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arXiv 2023年

作者： Tao, Yunbo Liu, Daizong Zhou, Pan Xie, Yulai Du, Wei Hu, Wei Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China Wangxuan Institute of Computer Technology Peking University China

With the maturity of depth sensors, the vulnerability of 3D point cloud models has received increasing attention in various applications such as autonomous driving and robot navigation. Previous 3D adversarial attackers either follow the white-box setting to iteratively update the coordinate perturbations based on gradients, or utilize the output model logits to estimate noisy gradients in the black-box setting. However, these attack methods are hard to be deployed in real-world scenarios since realistic 3D applications will not share any model details to users. Therefore, we explore a more challenging yet practical 3D attack setting, i.e., attacking point clouds with black-box hard labels, in which the attacker can only have access to the prediction label of the input. To tackle this setting, we propose a novel 3D attack method, termed 3D Hard-label attacker (3DHacker), based on the developed decision boundary algorithm to generate adversarial samples solely with the knowledge of class labels. Specifically, to construct the class-aware model decision boundary, 3DHacker first randomly fuses two point clouds of different classes in the spectral domain to craft their intermediate sample with high imperceptibility, then projects it onto the decision boundary via binary search. To restrict the final perturbation size, 3DHacker further introduces an iterative optimization strategy to move the intermediate sample along the decision boundary for generating adversarial point clouds with smallest trivial perturbations. Extensive evaluations show that, even in the challenging hard-label setting, 3DHacker still competitively outperforms existing 3D attacks regarding the attack performance as well as adversary quality. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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DESIGN AND ANALYSIS OF WIND TURBINE FAULT DIAGNOSIS SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK

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Diagnostyka 2025年第1期26卷

作者： Luo, Xiaoli Ning, Jinye Wu, Min Scientific Research Department Hunan Electrical College of Technology Xiangtan411101 China Institute of Big Data and Artificial Intelligence Application Technology Hunan Electrical College of Technology Xiangtan411101 China School of Mechanical Engineering Hunan University of Science and Technology Xiangtan City411201 China

Wind turbines are apt to diverse faults during long-term operation in natural environments, which affect their power generation efficiency and lifespan. Therefore, based on convolutional neural networks, gradient descent method was introduced to optimize their parameter training. Meanwhile, synchronous compressed wavelet transform was utilized to enhance the fault signal's time-frequency information. The fault detection correlation operation was optimized through Pearson correlation coefficient. Finally, a new type of fan fault detection model was proposed. The average fault detecting accuracy of this model was the highest at 98.98%, the average loss value was the lowest at 0.08%, and the average time consumption was the shortest at 16.52s. The minimum mean square error for detecting inner and outer ring pitting of fan bearings was 0.016 and 0.018, respectively. As a result, the proposed new model performs excellently in terms of accuracy and reliability in fault detection, with detection accuracy generally superior to other existing models. This model can significantly improve wind turbine fault detection, reduce false alarm and false alarm rates, and provide effective guarantees for wind turbines' stable operation. © 202x by the Authors.

关键词： Convolutional neural networks

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Evolutionary Dynamic Optimization-Based Calibration Framework for Agent-Based Financial Market Simulators

Evolutionary Dynamic Optimization-Based Calibration Framewor...

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Congress on Evolutionary Computation

作者： Zhenhua Yang Muyao Zhong Peng Yang Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Statistics and Data Science Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

The agent-based financial market simulators serve as an important validation tool for trading strategies. For high-fidelity simulation, it is pivotal to calibrate the parameters of a simulator so that the generated simulation data resembles the observed real market data of interest. In traditional calibration methods, it is typical that the parameters of the simulator are set to be time-invariant. However, the dynamic nature of the real financial market introduces various variability into the behaviors of the market participants over different time intervals, posing in-herent limitations to the traditional methods. A more reasonable approach might involve employing a simulator with time-variant parameters. This suggests that the model parameters can be dynamically adjusted at different stages of the simulation to adapt to the evolving market. Consequently, the calibration problem of the financial market simulators can be treated as a dynamic optimization problem. To dynamically calibrate the simulators, we introduce an Evolutionary Dynamic Optimization (EDO) framework. By monitoring the changes of the best fitness, the whole simulation time interval is adaptively divided into multiple stages. Then the Negatively Correlated Search (NCS) algorithm is employed to effectively adjust the parameters at different simulation stages to better simulate the real financial market. Empirical results on both synthetic and real data verify that our dynamic calibration framework significantly outperforms traditional calibration methods that fixing a parameter for the whole simulation interval. The proposed strategy of detecting dynamic changes is also shown to be more reliable than the naive method of manually segmenting stages. In terms of calibration time, our proposed method significantly improves by nearly 93% compared to the fixed parameter setting, and approximately 61% compared to manual segmentation calibration.

关键词： Adaptation models Fluctuations Heuristic algorithms Computational modeling Scalability Manuals data models

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A Novel Price-Oriented Supply-Demand Interaction Scheduling Model Optimizing Virtual Power Plant Operations

A Novel Price-Oriented Supply-Demand Interaction Scheduling ...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Yingxiao Wang Pengfei Ou Jianghua Wu Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Shenzhen China School of Mathematics Nanjing University Nanjing China School of Engineering The Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798350386127

ISBN: (纸本)9798350386134

Virtual Power Plants (VPPs) provide a promising direction for managing the complexities and uncertainties in operating Distributed Energy Resources (DERs). However, existing approaches rely on a “source-following-load” static strategy, falling short in dynamically leveraging market opportunities and transferable demands. To pursue optimal economic performance, this paper introduces a novel price-oriented supply-demand interaction scheduling (SDIS) model for VPP operation. SDIS integrates transferable demands and price signals from the market, renewable generators, and storage systems, enabling VPPs to dynamically balance supply and demand while optimizing both operational efficiency and economic returns. Case studies sampled from the Shanghai energy market with uncertain climatic conditions demonstrate that our model improves the operational efficiency of VPP, reduces economic costs, and enhances the grid stability. Our model paves a way to deal with the complex VPP operation, e.g. grid scenarios involving solar generators, urban metro systems, and central air conditioning clusters.

关键词： Costs Uncertainty Supply and demand Atmospheric modeling Decision making Virtual power plants Generators Stability analysis Complexity theory Solar power generation

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Differential Privacy-based Avatar Update in Cooperative intelligent Transportation System Metaverse

Differential Privacy-based Avatar Update in Cooperative Inte...

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International Conference on Computer and Communications (ICCC)

作者： Salabat Khan Fei Luo Mansoor Khan Insaf Ullah Farhan Amin Shamsher Ullah Mussadiq Abdul Rahim School of Computer and Enformation Engineering Qilu Institute of Technology Jinan China School of Computing and Information Technology Great Bay University Dongguan China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Institute for Analytics and Data Science University of Essex Colchester UK School of Computer Science and Engineering Yeungnam University South Korea National Engineering Laboratory for Big Data Shenzhen University Shenzhen China King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Metaverse is the next generation and the successor to the Internet. Various sectors allocated capital to adapt the metaverse due to its inherent importance. Cooperative intelligent Transportation Systems (C-ITS) is one of the important sectors that can benefit from metaverse adaptation and integration. The C-ITS and metaverse integration give rise to new use cases and cyber attacks. However, traditional Pseudonym Certificate (PCert) authentication based on Vehicular Public Key Infrastructure (VPKI) cannot meet the new use cases and thwart cyber attacks. We present a Differential Privacy (DP)-based PCert and avatar update model to ensure the privacy of end-devices (e.g., vehicles) in the C-ITS metaverse. The proposed DP-based model adds noise to avatar attributes (e.g., color, behavior) to prevent avatar linkage during the PCert update process. Our method ensures that PCerts and avatars cannot be linked to end-devices and the previous PCerts and avatars. We simulate the DP-based method to investigate it in terms of privacy, latency, color distortion, and adversarial success rate, and the results show that DP-based is effective and practical for avatar updates.

关键词： Privacy Metaverse Avatars Computational modeling Authentication Public key Color Colored noise intelligent transportation systems Cyberattack

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PC-CSP: An Efficient Partial Convolution in Cross Stage Partial Network for Small Targets Detection

PC-CSP: An Efficient Partial Convolution in Cross Stage Part...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Junmin Xue Yunbo Rao Jiangang Wu Mei Huang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Operation Data Center of Postal Savings Bank of China Beijing P. R. China Beijing China Chengdu Aerospace Science and industry big data Research Institute Corporation Limited Chengdu China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

The common approach for small object detection does not work well in complex scenes with objects of multiple sizes. This is because the detection of small objects and objects of other sizes often difficult to take into account at the same time. To overcome these limitations, we introduce an efficient Partial Convolution in Cross Stage Partial (PC-CSP) network for detecting small targets. First, the introduction of the DCNv3 module improves the feature extraction for detection, slimneck is utilized to enhance the attention mechanism suitable for objects of varying sizes. Then, PC-CSP is employed to streamline the detection backbone network. Last, the loss function is refined by incorporating the concept of Inner. Through extensive experiments, our method demonstrates a significant increase in accuracy of 6.8%, and mAP@0.5 by 8.97% on the standard dataset compared to the original model. Overall, the proposed method exhibits excellent performance for small objects, such as: mobile phones, food, and faces.

关键词： Visualization Convolution Semantics Refining Object detection Feature extraction Mobile handsets Pattern recognition Object recognition Standards

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Joint Participation Incentive and Network Pricing Design for Federated Learning

arXiv

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arXiv 2023年

作者： Ding, Ningning Gao, Lin Huang, Jianwei The Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Shenzhen Research Institute of Big Data Shenzhen518172 China The School of Electronics and Information Engineering The Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology Shenzhen China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Shenzhen518172 China

Federated learning protects users’ data privacy through sharing users’ local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users’ contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users’ private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under both vertical and horizontal interaction structures of the participants. We show that the vertical structure is better than the horizontal one, as it avoids the interests misalignment between the server and the network operator. Numerical results based on real-world datasets show that our proposed mechanisms decrease server’s cost by up to 24.87% comparing with the state-of-the-art benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Costs

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Five-user quantum virtual local area network with an AlGaAs entangled photon source

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science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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Exploring Beyond-Demonstrator via Meta Learning-Based Reward Extrapolation

Exploring Beyond-Demonstrator via Meta Learning-Based Reward...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Mingqi Yuan Man-on Pun School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9781665487696

Extrapolating beyond-demonstrator (BD) performance through the imitation learning (IL) algorithm aims to learn from and subsequently outperform the demonstrator. To that end, a representative approach is to leverage inverse rein-forcement learning (IRL) to infer a reward function from demon-strations before performing RL on the learned reward function. However, most existing reward extrapolation methods require massive demonstrations, making it difficult to be applied in tasks of limited training data. To address this problem, one simple solution is to perform data augmentation to artificially generate more training data, which may incur severe inductive bias and policy performance loss. In this paper, we propose a novel meta learning-based reward extrapolation (MLRE) algorithm, which can effectively approximate the ground-truth rewards using limited demonstrations. More specifically, MLRE first learns an initial reward function from a set of tasks that have abundant training data. Then the learned reward function will be fine-tuned using data of the target task. Extensive simulation results demonstrated that the proposed MLRE can achieve impressive performance improvement as compared to other similar BDIL algorithms. Our code is available at GitHub 1 1 https://***/yuanmingqi/MLRE.

关键词： Extrapolation Codes Simulation Computational modeling Training data Games Approximation algorithms

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Ontology Knowledge Based Multimodal Service Tasking Planning

Ontology Knowledge Based Multimodal Service Tasking Planning

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International Conference on Electronic engineering and Informatics (EEI)

作者： Xiaochen Zhang Guoliang Liu Wei Li Zhenhua Liu Shanmei Wang Jiwei Zhang Bin Wang Guohui Tian School of Control Science and Engineering Shandong University Jinan China Big Data and Artificial Intelligence Institute China Telecom Research Institute Beijing China Research Department Inspur Intelligent Technology Jinan China Department of Development Ji'nan Power Supply Company Jinan China Department of Investment and Development State Nuclear Electric Power Planning Design & Research Institute Co. LTD. Beijing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Considering smart device and robot having entered the home environment to provide human with convenient services, this paper proposes a multimodal service system to enhance the system dynamic understanding of the environment and dynamic task planning capabilities. The proposed system introduces a family ontology to record information about the object, device, and user. The multimodal information can be acquired and updated automatically to the family ontology. The task planning and execution are introduced to dynamically generate task. Finally, the experiments in a family environment prove that the system has outstanding task processing capabilities and environmental understanding.

关键词： System dynamics Knowledge based systems Ontologies Planning Smart devices Informatics Robots

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