检索结果-内蒙古大学图书馆

Integrating Artificial Intelligence into Electric Vehicle Energy systems: A Survey 1

10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023

作者： Hua, Weiqi Mullen, Daniel Wahid, Abdul Sitabkhan, Khadija Mason, Karl Department of Electronic Electrical and Systems Engineering University of Birmingham BirminghamB15 2TT United Kingdom School of Engineering University of Edinburgh EdinburghEH9 3DW United Kingdom School of Computer Science University of Galway GalwayH91 FYH2 Ireland

ISBN: (数字)9783031610691

ISBN: (纸本)9783031610684

Electric vehicles play an important role in the global transition to "Net Zero" and the decarbonisation of point source emissions from road transport, as their market share continues to grow each year. However, integrating these electric vehicles into existing electricity grids, refuelling stations and supply chains presents significant challenges. But most challenges can be effectively addressed through the implementation of artificial intelligence solutions. This paper starts with an introduction to artificial intelligence and an investigation of the challenges associated with the widespread adoption of electric vehicles in society. Then, the paper analyses in detail how artificial intelligence can facilitate the smooth integration of electric vehicles. In addition, the paper provides a detailed investigation of the life-cycle emissions of electric vehicles and examines the limitations of existing research in this area and suggests potential avenues for future research. © IFIP International Federation for Information Processing 2024.

关键词： Renewable energy

来源：评论

学校读者我要写书评

暂无评论

Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Drone-Enabled Connectivity: Advancements and Challenges in B5G / 6G Networks 8

Drone-Enabled Connectivity: Advancements and Challenges in B...

引用

8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Telli, Khaled Kraa, Okba Himeur, Yassine Boumehraz, Mohamed Ouamane, Abdelmalik Benbraika, Kamel Atalla, Shadi Mansoor, Wathiq Mohamed Khider University Energy-Systems-Modelling Laboratory Biskra Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates University of Biskra LI3C Laboratory Biskra Algeria University of El-Oued Department of Computer Science El-Oued Algeria

ISBN: (纸本)9798350309249

The rapid advancement of networking and broad-band communications has created a demand for efficient relaying methods, particularly in the context of high-fidelity and low-latency last-mile communications. This paper explores the utilization of cellular-enabled-drones as Aerial-Base-Station (Aerial-BS) in cellular networks next-generation, specifically focusing on their potential as flying base stations in Beyond 5G/6G networks. The study summarises various aspects, including energy availability, mobility, optimal positioning, and node localization, handover efficiency. Additionally, multi-objective optimizations for bandwidth allocation, accommodating highly mobile User Equipment (UE), mitigating interference, and ensuring quality of service (QoS) are discussed. The paper highlights the use of drone-base-stations (DBS) as extending terrestrial network capacity and coverage, assisting mobile ad-hoc-networks, and enabling beam-forming applications. The challenges associated with optimal positioning, energy availability, path planning mobility, security, and QoS are examined, providing insights into future research directions. The findings contribute to the enhancement of cellular connectivity and offer valuable insights into the utilization of drones as flying aerial base stations in B5G / 6G network. © 2024 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Developing educational serious games as a proposal for university training in the area of Child computer Interaction

引用

International Journal of Child-computer Interaction 2024年 41卷

作者： Sanz, Cecilia Verónica Artola, Verónica Baldassarri, Sandra Institute of Research at Computer Science III-LIDI School of Computer Science National University of La Plata CIC Engineering Research Institute of Aragon (I3A) Department of Computer Science and Systems Engineering University of Zaragoza

Training in the area of CCI has become a current topic of debate. It is necessary to address the specificities related to this target audience in terms of ethical aspects, evaluation techniques and instruments and the appropriate approach to content, forms of interaction, among others. This work focuses on a project-based learning experience that is being developed with students in the area of computer science, who work on the development of educational serious games aimed at children. Different axes related to CCI training and educational game design are approached in a phase-based process and an analysis of cognitive residues that are addressed is also included. The project developed in 2023, related to healthy eating, is presented as an example of the experiences carried out with students. The results indicate that this type of proposals are attractive and motivating for the students involved, who participate in real projects with stakeholders and children. © 2024 Elsevier B.V.

关键词： Child–computer interaction Educational serious games Nutrition and healthy eating Project-based learning Tangible interaction

来源：评论

学校读者我要写书评

暂无评论

Software-defined event-based camera and vision algorithms for digitizing highly dynamic manufacturing tasks

Software-defined event-based camera and vision algorithms fo...

引用

2024 Low-Cost Digital Solutions for Industrial Automation, LoDiSA 2024

作者： Walker, Emily Turner, Christopher J. Oyekan, John Department of Automatic Control & Systems Engineering The University of Sheffield SheffieldS1 3JD United Kingdom Surrey Business School The University of Surrey Surrey GuildfordGU2 7XH United Kingdom Real Time & Distributed Systems Group Department of Computer Science University of York Heslington YorkYO10 5GH United Kingdom

ISBN: (纸本)9781837242672

Event-based cameras are inspired by the way the retina in the eye process information. Rather than capturing image frames at a constant rate, event cameras generate data asynchronously, based on changes in pixel value between frames. As a result, these cameras have a high spatiotemporal accuracy, low power constraints and reduced artefacts from motion blur. Hence, they represent favourable properties for digitizing highly dynamic manufacturing tasks such as machine vibrations and rapid human assembly actions. However, the costs of event-based cameras are prohibitively high for small to medium sized enterprises (SMEs). In this research, we present the development of a rig that combines low-cost visual sensors with low-cost algorithms to perform action recognition for highly dynamic manual manufacturing tasks. We applied our algorithms to achieve an action classification system via a novel threshold-based approach and in parallel use a neural network to perform classification of objects in the video feed. The results achieved demonstrated that our approach was able to successfully identify various rapid manual tasks being performed on identified objects. In addition to SMEs, our approach would be suited for providing low-cost digitization on legacy machines. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/).

关键词： Machine vibrations

来源：评论

学校读者我要写书评

暂无评论

Handoff and Deposit: Designing Temporal Coordination in Cross-Device Transfer Techniques for Mixed-Focus Collaboration

引用

Proceedings of the ACM on Human-computer Interaction 2022年第CSCW2期6卷 1-23页

作者： Homaeian, Leila Wallace, James R. Scott, Stacey D. School of Public Health and Health Systems University of Waterloo WaterlooONN2L 3G1 Canada School of Computer Science University of Guelph GuelphONN1G 2W1 Canada Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

When working together, people frequently share information with each other to enable division of labour, assistance, and delegation of responsibility. The literature has explored both synchronous and asynchronous transfer techniques, known as Handoff and Deposit, respectively. However, current cross-device environments tend to only provide a single mechanism. Moreover, we have little understanding of the impact of different techniques on collaborative process. To understand how Handoff and Deposit may be designed to support complex sensemaking tasks, we followed a Research through Design process to iteratively design Handoff and Deposit techniques using paper and digital sketches and high-fidelity prototypes. We consulted the HCI literature to corroborate our findings with studies and descriptions of existing cross-device transfer designs and to understand the potential impact of those designs on mixed-focus collaboration. We learned that as we move away from a restricted physical workspace and leverage the flexibility of digital personal devices, there is a large design space for realizing cross-device transfer. To inform these designs, we provide five design considerations for cross-device transfer techniques: Transfer Acceptance, Action Dependencies, Immediate Usability, Interruption Potential, and Connection Actions. © 2022 ACM.

关键词： Design

来源：评论

学校读者我要写书评

暂无评论

Domain adaptive FL for edge-enabled privacy-preserving MRI analysis

引用

Alexandria engineering Journal 2025年 128卷 324-339页

作者： Saeed Iqbal Imran Arshad Choudhry Inam Ullah Kuljeet Kaur Bong Jun Choi Mohammad Mehedi Hassan College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 China Department of Computer Science Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan Department of Computer Engineering Gachon University Seongnam 13120 Republic of Korea Electrical Engineering Department École de technologie supérieure Montréal QC H3C1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India School of Computer Science and Engineering Soongsil University Seoul Republic of Korea Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Data heterogeneity, privacy leakage challenges, the ineffectiveness of conventional collaborative learning techniques, and unresolved managing non-IID data distributions are some of the major obstacles to implementing artificial intelligence (AI) in healthcare. Federated learning (FL) frameworks frequently have trouble distinguishing between privacy protection and model accuracy, especially when used for delicate medical imaging applications. This study presents a novel framework that synergizes federated learning (FL) with edge computing to address these issues while safeguarding patient privacy. Our proposed Domain Adaptive Federated (DAD) learning approach effectively mitigates both inter-client and intra-client data heterogeneity, enabling collaborative model training across diverse medical imaging modalities (MRI, CT, PET) through cross-domain adaptation. Experimental evaluations on MRI brain segmentation datasets demonstrate the superior performance of DAD compared to traditional FL methods, as evidenced by significant improvements in F1-score (96.3), sensitivity (96.0), specificity (97.1), and AUC (96.7). This enhanced accuracy and robustness in handling heterogeneous and privacy-sensitive data render DAD an ideal candidate for privacy-preserving AI in consumer healthcare. By pioneering innovative strategies for collaborative model training and data privacy, this research contributes to the emerging field of edge intelligence, paving the way for improved patient outcomes while adhering to stringent confidentiality and ethical mandates.

关键词： Multi-modal data Domain adaptive learning Federated learning Privacy preservation Domain-specific Data privacy

来源：评论

学校读者我要写书评

暂无评论

Energy-Aware Task Allocation for Teams of Multi-mode Robots

arXiv

引用

arXiv 2025年

作者： Ito, Takumi Funada, Riku Sampei, Mitsuji Notomista, Gennaro Department of Systems and Control Engineering Institute of Science Tokyo Tokyo152-8550 Japan Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

This work proposes a novel multi-robot task allocation framework for robots that can switch between multiple modes, e.g., flying, driving, or walking. We first provide a method to encode the multi-mode property of robots as a graph, where the mode of each robot is represented by a node. Next, we formulate a constrained optimization problem to decide both the task to be allocated to each robot as well as the mode in which the latter should execute the task. The robot modes are optimized based on the state of the robot and the environment, as well as the energy required to execute the allocated task. Moreover, the proposed framework is able to encompass kinematic and dynamic models of robots alike. Furthermore, we provide sufficient conditions for the convergence of task execution and allocation for both robot models. Copyright © 2025, The Authors. All rights reserved.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

引用

IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory Control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization Information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial Special Issue on Green Open Radio Access Networks: Architecture, Challenges, Opportunities, and Use Cases

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and Networking 2024年第3期8卷 891-894页

作者： Dev, Kapal Chih-Lin, I. Boutaba, Raouf Khowaja, Sunder Ali Lien, Shao-Yu Wang, Yue Munster Technological University Department of Computer Science CorkT12 P928 Ireland China Mobile Research Institute Beijing100053 China University of Waterloo School of Computer Science WaterlooONN2L 3G1 Canada University of Sindh Faculty of Engineering and Technology Jamshoro76080 Pakistan Institute of Intelligent Systems College of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu30010 Taiwan 6G Research Centre Samsung Electronics SurreyKT16 0RS United Kingdom

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：