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arXiv 2024年

作者： Bian, Shirui Wang, Zezhou Leng, Siyang Lin, Wei Shi, Jifan School of Mathematical Sciences Fudan University Shanghai200433 China Shanghai Center for Mathematical Sciences Fudan University Shanghai200433 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Fudan University Shanghai200032 China

Early-warning signals of delicate design are always used to predict critical transitions in complex systems, which makes it possible to render the systems far away from the catastrophic state by introducing timely interventions. Traditional signals including the dynamical network biomarker (DNB), based on statistical properties such as variance and autocorrelation of nodal dynamics, overlook directional interactions and thus have limitations in capturing underlying mechanisms and simultaneously sustaining robustness against noise perturbations. This paper therefore introduces a framework of causal network markers (CNMs) by incorporating causality indicators, which reflect the directional influence between variables. Actually, to detect and identify the tipping points ahead of critical transition, two markers are designed: CNM-GC for linear causality and CNM-TE for non-linear causality, as well as a functional representation of different causality indicators and a clustering technique to verify the system’s dominant group. Through demonstrations using benchmark models and real-world datasets of epileptic seizure, the framework of CNMs shows higher predictive power and accuracy than the traditional DNB indicator. It is believed that, due to the versatility and scalability, the CNMs are suitable for comprehensively evaluating the systems. The most possible direction for application includes the identification of tipping points in clinical disease. Copyright © 2024, The Authors. All rights reserved.

关键词： Autocorrelation

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High-Order Continuous-Variable Coherence of Phase-Dependent Squeezed state

arXiv

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arXiv 2021年

作者： Guo, Yanqiang Zhang, Haojie Guo, Xiaomin Zhang, Yuchi Zhang, Tiancai Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education College of Physics and Optoelectronics Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Cryptology Beijing100878 China State Key Laboratory of Quantum Optics and Quantum Optics Devices Institute of Opto-Electronics Shanxi University Taiyuan030006 China

We study continuous variable coherence of phase-dependent squeezed state based on an extended Hanbury Brown-Twiss scheme. High-order coherence is continuously varied by adjusting squeezing parameter r, displacement α, and squeezing phase θ. We also analyze effects of background noise γ and detection efficiency η on the measurements. As the squeezing phase shifts from 0 to π, the photon statistics of the squeezed state continuously change from the anti-bunching (g(n) (n) > n!) which shows a transition from particle nature to wave nature. The experiment feasibility is also examined. It provides a practical method to generate phase-dependent squeezed states with high-order continuous-variable coherence by tuning squeezing phase θ. The controllable coherence source can be applied to sensitivity improvement in gravitational wave detection and quantum imaging. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum optics

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A study of the limits of imaging capability due to water scattering effects in underwater ghost imaging

arXiv

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arXiv 2023年

作者： Yuliang, L.I. Chen, Mingliang Jinquan, Q.I. Deng, Chenjin Longkun, D.U. Zunwang, B.O. Chang, H.A.N. Zhihua, M.A.O. Yan, H.E. Shao, Xuehui Han, Shensheng Key Laboratory of Quantum Optics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Institute for Quantum Science and Technology College of Science National University of Defense Technology Changsha410073 China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography State Oceanic Administration Hangzhou310012 China Key Laboratory of Space Laser Communication and Detection Technology Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China National Laboratory of Aerospace Intelligent Control Technology Beijing100089 China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China

Underwater ghost imaging is an effective means of underwater detection. In this paper, a theoretical and experimental study of underwater ghost imaging is carried out by combining the description of underwater optical field transmission with the inherent optical parameters of the water body. This paper utilizes the Wells model and the approximate S-S scattering phase function to create a model for optical transmission underwater. The second-order Glauber function of the optical field is then employed to analyze the scattering field's degradation during the transmission process. This analysis is used to evaluate the impact of the water body on ghost imaging. The simulation and experimental results verify that the proposed underwater ghost imaging model can better describe the degradation effect of water bodies on ghost imaging. A series of experiments comparing underwater ghost imaging at different detection distances are also carried out in this paper. In the experiments, cooperative targets can be imaged up to 65.2m (9.3AL, at attenuation coefficient c=0.1426m-1 and the scattering coefficient b=0.052m-1) and non-cooperative targets up to 41.2m (6.4AL, at c=0.1569m-1 and b=0.081m-1) . By equating the experimental maximum imaged attenuation length for cooperative targets to Jerlov-I water (b=0.002m-1 and a=0.046m-1), the system will have a maximum imaging distance of 193m. Underwater ghost imaging is expected to achieve longer-range imaging by optimizing the system emission energy and detection sensitivity. © 2023, CC BY-NC-SA.

关键词： Light transmission

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Impacts of Instantaneous Heavy Load on DC Distribution Network and Their Solution

Impacts of Instantaneous Heavy Load on DC Distribution Netwo...

引用

2020 IEEE/IAS Industrial and Commercial Power System Asia, I and CPS Asia 2020

作者： Li, Changming Wang, Xiaohui Gong, Jinlin Ping, Chaochun Guo, Jiangyan Guo, Hong Wang, Zhidong Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China Cnooc Research Institute Co. Ltd Beijing China State Grid Economic and Technological Beijing China

ISBN: (数字)9781728143033

ISBN: (纸本)9781728143033

With the gradual maturity of power electronics technology, DC distribution network has attracted wide attention. Instantaneous heavy load has an adverse effect on system performance. It will cause fluctuation of the system, resulting in poor power quality and system instability. Therefore, this paper is focused on the impacts of instantaneous heavy load on DC distribution network and their solution. Firstly, the impacts are analyzed theoretically. Then, the simulation is carried out and it confirms the analysis. Finally, the method of using energy storage devices and capacitors at photovoltaic is proposed. The method can provide energy for the system to support instantaneous heavy load, so it can improve the performance of DC distribution network during instantaneous heavy load, and meanwhile it is validated by PSCAD/EMTDC model simulation. © 2020 IEEE.

关键词： Power quality

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Correction to: A compression pipeline for one-stage object detection model

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Journal of Real-Time Image Processing 2021年第6期18卷 1963-1964页

作者： Li, Zhishan Sun, Yiran Tian, Guanzhong Xie, Lei Liu, Yong Su, Hongye He, Yifan State Key Laboratory of Industrial Control Technology and Institute of Cyber-systems and Control Zhejiang University Hangzhou China Reconova Technologies Co. Ltd Xiamen China Institute of Intelligence Science and Engineering Shenzhen Polytechnic Shenzhen China

The original article can be found online.

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Practical fractional-order variable-gain super-twisting control with application to wafer stages of photolithography systems

arXiv

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arXiv 2021年

作者： Kuang, Zhian Sun, Liting Gao, Huijun Tomizuka, Masayoshi Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Mechanical Control System Lab Mechanical Engineering Department University of California BerkeleyCA94720 United States

In this paper, a practical fractional-order variable-gain super-twisting algorithm (PFVSTA) is proposed to improve the tracking performance of wafer stages for semiconductor manufacturing. Based on the sliding mode control (SMC), the proposed PFVSTA enhances the tracking performance from three aspects: 1) alleviating the chattering phenomenon via super-twisting algorithm and a novel fractional-order sliding surface (FSS) design, 2) improving the dynamics of states on the sliding surface with fast response and small overshoots via the designed novel FSS and 3) compensating for disturbances via variable-gain control law. Based on practical conditions, this paper analyzes the stability of the controller and illustrates the theoretical principle to compensate for the uncertainties caused by accelerations. Moreover, numerical simulations prove the effectiveness of the proposed sliding surface and control scheme, and they are in agreement with the theoretical analysis. Finally, practice-based comparative experiments are conducted. The results show that the proposed PFVSTA can achieve much better tracking performance than the conventional methods from various perspectives. Copyright © 2021, The Authors. All rights reserved.

关键词： Sliding mode control

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Towards Integrated Traffic control with Operating Decentralized Autonomous Organization

Towards Integrated Traffic Control with Operating Decentrali...

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International Conference on intelligent Transportation

作者： Shengyue Yao Jingru Yu Yi Yu Jia Xu Xingyuan Dai Honghai Li Fei-Yue Wang Yilun Lin Urban Computing Shanghai AI Lab Shanghai China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Research and Development Center of Transport Industry of Autonomous Driving Technology RIOH High and Technology Group Institute of Automation Chinese Academy of Sciences Beijing China Macau Institute of Systems Engineering Macau University of Science and Technology Macau China

With a growing complexity of the intelligent traffic system (ITS), an integrated control of ITS that is capable of considering plentiful heterogeneous intelligent agents is desired. However, existing control methods based on the centralized or the decentralized scheme have not presented their competencies in considering the optimality and the scalability simultaneously. To address this issue, we propose an integrated control method based on the framework of Decentralized Autonomous Organization (DAO). The proposed method achieves a global consensus on energy consumption efficiency (ECE), meanwhile to optimize the local objectives of all involved intelligent agents, through a consensus and incentive mechanism. Furthermore, an operation algorithm is proposed regarding the issue of structural rigidity in DAO. Specifically, the proposed operation approach identifies critical agents to execute the smart contract in DAO, which ultimately extends the capability of DAO-based control. In addition, a numerical experiment is designed to examine the performance of the proposed method. The experiment results indicate that the controlled agents can achieve a consensus faster on the global objective with improved local objectives by the proposed method, compare to existing decentralized control methods. In general, the proposed method shows a great potential in developing an integrated control system in the ITS.

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Urban Digital Twins for intelligent Road Inspection

Urban Digital Twins for Intelligent Road Inspection

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IEEE International Conference on Big Data

作者： Rui Fan Yikang Zhang Sicen Guo Jiahang Li Yi Feng Shuai Su Yanting Zhang Wenshuo Wang Yu Jiang Mohammud Junaid Bocus Xingyi Zhu Qijun Chen the Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous Systems and State Key Laboratory of Intelligent Autonomous Systems Robotics & Artificial Intelligence Laboratory (RAIL) Tongji University Shanghai P. R. China Department of Computer Science & Technology Donghua University Shanghai P. R. China Department of Civil Engineering McGill University Montreal Canada CTO office at ClearMotion Billerica MA USA Department of Electrical & Electronic Engineering the University of Bristol BS8 1UB U.K Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji University Shanghai P. R. China

ISBN: (纸本)9781665480468

Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at ***/view/udtiri-workshop/bigdata-2022.

关键词： Roads Data acquisition Inspection Big Data Rough surfaces Digital twins

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HARQ-IR Aided Short Packet Communications: BLER Analysis and Throughput Maximization

arXiv

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arXiv 2023年

作者： He, Fuchao Shi, Zheng Yang, Guanghua Li, Xiaofan Ye, Xinrong Ma, Shaodan the School of Intelligent Systems Science and Engineering and GBA B&R International Joint Research Center for Smart Logistics Jinan University Zhuhai519070 China the School of Physics and Electronic Information Anhui Normal University Wuhu241002 China the State Key Laboratory of Internet of Things for Smart City the Department of Electrical and Computer Engineering University of Macau China

This paper introduces hybrid automatic repeat request with incremental redundancy (HARQ-IR) to boost the reliability of short packet communications. The finite blocklength information theory and correlated decoding events tremendously preclude the analysis of average block error rate (BLER). Fortunately, the recursive form of average BLER motivates us to calculate its value through the trapezoidal approximation and Gauss–Laguerre quadrature. Moreover, the asymptotic analysis is performed to derive a simple expression for the average BLER at high signal-to-noise ratio (SNR). Then, we study the maximization of long term average throughput (LTAT) via power allocation meanwhile ensuring the power and the BLER constraints. For tractability, the asymptotic BLER is employed to solve the problem through geometric programming (GP). However, the GP-based solution underestimates the LTAT at low SNR due to a large approximation error in this case. Alternatively, we also develop a deep reinforcement learning (DRL)based framework to learn power allocation policy. In particular, the optimization problem is transformed into a constrained Markov decision process, which is solved by integrating deep deterministic policy gradient (DDPG) with subgradient method. The numerical results finally demonstrate that the DRL-based method outperforms the GP-based one at low SNR, albeit at the cost of increasing computational burden. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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BrainWave: A Brain Signal Foundation Model for Clinical Applications

arXiv

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arXiv 2024年

作者： Yuan, Zhizhang Shen, Fanqi Li, Meng Yu, Yuguo Gu, Gefei Wu, Fei Tan, Chenhao Yang, Yang Zhejiang University Zhejiang Hangzhou China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China INSIDE Institute for Biological and Artificial Intelligence Shanghai China Research Institute of Intelligent and Complex Systems State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Shanghai Artificial Intelligence Laboratory Fudan University Shanghai China University of Chicago ChicagoIL United States

Neural electrical activity is fundamental to brain function, underlying a range of cognitive and behavioral processes, including movement, perception, decision-making, and consciousness. Abnormal patterns of neural signaling often indicate the presence of underlying brain diseases. The variability among individuals, the diverse array of clinical symptoms from various brain disorders, and the limited availability of diagnostic classifications, have posed significant barriers to formulating reliable model of neural signals for diverse application contexts. Here, we present BrainWave, the first foundation model for both invasive and noninvasive neural recordings, pretrained on more than 40,000 hours of electrical brain recordings (13.79 TB of data) from approximately 16,000 individuals. Our analysis show that BrainWave outperforms all other competing models and consistently achieves state-of-the-art performance in the diagnosis and identification of neurological disorders. We also demonstrate robust capabilities of BrainWave in enabling zero-shot transfer learning across varying recording conditions and brain diseases, as well as few-shot classification without fine-tuning, suggesting that BrainWave learns highly generalizable representations of neural signals. We hence believe that open-sourcing BrainWave will facilitate a wide range of clinical applications in medicine, paving the way for AI-driven approaches to investigate brain disorders and advance neuroscience research. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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