检索结果-内蒙古大学图书馆

International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Sugandha Singh S China Ramu Ruchika Lalit Department of Computer Science and Engineering SGT University Gurugram India Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India University School of Automation and Robotics GGSIPU East Campus New Delhi India

Emerging technologies of today are altering the way we think about the built environment. In the meantime, many technologies have undergone revolutionary changes, and India, an expanding nation, offers opportunities for development in several new areas. The goal of the study is to show how organizations need to adapt their perspective and create new strategies for emerging technologies such as artificial intelligence (AI), machine learning (ML), big data, quantum, and space exploration in light of the swift advancements in technology. This paper presents that although India has come across a lot of advancements in this era of emerging technology, certain challenges have been faced in aspects like adapting, feasibility, and approval which brought many changes across the country, which in turn is impacting the pace to transform the country towards Industry 4.0 and Industry 5.0. Therefore a study is presented to understand the impact of emerging technologies for countries coming up as Industry 4.0 and Industry 5.0. This paper emphasizes on for India’s initiatives for emerging technologies to come up as VIKSIT BHARAT @2047.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced EEG Classification via Riemannian Normalizing Flows and Deep Neural Networks

Enhanced EEG Classification via Riemannian Normalizing Flows...

引用

iNnovative Informatics of Biskra (ISNIB), International Symposium on

作者： Imad Eddine Tibermacine Ahmed Tibermacine Meftah Zouai Samuele Russo Soufyane Bouchelaghem Christian Napoli Department of Computer Automation and Management Engineering Sapienza University of Rome Rome italy Department of Computer Science University of Biskra Biskra Algeria Department of Psychology Sapienza University of Rome Rome italy Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome italy

ISBN: (数字)9798331509576

ISBN: (纸本)9798331509583

Electroencephalography (EEG) signals present unique challenges for classification tasks due to their non-stationary and high-dimensional nature. In this paper, we propose a novel method that combines Riemannian geometry with deep learning to classify multi-class EEG data. Specifically, we compute covariance matrices of EEG signals and map them onto the tangent space of the Symmetric Positive Definite (SPD) manifold. A deep neural network architecture, termed NeuroSPDNet, is designed to effectively learn from these tangent space features. The method incorporates cross-validation for robust performance evaluation and utilizes Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves to assess classification effectiveness across four classes. Experimental results demonstrate that the proposed approach achieves an overall accuracy of 96.23% with high precision and recall, significantly outperforming traditional approaches in EEG signal classification.

关键词： Manifolds Deep learning Performance evaluation Pattern classification Artificial neural networks Receivers Electroencephalography Physiology Covariance matrices Informatics

来源：评论

学校读者我要写书评

暂无评论

Gait Optimization for Legged Systems Through Mixed Distribution Cross-Entropy Optimization

Gait Optimization for Legged Systems Through Mixed Distribut...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Ioannis Tsikelis Konstantinos Chatzilygeroudis Computer Engineering and Informatics Department University of Patras Patras Greece Department of Electrical & Computer Engineering Laboratory of Automation and Robotics (LAR) University of Patras Patras Greece Department of Mathematics Computational Intelligence Laboratory (CILab) University of Patras Patras Greece

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Legged robotic systems can play an important role in real-world applications due to their superior load-bearing capabilities, enhanced autonomy, and effective navigation on uneven terrain. They offer an optimal trade-off between mobility and payload capacity, excelling in diverse environments while maintaining efficiency in transporting heavy loads. However, planning and optimizing gaits and gait sequences for these robots presents significant challenges due to the complexity of their dynamic motion and the numerous optimization variables involved. Traditional trajectory optimization methods address these challenges by formulating the problem as an optimization task, aiming to minimize cost functions, and to automatically discover contact sequences. Despite their structured approach, optimization-based methods face substantial difficulties, particularly because such formulations result in highly nonlinear and difficult to solve problems. To address these limitations, we propose CrEGOpt, a bi-level optimization method that combines traditional trajectory optimization with a black-box optimization scheme. CrEGOpt at the higher level employs the Mixed Distribution Cross-Entropy Method to optimize both the gait sequence and the phase durations, thus simplifying the lower level trajectory optimization problem. This approach allows for fast solutions of complex gait optimization problems. Extensive evaluation in simulated environments demonstrates that CrEGOpt can find solutions for biped, quadruped, and hexapod robots in under 10 seconds. This novel bi-level optimization scheme offers a promising direction for future research in automatic contact scheduling.

关键词： Pipelines Closed box Humanoid robots Graphics processing units Real-time systems Planning Quadrupedal robots Optimization Trajectory optimization Convergence

来源：评论

学校读者我要写书评

暂无评论

Interactive Navigation in Environments with Traversable Obstacles Using Large Language and Vision-Language Models

Interactive Navigation in Environments with Traversable Obst...

引用

IEEE International Conference on Robotics and automation (ICRA)

作者： Zhen Zhang Anran Lin Chun Wai Wong Xiangyu Chu Qi Dou K. W. Samuel Au Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Multiscale Medical Robotics Centre Department of Computer Science and Engineering The Chinese University of Hong Kong Computer Aided Medical Procedures Technical University of Munich Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper proposes an interactive navigation framework by using large language and vision-language models, allowing robots to navigate in environments with traversable obstacles. We utilize the large language model (GPT-3.5) and the open-set Vision-language Model (Grounding DINO) to create an action-aware costmap to perform effective path planning without fine-tuning. With the large models, we can achieve an end-to-end system from textual instructions like "Can you pass through the curtains to deliver medicines to me?", to bounding boxes (e.g., curtains) with action-aware attributes. They can be used to segment LiDAR point clouds into two parts: traversable and untraversable parts, and then an action-aware costmap is constructed for generating a feasible path. The pre-trained large models have great generalization ability and do not require additional annotated data for training, allowing fast deployment in the interactive navigation tasks. We choose to use multiple traversable objects such as curtains and grasses for verification by instructing the robot to traverse them. Besides, traversing curtains in a medical scenario was tested. All experimental results demonstrated the proposed framework’s effectiveness and adaptability to diverse environments.

关键词： Training Point cloud compression Laser radar Navigation Grounding Large language models Path planning

来源：评论

学校读者我要写书评

暂无评论

IEconformer: A Robust Convolutional Transformer for EEG-Based Fatigue Driving Detection

IEconformer: A Robust Convolutional Transformer for EEG-Base...

引用

IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Jie Chen Yipeng Du Shuyi Kong Edith C. H. Ngai Jian Liu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China Electrical Engineering and Automation at the School of Automation Beijing Institute of Technology Beijing China

ISBN: (数字)9798350379167

ISBN: (纸本)9798350379174

Fatigue driving detection technology plays a pivotal role in ensuring road safety, and electroencephalography (EEG) signals can be employed as an objective measure of driver fatigue in intelligent vehicles. However, current EEG-based fatigue driving detection methods encounter certain limitations. Firstly, the restricted receptive field of convolutional neural networks struggles to effectively handle the non-stationary nature of fatigue EEG signals for feature extraction. Secondly, real-world training data often suffers from noisy labels, leading to model overfitting on mislabeled data and consequent degradation in the fatigue detection performance. In this paper, we propose the IE conformer ensemble, a robust EEG-based fatigue driving detection model. The IEconformer architecture integrates multi-scale convolutional layers for local feature extraction and the multi-head attention mechanism to capture global feature correlations. To tackle the challenge of noisy data during training, we introduce the co-teaching plus mechanism into our training scheme. This mechanism facilitates cross-updating each IEconformer using disagreement data that yields minimal loss on the respective IEconformer. Experimental results demonstrate the superiority of our proposed IEconformer ensemble over baseline models in fatigue detection. Particularly, the IEconformer ensemble demonstrates high performance even in the presence of noisy data during the training stage, underscoring the practicality of our approach in fatigue driving detection applications for intelligent vehicles.

关键词： Training Intelligent vehicles Training data Fatigue Feature extraction Brain modeling Electroencephalography Data models Noise measurement Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Embodiment-agnostic Action Planning via Object-Part Scene Flow

arXiv

引用

arXiv 2024年

作者： Tang, Weiliang Pan, Jia-Hui Zhan, Wei Zhou, Jianshu Yao, Huaxiu Liu, Yun-Hui Tomizuka, Masayoshi Ding, Mingyu Fu, Chi-Wing The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The Department of Mechanical Engineering UC Berkeley United States The Department of Computer Science UNC-Chapel Hill United States The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

Observing that the key for robotic action planning is to understand the target-object motion when its associated part is manipulated by the end effector, we propose to generate the 3D object-part scene flow and extract its transformations to solve the action trajectories for diverse embodiments. The advantage of our approach is that it derives the robot action explicitly from object motion prediction, yielding a more robust policy by understanding the object motions. Also, beyond policies trained on embodiment-centric data, our method is embodiment-agnostic, generalizable across diverse embodiments, and being able to learn from human demonstrations. Our method comprises three components: an object-part predictor to locate the part for the end effector to manipulate, an RGBD video generator to predict future RGBD videos, and a trajectory planner to extract embodiment-agnostic transformation sequences and solve the trajectory for diverse embodiments. Trained on videos even without trajectory data, our method still outperforms existing works significantly by 27.7% and 26.2% on the prevailing virtual environments MetaWorld and Franka-Kitchen, respectively. Furthermore, we conducted real-world experiments, showing that our policy, trained only with human demonstration, can be deployed to various embodiments. Copyright © 2024, 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

Design and Implementation of IoT Enabled Smart Assistive Systems for Healthcare Applications 2

Design and Implementation of IoT Enabled Smart Assistive Sys...

引用

2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Dankan Gowda, V. Srinivas, D. Srinivas, Rachakonda Ingole, Balaji Shesharao Jadhav, Priya Dongare Prasad, K.D.V. Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Sr University School of Business Telangana Warangal506371 India Aditya Institute of Technology and Management Department of Computer Science & Engineering Tekkali Andhra Pradesh Srikakulam India GA United States Department of Robotics and Automation Maharashtra Pune412115 India Symbiosis Institute of Business Management Hyderabad India Pune India

ISBN: (纸本)9798350388916

The use of IoT technology in healthcare systems has been adopted in patient monitoring because IoT technology has been adopted rapidly in the systems. This paper discusses on design and implementation of an IoT integrated smart assistive health care system to enhance the precision and speed of patient health assessment. In the proposed system, wearable sensors are used to capture respiratory rate, pulse rate, blood pressure, oxygen saturation, and body temperature with data to be sent to the cloud server as and when it is received. Some of the features are, however, small data transmission delays, high accuracy in the physiological signals and long durations of monitoring due to the low power consumption. The simulations showed that the response time falls below 500ms, and physicians retain up to a 2 % variance to clinical grade devices, which actualized robust and effective patient care. The incorporation of predicting capacities, thus providing ways of early identification of health complications, makes the system applicable in chronic disease treatments and telemedicine. © 2024 IEEE.

关键词： Blood pressure

来源：评论

学校读者我要写书评

暂无评论

Resilient Cooperative Control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

引用

IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Overview of Load Disaggregation Models, Methods and Hardware

Overview of Load Disaggregation Models, Methods and Hardware

引用

International Universities Power engineering Conference (UPEC)

作者： Mircea N. Lancranjan Alexandru Muresan Dacian I. Jurj Alexandru Berciu Alexis Polycarpou Levente Czumbil Dan D. Micu Department Electrical Engineering Technical University of Cluj-Napoca Cluj-Napoca Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Department of Electrical and Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (数字)9798350379730

ISBN: (纸本)9798350379747

In the dynamic landscape of energy efficiency, a domain garnering ever increasing attention and urgency, the cornerstone of informed decision-making resides in the acquisition of reliable data. This paper summarizes the main approaches, methods and hardware used to gather, process and disaggregate such data on an appliance-by-appliance basis. The paper explores a critical analysis of the various models and techniques that support the gathering of energy data at the appliance level. A range of approaches, including hardware-based solutions such as smart meters, submeters, and sensor arrays, placing a special focus on intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM) algorithms that employ advanced signal processing and machine learning techniques, are examined for their effectiveness, scalability, and suitability for both industrial and commercial contexts. Furthermore, the challenges of data acquisition capabilities, reconciling scalability, cost-effectiveness, and user-friendliness - prerequisites for widespread acceptance and uptake are also investigated.

关键词： Load monitoring Costs Scalability Signal processing algorithms Hardware Smart meters Energy efficiency Transient analysis Time complexity Load modeling

来源：评论

学校读者我要写书评

暂无评论

Manufacturing Process Selection Using Graph Neural Networks: A Novel Approach to CAD Model Semantic Segmentation 4

Manufacturing Process Selection Using Graph Neural Networks:...

引用

4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Niu, Shuai Cai, Yishu Li, Feng Li, Yibo Tong, Xiaomeng School of Automation Science and Electrical Engineering Beijing China University of Liverpool Department of Computer Science Liverpool United Kingdom AVIC Shenyang Aircraft Corporation Shenyang China Central South University State Key Laboratory of Precision Manufacturing for Extreme Service Performance Changsha China

ISBN: (纸本)9798331541729

Manufacturing process selection, as a core element in the manufacturing workflow, plays a crucial role in improving processing efficiency and manufacturing quality. However, due to the fragmented, experience-based, and often outdated nature of processing knowledge, current process selection methods heavily rely on human intervention. To address these challenges, this study proposes an innovative approach that reframes manufacturing process selection as a graph-based semantic segmentation task. Building on this concept, we introduce a graph neural network-based method for manufacturing process selection, capable of effectively identifying processing methods in CAD models. This method discretely samples the topological faces and edges of CAD models, storing them as node and edge attributes in an attributed adjacency graph. Finally, we design experiments to validate the effectiveness and accuracy of this method in identifying manufacturing processes. Experimental results demonstrate that representing 3D CAD models with attributed adjacency graphs effectively captures the geometric and topological relationships between different faces of the model. This method can accurately identify corresponding manufacturing processes at the topological face level of CAD models, offering a novel approach to automated process selection. © 2024 IEEE.

关键词： Semantic Segmentation

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：