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Food Safety and Health 2024年第0期 155-171页

作者： Sun, Yang Zhong, Kunli Nie, Lanmengya Xu, Yan Wang, Yiran Yuan, Jingtao Jiang, Shuai Li, Xu Zhang, Zhidong Li, Xiangfei Jiang, Ling State Key Laboratory of Materials-Oriented Chemical Engineering College of Food Science and Light Industry Nanjing Tech University Nanjing China Key Laboratory for Food Nutrition and Safety Control of Xinjiang Production and Construction Corps School of Food Science and Technology Shihezi University Xinjiang Shihezi China Xinjiang Key Laboratory of Special Environmental Microbiology Institute of Microbiology Xinjiang Academy of Agricultural Sciences Urumqi China Engineering Laboratory for Industrial Microbiology Molecular Beeding of Anhui Province College of Biologic & Food Engineering Anhui Polytechnic University Wuhu China

Food safety is essential for public health, with key hazards including microbial and chemical contamination and pesticide residues. Therefore, food testing is integral to ensuring food safety. Combining CRISPR/Cas with lateral flow assays (LFAs) leverages CRISPR's robust trans-acting activity, specific target recognition, and high enzymatic efficiency while retaining LFA's advantages of direct visualization, low cost, and simple operation. This combination enhances food safety detection capabilities. In this paper, we review the CRISPR-based lateral flow combined detection technology, summarize its basic principles and features, and focus on its integrated applications. The technology is fast, accurate, and cost-effective in detection, and is thus widely used in various testing fields, especially in food pathogen and viral infection detection. Finally, we highlight the technology's potential to improve various aspects, such as detection strength, and discuss the barriers and pre-commercialization issues of CRISPR/Cas detection platforms. We also provide solutions to increase the likelihood of its commercialization. Currently, CRISPR lateral flow assays are anticipated for broad detection applications. Researchers are investigating Cas protein properties to enhance assay stability and accuracy, promising enhanced food and bioassay safety in the future. © 2024 The Author(s). Food Safety and Health published by John Wiley & Sons Australia, Ltd on behalf of International Association of Dietetic Nutrition and Safety.

关键词： CRISPR detection platform food safety lateral flow assay low-cost manner

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Maximizing Wind Power Injection by Transmission Switching and DLR with Voltage Stability Constraints

Maximizing Wind Power Injection by Transmission Switching an...

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2024 IEEE IAS industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Wu, Ke Wang, Lei Ha, Hengxu Zhang, Mei School of Electrical and Electronic Engineering Shandong University of Technology Zibo China Electric Power Research Institute Hainan Power Grid China Southern Power Grid Company Ltd. Haikou China China Electric Power Research Institute State Key Laboratory of Operation and Control of Wind Energy & Storage Systems Beijing China

ISBN: (纸本)9798350352290

This paper proposes a maximum wind power injection (MVPI) problem under the engineering operation requirements of modern power systems and voltage stability constraints. To dig the access capacity of power grid for wind power, dynamic line rating (DLR) and optimal transmission switching (OTS) are jointly co-optimized to provide flexible operation of power systems. A novel DLR technique independent ambient parameter is proposed with the line parameter correction. The proposed problem is a mix-integer nonlinear programming problem that is hard to solve directly. Hence, a three-stage methodology is developed, which has been applied on the IEEE 24-bus RTS system, the IEEE 118-bus power system. The computational results show the effectiveness of the conducted model in maximizing the usage of wind power. © 2024 IEEE.

关键词： Windmill

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A Double Node Thin-Film Thermocouples for In-Situ Measurement

A Double Node Thin-Film Thermocouples for In-Situ Measuremen...

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2022 International Conference on Automation, Robotics and Computer Engineering, ICARCE 2022

作者： Ma, Rong Zhang, Meiju Zhang, Zhongkai Huang, Manguo Liang, Xiaobo Liu, Jiangjiang Liu, Zhaojun Tian, Bian Xi'An Jiaotong University State Key Laboratory for Mechanical Manufacturing Systems Engineering Xi'an China Aviation Key Laboratory of Science and Technology on Special Condition Monitoring Sensor Technology Beijing Changcheng Aeronautic Measurement and Control Technology Research Institute Beijing China

ISBN: (纸本)9781665475488

The design, fabrication, testing and implementation of a new high-performance tungsten-rhenium thin film thermocouple are introduced. In this paper, tungsten-rhenium TFTCs are taken as the research object, after simulation analysis in COMSOL software and design, it is made by screen-printing technology and magnetron sputtering. This kind of thin film thermocouple primarily utilizes two kinds of electrode materials with varying composition combination, mainly tungsten with 5% rhenium and tungsten with 26% rhenium. Furthermore, high-temperature annealing is performed to maintain the mechanical properties and thus improve thermoelectric performance. The results demonstrate that the performance is reliable in the temperature range of 150-600 C° with a repeatability of 2.02%, providing application value for high-temperature in-situ sensing. © 2022 IEEE.

关键词： Thermocouples

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Dynamic Modeling and control for an Offshore Semisubmersible Floating Wind Turbine

arXiv

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arXiv 2024年

作者： Gong, Yingjie Yang, Qinmin Geng, Hua Meng, Wenchao Wang, Lin The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering The Huzhou Institute Zhejiang University Hangzhou310027 China The Department of Automation Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China The Zhejiang Windey Co. Ltd. Hangzhou310027 China

Floating wind turbines (FWTs) hold significant potential for the exploitation of offshore renewable energy resources. Nevertheless, prior to the construction of FWTs, it is imperative to tackle several critical challenges, especially the issue of performance degradation under combined wind and wave loads. This study initiates with the development of a simplified nonlinear dynamical model for a semi-submersible FWT. In particular, both the rotor dynamics and the finite rotations of the platform are considered in presented modeling approach, thereby effectively capturing the complex interplay between the platform, tower, nacelle, and rotor under combined wind and wave loads. Subsequently, based on the developed FWT model, a novel adaptive nonlinear pitch controller is formulated with the goal of striking a trade-off between regulating power generation and reducing platform motion. Notably, the proposed control strategy adopts a continuous control approach, strategically beneficial in circumventing the chattering phenomenon commonly associated with sliding mode control. Furthermore, the controller integrates an online approximator and a robust integral of the sign of the tracking error, facilitating real-time learning of system unknown dynamics while compensating for bounded disturbances. Finally, both the accuracy of the established nonlinear FWT model in predicting key dynamics and the superiority of the presented pitch controller are validated through comprehensive comparative studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Sliding mode control

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Multi-Object Robot Visual Servo Based on YOLOv3

Multi-Object Robot Visual Servo Based on YOLOv3

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Data Driven control and Learning systems (DDCLS)

作者： Yulin Yang Shan Liu Polytechnic Institute Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

Aiming at the low robustness of image feature extractor in Image-Based Visual Servo (IBVS), a robot visual servo method based on object detection neural network YOLOv3 is proposed. By improving the output layer of YOLOv3 and adding attitude angle of camera, the pixel coordinate and depth information of feature points, the robustness of the IBVS system based on point features is improved while it can cope with multi-type and multi-instance objects, and the problem of the image Jacoby matrix falling into singularity caused by excessive rotation angle error of the optical axis is avoided. The visual servo convergence is accelerated. Meanwhile, the network training data generation algorithm of the desired image is used to replace the traditional manual data annotation, which reduces the cost of data acquisition, and the data enhancement method ensures the generalization performance of the training model.

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A DRL-enabled Adaptive Sparsity Promoting Wide-area Damping controller for Power System with Time-delay and Packet Dropout

A DRL-enabled Adaptive Sparsity Promoting Wide-area Damping ...

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2023 Panda Forum on Power and Energy, PandaFPE 2023

作者： Cao, Di Zhang, Guozhou Hu, Jiaxiang Hu, Weihao Huang, Yuehui Chen, Zhe State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Chengdu China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Aalborg University Department of Energy Technology Aalborg Denmark

ISBN: (纸本)9798350321173

This paper proposes a deep reinforcement learning (DRL) based adaptive sparsity promoting wide-area damping controller for power system. Different from traditional fixed wide-area control structure, the proposed method employs DRL algorithm to identify the optimal sparsity promoting control structure and obtain corresponding parameter settings under different operating conditions considering the time-delay and packet drop. Simulation are carried out on 10-machine 39-bus system to demonstrate the effectiveness of the proposed approach. © 2023 IEEE.

关键词： Time delay

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I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDAR Odometry

I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDA...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Wenlu Yu Jie Xu Chengwei Zhao Lijun Zhao Thien-Minh Nguyen Shenghai Yuan Mingming Bai Lihua Xie State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Hangzhou Guochen Robot Technology Company Limited Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

LiDAR odometry is a pivotal technology in the fields of autonomous driving and autonomous mobile robotics. However, most of the current works focus on nonlinear optimization methods, and still existing many challenges in using the traditional Iterative Extended Kalman Filter (IEKF) framework to tackle the problem: IEKF only iterates over the observation equation, relying on a rough estimate of the initial state, which is insufficient to fully eliminate motion distortion in the input point cloud; the system process noise is difficult to be determined during state estimation of the complex motions; and the varying motion models across different sensor carriers. To address these issues, we propose the Dual-Iteration Extended Kalman Filter (I 2 EKF) and the LiDAR odometry based on I 2 EKF (I 2 EKF-LO). This approach not only iterates over the observation equation but also leverages state updates to iteratively mitigate motion distortion in LiDAR point clouds. Moreover, it dynamically adjusts process noise based on the confidence level of prior predictions during state estimation and establishes motion models for different sensor carriers to achieve accurate and efficient state estimation. Comprehensive experiments demonstrate that I 2 EKF-LO achieves outstanding levels of accuracy and computational efficiency in the realm of LiDAR odometry. Additionally, to foster community development, our code is open-sourced. 1

关键词： Point cloud compression Laser radar Accuracy Nonlinear distortion Noise Robot sensing systems Mathematical models Odometry Kalman filters state estimation

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UGNA-VPR: A Novel Training Paradigm for Visual Place Recognition Based on Uncertainty-Guided NeRF Augmentation

arXiv

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arXiv 2025年

作者： Shen, Yehui Zhang, Lei Li, Qingqiu Zhao, Xiongwei Wang, Yue Lu, Huimin Chen, Xieyuanli College of Intelligence Science and Technology National University of Defense Technology China Faculty of Robot Science and Engineering Northeastern University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China SIASUN Robot& Automation Co. Ltd China School of Computer Science Fudan University China

Visual place recognition (VPR) is crucial for robots to identify previously visited locations, playing an important role in autonomous navigation in both indoor and outdoor environments. However, most existing VPR datasets are limited to single-viewpoint scenarios, leading to reduced recognition accuracy, particularly in multi-directional driving or feature-sparse scenes. Moreover, obtaining additional data to mitigate these limitations is often expensive. This paper introduces a novel training paradigm to improve the performance of existing VPR networks by enhancing multi-view diversity within current datasets through uncertainty estimation and NeRF-based data augmentation. Specifically, we initially train NeRF using the existing VPR dataset. Then, our devised self-supervised uncertainty estimation network identifies places with high uncertainty. The poses of these uncertain places are input into NeRF to generate new synthetic observations for further training of VPR networks. Additionally, we propose an improved storage method for efficient organization of augmented and original training data. We conducted extensive experiments on three datasets and tested three different VPR backbone networks. The results demonstrate that our proposed training paradigm significantly improves VPR performance by fully utilizing existing data, outperforming other training approaches. We further validated the effectiveness of our approach on self-recorded indoor and outdoor datasets, consistently demonstrating superior results. Our dataset and code have been released at https://***/nubot-nudt/UGNA-VPR. Copyright © 2025, The Authors. All rights reserved.

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Robust adaptive H∞ control for networked uncertain semi-Markov jump nonlinear systems with input quantization

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Science China(Information Sciences) 2022年第8期65卷 285-286页

作者： Shanling DONG Guanrong CHEN Meiqin LIU Zheng-Guang WU College of Electrical Engineering Zhejiang University Department of Electrical Engineering City University of Hong Kong National Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University Institute for Advanced Study Chengdu University

Dear editor,Markov jump systems(MJSs), as one kind of switched systems, are a good candidate for modeling physical systems subject to abrupt variation in their structures or parameters caused by many reasons such as s...

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Indirect Adaptive Robust Trajectory Tracking control of Hard Rock TBM with Load Variation of Tunneling Face

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Chinese Journal of Mechanical Engineering 2019年第2期32卷 168-179页

作者： Chengjun Shao Jianfeng Liao Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University State Key Laboratory of Fluid Power Transmission and Control Zhejiang University

Posture adjustment of open-type hard rock tunnel boring machine(TBM) can be achieved by properly adjusting the hydraulic pressure of gripper cylinder and torque cylinders. However, the time-varying inhomogeneous load acting on tunneling face of TBM and complex stratum working condition can cause the trajectory deviation. In this paper,the position and posture rectification kinematics and dynamics models of TBM have been established in order to track the trajectory. Moreover, there are uncertain parameters and uncertain loads from complex working conditions in the dynamic model. An indirect adaptive robust control strategy is applied to achieve precise position and posture trajectory tracking control. Simulation results show when the position deviation only occurs in Y-axis and the current orientation is parallel with the designed axis, the deviation can be corrected by controlling the pressure of gripper cylinder and the actual trajectory meets the designed axis when TBM is pushed forward 0.14 m in X-axis. If the deviation only occurs in Z-axis, then the deviation can be corrected by controlling torque cylinders. If the position deviation occurs both in Y-axis and Z-axis at the same time, the pressure of gripper cylinder and torque cylinders should be controlled at the same time to rectify the deviation. Simulation results are shown to illustrate the e ectiveness and robustness of the proposed controller. This research proposes an indirect adaptive robust controller that can track the planned tracking trajectory smoothly and rapidly.

关键词： Open type hard rock TBM Inhomogeneous load Position and posture rectification Indirect adaptive robust control

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