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arXiv 2023年

作者： Althoefer, Kaspar Ling, Yonggen Li, Wanlin Qian, Xinyuan Lee, Wang Wei Qi, Peng Queen Mary University of London London United Kingdom Tencent Robotics X Lab Shenzhen China Beijing China The Department of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai China

In conjunction with huge recent progress in camera and computer vision technology, camera-based sensors have increasingly shown considerable promise in relation to tactile sensing. In comparison to competing technologies (be they resistive, capacitive or magnetic based), they offer super-high-resolution, while suffering from fewer wiring problems. The human tactile system is composed of various types of mechanoreceptors, each able to perceive and process distinct information such as force, pressure, texture, etc. Camera-based tactile sensors such as GelSight mainly focus on high-resolution geometric sensing on a flat surface, and their force measurement capabilities are limited by the hysteresis and non-linearity of the silicone material. In this paper, we present a miniaturised dome-shaped camera-based tactile sensor that allows accurate force and tactile sensing in a single coherent system. The key novelty of the sensor design is as follows. First, we demonstrate how to build a smooth silicone hemispheric sensing medium with uniform markers on its curved surface. Second, we enhance the illumination of the rounded silicone with diffused LEDs. Third, we construct a force-sensitive mechanical structure in a compact form factor with usage of springs to accurately perceive forces. Our multi-modal sensor is able to acquire tactile information from multi-axis forces, local force distribution, and contact geometry, all in real-time. We apply an end-to-end deep learning method to process all the information. © 2023, CC BY.

关键词： Silicones

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Detection of Backdoor Attacks Using Targeted Universal Adversarial Perturbations for Deep Neural Networks

SSRN

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SSRN 2023年

作者： Qu, Yubin Huang, Song Chen, Xiang Wang, Xingya College of Command and Control Engineering Army Engineering University of PLA Nanjing210007 China School of Information Engineering Jiangsu College of Engineering and Technology Nantong226001 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China School of Information Science and Technology Nantong University Nantong226019 China College of Computer Science and Technology Nanjing Tech University China

The backdoors using targeted universal adversarial perturbations against deep neurall networks has been explored. This backdoor does not require data poisoning or model tampering. Rretraining deep neural network models using adversarial training for security is time-consuming and does not apply to deep neural networks in runtime. We want to detect backdoors using a black-box testing approach. We observe that after superimposing random noise on the input of a backdoor attack, the output still tends to remain the same, so we propose Sequential Analysis method based on the Metamorphosis Testing (SAMT). We designed two metamorphic relations for test case generation. Using sequential sampling, we calculate the label stability rate and infer whether the image to be verified contains a backdoor based on the sequential probability ratio change. The experimental results show that our method has a higher backdoor detection success rate than the state-of-the-art universal adversarial perturbations detection algorithms. Moreover, our method does not need to use the model structure of deep neural networks, which has more adaptability and generalization ability. Based on our proposed method, we can simply add a backdoor detection layer to detect backdoors as early as possible, which can eventually alleviate the harm of such backdoors. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Error Dynamics in Affine Group Systems

arXiv

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arXiv 2023年

作者： Li, Xinghan Chen, Jianqi Zhang, Han Wei, Jieqiang Wu, Junfeng The College of Control Science and Engineering Zhejiang University Hangzhou China The Department of Electronic and Computer Engineering HKUST Hong Kong Department of Automation Shanghai Jiao Tong University Shanghai China Ericsson Torshamnsgatan 21 16440 Sweden The School of Data Science CUHKShenzhen Shenzhen China

Error dynamics captures the evolution of the state errors between two distinct trajectories that are governed by the same system rule but initiated or perturbed differently. It is essential to analyze the error behaviour of a state observer in a matrix Lie group for precise estimations. This paper concentrates on the examination of the error dynamics in affine group systems when exposed to external disturbances or random noises. To this end, we first discuss the connections between the notions of affine group systems and linear group systems. We characterize linear group systems by homeomorphism of its transition flow and linearity of its Lie algebra. Next, we explore linear group systems that are spread by a Brownian motion in tangent spaces. We discover that the itô stochastic differential equation in the Lie algebra space has a linear drift component and an additional "pinning" term. We apply these findings in analyzing the error dynamics, a specific linear group system, with smooth disturbances and stochastic noise. The error dynamics can be in the form of the Lie group and the Lie algebra. An explicit and accurate derivation of error dynamics is provided for the concrete example SE2(3), which plays a vital role, especially in inertial-based navigation. Copyright © 2023, The Authors. All rights reserved.

关键词： Brownian movement

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Spreading dynamics of information on online social networks

arXiv

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arXiv 2025年

作者： Meng, Fanhui Xie, Jiarong Sun, Jiachen Xu, Cong Zeng, Yutian Wang, Xiangrong Jia, Tao Huang, Shuhong Deng, Youjin Hu, Yanqing School of Systems Science and Engineering Sun Yat-sen University Guangzhou510275 China Center for Computational Communication Research Beijing Normal University Zhuhai519087 China School of Journalism and Communication Beijing Normal University Beijing100875 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen518055 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China College of Computer and Information Science Chongqing Normal University Chongqing401331 China College of Computer and Information Science Southwest University Chongqing400715 China Institute of Neuroscience Technical University of Munich Munich80802 Germany Department of Modern Physics University of Science and Technology of China Hefei230026 China MinJiang Collaborative Center for Theoretical Physics College of Physics and Electronic Information Engineering Minjiang University Fuzhou350108 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen518055 China

Social media is profoundly changing our society with its unprecedented spreading power. Due to the complexity of human behaviors and the diversity of massive messages, the information spreading dynamics are complicated, and the reported mechanisms are different and even controversial. Based on data from mainstream social media platforms, including WeChat, Weibo, and Twitter, cumulatively encompassing a total of 7.45 billion users, we uncover a ubiquitous mechanism that the information spreading dynamics are basically driven by the interplay of social reinforcement and social weakening effects. Accordingly, we propose a concise equation, which, surprisingly, can well describe all the empirical large-scale spreading trajectories. Our theory resolves a number of controversial claims and satisfactorily explains many phenomena previously observed. It also reveals that the highly clustered nature of social networks can lead to rapid and high-frequency information bursts with relatively small coverage per burst. This vital feature enables social media to have a high capacity and diversity for information dissemination, beneficial for its ecological development. © 2025, CC BY.

关键词： Tweets

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A flexible analytic wavelet transform and ensemble bagged tree model for electroencephalogram-based meditative mind-wandering detection

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Healthcare Analytics 2024年 5卷 100286-100286页

作者： Dadhich, Ajay Patel, Jaideep Tiwari, Rovin Verma, Richa Mishra, Pratha Jain, Jay Kumar Department of Electronics Instrumentation and Control Engineering Engineering College Ajmer Rajasthan Ajmer India Department of Computer Science and Engineering Sagar Institute of Research & Technology MP Bhopal India Department of Electronics & Communication Engineering Amity University MP Gwalior India Department of Computer Science and Application Makhanlal Chaturvedi University of Journalism and Mass Communication MP Bhopal India Department of Electronics & Communication Engineering Technocrats TIT Group Madhya Pradesh Bhopal India Department of Computer Science & Engineering Indian Institute of Information Technology (IIIT) MP Bhopal India

Mind-wandering (MW) is when an individual's concentration drifts away from the task or activity. Researchers found a greater variability in electroencephalogram (EEG) signals due to MW. Collecting more nuanced information from raw EEG data to examine the harmful effects of MW is time-consuming. This study proposes a multi-resolution assessment of EEG signals using the flexible analytic wavelet transform (FAWT). The FAWT algorithm decomposes raw EEG data into more representative sub-bands (SBs). Several statistical characteristics are derived from the obtained SBs, and the effects of MW during meditation on the EEG signals are investigated. A set of significant characteristics is chosen and fed into the machine learning modules using a 10-fold validation approach to detect MW subjects automatically. Our proposed framework attained the highest classification accuracy of 92.41%, the highest sensitivity of 93.56%, and the highest specificity of 91.97%. The proposed framework can be used to design a suitable brain-computer interface (BCI) system to reduce MW and increase meditation depth for holistic and long-term health in society. © 2023 The Author(s)

关键词： Brain–computer interface Classification Electroencephalogram signals Flexible analytic wavelet transform Machine learning Mind-wandering

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Trajectory Tracking for the Quadcopter UAV utilizing Fuzzy PID control Approach

Trajectory Tracking for the Quadcopter UAV utilizing Fuzzy P...

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2020 International Conference on computer, control, Electrical, and Electronics engineering, ICCCEEE 2020

作者： Eltayeb, Ahmed Rahmat, Mohd Fuaad Eltoum, M. A Mohammed Ibrahim, M. H. Sanhoury Basri, Mohd Ariffanan Mohd Universiti Teknologi Malaysia School of Electrical Engineering Faculty of Engineering Skudai Johor1310 Malaysia Kfupm University Faculty of Computer Science and Engineering System Engineering Dept Dhahran31261 Saudi Arabia Alneelain University College of Engineering Dept. of Control Engineering Khartoum Sudan

ISBN: (纸本)9781728191119

Currently, the quadcopter Unmanned Aerial Vehicles (UAVs) are playing a significant role in combating the COVID-19 pandemic crisis, which induced the researchers to design robust control techniques. In this paper, a fuzzy PID controller is designed to stabilize and/or track the desired trajectory of the quadcopter UAV. The mathematical model of the quadcopter UAV has been briefly presented, where it has been divided into two portions, the position dynamic and the attitude dynamic subsystems. Subsequently, a robust fuzzy PID controller has been designed for both the inner loop and outer loop to control and stabilize the position and the attitude of the quadcopter, which adaptively manipulate the system's input based on the tracking error. The proposed controller is benchmarked with the conventional PID controller to show the robustness of the fuzzy PID controller. Fuzzy PID controller has been verified through simulation work utilizing Matlab/Simulink, where better performance is achieved compared with the conventional PID controller. It is found that the errors in the quadcopter's attitude and position have been significantly reduced through using fuzzy PID controller by 70% and 87%, respectively. © 2021 IEEE.

关键词： Simulink

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Large-Scale Data Generation Using SWLBM on the Sunway TaihuLight Supercomputer and Subsequent Data Mining with Physics-Informed Neural Networks 12th

Large-Scale Data Generation Using SWLBM on the Sunway TaihuL...

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12th CCF Conference on BigData, BigData 2024

作者： Chu, Xuesen Guo, Wei Wu, Tianqi Cai, Shengze Yang, Guangwen China Ship Scientific Research Center Wuxi214082 China Taihu Lake Laboratory of Deep Sea Technology and Science Wuxi214082 China Department of Computer Science and Technology Tsinghua University Beijing100084 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China National Supercomputing Center in Wuxi Wuxi214072 China

ISBN: (纸本)9789819610235

Generating large-scale datasets on supercomputers is a critical component of modern research, enabling sophisticated analysis and mining applications. In the context of SUBOFF model studies for advanced submarine development, we emphasize the significance of supercomputer-generated big data and its subsequent application. Utilizing the Sunway TaihuLight supercomputer and the SWLBM software, we employ the lattice Boltzmann method (LBM) to generate a comprehensive dataset of flows over a SUBOFF model. This dataset is then used to train a physics-informed neural network (PINN) model, which aims to reconstruct flow fields from sparse velocity measurements. The PINN is designed to perform super-resolution velocity reconstruction and concurrent pressure field inference. Our results show that the reconstructed flow fields, including pressure, are in good agreement with the full-resolution LBM references, highlighting the promise of this approach for complex flow motion reconstruction. This research demonstrates the potential of supercomputer-generated big data in enhancing simulations and experiments in the study of SUBOFF and beyond. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computational fluid dynamics

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Community structure detection algorithm based on link prediction

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International Journal of Information and Communication Technology 2021年第4期19卷 432-448页

作者： Dai, Gang Wang, Quanxin Xu, Baomin Sun, Lijun School of Computer and Information Technology Beijing Jiaotong University Beijing China China University of Petroleum-Beijing at Karamay No. 355 Anding Road Karamay District Xinjiang Karamay City China School of Computer Science Beijing Jiaotong University Haibin College Huanghua China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou China College of Information Science and Engineering Henan University of Technology Zhengzhou China

Community structure identification has received a great effort among computer scientists who are focusing on the properties of complex networks. The label propagation algorithm is a near linear time algorithm to find a good community structure. Despite various subsequent advances, an important issue of this algorithm is the efficiency and accuracy of the identified community structure. In this paper, we propose a novel community detection algorithm by using link prediction algorithm based on label propagation. The method is the first to introduce the idea of link prediction into community detection. The experimental results show that the proposed method is less resolution limited than modularity optimising methods, and it can be more effective in detecting communities. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Complex networks

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

A Multi-Player Potential Game Approach for Sensor Network Lo...

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IEEE Conference on Decision and control

作者： Gehui Xu Guanpu Chen Baris Fidan Yiguang Hong Hongsheng Qi Thomas Parisini Karl H. Johansson Department of Electrical and Electronic Engineering Imperial College London London UK Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Control Science and Engineering Tongji University shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies.

关键词： Location awareness Noise Games Position measurement Nash equilibrium Distance measurement Noise measurement

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Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

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science China(Information sciences) 2021年第7期64卷 5-24页

作者： Hanwen ZHANG Maoyin CHEN Jun SHANG Chunjie YANG Youxian SUN School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University Department of Automation Tsinghua University School of Automation and Electrical Engineering Linyi University Department of Electrical and Computer Engineering University of Alberta

Brownian motion(BM) has been widely used for degradation modeling and remaining useful life(RUL) prediction, but it is essentially Markovian. This implies that the future state in a BM-based degradation process relies only on its current state, independent of the past states. However, some practical industrial devices such as Li-ion batteries, ball bearings, turbofans, and blast furnace walls show degradations with long-range dependence(LRD), where the future degradation states depend on both the current and past degradation states. This type of degradation naturally brings two interesting problems, that is, how to model the degradations and how to predict their RULs. Recently, in contrast to the work that uses only BM, fractional Brownian motion(FBM) is introduced to model practical degradations. The most important feature of the FBM-based degradation models is the ability to characterize the non-Markovian degradations with LRD. Although FBM is an extension of BM, it is neither a Markovian process nor a semimartingale. Therefore, how to obtain the first passage time of an FBM-based degradation process has become a challenging task. In this paper, a review of the transition of RUL prediction from BM to FBM is provided. The peculiarities of FBM when addressing the LRD inherent in some practical degradations are discussed. We first review the BM-based degradation models of the past few decades and then give details regarding the evolution of FBM-based research. Interestingly, the existing BM-based models scarcely consider the effect of LRD on the prediction of RULs. Two practical cases illustrate that the newly developed FBMbased models are more generalized and suitable for predicting RULs than the BM-based models, especially for degradations with LRD. Along with the direction of FBM-based RUL prediction, we also introduce some important and interesting problems that require further study.

关键词： remaining useful life degradation model Brownian motion fractional Brownian motion long-range dependence

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