检索结果-内蒙古大学图书馆

IEEE Symposium on Intelligent Vehicle

作者： Suiyi He Shian Wang Yunli Shao Zongxuan Sun Michael W. Levin Department of Mechanical Engineering University of Minnesota Minneapolis MN USA Department of Electrical and Computer Engineering The University of Texas at El Paso El Paso TX USA Oak Ridge National Laboratory Oak Ridge TN USA Department of Civil Environmental and Geo-Engineering University of Minnesota Minneapolis MN USA

Emerging vehicle sensing and communication technologies allow for real-time information exchange between connected vehicles (CVs) and intelligent infrastructure. This presents a unique opportunity for predicting traffic states such as speed and density. A promising application of traffic prediction is eco-driving speed control of CVs, which requires future traffic information along the look-ahead time horizon. However, it is challenging to obtain accurate real-time traffic prediction for the next 10-15 s, particularly for mixed traffic involving both CVs and human-driven vehicles (HVs), complicated further by the presence of lane changing maneuvers. In this article, we address this pressing problem by integrating a macroscopic traffic flow model for prediction with a microscopic vehicle model for speed control. Specifically, we modify the well-known second-order Payne-Whitham (PW) model to account for the impacts of lane changing on traffic state evolution, based on which we develop a traffic prediction framework capable of handling mixed traffic. CVs provide partial measurements of traffic states, while the unknown states are estimated using an unscented Kalman filter (UKF). Consequently, future traffic states are obtained by propagating the PW model forward in time, and optimal eco-driving speed controls are obtained for electric vehicles (EVs) using the prediction results. The proposed approach is evaluated using ample traffic data collected from Simulation of Urban MObility (SUMO). The results show an average energy benefit of 6.6% for the ego vehicle considering all the simulated scenarios, among which the maximum energy benefit is about 16.18%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The effect of B – site cation on the supercapacitive properties of LaBO3 (B = Cr, Mn, Fe and Co) porous perovskites

引用

Journal of Energy Storage 2024年 86卷

作者： Ashok C, Shyamli Vazhayil, Ashalatha Vinaykumar, R. Thomas, Jasmine Jeffery, A. Anto Hasan, Imran Thomas, Nygil Yadav, Akhilesh Kumar Ahn, Young-Ho Department of Chemistry Nirmalagiri College Kerala Kannur India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Karnataka Bangalore5600064 India Environmental Science and Engineering Laboratory Department of Civil Engineering Yeungnam University Gyeongsan38541 Korea Republic of Department of BioEngineering Saveetha School of Engineering Tamil Nadu Chennai602105 India Department of Chemistry College of Science King Saud University Riyadh11451 Saudi Arabia College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei10608 Taiwan Varanasi221005 India

Perovskite oxides have attracted great attention as favorable electrode materials for supercapacitors due to their sole structure, intrinsic oxygen vacancy and compositional flexibility. However, a facile synthetic route which is simple and economical is a challenging issue related to the synthesis of such complex functional oxides. We present a facile and ultrafast way to synthesize LaBO3 (B = Cr, Mn, Fe and Co) perovskite nanocrystallites using glycine-nitrate autocombustion followed by annealing. The structure, phase purity, thermal stability, morphology and surface features of thus synthesized perovskite oxides were analyzed by X-ray diffraction, Fourier transform infrared spectroscopy, Field emission scanning electron microscopy, High resolution transmission electron microscopy, X-ray photoelectron spectroscopy, and Brunauer-Emmet-Teller measurements. The role of B – site cation on the electrochemical performance has been investigated by cyclic voltammetry, galvanostatic charge discharge measurements and electrochemical impedance spectroscopy. Electrochemical measurements showed that, among the synthesized materials, LaBO3 nanoparticles with smallest transition metal cation at its B – site, that is LaCoO3 delivered highest value of specific capacitance (750 F/g) and the value of specific capacitance decreases in the order LaCoO3 ˃ LaMnO3 ˃ LaCrO3 ˃ LaFeO3. Therefore, B – site transition metal cation holds the key to regulate perovskite's properties which are essential prerequisites for supercapacitors. An asymmetric device fabricated using LaCoO3 and activated carbon as the positive and negative electrode respectively acted in broad potential window (1.7 V) and delivered an energy density of 22.2 Wh/kg at a power density of 997.7 W/kg and two supercapacitors connected in series could power a red light emitting diode efficiently. © 2024 Elsevier Ltd

关键词： Supercapacitor

来源：评论

学校读者我要写书评

暂无评论

A Systematic Evaluation of Different Indoor Localization Methods in Robotic Autonomous Luggage Trolley Collection at Airports

arXiv

引用

arXiv 2023年

作者： Sun, Zhirui Chen, Weinan Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Guangdong University of Technology Guangzhou China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada

This article addresses the localization problem in robotic autonomous luggage trolley collection at airports and provides a systematic evaluation of different methods to solve it. The robotic autonomous luggage trolley collection is a complex system that involves object detection, localization, motion planning and control, manipulation, etc. Among these components, effective localization is essential for the robot to employ subsequent motion planning and end-effector manipulation because it can provide a correct goal position. In this article, we survey four popular and representative localization methods to achieve object localization in the luggage collection process, including radio frequency identification (RFID), Keypoints, ultrawideband (UWB), and Reflectors. To test their performance, we construct a qualitative evaluation framework with Localization Accuracy, Mobile Power Supplies, Coverage Area, Cost, and Scalability. Besides, we conduct a series of quantitative experiments regarding Localization Accuracy and Success Rate on a real-world robotic autonomous luggage trolley collection system. We further analyze the performance of different localization methods based on experiment results, revealing that the Keypoints method is most suitable for indoor environments to achieve the luggage trolley collection. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

From OTFS to DD-ISAC: Integrating Sensing and Communications in the Delay Doppler Domain

arXiv

引用

arXiv 2023年

作者： Yuan, Weijie Zhou, Lin Dehkordi, Saeid K. Li, Shuangyang Fan, Pingzhi Caire, Giuseppe Poor, H. Vincent The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The School of Cyber Science and Technology the Beijing Laboratory for General Aviation Technology Beihang University Beijing100191 China The Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin10587 Germany The Institute of Mobile Communications Southwest Jiaotong University Chengdu611756 China The Department of Electrical and Computer Engineer- ing Princeton University PrincetonNJ08544 United States

Next-generation vehicular networks are expected to provide the capability of robust environmental sensing in addition to reliable communications to meet intelligence requirements. A promising solution is the integrated sensing and communication (ISAC) technology, which performs both functionalities using the same spectrum and hardware resources. Most existing works on ISAC consider the Orthogonal Frequency Division Multiplexing (OFDM) waveform. Nevertheless, vehicle motion introduces Doppler shift, which breaks the subcarrier orthogonality and leads to performance degradation. The recently proposed Orthogonal Time Frequency Space (OTFS) modulation, which exploits various advantages of Delay Doppler (DD) channels, has been shown to support reliable communication in high-mobility scenarios. Moreover, the DD waveform can directly interact with radar sensing parameters, which are actually delay and Doppler shifts. This paper investigates the advantages of applying the DD communication waveform to ISAC. Specifically, we first provide a comprehensive overview of implementing DD communications, based on which several advantages of DD-ISAC over OFDM-based ISAC are revealed, including transceiver designs and the ambiguity function. Furthermore, a detailed performance comparison are presented, where the target detection probability and the mean squared error (MSE) performance are also studied. Finally, some challenges and opportunities of DD-ISAC are also provided. © 2023, CC BY.

关键词： Doppler effect

来源：评论

学校读者我要写书评

暂无评论

A cross Transformer for image denoising

引用

Information Fusion 2024年 102卷

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Shichao Zhang, Yanning Lin, Chia-Wen School of Software Northwestern Polytechnical University Xi'anShaanxi710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'anShaanxi710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi'an710129 China Department of Electrical Engineering and the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://***/hellloxiaotian/CTNet. © 2023 Elsevier B.V.

关键词： Image denoising

来源：评论

学校读者我要写书评

暂无评论

Multi-behavior Learning for Socially Compatible Autonomous Driving

Multi-behavior Learning for Socially Compatible Autonomous D...

引用

International Conference on Intelligent Transportation

作者： Sanjula Jayawardana Vindula Jayawardana Kaneeka Vidanage Cathy Wu University of Westminster UK Informatics Institute of Technology Colombo Sri Lanka Department of Electrical Engineering and Computer Science MIT Laboratory for Information & Decision Systems Cambridge MA USA Department of Civil and Environmental Engineering MIT Laboratory for Information Decision Systems Institute of Data Systems and Society Cambridge MA USA

Autonomous vehicles (AVs) have the potential to revolutionize transportation, but their effective integration into the real world requires addressing the challenge of interacting with human drivers. Real-world driving involves negotiating and cooperating with fellow drivers through social cues, necessitating AVs to also demonstrate such social compatibilities. However, despite the popularity, current learning-based control methods for AV policy synthesis often overlook this crucial aspect. In this work, we look at the problem of enabling socially compatible driving when AV control policies are learned. We leverage human driving data to learn a social preference model of human driving and then integrate it with reinforcement learning-based AV policy synthesis using Social Value Orientation theory. In particular, we propose to use multi-task reinforcement learning to learn diverse social compatibility levels in driving (ex: altruistic, prosocial, individualistic, and competitive), focusing on the requirement of having diverse behaviors in real-world driving. Using highway driving scenarios, we demonstrate through experiments that socially compatible AV driving not only enables naturalistic driving behaviors but also reduces collision rates from the baseline. Our findings reveal that without social compatibility, AV policies tend to adopt dangerously competitive driving behaviors, while the incorporation of social compatibility fosters smoother vehicle maneuvers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Region targeted robotic needle guidance using a cameraintegrated optical coherence tomography

Region targeted robotic needle guidance using a cameraintegr...

引用

Optical Coherence Tomography, OCT 2022

作者： Wei, Shuwen Kim, Ji Woong Martin-Gomez, Alejandro Zhang, Peiyao Iordachita, Iulian Kang, Jin U. Department of Electrical and Computer Engineering Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States Laboratory for Computational Sensing and Robotics Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States Department of Mechanical Engineering Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States

We implement a low-cost camera-integrated optical coherence tomography system and demonstrate its feasibility to guide a surgical needle to a general targeted region for subretinal injection combining manual control a... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical tomography

来源：评论

学校读者我要写书评

暂无评论

Multimodal Learning for Materials

arXiv

引用

arXiv 2023年

作者： Moro, Viggo Loh, Charlotte Dangovski, Rumen Ghorashi, Ali Ma, Andrew Chen, Zhuo Kim, Samuel Lu, Peter Y. Christensen, Thomas Soljačić, Marin Department of Physics Massachusetts Institute of Technology United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Research and Exploratory Development John Hopkins University Applied Physics Laboratory United States Data Science Institute University of Chicago United States Department of Electrical and Photonics Engineering Technical University of Denmark Denmark

Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials. Recently, publicly available material data repositories have grown rapidly. This growth encompasses not only more materials, but also a greater variety and quantity of their associated properties. Existing machine learning efforts in materials science focus primarily on single-modality tasks, i.e., relationships between materials and a single physical property, thus not taking advantage of the rich and multimodal set of material properties. Here, we introduce Multimodal Learning for Materials (MultiMat), which enables self-supervised multi-modality training of foundation models for materials. We demonstrate our framework’s potential using data from the Materials Project database on multiple axes: (i) MultiMat achieves state-of-the-art performance for challenging material property prediction tasks;(ii) MultiMat enables novel and accurate material discovery via latent space similarity, enabling screening for stable materials with desired properties;and (iii) MultiMat encodes interpretable emergent features that may provide novel scientific insights. Copyright © 2023, The Authors. All rights reserved.

关键词： Materials properties

来源：评论

学校读者我要写书评

暂无评论

Graph partitioning and sparse matrix ordering using reinforcement learning and graph neural networks

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2022年第1期23卷 13675-13702页

作者： Alice Gatti Zhixiong Hu Tess Smidt Esmond G. Ng Pieter Ghysels Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA LinkedIn Corporation Sunnyvale CA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA

We present a novel method for graph partitioning, based on reinforcement learning and graph convolutional neural networks. Our approach is to recursively partition coarser representations of a given graph. The neural network is implemented using SAGE graph convolution layers, and trained using an advantage actor critic (A2C) agent. We present two variants, one for finding an edge separator that minimizes the normalized cut or quotient cut, and one that finds a small vertex separator. The vertex separators are then used to construct a nested dissection ordering to permute a sparse matrix so that its triangular factorization will incur less fill-in. The partitioning quality is compared with partitions obtained using METIS and SCOTCH, and the nested dissection ordering is evaluated in the sparse solver SuperLU. Our results show that the proposed method achieves similar partitioning quality as METIS, SCOTCH and spectral partitioning. Furthermore, the method generalizes across different classes of graphs, and works well on a variety of graphs from the SuiteSparse sparse matrix collection.

关键词： graph partitioning sparse matrix ordering graph neural networks reinforcement learning machine learning for scientific computing

来源：评论

学校读者我要写书评

暂无评论

Misalignment Tolerance of Inductive Power Transfer Coupler with Low Loss and High Magnetic Induction Ferromagnetic Materials

Misalignment Tolerance of Inductive Power Transfer Coupler w...

引用

2022 IEEE Industry Applications Society Annual Meeting, IAS 2022

作者： Kang, Jinping Cheng, Shaoyu Wang, Yubo Zhang, Xueying Yang, Fuyao Xu, Guorui Eldeeb, Hassan H. Zhao, Haisen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China State Grid Smart Grid Research Institute Co. LTD. State Key Laboratory of Advanced Power Transmission Technology 102211 China Noblesville Technical Center of BorgWarner Inc. NoblesvilleIN United States Florida International University Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9781665478151

In an inductive power transfer (IPT) system, various magnetic materials have difference characteristics in enhancing the degree of coupling and misalignment tolerance between the primary and secondary coils. However, the iron losses in ferromagnetic materials may decrease the transfer efficiency of the IPT system significantly. Therefore, in this study, performance and misalignment tolerance of IPT systems with four types of ferromagnetic materials are investigated. Firstly, an equivalent circuit model of the IPT system is established and the variations of system efficiency with different ferromagnetic materials are also revealed. Then, a 3-D finite element analysis (FEA) model of the magnetic coupler is established. In addition, loss test platform is established to obtain the accurate loss coefficients. Finally, the transfer efficiency and magnetic field distributions are both investigated. The results show that the high efficiency and magnetic flux density can be achieved when nanocrystalline core with vertical laminations is used. © 2022 IEEE.

关键词： Ferromagnetic materials

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：