检索结果-内蒙古大学图书馆

SpaGRA:Graph augmentation facilitates domain identification for spatially resolved transcriptomics

Journal of Genetics and Genomics 2025年第1期52卷 93-104页

作者： Xue Sun Wei Zhang Wenrui Li Na Yu Daoliang Zhang Qi Zou Qiongye Dong Xianglin Zhang Zhiping Liu Zhiyuan Yuan Rui Gao Center of Intelligent Medicine School of Control Science and EngineeringShandong UniversityJinanShandong 250061China MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST Department of AutomationTsinghua UniversityBeijing 100084China Institute of Precision Medicine Peking University Shenzhen HospitalShenzhenGuangdong 518036China Department of Clinical Laboratory The Second HospitalCheeloo College of MedicineShandong UniversityJinanShandong 250033China Institute of Science and Technology for Brain-Inspired Intelligence Center for Medical Research and InnovationShanghai Pudong HospitalFudan University Pudong Medical CenterFudan UniversityShanghai 200433China

Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various ***-based geometric deep learning has gained widespread adoption for spatial domain identification ***,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain *** tackle this challenge,we propose a novel method,SpaGRA(https://***/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph *** uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive ***,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random *** results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different *** SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD ***,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.

关键词： Spatial domain identification Spatially resolved transcriptomics Multi-head graph attention networks Graph augmentation Geometric contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Effect of Air Content on the Internal Fluid Characteristics of Liquid Floated Gyro

引用

Journal of Thermal Science and engineering Applications 2025年第4期17卷 041005页

作者： Zhang, Yaping Wang, Yanzhong Niu, Wentao Yang, Kai Rao, Sixian Qiao, Baiyu School of Mechanical Engineering Anhui University of Technology Ma’anshan 243032 China School of Mechanical Engineering and Automation Beihang University Beijing 100191 China Beijing Institute of Aerospace Control Devices Beijing 100853 China Beijing Spacecraft Beijing 100094 China

The liquid floated gyro (LFG) is an inertial instrument widely used in aviation, aerospace, navigation, and other high-tech fields, with the advantages of high accuracy and good in-orbit reliability. The filling of internal floating oil is a key process in the manufacture of LFG, however, due to the limitation of technology level, environmental conditions, and other factors, the floating oil may be mixed with a certain amount of air, which affects the rheological characteristics of the oil, thus leading to the accuracy drift of the gyro instrument. Therefore, this paper analyzes the effect of air content on the fluid characteristics of the gyro oil by using the computational fluid dynamics method. The results show that the air content has a significant effect on the oil dynamics: when the air content increases from 0% to 3%, the average flow velocity of the oil decreases by 4.15%, and the viscous interference torque increases by 0.02%, which reduces the stability of the instrument accuracy to a certain extent, so the generation of air bubbles should be avoided or necessary measures should be taken to remove air bubbles during the oil filling process.

关键词： thermodynamic characteristics aerospace heat transfer and droplets Torque bubbles Drag (Fluid dynamics) Temperature Computational fluid dynamics air content Viscosity Instrumentation Flow (Dynamics) inertial navigation Heat transfer liquid floated gyro Stability Navigation Rheology Fluids particles thermophysical properties Bubbles Aerospace industry

来源：评论

学校读者我要写书评

暂无评论

Development of a Learning-Based control System for Robot-Assisted Grinding of Rubber Roller

Development of a Learning-Based Control System for Robot-Ass...

引用

International Conference on Autonomous Robots and Agents, ICARA

作者： Fabian Reiling Maximilian Bause Stefan Gröger Christian Henke Steven Koppert Ansgar Trächtler Scientific Automation Fraunhofer IEM Paderborn Germany Intelligent Automotive Industry Capgemini Invent Hamburg Germany Control Engineering and Mechatronics Heinz Nixdorf Institute Paderborn Germany

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Grinding rubber rollers to a specific geometry presents significant challenges due to the material's non-linear behavior. Currently, this process has not been successfully automated, primarily because the control concepts implemented thus far do not achieve the desired accuracy for economical operation. Consequently, these rollers are manually ground to the required geometry on a grinding wheel. Previous work has led to the development of a system that automates the grinding of rollers using a robot. The objective of this paper is to demonstrate, through modeling, how a learning-based control system can be developed for this process to reduce production time and facilitate an economical operation. To this end, we propose two control concepts: one that directly predicts the required control variable using a learning-based controller, and another that employs an AI-corrected control mechanism, which utilizes the control variable provided by a proportional controller to correct the error between the control variable and the control difference. Finally, we conduct a comparative simulation of the performance of these concepts against a conventional controller.

关键词： Geometry Hands Manufacturing processes Process control Wheels Production control systems Parametric statistics Rubber Mobile robots

来源：评论

学校读者我要写书评

暂无评论

Coordinated control of Deformation and Flight for Morphing Aircraft via Meta-Learning and Coupled State-Dependent Riccati Equations

arXiv

引用

arXiv 2025年

作者： Che, Hao-Chi Wu, Huai-Ning School of Automation Science and Electrical Engineering Beihang University Beijing China The Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Hangzhou International Innovation Institute Beihang University Hangzhou311115 China

In this paper, the coordinated control problem of deformation and flight for morphing aircraft (MA) is studied by using meta-learning (ML) and coupled state-dependent Riccati equations (CSDREs). Our method is built on two principal observations that dynamic models of MA under varying morphing conditions share a morphing condition independent representation function and that the specific morphing condition part lies in a set of linear coefficients. To that end, the domain adversarially invariant meta-learning (DAIML) is employed to learn the shared representation with offline flight data. Based on the learned representation function, the coordinated control of the deformation and flight for MA is formulated as a non-cooperative differential game. The state-dependent feedback control solutions can be derived by addressing a pair of CSDREs. For this purpose, Lyapunov iterations are extended to obtain the positive semidefinite (definite) stabilizing solutions of the CSDREs, and the convergence proof of the proposed algorithm is provided. Finally, a simulation study is carried out to validate the efficacy of the developed coordinated game control strategies. Copyright © 2025, The Authors. All rights reserved.

关键词： Riccati equations

来源：评论

学校读者我要写书评

暂无评论

Discrete control algorithms for systems with big dead time for control101 MATLAB toolbox

Discrete control algorithms for systems with big dead time f...

引用

Cybernetics & Informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

来源：评论

学校读者我要写书评

暂无评论

Aggregation Modeling of System Frequency Response Under Inertia control of Grid-following Direct-drive Wind Turbine

引用

Dianli Xitong Zidonghua/automation of Electric Power Systems 2025年第10期49卷 39-48页

作者： Jiang, Chao Yang, Dongfeng Guo, Shujia Liu, Xiaojun Zhou, Shuyu Cao, Zhichong Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China School of Automation and Electrical Engineering Zhejiang University of Science and Technology Hangzhou310023 China State Grid Jilin Electric Power Research Institute Changchun130000 China

The inertia control of grid-following direct-drive wind turbines significantly influences the system frequency response pattern afrer disturbances. However, current research has not clearly identified the dominant response link of direct-drive wind turbines in frequency regulation, while the modeling is too complicated to be suitable for frequency characteristic analysis. Therefore, the linearized models under inertia control are derived for two typical types of direct-drive wind turbines according to the aerodynamic equations, generator models, power control link, and control circuit structure. The fast physical process of current control-boost circuit/stator voltage dynamic loop is clearly understood and reasonably ignored. It is revealed that the two types of direct-drive wind turbines have a common frequency response mechanism which can be described as the cascade effect of the speed regulator-wind rotor motion equation. Furthermore, combined with the frequency response model of synchronous unit, the aggregation model of system frequency response under the inertia control of grid-following direct-drive wind turbines is constructed, and the aggregation model is reduced to a second-order model by the balance truncation method for analytical calculation of frequency extremes. The accuracy and applicability of the aggregation model and its reduced-order model are verified by MATLAB/Simulink simulation, and the influence of control parameters and system states on frequency dynamic characteristics and stability margin is analyzed. © 2025 automation of Electric Power Systems Press. All rights reserved.

关键词： Electric power system control

来源：评论

学校读者我要写书评

暂无评论

Development and Optimization of a WebGL-Based Mechanical Model Simulation Platform 4th

Development and Optimization of a WebGL-Based Mechanical Mod...

引用

4th International Conference on Mechanical Design and Simulation, MDS 2024

作者： Wang, Xintong Tian, Hao Fang, Jingzhe Zhang, Hualiang Zhang, Tao Software College Shenyang University of Technology Liaoning Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Electrical Engineering Shenyang University of Technology Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9789819778867

The need for mechanical model simulation platforms is growing as a result of the ongoing advancements in digital technology and the mechanical manufacturing sector. The application scope and efficacy of simulation platforms are severely limited by the issue of sluggish rendering performance, which frequently arises when obtaining high-quality rendering. When drawing high demand mechanical models, a lot of processing power is frequently needed, which makes real-time rendering speed unsatisfactory for practical purposes. In order to achieve real-time rendering and simulation of 3D robots, this experiment built and implemented a WebGL-based 3D simulation platform. Additionally, the simulation platform can be optimized by increasing frame rate and rendering speed without sacrificing rendering effect by building a mixed bounding box. © The Author(s) 2025.

关键词： Digital elevation model

来源：评论

学校读者我要写书评

暂无评论

Contrastive Conditional Adversarial Autoencoder with Class-specific Forces for Imbalanced Open-set Fault Detection

引用

IEEE Transactions on automation Science and engineering 2025年 22卷 15757-15767页

作者： Zuo, Zhonglin Meng, Tao Li, Zheng Zhang, Hao Liu, Tong Chen, Zhansheng Pang, Zhibo Zhejiang University College of Control Science and Engineering Hangzhou310027 China Zhejiang University School of Aeronautics and Astronautics Hangzhou310027 China Hainan Institute of Zhejiang University Sanya572025 China Chongqing University School of Automation Chongqing400044 China University of Sheffield Department of Computer Science SheffieldS1 4DP United Kingdom Shanghai Aerospace Space Technology Co. Ltd Shanghai201109 China ABB Corporate Research Sweden Department of Automation Technology Vasteras Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm Sweden

In real-world industrial scenarios, fault detection faces the widely recognized challenge of data imbalance, which not only refers to the scarcity of fault data but also includes the imbalance in healthy data. This article is concerned with imbalanced open-set fault detection (IOSFD), a practical yet challenging scenario in industrial applications where multiple healthy operating conditions and multiple fault types are imbalanced. In this article, we propose a new contrastive conditional adversarial autoencoder for IOSFD. It constructs an end-to-end unified model based on multi-class known healthy and faulty data to address the reliance of traditional methods on fault samples, while optimizing with class-specific weighted forces to ensure equal attention to imbalanced known classes. Input and feature reconstruction conditioned on operating modes are utilized to learn a compact decision plane and achieve both unknown fault detection and known data classification. Significantly, we formulate the optimization objective of conditional reconstruction based on contrastive learning and introduce adversarial training to further enhance the model’s performance. The effectiveness of the proposed method is validated through real-world pipeline leak detection and Tennessee-Eastman multi-fault detection. © 2004-2012 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Object Detection and Multiple Objective Optimization Manipulation Planning for Underwater Autonomous Capture in Oceanic Natural Aquatic Farm

引用

Journal of Field Robotics 2025年第0期

作者： Hai, Huang Tao, Jiang Xinyu, Bian Hao, Zhou Xu, Yang Gang, Wang Hongde, Qin Xinyue, Han National Key Laboratory of Science and Technology on Underwater Vehicle Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Bei Jing China

Underwater autonomous capture operations offer significant potential for reducing labor and health risks in sea organism industries. This study presents a comprehensive solution for cross-domain underwater object detection and autonomous capture. A novel unsupervised domain adaptive learning method is proposed, integrating multiscale domain adaptive modules and attention mechanisms into a Faster Region-Convolutional Neural Network framework. This approach enhances feature alignment across diverse aquatic domains without parameter tuning. Additionally, an efficient, parameterless constrained multiobjective optimization algorithm is introduced for underwater autonomous mobile capture, integrating parameterized trajectory planning with innovative features, such as adaptive mutation strategies and constraint violation tolerance. The proposed approaches are extensively validated through simulations, tank experiments, and real-world oceanic trials in the Natural Aquatic Farm of Zhangzidao Island. Results demonstrate the system's robustness in complex underwater environments with varying currents, with experimental outcomes validating the accuracy and reliability of detection and capture capabilities. This research significantly advances autonomous underwater systems' capabilities in object detection and capture tasks, addressing complex challenges in realistic organism capture applications across diverse aquatic environments. © 2025 Wiley Periodicals LLC.

关键词： Health risks

来源：评论

学校读者我要写书评

暂无评论

Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive control 26

Trajectory Tracking for Directional Drilling in Underground ...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Wu, Jiajun Lu, Chengda Li, Hao Wei, Hongchao Wu, Min School of Future Technology 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 Wuhan430074 China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an710077 China China Coal Research Institute Beijing100013 China

ISBN: (纸本)9798331521950

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the H∞ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment. © 2025 IEEE.

关键词： Predictive control systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：