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Multi-block SSD based on small object detection for UAV railway scene surveillance

Chinese Journal of Aeronautics 2020年第6期33卷 1747-1755页

作者： Yundong LI Han DONG Hongguang LI Xueyan ZHANG Baochang ZHANG Zhifeng XIAO North China University of Technology Beijing 100144China Key Laboratory of Large Structure Health Monitoring and Control Shijiazhuang 050043China Unmanned System Research Institute Beihang UniversityBeijing 100083China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China State Key Laboratory of Information Engineering in Surveying Wuhan UniversityWuhan 430079China

A method of multi-block Single Shot Multi Box Detector(SSD)based on small object detection is proposed to the railway scene of unmanned aerial vehicle *** address the limitation of small object detection,a multi-block SSD mechanism,which consists of three steps,is ***,the original input images are segmented into several overlapped ***,each patch is separately fed into an SSD to detect the ***,the patches are merged together through two *** the first stage,the truncated object of the sub-layer detection result is *** the second stage,a sub-layer suppression and filtering algorithm applying the concept of non-maximum suppression is utilized to remove the overlapped boxes of *** boxes that are not detected in the main-layer are *** addition,no sufficient labeled training samples of railway circumstance are available,thereby hindering the deployment of SSD.A two-stage training strategy leveraging to transfer learning is adopted to solve this *** deep learning model is preliminarily trained using labeled data of numerous auxiliaries,and then it is refined using only a few samples of railway scene.A railway spot in China,which is easily damaged by landslides,is investigated as a case *** results show that the proposed multi-block SSD method produces an overall accuracy of 96.6%and obtains an improvement of up to 9.2%compared with the traditional SSD.

关键词： Deep learning Multi-block Single Shot MultiBox Detector(SSD) Objection detection Railway scene Unmanned aerial vehicle remote sensing

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Stochastic Stability of Discrete-time Phase-coupled Oscillators over Uncertain and Random Networks

arXiv

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arXiv 2021年

作者： Jafarian, Matin Mamduhi, Mohammad H. Johansson, Karl H. The Delft Center for Systems and Control Delft University of Technology Netherlands The Automatic Control Laboratory ETH Zürich Switzerland The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures Sweden

This article studies stochastic relative phase stability, i.e., stochastic phase-cohesiveness, of discrete-time phase-coupled oscillators. Stochastic phase-cohesiveness in two types of networks is studied. First, we consider oscillators coupled with 2π-periodic odd functions over underlying undirected graphs subject to both multiplicative and additive stochastic uncertainties. We prove stochastic phase-cohesiveness of the network with respect to two specific, namely in-phase and anti-phase, sets by deriving sufficient coupling conditions. We show the dependency of these conditions on the size of the mean values of additive and multiplicative uncertainties, as well as the sign of the mean values of multiplicative uncertainties. Furthermore, we discuss the results under a relaxation of the odd property of the coupling function. Second, we study an uncertain network in which the multiplicative uncertainties are governed by the Bernoulli process representing the well-known Erdös-Rényi network. We assume constant exogenous frequencies and derive sufficient conditions for achieving both stochastic phase-cohesive and phase-locked solutions, i.e., stochastic phase-cohesiveness with respect to the origin. For the latter case, where identical exogenous frequencies are assumed, we prove that any positive probability of connectivity leads to phase-locking. Thorough analyses are provided, and insights obtained from stochastic analysis are discussed, along with numerical simulations to validate the analytical results. © 2021, CC BY.

关键词： Stochastic systems

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

引用

Chinese Automation Congress (CAC)

作者： Yanxing Bai Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene.

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Synthesis of churros-like V2Se9 nanostructures for ultrasensitive electrochemical detection of oxalic acid in urine with portable point-of-care diagnostic devices

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Biosensors and Bioelectronics 2025年 285卷 117589页

作者： Shi, Peizheng Ye, Chen Sun, Zhuang Sun, Kaiqiang Zhao, Ningbin Pourfakhr, Mahan Cai, Tao Nishimura, Kazuhito Jiang, Nan Tsai, Hsu-Sheng Wu, Lidong Wu, Wenqi Yu, Jiancheng Fu, Li Xu, Lixin Chen, Jun Lin, Cheng-Te College of Materials Science and Engineering Zhejiang University of Technology Hangzhou310014 China Qianwan Institute of CNITECH Ningbo315336 China State Key Laboratory of Advanced Marine Materials Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Department of Bioengineering University of California Los AngelesCA90095 United States School of Physics Harbin Institute of Technology Harbin150001 China Key Laboratory of Control of Quality and Safety for Aquatic Products Chinese Academy of Fishery Sciences Beijing100141 China Department of Urology The Second Affiliated Hospital of Guangzhou Medical University Guangdong 510260 China Guangdong Key Laboratory of Urology Guangzhou Medical University Guangdong 511495 China Institute of Mass Spectrometry Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application Ningbo University Ningbo315211 China Zhenhai Institute of Mass Spectrometry Ningbo315211 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China College of Materials and Environmental Engineering Hangzhou Dianzi University Hangzhou310018 China

The binding of oxalic acid (OA) with metal ions, particularly calcium ions, is a key factor in kidney stone formation, as it produces insoluble calcium oxalate, which crystallizes and accumulates in the kidneys. Consequently, OA in urine is commonly considered a significant biomarker for clinical diagnosis and early screening of kidney stones. This study reports churros-like V2Se9 nanostructures synthesized using a solvothermal method (high pressure and temperature) for sensitive and selective electrochemical detection of OA in urine. The electrodes made of churros-like V2Se9 hybridized with reduced graphene oxide (rGO) for OA recognition, demonstrated a linear range from 20 nM to 4 mM, with an extremely low detection limit of 9.7 nM. To enable fast, user friendly, and accurate point-of-care testing (POCT) for OA in urine samples, we designed and developed a portable workstation integrated with smartphone for data visualization. This is the first study to utilize the V2Se9 system for highly sensitive biosensor applications, highlighting its potential to advance screening techniques for key indicators of kidney stones by incorporating novel transition metal chalcogenide nanomaterials in POCT devices. © 2025 Elsevier B.V.

关键词： Electrochemical biosensors

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Tabular Q-learning Based Reinforcement Learning Agent for Autonomous Vehicle Drift Initiation and Stabilization

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IFAC-PapersOnLine 2023年第2期56卷 4896-4903页

作者： Szilárd H. Tóth Ádám Bárdos Zsolt J. Viharos Department of Automotive Technologies Budapest University of Technology and Economics Budapest Hungary Research Laboratory on Engineering and Management Intelligence Intelligent Processes Research Group Institute of Computer Science and Control Centre of Excellence in Production Informatics and Control Eötvös Loránd Research Network (ELKH) Centre of Excellence of the Hungarian Academy of Sciences (MTA) Budapest Hungary Department of Management and Business Law Faculty of Economics John von Neumann University Kecskemét Hungary

This paper aims to report on novel research results about developing a reinforcement learning agent for steady-state vehicle drift motion control. Based on the previous results of this research, the primary goal was to eliminate the problems causing learning instability experienced with the Soft Actor-Critic (SAC) algorithm applying Tabular Q-learning in this work. Trained in a MATLAB/Simulink-based simulation environment, the resulting agent succeeded in this task while being able to smoothly operate the vehicle to achieve and retain the desired target drift state, regardless of the discreet nature of the algorithm used for solving an inherently continuous task.

关键词： Motion control Autonomous Vehicles Vehicle Dynamics control Reinforcement Learning Vehicle Drifting Handling Limits

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FPGA Implementation of SIMON-128 Cryptographic Algorithm Using Artix-7

arXiv

引用

arXiv 2023年

作者： Ghayoula, Ridha Fattahi, Jaouhar Smida, Amor Gmati, Issam El Pricop, Emil Ziadia, Marwa Unit of Research in High Frequency Electronic Circuits and Systems University of Tunis El Manar Tunis2092 Tunisia Department of Electrical and Computer Engineering Laval University Quebec Canada Department of Computer Science and Software Engineering Laval University Quebec Canada Department of Medical Equipment Technology College of Applied Medical Sciences Majmaah University Majmaah11952 Saudi Arabia College of Engineering Al Gunfudha Umm Al Qura University Saudi Arabia Automatic Control Computers and Electronics Department Petroleum-Gas University of Ploiesti Romania

FPGA is a hardware architecture based on a matrix of programmable and configurable logic circuits thanks to which a large number of functionalities inside the device can be modified using a hardware description language. These functionalities must often be secured especially when the context is sensitive (military, banking, medical, legal, etc.). In this paper, we put forward an efficient implementation of SIMON’s block cipher algorithm using Xilinx Vivado 2018.2. The proposed design is analyzed through simulation on Xilinx Artix-7. A prototype of our design is implemented using the xc7a35tcsg324-1 FPGA chip. Performance and results are discussed. © 2023, CC BY.

关键词： Field programmable gate arrays (FPGA)

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Defective Sic Nanotube Based Single-Atom Catalysts for Electrocatalytic Nitrogen Fixation with Curvature Effect

SSRN

引用

SSRN 2023年

作者： Wang, Zhiwei Gao, Shuaishuai Liu, Xiaojing Chen, Xin Zhang, Xintao Sa, Rongjian Li, Qiaohong Sun, Chenghua Ma, Zuju School of Environmental and Materials Engineering Yantai University Yantai264005 China School of Computer and Control Engineering Yantai University Yantai264005 China College of Materials and Chemical Engineering Minjiang University Fuzhou350108 China State Key Laboratory of Structural Chemistry Fujian Institute of Research the Structure of Matter Chinese Academy of Sciences Fuzhou350002 China Department of Chemistry and Biotechnology Faculty of Science Engineering & Technology Swinburne University of Technology HawthornVIC3122 Australia

The production of fertilizers, explosives, and various other chemicals relies heavily on ammonia (NH3). Electrochemical reduction of nitrogen to ammonia, also known as the nitrogen reduction reaction (NRR), is a potential alternative to the Haber-Bosch process that can be carried out at ambient conditions. However, many NRR catalysts suffer from low selectivity and require a large overpotential to drive the reaction, limiting their practical application. In this study, we used density functional theory (DFT) calculations to investigate the mechanistic aspects of the NRR and the effect of curvature variation on the catalytic activity of silicon carbide nanotubes (SiCNTs) decorated with 3d-5dtransition metal single-atom catalysts. Our results suggest that the Os@SiCNT catalyst exhibits promising activity as an eNRR catalyst with an overpotential of 0.47 V and high selectivity over hydrogen evolution reaction (HER) competition. The electronic "acceptance-donation" interaction between N2 and Os promotes the adsorption and activation of N2 molecules on the catalyst surface. Additionally, our findings demonstrate that as the nanotube diameter decreases, the energy barrier for the NRR reaction decreases as well. However, this decrease in diameter also leads to a weaker capacity for *N2 adsorption, which may ultimately reduce the selectivity of the NRR reaction. These findings offer valuable insights that can aid in the rational design of nanotube-based electrocatalysts for eNRR. © 2023, The Authors. All rights reserved.

关键词： Density functional theory

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Hybrid federated learning with brain-region attention network for multi-center Alzheimer's disease detection

引用

Pattern Recognition 2024年 153卷

作者： Lei, Baiying Liang, Yu Xie, Jiayi Wu, You Liang, Enmin Liu, Yong Yang, Peng Wang, Tianfu Liu, ChuanMing Du, Jichen Xiao, Xiaohua Wang, Shuqiang School of Biomedical Engineering National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging Shenzhen University Medical School Shenzhen University Shenzhen518060 China Department of Automatic Tsinghua University Beijing100084 China School of Artifiicial Intelligence Beijing University of Posts and Telecomunications Beijing China Computer Science and Information Engineering National Taipei University of Technology Taipei China Aerospace Medical Center Aerospace Center Hospital Peking University Aerospace Clinical College Beijing100049 China The First Affiliated Hospital of Shenzhen University Shenzhen University Medical School Shenzhen University Shenzhen Second People's Hospital Shenzhen530031 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Identifying reproducible and interpretable biomarkers for Alzheimer's disease (AD) detection remains a challenge. AD detection using multi-center datasets can expand the sample size to improve robustness but might lead to a data privacy problem. Moreover, due to the high cost of labeling data, a lot of unlabeled data in each center is not fully utilized. To address this, a hybrid FL (HFL) framework is proposed that not only uses unlabeled data to train deep learning networks, but also achieves data privacy protection. We propose a novel Brain-region Attention Network (BANet), which highlights important regions via attention to represent the region of interest (ROIs).Specifically, we use a brain template to extract ROI signals from the preprocessed structure magnetic resonance imaging (sMRI) data. In addition, we add a self-supervised loss to the current loss to guide the attention map generation to learn the representations from unlabeled data. Finally, we evaluate our method on a multi-center database which is constructed using five AD datasets. The experimental results show that the proposed method performs better than state-of-the-art methods, achieving mean accuracy rates of 85.69 %, 63.34 %, and 69.89 % on the AD vs. NC, MCI vs. NC, and AD vs. MCI respectively. The source code is available for reproducibility at: https://***/yuliangCarmelo/HFL. © 2024 Elsevier Ltd

关键词： Magnetic resonance imaging

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain technology for Al-Najaf/Iraq Oil Refinery 19

Secure and Privacy-preserving Data-sharing Framework based o...

引用

2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Umran, Samir M. Lu, SongFeng Abduljabbar, Zaid Ameen Lu, Zhi Feng, Bingyan Zheng, Lu Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China University of Basrah College of Education for Pure Sciences Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co. Ltd Wuhan430074 China South-Central University for Nationalities College of Computer Science Wuhan430074 China

ISBN: (纸本)9798350346558

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network;provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality. © 2022 IEEE.

关键词： Digital storage

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Fast projection onto convex smooth constraints

arXiv

引用

arXiv 2021年

作者： Usmanova, Ilnura Kamgarpour, Maryam Krause, Andreas Levy, Kfir Yehuda Automatic Control Laboratory D-ITET ETH Zürich Switzerland Department of Electrical and Computer Engineering University of British Columbia Canada Department of Computer Science ETH Zürich Switzerland Viterby Department of Electrical & Computer Engineering Technion - Israel Institute of Technology

The Euclidean projection onto a convex set is an important problem that arises in numerous constrained optimization tasks. Unfortunately, in many cases, computing projections is computationally demanding. In this work, we focus on projection problems where the constraints are smooth and the number of constraints is significantly smaller than the dimension. The runtime of existing approaches to solving such problems is either cubic in the dimension or polynomial in the inverse of the target accuracy. Conversely, we propose a simple and efficient primal-dual approach, with a runtime that scales only linearly with the dimension, and only logarithmically in the inverse of the target accuracy. We empirically demonstrate its performance, and compare it with standard baselines. © 2021, CC BY.

关键词： Constrained optimization

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