检索结果-内蒙古大学图书馆

Lecture Notes on Data Engineering and Communications Technologies 2022年 130卷 19-29页

作者： Abidemi, Sarumi Usman Oghenetega, Omoraka Gershion Daniel, Sekyere-Asiedu Al-Turjman, Fadi Artificial Intelligence Engineering Department Research Center for AI and IoT AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey Department of Computer Information Systems Near East University Mersin 10 Nicosia Turkey

Traditionally, attendance is marked manually by students writing down their name on a paper. This whole process wastes some of lecture time and might even get lost. Maintaining the attendance of the students in an institution is a hefty task. This project aims at designing a smart attendance system that automatically monitors and manages attendance of the students in an institution efficiently. The system is developed with an Arduino ESP8266 Wi-Fi microcontroller deployed in the class room. Further, Wi-Fi communication modules was used to make convenient communication between the student’s devices e.g. phone or laptop and the ESP8266 Wi-Fi microcontroller. Database was created where the attendance submitted will be logged and saved. Internet is utilized to transfer the information collected by the micro controller to the database and from the database to the front-end web page GUI graphical user interface. The students and admin will make one-time registration and subsequently establishing a Wi-Fi connection between the student’s device and the microcontroller in the class to mark an attendance. This system will arrange the attendance in a list with a real time timestamp and save it to database, and also be accessible to the lecturer/Admin over internet any time with uses of web browser e.g. Google chrome through the front end graphic user interface (GUI). This system is useful to reduce manual method of attendance by teachers and to alleviate the spread of COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Survey on studies investigating the effect of simulated microgravity on the musculoskeletal system 73

Survey on studies investigating the effect of simulated micr...

引用

73rd International Astronautical Congress, IAC 2022

作者： Habenicht, Julia Tabie, Marc Will, Niels Kirchner, Elsa Andrea Institute of Medical Technology Systems University of Duisburg-Essen Duisburg Germany Robotics Innovation Center German Research Center for Artificial Intelligence Bremen Germany

Purpose: Astronaut training for space missions is expensive and elaborative. Astronauts need extensive preparation for the circumstances they are exposed to in space. One aspect is microgravity, which can be simulated with parabolic flights or in underwater training scenarios making use of the buoyancy effect. Both methods are expensive and need special facilities and a lot of personnel. We strive to optimize and intensify astronaut training by simulating microgravity for the upper limbs of the human by means of an active exoskeleton. Methodology: To evaluate the need and potential success of such a training scenario we performed intensive literature research analysing what kind of comparable experiments have already been done on parabolic flight campaigns. To this end, we analysed previous European parabolic flight campaigns to see if there had already been similar investigations. We searched for experiments containing the keywords EMG and exoskeleton. Additionally, we expanded our search to PubMed and Scopus and added the keyword parabolic flight to find studies which were not conducted in Europe. We focused on studies published in the last two decades. Results: In previous experiments, the influence of microgravity on EMG signals was investigated. Most of them only compared the effects of microgravity with Earth gravity. There are studies with simulated microgravity using an end-effector based robotic support system. The results showed differences of EMG activity under real microgravity compared to simulated microgravity. What is missing is the comparison between the effects of real microgravity and simulated microgravity based on multi-contact full-arm approach by means of an actuated exoskeleton on the human body. Our approach is comparable, but due to the multiple contact points we assume that the arm weight is better compensated and therefore improved results can be Conclusions: Exoskeletons that simulate microgravity have high potential to optimize training

关键词： Microgravity

来源：评论

学校读者我要写书评

暂无评论

A Multi-View fMRI Model with Voxel Inner State for Vision Encoding

A Multi-View fMRI Model with Voxel Inner State for Vision En...

引用

2023 China Automation Congress, CAC 2023

作者： Li, Yueying Wu, Hao Chen, Badong Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Xi'an710049 China School of Electrical Engineering Xi'An University of Technology Xi'an710048 China

ISBN: (纸本)9798350303759

Visual encoding and decoding is an important tool to explore brain neural function, how to establish an efficient and accurate encoding model is an important issue in fMRI brain visual encoding tasks. However, due to the low signal-to-noise ratio and the high-dimensional characteristics of fMRI signal, it is difficult to extract response component from voxels which has high correlation with external image stimulus. Traditional fMRI encoding models ignore the voxel inner state and noise components in fMRI response, In this work, We present a novel approach of encoding by adding voxel mode into the encoding stage, which is ignored in previous study. We propose a multi-view voxel-wise encoding model with two input modes, independently extract voxel features and stimuli features. Our multi-view encoding model takes voxel inner state into encoding stage, and uses feature-irrelevant voxels to perform noise reduction for feature-relevant voxels simultaneously, thus reach a higher explain variance (R2) of encoding result and higher image identification accuracy of decoding result. Our work is helpful for further exploring the characteristics of visual cortex and understanding the mechanism of brain visual pathway. © 2023 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Intelligent Diagnosis of Diabetic Kidney Disease Based on Heterogeneous Data

Intelligent Diagnosis of Diabetic Kidney Disease Based on He...

引用

2022 Chinese Automation Congress, CAC 2022

作者： Yu, Hongxuan Xin, Jingmin Wu, Jiayi Wang, Lu Chen, Shitao Zuo, Weiliang Cui, Wei Zheng, Nanning Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an China The First Affiliated Hospital Xi'an Jiaotong University Endocrine Department Xi'an China

ISBN: (纸本)9781665465335

Diabetic kidney disease (DKD) is a complication of diabetes, with high incidence rate, low prevention rate, and low cure rate. The diagnostic process for DKD is difficult and most of the screening techniques for DKD are invasive. In this paper, we try to use the non-invasive heterogeneous data consisting of fundus images and urine albumin-to-creatinine ratio (UACR) to develop a deep learning method to diagnose DKD. More specifically, we design an image feature module to extract the image information from fundus images while creating a biochemistry feature module to extract the information from UACR. After concatenating the two kinds of information, we propose a decision module to classify DKD or non-DKD. We apply the method to the heterogeneous dataset, where the results show that our method has 82.4% accuracy, 85.7% area under the receiver operating characteristic curve (AUC), 87.2% sensitivity, and 12.8% false negative rate (FNR). The results prove that the method we proposed helps diagnose DKD by using non-invasive heterogeneous data, such as fundus images and UACR. Besides, the ablation experiments indicate that fundus images contain much information about DKD and can be an important reference for diagnosing DKD. © 2022 IEEE.

关键词： Creatinine

来源：评论

学校读者我要写书评

暂无评论

Reusable Architecture Growth for Continual Stereo Matching

arXiv

引用

arXiv 2024年

作者： Zhang, Chenghao Meng, Gaofeng Fan, Bin Tian, Kun Zhang, Zhaoxiang Xiang, Shiming Pan, Chunhong The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Centre for Artificial Intelligence and Robotics HK Institute of Science & Innovation Chinese Academy of Sciences China The School of Intelligence Science and Technology The Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China

The remarkable performance of recent stereo depth estimation models benefits from the successful use of convolutional neural networks to regress dense disparity. Akin to most tasks, this needs gathering training data that covers a number of heterogeneous scenes at deployment time. However, training samples are typically acquired continuously in practical applications, making the capability to learn new scenes continually even more crucial. For this purpose, we propose to perform continual stereo matching where a model is tasked to 1) continually learn new scenes, 2) overcome forgetting previously learned scenes, and 3) continuously predict disparities at inference. We achieve this goal by introducing a Reusable Architecture Growth (RAG) framework. RAG leverages task-specific neural unit search and architecture growth to learn new scenes continually in both supervised and self-supervised manners. It can maintain high reusability during growth by reusing previous units while obtaining good performance. Additionally, we present a Scene Router module to adaptively select the scene-specific architecture path at inference. Comprehensive experiments on numerous datasets show that our framework performs impressively in various weather, road, and city circumstances and surpasses the state-of-the-art methods in more challenging cross-dataset settings. Further experiments also demonstrate the adaptability of our method to unseen scenes, which can facilitate end-to-end stereo architecture learning and practical deployment. Copyright © 2024, The Authors. All rights reserved.

关键词： Reusability

来源：评论

学校读者我要写书评

暂无评论

Underwater Image Clearing Algorithm Based on the Laplacian Edge Detection Operator 1

引用

2nd International Conference on artificial intelligence, robotics, and Communication, ICAIRC 2022

作者： Li, Xingzhen Yu, Siquan Gu, Haitao Tan, Yuanyuan Xing, Lin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang11000 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang11000 China School of Artificial Intelligence Shenyang University of Technology Liaoning Shenyang110870 China

ISBN: (数字)9789819945542

ISBN: (纸本)9789819945535

When shooting underwater, limited by different camera hardware and lighting, underwater images are generally of low quality and blurred details. Moreover, due to the different absorption rates of the R, G, and B three-color wavelengths, the color of underwater photos is distorted. This affects the feasibility of studying underwater images. The majority of currently available techniques for improving underwater photographs mainly solve the problem of overall denoising and brightness enhancement of underwater images while ignoring the edge details of the image. As a solution to the aforementioned issues, we suggest a method using a double extraction network structure model based on the Laplacian operator, which can dehaze underwater images more quickly. One is to use downsampling and upsampling to reduce the excessive distortion of the image during training, and the other is to use the Laplacian operator to reduce the blurring of the edge to enhance the clarity of the edge and use the blue‒green model for multiscale fusion. We adopted MSE loss and proved its superiority through experiments and index analysis by training images of different underwater scenes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Laplace transforms

来源：评论

学校读者我要写书评

暂无评论

SharedAssembly: A Data Collection Approach via Shared Tele-Assembly

arXiv

引用

arXiv 2025年

作者： Wu, Yansong Chen, Xiao Chen, Yu Sadeghian, Hamid Wu, Fan Bing, Zhenshan Haddadin, Sami König, Alexander Knoll, Alois CMIRMI - Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Assembly is a fundamental skill for robots in both modern manufacturing and service robotics. Existing datasets aim to address the data bottleneck in training general-purpose robot models, falling short of capturing contact-rich assembly tasks. To bridge this gap, we introduce SharedAssembly, a novel bilateral teleoperation approach with shared autonomy for scalable assembly execution and data collection. User studies demonstrate that the proposed approach enhances both success rates and efficiency, achieving a 97.0% success rate across various sub-millimeter-level assembly tasks. Notably, novice and intermediate users achieve performance comparable to experts using baseline teleoperation methods, significantly enhancing large-scale data *** Codes 68T40 © 2025, CC BY-NC-ND.

关键词： Robotic assembly

来源：评论

学校读者我要写书评

暂无评论

Depth-Guided Vision Transformer With Normalizing Flows for Monocular 3D Object Detection

引用

IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 673-689页

作者： Cong Pan Junran Peng Zhaoxiang Zhang the Center for Research on Intelligent Perception and Computing(CRIPAC) National Laboratory of Pattern Recognition(NLPR)Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 the School of Future Technology University of Chinese Academy of Sciences(UCAS)Beijing 100049China the Huawei Inc. Beijing 100085China IEEE the Institute of Automation Chinese Academy of Sciences(CASIA)Beijing 100190 the University of Chinese Academy of Sciences(UCAS) Beijing 100049 the Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science&Innovation Chinese Academy of Sciences(HKISI CAS)Hong Kong 999077China

Monocular 3D object detection is challenging due to the lack of accurate depth *** methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB ***-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion ***,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with *** from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth *** we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each ***,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input *** method is the first Swin-Transformer-based backbone architecture for monocular 3D object *** experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.

关键词： Monocular 3D object detection normalizing flows Swin Transformer

来源：评论

学校读者我要写书评

暂无评论

Learning End-to-End Action Interaction by Paired-Embedding Data Augmentation 15th

Learning End-to-End Action Interaction by Paired-Embedding D...

引用

15th Asian Conference on Computer Vision, ACCV 2020

作者： Song, Ziyang Yuan, Zejian Zhang, Chong Chi, Wanchao Ling, Yonggen Zhang, Shenghao Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Tencent Robotics X Shenzhen China

ISBN: (纸本)9783030695439

In recognition-based action interaction, robots’ responses to human actions are often pre-designed according to recognized categories and thus stiff. In this paper, we specify a new Interactive Action Translation (IAT) task which aims to learn end-to-end action interaction from unlabeled interactive pairs, removing explicit action recognition. To enable learning on small-scale data, we propose a Paired-Embedding (PE) method for effective and reliable data augmentation. Specifically, our method first utilizes paired relationships to cluster individual actions in an embedding space. Then two actions originally paired can be replaced with other actions in their respective neighborhood, assembling into new pairs. An Act2Act network based on conditional GAN follows to learn from augmented data. Besides, IAT-test and IAT-train scores are specifically proposed for evaluating methods on our task. Experimental results on two datasets show impressive effects and broad application prospects of our method. © 2021, Springer Nature Switzerland AG.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Robust Tracking and Localization using Stable Features of Static Planes in Dynamic Scenes

Robust Tracking and Localization using Stable Features of St...

引用

2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Zhang, Yu Zhang, Xuefang Feng, Shiyu Shen, Qiguang Dang, Shaobo Xu, Kun Wuhan Institute of Post and Telecommunications Wuhan430074 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenZhen518055 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Siat Branch China Guangxi University of Science and Technology College of Automation China

ISBN: (纸本)9798350327182

Dynamic interference by moving objects and localization failure induced by low-texture scenes are two urgent problems to be solved in the practical applications of visual simultaneous localization and mapping (SLAM). This study proposes a robust tracking and localization approach with a monocular camera sensor that leverages points within extracted lines and planes to deal with the challenges of dynamic objects and low-texture features. Firstly, we use a line segment detector and an improved CNN-based plane segmentation network to extract lines and planes, respectively, without detecting dynamic objects, thus avoiding complex semantic models. Then, the tracking thread tightly uses plane and line information to promote front-end pose tracking. Finally, the geometric constraint is added to the BA constraints in the pose optimization stage to enhance the robustness. We have evaluated the proposed approach on public datasets and real-scene experiments. Results demonstrate that our method outperforms several state-of-the-art dynamic filtering-based methods in highly dynamic scenes, significantly improving robustness and positioning accuracy. © 2023 IEEE.

关键词： Textures

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：