检索结果-内蒙古大学图书馆

IEEE International Conference on robotics and Automation (ICRA)

作者： Nikolaos Stathoulopoulos Anton Koval Ali-akbar Agha-mohammadi George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden Jet Propulsion Laboratory California Institute of Technology Pasadena CA

This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots' poses. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework's main pipeline, offer a fast and robust region overlap estimation, hence eliminating the need for the time-consuming global feature extraction and feature matching process that is typically used in 3D map integration. The region overlap estimation provides a homogeneous rigid transform that is applied as an initial condition in the point cloud registration algorithm Fast-GICP, which provides the final and refined alignment. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments, where both ground and aerial robots are deployed, with different sensor configurations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Insights into Household Energy Consumption: Disaggregating Cooling and Heating Profiles using Principal Component Analysis

Insights into Household Energy Consumption: Disaggregating C...

引用

International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Stylianos Loizidis Fanourios Kourougianni Andreas V. Olympios Andreas Kyprianou George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Department of Mechanical and Manufacturing Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

This paper develops a methodology based on Principal Component Analysis (PCA) to disaggregate distinct baseline electricity, heating and cooling components from the daily total electricity consumption data of 68 houses in Cyprus over the course of one year. The results show that the electricity data involves several principal components, with the two main ones accounting for 52.27% and 14.83% of the total variance, representing cooling and heating, respectively. The findings highlight the potential of using PCA for effective energy disaggregation, which could be extended to various building types and communities. By analyzing energy consumption patterns, targeted energy-saving and improved energy efficiency measures can be identified. Future research will compare PCA results with physics-driven building models, explore alternative disaggregation methods, and apply the methodology to broader datasets.

关键词： Energy consumption Electric potential Cooling Electricity Buildings Energy measurement Energy efficiency Reliability Resistance heating Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Design and Modeling of a Proton Exchange Membrane Electrolyzer-Compressed Hydrogen Storage-Proton Exchange Membrane Fuel Cell Green Hydrogen Subsystem for Integration to a Solar Photovoltaic-Battery Nanogrid

Design and Modeling of a Proton Exchange Membrane Electrolyz...

引用

International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Alexandros Arsalis Fanourios Kourougianni Andreas V. Olympios Panos Papanastasiou George E. Georghiou Charalampos Konstantinou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Department of Civil and Environmental Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

A Photovoltaic-Battery-Green Hydrogen nanogrid system is designed and modeled to investigate its potential as a decentralized energy solution able to satisfy the load profile of Near Zero Energy Buildings. Such a system could potentially maximize self-consumption through onsite power generation, which also achieves minimization of dependence on the central power grid. The proposed system includes battery and hydrogen energy storage for various purposes. Such a combination could succeed in satisfying both the need for rapid power supply when needed (through battery energy storage), but it can also provide seasonal energy storage when the weather pattern of available solar energy changes between summer and winter (through hydrogen energy storage). The Green Hydrogen subsystem includes electrolyzer and fuel cell units, both based on proton exchange membrane technology, a hydrogen storage unit, and auxiliary components. The proposed system generates 110 kg of hydrogen per year, with electrolyzer and fuel cell efficiencies (at full load design conditions) at 58.4% and 45.8%, respectively. Self-sufficiency rate is close to 100%, while self-consumption rate is slightly below 48%.

关键词： Protons Photovoltaic systems Power supplies Fuel cells Solar energy Green hydrogen Minimization Power grids Batteries Load modeling

来源：评论

学校读者我要写书评

暂无评论

Constrained Generative Sampling of 6-DoF Grasps

Constrained Generative Sampling of 6-DoF Grasps

引用

IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Jens Lundell Francesco Verdoja Tran Nguyen Le Arsalan Mousavian Dieter Fox Ville Kyrki KTH Royal Institute of Technology Sweden Department of Electrical Engineering and Automation Intelligent Robotics Group School of Electrical Engineering Aalto University Finland NVIDIA Corporation USA Paul G. Allen School of Computer Science & Engineering University of Washington Seattle USA

Most state-of-the-art data-driven grasp sampling methods propose stable and collision-free grasps uniformly on the target object. For bin-picking, executing any of those reachable grasps is sufficient. However, for completing specific tasks, such as squeezing out liquid from a bottle, we want the grasp to be on a specific part of the object's body while avoiding other locations, such as the cap. This work presents a generative grasp sampling network, VCGS, capable of constrained 6-Degrees of Freedom (DoF) grasp sampling. In addition, we also curate a new dataset designed to train and evaluate methods for constrained grasping. The new dataset, called CONG, consists of over 14 million training samples of synthetically rendered point clouds and grasps at random target areas on 2889 objects. VCGS is benchmarked against GraspNet, a state-of-the-art unconstrained grasp sampler, in simulation and on a real robot. The results demonstrate that VCGS achieves a 10-15% higher grasp success rate than the baseline while being 2–3 times as sample efficient. Supplementary material is available on our project website.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning a Better Control Barrier Function

Learning a Better Control Barrier Function

引用

IEEE Conference on Decision and Control

作者： Bolun Dai Prashanth Krishnamurthy Farshad Khorrami Electrical & Computer Engineering Department Control/Robotics Research Laboratory Tandon School of Engineering New York University Brooklyn NY

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Control barrier functions (CBFs) are widely used in safety-critical controllers. However, constructing a valid CBF is challenging, especially under nonlinear or non-convex constraints and for high relative degree systems. Meanwhile, finding a conservative CBF that only recovers a portion of the true safe set is usually possible. In this work, starting from a "conservative" handcrafted CBF (HCBF), we develop a method to find a CBF that recovers a reasonably larger portion of the safe set. Since the learned CBF controller is not guaranteed to be safe during training iterations, we use a model predictive controller (MPC) to ensure safety during training. Using the collected trajectory data containing safe and unsafe interactions, we train a neural network to estimate the difference between the HCBF and a CBF that recovers a closer solution to the true safe set. With our proposed approach, we can generate safe controllers that are less conservative and computationally more efficient. We validate our approach on two systems: a second-order integrator and a ball-on-beam.

关键词： Training Heuristic algorithms Neural networks Predictive models Data collection Data models Safety

来源：评论

学校读者我要写书评

暂无评论

Joint Interference Cancellation with Imperfect CSI

Joint Interference Cancellation with Imperfect CSI

引用

MILCOM, Military Communications Conference

作者： Zhibin Zou Xue Wei Xin Tian Genshe Chen Aveek Dutta Khanh Pham Erik Blasch Intelligent Fusion Technology Inc. Germantown MD USA Department of Electrical and Computer Engineering SUNY University at Albany NY USA Air Force Research Laboratory Arlington VA USA

ISBN: (数字)9798350374230

ISBN: (纸本)9798350374247

Precoding and Modulation techniques are widely investigated to mitigate interference in the space, time, and frequency domains, respectively. However, next-generation (xG) channels are increasingly dense and mobile, making these domains highly correlated and resulting in high-dimensional channel tensors. Separate interference cancellation methods treating the channel as independent 2-D matrices fail to cancel the joint interference across multiple Degrees of Freedom (DoF). High Order Generalized Mercer’s Theorem (HOGMT) based precoding and Multidimensional Eigenwave Multiplexing (MEM) modulation have been proposed to cancel the joint interference by leveraging the jointly orthogonal eigencomponents of the channel. However, these methods rely heavily on perfect Channel State Information (CSI), and their performance degrades with imperfect CSI. In this paper, we propose a joint interference cancellation method based on the Convolutional Neural Network (CNN) with a single linear kernel (JIC-CLK). We show that a CNN with a single linear kernel is equivalent to a wireless system, where the linear kernel itself is identical to the transmitted signal with the CSI as input. By training the output to approach the desired signal at the receiver with MSE loss, the kernel converges to the optimal transmitted signal in the MMSE sense. The proposed method adapts to both perfect and imperfect CSI and can be extended to high-dimensional channels. The accuracy and generality of the proposed method are validated by simulations in three cases: 1) Perfect CSI, 2) Imperfect CSI, and 3) 4-D spatio-temporal channels with imperfect CSI.

关键词： Wireless communication Micromechanical devices Training Interference cancellation Tensors Precoding Modulation Convolutional neural networks Kernel Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

DSDFormer: An Innovative Transformer-Mamba Framework for Robust High-Precision Driver Distraction Identification

arXiv

引用

arXiv 2024年

作者： Chen, Junzhou Zhang, Zirui Yu, Jing Huang, Heqiang Zhang, Ronghui Xu, Xuemiao Sheng, Bin Yan, Hong Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-Sen University Guangzhou510275 China The School of Computer Science and Engineering South China University of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University China The Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong

Driver distraction remains a leading cause of traffic accidents, posing a critical threat to road safety globally. As intelligent transportation systems evolve, accurate and real-time identification of driver distraction has become essential. However, existing methods struggle to capture both global contextual and fine-grained local features while contending with noisy labels in training datasets. To address these challenges, we propose DSDFormer, a novel framework that integrates the strengths of Transformer and Mamba architectures through a Dual State Domain Attention (DSDA) mechanism, enabling a balance between long-range dependencies and detailed feature extraction for robust driver behavior recognition. Additionally, we introduce Temporal Reasoning Confident Learning (TRCL), an unsupervised approach that refines noisy labels by leveraging spatiotemporal correlations in video sequences. Our model achieves state-of-the-art performance on the AUC-V1, AUC-V2, and 100-Driver datasets and demonstrates real-time processing efficiency on the NVIDIA Jetson AGX Orin platform. Extensive experimental results confirm that DSDFormer and TRCL significantly improve both the accuracy and robustness of driver distraction detection, offering a scalable solution to enhance road safety. Copyright © 2024, The Authors. All rights reserved.

关键词： Highway accidents

来源：评论

学校读者我要写书评

暂无评论

Bayesian Neural Networks for Direct Short-Term Net Load Forecasting in Solar-Integrated Residential Buildings

Bayesian Neural Networks for Direct Short-Term Net Load Fore...

引用

International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Georgios Tziolis Andreas Livera Javier Lopez-Lorente Panagiotis Herodotou George Makrides George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus DNV Arnhem Netherlands

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Short-term net load forecasting (STNLF) is of utmost importance for the efficient operation and control of residential buildings, with integrated photovoltaic (PV) systems. The intermittent PV generation increases the uncertainty of STNLF and therefore the error. This study presents a machine learning model using Bayesian neural networks (BNNs) for direct STNLF in solar-integrated residential buildings. Historical data from PV-integrated households in Cyprus, with self-sufficiency between 88.59% and 294.12%, were used for assessing the performance of the BNN model. The proposed model was applied to data from 68 individual and aggregated households in Cyprus. The results indicated that the machine learning model exhibited normalized root mean square error of 7.20% for the aggregated data, rendering it suitable for STNLF in residential buildings. Finally, the model was compared to a naïve persistence model, achieving a skill score of 46.37% at the aggregated household level.

关键词： Accuracy Uncertainty Load forecasting Buildings Neural networks Machine learning Predictive models Data models Bayes methods Load modeling

来源：评论

学校读者我要写书评

暂无评论

Adaptive position tracking control of the lower limb exoskeleton robot with an uncertain dynamic model 18

Adaptive position tracking control of the lower limb exoskel...

引用

18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Yuan, Ting Zhang, Chi Yi, Feng Lv, Pingping Li, Shupei Zhang, Meitong Ningbo University Ningbo Instituteof Materials Technology and Engineering Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo315201 China Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering Ningbo315201 China University of Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo315201 China Hebei University of Science and Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences School of Electrical Engineering Ningbo315201 China

ISBN: (纸本)9798350312201

In this paper, an adaptive position control method combining proportional-derivative (PD) control, RBF neural network-based sliding mode (SM) control, and feedforward (FF) control, i.e., PD-FF-SM control, is proposed to deal with the dynamic uncertainties, disturbances, and slow response in lower limb exoskeleton robot system. The Lagrange method is utilized to establish dynamic models that ignore the uncertainties and disturbances in the swing phase and supporting phase. Then, the PD feedback controller is designed with a sliding surface function, to keep the system stable. The FF controller is adopted to improve the response by using dynamic models. And the RBF neural network-based SM controller is designed to compensate for the uncertainties and disturbances. The system's stability is verified by Lyapunov's theorem. The simulation results show that the proposed control method can effectively reduce the error of position tracking and response time with inaccurate dynamic modeling. © 2023 IEEE.

关键词： Sliding mode control

来源：评论

学校读者我要写书评

暂无评论

The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration

arXiv

引用

arXiv 2024年

作者： Greer, Ross Antoniussen, Bjørk Andersen, Mathias V. Møgelmose, Andreas Trivedi, Mohan M. The Laboratory for Intelligent and Safe Automobiles Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92092 United States The Faculty of Electronic Systems Aalborg University Denmark

Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data. We demonstrate the effectiveness of entropy querying to select informative samples, aiming to reduce annotation costs and improve model performance. We experiment using the BEVFusion model for 3D object detection on the nuScenes dataset, comparing active learning to random sampling and demonstrating that entropy querying outperforms in most cases. The method is particularly effective in reducing the performance gap between majority and minority classes. Class-specific analysis reveals efficient allocation of annotated resources for limited data budgets, emphasizing the importance of selecting diverse and informative data for model training. Our findings suggest that entropy querying is a promising strategy for selecting data that enhances model learning in resource-constrained environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Entropy

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：