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3rd International Symposium on Artificial Intelligence for Medical sciences, ISAIMS 2022

作者： Chen, Hao Tang, Wending Tao, Ran Department of Electrical and Computer Engineering Northeastern University United States Department of Engineering and Physics Tsinghua University China State Key Laboratory of Surface Physics Department of Physics Advanced Materials Laboratory Fudan University China

ISBN: (纸本)9781450398442

The extraction of local texture information using the traditional Local Binary Mode (LBP) is limited, and it ignores the representation of global texture information, which leads to an unsatisfactory outcome for the texture classification task. Local Binary Mode (LBP) has been widely used in texture classification. This paper utilizes LBPV to resolve this issue (Local Binary Pattern Variance) and proposes a novel adaptive weight joint multi-scale LBPV2 texture picture classification algorithm. The typical variance weight is replaced by the square of covariance as the cumulative weight of the histogram in this method, and the multi-scale texture information is retrieved using an adaptive weight and multi-scale scheme. Thus, the texture classification performance is further improved. Simulation experiments on the commonly used Outex reference texture database show that the proposed adaptive weight combined with multi-scale LBPV2 can significantly improve the performance of texture classification. In the fields of computer vision and pattern recognition, texture analysis is a fundamental visual problem with a wide range of applications, including object detection, remote sensing, content-based image retrieval, and medical picture analysis. For various research questions, numerous academics have put forth various LBP versions in recent years. The dominant LBP [2] model was proposed by Liao et al. in 2009, and it was empirically chosen as the best model out of all the models. Guo et al. proposed LBPV [3], which expresses local contrast information into the straight square of texture images using a local variance confidence and global matching scheme. In order to increase classification performance, the author also proposed a Completed Local Binary Pattern (CLBP) [4] in the same year. This pattern combines three complimentary groups - CLBPS, CLBPM, and CLBPC - using a combined probability distribution. To enhance the traditional local binary pattern's noise resistance and

关键词： Local binary pattern

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On the System Performance of Mobile Edge Computing in an Uplink NOMA WSN With a Multiantenna Access Point Over Nakagami-m Fading

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 668-685页

作者： Van-Truong Truong Van Nhan Vo Dac-Binh Ha Chakchai So-In the Faculty of Electrical-Electronic Engineering Duy Tan University the Institute of Research and Development Duy Tan University the Faculty of Information Technology Duy Tan University IEEE the Applied Network Technology(ANT)Laboratory Department of Computer ScienceCollege of ComputingKhon Kaen University

In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m *** proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)*** derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered *** results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power ***,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system *** approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar *** use Monte Carlo simulations to confirm the validity of our analysis.

关键词： Mobile edge computing(MEC) Nakagami-m fading offloading selection combining(SC)/maximal ratio combining(MRC) successful computation probability(SCP) uplink nonortho-gonal multiple access(NOMA) wireless sensor networks(WSNs)

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State of Charge Management for Grid-Connected PV with Supercapacitor System Considering LVRT Operation

State of Charge Management for Grid-Connected PV with Superc...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Kritprajun, Paychuda Tolbert, Leon M. Sun, Jingjing Wang, Jingxin Praisuwanna, Nattapat Liu, Yunting Ferrari, Maximiliano The University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States The Pennsylvania State University Department of Electrical Engineering University ParkPA United States Oak Ridge National Laboratory Grid Components and Control Oak RidgeTN United States

ISBN: (纸本)9798350316445

This paper presents a state of charge (SOC) management strategy to ensure low-voltage ride-through (LVRT) services of a grid-connected photovoltaic (PV) with a supercapacitor (SC) energy storage system (PVSS). A concept of SOC reservation for LVRT operations is introduced to guarantee that the PV grid-connected system can provide LVRT services complying with grid code requirements. SOC management is derived by considering SC limitations, the current rating of the converters, and grid conditions. Therefore, safe operation of the power system and PVSS can be achieved. The study also adopts an electrothermal model of the SC to adjust the current set points during SOC management by considering the temperature-dependent SC parameters;thus, both safety and full utilization of the SC can be achieved. Consequently, SOC management can rapidly be accomplished, thereby increasing the certainty of providing LVRT services to the system. © 2023 IEEE.

关键词： Grid-connected photovoltaic system low-voltage ride-through state of charge management supercapacitor

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Toward Consistent High-Fidelity Quantum Learning on Unstable Devices via Efficient In-Situ Calibration 4

Toward Consistent High-Fidelity Quantum Learning on Unstable...

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Hu, Zhirui Wolle, Robert Tian, Mingzhen Guan, Qiang Humble, Travis Jiang, Weiwen George Mason University Department of Electrical and Computer Engineering VA United States George Mason University Quantum Science and Engineering Center VA United States George Mason University Department of Physics & Astronomy VA United States Kent State University Department of Computer Science OH United States Oak Ridge National Laboratory TN United States

ISBN: (纸本)9798350343236

In the near-term noisy intermediate-scale quantum (NISQ) era, high noise will significantly reduce the fidelity of quantum computing. What's worse, recent works reveal that the noise on quantum devices is not stable, that is, the noise is dynamically changing over time. This leads to an imminent challenging problem: At run-time, is there a way to efficiently achieve a consistent high-fidelity quantum system on unstable devices? To study this problem, we take quantum learning (a.k.a., variational quantum algorithm) as a vehicle, which has a wide range of applications, such as combinatorial optimization and machine learning. A straightforward approach is to optimize a variational quantum circuit (VQC) with a parameter-shift approach on the target quantum device before using it;however, the optimization has an extremely high time cost, which is not practical at run-time. To address the pressing issue, in this paper, we proposed a novel quantum pulse-based noise adaptation framework, namely QuPAD. In the proposed framework, first, we identify that the CNOT gate is the fidelity bottleneck of the conventional VQC, and we employ a more robust parameterized multi-qubit gate (i.e., Rzx gate) to replace CNOT gate. Second, by benchmarking Rzx gate with different parameters, we build a fitting function for each coupling qubit pair, such that the deviation between the theoretic output of Rzx gate and its on-device output under a given pulse amplitude and duration can be efficiently predicted. On top of this, an evolutionary algorithm is devised to identify the pulse amplitude and duration of each Rzx gate (i.e., calibration) and find the quantum circuits with high fidelity. Experiments show that the runtime on quantum devices of QuPAD with 8-10 qubits is less than 15 minutes, which is up to 270 x faster than the parameter-shift approach. In addition, compared to the vanilla VQC as a baseline, QuPAD can achieve 59.33% accuracy gain on a classification task, and average 66.34% clo

关键词： Combinatorial optimization

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Longitudinal velocity control of autonomous driving based on extended state observer

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 36-46页

作者： Hongbo Gao Hanqing Yang Xiaoyu Zhang Xiangyun Ren Fenghua Liang Ruidong Yan Qingchao Liu Mingmao Hu Fang Zhang Jiabing Gao Siyu Bao Keqiang Li Deyi Li Danwei Wang Department of Automation University of Science and Technology of ChinaHefeiChina State Key Laboratory of Intelligent Vehicle Safety Technology ChongqingChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Traffic and Transportation Beijing Jiaotong UniversityBeijingChina Automotive Engineering Research Institute Jiangsu UniversityZhenjiangChina School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore School of Automotive Engineering Hubei University of Automotive TechnologyShiyanChina Beijing Idriverplus Technology Co. Ltd.BeijingChina Chery Automobile Co. Ltd.WuhuChina State Key Laboratory of Automotive Safety and Energy Tsinghua UniversityBeijingChina Department of Computer Science and Technology Tsinghua UniversityBeijingChina

Active Disturbance Rejection Control(ADRC)possesses robust disturbance rejection capabilities,making it well-suited for longitudinal velocity ***,the conventional Extended State Observer(ESO)in ADRC fails to fully exploit feedback from first-order and higher-order estimation errors and tracking error simultaneously,thereby diminishing the control performance of *** address this limitation,an enhanced car-following algorithm utilising ADRC is proposed,which integrates the improved ESO with a feedback *** comparison to the conventional ESO,the enhanced version effectively utilises multi-order estimation and tracking ***,it enhances convergence rates by incorporating feedback from higher-order estimation errors and ensures the estimated value converges to the reference value by utilising tracking error *** improved ESO significantly enhances the disturbance rejection performance of ***,the effectiveness of the proposed algorithm is validated through the Lyapunov approach and experiments.

关键词： active disturbance rejection control(ADRC) autonomous vehicle extended state observer(ESO) intelligent control longitudinal velocity control

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Skyrmion Dynamics in the Presence of Deformation

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Physical Review Applied 2022年第1期17卷 L011002-L011002页

作者： Zehan Chen Xichao Zhang Yan Zhou Qiming Shao Department of Physics The Hong Kong University of Science and Technology Hong Kong China Department of Electrical and Computer Engineering Shinshu University 4-17-1 Wakasato Nagano 380-8553 Japan School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong 518172 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology The Hong Kong University of Science and Technology Hong Kong China

Magnetic skyrmions are topological spin textures promising for future high-density and nonvolatile memory. It is crucial to understand the current-driven skyrmion dynamics in the presence of deformation, of which an analytical model, however, remains elusive. Here we extend Thiele’s model by considering both the radial and tangential forces. Our model attributes the skyrmion deformation to the current-induced rotational symmetry breaking, which includes magnetization canting and domain-wall width variation. Our predictions of skyrmion radius and nonlinear dynamics are consistent with micromagnetic simulation results. Besides, we show that by applying an in-plane magnetic field, the deformation of a skyrmion can be suppressed, and even the compression of a skyrmion can be achieved. Our model provides a generic way to analyze the skyrmion deformation and may inspire applications based on nonlinear skyrmion dynamics.

关键词： Magnetic texture Skyrmions Spin dynamics Spin torque Spin-orbit coupling Spintronics Chaos & nonlinear dynamics Landau-Lifschitz-Gilbert equation

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How to leverage diverse demonstrations in offline imitation learning 24

How to leverage diverse demonstrations in offline imitation ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Sheng Yue Jiani Liu Xingyuan Hua Ju Ren Sen Lin Junshan Zhang Yaoxue Zhang Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China and Zhongguancun Laboratory Beijing China Department of Computer Science University of Houston Texas Department of Electrical and Computer Engineering University of California Davis

Offline Imitation Learning (IL) with imperfect demonstrations has garnered increasing attention owing to the scarcity of expert data in many real-world domains. A fundamental problem in this scenario is how to extract positive behaviors from noisy data. In general, current approaches to the problem select data building on state-action similarity to given expert demonstrations, neglecting precious information in (potentially abundant) diverse state-actions that deviate from expert ones. In this paper, we introduce a simple yet effective data selection method that identifies positive behaviors based on their resultant states – a more informative criterion enabling explicit utilization of dynamics information and effective extraction of both expert and beneficial diverse behaviors. Further, we devise a lightweight behavior cloning algorithm capable of leveraging the expert and selected data correctly. In the experiments, we evaluate our method on a suite of complex and high-dimensional offline IL benchmarks, including continuous-control and vision-based tasks. The results demonstrate that our method achieves state-of-the-art performance, outperforming existing methods on 20/21 benchmarks, typically by 2-5x, while maintaining a comparable runtime to Behavior Cloning (BC).

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Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion

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IEEE Wireless Communications Letters 2025年第6期14卷 1748-1752页

作者： Guo, Lei Chen, Wei Sun, Yuxuan Ai, Bo Pappas, Nikolaos Quek, Tony Q. S. Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation School of Electronic and Information Engineering Beijing100044 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Linköping University Department of Computer and Information Science Linköping581 83 Sweden Singapore University of Technology and Design Information Systems Technology and Design Tampines Singapore

Obtaining high-resolution hyperspectral images (HR-HSI) is costly and data-intensive, making it necessary to fuse low-resolution hyperspectral images (LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications. However, traditional fusion techniques, which integrate detailed information into the reconstruction, significantly increase bandwidth consumption compared to directly transmitting raw data. To overcome these challenges, we propose a hierarchy-aware and channel-adaptive semantic communication approach for bandwidth-limited data fusion. A hierarchical correlation module is proposed to preserve both the overall structural information and the details of the image required for super-resolution. This module efficiently combines deep semantic and shallow features from LR-HSI and HR-RGB. To further reduce bandwidth usage while preserving reconstruction quality, a channel-adaptive attention mechanism based on Transformer is proposed to dynamically integrate and transmit the deep and shallow features, enabling efficient data transmission and high-quality HR-HSI reconstruction. Experimental results on the CAVE and Washington DC Mall datasets demonstrate that our method outperforms single-source transmission, achieving up to a 2 dB improvement in peak signal-to-noise ratio (PSNR). Additionally, it reduces bandwidth consumption by two-thirds, confirming its effectiveness in bandwidth-constrained environments for HR-HSI reconstruction tasks. © 2012 IEEE.

关键词： Bandwidth

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OLLIE: imitation learning from offline pretraining to online finetuning 24

OLLIE: imitation learning from offline pretraining to online...

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Proceedings of the 41st International Conference on Machine Learning

作者： Sheng Yue Xingyuan Hua Ju Ren Sen Lin Junshan Zhang Yaoxue Zhang Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China and Zhongguancun Laboratory Beijing China Department of Computer Science University of Houston Texas Department of Electrical and Computer Engineering University of California Davis

In this paper, we study offline-to-online Imitation Learning (IL) that pretrains an imitation policy from static demonstration data, followed by fast finetuning with minimal environmental interaction. We find the naïve combination of existing offline IL and online IL methods tends to behave poorly in this context, because the initial discriminator (often used in online IL) operates randomly and discordantly against the policy initialization, leading to misguided policy optimization and un-learning of pretraining knowledge. To overcome this challenge, we propose a principled offline-to-online IL method, named OLLIE, that simultaneously learns a near-expert policy initialization along with an aligned discriminator initialization, which can be seamlessly integrated into online IL, achieving smooth and fast finetuning. Empirically, OLLIE consistently and significantly outperforms the baseline methods in 20 challenging tasks, from continuous control to vision-based domains, in terms of performance, demonstration efficiency, and convergence speed. This work may serve as a foundation for further exploration of pretraining and finetuning in the context of IL.

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Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1262-1272页

作者： Mingming Ha Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Information Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA IEEE

The core task of tracking control is to make the controlled plant track a desired *** traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps *** this paper,a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control *** the regulator problem,the iterative value function of tracking control problem cannot be regarded as a Lyapunov function.A novel stability analysis method is developed to guarantee that the tracking error converges to *** discounted iterative scheme under the new cost function for the special case of linear systems is ***,the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.

关键词： Adaptive critic design adaptive dynamic programming(ADP) approximate dynamic programming discrete-time nonlinear systems reinforcement learning stability analysis tracking control value iteration(VI)

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