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Electronic Information engineering and Computer Technology (EIECT), International Conference on

作者： XueYan Lie XiaoPeng Huang HuanYu Liu YanLong Li Hua Zhong FeiFei Gao Department of Electrical Engineering and Automation University of South China China Department of Electronic Information Engineering University of South China China Department of Measurement and Control Technology University of South China China

With the outbreak and normal development of COVID-19, the effective detection and recording of body temperature has become a new focus of our attention. At present, there is no complete system to measure temperature, automatic record and specific information at home and abroad. To this end, combined with professional knowledge, our team designed a two-dimensional code scanning and human body temperature automatic recording device with STM32F1 as the core. The device STM32F1 development board is the main control chip. By connecting the WIFI module through the serial port, STM32F1 uses the function of wireless communication. Through the communication protocol, the link between the router and the ESC cloud server of Ali Cloud is utilized. The router or mobile data is transmitted to the user side (APP, applets) according to the specified communication protocol. Inside the development board, the code of each part is written to complete the device integrating code scanning and temperature measurement, which can be displayed and alarm through the node (OLED display screen). This will play a good role in preventing the spread of COVID-19. The system can be used in hospitals, communities, railway stations, shopping malls and many other public places.

关键词： Temperature measurement COVID-19 Wireless communication Codes Clouds Routing protocols Real-time systems

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Data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings 13

Data Augmentation Integrated with Feature-Enhanced Convoluti...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wang, Li Qu, Qilin Wang, Yanwei Wong, David Shan-Hill Zheng, Ying School of Artificial Intelligence and Automation Huazhong University of Science and Technology China -Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China School of Mechanical & Electrical Engineering Wuhan Institute of Technology 693 Chuxiong Road Wuhan430073 China National Tsing-Hua University Department of Chemical Engineering Hsinchu30071 Taiwan

ISBN: (纸本)9798350361674

Imbalance in fault samples is a key problem with most rolling bearing datasets in real industrial processes, limiting the performance of fault detection. In addition, existing fault diagnosis methods for rolling bearings do not adopt a modular division between the initial layer and the classification layer. This lack of modularization inhibits the full activation of the network's feature extraction capabilities and impedes subsequent model improvements. In this paper, a novel fault diagnosis method based on data augmentation integrated with a feature-enhanced convolutional neural network (DA-FECNN) is proposed. First, Variational Autoencoder-Generative Adversarial Network (VAE-GAN) is utilized to expand the number of fault samples and improve the diversity of few-shot. Second, a uniform feature-enhanced structure is designed with a feature extraction module, a multi-scale classification module, and a reconstruction module. The network's ability on feature extraction is improved by focusing on high quality features. Thereby, accurate fault classification is realized simultaneously. Experiments on real world data show the effectiveness of the proposed method on fault diagnosis task. © 2024 IEEE.

关键词： Convolutional neural networks

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Improved Res2Net Based on Multi-scale Attention Mechanism for Breast Cancer Image Classification 12th

Improved Res2Net Based on Multi-scale Attention Mechanism fo...

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12th International Conference on Computer engineering and Networks, CENet 2022

作者： Lei, Suqin Deng, Xing Shao, Haijian Xia, Wang Department of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China College of Automation Key Laboratory of Ministry of Education of Complex Engineering System Measurement and Control Southeast University Jiangsu Nanjing210009 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Breast cancer is one of the most common cancers among female diseases. Since the classification accuracy of pathological images is crucial to the diagnosis of breast cancer, in order to reduce the error of manual diagnosis and improve the automatic classification accuracy of pathological images, this paper proposes a high-accuracy breast cancer classification method based on multi-scale attention mechanism enhanced Res2Net. By constructing a multi-channel residual connection in the residual block, and introducing a convolutional attention mechanism to enhance the feature extraction ability of Res2Net, and using group convolution to replace part of the ordinary convolution. In addition, since the channel information interaction across groups cannot be realized in the group convolution, which reduces the feature extraction ability. In this paper, the channel shuffling operation is used to reorganize the feature maps after the group convolution to achieve the information interaction between different groups. The evaluation results on the BreaKHis dataset show that, the accuracy and precision of the method in this paper have reached 98.5% and 98.9% respectively, in binary classification of histopathological images compared with the benchmark method, and the advantages of recall rate and F1 value are also more obvious. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Robust control reconfiguration of resource allocation systems with Petri nets and integer programming

Robust control reconfiguration of resource allocation system...

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作者： Li, Jun Zhou, Mengchu Guo, Tao Gan, Yahui Dai, Xianzhong Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education China School of Automation Southeast University Nanjing 210096 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 United States

Supervisory control reconfiguration can handle the uncertainties including resource failures and task changes in discrete event systems. It was not addressed to exploit the robustness of closed-loop systems to accommodate some uncertainties in the prior studies. Such exploitation can cost-efficiently achieve reconfigurability and flexibility for real systems. This paper presents a robust reconfiguration method based on Petri nets (PNs) and integer programming for supervisory control of resource allocation systems (RASs) subject to varying resource allocation relationships. An allocation relationship is seen as a control specification while the execution processes requiring resources as an uncontrolled plant. First, a robust reconfiguration mechanism is proposed. It includes updating the P-invariant-based supervisor and evolving the state of the closed-loop system. The latter adapts to the control specification changes by the self-regulation of the closed-loop system's state. Next, two novel integer programming models for control reconfiguration are proposed, called a reconfiguration model with acceptability and reconfiguration one with specification correction. Since both models integrate the firability condition of transitions, no additional efforts are required for the state reachability analysis. Finally, a hospital emergency service system is used as an example to illustrate them. © 2014 Elsevier Ltd. All rights reserved.

关键词： Integer programming

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LiDAR-Inertial-Encoder Fusion for Real-Time Simultaneous Localization and Mapping of Rubber Tire Gantry Cranes

LiDAR-Inertial-Encoder Fusion for Real-Time Simultaneous Loc...

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第43届中国控制会议

作者： Wangchao Yu Jun Yang Jinya Su School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education Southeast University Department of Aeronautical and Automotive Engineering Loughborough University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Simultaneous Localization and Mapping(SLAM) plays a vital role in ensuring port safety and efficiency by augmenting accurate, robust, and real-time perception. SLAM has been widely applied in automated guided vehicles in ports, there is still limited exploration into rubber tire gantry(RTG) crane applications. To address this gap, this paper introduces a robust SLAM framework for RTGs, leveraging a tightly coupled Iterated Error-State Kalman Filter to integrate multiple solid-state LiDARs with IMU and encoder measurements. The proposed approach, mainly encompassing preprocessing for sensor synchronization and a novel feature point based on LiDAR point cloud intensity, can significantly enhance system robustness. During prediction,the IMU first infers the system's state and de-skews the LiDAR point cloud with an initial estimate, which is then optimized using Li DAR observations in the update stage. Tested on extensive self-collected port datasets against the SOTA FAST-LIO2,the proposed method achieves considerable accuracy and robustness in RTG applications.

关键词： SLAM Multi-LiDARs Multi-sensor fusion Rubber Tire Gantry Cranes

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Container Distance Estimation for Automated RTG via Improved SOLOv2 Instance Segmentation

Container Distance Estimation for Automated RTG via Improved...

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第43届中国控制会议

作者： Zhengtao Yu Jun Yang Jinya Su School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education Southeast University Department of Aeronautical and Automotive Engineering Loughborough University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This study develops a simple real-time algorithm inferring the relative distance between containers stacked in operational yards and spreaders of Rubber Tire Gantry(RTG) in support of fully automated RTG operations in ports. The proposed scheme is based on SOLOv2 instance segmentation network with image captured by camera mounted on RTG driver's ***, distance calculation is achieved by containers' surface recognition via instance segmentation and geometric information pertaining to the containers. To meet the dual demands of accuracy and reduced resource consumption, the original backbone network of SOLOv2, namely resnet18, is replaced by GhostNet. This substitution enables the extraction of multi-level feature maps for fusion, significantly reducing network complexity without sacrificing accuracy. The proposed algorithm is validated by comparative experiments on a self-curated real-life dataset, demonstrating a remarkable reduction of 91.6% in FLOPS and 55.7% in Params for the improved SOLOv2 framework.

关键词： Distance Estimation Instance Segmentation Rubber Tire Gantry Network lightweight

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engineering METHODS OF control DESIGN FOR DISTRIBUTED PARAMETER SYSTEMS

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IFAC Proceedings Volumes 2005年第1期38卷 159-164页

作者： G. Hulkó C. Belavý Ŝ. Cibiri J. Szuda University Center for Control of Distributed Parameter Systems Department of Automation and Measurement Faculty of Mechanical Engineering Slovak University of Technology Nám. Slobody 17 812 31 Bratislava Slovak Republic

In the paper engineering methods for control of distributed parameter systems given by numerical structures on complex shape 3D definition domains are presented. controlled systems are interpreted as lumped-input/distributed-output systems. Schemes of distributed parameter control loops are arranged. A web-based control design environment from *** will be introduced: Distributed Parameter Systems Blockset for MATLAB & Simulink – third-party MathWorks product, web service and internet monograph. – In general the paper indicates a new direction for engineering control technology, operating mainly on the time axis, to expand towards time/space coordinates of real world.

关键词： control design distributed parameter systems lumped-input/distributed-output systems time/space decomposition of dynamics and control synthesis distributed parameter PID algebraic state space robust control web based control design environment finite element method

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WEB-BASED control DESIGN ENVIRONMENT FOR DISTRIBUTED PARAMETER SYSTEMS control EDUCATION

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IFAC Proceedings Volumes 2005年第1期38卷 25-30页

In the paper a web-based control design environment for distributed parameter systems control education is presented. It is arranged on *** and includes: full downloadable demo version of Distributed Parameter Systems Blockset for MATLAB & Simulink with Show, Demos and DPS Wizard –third-party Math Works product; web Service for support control solutions via internet and internet monograph Modeling, control and Design of Distributed Parameter Systems with Demonstrations in MATLAB. Some possibilities of this environment are indicated by control of complex shape 3D metal body heating – modeled by numerical structures of finite element method.

关键词： control education internet distributed parameter systems lumped-input/distributed-output systems time/space decomposition of dynamics and control synthesis distributed parameter PID algebraic state space robust control web based control design environment finite element method

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Attentive online sequential extreme learning machine for nonlinear dynamic process monitoring

Attentive online sequential extreme learning machine for non...

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第34届中国过程控制会议

作者： Jingxin Zhang Yuying Cao Hai Zhou Department of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education Department of Automation Southeast University New Energy Research Center China Electric Power Research Institute

This paper provides a new monitoring method based on the attention mechanism and online sequential extreme learning machines(OS-ELM) for nonlinear dynamic process,referred as attentive ***-ELM provides the prediction of outputs and the update of parameters by sequential *** cope with nonlinearity,the attention mechanism is utilized to map data to a high-dimensional feature *** effect of the attention mechanism and the single hidden layer feedforward network trained with OS-ELM work collaboratively in monitoring the real-time process data to detect possible *** addition,fault detection rate and false alarm rate are adopted to evaluate the monitoring *** effectiveness of the proposed method is illustrated by a typically complicated industrial system.

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Wheat Drought Assessment by Remote Sensing Imagery Using Unmanned Aerial Vehicle

Wheat Drought Assessment by Remote Sensing Imagery Using Unm...

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第37届中国控制会议

作者： Jinya Su Matthew Coombes Cunjia Liu Lei Guo Wen-Hua Chen Department of Aeronautical and Automotive Engineering Loughborough University Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University) Ministry of Education School of Electrical Engineering and Automation Beihang University

This work aims at evaluating the usability of remote sensing RGB imagery by an Unmanned Aerial Vehicle（UAV） in assessing wheat drought status. A UAV survey is conducted to collect high-resolution RGB imageries by using DJI S1000 for the experimental wheat fields of Gucheng town, Heibei Province, China. The soil moisture for different plots of the experimental filed is kept at an approximately constant level for the whole growing season in a well controlled environment, where field measurements are performed just after the UAV survey to obtain the soil water content for each plot. A machine learning based wheat drought assessment framework is proposed in this work. In the proposed framework, wheat pixels are first segmented from the soil background using the classical normalized excess green index（NEx G）. Rather than using pixel-wise classification,a pixel square of appropriate dimension is defined as the samples, based on which various features are extracted for the wheat pixels including statistical features and spectral index features. Different classification algorithms are experimented to identify a suitable one in terms of classification accuracy and computation time. It is discovered that Support Vector Machine with Gaussian kernel can obtain an accuracy over 90%, which demonstrates the usefulness of RGB imagery in wheat drought assessment.

关键词： Classification Remote sensing UAV imagery Wheat drought

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