检索结果-内蒙古大学图书馆

Information Science and Education (ICISE-IE), International Conference on

作者： Xiaoke Zhu Zhiwei Dong Xiaopan Chen Yufeng Shi Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

After the outbreak of the COVID-19 pandemic, online teaching has gradually emerged as an indispensable component of education. Despite its convenience, online education lacks the immediate interactive experience inherent in traditional classrooms. This paper presents a meticulously designed DS-YOLOv8 network model, specifically tailored for real-time recognition of students raising their hands under cameras. On our proprietary dataset, this network model exhibits exceptional accuracy in hand-raising detection, showcasing a 1.3% improvement in the AP50 index compared to the baseline YOLOv8 model. Our focus lies on the application of hand-raising detection within online classrooms with an objective to provide enhanced support for teachers by promptly identifying students raising their hands and thereby improving teaching efficiency while enriching the interactive experience of students in virtual learning environments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Handover Scheme Based on Mobility Prediction for Autonomous Moving Platforms in 5G Networks

A Handover Scheme Based on Mobility Prediction for Autonomou...

引用

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Lei Zhang Xuefei Chen Yuxiang Ma School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

In recent years, Autonomous Moving Platforms (AMP) have gradually become an essential part of industry and life, including a variety of water, land and air mobile platforms. 5G networks provide AMP with low-latency and high-speed communication services which can promote the construction of smart cities. However, the mobility of smart-AMP affects the handover in 5G networks. With the intensive deployment of gNBs, unreasonable handover will increase the power con-sumption of AMP and reduce the quality of service (QoS) of AMP communications. Thus, we propose a 5G handover method based on AMP trajectory prediction in this paper. A CNN-STLSTM neural network which combines a convolutional neural network (CNN) and Spatioal-Timporallong short-term memory (ST- LSTM) is proposed to predict the trajectory of AMP. Then, the gNB is selected to handover through the AMP's trajectory points predicted by the prediction model. The evaluation results show that the proposed mechanism can effectively reduce the number of hand over and improve the efficiency and stability of AMP networks.

关键词： Wireless communication 5G mobile communication Smart cities Neural networks Quality of service Handover Predictive models

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Max-Min Ant System Algorithm for Electric Capacitated Vehicle Routing Problem

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer Science 2024年第3期51卷 195-203页

作者： Hou, Yan-e Wang, Congran Zhang, Chunyang Dang, Lanxue Xiao, Chunjing Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China

Electric vehicles have become the first choice for products delivery in logistics, due to their environmental friendliness and low cost compared to the traditional fuel vehicles. This paper deals with an electric capacitated vehicle routing problem with variable energy consumption rate (E-CVRP), which aims to minimize the total cost including vehicle fixed cost, traveled distance cost and energy cost. First, a mixed integer linear programming model is constructed based on the fact that the energy consumption is affected by the vehicle load. Second, a hybrid Max-Min Ant System (HMMAS) algorithm is proposed to solve this problem. Three neighborhood structures are introduced and executed in a Variable Neighborhood Descent (VND) algorithm to improve the current solution. In addition, an effective charging station adjustment strategy is adopted to further improve the solution obtained by the local search process. Finally, some experiments have been conducted on public instances, and the results demonstrate the effectiveness and stability of the algorithm. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

An efficient backbone network for target detection in aerial images 4

An efficient backbone network for target detection in aerial...

引用

2021 4th International Conference on Computer Information Science and Application Technology, CISAT 2021

作者： Zhang, Sihan Liu, Yaping Wang, Xile Zhang, Miaohui School of Artificial Intelligence Henan University Henan Kaifeng475004 China School of Mathematics and Statistics Henan University Henan Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Kaifeng475004 China

Target detection in aerial images by high altitude unmanned aerial vehicles is one of the hot research topics. A detection that is efficient and accurate is of a high value in the military and civilian fields. However, due to the irregular distribution of targets and their various scales shown in aerial images, it is difficult for existing backbone networks to effectively extract target features based on deep learning. To address these problems, in the paper, an efficient backbone network called AerNet is proposed, to which a local feature enhancement module (LFEM) is added to fully extract discriminative features of aerial targets via multi-scale convolutional layers. The network AerNet consists of 55 layers, which can keep high spatial resolution in deeper layers and maintain a large receptive field. Experimental results show that our AerNet has achieved a satisfying detection performance on the DOTA benchmark. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment analysis

A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment An...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Lanxue Dang Chunyu Wang Hongyu Han Yan-E Hou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science and Information Engineering Henan University Kaifeng China

Sentiment analysis is an important and challenging task in the field of natural language processing. Researchers have used vocabulary-based methods and machine learning methods to conduct research on sentiment analysis tasks. In recent years, deep learning has achieved great success in the field of sentiment analysis. However, for complex textual data, using a single model is often insufficient. Inspired by deep learning models, in this study, we proposed a hybrid model using BiLSTM and attention mechanism, called BiLSTM-ATT model, to solve the problem of sentiment analysis. First, we adopted the GloVe method to train the initialized word embeddings. GloVe converts textual information into word vectors, which can calculate the distance between words. Next, we used a convolutional neural network that can extract local features, and a BiLSTM that can extract long-range semantic features of text with bidirectional extraction of long-term dependencies. Finally, the attention mechanism was used to improve the performance of the model by calculating the weight of the data. Experimental results demonstrate that our proposed hybrid BiLSTM-ATT model outperforms traditional deep learning methods in Accuracy, Recall, and Fl-Score. Our method was compared with deep learning methods on the IMDB movie review dataset.

关键词： Deep learning Analytical models Sentiment analysis Semantics Feature extraction Motion pictures data models

来源：评论

学校读者我要写书评

暂无评论

Multi-UAVs cooperative task assignment and path planning scheme

Multi-UAVs cooperative task assignment and path planning sch...

引用

2021 International Conference on Computer Network Security and Software Engineering, CNSSE 2021

作者： Du, Xiaoyu Guo, Qicheng Li, Hui Zhang, Yanyu International Joint Research Laboratory for Cooperative Vehicular Networks of Henan Henan University Kaifeng Henan475000 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng Henan475000 China Henan University Minsheng College Kaifeng Henan475000 China

The task assignment and path planning problems of multiple unmanned aerial vehicles (multi-UAVs) cooperative assistance roadside units (RSUs) for data collection are optimization problems with the goal of minimizing time and energy consumption. This paper proposes a hierarchical optimization scheme for multi-UAVs collaborative assistance RSUs data collection. This solution solves the problems that the number of UAVs needs to be set in advance, the convergence speed is slow when the number of tasks increases, and it is easy to fall into a local optimal solution, and the convergence accuracy is poor. First, the solution uses the K-means algorithm to allocate tasks and group RSUs to find the right number of UAVs to perform the task. Then, this paper proposes a hybrid optimization algorithm based on bionic learning for path planning. Finally, we set up a reasonable evaluation mechanism and conducted simulation experiments. The algorithm in this paper is compared with genetic algorithm, gray wolf algorithm and whale algorithm, the results show that the total cost of the task obtained by the proposed algorithm is the lowest, the algorithm stability is better, and the convergence accuracy is the highest. © Published under licence by IOP Publishing Ltd.

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

EFFECTS OF IONOSPHERE ON LOWER-FREQUENCY SPACEBORNE SAR IMAGING

EFFECTS OF IONOSPHERE ON LOWER-FREQUENCY SPACEBORNE SAR IMAG...

引用

2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

作者： Wang, Kuan Chen, Bingxu Li, Ning Guo, Zhengwei Fu, Zewen College of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China

The propagation of spaceborne synthetic aperture radar (SAR) signal is affected by the ionosphere, which will lead to the degradation of the imaging quality, especially in the conditions of low-frequency and wide-bandwidth. This paper analyzed the influence of ionosphere on SAR imaging under different carrier frequency, different bandwidth, different ionosphere total electron content (TEC), different azimuth resolution and different scintillation strength by time-domain simulation. The results show that the ionospheric dispersion effect leads to SAR image defocusing, which is closely related to the radar carrier frequency, signal bandwidth, azimuth resolution and TEC value on the propagation path. At the same time, due to the influence of time delay, the dispersion effect also leads to the range displacement of SAR image. The ionospheric scintillation effect reduces the azimuth resolution significantly and in extreme cases imaging is not possible. © 2021 IEEE

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

A Dynamic Acceleration Method for Remote Sensing Image processing Based on CUDA 12th

A Dynamic Acceleration Method for Remote Sensing Image Proce...

引用

12th EAI International Conference on Simulation Tools and Techniques, SIMUtools 2020

作者： Zuo, Xianyu Zhang, Zhe Qiao, Baojun Tian, Junfeng Zhou, Liming Zhang, Yunzhou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Laboratory of Spatial Information Processing Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China National Cultural Heritage Administration Beijing100010 China

ISBN: (纸本)9783030727918

The incredible increase in the volume of remote sensing data has made the concept of Remote Sensing as big data reality with recent technological developments. Remote sensing image processing is characterized with features of massive data processing and intensive computation, which makes the processes difficult. To optimize the remote sensing image processing for GPU, compute unified device architecture (CUDA) is widely used to implement remote sensing algorithms. However, the usage of GPU in remote sensing image processing has been constrained by the complexity of its implementation and configuration. Therefore, how to take fully advantage of the parallel organization of GPU architecture is awfully challenging. In this paper, a dynamic adaptive acceleration (DAA) method is proposed to determine calculation parameters of GPU adaptively and preprocess the input remote sensing images on host dynamically. By this method, we determine calculation parameters according to the hardware parameters of GPU firstly. And then, the input remote sensing images are reconstructed based on the calculation parameters. Finally, the preprocessed image blocks are arranged to stream tasks and executed on GPU respectively. Effectiveness of the proposed DAA method in accelerate remote sensing algorithm with point operations were verified by experiments in this paper, and the experimental results indicated that the DAA method can obtain better performance than traditional methods. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

A Systematic Review on Long-Tailed Learning

arXiv

引用

arXiv 2024年

作者： Zhang, Chongsheng Almpanidis, George Fan, Gaojuan Deng, Binquan Zhang, Yanbo Liu, Ji Kamel, Aouaidjia Soda, Paolo Gama, João Henan University Henan Key Lab of Big Data Analysis and Processing Kaifeng China Baidu Inc. Beijing China University Campus Bio-Medico di Roma Rome Italy The Laboratory of Artificial Intelligence and Decision Support Faculty of Economics University of Porto Porto Portugal

Long-tailed data is a special type of multi-class imbalanced data with a very large amount of minority/tail classes that have a very significant combined influence. Long-tailed learning aims to build high-performance models on datasets with long-tailed distributions, which can identify all the classes with high accuracy, in particular the minority/tail classes. It is a cutting-edge research direction that has attracted a remarkable amount of research effort in the past few years. In this paper, we present a comprehensive survey of latest advances in long-tailed visual learning. We first propose a new taxonomy for long-tailed learning, which consists of eight different dimensions, including data balancing, neural architecture, feature enrichment, logits adjustment, loss function, bells and whistles, network optimization, and post hoc processing techniques. Based on our proposed taxonomy, we present a systematic review of long-tailed learning methods, discussing their commonalities and alignable differences. We also analyze the differences between imbalance learning and long-tailed learning approaches. Finally, we discuss prospects and future directions in this field. © 2024, CC BY.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification

arXiv

引用

arXiv 2023年

作者： Liu, Chun Yang, Longwei Li, Zheng Yang, Wei Han, Zhigang Guo, Jianzhong Yu, Junyong School of Computer and Information Engineering Henan University Henan Zhengzhou450046 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Zhengzhou450046 China Henan Engineering Laboratory of Spatial Information Processing Henan University Henan Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Henan Zhengzhou450046 China College of Geography and Environmental Science Henan University Henan Zhengzhou450046 China Henan DeFan High-tech Software Co. Ltd Henan Zhengzhou450046 China

Cross-domain few-shot hyperspectral image classification focuses on learning prior knowledge from a large number of labeled samples from source domains and then transferring the knowledge to the tasks which contain few labeled samples in target domains. Following the metric-based manner, many current methods first extract the features of the query and support samples, and then directly predict the classes of query samples according to their distance to the support samples or prototypes. The relations between samples have not been fully explored and utilized. Different from current works, this paper proposes to learn sample relations on different levels and take them into the model learning process, to improve the cross-domain few-shot hyperspectral image classification. Building on current method of"Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification" which adopts a domain discriminator to deal with domain-level distribution difference, the proposed method applies contrastive learning to learn the class-level sample relations to obtain more discriminable sample features. In addition, it adopts a transformer based cross-attention learning module to learn the set-level sample relations and acquire the attention from query samples to support samples. Our experimental results have demonstrated the contribution of the multi-level relation learning mechanism for few-shot hyperspectral image classification when compared with the state of the art methods. All the codes are available at github https://***/HENULWY/STBDIP. © 2023, CC BY.

关键词： Image enhancement

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：