检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Li, Mengtian Lin, Shaohui Wang, Zihan Shen, Yunhang Zhang, Baochang Ma, Lizhuang Shanghai University China Shanghai Engineering Research Center of Motion Picture Special Effects China East China Normal University China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China Beihang University China Tencent Youtu Lab China

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, mainly due to class imbalance and long-tail distributions of the point cloud data. As a result, they lead to a biased prediction for the tail class segmentation. In this paper, we introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively. In particular, we first employ two-round pseudo-lab.l generation to select unlab.led points across head-to-tail classes. We further introduce multi-class imbalanced focus loss to adaptively pay more attention to feature learning across head-to-tail classes. We fix the backbone parameters after feature learning and retrain the classifier using ground-truth points to update its parameters. Extensive experiments demonstrate the effectiveness of our method outperforming previous state-of-the-art methods on both indoor and outdoor 3D point cloud datasets (i.e., S3DIS, ScanNet-V2, Semantic3D, and SemanticKITTI) using 1% and 1pt evaluation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension 2

Extremely Short Chinese Text Classification Method Based on ...

引用

2019 2nd International Symposium on Big data and Applied Statistics, ISBDAS 2019

作者： Yue, Yongzeng Zhang, Yuhong Hu, Xuegang Li, Peipei Key Laboratory of Knowledge Engineering with Big Data [Hefei University of Technology] Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei23061 China

Short text classification methods have achieved significant progress and wide application on text data such as Twitter and Weibo. However, the extremely short chinese texts like tax invoice data are different with traditional short texts in lackness of contextual semantic information, feature sparseness and extremely short length. The existing short text classification methods are difficult to achieve a satisfactory performance in these texts. To address these problems, this paper proposes a text classification method based on bidirectional semantic extension for extremely short texts like Chinese tax invoice data. More specifically, firstly, the Chinese knowledge graph is introduced for extending bidirectional semantic of texts and lab.l data to expand the extremely short texts and ease the problem of feature sparseness;secondly, the hash vectorization is used to avoid the semantic problem caused by the lackness of contextual information. Experimental results conducted the real tax invoice dataset demonstrate the effectiveness of our proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Investigation on Skidding Characteristics and Dynamic Responses of Rolling Element Bearing Under Thermal Elastohyrodynamic Lubrication

SSRN

引用

SSRN 2023年

作者： Kang, Jianxiong Luo, Wei Yan, Changfeng Liu, Yaofeng Lü, Yanjun Wu, Lixiao Lanzhou University of Technology School of Mechanical & Electrical Engineering Lanzhou730050 China Key Lab. of NC Machine Tools and Integrated Manufacturing Equipment of the Education Ministry Xi’an710048 China Xi’an University of Technology School of Mechanical and Precision Instrument Engineering Xi’an710048 China

The unstable movement of the cage will lead to severe and frequent collisions between the rolling element and cage, it inevitably leads to wear and failure of the cage. However, the dynamics can accurately analyze the problem of cage movement. Hence, this study abandons the assumption of pure rolling and considers the thermal elastohyrodynamic lubrication, cage flexibility and pocket clearance to establish a multiple-DOF skidding dynamic model of rolling element bearings based on time-varying comprehensive stiffness and dampers. The intrinsic correlation between thermal elastohyrodynamic lubrication and oil film thickness, contact damping, and stiffness is studied. Considering the viscous resistance and centrifugal force of rolling element, a flexible cage model is established, and the effects of pocket clearance, radial clearance, and rolling element deformation on the interaction force between the cage and rolling element are analyzed. The influence of service conditions and bearing parameters on skidding characteristics is explored, and the model is validated using existing models and experiments. The results indicate that thermal elastohyrodynamic lubrication has a significant impact on oil thickness, pressure, and temperature. The skidding speed and slipping rate increase with the increase of rotational speed, and decrease with the increase of radial load. With the increase of the cage pocket clearance and equivalent stiffness, the skidding speed and slipping rate decrease. This study can provide support for the optimization design, friction reduction and early failure of rolling element bearings. © 2023, The Authors. All rights reserved.

关键词： Friction

来源：评论

学校读者我要写书评

暂无评论

Intrusion signal discrimination method based on MFCC-Energy entropy feature and FTO-SVM

Intrusion signal discrimination method based on MFCC-Energy ...

引用

2022 International Conference on Electronic Information Technology, EIT 2022

作者： Chen, Hepu Wu, Huaming Zhang, Yechao Xiao, Wenbo Xiao, Yongsheng Huang, Lizhen Zeng, Jie Natl. Eng. Lab. for Non-Destructive Testing and Optoelectronic Sensing Technology and Applications Ministry of Education Nanchang330063 China Jiangxi Engineering Laboratory for Optoelectronics Testing Technology School of Measuring and Optical Engineering Nanchang Hangkong University Nanchang330063 China School of Information Engineering Nanchang Hangkong University Nanchang330063 China State Key Laboratory of Mechanical Structure Mechanics and Control Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9781510655096

Aiming at the problem that Distributed Optical Fiber Acoustic Sensing (DAS) system will misjudge external intrusion signals, an intrusion signal discrimination method based on MFCC-Energy entropy feature and FTO-SVM is proposed. Firstly, the former 13-dimensional Mel-Frequency Cepstral Coefficients (MFCC) is extracted from the collected sound signal, and Principal Component Analysis (PCA) is used to reduce the dimension of MFCC;Secondly, the energy entropy of the collected sound signal is calculated, which fused with the reduced MFCC as the feature parameter of the collected sound signal;Finally, the extracted feature parameters are discriminated by using Support Vector Machine (SVM) with the hyperparameters optimized by the Fibonacci Tree Optimization (FTO) algorithm. The results show that the proposed method can effectively improve the system discrimination accuracy for intrusion signals, and is of great significance to perimeter security and fault diagnosis and other related fields. © 2022 SPIE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Adaptive Student Inference Network for Efficient Single Image Super-Resolution

Adaptive Student Inference Network for Efficient Single Imag...

引用

IEEE International Conference on data Mining (ICDM)

作者： Kang Miao Zhao Zhang Jiahuan Ren Mingbo Zhao Haijun Zhang Richang Hong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China School of Information Science and Technology Donghua University Shanghai China Department of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China

Recent advances in single image super-resolution (SISR) have achieved remarkable performance through deep learning. However, the high computational cost hinders the deployment of SISR models on edge devices. Instead of proposing new SISR models, a new trend is emerging to improve network efficiency by reducing parameters, FLOPs, and inference time through slight modifications to the original models. However, recent methods usually focus on reducing only one of three metrics, i.e., FLOPs, parameters and inference time, which inevitably increases the other two metrics. In this paper, we propose a novel Adaptive Student Inference Network (ASIN) on popular SISR models, which aims at reducing FLOPs and inference time while maintaining the number of parameters and restoring clearer high-resolution images. Specifically, our ASIN divides a SISR model into three components (head, body and tail) and adopts various strategies for each part. For head and tail parts, to ensure the restored images contain more detailed information, a novel auxiliary Enhanced Teacher Network (ETNet) is designed, which is trained with the ground-truth images to obtain more prior knowledge to guide student network to extract more accurate textures using a new knowledge distillation method. For the body part, owing to the varying difficulties of the reconstructions in different regions, we propose an Adaptive Depth Predicted Module (ADPM) to dynamically shorten average depth of network to reduce the computational cost of overall network. Extensive experiments on two datasets demonstrate the effectiveness and state-of-the-art performance of our ASIN compared to its counterparts.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Context Modeling Network for Landmark Recognition

Hierarchical Context Modeling Network for Landmark Recogniti...

引用

IEEE International Conference on data Mining (ICDM)

作者： Xing Bao Huan Zheng Zhao Zhang Zhengjun Zha Meng Wang School of Information Science and Technology University of Science and Technology of China Hefei China Aerospace Information Research Institute Suzhou China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China

Landmark recognition stands as a prominent classification challenge within the domain of vision and perception, involving the identification and localization of landmarks in images. However, existing landmark recognition methods often fall short in delivering satisfactory performance. A critical issue lies in the lack of simultaneous exploration of local, regional, and global modeling. Intuitively, the local details, regional features, and global structure within an image, especially of a building, significantly contribute to landmark recognition. To address this issue, we propose a novel approach, termed Hierarchical Context Modeling Network (HCMNet), for landmark recognition. In our methodology, we introduce a Hierarchical Context Modeling Block (HCMB) designed with a triplet-branch structure to model image context in a hierarchical manner. These branches consist of the local branch, which leverages a convolutional layer to capture local details, the regional branch focused on extracting regional features through large-kernel asymmetrical convolutional layers, and the global branch aims at encompassing the broader context by incorporating channel attention. To enhance the modeling capacity, we assemble multiple HCMBs into the HCMNet, creating a hierarchical structure. Comprehensive experiments validate the efficacy of the proposed HCMNet for landmark recognition, showcasing its superior performance compared to current methods. The proposed hierarchical context modeling approach proves instrumental in capturing the intricate features essential for accurate landmark recognition.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Aerial Reliable Collab.rative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

arXiv

引用

arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collab.rative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalab.lity and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Syntax-enhanced pre-trained model 59

Syntax-enhanced pre-trained model

引用

Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Xu, Zenan Guo, Daya Tang, Duyu Su, Qinliang Shou, Linjun Gong, Ming Zhong, Wanjun Quan, Xiaojun Jiang, Daxin Duan, Nan School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Microsoft Research Asia Beijing China Microsoft Search Technology Center Asia Beijing China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781954085527

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens. © 2021 Association for Computational Linguistics

关键词： Syntactics

来源：评论

学校读者我要写书评

暂无评论

High-Performance Sub-Micron Cigsse Solar Cells Optimized for Sodium Doping by Adjusting Diffusion Barriers

SSRN

引用

SSRN 2022年

作者： Wang, C. Lia, Y. Zhuang, Daming Zhao, Ming Tong, H. Wang, H. Wei, J. Gong, Q. School of Materials Science and Engineering Tsinghua University Beijing100084 China Key Lab. for Advanced Materials Processing Technology Ministry of Education State Key Lab. of New Ceramics and Fine Processing College of Mechanical and Vehicle Engineering Taiyuan University of Technology Taiyuan030024 China

Alkali metal Na is beneficial to the performance of CuInGaSSe thin film solar cells. This work studies the addition and optimization of a sputtering SiO 2 barrier layer on the SLG to optimize the Na content that diffuses into the absorber layer from the SLG. The resulting Se content and distribution, the influence and change of the MoSe 2 thickness are studied. Also, the changes in quality of the p-n junction and the defects of the absorption layer are studied. Last, the resulting carrier separation and changes in the carrier recombination rate of each part of the absorber are analyzed. For sub-micron CIGS devices, it is clear that untreated SLG provides excessive amounts of Na. After optimization and analysis of the barrier layer thickness, the average conversion efficiency is increased by 34% compared with the sub-optimal sample, and the efficiency reaches 18.9%. © 2022, The Authors. All rights reserved.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

HUSS: A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

HUSS: A Heuristic Method for Understanding the Semantic Stru...

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S. Sheng Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science Texas Tech University Lubbock TX USA

Spreadsheets contain a lot of valuable data and have many practical applications. The key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets, e.g., identifying cell function types and discovering relationships between cell pairs. Most existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells. A few studies do, but they ignore the layout structure information of spreadsheets, which affects the performance of cell function classification and the discovery of different relationship types of cell pairs. In this paper, we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets (HUSS). Specifically, based on the existing cell function classification model [11], we propose five types of heuristic rules to extract four different types of cell pairs, based on the cell style and spatial location information. Our experimental results on two real-world datasets demonstrate that the proposed method HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Analytical models Heuristic algorithms Semantics Layout Feature extraction Classification algorithms data mining

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：