检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Chen, Junzhou Huang, Heqiang Zhang, Ronghui Lyu, Nengchao Guo, Yanyong Dai, Hong-Ning Yan, Hong Guangdong Provincial Key Laboratory of Intelligent Transport System School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China School of Transportation Southeast University Nanjing210097 China Department of Computer Science Hong Kong Baptist University Hong Kong Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong

Ensuring safety in both autonomous driving and advanced driver-assistance systems (ADAS) depends critically on the efficient deployment of traffic sign recognition technology. While current methods show effectiveness, they often compromise between speed and accuracy. To address this issue, we present a novel real-time and efficient road sign detection network, YOLO-TS. This network significantly improves performance by optimizing the receptive fields of multi-scale feature maps to align more closely with the size distribution of traffic signs in various datasets. Moreover, our innovative feature-fusion strategy, leveraging the flexibility of Anchor-Free methods, allows for multiscale object detection on a high-resolution feature map abundant in contextual information, achieving remarkable enhancements in both accuracy and speed. To mitigate the adverse effects of the grid pattern caused by dilated convolutions on the detection of smaller objects, we have devised a unique module that not only mitigates this grid effect but also widens the receptive field to encompass an extensive range of spatial contextual information, thus boosting the efficiency of information usage. Evaluation on challenging public datasets, TT100K and CCTSDB2021, demonstrates that YOLO-TS surpasses existing state-of-the-art methods in terms of both accuracy and speed. The code for our method will be available. Copyright © 2024, The Authors. All rights reserved.

关键词： Advanced driver assistance systems

来源：评论

学校读者我要写书评

暂无评论

Assessment of Power Battery Health in New Energy Vehicles Through Driving Data Analytics 8

Assessment of Power Battery Health in New Energy Vehicles Th...

引用

8th International Conference on electrical, Mechanical and computer engineering, ICEMCE 2024

作者： Yang, Jia Wei, Juan Wei, Tongbian Liu, Kegui Tan, Yijing Lu, Liping Huang, Pengren Wei, Min Wuhan University of Technology Intelligent Transportation Systems Research Center Wuhan China Saic Gm Wuling Automobile Co. Ltd Technical Center Liuzhou China Wuhan University of Technology School of Computer and Artificial Intelligence Hubei Wuhan China Saic Gm Wuling Automobile Co. Ltd. Quality Department Guangxi Liuzhou China Wuhan University of Technology School of Automotive Engineering Wuhan China

ISBN: (纸本)9798331506230

With the rapid development of modern society's economy and the increase in the number of automobiles, issues of environmental pollution and energy shortages have become increasingly severe. Each country has started to vigorously promote the new energy vehicle industry. As the power source of the vehicle, the power battery is also the core component of electric vehicles. Accurate estimation of the state of the vehicle's power battery can effectively enhance the operational safety of the entire vehicle and adjust the battery's operation strategy, which is significant for delaying the performance degradation of the power battery. Estimating the state of health (SOH) of the power battery is particularly crucial. This paper proposes an estimation method for new energy vehicle batteries based on real vehicle operation data, including battery label capacity extraction, feature engineering selection, and neural network prediction based on TCN-LSTM. The predictive accuracy of this method has been enhanced in comparison to the single-method approach. © 2024 IEEE.

关键词： Data analysis Deep learning New energy vehicle

来源：评论

学校读者我要写书评

暂无评论

Towards a Theoretical Framework of Autonomous systems Underpinned by Intelligence and systems Sciences

引用

IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 52-63页

作者： Yingxu Wang Ming Hou Konstantinos NPlataniotis Sam Kwong Henry Leung Edward Tunstel Imre JRudas Ljiljana Trajkovic International Institute of Cognitive Informatics and Cognitive Computing Department of Electrical and Computer EngineeringSchulich School of Engineering and Hotchkiss Brain InstituteUniversity of CalgaryCalgary T2N 1N4Canada Toronto Research Centre Defence Research&Development CanadaToronto M3K 2C9Canada Department of Electrical&Computer Engineering University of TorontoToronto M5S3G4Canada Department of Computer Science City University of Hong KongHong KongChina Department of Electrical and Computer Engineering Schulich School of EngineeringUniversity of CalgaryCalgary T2N 1N4Canada Autonomous&Intelligent Systems Department United Technologies Research CenterCT 06108 USA University Research&Innovation Center(EKIK) Óbuda UniversityBudapest H-1034Hungary School of Engineering Science Simon Fraser UniversityBurnaby V5A 1S6Canada

Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems *** paper explores the intelligent and mathematical foundations of autonomous *** focuses on structural and behavioral properties that constitute the intelligent power of autonomous *** explains how system intelligence aggregates from reflexive,imperative,adaptive intelligence to autonomous and cognitive intelligence.A hierarchical intelligence model(HIM)is introduced to elaborate the evolution of human and system intelligence as an inductive *** properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems *** paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are *** in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.

关键词： Autonomous systems(AS) cognitive systems computational intelligence engineering paradigms intelligence science intelligent mathematics

来源：评论

学校读者我要写书评

暂无评论

SVM-based Extended Kalman Filter State Estimation of Biped Robot

SVM-based Extended Kalman Filter State Estimation of Biped R...

引用

IEEE International Conference on robotics and Biomimetics

作者： Chengzhi Gao Ye Xie Shiqiang Zhu Guanyu Huang Lingyu Kong Anhuan Xie Jianjun Gu Dan Zhang Zhejiang Laboratory Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Zhejiang University Hangzhou Zhejiang China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada York University North York Toronto Canada

ISBN: (纸本)9781665481106

At present, more and more attention has been paid to the exploration of biped robots. As the precondition of biped stable walking, the state estimation of robot has also received the attention of many experts. Biped robot has special motion characteristics, has a hybrid dynamic system and belongs to a floating base, so it must obtain the contact information of two feet before realizing the motion state estimation of the robot. The contribution of this paper is to introduce soft support vector machine to detect contact phase. This method based on support vector machine (SVM) eliminates the need for force sensors on two feet because they are vulnerable to the impact between the foot and the contact surface. In addition, precise dynamic modeling is not required. Furthermore, this paper uses SVM to obtain the touchdown information, and proposes a state estimation method using extended Kalman filter to combine motion estimation with IMU data. Finally, the touchdown detection algorithm and the state estimation algorithm are verified through experiments, and the accuracy and feasibility of the SVM method are proved through the touchdown detection experiments. Based on this, the full state estimation of the robot is realized through the state estimation experiments.

关键词： Support vector machines Legged locomotion Technological innovation Motion estimation Switches Robot sensing systems Kalman filters

来源：评论

学校读者我要写书评

暂无评论

Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models 22

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

引用

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth IoanninaGR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory HeraklionGR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering HeraklionGR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

引用

eLight 2022年第1期2卷 132-142页

作者： Qian Ma Wei Gao Qiang Xiao Lingsong Ding Tianyi Gao Yajun Zhou Xinxin Gao Tao Yan Che Liu Ze Gu Xianghong Kong Qammer HAbbasi Lianlin Li Cheng‑Wei Qiu Yuanqing Li Tie Jun Cui Institute of Electromagnetic Space Southeast UniversityNanjing 210096China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China State Key Laboratory of Millimeter Wave Southeast UniversityNanjing 210096China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou 510330China Research Center for Brain-Computer Interface Pazhou LabGuangzhou 510330China Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore University of Glasgow James Watt School of EngineeringGlasgow G128QQUK State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsPeking UniversityBeijing 100871China

Brain-computer interfaces(BCIs),invasive or non-invasive,have projected unparalleled vision and promise for assisting patients in need to better their interaction with the *** by the BCI-based rehabilitation technologies for nerve-system impairments and amputation,we propose an electromagnetic brain-computer-metasurface(EBCM)paradigm,regulated by human’s cognition by brain signals directly and *** experimentally show that our EBCM platform can translate human’s mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively,which can be further processed and transported by an information metasurface in automated and wireless *** wireless communications of the human minds are performed between two EBCM operators with accurate text ***,several other proof-of-concept mind-control schemes are presented using the same EBCM platform,exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning,wave modulations,and pattern encoding.

关键词： communication computer surface

来源：评论

学校读者我要写书评

暂无评论

Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation

arXiv

引用

arXiv 2023年

作者： Cai, Danwei Li, Ming The Department of Electrical and Computer Engineering Duke University DurhamNC27705 United States The Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Data Science Research Center Duke Kunshan University Kunshan China

This paper focuses on the application of Conformers in speaker verification. Conformers, initially designed for Automatic Speech Recognition (ASR), excel at modeling both local and global contexts within speech signals effectively. Previous research has established that ASR and speaker verification tasks can naturally complement each other. Building on this synergistic relationship, this study introduces three strategies for leveraging ASR-pretrained Conformers in speaker verification: (1) Transfer learning: We use a pretrained ASR Conformer encoder to initialize the speaker embedding network, thereby enhancing model generalization and mitigating the risk of overfitting. (2) Knowledge distillation: We distill the complex capabilities of an ASR Conformer into a speaker verification model. This not only allows for flexibility in the student mode's network architecture but also incorporates frame-level ASR distillation loss as an auxiliary task to reinforce speaker verification. (3) Parameter-efficient transfer learning with speaker adaptation: A lightweight speaker adaptation module is proposed to convert ASR-derived features into speaker-specific embeddings, without altering the core architecture of the original ASR Conformer. This strategy facilitates the concurrent execution of ASR and speaker verification tasks within a singular model. Experiments were conducted on VoxCeleb datasets. The results are compelling: models employing ASR pretraining and knowledge distillation significantly outperform standard Conformers. Specifically, the best model using the ASR pretraining method achieved a 0.43% equal error rate (EER) on the VoxCeleb1-O test trial, while the knowledge distillation approach yielded a 0.38% EER. Furthermore, by adding a mere 4.92 million parameters to a 130.94 million-parameter ASR Conformer encoder, the speaker adaptation approach achieved a 0.45% EER, enabling parallel speech recognition and speaker verification within a single ASR Conformer encoder.

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

FogROS2-FT: Fault Tolerant Cloud robotics

FogROS2-FT: Fault Tolerant Cloud Robotics

引用

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

作者： Kaiyuan Chen Kush Hari Trinity Chung Michael Wang Nan Tian Christian Juette Jeffrey Ichnowski Liu Ren John Kubiatowicz Ion Stoica Ken Goldberg Department of Electrical Engineering and Computer Science University of California Berkeley CA USA Robert Bosch Research and Technology Center North America Sunnyvale CA USA Robotics Institute Carnegie Mellon University Department of Industrial Engineering and Operations Research University of California Berkeley CA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Cloud robotics enables robots to offload complex computational tasks to cloud servers for performance and ease of management. However, cloud compute can be costly, cloud services can suffer occasional downtime, and connectivity between the robot and cloud can be prone to variations in network Quality-of-Service (QoS). We present FogROS2-FT (Fault Tolerant) to mitigate these issues by introducing a multi-cloud extension that automatically replicates independent stateless robotic services, routes requests to these replicas, and directs the first response back. With replication, robots can still benefit from cloud computations even when a cloud service provider is down or there is low QoS. Additionally, many cloud computing providers offer low-cost "spot" computing instances that may shutdown unpredictably. Normally, these low-cost instances would be inappropriate for cloud robotics, but the fault tolerance nature of FogROS2-FT allows them to be used reliably. We demonstrate FogROS2-FT fault tolerance capabilities in 3 cloud-robotics scenarios in simulation (visual object detection, semantic segmentation, motion planning) and 1 physical robot experiment (scan-pick-and-place). Running on the same hardware specification, FogROS2-FT achieves motion planning with up to 2.2x cost reduction and up to a 5.53x reduction on 99 Percentile (P99) long-tail latency. FogROS2-FT reduces the P99 long-tail latency of object detection and semantic segmentation by 2.0x and 2.1x, respectively, under network slowdown and resource contention. Videos and code are available at https://***/view/fogros2-ft.

关键词： Fault tolerance Heavily-tailed distribution Semantic segmentation Fault tolerant systems Quality of service Object detection Planning Servers Robots Cloud robotics

来源：评论

学校读者我要写书评

暂无评论

Realizing RF Wavefront Copying with RIS for Future Extended Reality Applications

Realizing RF Wavefront Copying with RIS for Future Extended ...

引用

Communications and Networking (BalkanCom), International Balkan Conference on

作者： Stavros Tsimpoukis Dimitrios Tyrovolas Sotiris Ioannidis Ian F. Akyildiz George K. Karagiannidis Christos Liaskos Institute of Computer Science Foundation for Research and Technology Hellas Greece Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Dept. of Electrical and Computer Engineering Technical University of Crete Chania Greece Truva Inc Alpharetta GA USA Artificial Intelligence & Cyber Systems Research Center Lebanese American University (LAU) Lebanon Computer Science Engineering Department University of Ioannina Ioannina Greece

ISBN: (数字)9798350365955

ISBN: (纸本)9798350365962

Lately a new approach to Extended Reality (XR), denoted as XR-RF, has been proposed which is realized by combining Radio Frequency (RF) Imaging and programmable wireless environments (PWEs). RF Imaging is a technique that aims to detect geometric and material features of an object through RF waves. On the other hand, the PWE focuses on the the conversion of the wireless RF propagation in a controllable, by software, entity through the utilization of Reconfigurable intelligent Surfaces (RISs), which can have a controllable interaction with impinging RF waves. In that sense, this dynamic synergy leverages the potential of RF Imaging to detect the structure of an object through RF wavefronts and the PWE’s ability to selectively replicate those RF wavefronts from one spatial location to wherever an XR-RF mobile user is presently located. Then the captured wavefront, through appropriate hardware, is mapped to the visual representation of the object through machine learning models. As a key aspect of the XR-RF’s system workflow is the wavefront copying mechanism, this work introduces a new PWE configuration algorithm for XR-RF. Moreover, it is shown that the waveform replication process inevitably yields imprecision in the replication process. After statistical analysis, based on simulation results, it is shown that this imprecision can be effectively modeled by the gamma distribution.

关键词： Radio frequency Wireless communication Wireless sensor networks Analytical models Extended reality Simulation Receiving antennas

来源：评论

学校读者我要写书评

暂无评论

Distributed formation control of networked mechanical systems

引用

IFAC-PapersOnLine 2022年第13期55卷 294-299页

作者： N. Javanmardi P. Borja M.J. Yazdanpanah J.M.A. Scherpen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft The Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen The Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach.

关键词： Autonomous systems Cooperative control Networked control systems Large-scale systems Port-Hamiltonian systems Scalability

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：