检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

444 篇 会议
85 篇 期刊文献

馆藏范围

529 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

206 篇 工学
- 162 篇 计算机科学与技术...
- 143 篇 软件工程
- 56 篇 信息与通信工程
- 47 篇 电气工程
- 29 篇 控制科学与工程
- 18 篇 生物工程
- 15 篇 机械工程
- 15 篇 电子科学与技术（可...
- 15 篇 交通运输工程
- 14 篇 网络空间安全
- 13 篇 光学工程
- 11 篇 安全科学与工程
- 10 篇 仪器科学与技术
- 10 篇 土木工程
- 9 篇 建筑学
- 8 篇 生物医学工程（可授...
- 4 篇 化学工程与技术
91 篇 理学
- 58 篇 数学
- 20 篇 物理学
- 16 篇 生物学
- 10 篇 统计学（可授理学、...
- 7 篇 系统科学
53 篇 管理学
- 32 篇 图书情报与档案管...
- 31 篇 管理科学与工程(可...
- 8 篇 工商管理
14 篇 法学
- 8 篇 法学
- 8 篇 社会学
10 篇 医学
- 9 篇 临床医学
- 6 篇 基础医学(可授医学...
- 5 篇 公共卫生与预防医...
3 篇 军事学
2 篇 教育学
2 篇 农学
1 篇 经济学
1 篇 文学

主题

74 篇 feature extracti...
62 篇 accuracy
47 篇 semantics
41 篇 computational mo...
38 篇 data models
30 篇 federated learni...
29 篇 training
27 篇 neural networks
26 篇 transformers
21 篇 predictive model...
19 篇 deep learning
18 篇 data mining
17 篇 noise
17 篇 adaptation model...
16 篇 visualization
16 篇 attention mechan...
15 篇 measurement
14 篇 object detection
14 篇 image segmentati...
14 篇 analytical model...

机构

199 篇 key laboratory o...
197 篇 shandong provinc...
44 篇 shandong enginee...
42 篇 shaanxi key labo...
36 篇 shandong fundame...
35 篇 school of comput...
35 篇 school of comput...
32 篇 shaanxi key labo...
29 篇 shandong provinc...
22 篇 qilu university ...
21 篇 shandong provinc...
15 篇 quan cheng labor...
13 篇 key laboratory o...
13 篇 qilu university ...
12 篇 key laboratory o...
11 篇 xi'an university...
11 篇 shandong fundame...
11 篇 key laboratory o...
10 篇 shandong enginee...
10 篇 faculty of data ...

作者

28 篇 yichuan wang
24 篇 xinhong hei
22 篇 wang yichuan
18 篇 xiaoming wu
17 篇 hei xinhong
15 篇 haiyan jin
14 篇 li wei
14 篇 jin haiyan
13 篇 xiangzhi liu
13 篇 guohua lv
12 篇 aimin li
11 篇 jing zhao
11 篇 mingle zhou
11 篇 wenpeng lu
10 篇 wang lei
10 篇 xiaohui han
10 篇 lei zhu
10 篇 wei wei
9 篇 bin wang
9 篇 guixin zhao

语言

504 篇 英文
24 篇 其他
3 篇 中文

检索条件"机构=Shaanxi Provincial Key Laboratory of Network Computing and Security Technology"

共 529 条记录，以下是371-380 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Malicious Traffic Classification for IoT based on Graph Attention network and Long Short-Term Memory network

Malicious Traffic Classification for IoT based on Graph Atte...

引用

Asia-Pacific network Operations and Management Symposium (APNOMS)

作者： Lei Zhang Lizhuang Tan Huiling Shi Hongyang Sun Wei Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

With the rapid development of digital services and the Internet of Things (IoT), the number and diversity of cyber attacks have reached unprecedented levels, making network traffic classification a crucial means of maintaining network security. However, in the face of increasingly complex traffic attacks, traditional analysis of individual traffic flows is no longer sufficient for effective detection. Therefore, to reveal the potential correlations between network traffic flows, we propose a novel IoT malicious traffic classification model called EGAT-LSTM. This model combines an improved Graph Attention network (GAT) with Long Short-Term Memory (LSTM) network to effectively capture the spatial topology and temporal features of network traffic, ultimately achieving efficient classification. Moreover, we optimize the aggregation function of network node features within the attention mechanism to effectively address the issue of essential information retention during the feature learning process. The experimental results on two publicly available IoT datasets demonstrate that our method achieves improvements in both binary and multi-class classification tasks, exhibiting significant advantages over some current approaches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Job2Vec: A Self-Supervised Contrastive Learning Based HPC Job Power Consumption Prediction Framework

Job2Vec: A Self-Supervised Contrastive Learning Based HPC Jo...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jie Zhang Jian Song Xiang Li Xuesen Tian Zhigang Zhao Lu Wu Chunxiao Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In the context of the rapid development of big data and artificial intelligence, the field of HPC is facing significant challenges in energy consumption. To address this challenge, this paper proposes a power consumption prediction framework for HPC jobs based on self-supervised contrastive learning, named Job2Vec. Firstly, HPC jobs are clustered into different clusters based on job logs and power consumption curve shapes. Next, high-quality features of power consumption data at different granularities within each cluster are captured through self-supervised contrastive learning. Lastly, an incremental update strategy is introduced to handle the dynamic changes in HPC data. The experimental results demonstrate that, on the Jinan Supercomputing dataset comprising 1170 HPC jobs, the Job2Vec framework outperforms the baseline models overall in terms of prediction performance. Additionally, we also investigated the model’s performance in single-step and multi-step forecasting, the impact of different indicators on prediction accuracy, and conducted ablation experiments to confirm the indispensability of each module. This is the first time that self-supervised contrastive learning has been introduced into the HPC field, aiming to provide support for energy scheduling in data centers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Student Behavior Detection Method Based on Improved YOLOv5 Algorithm

Student Behavior Detection Method Based on Improved YOLOv5 A...

引用

Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Haitao Hu Meihong Yang Ying Guo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

When using computer vision technology to identify students in the classroom, it will be affected by the inconsistency of the seating layout in different classrooms, the complexity of the background, as well as the existence of back row students with smaller portraits, mutual occlusion and other problems resulting in poor recognition of student behavior and other problems. In order to solve this problem, this paper firstly takes the real classroom video of a university, summarizes five common student behaviors, intercepts the video frame by frame, labels the data, and constructs the dataset, and then proposes a target detection technique based on the improved YOLOv5 algorithm, which improves the YOLOv5 algorithm by replacing the SPP module in the original network with the BasicRFB module and introducing the SACA attention mechanism. algorithm, which enhances the feature extraction capability of the model and ensures the detection speed at the same time; in the post-processing stage of the model, Soft-NMS is used to replace the original NMS non-maximal value suppression method, which reduces the probability of the model leakage detection. Finally, through the ablation experiment, it is verified that the improved method proposed in this paper has better detection effect for real classroom scenarios, which can help teachers grasp the learning status of students in time and optimize the teaching methods accordingly, and at the same time, it provides solid support for the dynamic evaluation of classroom teaching.

关键词： YOLO Computer vision Attention mechanisms Accuracy Heuristic algorithms Education Layout

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Residual Coordinate Attention-Based network for Hat and Mask Wearing Detection in Kitchen Environments

An Adaptive Residual Coordinate Attention-Based Network for ...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Ying Zhao Xiaoming Wu Xiangzhi Liu Hao Chen Bei Qi Huomin Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In order to ensure food safety, it is required for personnel to wear hats and masks during food handling processes. To accurately detect the wearing status of kitchen staff, the ARP-YOLO model is proposed. Firstly, images are obtained from multiple kitchens and angles to construct a dataset reflecting the wearing status of hats and masks. To simulate more complex kitchen environments, Gaussian noise is added to the data and lighting conditions are adjusted for data augmentation. Lighting conditions in the kitchen can affect detection, causing the same target to exhibit different shapes and features under different lighting conditions, leading to missed detections. To address the above issues, we propose ARCA (Adaptive-Residual-Coordinate-Attention), which uses residual connections to strengthen attention to important features while preserving original features, and employs adaptive convolution reduction to reduce module parameters. To improve target localization accuracy, P2 detection layers are added in the Neck to obtain more accurate target position information. The ARP-YOLO model demonstrates significant improvements over the baseline model, with a 13.6% increase in Recall, allowing for more effective target capture and reduced missed detection risk. Additionally, mAP@0.5 has increased by 10%, enhancing target localization accuracy. The F1-Score has also increased by 7.4%, better balancing the relationship between Precision and Recall. To validate the effectiveness of the model, comparative experiments with other models are conducted, showing that ARP-YOLO model's Recall and Average Precision(AP) are higher than those of other models.

关键词： Location awareness Adaptation models Accuracy Convolution Shape Computational modeling Gaussian noise Lighting Feature extraction Personnel

来源：评论

学校读者我要写书评

暂无评论

BTP-CAResNet: An Encrypted Traffic Classification Method Based on Byte Transfer Probability and Coordinate Attention Mechanism

BTP-CAResNet: An Encrypted Traffic Classification Method Bas...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Junhao Li Huiling Shi Wei Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the extensive application of network traffic encryption technology, the accurate and efficient classification of encrypted traffic has become a critical need for network management. Deep learning has become the predominant method for traffic classification, primarily involving the transformation of network traffic into grayscale images and their subsequent classification using Convolutional Neural networks (CNNs). However, traditional grayscale image generation methods are plagued with issues of redundant and lost information, and conventional channel attention mechanisms are still insufficient in capturing key traffic features, collectively hindering the enhancement of classification performance. To tackle these issues, this paper introduces a classification method based on Byte Transfer Probability and Coordinate Attention Mechanism in Residual network (BTP-CAResNet). This method, on the foundation of the classic ResNet architecture, incorporates a new grayscale image generation method that utilizes Byte Transfer Probability, effectively overcoming the deficiencies of traditional approaches. Additionally, this paper integrates a Coordinate Attention Mechanism into the ResNet model, which effectively overcomes the limitations of traditional channel attention mechanisms and further improves the performance of traffic classification. Experimental validation on the ISCX VPN-nonVPN dataset demonstrates that, compared to previous CNN-based methods, the method proposed in this paper exhibits superior performance in key metrics such as accuracy, precision, recall, and F1 score. It provides a new perspective for traffic classification based on convolutional neural networks.

关键词： Measurement Attention mechanisms Accuracy Image synthesis Redundancy Telecommunication traffic Gray-scale Robustness Convolutional neural networks Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Lightweight Fusion Model with Time-Frequency Features for Speech Emotion Recognition

Lightweight Fusion Model with Time-Frequency Features for Sp...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Peng Zhang Meijuan Li Hui Zhao Yida Chen Fuqiang Wang Ye Li Wei Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Automatic speech emotion recognition (SER) is the task to predict the emotional state from speech, and has become a vital part of human-machine interaction. Recent studies focus on learning from multiple features via feature fusion. This paper proposes a lightweight fusion model with time-frequency features for SER. First, time-frequency features are extracted from the Mel frequency cepstral coefficients (MFCC) of speech by using parallel convolutional neural networks, and enhanced by using a dimension-specific weighting method. Then, a two-stage feature fusion model is proposed to fully capture the complementary information between time and frequency features and to reduce the redundancy. It consists of an adaptive fusion module to redistribute the features for a local fusion, and a global fusion module to enhance the cross-dimension feature communication. Experimental results show that our model achieves competitive performance on the datasets of IEMOCAP and RAVDESS, with recognition accuracies of 74.62% and 86.11%, respectively. The model contains only 0.82M and 0.85M parameters for the two datasets, which is lightweight for SER tasks.

关键词： Adaptation models Time-frequency analysis Emotion recognition Accuracy Computational modeling Redundancy Speech recognition

来源：评论

学校读者我要写书评

暂无评论

YOLO-RDD: A road defect detection algorithm based on YOLO

YOLO-RDD: A road defect detection algorithm based on YOLO

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Jiabin Pei Xiaoming Wu Xiangzhi Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Road maintenance is an indispensable part of the transportation system, ensuring road safety and maximizing road efficiency. The rapid and accurate acquisition of road surface information is crucial for effective road maintenance management. With the development of artificial intelligence, computer vision-based road defect detection methods are becoming popular. In this study, we propose a universal road defect detection model named YOLO-RDD, using You Only Look Once version 8 as the basic framework. Firstly, we introduce a new feature extraction and fusion approach to enhance the interaction between shallow and deep feature information, enabling multi-scale defect detection. Secondly, inspired by dynamic snake convolution, as cracks are the major road defects, we propose a new feature extraction module called DSC-C2f to adapt to the elongated and continuous morphology of cracks. Finally, we establish cross-layer connections between the backbone, neck, and prediction stages and integrate a coordinate attention module that considers inter-channel relationships and long-range positional dependencies. This allows the model to selectively focus on relevant parts of shallow and deep semantic information. We evaluate our model using the dataset provided by the crowdsensing-based road damage detection challenge (CRDDC2022) and further assess its performance on a publicly available crack and sealed crack dataset. The experiments demonstrated that our model achieved the best performance compared to the state-of-the-art (SOTA) models. Compared to the baseline model YOLOv8, our model achieved improvements of 1.2% in Precision, 1.9% in Recall, 1.9% in mAP@0.5, 0.8% in mAP@0.5:0.95, and 1.6% in F1-Score.

关键词： YOLO Cross layer design Roads Semantics Transportation Surface morphology Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Method and system of power load classification and decomposition based on deep learning

Method and system of power load classification and decomposi...

引用

Smart Grid and Energy Engineering (SGEE), International Conference on

作者： Hao Sun Hongtai Yuan Yudong Geng Jing Chen Chuanfu Zhang Wen Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

This paper introduces a system for the classification and decomposition of electrical loads, addressing the shortcomings of current non-intrusive load decomposition methods that are prone to errors. We propose a method that first classifies electrical loads and then performs decomposition, along with the corresponding system workflow. Initially, we collect household electricity consumption data using a proprietary load collection device developed by the company, constructing an electrical load dataset. Subsequently, we train classification and decomposition methods using this dataset, conducting comparative experiments with various methods to select the most effective one. Based on the trained electrical load classification and decomposition models, we develop an electrical load inference program. The key implementation lies in applying the electrical load classification and decomposition inference program to edge devices. Leveraging the cloud server hosting a backend web service for the mobile app, we interact with users through the app, achieving precise decomposition and monitoring of electrical loads. Integrating edge computing and cloud computing, coupled with a mobile app serving as the interaction platform, this paper presents a high-precision, simplified, and highly scalable solution for electrical load analysis and management. In experimental comparisons, we find that using the Gated Recurrent Unit (GRU) method from deep learning and the Sequence-to-Point Neural network (Seq2point) method outperform other methods in classification and decomposition, significantly improving the accuracy of electrical load classification. Therefore, we select the GRU method and Seq2Point method as the core techniques for the electrical load classification and decomposition system. This system not only demonstrates significant advantages in performance but also possesses high accuracy, model simplification, and scalability, providing an efficient and reliable solution for elec

关键词：

来源：评论

学校读者我要写书评

暂无评论

Collaborative Prediction of Drug-target Interaction using Sequence-based CNN and Transformer

Collaborative Prediction of Drug-target Interaction using Se...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Huiting Li Weiyu Zhang Yong Shang Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Drug-target interaction (DTI) prediction is a critical step in drug discovery. Deep learning has shown great potential in DTI prediction. In recent years, many sequence-based DTI prediction methods have been proposed. However, the current methods do not fully represent drugs and proteins and ignore the local and global features of these molecules. In response to these challenges, we propose a sequence-based CNN and Transformer collaborative prediction method (SCTDTI) for drug-target interaction. We train and evaluate our proposed approach on two public drug-target datasets, and experimental results show that SCTDTI improves DTI prediction performance compared to state-of-the-art baselines.

关键词： Proteins Drugs Deep learning Federated learning Computational modeling Neural networks Collaboration

来源：评论

学校读者我要写书评

暂无评论

Chinese named entity recognition method for the field of network security based on RoBERTa

Chinese named entity recognition method for the field of net...

引用

International Conference on networking and network Applications (NaNA)

作者： Xiaoyan Zhu Yu Zhang Lei Zhu Xinhong Hei Yichuan Wang Feixiong Hu Yanni Yao School of Computer Science and Engineering Xi’an Jiaotong University Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China School of Computer Science and Technology Xi’an University of technology Xi’an China Shenzhen Tencent Computer System Co. Ltd Shenzhen China

As the mobile Internet is developing rapidly, people who use cell phones to access the Internet dominate, and the mobile Internet has changed the development environment of online public opinion and made online public opinion events spread more widely. In the online environment, any kind of public issues may become a trigger for the generation of public opinion and thus need to be controlled for network supervision. The method in this paper can identify entities from the event texts obtained from mobile Today's Headlines, People's Daily, etc., and informatize security of public opinion in event instances, thus strengthening network supervision and control in mobile, and providing sufficient support for national security event management. In this paper, we present a SW-BiLSTM-CRF model, as well as a model combining the RoBERTa pre-trained model with the classical neural network BiLSTM model. Our experiments show that this approach provided achieves quite good results on Chinese emergency corpus, with accuracy and F1 values of 87.21% and 78.78%, respectively.

关键词： Text recognition Cellular phones Semantics Neural networks network security Feature extraction Internet

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共53页 << < 34 35 36 37 38 39 40 41 42 43 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：