict constraints on system models imposed by special target states. Furthermore, we identify conditions under which the open qubit system achieves finite-time stability (FTS) and finite-time contractive stability (FTCS), respectively. This represents a critical improvement in quantum state transitions, especially considering the asymptotic stability of arbitrary target states is unattainable in open quantum systems. The effectiveness of our proposed method is convincingly demonstrated through its application in a qubit system affected by various types of decoherence, including amplitude, dephasing and polarization decoherence. Copyright © 2024, The Authors. All rights reserved.

关键词： Asymptotic stability

A Real-time Framework of Multi-Camera Vehicle Tracking System*

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A Real-time Framework of Multi-Camera Vehicle Tracking Syste...

IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Hanyang Zhuang Xingyuan Chen Jianglin Lan Ying Wang Zhaoxuan Lu Chunxiang Wang Ming Yang University of Michigan - Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China James Watt School of Engineering University of Glasgow Glasgow United Kingdom School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Intelligent and Connected Vehicle R&D Center Co. LTD Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Multi-camera vehicle tracking (MCVT) system is a key technology to build an intelligent city and intelligent transportation system. The MCVT system utilizes roadside monitoring devices and computing platforms to achieve city-level road traffic perception at a finer scale by tracking each vehicle on the road to obtain the trajectory. The existing MCVT system mainly focuses on dealing with the algorithm modules separately, without studying the real-time performance and system architecture of the entire pipeline. However, in real-world applications, many of the tricks in improving evaluation metrics are not applicable since there is endless traffic flow running all the time. Therefore, this paper proposes a real-time framework of the MCVT system to streamline all the algorithm modules with proper data flow. Firstly, a multi-threaded multi-vehicle tracking (MVT) module is developed to detect vehicles in each camera. Re-identification is implemented during MVT to provide the feature for tracking in the same camera and across different cameras. Then, after the vehicle leaves the camera field of view, the feature of vehicle trajectory is calculated by averaging the feature at each frame. Meanwhile, a background thread runs continuously to match the incoming feature of vehicle trajectory with past features by conducting a topology searching method. In the end, the vehicle trajectories with well-matched features are considered to be the same vehicle. The entire pipeline has been validated on two different datasets, the precision is 0.64 in the CityFlow dataset which uses 6 cameras simultaneously. In the customized dataset, the same method can still reach real-time when using 16 cameras at the same time with smaller network modules.

关键词： Visualization Target tracking Roads Human-machine systems Pipelines Urban areas Cameras

On fuzzy alternate control systems

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On fuzzy alternate control systems

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Babatunde Oluwaseun Onasanya Yuming Feng Wei Zhang Shiping Wen Ning Tang Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing China University of Ibadan Ibadan Nigeria Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology Chongqing Three Gorges University Wanzhou Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China Australian AI Institute Faculty of Engineering and Information Technology University of Technology Sydney NSW Australia School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing China

Some researchers have worked on intermittent control and alternate control. In both cases, the choice of the control was a constant quantity. But, in real life, this situation is hard to come by for the reason that control may vary with time and circumstances owing to machine or human errors or both. Hence is the need to employ a method in which the control matrix is rather uncertain as is obtainable in real life. In this paper, we consider a more general model of both intermittent and alternate control which allows the control matrix to be fuzzy (uncertain). It turns out that both the classical intermittent and classical alternate controls are recovered in this mode.

关键词： Adaptation models Computational modeling control systems Computational intelligence

Spectral2Spectral: Image-spectral Similarity Assisted Deep Spectral CT Reconstruction without Reference

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arXiv 2022年

作者： Guo, Xiaodong Li, Yonghui Chang, Dingyue He, Peng Feng, Peng Yu, Hengyong Wu, Weiwen The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China Chongqing University Chongqing China The China Academy of Engineering Physics Institute of Materials Mianyang China The Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States The School of Biomedical Engineering Sun Yat-Sen University Guangdong Shenzhen China The Henan Key Laboratory of Imaging and Intelligent Processing PLA Strategic Support Force Information Engineering University Zhengzhou China

Spectral computed tomography based on a photon-counting detector (PCD) attracts more and more attentions since it has the capability to provide more accurate identification and quantitative analysis for biomedical materials. The limited number of photons within narrow energy bins leads to imaging results of low signal-noise ratio. The existing supervised deep reconstruction networks for CT reconstruction are difficult to address these challenges because it is usually impossible to acquire noise-free clinical images with clear structures as references. In this paper, we propose an iterative deep reconstruction network to synergize unsupervised method and data priors into a unified framework, named as Spectral2Spectral. Our Spectral2Spectral employs an unsupervised deep training strategy to obtain high-quality images from noisy data in an end-to-end fashion. The structural similarity prior within image-spectral domain is refined as a regularization term to further constrain the network training. The weights of neural network are automatically updated to capture image features and structures within the iterative process. Three large-scale preclinical datasets experiments demonstrate that the Spectral2spectral reconstructs better image quality than other the state-of-the-art methods. © 2022, CC0.

关键词： Image reconstruction

Self-adaptation of chimera states

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Physical Review E 2019年第1期99卷 010201(R)-010201(R)页

作者： Nan Yao Zi-Gang Huang Hai-Peng Ren Celso Grebogi Ying-Cheng Lai Department of Applied Physics Xi'an University of Technology Xi'an 710048 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education National Engineering Research Center of Health Care and Medical Devices The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China Institute for Complex Systems and Mathematical Biology King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of “intelligence” in achieving robustness through self-adaptation. The behavior can be exploited for the controlled generation of chimera states with their coherent component placed in any desired spatial region of the system.

关键词： Chimera states Collective dynamics Coupled oscillators Chaos & nonlinear dynamics

Joint Observer Gain and Input Design for Asymptotic Active Fault Diagnosis

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arXiv 2024年

作者： Xu, Feng Wan, Yiming Wang, Ye Puig, Vicenç Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education China Wuhan430074 China Department of Electrical and Electronic Engineering The University of Melbourne ParkvilleVIC3010 Australia Universitat Politècnica de Catalunya Barcelona08028 Spain

This paper proposes a joint gain and input design method for observer-based asymptotic active fault diagnosis, which is based on a newly-defined notion named the excluding degree of the origin from a zonotope. Using the excluding degree, a quantitative specification is obtained to characterize the performance of set-based robust fault diagnosis. Furthermore, a single gain design method and a joint gain and input design method are proposed, respectively. This is the first work to achieve a joint observer gain and input design for set-based active fault diagnosis. Compared with the existing methods that design gains and input separately, the proposed joint gain and input design method has advantages to exploit the fault diagnosis potential of observer-based schemes. Finally, several examples are used to illustrate the effectiveness of the proposed methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Design

Graph Convolutional Networks Based Muti-Label Deep Cross-Modal Hashing

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Graph Convolutional Networks Based Muti-Label Deep Cross-Mod...

International Conference on Information Science and Technology (ICIST)

作者： Yi Peng Xin Jiang Nian Zhang Jiang Xiong School of Electronic and Information Engineering Chongqing Three Gorges University Chongqing China Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Chongqing China Department of Electrical and Computer Engineering University of the District of Columbia Washington D.C. USA College of Mathematics and Statistics Chongqing Three Gorges University Chongqing China

Recently, multi-label deep cross-modal hashing (MDCH), which incorporates deep neural networks, hashing and multi-label learning for cross-modal retrieval tasks, has achieved excellent cross-modal retrieval results and thus became a highly popular area of research. Nevertheless, many existing MDCH methods concentrate on extracting information from multi-modal data, while neglecting the abundant semantic information in multiple labels. Few MDCH methods incorporate multi-label information, but they often treat labels as independent entities, ignoring the relationships between categories, which hinders the establishment of semantic connections among multi-modal data. In order to tackle the aforementioned challenges, we propose a graph convolutional networks based multi-label deep cross-modal hashing method (GMCH) in this paper. GMCH leverages two deep neural networks to generate hash representations from the original image-text pairs, during this process, a graph convolutional network is introduced to capture the category correlations of multi-labels and supervise the training of hash mapping. Experimental results on two commonly employed datasets validate the efficacy of the proposed GMCH method. You can find the code for our proposed GCMH at https://***/licher12/***.

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Multi-View broad learning system for primate oculomotor decision decoding

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arXiv 2019年

作者： Shi, Zhenhua Chen, Xiaomo Zhao, Changming He, He Stuphorn, Veit Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Zanvyl Krieger Mind and Brain Institute Johns Hopkins University School of Medicine Department of Neurobiology Stanford University StanfordCA United States Department of Neuroscience Johns Hopkins University BaltimoreMD United States

Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source. This approach is suitable for primate brain state decoding using cortical neural signals. This is because the complementary components of simultaneously recorded neural signals, local field potentials (LFPs) and action potentials (spikes), can be treated as two views. In this paper, we extended broad learning system (BLS), a recently proposed wide neural network architecture, from single-view learning to multi-view learning, and validated its performance in monkey oculomotor decision decoding from medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in non-human primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approach to classify the oculomotor decision, than several classical and state-of-the-art single-view and multi-view learning approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Electrophysiology

Cross-Modality Person Re-Identification Based on Shared Feature Attention Aggregation Algorithm

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Cross-Modality Person Re-Identification Based on Shared Feat...

International Conference on Information Science and Technology (ICIST)

作者： Jiong Wang Linjuan Xie Nian Zhang Jiang Xiong XiaoLing Song Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Chongqing China Department of Electrical and Computer Engineering University of the District of Columbia Washington D.C. USA College of Mathematics and Statistics Chongqing Three Gorges University Chongqing China College of Computer Science and Engineerings Chongqing Three Gorges University Chongqing China

Person re-identification in the visible-infrared spectrum (VI-ReID) is a challenging task due to significant difference of modalities, making it difficult to extract salient features across modalities. To solve this problem, in this paper, we propose a novel Cross-Modality Shared Feature Attention Aggregation algorithm (cm-SFAA) for VI-ReID. In the cm-SFAA approach, we first design a Random Channel Alternation Erasure Network to better learn robust features against occlusions and noise, as well as diverse feature representations under different channel combinations. This enhances the generalization ability of the model. Furthermore, a Local Feature Aggregation module is introduced in cm-SFAA, which utilizes an adaptive weighting mechanism to aggregate features from different levels and domains, thereby improving the performance of the model. Extensive experiments on two mainstream datasets SYSU-MMOI and RegDB show that cm-SFAA outperforms several baseline methods and acquires state-of-the-art performance. The source code of our proposed cm-SFAA is available at https://***/wdqlndVcm-SFAA.

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