检索结果-内蒙古大学图书馆

14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Jiang, Zhiyong Ma, Dongxiao Zhang, Jiawei Wang, Yu Chen, Jiangcheng Chen, Liyan The 21st Research Institute of China Electronics Technology Group Corporation Robotics Engineering Center Shanghai China The 21st Research Institute of CETC China SZAR China Guangdong Province Shenzhen China

ISBN: (纸本)9798331506056

Bipedal humanoid robots recently advanced in control theory and technology, which enable complex movements independently and with stability. center of Mass (CoM) trajectory planning is significant for the walking tasks of bipedal humanoid robots. In practice, a bipedal support period should be interpolated during the switching of the supports to smooth the velocity curves and avoid sudden changes in acceleration. Commonly, the coordinates of the CoM can be modeled by fifth-degree polynomials;however, the polynomial approximation cannot model a more general change in the accelerations. To handle this problem, we proposed a general B-spline curve to model the CoM trajectory for bipedal support interpolation. Simulations showed that the proposed method improved the expressive power compared with the fifth-degree polynomial model. Experiments demonstrated that the proposed method could be applied to a real-world bipedal humanoid robot system. © 2024 IEEE.

关键词： Anthropomorphic robots

来源：评论

学校读者我要写书评

暂无评论

A Diving Glove with Inertial Sensors for Underwater Gesture Recognition 16th

A Diving Glove with Inertial Sensors for Underwater Gestur...

引用

16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Tang, Qi Mai, Jingeng Wang, Tiantong Wang, Qining Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing100871 China Beijing Engineering Research Center of Intelligent Rehabilitation Engineering Peking University Beijing100871 China Medical Robotics Laboratory University of Health and Rehabilitation Sciences Qingdao266071 China Institute for Artificial Intelligence Peking University Beijing100871 China

ISBN: (纸本)9789819964857

Underwater gesture recognition has emerged as a popular research area for achieving efficient and secure underwater human-human interaction and human-robot collaboration. Previous research primarily relied on visual methods, which face challenges related to low visibility and motion blur in underwater environments. In this paper, we introduce a diving glove embedded with inertial sensors for underwater gesture recognition. The Nearest Centroid (NC), Random Forest (RF) and Support Vector Machine (SVM) classifiers are used in our diving glove. To demonstrate the underwater gesture recognition performance, we conducted a two-stage underwater experiment with ten underwater gestures. For the same user, The average recognition accuracies of the NC, RF and SVM classifiers are 97.5 % ± 5.4 %, 98.6 % ± 0.6 % and 99.2 % ± 0.3 %, respectively. For new users, the average recognition accuracies of the NC, RF and SVM classifiers are 86.4 % ± 2.18 %, 88.6 % ± 7.4 % and 96.5 % ± 0.31 %, respectively. The study suggests that the diving glove with inertial sensors is a feasible solution for underwater gesture recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

Additive manufacturing of high-quality tritium breeding pebbles using ceramic precursors

引用

Journal of the European Ceramic Society 2024年第7期44卷 4719-4730页

作者： Su, Fang Li, Yuzeng Xing, Huanhuan Liu, Yu Liu, Zhiyuan Liu, Changyong Jiang, Cho-Pei Chen, Zhangwei Additive Manufacturing Institute Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robotics Shenzhen University Shenzhen518060 China College of Mechanical & Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an710049 China

In this study, the fabrication of advanced lithium orthosilicate (Li4SiO4) ceramic tritium breeding pebbles through photopolymerization using ceramic precursors (i.e., SiO2 and Li2CO3) is reported for the first time. The pebbles exhibit excellent mechanical and physical properties. Through optimization of manufacturing parameters and a geometrical correction model to compensate printing deviations, the printing accuracy of the pebbles was enhanced. The resulting pebbles had a diameter of approximately 1 mm, a sphericity of 1.01, a uniformly distributed pore structure, a phase purity > 95%, and a satisfying relative density reaching 91.39%. The average crush load 25.7 ± 0.3 N, surpassed that achieved by previously reported methods. Furthermore, the thermal conductivity was enhanced compared to that of solid-phase synthesized Li4SiO4 powder, facilitating the tritium release process for fusion applications. This study may serve as a guidance for designing appropriate ceramic breeders, providing a cost-effective approach applicable to produce other high-precision ceramic components with complex compositions. © 2024 Elsevier Ltd

关键词： Lithium compounds

来源：评论

学校读者我要写书评

暂无评论

A hierarchical energy sharing strategy for multi-communities with heterogeneous prosumers

A hierarchical energy sharing strategy for multi-communities...

引用

2024 China Automation Congress, CAC 2024

作者： Yang, Yan Chen, Fan Hu, Mian Wu, Huaiyu Chen, Yang Engineering Research Center for Metallurgical Automation and Measurement Technology Ministry of Education Wuhan University of Science and Technology 430081 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology 430081 China

ISBN: (纸本)9798350368604

Inter-community energy sharing is of positive significance in the energy transacion market, which can effectively reduce the cost of purchasing energy from the utility grid and improve the local consumption of renewable energy. In this study, a hierarchical energy sharing framework for multi-communities with heterogeneous prosumers is proposed. Each community consists of different types of prosumers, that is, photovoltaic (PV) prosumers, wind turbine (WT) prosumers and aggregate electric vehicle (EV) prosumers. A hierarchical energy sharing strategy based on Stackelberg game is proposed. In the inner cooperation stage, the total cost minimization model of operator cluster is established. In the outer non-cooperative stage, the Stackelberg game model between each operator and heterogeneous prosumers in each community is established, and the robust optimization method is adopted to solve the time-varying uncertainty problem of renewable energy generations. A hierarchical iterative algorithm is designed to solve the Stackelberg game equilibrium strategy, which maximizes each prosumer’s income and minimizes the operators’ total cost. The simulation results show that the proposed multi-community hierarchical energy sharing strategy can not only reduce the impact of load fluctuation on the utility grid, but also improve the income of prosumers. © 2024 IEEE.

关键词： Distributed energy

来源：评论

学校读者我要写书评

暂无评论

Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

引用

2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Applying CNN Models with Stereo Camera for Object Detection and Distance Estimation in Automatic Micro Electric Vehicle (MEVi) 1

Applying CNN Models with Stereo Camera for Object Detection ...

引用

1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Harimanto, Farrell Putra Hasanah, Nurul Pradana, Muhammad Khristamto Aditya Puji, Muhammad Nurul Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia National Research and Innovation Agency Kst Samaun Samadikun Research Center for Smart Mechatronics Bandung40135 Indonesia

ISBN: (纸本)9798350368918

Technological advancements in the automotive industry are currently focused on autonomous driving systems or driver assistance systems. Depth estimation is also an important feature of the autonomous driving system as it can accurately estimate the distance to the detected objects. In current developments technologies such as stereo cameras are more popular for a more accurate depth estimation. The process for object detection consists of a combination of classification, localization, and segmentation. Deep convolutional neural networks are the basis of object detection. The methods for object detection are divided into two categories namely the Region Proposal-based method such as RCNN, and the Classification-based method which provides for methods such as YOLO and MobileNet. As a part of the research conducted by the national Research and Innovation Agency (BRIN) on Automatic Micro Electric Vehicles or MEVi, this paper analyzed and compared the performance of several CNN models for object detection as well as the performance of depth sensing using ZED stereo camera which was used for the autonomous system in MEVi. Among several models that had been compared, YOLO v8 model demonstrates high accuracy and moderate speed as indicated by its high mAP (0.50) and FPS (38.46). Based on depth sensing results, the accuracy of distance measurement also depended on brightness and environment with the average of accuracy achieved indoors was 98.2% and outdoors was 96.3%. © 2024 IEEE.

关键词： Automotive industry

来源：评论

学校读者我要写书评

暂无评论

Multi-Class Gait Phase Recognition using Machine Learning Models with Two Training Methods 15

Multi-Class Gait Phase Recognition using Machine Learning Mo...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mekni, Amal Narayan, Jyotindra Gritli, Hassene Borj Cedria Tunis1164 Tunisia Indian Institute of Technology Guwahati Mechtronics and Robotics Laboratory Department of Mechanical Engineering Assam Guwahati781039 India University of Tunis El Manar le Belvédère Laboratory of Robotics Informatics and Complex Systems RISC Lab-LR16ES07 National Engineering School of Tunis Tunis1002 Tunisia

ISBN: (纸本)9798350370249

Walking is a fundamental human activity, yet understanding its intricacies is crucial for diagnosing and treating various gait abnormalities and musculoskeletal disorders. This study investigates the machine learning-based classification of gait phases into five subphases with two training methods. Using data from 100 individuals obtained from an open-source platform. The gait cycle was divided into the following subphases: Initial Contact, Loading Response, Mid Stance, Terminal Stance, and Pre-Swing. In the first approach, a stratified random sampling method was employed, allocating 80% of data from each subphase for training and 20% for testing. The second method involved training with data from 80% participants and testing with data from 20% participants. Out of different ML algorithms, the Random Forest (RF) achieves the highest performance (99.2%) with similar results across both methods. The k-Nearest Neighbors (k-NN), the Logistic Regression (LR), the Decision Tree (DT), and the Support Vector Machine (SVM) show comparable performance ( 91%) with marginal improvement with the second method. Naive Bayes (NB) has a significantly lower performance ( 77.7%). These findings suggest that RF is a robust and effective algorithm for this task. Furthermore, the study indicates that applying machine learning techniques to datasets divided by subjects presents promising opportunities for creating precise gait analysis systems. This can assist clinicians and researchers in enhancing patient care and gaining deeper insights into human locomotion. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

MateRobot: Material Recognition in Wearable robotics for People with Visual Impairments

MateRobot: Material Recognition in Wearable Robotics for Peo...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Junwei Zheng Jiaming Zhang Kailun Yang Kunyu Peng Rainer Stiefelhagen Institute for Robotics and Anthropomatics Karlsruhe Institute of Technology Karlsruhe Germany Department of Engineering Science University of Oxford UK School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

People with Visual Impairments (PVI) typically recognize objects through haptic perception. Knowing objects and materials before touching is desired by the target users but under-explored in the field of human-centered robotics. To fill this gap, in this work, a wearable vision-based robotic system, MATERobot, is established for PVI to recognize materials and object categories beforehand. To address the computational constraints of mobile platforms, we propose a lightweight yet accurate model MATEViT to perform pixel-wise semantic segmentation, simultaneously recognizing both objects and materials. Our methods achieve respective 40.2% and 51.1% of mIoU on COCOStuff-10K and DMS datasets, surpassing the previous method with +5.7% and +7.0% gains. Moreover, on the field test with participants, our wearable system reaches a score of 28 in the NASA-Task Load Index, indicating low cognitive demands and ease of use. Our MATERobot demonstrates the feasibility of recognizing material property through visual cues and offers a promising step towards improving the functionality of wearable robots for PVI. The source code has been made publicly available at MATERobot.

关键词： Visualization Source coding Semantic segmentation Visual impairment Semantics Wearable robots Mobile applications

来源：评论

学校读者我要写书评

暂无评论

Machine Learning In Sustainable Development-An Overview 1

Machine Learning In Sustainable Development-An Overview

引用

1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Altrjman, Chadi Gupta, Richa Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Turkey University of Kyrenia Faculty of Engineering Kyrenia Turkey Waterloo University Canada Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350338027

I have studied how artificial intelligence (AI) or machine learning (ML) is applied in a sustainable system called "Eugenie"for my work, project, and thesis. In this project, I get to discuss Eugenie's role in ensuring the sustainability of large environmental business organizations. I also get to discuss how it aids operation managers in making predictions about when their machines will break down days, weeks, and months in advance, allowing them to schedule maintenance cycles more effectively. In other words, Eugenie provides plant operators with data so they can prioritize repairs. Eugenie executes this process by utilizing real-time data from numerous sensors attached to large machinery and equipment in the process industry, metals and mining, oil and gas, and smart cities industries. Two products make up this program;the first is named RAY-FINN and the second is called PAPILLON. Eugenie assists businesses in a variety of ways, including:•Aids in the attainment of operational excellence by process organizations•Always raise overall profitability.•Contributes to a greener and safer future. © 2023 IEEE.

关键词： Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Trajectory Optimization Algorithm of Trajectory Rehabilitation Training Mode for Rehabilitation Robot

Trajectory Optimization Algorithm of Trajectory Rehabilitati...

引用

2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Cen, Yuhui Yuan, Jianjun Ma, Shugen Luo, Jingjing Wang, Hongbo Human Phenome Institute Fudan University Shanghai200438 China The Shanghai Robotics Institute Shanghai University 99 Shangda RD Baoshan District Shanghai China The Department of Robotics Faculty of Science and Engineering Ritsumeikan University 1-1-1 Nojihigashi Shiga Kusatsu Japan Institute of AI and Robotics Academy for Engineering and Technology Fudan University Engineering Research Center of AI & Robotics Ministry of Education Shanghai210043 China

ISBN: (纸本)9781665481090

It is a challenge for robot-assisted rehabilitation therapy to develop a training program with both customized and optimized characteristics. To optimize the scheme of the trajectory rehabilitation training mode, we design a trajectory optimization algorithm with an intelligent decision mechanism. A trajectory smoothing improvement algorithm is proposed to make the trajectory more suitable for rehabilitation training. In addition, we propose an improved genetic algorithm that reduces the optimization time and obtains a better solution. An adaptive variable time weight improved trapezoidal velocity algorithm is innovatively designed by fusing the above two algorithms with an optimal rehabilitation training program as a guide. Based on the experimental platform of the upper limb rehabilitation robot, it is demonstrated that the algorithm can provide comfortable and high-quality rehabilitation training for patients by adaptively optimizing the parameters of the trajectory model according to the training constraints. By combining an intelligent optimization algorithm with a rehabilitation robot trajectory planning algorithm, this paper realizes the unification of customization and optimization of a trajectory rehabilitation training program. © 2022 IEEE.

关键词： Trajectories

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：