检索结果-内蒙古大学图书馆

IEEE International Conference on robotics and Biomimetics

作者： Yuhui Cen Jingjing Luo Hongbo Wang Li Chen Xing Zhu Jingchun Luo Human Phenome Institute Fudan University Shanghai China The Institute of AI and Robotics Academy for Engineering and Technology Fudan University Ministry of Education Engineering Research Center of AI & Robotics Shanghai P.R.China

Blood pressure (BP) estimation through cuffless approaches is gaining prominence in precision healthcare, and the emergence of robotic medical services adds impetus to this trend with enhanced diagnostic capabilities. In our endeavor to enable medical service robots for cuffless BP self-estimation, we propose OCViTnet, a deep-learning network model grounded in pulse wave analysis (PWA). The model introduces a novel waveform processing architecture: an omni-scale convolution subnet captures single-period waveform characteristics, the Vision Transformer subnet extracts multi-period waveform features, and a regressor fuses phenotypic information with pulse wave features and performs BP regression. Experimental results perform exceptionally well in non-contact BP estimation from forehead imaging photoplethysmography. Additionally, this paper pioneers the utilization of the radial artery pulse wave for BP estimation. Leveraging the British Hypertension Society standard, our approach yields notable results, assessing the pulse diagnosis robot’s BP estimation performance at grade C/B (systolic/diastolic BP). This effort advances the potential of cuffless BP estimation in pulse diagnosis robotics and underscores the practical viability of our proposed intelligent methodology based on PWA.

关键词：

来源：评论

学校读者我要写书评

暂无评论

POMDP-Based Real-Time Path Planning for Manipulation of Multiple Microparticles via Optoelectronic Tweezers

引用

Cyborg and Bionic Systems 2022年第1期2022卷 303-313页

作者： Jiaxin Liu Huaping Wang Menghua Liu Ran Zhao Yanfeng Zhao Tao Sun Qing Shi Intelligent Robotics Institute School of Mechatronical Engineering Bejing Institute of TechnologyBeijing 10081China Key Laboratory of Biomimetic Robots and Systems(Beijing Institute of Technology) Ministry of EducationBeijing 100081China School of Medical Technology Beijing Institute of TechnologyBeijing 100081China Bejing Advanced Innovation Center for Itelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China

With high throughput and high flexibility,optoelectronic tweerers(OETs)hold huge potential for massively parallel ***,the trajectory of the virtual electrode has been planned in advance in most synchronous manipulations for multiple targets based on an optically induced dielectrophoresis(ODEP)mechanism,which is insuficient to ensure the stability and eficiency in an environment with potential collision *** this paper,a synchronously discretized manipulation method based on a centralized and decoupled path planner is proposed for transporting microparticles of diferent types with an OET *** approach based on the K uhn-Munkres algorithm is utilized to achieve the goal assignment between target microparticles and goal *** the assistance of a visual feedback module,a path planning approach based on the POMDP algorithm dynamically determines the motion strategies of the particle movement to avoid potential *** geometrical parameters of the virtual electrodes are optimized for different types of particles with the goal of maximum transport *** experiments of micropatterning with different morphologies and transporting multiple micropartides(e.g,polystyrene microspheres and 3T3 cells)to goal positions are *** results demonstrate that the proposed manipulation method based on optoelectronic tweezers is effective for multicell transport and promises to be used in biomedical manipulation tasks with high flexibility and efficiency.

关键词： manipulation positions assignment

来源：评论

学校读者我要写书评

暂无评论

MDRPC: Music-Driven Robot Primitives Choreography 36

MDRPC: Music-Driven Robot Primitives Choreography

引用

36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Guan, Haiyang Wei, Xiaoyi Long, Weifan Yang, Dingkang Zhai, Peng Zhang, Lihua Fudan University Academy for Engineering and Technology Shanghai200433 China Ji Hua Laboratory Foshan528251 China Engineering Research Center of AI and Robotics Ministry of Education Shanghai200433 China Institute of Meta-Medical Fudan University Shanghai200433 China Jilin Provincial Key Laboratory of Intelligence Science and Engineering Changchun130013 China

ISBN: (纸本)9798331527235

Dance has been an important art form and means of communication since the dawn of human civilization. Equipping humanoid robots with the ability to perform smooth dance movements to music is a key research priority in artificial intelligence, robotics and human-computer interaction. However, existing kinematics-based dance generation methods often violate real-world physical laws as they do not consider physical constraints, leading to unrealistic movements. Additionally, due to the diversity and dynamic variability of input music, most existing physics-based methods, which rely on task-specific reward functions, face significant challenges in effectively handling music-driven dance generation tasks. To address these issues, we introduce MDRPC, the first physics-based, music-driven dance generation method for humanoid robots. Inspired by human choreographic principles, MDRPC is defined as a two-phase framework. The initial phase utilizes adversarial imitation learning to acquire a rich set of reusable dance primitives from a music-dance dataset. In the subsequent phase, these dance primitives are orchestrated under the guidance of musical theory and choreographic rules to generate complex humanoid dance sequences. Specifically, we propose beat alignment and dance diversity reward functions to synchronize motion rhythms with music beats and enhance the diversity of dance movements. We implement MDRPC on a simulated humanoid robot, and the results confirm that our method effectively controls the humanoid, enabling it to perform dance movements harmoniously synchronized with the music. © 2024 IEEE.

关键词： Anthropomorphic robots

来源：评论

学校读者我要写书评

暂无评论

Query-Enhanced Motion Transformer with Dilated Static Query and Bridged Dynamic Query

SSRN

引用

SSRN 2024年

作者： Kang, Miao Shi, Liushuai Ye, Ke Zhou, Sanping Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence China National Engineering Research Center for Visual Information and Applications China Institute ofArtificial Intelligence and Robotics Xi'an Jiaotong University China

Motion forecasting is integral to autonomous driving, as it aims to anticipate future trajectory of traffic agents within the complicated environment by comprehending their various motion intentions. Recent effective query-based methods have focused on designing static queries and learning dynamic queries through cascaded Transformer decoders to represent motion multimodality. However, static queries are usually generated by clustering the goal of motion samples, whose coverage of motion patterns suffers from the limited valid range of the clustered samples. Additionally, dynamic queries, affected by the sequential connections between decoder layers, lack communication between distant decoders, leading to suboptimal optimization. In this paper, we propose a simple yet effective Query-Enhanced Motion TRansformer (QE-MTR) for motion forecasting. This approach includes both Dilated Static Query (DSQ) and Bridged Dynamic Query (BDQ) to enhance the representation of motion multimodality. Specifically, the DSQ expands the valid range of motion samples from their future sequence to complete sequence, providing more diverse motion patterns for query generation and effectively dilating the coverage of static queries. The BDQ introduces cross connection across decoders instead of direct connections, promoting harmonious dynamic optimization and improving the effectiveness of final dynamic queries. QE-MTR achieves state-of-the-art performance on the Waymo Open Motion Dataset, earning 2nd place finish in the 2023 Waymo Motion Prediction Challenge. © 2024, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Time-synchronous-averaging-spectrum based on super-resolution analysis and application in bearing fault signal identification

引用

Journal of Zhejiang University-Science A(Applied Physics & engineering) 2024年第7期25卷 573-585页

作者： Zengle REN Yuan WANG Huiyue TANG Xin'an CHEN Wei FENG Guangdong Provincial Key Laboratory of Construction Robotics and Intelligent Construction Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China Shenzhen EBORAIL Technology Co. Ltd.Shenzhen518038China National Supercomputing Center in Shenzhen Shenzhen518055China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing100044China University of Chinese Academy of Sciences Beijing100190China Shenzhen University of Advanced Technology Shenzhen518107China

Time-synchronous-averaging(TSA)is based on the idea of denoising by averaging,and it extracts the periodic components of a quasiperiodic signal and keeps the extracted waveform *** paper studies the mathematical properties of TSA,where three propositions are given to reveal the nature of *** paper also proposes a TSA-spectrum based on super-resolution analysis and it decomposes a signal without using any base *** contrast to discrete Fourier transform spectrum(DFT-spectrum),which is a spectrum in frequency domain,TSA-spectrum is a period-based spectrum,which can present more details of the cross effects between different periodic components of a quasiperiodic ***,a case study is carried out using bearing fault analysis to illustrate the performance of TSA-spectrum,where the rotation speed fluctuation of the shaft is estimated,which is about 0.12 ms *** extracted fault signals are presented and some insights are *** believe that this paper can provide new motivation for TSA-spectrum to be widely used in applications involving quasiperiodic signal processing(QSP).

关键词： Time-synchronous-averaging(TSA) Spectrum Quasiperiodic signal processing(QSP) Super-resolution analysis Bearingfaultdetection

来源：评论

学校读者我要写书评

暂无评论

Affine Formation Maneuver Control for Multiagent Systems Based on Unknown Input Reconstruction

引用

IEEE Access 2025年 13卷 102311-102322页

作者： Liduo Hu Wei Zhang Chenhang Yan Yuhang Cai Hao Chen Zhi Hu Laboratory of Intelligent Control and Robotics Shanghai University of Engineering Science Shanghai China National Key Laboratory of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing China

Disturbances can significantly impact the dynamic performance and stability of a system, making them a key focus in stability analysis. However, direct measurement of disturbances is often challenging due to technological and cost limitations. These disturbances introduce uncertainties that complicate multi-agent coordination, particularly in formation control tasks. Consequently, designing effective control strategies for multi-agent systems becomes both challenging and crucial. This paper addresses the affine transformation formation control problem of multi-agent systems in the presence of unknown disturbances. We begin by using a state-space model to describe agent dynamics and employing an unknown input observer to estimate both the state and the unknown disturbances. Based on these estimates, we redesign the control law to ensure that tracking errors converge to an adjustable residual set. This distributed control approach allows agents to operate without global information, thereby reducing communication requirements. Finally, a simulation example demonstrates the feasibility and effectiveness of the proposed control law.

关键词： Formation control Observers Laplace equations Directed graphs Vectors Multi-agent systems Protocols Matrix decomposition Aerospace electronics Vehicle dynamics

来源：评论

学校读者我要写书评

暂无评论

Simultaneous Estimation of Shape and Force along Highly Deformable Surgical Manipulators Using Sparse FBG Measurement

arXiv

引用

arXiv 2024年

作者： Lu, Yiang Li, Bin Chen, Wei Yan, Junyan Cheng, Shing Shin Wang, Jiangliu Zhou, Jianshu Dou, Qi Liu, Yun-Hui T Stone Robotics Institute Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong

Recently, fiber optic sensors such as fiber Bragg gratings (FBGs) have been widely investigated for shape reconstruction and force estimation of flexible surgical robots. However, most existing approaches need precise model parameters of FBGs inside the fiber and their alignments with the flexible robots for accurate sensing results. Another challenge lies in online acquiring external forces at arbitrary locations along the flexible robots, which is highly required when with large deflections in robotic surgery. In this paper, we propose a novel data-driven paradigm for simultaneous estimation of shape and force along highly deformable flexible robots by using sparse strain measurement from a single-core FBG fiber. A thin-walled soft sensing tube helically embedded with FBG sensors is designed for a robotic-assisted flexible ureteroscope with large deflection up to 270◦ and a bend radius under 10 mm. We introduce and study three learning models by incorporating spatial strain encoders, and compare their performances in both free space without interactions as well as constrained environments with contact forces at different locations. The experimental results in terms of dynamic shape-force sensing accuracy demonstrate the effectiveness and superiority of the proposed methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Fiber Bragg gratings

来源：评论

学校读者我要写书评

暂无评论

Neural Text to Articulate Talk: Deep Text to Audiovisual Speech Synthesis achieving both Auditory and Photo-realism

arXiv

引用

arXiv 2023年

作者： Milis, Georgios Filntisis, Panagiotis P. Roussos, Anastasios Maragos, Petros School of Electrical & Computer Engineering National Technical University of Athens Greece Institute of Robotics Athena Research Center Maroussi15125 Greece Greece

and is expected to receive more and more attention, following the recent breakthroughs in large language models. For that, most methods implement a cascaded 2-stage architecture of a text-to-speech module followed by an audio-driven talking face generator, but this ignores the highly complex interplay between audio and visual streams that occurs during speaking. In this paper, we propose the first, to the best of our knowledge, text-driven audiovisual speech synthesizer that uses Transformers and does not follow a Recent advances in deep learning for sequential data have given rise to fast and powerful models that produce realistic videos of talking humans. The state of the art in talking face generation focuses mainly on lip-syncing, being conditioned on audio clips. However, having the ability to synthesize talking humans from text transcriptions rather than audio is particularly beneficial for many applications cascaded approach. Our method, which we call NEUral Text to ARticulate Talk (NEUTART), is a talking face generator that uses a joint audiovisual feature space, as well as speech-informed 3D facial reconstructions and a lipreading loss for visual supervision. The proposed model produces photorealistic talking face videos with human-like articulation and well-synced audiovisual streams. Our experiments on audiovisual datasets as well as in-the-wild videos reveal state-of-the-art generation quality both in terms of objective metrics and human evaluation. © 2023, CC BY.

关键词： Speech synthesis

来源：评论

学校读者我要写书评

暂无评论

Simultaneous Estimation of Shape and Force along Highly Deformable Surgical Manipulators Using Sparse FBG Measurement

Simultaneous Estimation of Shape and Force along Highly Defo...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Yiang Lu Bin Li Wei Chen Junyan Yan Shing Shin Cheng Jiangliu Wang Jianshu Zhou Qi Dou Yun-Hui Liu Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Recently, fiber optic sensors such as fiber Bragg gratings (FBGs) have been widely investigated for shape reconstruction and force estimation of flexible surgical robots. However, most existing approaches need precise model parameters of FBGs inside the fiber and their alignments with the flexible robots for accurate sensing results. Another challenge lies in online acquiring external forces at arbitrary locations along the flexible robots, which is highly required when with large deflections in robotic surgery. In this paper, we propose a novel data-driven paradigm for simultaneous estimation of shape and force along highly deformable flexible robots by using sparse strain measurement from a single-core FBG fiber. A thin-walled soft sensing tube helically embedded with FBG sensors is designed for a robotic-assisted flexible ureteroscope with large deflection up to 270° and a bend radius under 10 mm. We introduce and study three learning models by incorporating spatial strain encoders, and compare their performances in both free space without interactions as well as constrained environments with contact forces at different locations. The experimental results in terms of dynamic shape-force sensing accuracy demonstrate the effectiveness and superiority of the proposed methods.

关键词： Optical fiber sensors Shape Soft sensors Force Estimation Robot sensing systems Fiber gratings

来源：评论

学校读者我要写书评

暂无评论

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

引用

Computers, Materials & Continua 2022年第2期70卷 4199-4215页

作者： Nebojsa Bacanin Khaled Alhazmi Miodrag Zivkovic K.Venkatachalam Timea Bezdan Jamel Nebhen Singidunum University Danijelova11000BelgradeSerbia National Center for Robotics and IoT Communication and Information Technology Research InstituteKing Abdulaziz City for Science and Technology(KACST)Riyadh12371Saudi Arabia Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia

In the domain of artificial neural networks,the learning process represents one of the most challenging *** the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at ***,to a very large search space,it is very difficult to find the proper values of connection weights and *** traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local *** commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient *** an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network *** thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural *** the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization *** results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local *** proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on *** mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.

关键词： Artificial neural network optimization metaheuristics algorithm hybridization brain storm optimization

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：