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Output Constrained Adaptive Controller Design for Nonlinear Saturation Systems

IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 441-454页

作者： Yongliang Yang Zhijie Liu Qing Li Donald C.Wunsch the Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China the Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA

This paper considers the adaptive neuro-fuzzy control scheme to solve the output tracking problem for a class of strict-feedback nonlinear *** asymmetric output constraints and input saturation are *** asymmetric barrier Lyapunov function with time-varying prescribed performance is presented to tackle the output-tracking error constraints.A high-gain observer is employed to relax the requirement of the Lipschitz continuity about the nonlinear *** avoid the"explosion of complexity",the dynamic surface control(DSC)technique is employed to filter the virtual control signal of each *** deal with the actuator saturation,an additional auxiliary dynamical system is *** is theoretically investigated that the parameter estimation and output tracking error are semi-global uniformly ultimately *** simulation examples are conducted to verify the presented adaptive fuzzy controller design.

关键词： Asymmetric barrier Lyapunov function input saturation Nussbaum function time-varying prescribed performance

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A Visual Analytics Framework for Ocean Scalar Volume Data

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Ocean-Land-Atmosphere Research 2023年第1期2卷 173-189页

作者： Jinyu Li Tianyi Huang Ping Hu Weicheng Cui Shenghui Cheng Research Center for Industries of the Future and School of Engineering Westlake UniversityHangzhouChina Department of Computer Science Stony Brook UniversityStony BrookNYUSA. Key Laboratory of Coastal of Environment and Resources of Zhejiang Province School of EngineeringWestlake UniversityHangzhouChina

The processes and phenomena hidden in massive marine data importantly impact heat transportation,material transportation,and climate *** can assist people in mining and understanding marine data to gain ***,oceanographers must study ocean processes and ***,one remaining challenge in the existing visualization methods is efficiently rendering marine data with large volumes and illustrating the internal structure of marine *** solve this problem,we propose a new visual analytics framework involving 4 parts for visualizing extensive marine scalar volume *** first use a single box and double spheres separately as proxy geometries to draw a flat Earth and spherical ***,we design a new ray-casting algorithm based on graphics processing unit to reduce the volume of marine *** algorithm accelerates volume rendering by using an adaptive texture sampling rate and step ***,we use a depth correction algorithm to accurately restore the ocean ***,we develop an internal roaming algorithm to observe the internal structure of marine *** this way,users can dynamically observe the internal structure of marine *** illustrate the effectiveness of our algorithms,we use them to visualize Hybrid Coordinate Ocean Model data and Argo data.

关键词： Visual graphics rendering

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BI-based Reasoning about Quantum Programs with Heap Manipulations

arXiv

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arXiv 2024年

作者： Su, Bonan Zhou, Li Feng, Yuan Ying, Mingsheng Department of Computer Science and Technology Tsinghua University Beijing China Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information University of Technology Sydney Ultimo Australia

We provide well-founded semantics for a quantum programming language Qwhile-hp with heap manipulations, where allocation statements follow a dirty pattern, meaning that newly allocated qubits can nondeterministically assume arbitrary initial states. To thoroughly characterize heap manipulations in the quantum setting, we develop a quantum BI-style logic that includes interpretations for separating implication (−∗) and separating conjunction (∗). We then adopt this quantum BI-style logic as an assertion language to reason about heap-manipulated quantum programs and present a quantum separation logic which is sound and relatively complete. Finally, we apply our framework to verify the correctness of various practical quantum programs and to prove the correct usage of dirty ancilla qubits. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

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Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise? 6

Can GPT-4 Support Analysis of Textual Data in Tasks Requirin...

引用

6th Workshop on Automated Semantic Analysis of Information in Legal Text, ASAIL 2023

作者： Savelka, Jaromir Ashley, Kevin D. Gray, Morgan A. Westermann, Hannes Xu, Huihui Computer Science Department Carnegie Mellon University PittsburghPA United States Intelligent Systems Program University of Pittsburgh PA United States Cyberjustice Laboratory Faculté de Droit Université de Montréal Montréal Canada

We evaluated the capability of generative pre-trained transformers (GPT-4) in analysis of textual data in tasks that require highly specialized domain expertise. Specifically, we focused on the task of analyzing court opinions to interpret legal concepts. We found that GPT-4, prompted with annotation guidelines, performs on par with well-trained law student annotators. We observed that, with a relatively minor decrease in performance, GPT-4 can perform batch predictions leading to significant cost reductions. However, employing chain-of-thought prompting did not lead to noticeably improved performance on this task. Further, we demonstrated how to analyze GPT-4's predictions to identify and mitigate deficiencies in annotation guidelines, and subsequently improve the performance of the model. Finally, we observed that the model is quite brittle, as small formatting related changes in the prompt had a high impact on the predictions. These findings can be leveraged by researchers and practitioners who engage in semantic/pragmatic annotations of texts in the context of the tasks requiring highly specialized domain expertise. © 2023 CEUR-WS. All rights reserved.

关键词： Forecasting

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Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

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npj Computational Materials 2022年第1期8卷 378-391页

作者： Marcel F.Langer Alex Goeßmann Matthias Rupp NOMAD Laboratory Fritz Haber Institute of the Max Planck SocietyBerlinGermany Machine Learning Group Technische Universität BerlinBerlinGermany Institute of Mathematics Technische Universität BerlinBerlinGermany Citrine Informatics Redwood CityCAUSA Department of Computer and Information Science University of KonstanzKonstanzGermany

Computational study of molecules and materials from first principles is a cornerstone of physics,chemistry,and materials science,but limited by the cost of accurate and precise *** settings involving many simulations,machine learning can reduce these costs,often by orders of magnitude,by interpolating between reference *** requires representations that describe any molecule or material and support *** comprehensively review and discuss current representations and relations between *** selected state-of-the-art representations,we compare energy predictions for organic molecules,binary alloys,and Al–Ga–In sesquioxides in numerical experiments controlled for data distribution,regression method,and hyper-parameter optimization.

关键词： alloys corners precise

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Anatomical-Functional Fusion Network for Lesion Segmentation Using Dual-View CEUS 9th

Anatomical-Functional Fusion Network for Lesion Segmentatio...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Wan, Peng Liu, Chunrui Zhang, Daoqiang Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Ultrasound Affiliated Drum Tower Hospital Medical School of Nanjing University Nanjing China

ISBN: (纸本)9783031466632

Dual-view contrast-enhanced ultrasound (CEUS) has been widely applied in lesion detection and characterization due to the provided anatomical and functional information of lesions. Accurate delineation of lesion contour is important to assess lesion morphology and perfusion dynamics. Although the last decade has witnessed the unprecedented progress of deep learning methods in 2D ultrasound imaging segmentation, there are few attempts to discriminate tissue perfusion discrepancy using dynamic CEUS imaging. Combined with the side-by-side gray-scale US view, we propose a novel anatomical-functional fusion network (AFF-Net) to fuse complementary imaging characteristics from dual-view dynamic CEUS imaging. Towards a comprehensive characterization of lesions, our method mainly tackles with two challenges: 1) how to effectively represent and aggregate enhancement features of the dynamic CEUS view;2) how to efficiently fuse them with the morphology features of the US view. Correspondingly, we design the channel-wise perfusion (PE) gate and anatomical-functional fusion (AFF) module with the goal to exploit dynamic blood flow characteristics and perform layer-level fusion of the two modalities, respectively. The effectiveness of the AFF-Net method on lesion segmentation is validated on our collected thyroid nodule dataset with superior performance compared with existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Deep Reinforcement Learning-Based LSTM Model for Traffic Flow Forecasting in Internet of Vehicles

Deep Reinforcement Learning-Based LSTM Model for Traffic Flo...

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Chinese Intelligent Automation Conference, CIAC 2021

作者： Chen, Zekuan Luo, Xiong Wang, Ting Wang, Weiping Zhao, Wenbing School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China Shunde Graduate School University of Science and Technology Beijing Foshan528399 China Department of Electrical Engineering and Computer Science Cleveland State University ClevelandOH44115 United States

ISBN: (纸本)9789811663710

Nowadays many large cities often suffer from road congestion. Then, it is necessary to improve the working efficiency of urban road network with the help of computational intelligence technologies, within the Internet of vehicles (IoV) environment. We are committed to designing traffic flow prediction methods through the use of machine learning models. In this paper, we predict the traffic flow and traffic situation of the selected road segment. Specifically, we propose a deep reinforcement learning (DRL)-based long short-term memory (LSTM) model to predict the traffic flow. On the basis of it, we characterize the traffic situation with fuzzy comprehensive evaluation (FCE)-based model. The experimental results show that the constructed DRL-based LSTM model can accurately predict the traffic flow data. Meanwhile, the traffic situation characterization model can represent the road traffic situation well. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Statistical Device Simulation and Machine Learning of Process Variation Effects of Vertically Stacked Gate-All-Around Si Nanosheet CFETs

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IEEE Transactions on Nanotechnology 2024年 23卷 386-392页

作者： Kola, Sekhar Reddy Li, Yiming Butola, Rajat National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Biomedical Engineering Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

In this study, we report the process variation effect (PVE) including the work function fluctuation (WKF) on the DC/AC characteristic fluctuation of stacked gate-all-around silicon complementary field-effect transistors (CFETs). The PVE affects characteristic fluctuation significantly;in particular, for the variability of off-state current. Owing to the bottom channel of a fin-type, the P-FET suffers from the worst off-state current fluctuation (more than 200% variation) compared to the N-FET. The device variability induced by the WKF is marginal because of amorphous-type metal grains. As input features to an artificial neural network (ANN) model, low and high work function values, as well as parameters of PVE that have prevalent effects on CEFT transfer characteristics are further considered and modeled. The estimated values of R2-score prove that the ANN model properly grasps information from the dataset successfully;thus, it can be used to model emerging CFETs for circuit simulation. © 2002-2012 IEEE.

关键词： Neural networks

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Arbitrarily polarized bound states in the continuum with twisted photonic crystal slabs

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Light(science & Applications) 2023年第4期12卷 542-550页

作者： Haoye Qin Zengping Su Mengqi Liu Yixuan Zeng Man-Chung Tang Mengyao Li Yuzhi Shi Wei Huang Cheng-Wei Qiu Qinghua Song Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Institute of Precision Optical Engineering School of Physics Science and EngineeringTongji UniversityShanghai 200092China Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou institute of Nano-Tech and Nano-Bionics(SINANO)Chinese Academy of Sciences(CAS)Suzhou 215123China

Arbitrary polarized vortex beam induced by polarization singularity offers a new platform for both classical optics and quantum entanglement *** states in the continuum(BICs)have been demonstrated to be associated with topological charge and vortex polarization singularities in momentum *** conventional symmetric photonic crystal slabs(PhCSs),BIC is enclosed by linearly polarized far fields with winding angle of 2π,which is unfavorable for high-capacity and multi-functionality integration-optics ***,we show that by breakingσz-symmetry of the PhCS,asymmetry in upward and downward directions and arbitrarily polarized BIC can be realized with a bilayer-twisted *** exhibits elliptical polarization states with constant ellipticity angle at every point in momentum space within the vicinity of *** topological nature of BIC reflects on the orientation angle of polarization state,with a topological charge of 1 for any value of ellipticity *** coverage of Poincarésphere(i.e.,-π/4≤X≤4 and-π/2≤ψ≤π/2)and higher-order Poincarésphere can be realized by tailoring the twist *** findings may open up new avenues for applications in structured light,quantum optics,and twistronics for photons.

关键词： Poincaré polarized twisted

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Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework

arXiv

引用

arXiv 2025年

作者： Li, Xiaolong Wei, Jianhao Wang, Haidong Dong, Li Chen, Ruoyang Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin The Xiangjiang Laboratory Hunan Provincial General University Key Laboratory of IoT Intelligent Sensing and Distributed Collaborative Optimization Hunan University of Technology and Business Changsha410205 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China The Department of Electrical and Computer Engineering University of Concordia Montreal Canada The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada

In intelligent transportation systems (ITSs), incorporating pedestrians and vehicles in-the-loop is crucial for developing realistic and safe traffic management solutions. However, there is falls short of simulating complex real-world ITS scenarios, primarily due to the lack of a digital twin implementation framework for characterizing interactions between pedestrians and vehicles at different locations in different traffic environments. In this article, we propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop. Specifically, SVFDT builds comprehensive pedestrian-vehicle interaction models by leveraging multi-source traffic surveillance videos. Its architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources;(ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions;and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime. We analyze key design requirements and challenges and present core guidelines for SVFDT’s system implementation. A testbed evaluation demonstrates its effectiveness in optimizing traffic management. Comparisons with traditional terminal-server frameworks highlight SV-FDT’s advantages in mirroring delays, recognition accuracy, and subjective evaluation. Finally, we identify some open challenges and discuss future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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