检索结果-内蒙古大学图书馆

Adaptive model-based dynamic event-triggered output feedback control of a robotic manipulator with disturbance

ISA TRANSACTIONS 2022年 122卷 63-78页

作者： Gao, Jie Kang, Erlong He, Wei Qiao, Hong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Key Lab Res & Applicat Robot Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn 320 Yue Yang Rd Shanghai 200031 Peoples R China

This paper focuses on the stable tracking control of the manipulator with constrained communication, unmeasurable velocity, and nonlinear uncertainties. An NN observer-depended output feedback scheme in the discrete-time domain is developed by virtue of the model-based dynamic eventtriggered backstepping technique in the channel of sensor to controller. For generalizing the zeroorder-holder (ZOH) implementation, a plant model is built to approximate the triggered states in the time flow, and according to which, the control law is fabricated. Based on model-based error events, we construct a dead-zone triggered condition with a dynamically adjustable threshold, making the threshold evolve with the system performance, to achieve flexible communication scheduling and avoid the accumulation of triggers in small tracking errors. The internal and external nonlinear uncertainties are online compensated by the neural network, and the aperiodic adaptive law is derived in the sense of control stability to save the computation. Finally, the conditions for semi-global ultimate uniform bounded (SGUUB) of all variables are given via impulse Lyapunov analysis, and a positive lower bound in the time interval between consecutive executions to guarantee the Zeno free behavior is obtained. Simulations are conducted on a three-link manipulator to illustrate the effectiveness of our method. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Adaptive neural network control Event-triggered mechanism Robotic manipulator Model-based control state observer

来源：评论

学校读者我要写书评

暂无评论

An Approximate Neuro-Optimal Solution of Discounted Guaranteed Cost control Design

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第1期52卷 77-86页

作者： Wang, Ding Qiao, Junfei Cheng, Long Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Beijing Artificial Intelligence Inst Beijing 100124 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

The adaptive optimal feedback stabilization is investigated in this article for discounted guaranteed cost control of uncertain nonlinear dynamical systems. Via theoretical analysis, the guaranteed cost control problem involving a discounted utility is transformed to the design of a discounted optimal control policy for the nominal plant. The size of the neighborhood with respect to uniformly ultimately bounded stability is discussed. Then, for deriving the approximate optimal solution of the modified Hamilton-Jacobi-Bellman equation, an improved self-learning algorithm under the framework of adaptive critic designs is established. It facilitates the neuro-optimal control implementation without an additional requirement of the initial admissible condition. The simulation verification toward several dynamics is provided, involving the F16 aircraft plant, in order to illustrate the effectiveness of the discounted guaranteed cost control method.

关键词： control design Cost function Optimal control Nonlinear systems Adaptive systems Switches Adaptive learning system discount factor guaranteed cost function neuro-optimal control uncertainty

来源：评论

学校读者我要写书评

暂无评论

DriveRP: RAG and Prompt Engineering Embodied Parallel Driving in Cyber-Physical-Social Spaces

DriveRP: RAG and Prompt Engineering Embodied Parallel Drivin...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Jun Huang Hao Ma Tengchao Zhang Fei Lin Siji Ma Xiao Wang Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China School of Artificial Intelligence Anhui University Hefei China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

In recent years, numerous technological advancements in Artificial Generative Intelligences (AGIs) have demonstrated significant potential to transform the intelligence acquisition mechanisms in connected autonomous vehicles (CAVs). Integrating technologies like ChatGPT into CAVs can enhance human-machine interactions. However, the emergence of such new traffic entities may introduce unforeseen hallucinations and complex risks that surpass our current understanding. To address these challenges, Retrieval-Augmented Generation (RAG) and prompt engineering technologies are being explored to enhance the reliability and safety of autonomous driving systems. RAG retrieves relevant contextual information, such as driving experiences and real-time road network status, from external databases to ensure that foundation models have access to accurate and timely data for informed decision-making. Prompt engineering optimizes the performance of large language models in autonomous driving systems by designing and refining prompts that guide the models’ responses, thereby improving their relevance and accuracy in various driving scenarios. Together, these technologies enhance the robustness and trustworthiness of autonomous driving systems. This paper proposes DriveRP, a framework that integrates RAG and prompt engineering within the Descriptive-Predictive-Prescriptive Intelligence framework of Parallel Driving theory. DriveRP aims to enhance the safety and interpretability of autonomous vehicle trajectory planning, decision-making, and motion control, ultimately achieving the "6S" goals. Grounded in Digital Twins and Metaverse-embodied parallel driving theory, DriveRP provides the infrastructure and foundational intelligence for parallel driving with Multi-modal Large Lange Models(MLLMs). Additionally, the paper discusses future trends and potential research directions, focusing on the "6S" goals of parallel driving: Smart, Safe, Secure, Sensitive, Sustainable, and Serviceable.

关键词： Accuracy Trajectory planning Decision making Transportation Transforms Safety Digital twins Prompt engineering Motion control Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Abnormal area identification of corn ear based on semi-supervised learning

引用

IET IMAGE PROCESSING 2022年第9期16卷 2351-2360页

作者： Wei, Jian Ma, Qin Wang, Weitao Guo, Hao Liu, Zhe Zhang, Jiajing China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr & Rural Affairs Key Lab Agr Machinery Monitoring & Big Data Appli Beijing Peoples R China China Agr Univ Coll Land Sci & Technol Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

Screening corn ear is the key link in the breeding process of new varieties. But manual testing is difficult to measure the proportion of abnormal area. Meanwhile, the abnormal areas are mainly caused by mildew, moth and mechanical collision. A new refined semantic segmentation model was proposed based on the semi-supervised learning method of generating antagonistic networks (GAN). Besides k-means algorithm was used to remove a large amount of background information for data preprocessing. By using feature fusion and weighted loss function the model performance was improved. The introduction of transfer learning accelerated model convergence. Through the high-throughput corn ear collection system, 1448 ear images (including abnormal conditions such as mildew, moth and mechanical damage) were collected and labelled. The proposed method was tested on real corn ear images with an accuracy of 0.950, mean precision of 0.933, mean IoU of 0.884, and FwIoU of 0.908. Experimental results show that the proposed method has better performance than general networks.

关键词： transfer learning accelerated model convergence weighted loss refined semantic segmentation model antagonistic networks convolutional neural nets geophysical image processing 1448 ear images breeding process learning (artificial intelligence) manual testing mechanical collision corn ear collection system feature extraction abnormal area identification deep learning (artificial intelligence) ear image segmentation feature fusion key link corn ear images mechanical damage abnormal conditions semisupervised learning method image classification moth

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Structure Determination in Single-Particle Cryo-Electron Microscopy: A Systematic Review

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2022年第2期33卷 452-472页

作者： Wu, Jia-Geng Yan, Yang Zhang, Dong-Xu Liu, Bo-Wen Zheng, Qing-Bing Xie, Xiao-Liang Liu, Shi-Qi Ge, Sheng-Xiang Hou, Zeng-Guang Xia, Ning-Shao Xiamen Univ Sch Publ Hlth State Key Lab Mol Vaccinol & Mol Diagnost Xiamen 361102 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China Xiamen Univ State Key Lab Mol Vaccinol & Mol Diagnost Natl Inst Diagnost & Vaccine Dev Infect Dis Sch Publ Hlth Xiamen 361102 Peoples R China Chinese Acad Med Sci Res Unit Frontier Technol Struct Vaccinol Xiamen 361102 Peoples R China

Recently, single-particle cryo-electron microscopy (cryo-EM) has become an indispensable method for determining macromolecular structures at high resolution to deeply explore the relevant molecular mechanism. Its recent breakthrough is mainly because of the rapid advances in hardware and image processing algorithms, especially machine learning. As an essential support of single-particle cryo-EM, machine learning has powered many aspects of structure determination and greatly promoted its development. In this article, we provide a systematic review of the applications of machine learning in this field. Our review begins with a brief introduction of single-particle cryo-EM, followed by the specific tasks and challenges of its image processing. Then, focusing on the workflow of structure determination, we describe relevant machine learning algorithms and applications at different steps, including particle picking, 2-D clustering, 3-D reconstruction, and other steps. As different tasks exhibit distinct characteristics, we introduce the evaluation metrics for each task and summarize their dynamics of technology development. Finally, we discuss the open issues and potential trends in this promising field.

关键词： Machine learning Periodic structures Task analysis Proteins Photomicrography Coronaviruses Machine learning algorithms Clustering deep learning (DL) image processing machine learning neural network (NN) single-particle cryo-electron microscopy (cryo-EM)

来源：评论

学校读者我要写书评

暂无评论

VistaScenario: Interaction Scenario Engineering for Vehicles with Intelligent systems for Transport Automation

arXiv

引用

arXiv 2024年

作者： Chang, Cheng Zhang, Jiawei Ge, Jingwei Zhang, Zuo Wei, Junqing Li, Li Wang, Fei-Yue DiDi Autonomous Driving Company Beijing100094 China The Department of Automation BNRist Tsinghua University Beijing100084 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Intelligent vehicles and autonomous driving systems rely on scenario engineering for intelligence and index (I&I), calibration and certification (C&C), and verification and validation (V&V). To extract and index scenarios, various vehicle interactions are worthy of much attention, and deserve refined descriptions and labels. However, existing methods cannot cope well with the problem of scenario classification and labeling with vehicle interactions as the core. In this paper, we propose VistaScenario framework to conduct interaction scenario engineering for vehicles with intelligent systems for transport automation. Based on the summarized basic types of vehicle interactions, we slice scenario data stream into a series of segments via spatiotemporal scenario evolution tree. We also propose the scenario metric Graph-DTW based on Graph Computation Tree and Dynamic Time Warping to conduct refined scenario comparison and labeling. The extreme interaction scenarios and corner cases can be efficiently filtered and extracted. Moreover, with naturalistic scenario datasets, testing examples on trajectory prediction model demonstrate the effectiveness and advantages of our framework. VistaScenario can provide solid support for the usage and indexing of scenario data, further promote the development of intelligent vehicles and transport automation. Copyright © 2024, The Authors. All rights reserved.

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Nonfungible Tokens: Constructing Value systems in Parallel Societies

引用

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2021年第5期8卷 1062-1067页

作者： Wang, Fei-Yue Qin, Rui Yuan, Yong Hu, Bin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Renmin Univ China Sch Math Beijing 100872 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Gansu Peoples R China

Welcome to the fifth issue of Ieee Transactions on Computational Social systems (TCSS) of 2021. As usual, we would like to share some great news first. Since April 2021, IEEE TCSS has been added to the Science Citation Index Expanded (SCIE) database in Clarivate Web of Science. We are excited to report that all TCSS articles published since 2018 have been backtracked and indexed by SCIE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

FIT: Frequency-based Image Translation for Domain Adaptive Object Detection

arXiv

引用

arXiv 2023年

作者： Zhang, Siqi Zhang, Lu Liu, Zhiyong Feng, Hangtao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China

Domain adaptive object detection (DAOD) aims to adapt the detector from a labelled source domain to an unlabelled target domain. In recent years, DAOD has attracted massive attention since it can alleviate performance degradation due to the large shift of data distributions in the wild. To align distributions between domains, adversarial learning is widely used in existing DAOD methods. However, the decision boundary for the adversarial domain discriminator may be inaccurate, causing the model biased towards the source domain. To alleviate this bias, we propose a novel Frequency-based Image Translation (FIT) framework for DAOD. First, by keeping domain-invariant frequency components and swapping domain-specific ones, we conduct image translation to reduce domain shift at the input level. Second, hierarchical adversarial feature learning is utilized to further mitigate the domain gap at the feature level. Finally, we design a joint loss to train the entire network in an end-to-end manner without extra training to obtain translated images. Extensive experiments on three challenging DAOD benchmarks demonstrate the effectiveness of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Planning-inspired Hierarchical Trajectory Prediction for Autonomous Driving

arXiv

引用

arXiv 2023年

作者： Li, Ding Zhang, Qichao Xia, Zhongpu Zhang, Kuan Yi, Menglong Jin, Wenda Zhao, Dongbin School of Artificial Intelligence University of Chinese Academy of Sciences China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Baidu Inc. China

Recently, anchor-based trajectory prediction methods have shown promising performance, which directly selects a final set of anchors as future intents in the spatio-temporal coupled space. However, such methods typically neglect a deeper semantic interpretation of path intents and suffer from inferior performance under the imperfect High-Definition (HD) map. To address this challenge, we propose a novel Planning-inspired Hierarchical (PiH) trajectory prediction framework that selects path and speed intents through a hierarchical lateral and longitudinal decomposition. Especially, a hybrid lateral predictor is presented to select a set of fixed-distance lateral paths from map-based road-following and cluster-based free-move path candidates. Then, the subsequent longitudinal predictor selects plausible goals sampled from a set of lateral paths as speed intents. Finally, a trajectory decoder is given to generate future trajectories conditioned on a categorical distribution over lateral-longitudinal intents. Experiments demonstrate that PiH achieves competitive and more balanced results against state-of-the-art methods on the Argoverse motion forecasting benchmark and has the strongest robustness under the imperfect HD map. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

Deep Domain Adaptation Regression for Force Calibration of O...

引用

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

作者： Zhuo Chen Ni Ou Jiaqi Jiang Shan Luo Department of Engineering Robot Perception Lab Centre for Robotics Research King’s College London London United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.

关键词： Accuracy Force Tactile sensors Lighting Elastomers Data collection Optical imaging Adaptive optics Calibration Optical sensors

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：