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Research Square 2021年

作者： Yi, Ming Dai, Shiqi Lu, Lulu Wei, Zhouchao Zhu, Yuan School of Mathematics and Physics China University of Geosciences Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Hubei Wuhan430074 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Fudan University Shanghai200433 China

Temperature is an important environmental factor that all creatures depend on. Under the appropriate temperature, the neural system shows good biological performance. Based on an improved Hodgkin-Huxley (HH) neuron model considering temperature and noise, the ten-layers pure excitatory feedforward neural network and the ten-layers excitatory-inhibitory (EI) neural network are constructed to study the subthreshold signal propagation. It’s found that increasing temperature can restrain the signal propagation, and raise the noises intensity threshold where the failed signal propagation can transform into succeed signal propagation. Under the large noise, the signal propagation in network in different temperatures exhibits different anti-noise capabilities. There exists the saturation value of interlayer connection probability, that is, the signal propagation maintains constant when interlayer connection probability beyond a certain value. Moreover, in EI network with large noise, the network’s intrinsic oscillation activity will completely cover subthreshold signal, and block the signal propagation. The jumping phenomenon in the value of fidelity, which measures the similarity between input signal and output signal, appears in both pure excitatory network and EI network. This paper provides potential value for understanding the regulation of both temperature and noise in information propagation in neural network. © 2021, CC BY.

关键词： Backpropagation

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Inverse Covariance Intersection Fusion for Lie-Group-Based Pose Estimation

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IEEE Signal Processing Letters 2025年

作者： Zou, Tao Wang, Rusheng Chen, Bo Hu, Zhongyao Zhejiang University of Technology Department of Automation Hangzhou310023 China Zhejiang Key Laboratory of Intelligent Perception and Control for Complex Systems Hangzhou310023 China

This letter is concerned with the pose estimation problem on Lie groups. In general, robots naturally move on the special Euclidean Lie groups, which provides the motivation to model the measurement uncertainty on Lie algebras and project it onto Lie groups. Then, a Lie-group-based pose estimation method under inverse covariance intersection fusion is proposed, in which the obtained estimates are more accurate than in vector spaces. Since the unknown common information shared among the measurements is taken into account, the proposed Lie-group-based pose estimation method has the advantages of higher precision and better consistency. Finally, the effectiveness of the proposed method is verified through simulations. © 1994-2012 IEEE.

关键词： Lie groups

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Distributed Stochastic Optimization for Non-Smooth and Weakly Convex Problems under Heavy-Tailed Noise

arXiv

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arXiv 2025年

作者： Hu, Jun Sun, Chao Chen, Bo Wang, Jianzheng Wang, Zheming Department of Automation Zhejiang University of Technology Hangzhou310023 China Zhejiang Key Laboratory of Intelligent Perception and Control for Complex Systems Hangzhou310023 China

In existing distributed stochastic optimization studies, it is usually assumed that the gradient noise has a bounded variance. However, recent research shows that the heavy-tailed noise, which allows an unbounded variance, is closer to practical scenarios in many tasks. Under heavy-tailed noise, traditional optimization methods, such as stochastic gradient descent, may have poor performance and even diverge. Thus, it is of great importance to study distributed stochastic optimization algorithms applicable to the heavy-tailed noise scenario. However, most of the existing distributed algorithms under heavy-tailed noise are developed for convex and smooth problems, which limits their applications. This paper proposes a clipping-based distributed stochastic algorithm under heavy-tailed noise that is suitable for non-smooth and weakly convex problems. The convergence of the proposed algorithm is proven, and the conditions on the parameters are given. A numerical experiment is conducted to demonstrate the effectiveness of the proposed algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage

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Plant Phenomics 2023年第2期5卷 226-238页

作者： Yinglun Li Xiaohai Zhan Shouyang Liu Hao Lu Ruibo Jiang Wei Guo Scott Chapman Yufeng Ge Benoit de Solan Yanfeng Ding Frédéric Baret Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesJiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjingChina Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Graduate School of Agricultural and Life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan School of Agriculture and Food Sciences The University of QueenslandSt.LuciaQueensland 4072Australia Department of Biological Systems Engineering University of Nebraska-LincolnLincolnNebraska 68583United States INRAE Avignon UniversitéUMR EMMAHUMT CAPTE228route de l’aérodrome-CS 4050984914 Avignon Cedex 9France ARVALIS Institut du Végétal 3 Rue Joseph et Marie Hackin75116 ParisFrance

The number of leaves at a given time is important to characterize plant growth and *** this work,we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB *** digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages(150,000 images with over 2 million labels).The realism of the images was then improved using domain adaptation methods before training deep learning *** results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset,collecting measurements from 5 countries obtained under different environments,growth stages,and lighting conditions with different cameras(450 images with over 2,162 labels).Among the 6 combinations of deep learning models and domain adaptation techniques,the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance(R^(2)=0.94,root mean square error=8.7).Complementary studies show that it is essential to simulate images with sufficient realism(background,leaf texture,and lighting conditions)before applying domain adaptation ***,the spatial resolution should be better than 0.6 mm per pixel to identify leaf *** method is claimed to be self-supervised since no manual labeling is required for model *** self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping *** trained networks are available at https://***/YinglunLi/Wheat-leaf-tip-detection.

关键词： Wheat seedling Plant

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Research on the Stability of Aircraft With Nose Wheel Autonomous Taxiing Configuration

Research on the Stability of Aircraft With Nose Wheel Autono...

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2022 CSAA/IET International Conference on Aircraft Utility systems, AUS 2022

作者： Huang, Haixiang Zhang, Hao Liu, Xiaochao Shang, Yaoxing Beihang University School of Automation Science and Electrical Engineering Beijing China Ningbo Institute of Technology Beihang University Ningbo China Science and Technology on Aircraft Control Laboratory Beihang University Beijing China Research Institute for Frontier Science Beihang University Beijing China Key Laboratory of Advanced Aircraft Utility Systems Beihang University Ministry of Industry and Information Technology China Beijing Tianma Intelligent Control Technology Co. Ltd. Beijing China General Research Institute of Coal Science Beijing China

ISBN: (数字)9781839537769

ISBN: (纸本)9781839537769

The traditional main engine-driven aircraft ground taxiing method has the problems of low efficiency, heavy pollution and high fuel consumption. However, today's aircraft is facing the urgent need for efficient ground maneuvering, it is urgent to further optimize the ground taxiing method of the aircraft. In response to this demand, a taxiing strategy of autonomously dragging the nose wheels of the aircraft is proposed. However, the ground dynamics research on the nose-wheel drive taxiing configuration aircraft is still insufficient. Therefore, in this paper, the ground dynamics modeling and simplification of the nose wheel autonomous drag configuration aircraft are carried out;the stability boundary of the system under different nose wheel rotation angles and rotation angle change rates during ground taxiing is analyzed and calculated;The traditional configuration aircraft with thrust to achieve the function of ground taxiing is compared and analyzed. It provides a theoretical basis for the efficient and autonomous taxiing stability control of aircraft on the ground. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Wheels

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The CASTLab in CASIA [ITS Research Lab]

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IEEE intelligent TRANSPORTATION systems MAGAZINE 2020年第2期12卷 85-94页

作者： Olaverri-Monreal, Cristina Yisheng Lv is currently an associate professor with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences. His research interests include artificial intelligence intelligent control intelligent transportation systems and parallel traffic management and control systems. He is currently an ITS Society Board of Governors member. He is an associate editor of IEEE Transactions on Intelligent Transportation Systems and is on the editorial board of Acta Automatica Sinica. He received the 2015 IEEE ITS Outstanding Application Award. Contact him at yisheng.lv@***.

The complex Adaptive systems for Transportation laboratory (CASTLab) was established by Prof. Fei-Yue Wang in July 1999 for the task of designing and implementing the proposed intelligent traffic system for the city of Xinxiang, Henan, one of the first initiatives in intelligent transportation systems (ITS) in China. At the end of 1999, the CASTLab became a part of the newly created Center for intelligent control and systems in the Institute of automation, Chinese Academy of Sciences (CASIA), one of the premier and oldest national research organizations in information, automation, and artificial intelligence in China and worldwide.

关键词： Research laboratories

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Enhancing the sensitivity of amplification of rotation velocity in Sagnac’s interferometer with biased weak measurement

arXiv

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arXiv 2021年

作者： Huang, Jing-Hui Hu, Xiang-Yun Duan, Xue-Ying Wang, Guang-Jun Institute of Geophysics and Geomatics China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Recently, biased weak measurement(BWM) has shown higher precision than both conventional measurement and standard weak measurement(SWM) in optical metrology. In this paper, we propose a scheme of detecting rotation velocity in Sagnac’s interferometer with BWM. In particular, BWM employs an additional reduction of photons in the post-selection by introducing a pre-coupling, and the remaining photons have been shown to be extremely sensitive to the estimated parameter. In addition, our numerical results show that the scheme with BWM can obtain a higher sensitivity than the scheme with SWM. © 2021, CC BY.

关键词： Interferometers

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Artificial intelligence in gastric cancer:applications and challenges

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Gastroenterology Report 2022年第1期10卷 227-242页

作者： Runnan Cao Lei Tang Mengjie Fang Lianzhen Zhong Siwen Wang Lixin Gong Jiazheng Li Di Dong Jie Tian School of Artificial Intelligence University of Chinese Academy of SciencesBeijingP.R.China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijingP.R.China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Radiology DepartmentPeking University Cancer Hospital&InstituteBeijingP.R.China College of Medicine and Biological Information Engineering School Northeastern UniversityShenyangLiaoningP.R.China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijingP.R.China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxiP.R.China

Gastric cancer(GC)is one of the most common malignant tumors with high *** diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical ***,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer ***,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data *** are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including ***,we aim to provide a broad framework to summarize current research on AI in *** the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological *** the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype *** treatment decisions,AI can help with surgical margin determination and prognosis ***,current approaches are challenged by data scarcity and poor *** tackle these problems,more regulated data,unified processing procedures,and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

关键词： gastric cancer artificial intelligence radiomics endoscopy computed tomography pathology

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Forward Modeling of Shallow Transient Electromagnetic Method under Ramp Turn-off Current

Forward Modeling of Shallow Transient Electromagnetic Method...

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第三十九届中国控制会议

作者： Guangjun Wang Menglan Song School of Automation China University of Geosciences China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System

In order to reduce the shallow detection blind zone and improve the shallow detection accuracy, this paper presents a shallow transient electromagnetic direct forward modeling method based on ramp turn-off current. The main idea is to obtain the spectrum of the ramp turn-off current signal by Fourier transform at first, then using Maxwell’s equation to solve the propagation process of the electromagnetic signal in the medium, thus obtaining the early signal of transient electromagnetic *** theory, this method is more suitable for the detection of shallow subsurface media than the ideal step signal. Simulation experiments on the ramp step signals with different turn-off times show that the complete ideal coincidence with the traditional ideal step signal when the turn-off time approaches zero, indicating the correctness of the algorithm. At the same time, compared with the current element superposition forward method, the forward curve obtained by this method is closer to the actual situation,and the calculation speed is much faster than the current element superposition method. The research results in this paper are of great value to reduce the detection blind zone, improve the detection accuracy and real-time performance of the transient electromagnetic detecting.

关键词： Ramp turn-off current shallow transient electromagnetic method forward modeling

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Improved Magnetic Coupling Resonance Wireless Power Transfer System

Improved Magnetic Coupling Resonance Wireless Power Transfer...

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第三十九届中国控制会议

作者： Guangjun Wang Jianhui Sun School of Automation China University of Geosciences China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System

For the power supply of spaceborne cameras on two-body satellites, an improved magnetic coupling resonant wireless power transfer system(WPT) is proposed. By establishing the equivalent circuit model of WPT system, the influence of topology model and high-Q repeating coil on system performance is analyzed. Finally, the correctness of theory and analysis was verified by experiments. The experimental results show that when the transmission distance of the improved WPT system is 11 cm, the load can receive 18.66 W of power and the transmission efficiency is 69%. Compared with the traditional two-coil WPT system,the system has a significant improvement in transmission distance, transmission power, and transmission efficiency, and can meet the technical requirements of spaceborne cameras in transmission distance, transmission power and transmission efficiency.

关键词： Magnetic coupling resonance WPT Transmission distance Transmission power Transmission efficiency

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