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A review of smartphones-based indoor positioning:Challenges and applications

IET Cyber-Systems and Robotics 2021年第1期3卷 1-30页

作者： Khuong An Nguyen Zhiyuan Luo Guang Li Chris Watkins School of Computing Engineering and MathematicsUniversity of BrightonBrightonUK Computer Science Department Royal HollowayUniversity of LondonEghamUK Institute of Cyber-Systems and Control Zhejiang UniversityHangzhouChina

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor *** article is dedicated to review the most recent and interesting smartphones-based indoor navigation systems,ranging from electromagnetic to inertia to visible light ones,with an emphasis on their unique challenges and potential realworld applications.A taxonomy of smartphone sensors will be introduced,which serves as the basis to categorise different positioning systems for reviewing.A set of criteria to be used for the evaluation purpose will be *** each sensor category,the most recent,interesting,and practical systems will be examined,with detailed discussion on the open research questions for the academics,and the practicality for the potential clients.

关键词： client smart taxonomy

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A Vein Segmentation Method Based on Improved U-Net 12

A Vein Segmentation Method Based on Improved U-Net

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12th International Conference of Information and Communication Technology, ICTech 2023

作者： Wang, Hongliang Zhu, Liang Deng, Changyi Xiu, Pengfei Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

ISBN: (纸本)9798350332919

Recently, medical research has revealed that diffusion weighted imaging (DWI) is less sensitive than susceptibility-weighted imaging (SWI) for acute ischemic stroke. Brain vein analysis in SWI is very important for personalised therapy, quantitative diagnosis, and prognosis of acute ischemic stroke. Therefore, the accurate segmentation of veins in SWI images has important significance in supplementary diagnosis and medical research. However, vein segmentation in SWI is a challenging task because different veins show distinct variability and uneven intensity and are difficult to manually split. This paper proposes an improvement method based on U-net, by using dense connection and mixing loss function, the best Dice coefficient and the ASSD were obtained by other comparison methods. The method presented in this article can be used as a potential tool to accurately measure SWI cerebral veins and help physicians in decision making. © 2023 IEEE.

关键词： Image segmentation

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Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

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computers, Materials & Continua 2022年第6期71卷 5837-5854页

作者： El-Sayed M.El-Kenawy Abdelhameed Ibrahim Nadjem Bailek Kada Bouchouicha Muhammed A.Hassan Basharat Jamil Nadhir Al-Ansari Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansouraEgypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of Tamanghasset10034TamanghassetAlgeria URERMS Centre de Developpement des Energies Renouvelables(CDER)01000AdrarAlgeria Mechanical Power Engineering Department Faculty of EngineeringCairo UniversityGiza12613GizaEgypt Department Computer Sciences Universidad Rey Juan CarlosMostoles28933MadridSpain Department of Civil Environmental and Natural Resources EngineeringLulea University of Technology97187LuleaSweden

In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing Advanced Squirrel-Search Optimization Algorithm(ASSOA)and support vector regression,is utilized to estimate the hourly tilted solar irradiation for selected arid regions in ***-term measured meteorological data,including mean-air temperature,relative humidity,wind speed,alongside global horizontal irradiation and extra-terrestrial horizontal irradiance,were obtained for the two cities of Tamanrasset-and-Adrar for two *** computational algorithms were considered and analyzed for the suitability of *** two new algorithms,namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization *** accuracy of the developed models was analyzed in terms of five statistical error metrics,as well as theWilcoxon rank-sum and ANOVA *** the previously selected algorithms,K Neighbors Regressor and support vector regression exhibited good ***,the newly proposed ensemble algorithms exhibited even better *** proposed model showed relative root mean square errors lower than 1.448%and correlation coefficients higher than *** was further verified by benchmarking the new ensemble against several popular swarm intelligence *** is concluded that the proposed algorithms are far superior to the commonly adopted ones.

关键词： Renewable energy resources hybrid modeling tilted solar irradiation arid region

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A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network

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Biomedical and Environmental Sciences 2022年第6期35卷 494-503页

作者： JI Tian Jiao CHENG Qiang ZHANG Yong ZENG Han Ri WANG Jian Xing YANG Guan Yu XU Wen Bo LIU Hong Tu NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 100026China Academy of Cyber Science and Engineering Southeast UniversityNanjing 211189JiangsuChina Center for Biosafety Mega Science Chinese Academy of SciencesWuhan 430071HubeiChina Guangdong Center for Disease Control and Prevention Guangzhou 511430GuangdongChina Shandong Center for Disease Control and Prevention Jinan 250014ShandongChina LIST Key Laboratory of Computer Network and Information Integration(Southeast University)Ministry of EducationSoutheast UniversityNanjing 211189JiangsuChina

Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on *** achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of *** We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and *** 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction *** important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute *** As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant *** This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.

关键词： HFMD Early warning model STGCN Disease prediction

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Forecasting-Aided Graphical Learning for Robust State Estimation of Distribution System 1

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8th Purple Mountain Forum on Smart Grid Protection and control, PMF 2023

作者： Di, Cao Zhao, Junbo Hu, Jiaxiang Huang, Yuehui Huang, Qi Chen, Zhe Hu, Weihao University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing China Department of Energy Technology Aalborg University Pontoppidanstraede 111 Aalborg Denmark

ISBN: (数字)9789819992515

ISBN: (纸本)9789819992508

The forecasting of of pseudo-measurements play an important role in distribution system state estimation (DSSE). This paper proposes robust DSSE method based on forecasting-aided graphical learning method. The nodal power consumption models are first built to produce pseudo-measurements based on deep neural network. Then, the pseudo-measurements and real-time measurements are represented as a graph according to the topology of the distribution network, which are further processed by a graph attention network to capture the mapping relationship between the graphical measurements and the state variables based on the error modeling of pseudo-measurements. The robustness against anomalous measurements is achieved through the embedding of structural information of DN. The modeling of pseudo-measurements further enhance its robustness by guiding the formulation of edge weights of the graph neural network. Comparative tests are carried out on a IEEE 119-node system to demonstrate the effectiveness and robustness of the proposed method. © 2024, State Grid Electric Power.

关键词： Electric power distribution

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Development of a novel visual servoing probe test method for fault diagnosis of printed circuit boards

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IFAC-PapersOnLine 2023年第2期56卷 5248-5254页

作者： B. Tipary Á. Juniki F.G. Erdős E. Takács K. Németh Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eötvös Loránd Research Network Budapest Hungary Department of Manufacturing Science and Engineering Faculty of Mechanical Engineering Budapest University of Technology and Economics Budapest Hungary University of Pannonia Veszprém Hungary

Printed circuit board (PCB) measurement and repair is a challenging task that requires experience and expertise to perform. PCB diagnosis and repair shops employ skilled operators to carry out the corresponding measurement tasks using measuring instruments (e.g., oscilloscopes, multimeters) in order to uncover the condition of a particular product. However, these tasks are often repetitive and meticulous, and additionally, the results need to be collected and carefully documented so that the gathered experience regarding the product can be re-used when the next product of the same type arrives into the shop. Nevertheless, the diagnosis of used PCBs is less researched and current flexible automation possibilities are limited. In this paper, a novel visual servoing probe test method and measurement tool are proposed to provide a flexible solution for PCB diagnosis with a higher level of automation. The aim of the approach is to reduce the burden on the operators by carrying out the repetitive measurement tasks and automatically storing the results while leaving the responsibility of measurement profile setup to the human expert. The proposed visual servo system uses manually teached-in measurement points, where template patterns are recorded using cameras, and it is capable of compensating positioning errors in the range of a couple of millimeters. The proof of concept of the proposed method is presented through motherboard measuring experiments, with a 99.7% success rate.

关键词： Flexible and reconfigurable manufacturing systems Quality assurance and maintenance Visual servo control Printed circuit board measurement Fault diagnosis

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Hierarchical Hybrid Model Based on Deep Learning for Myocardial Infarction Detection 25

Hierarchical Hybrid Model Based on Deep Learning for Myocard...

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Proceedings of the 2025 4th International Conference on Big Data, Information and computer Network

作者： Mingxi Han Chi Ma Siyuan Li Baoming Pu School of Software Liaoning Technical University Huludao Liaoning China School of Computer Science and Engineering Northeastern University Shenyang Liaoning China School of Electrical and Intelligent Control Liaoning Institute of Science and Engineering Jinzhou Liaoning China

ISBN: (纸本)9798400712425

Myocardial Infarction (MI) is a major global health threat, where rapid and accurate diagnosis is essential for improving treatment outcomes. This study proposes MSRC-TransBLSTM, a deep learning-based hierarchical hybrid model for the automatic detection of MI. The model combines spatial and temporal features through a hierarchical modeling strategy: multi-layer convolutional blocks and improved MSRC modules extract and optimize spatial features, strengthening the representation of both local and global features. For temporal modeling, the Transformer Encoder captures global dependencies, while the BLSTM focuses on refining local dynamics features. Experiments on the PTB-XL dataset demonstrated the model's strong performance across key metrics (Acc = 98.68%, Sen = 97.33%, F1 = 97.43%). Compared to other models, it achieves notable improvements in accuracy and feature representation, confirming its effectiveness in MI detection.

关键词： Deep Learning

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SVD based scale transform invariant observable degree for LTI system

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Science China(Information Sciences) 2021年第3期64卷 246-248页

作者： Quanbo GE Peng ZHUO Hongli HE Zhentao HU Zhansheng DUAN Junzhi YU Shenzhen Institute of Guangdong Ocean University School of Automation Hangzhou Dianzi University Test Institute Chinese Flight Test Establishment College of Computer and Information Engineering Henan University The Center for Information Engineering Science Research Xi'an Jiaotong University State Key Laboratory of Complex System Management and Control Institute of AutomationChinese Academy of Sciences

Dear editor,The basic concept of observability is used to express the possibility to recover the state from measurements in modern control theory [1–3]. It is a qualitative parameter,which cannot reflect the observable degree(OD) ability [4,5].Therefore, the OD is presented to quantitatively obtain the function. The quantitative parameter is effective for indicating the exact degree of estimation ability.

关键词： observability matrix observable degree singular value decomposition scale transform invariance linear system linear relation

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MaxCUCL: Max−Consensus with Deterministic Convergence in Networks with Unreliable Communication Links

arXiv

引用

arXiv 2024年

作者： Rikos, Apostolos I. Charalambous, Themistoklis Johansson, Karl H. Department of Electrical and Computer Engineering Division of Systems Engineering Boston University Boston United States Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Division of Decision and Control Systems KTH Royal Institute of Technology also affiliated with Digital Futures StockholmSE-100 44 Sweden

In this paper, we present a novel distributed algorithm (herein called MaxCUCL) designed to guarantee that max−consensus is reached in networks characterized by unreliable communication links (i.e., links suffering from packet drops). Our proposed algorithm is the first algorithm that achieves max−consensus in a deterministic manner (i.e., nodes always calculate the maximum of their states regardless of the nature of the probability distribution of the packet drops). Furthermore, it allows nodes to determine whether convergence has been achieved (enabling them to transition to subsequent tasks). The operation of MaxCUCL relies on the deployment of narrowband error-free feedback channels used for acknowledging whether a packet transmission between nodes was successful. We analyze the operation of our algorithm and show that it converges after a finite number of time steps. Finally, we demonstrate our algorithm’s effectiveness and practical applicability by applying it to a sensor network deployed for environmental monitoring. © 2024, CC BY.

关键词： Probability distributions

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FPGA-based Low Power Acceleration of HARK Sound Source Localization

FPGA-based Low Power Acceleration of HARK Sound Source Local...

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IEEE Cool Chips

作者： Zirui Lin Katsutoshi Itoyama Kazuhiro Nakadai Hideharu Amano Dept. of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Honda Research Institute Japan Co. Ltd. Saitama Japan Dept. of Information and Computer Science Keio University Kanagawa Japan

ISBN: (数字)9798350384147

ISBN: (纸本)9798350384154

This paper presents an FPGA-based low-power acceleration of sound source localization in HARK, open-source software for robot audition. Due to the massive matrix operations, sound source localization in HARK takes substantial processing time in edge computing devices. To balance processing time and low power consumption, two functions in sound source localization that include many matrix operations are targeted and migrated on an FPGA SoC board called M-KUBOS. Compared to CPU-based computing on ARM Cortex A53, our implementation achieved a 2.0× speedup and 1.7× lower energy consumption.

关键词： Location awareness Program processors Power demand Parallel processing Energy efficiency Matrix decomposition Robots

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