检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Ye, Lianxu Wu, Fan Xu, Ruixing Zhang, Di Lu, Juanjuan Wang, Chuanlong Dong, Anjiang Xu, Sichen Xue, Lejun Fan, Zixin Xu, Longjie Li, Kaifeng Li, Dong Kursumovic, Ahmed Zhao, Run Tang, Rujun Qiu, Lei Wang, Haiyan MacManus-Driscoll, Judith L. Jing, Qingshen Li, Weiwei Yang, Hao College of Physics MIIT Key Laboratory of Aerospace Information Materials and Physics Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Microelectronics & Data Science Anhui University of Technology Maanshan243002 China School of Materials Engineering School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Jiangsu Key Laboratory of Thin Films School of Physical Science and Technology Soochow University Suzhou215006 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Department of Materials Science & Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom James Watt School of Engineering University of Glasgow GlasgowG12 8LT United Kingdom State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing211106 China

Face masks are key personal protective equipment for reducing exposure to viruses and other environmental hazards such as air pollution. Integrating flexible and wearable sensors into face masks can provide valuable insights into personal and public health. The advantages that a breath-monitoring face mask requires, including multi-functional sensing ability and continuous, long-term dynamic breathing process monitoring, have been underdeveloped to date. Here, we design an effective human breath monitoring face mask based on a flexible La0.7Sr0.3MnO3 (LSMO)/Mica respiration sensor. The sensor’s capabilities and systematic measurements are investigated under two application scenes, namely clinical monitoring mode and daily monitoring mode, to monitor, recognise, and analyse different human breath status, i.e., cough, normal breath, and deep breath. This sensing system exhibits super-stability and multi-modal capabilities in continuous and long-time monitoring of the human breath. We determine that during monitoring human breath, thermal diffusion in LSMO is responsible for the change of resistance in flexible LSMO/Mica sensor. Both simulated and experimental results demonstrate good discernibility of the flexible LSMO/Mica sensor operating at different breath status. Our work opens a route for the design of novel flexible and wearable electronic devices. © 2023, The Authors. All rights reserved.

关键词： Mica

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Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers

arXiv

引用

arXiv 2021年

作者： Liu, Wanli Li, Chen Rahaman, Md Mamunur Jiang, Tao Sun, Hongzan Wu, Xiangchen Hu, Weiming Chen, Haoyuan Sun, Changhao Yao, Yudong Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China School of Control Engineering Chengdu University of Information Technology Chengdu610225 China Shengjing Hospital China Medical University Shenyang110001 China Suzhou Ruiguan Technology Company Ltd. Suzhou215000 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States Institute of Medical Informatics University of Luebeck Luebeck Germany

Cervical cancer is a very common and fatal type of cancer in women. Cytopathology images are often used to screen for this cancer. Given that there is a possibility that many errors can occur during manual screening, a computer-aided diagnosis system based on deep learning has been developed. Deep learning methods require a fixed dimension of input images, but the dimensions of clinical medical images are inconsistent. The aspect ratios of the images suffer while resizing them directly. Clinically, the aspect ratios of cells inside cytopathological images provide important information for doctors to diagnose cancer. Therefore, it is difficult to resize directly. However, many existing studies have resized the images directly and have obtained highly robust classification results. To determine a reasonable interpretation, we have conducted a series of comparative experiments. First, the raw data of the SIPaKMeD dataset are pre-processed to obtain standard and scaled datasets. Then, the datasets are resized to 224 × 224 pixels. Finally, 22 deep learning models are used to classify the standard and scaled datasets. The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images. This conclusion is also validated via the Herlev dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Aspect ratio

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Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms

arXiv

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arXiv 2025年

作者： Zhou, Junchi Wang, Haozhou Kato, Yoichiro Nampally, Tejasri Rajalakshmi, P. Balram, M. Katsura, Keisuke Lu, Hao Mu, Yue Yang, Wanneng Gao, Yangmingrui Xiao, Feng Chen, Hongtao Chen, Yuhao Li, Wenjuan Wang, Jingwen Yu, Fenghua Zhou, Jian Wang, Wensheng Hu, Xiaochun Yang, Yuanzhu Ding, Yanfeng Guo, Wei Liu, Shouyang Engineering Research Center of Plant Phenotyping Ministry of Education Jiangsu Collaborative Innovation Center for Modern Crop Production Academy for Advanced Interdisciplinary Studies Sanya Institute of Nanjing Agricultural University Nanjing Agricultural University Nanjing China Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan Department of Artificial Intelligence Indian Institute of Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Hyderabad India Institute of Biotechnology Professor Jayashankar Telangana Agricultural State University Hyderabad India Graduate School of Agriculture Kyoto University Kyoto Japan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Crop Genetic Improvement National Center of Plant Gene Research Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural University Wuhan China State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China the Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing China Center for Geospatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China School of Information and Electrical Engineering Shenyang Agricultural University Shenyang China Rice Research Institute Jilin Academy of Agricultural Sciences Changchun China Institute of Crop Sciences National Key Facility for Crop Gene Resources and Genetic Improvement Chinese Academy of Agricultural Sciences Beijing China Yuan Long Ping High-Tech Agriculture Co. Ltd. Changsha China

Developing computer vision–based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production. Among phenotyping tasks, distinguishing image components is a key prerequisite for characterizing plant growth and development at the organ scale, enabling deeper insights into eco-physiological processes. However, due to the fine structure of rice organs and complex illumination within the canopy, this task remains highly challenging, underscoring the need for a high-quality training dataset. Such datasets are scarce, both due to a lack of large, representative collections of rice field images and the time-intensive nature of annotation. To address this gap, we established the first comprehensive multi-class rice semantic segmentation dataset, RiceSEG. We gathered nearly 50,000 high-resolution, ground-based images from five major rice-growing countries (China, Japan, India, the Philippines, and Tanzania), encompassing over 6,000 genotypes across all growth stages. From these original images, 3,078 representative samples were selected and annotated with six classes (background, green vegetation, senescent vegetation, panicle, weeds, and duckweed) to form the RiceSEG dataset. Notably, the sub-dataset from China spans all major genotypes and rice-growing environments from the northeast to the south. Both state-of-the-art convolutional neural networks and transformer-based semantic segmentation models were used as baselines. While these models perform reasonably well in segmenting background and green vegetation, they face difficulties during the reproductive stage, when canopy structures are more complex and multiple classes are involved. These findings highlight the importance of our dataset for developing specialized segmentation models for rice and other crops. The RiceSEG dataset is publicly available at ***. © 2025, CC BY.

关键词： Semantic Segmentation

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Formation control for Mobile Robot using Fuzzy - PI controller

Formation Control for Mobile Robot using Fuzzy - PI Controll...

引用

International Conference on Advanced Robotics and Intelligent Systems (ARIS)

作者： Min-Fan Ricky Lee H.P.M Willybrordus Sukamto Sharfiden Hassen Asep Nugroho National Taiwan University of Science and Technology Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan College of Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9781728198231

ISBN: (纸本)9781728198248

Protecting soldiers in the long march of platoon formation is crucial mission in the military operation. Autonomous ground mobile robots can be deployed to carry out such kind of the mission. The primary task is maintaining robot position automatically based on the movement of soldier in a platoon formation. The GPS is employed to determine the soldier current pose. This information is used as an input to create a dynamic convex hull around a platoon. The Proportional-Integral (PI) controller is applied to control each robot so it can move in a desired trajectory. The fuzzy logic control (FLC) is involved to tune the gain of PI controller to optimize the performance. Three protective robots and nine soldiers are used to evaluate the algorithm in simulation. The proposed algorithm will provide a platoon soldiers with optimal protection encirclement and enhance their safety. The simulation results show good performance using the proposed controller.

关键词： Robot kinematics Mobile robots Wheels Task analysis Fuzzy logic Kinematics

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The Performance Evaluation and Resilience Analysis of Supply Chain Based on Logistics Network

The Performance Evaluation and Resilience Analysis of Supply...

引用

第三十九届中国控制会议

作者： Weiqi Sun Yuanlong Li Liangren Shi Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of the Ministry of Education Shanghai Key Laboratory of Navigation and Location-based Services and Academy of Information Technology and Electrical Engineering Shanghai Jiao Tong University

With the development of globalization, more and more enterprises are involved in the supply chain network with increasingly complex structure. In this paper, enterprises and relations in the logistics network are abstracted as nodes and edges of the complex network. A graph model for a supply chain network to specified industry is constructed, and the Neo4 j graph database is employed to store the graph data. This paper uses the theoretical research tool of complex network to model and analyze the supply chain, and designs a supply chain network evaluation system which include static and dynamic measurement indexes according to the statistical characteristics of complex network. In this paper both the static and dynamic resilience characteristics of the the constructed supply chain network are evaluated from the perspective of complex network. The numeric experimental simulations are conducted for validation. This research has practical and theoretical significance for enterprises to make strategies to improve the anti-risk capability of supply chain network based on logistics network information.

关键词： Complex network Supply chain network Graph database Neo4j Resilience evaluation

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Bayesian Neural Networks Uncertainty Quantification with Cubature Rules

Bayesian Neural Networks Uncertainty Quantification with Cub...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Peng Wang Nidhal C. Bouaynaya Lyudmila Mihaylova Jikai Wang Qibin Zhang Renke He Automatic Control & Systems Engineering Dept. The University of Sheffield Information and Navigation College Air Force Engineering University The 32nd Research Institute of CETC Department of Automation University of Science and Technology of China Department of Electrical and Computer Engineering Rowan University

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.

关键词： Neural networks Uncertainty Bayes methods Computational modeling Gaussian distribution Two dimensional displays Weather forecasting

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PoCET: a Polynomial Chaos Expansion Toolbox for Matlab

arXiv

引用

arXiv 2020年

作者： Petzke, Felix Mesbah, Ali Streif, Stefan Automatic Control & System Dynamics Lab Technische Universität Chemnitz Faculty for Electrical Engineering and Information Technology Chemnitz Germany Department of Chemical and Biomolecular Engineering University of California BerkeleyCA United States

We introduce PoCET: a free and open-scource Polynomial Chaos Expansion Toolbox for Matlab, featuring the automatic generation of polynomial chaos expansion (PCE) for linear and nonlinear dynamic systems with time-invariant stochastic parameters or initial conditions, as well as several simulation tools. It offers a built-in handling of Gaussian, uniform, and beta probability density functions, projection and collocation-based calculation of PCE coefficients, and the calculation of stochastic moments from a PCE. Efficient algorithms for the calculation of the involved integrals have been designed in order to increase its applicability. PoCET comes with a variety of introductory and instructive examples. Throughout the paper we show how to perform a polynomial chaos expansion on a simple ordinary differential equation using PoCET, as well as how it can be used to solve the more complex task of optimal experimental design. Copyright © 2020, The Authors. All rights reserved.

关键词： Nonlinear dynamical systems

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An improved weight control system for slender cigarette production

An improved weight control system for slender cigarette prod...

引用

International Conference on Smart City and Intelligent Building, ICSCIB 2018

作者： Han, Zhonghua Yang, Xu Bi, Kaiyuan Dong, Xiaoting Sun, Xixian Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang China Department of Digital Factory Shenyang Institute of Automation CAS Shenyang China Intelligent Detection and Equipment Department Shenyang Institute of Automation CAS Shenyang China Department of Electric Engineering Sichuan College of Architectural Technology Deyang China Shenyang Jingdaxi Machine and Electrical Equipment Co. Ltd Shenyang China

ISBN: (纸本)9789811367328

In this paper, a weight control system is developed based on Beckhoff PLC for a ZJ19 cigarette machine, which improved the performance of the existing system, and the control system is also introduced. The system uses the microwave to measure cigarette weight, and uses the digital PID control algorithm to achieve weight control. The system hardware structure is simple, the performance of the system is stable and reliable with quick response set aside the upgrade space. The original machine control system was completely replaced with the designed system to meet the requirements of slender cigarette production precision and the production efficiency and product quality were ensured. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Quality control

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control System for Spinning Machine Induction Motor using PLC 1

Control System for Spinning Machine Induction Motor using PL...

引用

1st International Conference on electrical, Communication and Computer engineering, ICECCE 2019

作者： Awais, Muhammad Hassan, Syed Zulqadar Kamal, Tariq Zahoor, Amir Khan, Muhammad Abbas Riaz, Muhammad Tanveer National University of Computer and Emerging Sciences Department of Electrical Engineering Chiniot35400 Pakistan Sakarya University Faculty of Engineering Department of Electrical and Electronics Engineering Serdivan/Sakarya54050 Turkey Balochistan University of Information Technology Engineering Management Sciences Quetta Balochistan87650 Pakistan University of Engineering Technology Department of Mechatronics and Control Engineering Faisalabad38000 Pakistan

ISBN: (纸本)9781728138251

Programmable logic controllers (PLCs) are at the vanguard of industrial automation. In this article a PLC based control system is design and later on implemented on an induction motor used inside textile industry in spinning plant, further it can be implemented on any other industry. Also, it is implemented by designing hardware circuitry for induction motor's speed control by attaching motor to PLC and tested it. The results are collected for at two conditions;normal and trip operation conditions. With the addition of PLC in the existing system for control and automation of spinning machine's induction motor accuracy is higher than the normal traditional voltage-frequency (V/f) control mechanism and control system. This control system using PLC increases the efficiency of induction motor to 95%. In terms of speed regulation and efficiency, the PLC is very adaptable tool for industry and electric drives control. © 2019 IEEE.

关键词： controllers

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Low-latency federated learning over wireless channels with differential privacy

arXiv

引用

arXiv 2021年

作者： Wei, Kang Li, Jun Ma, Chuan Ding, Ming Chen, Cailian Jin, Shi Han, Zhu Poor, H. Vincent School of Electrical and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Data61 CSIRO SydneyNSW2015 Australia The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul Korea Republic of The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. The performance of uploaded models in such situations can vary widely due to imbalanced data distributions, potential demands on privacy protections, and quality of transmissions. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement. We solve this problem in the framework of multi-agent multiarmed bandit (MAMAB) to deal with the situation where there are multiple clients confornting different unknown transmission environments, e.g., channel fading and interferences. Specifically, we first transform the long-term constraints on both training performance and each client's DP into a virtual queue based on the Lyapunov drift technique. Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach. More importantly, we propose two efficient solutions to this matching problem, i.e., modified Hungarian algorithm and greedy matching with a better alternative (GMBA), in which the first one can achieve the optimal solution with a high complexity while the second one approaches a better tradeoff by enabling a verified low-complexity with little performance loss. In addition, we develop an upper bound on the expected regret of this MAMAB based FL framework, which shows a linear growth over the logarithm of communication rounds, justifying its theoretical feasibility. Extensive experimental results are conducted to validate the effectiveness of our proposed algorithms, and the impacts of various parameters on the FL performance over wireless edge networks are also discussed. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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