检索结果-内蒙古大学图书馆

4th International Conference on computer engineering and Application, ICCEA 2023

作者： Chen, Guanyu Zhang, Suli Zhang, Chenlin Qian, XiaoChun Changchun University of Technology School of Computer Science & Engineering Jilin Provincial Key Laboratory of Changbai Historical Culture and Vr Reconstruction Technology Jilin Prov. Changbai Mt. Ecol. Culture and Virtual Reality Simulation Engineering Research Center Changchun China Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin Provincial Key Laboratory of Changbai Historical Culture and Vr Reconstruction Technology Jilin Prov. Changbai Mt. Ecol. Culture and Virtual Reality Simulation Engineering Research Center Changchun China

ISBN: (纸本)9798350347548

In the study of hydraulic seepage prediction, it is often necessary to target multiple feature dimensions. In this paper, a variety of traditional machine learning algorithms based on stepwise regression filtering features are used to train a dam seepage prediction model based on isolated forest processing of data outliers. The results show that the algorithm can be applied to predict the seepage data of a reservoir more accurately, and further suggest improvements to the dam's seepage control function and observation facilities. The experimental results show that the correlation coefficient of the linear regression model is significantly improved when the training sample ratio is 0.8 after the isolated forest treatment of outliers and stepwise regression screening features. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Training Human-Robot Teams by Improving Transparency Through a Virtual Spectator Interface

arXiv

引用

arXiv 2025年

作者： Dallas, Sean Qiang, Hongjiao AbuHijleh, Motaz Jo, Wonse Riegner, Kayla Smereka, Jon Robert, Lionel Louie, Wing-Yue Tilbury, Dawn M. Robotics Department University of Michigan Ann ArborMI48105 United States Department of Electrical and Computer Engineering Oakland University RochesterMI48309 United States Department of Mechanical Engineering Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48105 United States School of Information University of Michigan Ann ArborMI48105 United States United States

After-action reviews (AARs) are professional discussions that help operators and teams enhance their task performance by analyzing completed missions with peers and professionals. Previous studies comparing different formats of AARs have focused mainly on human teams. However, the inclusion of robotic teammates brings along new challenges in understanding teammate intent and communication. Traditional AAR between human teammates may not be satisfactory for human-robot teams. To address this limitation, we propose a new training review (TR) tool, called the Virtual Spectator Interface (VSI), to enhance human-robot team performance and situational awareness (SA) in a simulated search mission. The proposed VSI primarily utilizes visual feedback to review subjects’ behavior. To examine the effectiveness of VSI, we took elements from AAR to conduct our own TR, and designed a 1 × 3 between-subjects experiment with experimental conditions: TR with (1) VSI, (2) screen recording, and (3) non-technology (only verbal descriptions). The results of our experiments demonstrated that the VSI did not result in significantly better team performance than other conditions. However, the TR with VSI led to more improvement in the subjects’ SA over the other conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Social robots

来源：评论

学校读者我要写书评

暂无评论

Generalized Polarization-Spatial Modulation With Multi-Mode Transmission

引用

IEEE Internet of Things Journal 2025年第12期12卷 18570-18581页

作者： Li, Jun Li, Shuangyuan Dang, Shuping Chen, Xuan Chen, Chuanxi Peng, Yuyang Yu, Ling Guangzhou University Research Center of Intelligent Communication Engineering School of Electronics and Communication Engineering Guangzhou510006 China Guangdong Provincial Hospital of Chinese Medicine Guangzhou510120 China University of Bristol School of Electrical Electronic and Mechanical Engineering BristolBS8 1UB United Kingdom Fujian Normal University College of Computer and Cyber Security Fuzhou350007 China Macau University of Science and Technology School of Computer Science and Engineering Taipa999078 China State Key Laboratory of Traditional Chinese Medicine Syndrome Guangzhou510120 China

Polarization-spatial modulation (PSM) has been recently proposed to improve the system performance and energy efficiency of the conventional polarization modulation (PM) by activating only a single dual-polarized (DP) antenna for signal transmission. However, the spectral efficiency (SE) of PSM is significantly degraded due to the single DP antenna transmission. To tackle this problem, we propose a generalized PSM (GPSM) scheme to enhance the SE of PSM through transmission using multiple DP antennas. In the GPSM scheme, the information bits are mapped to antenna activation patterns (AAPs), polarization matrix activation patterns (PAPs), and modulated symbols. To further enhance the SE of GPSM, we propose a more practical scheme termed multi-mode GPSM (MM-GPSM), which transmits information bits not only through the AAPs, PAPs, and modulated symbols but also via the constellation activation patterns (CAPs). To reduce the computational burden of the optimal maximum likelihood (ML) detector, we propose a low-complexity ML detector and a log-likelihood ratio (LLR) detector for both GPSM and MM-GPSM, with the negligible performance degradation. A closed-form upper bound on the bit error rate (BER) is derived to evaluate the performance of both GPSM and MM-GPSM. Simulation results demonstrate that GPSM and MM-GPSM outperform conventional PM and PSM schemes, particularly in the high signal-to-noise ratio (SNR) region, and validate the accuracy of the theoretical BER analysis. © 2014 IEEE.

关键词： Global positioning system

来源：评论

学校读者我要写书评

暂无评论

Simplified Skip-Connected UNet for Robust Speaker Verification Under Noisy Environments

Simplified Skip-Connected UNet for Robust Speaker Verificati...

引用

International Symposium on Chinese Spoken Language Processing

作者： Zonghui Wang Zhihua Fang Zhida Song Liang He School of Computer Science and Technology Xinjiang University Urumqi Xinjiang Key Laboratory of Signal Detection and Processing Urumqi School of Intelligence Science and Technology Xinjiang University Urumqi Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

In recent years, deep neural network based methods for speaker verification have made remarkable progress in clean environments. However, background noise significantly reduces the accuracy and reliability of speaker verification systems by masking or changing the voice characteristics of the speaker. In this paper, we propose a cascaded framework optimized with multiobjective loss to mitigate the interference of different levels and types of noise on the speaker verification task. The proposed architecture consists of two components: a speech enhancement module based on improved 2D-UNet, which reduces the structural limitations of directly using classical UNet for noise reduction, and a back-end speaker embedding extraction module. We carry our experiments on the VoxCeleb1 and VOiCES datasets, as well as in the presence of out-of-domain noise conditions. The evaluations have demonstrated this method shows great potential for speaker verification in noisy environments.

关键词： Training Semantics Noise reduction Interference Speech enhancement Complexity theory Noise measurement Reliability Optimization Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Dynamic Path Based DNN Synergistic Inference Acceleration in Edge Computing Environment 27

Dynamic Path Based DNN Synergistic Inference Acceleration in...

引用

27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Zhou, Meng Zhou, Bowen Wang, Huitian Dong, Fang Zhao, Wei School of Computer Science and Engineering Southeast University Nanjing210000 China Institute for Artficial Intelligence Hefei Comprehensive National Science Center Hefei230000 China School of Computer Science and technology Anhui University of Technology Ma'anshan243000 China

ISBN: (纸本)9781665408783

Deep Neural Networks (DNNs) have achieved excellent performance in intelligent applications. Nevertheless, it is elusive for devices with limited resources to support computationally intensive DNNs, while employing the cloud may lead to prohibitive latency. Better solutions are exploiting edge computing and reducing unnecessary computation. Multi-exit DNN based on the early exit mechanism has an impressive effect in the latter, and in edge computing paradigm, model partition on multi-exit chain DNNs is proved to accelerate inference effectively. However, despite reducing computations to some extent, multiple exits may lead to instability of performance due to variable sample quality, performance inferior to the original model especially in the worst case. Furthermore, nowadays DNNs are universally characterized by a directed acyclic graph (DAG), complicating the partition of multi-exit DNN exceedingly. To solve the issues, in this paper, considering online exit prediction and model execution optimization for multi-exit DNN, we propose a Dynamic Path based DNN Synergistic inference acceleration framework (DPDS), where exit designators are designed to avoid iterative entry for exits;to further promote computational synergy in the edge, the multi-exit DNN is dynamically partitioned according to network environment to achieve fine-grained computing offloading. Experimental results show that DPDS can significantly accelerate DNN inference by 1.87× to 6.78×. © 2021 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Emergency Caching: Coded Caching-Based Reliable Map Transmission in Emergency Networks

Emergency Caching: Coded Caching-Based Reliable Map Transmis...

引用

IEEE Conference on Wireless Communications and Networking

作者： Zeyu Tian Lianming Xu Liang Li Li Wang Aiguo Fei School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Frontier Research Center Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Many rescue missions demand effective perception and real-time decision making, which highly rely on effective data collection and processing. In this study, we propose a three-layer architecture of emergency caching networks focusing on data collection and reliable transmission, by leveraging efficient perception and edge caching technologies. Based on this architecture, we propose a disaster map collection framework that integrates coded caching technologies. Our framework strategically caches coded fragments of maps across unmanned aerial vehicles (UAVs), fostering collaborative uploading for augmented transmission reliability. Additionally, we establish a comprehensive probability model to assess the effective recovery area of disaster maps. Towards the goal of utility maximization, we propose a deep reinforcement learning (DRL) based algorithm that jointly makes decisions about cooperative UAVs selection, bandwidth allocation and coded caching parameter adjustment, accommodating the real-time map updates in a dynamic disaster situation. Our proposed scheme is more effective than the non-coding caching scheme, as validated by simulation.

关键词： Wireless communication Wireless sensor networks Disasters Heuristic algorithms Data collection Network architecture Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Indoor Localization Method Based on Fingerprint Expansion and Deep Learning 23

Indoor Localization Method Based on Fingerprint Expansion an...

引用

Proceedings of the 2023 7th International Conference on Electronic Information Technology and computer engineering

作者： Yun He Licai Zhu Yong Li Hao Yang School of Computer Science and Technology Nanjing Tech University China School of Information Engineering Yancheng Teachers University China Joint Technology Transfer Center of Yancheng Ploytechnic College Yancheng Ploytechnic College China

ISBN: (纸本)9798400708305

In the field of indoor positioning, maintaining accuracy and robustness has been challenging due to the complexity of environments and signal instability. To address this issue, this paper introduces the FE-DeepLoc system. Firstly, a fingerprint data augmentation method is proposed to increase training samples and enhance model robustness. Secondly, a stacked convolutional autoencoder model is employed to process fingerprint data and extract more discriminative features. Finally, a similarity score is introduced to estimate the nearest fingerprint location to the test data. The trained model is used in combination with the similarity score for positioning. Experiments conducted in large indoor environments demonstrate that this system not only outperforms traditional methods in terms of positioning accuracy but also exhibits a degree of stability, providing users with accurate and reliable positioning services.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning-driven Physically-aware Large-scale Circuit Gate Sizing

arXiv

引用

arXiv 2024年

作者： Ye, Yuyang Xu, Peng Ren, Lizheng Chen, Tinghuan Yan, Hao Yu, Bei Shi, Longxing National ASIC Research Center Southeast University Nanjing210096 China National Center of Technology Innovation for EDA Nanjing China Department of Computer Science and Engineering The Chinese University of Hong Kong NT Hong Kong School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

Gate sizing plays an important role in timing optimization after physical design. Existing machine learning-based gate sizing works cannot optimize timing on multiple timing paths simultaneously and neglect the physical constraint on layouts. They cause sub-optimal sizing solutions and low-efficiency issues when compared with commercial gate sizing tools. In this work, we propose a learning-driven physically-aware gate sizing framework to optimize timing performance on large-scale circuits efficiently. In our gradient descent optimization-based work, for obtaining accurate gradients, a multi-modal gate sizing-aware timing model is achieved via learning timing information on multiple timing paths and physical information on multiple-scaled layouts jointly. Then, gradient generation based on the sizing-oriented estimator and adaptive back-propagation are developed to update gate sizes. Our results demonstrate that our work achieves higher timing performance improvements in a faster way compared with the commercial gate sizing tool. Copyright © 2024, The Authors. All rights reserved.

关键词： Timing circuits

来源：评论

学校读者我要写书评

暂无评论

End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions

arXiv

引用

arXiv 2023年

作者： Qin, Libo Pan, Wenbo Chen, Qiguang Liao, Lizi Yu, Zhou Zhang, Yue Che, Wanxiang Li, Min School of Computer Science and Engineering Central South University China Harbin Institute of Technology China Research Center for Social Computing and Information Retrieval Cyprus Singapore Management University Singapore Department of Computer Science Columbia University United States School of Engineering Westlake University China

End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) First survey: to our knowledge, we take the first step to present a thorough survey of this research field;(2) New taxonomy: we first introduce a unified perspective for EToD, including (i) Modularly EToD and (ii) Fully EToD;(3) New Frontiers: we discuss some potential frontier areas as well as the corresponding challenges, hoping to spur breakthrough research in EToD field;(4) Abundant resources: we build a public website1, where EToD researchers could directly access the recent progress. We hope this work can serve as a thorough reference for the EToD research community. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Robot Trust and Self-Confidence Based Role Arbitration Method for Physical Human-Robot Collaboration

Robot Trust and Self-Confidence Based Role Arbitration Metho...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Qiao Wang Dikai Liu Marc G. Carmichael Chin-Teng Lin Faculty of Engineering and Information Technology Robotics Institute University of Technology Sydney Ultimo NSW Australia Faculty of Engineering and Information Technology Australian Artificial Intelligence Institute School of Computer Science University of Technology Sydney Ultimo NSW Australia

Role arbitration in human-robot collaboration (HRC) is a dynamically changing process that is affected by many factors such as physical workload, environmental changes and trust. In order to address this dynamic process, a trust-based role arbitration method is studied in this research. A computational model of robot trust and self-confidence (TSC) in physical human-robot collaboration (pHRC) is proposed. The TSC model is defined as a function of objective robot and human co-worker performance. A role arbitration method is then proposed based on the TSC model presented. The human-in-the-loop experiments with a collaborative robot are conducted to verify the TSC-based role arbitration method. The results show that the proposed method could achieve superior human-robot combined performance, reduce human co-workers' workload, and improve subjective preference.

关键词：

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：