检索结果-内蒙古大学图书馆

2nd International Conference on Digital Transformation in Education and artificial intelligence applications, MoStart 2024

2nd International Conference on Digital Transformation in Ed...

引用

2nd International Conference on Digital Transformation in Education and artificial intelligence applications, MoStart 2024

ISBN: (纸本)9783031620577

The proceedings contain 17 papers. The special focus in this conference is on Digital Transformation in Education and artificial intelligence applications. The topics include: artificial intelligence in Elementary Math Education: Analyzing Impact on Students Achievements;Gamification in Learning Process Enhanced with AI;impact of the e-Schools Programme on the Use of the e-Class Register;students’ Digital Learning Behavior Using the Mandatory and Non-mandatory Platforms in an Online Learning Environment;Assessing the Impact of Large-Scale ICT Investment in Education Through Measuring the Digital Maturity of Schools;opportunities for the Professional Development of Teachers in Digital Competences Related to the Use of artificial intelligence in Education in Croatia;crop-Guided Neural Network Segmentation of High-Resolution Skin Lesion Images;artificial intelligence-Based Control of Autonomous Vehicles in Simulation: A CNN vs. RL Case Study;fuzzy-Based Knowledge Design and Delivery Model for Personalised Learning;the Development of Assistive robotics: A Comprehensive Analysis Integrating machine Learning, Robotic vision, and Collaborative Human Assistive Robots;development of a Dynamic Multi-object Planning Framework for Autonomous Mobile Robots;application of artificial intelligence in the Economic and Legal Affairs of Companies;Impact of AI Tools on Software Development Code Quality;a Systematic Review of robotics’ Transformative Role in Education;application of the Decision Tree in the Business Process.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Iterative Overlap Attention-Aware Network With Similarity Learning for Partial Point Cloud Registration

引用

IEEE SENSORS JOURNAL 2024年第8期24卷 12782-12794页

作者： Chen, xinyu Luo, Jiahui Ren, Yan Wang, Chuanyun Shenyang Aerosp Univ Coll Artificial Intelligence Shenyang 110136 Liaoning Peoples R China

Point cloud registration aims at accurately aligning and integrating multiple point cloud data by scanning the same object from different viewpoints into a more comprehensive 3-D model or scene. It has significant applications in the field of computer vision and robotics. In recent years, with the continuous impact of machine vision technology, more and more registration methods based on deep network models have emerged. However, most deep learning-based methods perform poorly in low overlap scenarios. Therefore, this article proposes a novel network architecture to pursue better performance with low overlapping regions. A spatial rotation feature encoder with point attention (PASR) is addressed to improve the rotation invariance of point clouds and enhance the network's perception of local feature extraction. Then this article introduces the overlap attention prediction (OAP) module for the estimation process of point cloud overlap factors. On this basis, the cross-attention mechanism is introduced to regress the initial transformation between two input point clouds. In addition, by combining the previous overlap factors, we constructed an iterative dual-branch similarity matrix learning (DBSML) integrated network which guides similarity estimation and further eliminates interference from non-overlapping points. Extensive experiments on ModelNet40 and our real datasets with noisy and partially overlapping point clouds show that the proposed method outperforms the traditional and mainstream learning-based methods, achieving the state-of-the-art performance. In particular, we also verify the effectiveness and superiority of the network model in coping with multiple registration task scenarios.

关键词： Dual-branch feature fusion overlap factor point cloud registration rotation invariance similarity matrix

来源：评论

学校读者我要写书评

暂无评论

Evaluation of mobile autonomous robot in trajectory optimization

引用

COMPUTING 2023年第12期105卷 2725-2745页

作者： Palacios, Rodrigo Henrique Cunha Bertoncini, Joao Paulo Scarabelo Uliam, Gabriel Henrique Oliveira Mendonca, Marcio de Souza, Lucas Botoni Fed Technol Univ Parana Dept Comp Ave Alberto Carazzai 1640Ctr BR-86300000 Cornelio Procopio PR Brazil Fed Technol Univ Parana Dept Elect Engn Ave Alberto Carazzai 1640Ctr BR-86300000 Cornelio Procopio PR Brazil

The demand for mobile robotics applications has grown considerably in recent years, especially due to the advent of industry 4.0, which has as one of its pillars the autonomous robotics field, the subject of this research. In this context, autonomous mobile robots must interact with the world to achieve their goals. One of the main challenges regarding mobile robots is the navigation problem: a robot can face several problems according to the type of sensor that is chosen in each application. The use of computer vision as a navigation tool in robotics represents an interesting alternative for controlling the movement of a mobile robot, and represent several vision techniques that gained more space in the last few years. Therefore, this work's research proposes the development of a control center to assist navigation and location of mobile robots in closed environments using the global view technique. In addition to computer vision, wireless communication (WiFi) between the exchange and the robots has been investigated to date. The results obtained in the initial steps of the project's development were promising, in which data from an autonomous robot is compared with a human-guided robot. Through the algorithm developed for the project, it was possible to transform the collected data into the robot's kinematics necessary to take the correct path to the destination using multivalued logic as a control algorithm. The optimization of the trajectory between the origin and the destination is performed using the A* and Dijkstra algorithms for calculating the shortest path.

关键词： Mobile robotics Computer vision artificial intelligence Image processing Microcontrollers

来源：评论

学校读者我要写书评

暂无评论

Classification and Featurization of Motherboard Components Using YOLO Through machine Learning

Classification and Featurization of Motherboard Components U...

引用

2024 IEEE International Conference on Computing, applications and Systems, COMPAS 2024

作者： Rahman Fahim, Md. Imadur Mahazabin Senthia, Sabrina Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331529765

The role of technology is vital and can be observed through the 5th industrial revolution. As a matter of fact, the impact is so severe, it can be felt almost everywhere. As technology advances, one of the most promising frontiers lies in the realm of artificial intelligence (AI), with machine learning at its forefront. Object detection is a crucial such part with applications in robotics and image analysis. This paper presents a comprehensive study on vision AI, focusing on the detection of motherboard components utilizing the YOLOv8 object detection framework. The results demonstrate the effectiveness and real time effectiveness of YOLOv8 in terms of motherboard object detection. Simultaneously, this work validates the feasibility of employing YOLOv8 for in-depth analysis and object detection on various circuit boards. This will allow many automated devices to repair circuit boards with the help of artificial intelligence. Consequently, allowing robots to detect flaws and repair integrated hardware components in situations considered difficult for humans. © 2024 IEEE.

关键词： Robot vision

来源：评论

学校读者我要写书评

暂无评论

machine Learning applications: From Computer vision to robotics 1st

引用

2023年

作者： Indranath Chatterjee Sheetal Zalte

ISBN: (数字)9781394173358

ISBN: (纸本)9781394173327

machine Learning applications Practical resource on the importance of machine Learning and Deep Learning applications in various technologies and real-world situations machine Learning applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader’s active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective. Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, machine Learning applications includes information on: Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records With its practical approach to the subject, Ma

关键词： artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

The JPEG AI Standard: Providing Efficient Human and machine Visual Data Consumption

引用

IEEE MULTIMEDIA 2023年第1期30卷 100-111页

作者： Ascenso, Joao Alshina, Elena Ebrahimi, Touradj Inst Super Tecn Inst Telecomunicacoes P-1049001 Lisbon Portugal Huawei Technol Duesseldorf GmbH D-80992 Munich Germany Ecole Polytech Fed Lausanne Multimedia Signal Proc Grp CH-1015 Lausanne Switzerland

The Joint Photographic Experts Group (JPEG) AI learning-based image coding system is an ongoing joint standardization effort between International Organization for Standardization (ISO), International Electrotechnical Commission (IEC), and International Telecommunication Union - Telecommunication Sector (ITU-T) for the development of the first image coding standard based on machine learning (a subset of artificial intelligence), offering a single stream, compact compressed domain representation, targeting both human visualization and machine consumption. The main motivation for this upcoming standard is the excellent performance of tools based on deep neural networks, in image coding, computer vision, and image processing tasks. The JPEG AI aims to develop an image coding standard addressing the needs of a wide range of applications such as cloud storage, visual surveillance, autonomous vehicles and devices, image collection storage and management, live monitoring of visual data, and media distribution. This article presents and discusses the rationale behind the JPEG AI vision, notably how this new standardization initiative aims to shape the future of image coding, through relevant application-driven use cases. The JPEG AI requirements, the JPEG AI history, and current status are also presented, offering a glimpse of the development of the first learning-based image coding standard.

关键词： Performance evaluation Visualization Image coding artificial intelligence Surveillance Transform coding Streaming media

来源：评论

学校读者我要写书评

暂无评论

vision-Based Mobile Robots Control Along a Given Trajectory 22nd

Vision-Based Mobile Robots Control Along a Given Trajectory

引用

22nd International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Rodziewicz-Bielewicz, Jan Korzen, Marcin West Pomeranian Univ Technol Szczecin Ul Zolnierska 49 PL-71210 Szczecin Poland

ISBN: (纸本)9783031425073;9783031425080

The paper presents the application of a computer vision approach to tracking the mobile robot's state. As an exemplary environment, we use a feedback control system for the trajectory planning and control. The system in the feedback loop use images taken from a centrally placed camera and, based on this, calculates the robots states, i.e. position and angle of rotation. The solution is adopted for indoor experiments. The experimental part shows the application of trajectory planning for multiple robots to cover a given area. The robot state is calculated using the YOLO model. We show that current machine learning techniques are fast and accurate for such applications and do not require image preprocessing or camera calibration.

关键词： Mobile robotics Computer vision vision-based control Visual servoing iRobot YOLO Trajectory planing

来源：评论

学校读者我要写书评

暂无评论

Raspberry Pi-powered door lock with facial recognition

Raspberry Pi-powered door lock with facial recognition

引用

2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Jha, Anjali Bulbule, Rajesh Nagrale, Nilesh Belambe, Tanavi Dit E&tc Dept. Pune India

ISBN: (纸本)9798350348460

Facial recognition is a widely-used process that aims to detect and verify an individual's identity. This technique is employed in various applications, such as image and video analysis, surveillance, and security systems. OpenCV is a computer vision technique that enables users to comprehend the storage, modification and feature extraction of images and videos. The advancement of artificial intelligence is heavily reliant on computer vision techniques, particularly in self-driving cars, robotics, and image-editing software. The OpenCV Library is a valuable tool for processing and displaying camera images. This research utilizes the Haar Cascade Classifier, a machine learning-based algorithm, to process a user's face image and detect faces accurately. © 2024 IEEE.

关键词： computer vision Facial recognition Haar Cascade Classifier image processing machine learning OpenCV robotics security systems self-driving cars

来源：评论

学校读者我要写书评

暂无评论

Person Re-Identification for Robot Person Following With Online Continual Learning

引用

IEEE robotics AND AUTOMATION LETTERS 2024年第11期9卷 9151-9158页

作者： Ye, Hanjing Zhao, Jieting Zhan, Yu Chen, Weinan He, Li Zhang, Hong Southern Univ Sci & Technol Shenzhen Key Lab Robot & Comp Vis Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Guangdong Univ Technol Guangzhou 510006 Peoples R China

Robot person following (RPF) is a crucial capability in human-robot interaction (HRI) applications, allowing a robot to persistently follow a designated person. In practical RPF scenarios, the person can often be occluded by other objects or people. Consequently, it is necessary to re-identify the person when he/she reappears within the robot's field of view. Previous person re-identification (ReID) approaches to person following rely on a fixed feature extractor. Such an approach often fails to generalize to different viewpoints and lighting conditions in practical RPF environments. In other words, it suffers from the so-called domain shift problem where it cannot re-identify the person when his re-appearance is out of the domain modeled by the fixed feature extractor. To mitigate this problem, we propose a ReID framework for RPF where we use a feature extractor that is optimized online with both short-term and long-term experiences (i.e., recently and previously observed samples during RPF) using the online continual learning (OCL) framework. The long-term experiences are maintained by a memory manager to enable OCL to update the feature extractor. Our experiments demonstrate that even in the presence of severe appearance changes and distractions from visually similar people, the proposed method can still re-identify the person more accurately than the state-of-the-art methods.

关键词： Feature extraction Target tracking Training Continuing education Computer vision Lighting Human-robot interaction Human-centered automation human-centered robotics human detection and tracking robot companions and continual learning

来源：评论

学校读者我要写书评

暂无评论

Active vision for Physical Robots Using the Free Energy Principle 33rd

Active Vision for Physical Robots Using the Free Energy Prin...

引用

33rd International Conference on artificial Neural Networks and machine Learning (ICANN)

作者： Haddon-Hill, Gabriel W. Murata, Shingo Keio Univ Yokohama Kanagawa Japan

ISBN: (纸本)9783031723582;9783031723599

This paper explores the application of active inference and the free energy principle (FEP) to enable active vision in physical robots, using pixel-level RGB camera observations. By adapting existing methodologies previously limited to simulated environments, we introduce architectural improvements, including spatial softmax, to address the challenges of real-world application. Our model demonstrates proficiency in both exploratory and goal-directed behaviors within complex environments, achieving a dynamic understanding of visual scenes from pixel data. Our findings further demonstrate the potential of active inference and the FEP for tackling active vision in real-world robotics, and in bridging the gap between artificial and biological systems, offering a robust framework for developing more adaptive and aware robotic agents.

关键词： Active vision Active Inference Neurorobotics

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：