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

7,888 篇 会议
81 篇 期刊文献
76 册 图书

馆藏范围

8,045 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

5,021 篇 工学
- 3,891 篇 计算机科学与技术...
- 2,375 篇 信息与通信工程
- 1,786 篇 软件工程
- 1,352 篇 电气工程
- 629 篇 控制科学与工程
- 417 篇 动力工程及工程热...
- 368 篇 电子科学与技术（可...
- 313 篇 生物工程
- 220 篇 生物医学工程（可授...
- 206 篇 仪器科学与技术
- 203 篇 机械工程
- 173 篇 测绘科学与技术
- 161 篇 光学工程
- 125 篇 化学工程与技术
- 119 篇 材料科学与工程（可...
- 94 篇 建筑学
- 87 篇 土木工程
- 84 篇 安全科学与工程
- 83 篇 石油与天然气工程
1,364 篇 理学
- 627 篇 数学
- 404 篇 物理学
- 359 篇 生物学
- 213 篇 统计学（可授理学、...
- 138 篇 化学
- 121 篇 系统科学
681 篇 管理学
- 391 篇 管理科学与工程(可...
- 305 篇 图书情报与档案管...
- 147 篇 工商管理
236 篇 医学
- 208 篇 临床医学
- 130 篇 基础医学(可授医学...
83 篇 法学
81 篇 农学
57 篇 经济学
23 篇 军事学
20 篇 教育学
15 篇 文学
1 篇 哲学

主题

1,587 篇 wireless sensor ...
499 篇 neural networks
322 篇 wireless communi...
290 篇 convolutional ne...
247 篇 deep learning
237 篇 sensor nodes
221 篇 routing
215 篇 feature extracti...
181 篇 monitoring
179 篇 image processing
174 篇 signal processin...
173 篇 wireless sensor ...
168 篇 sensor networks
166 篇 energy consumpti...
164 篇 sensors
148 篇 accuracy
146 篇 sensor systems
141 篇 real-time system...
141 篇 protocols
141 篇 routing protocol...

机构

15 篇 chitkara univers...
10 篇 univ chinese aca...
10 篇 beijing universi...
10 篇 gla university m...
9 篇 south china univ...
9 篇 university of ch...
9 篇 beijing institut...
8 篇 school of inform...
8 篇 department of co...
8 篇 department of el...
7 篇 school of inform...
7 篇 chinese academy ...
6 篇 univ wisconsin d...
6 篇 karnataka bangal...
6 篇 computer network...
6 篇 shanghai jiao to...
6 篇 school of comput...
6 篇 school of electr...
6 篇 amrita vishwa vi...
6 篇 shihezi universi...

作者

12 篇 wang wei
11 篇 chen chen
9 篇 liu yang
9 篇 li qiang
8 篇 zhang tao
8 篇 wang lei
7 篇 wei liu
7 篇 liu wei
7 篇 cao jiannong
7 篇 wei wang
6 篇 badache nadjib
6 篇 jiao licheng
5 篇 zhou jie
5 篇 li xin
5 篇 hui wang
5 篇 pitchaimanickam ...
5 篇 soman k. p.
5 篇 liu ying
5 篇 zhang sheng
5 篇 qiang li

语言

7,910 篇 英文
125 篇 其他
18 篇 中文
1 篇 俄文

检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8045 条记录，以下是31-40 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：

An Energy-Efficient Data Management Approach Using Sparse Compression in Future-Ready IoT networks 5th

An Energy-Efficient Data Management Approach Using Sparse Co...

引用

5th international conference on Computing and Network Communications, CoCoNet 2023

作者： George, Gemini Department of Computer Applications St. George’s College Aruvithura Erattupetta Kerala Kottayam686122 India

ISBN: (纸本)9789819745395

IoT networks deployed for various industrial applications often consist of a large number of wireless sensors deployed over an area. Minimizing energy consumption and optimizing network longevity through efficient data traffic management are open research problems in such networks. Data transmission contributes to a major portion of sensor energy consumption. Hence, efficient data management schemes are to be deployed to improve the longevity and latency in such networks. Sparse compression is a technique by which the data can be sampled much below the Nyquist rate, enabling a compact data representation. In this work, we extend this approach to manage the data traffic in IoT networks and evaluate the performance of the sparse compression technique in data traffic management in future-ready IoT networks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data compression

来源：评论

学校读者我要写书评

暂无评论

5th Polish Congress of Mechanics and 25th international conference on Computer Methods in Mechanics, PCM-CMM 2023

5th Polish Congress of Mechanics and 25th International Conf...

引用

5th Polish Congress of Mechanics and 25th international conference on Computer Methods in Mechanics, PCM-CMM 2023

ISBN: (纸本)9783031731600

the proceedings contain 18 papers. the special focus in this conference is on Mechanics and Computer Methods in Mechanics. the topics include: Inspection of 2D Composite Structures Using Self-Heating-Based Vibrothermography;selected Aspects of Validation of Numerical Models of Bridge Structures in Terms of Dynamic Technical Diagnostics;influence of Lighting Column Wall thickness on Passive Safety Coefficient During Car Crash;contact Phenomena of Soft Polymers – Insights from Experiments and Simulations;jaya Algorithm for Optimization of 2D Reinforced Concrete Frame Structure;specific Strength and Modulus of Epoxy Composites with Different Reinforcing Materials;modelling the Cooperation of Gravity Energy Storage with a Renewable Energy Source;elastic Force sensor Based on Velostat® Characteristics Investigation;numerical Modeling of Tissue Laser Irradiation Using the Finite Pointset Method;the Impact of Multilayer Composite Materials with a Duroplastic Matrix at Diversified Temperature Conditions;the Implementation of Biomimetic Structural Optimization Software in a Supercomputing Environment;design of Electric Crawler Platform;Development of Numerical Model to Simulate PM-FBG Response to Propagating Guided Waves;friction and Stick-Slip of Soft Materials – Challenge for Measurement and Prediction;Hybrid PZT-FBG Sensing for Damage Detection in Structural Health Monitoring;numerical Analysis of Shock Wave Impact on a Gas Cylinder.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Survey on Adversarial Text Attacks on Deep Learning Models in Natural Language processing 5th

A Survey on Adversarial Text Attacks on Deep Learning Models...

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Deepan, S. Torres-Cruz, Fred Placido-Lerma, Ruben L. Udhayakumar, R. Anuradha, S. Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Univ Nacl Altiplanode Puno Unit Stat & Comp Engn Puno Peru Univ Andina Nestor Caceres Velasquez Syst Engn Juliaca Peru VelTech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai Tamil Nadu India Sri Sairam Engn Coll Dept English Chennai Tamil Nadu India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep neural networks have recently seen a significant surge in adoption for different Artificial Intelligence technologies due to the development of powerful computer systems. However, because of the growing security concerns, they are susceptible to dangerous risks. Adversarial instances were initially discovered in the field of computer vision (CV), where systems were deceived by altering their initial inputs. In the field of natural language processing (NLP), additionally they occur. Several approaches are put up to address this gap and handle an extensive variety of NLP applications. We give an organized survey of these works in this *** text is distinct and meaningful in nature, in contrast to the image, which makes the creation of hostile assaults much more challenging. In this study, we present a thorough analysis of adversarial attacks and counterattacks in the textual domain. In order to make the essay self-contained, we examine related important works in computer vision and cover the fundamentals of NLP. We explore unresolved concerns to close the gap between current advancements and increasingly powerful adversarial assaults on NLP DNNs in our survey's conclusion.

关键词： NLP DNN adversarial text attacks adversarial defense textual data

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence-based disinformation discovery for social networks 5

Artificial Intelligence-based disinformation discovery for s...

引用

5th international conference on Signal processing and Computer Science, SPCS 2024

作者： Zhu, Zhiying Zhujiang College South China Agricultural University Guangdong Guangzhou510900 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In this era of rapid development of the Internet, disinformation, as a new public opinion phenomenon, is generated and disseminated through the Internet. the most important feature that distinguishes it from general disinformation is that the means of dissemination have changed from verbal transmission to the dissemination of textual information or multimedia information on the Internet. Its untruthful and deceptive information content has a great impact on society and people's lives. the wide range and fast speed of dissemination of false information makes it difficult to judge its dissemination channels and the scope of dissemination, and therefore it is impossible to detect false information in an effective and timely manner. In this paper, through the simulation of the BSS virtual community propagation model, the SIR model, we can discover its propagation mode in social networks. After getting a rough idea of the pattern of disinformation dissemination. From the perspective of artificial intelligence through the feature extraction model detection method and Web content filtering model method to effectively detect false information, to reduce the negative impact of false information dissemination on society. © 2025 SPIE.

关键词： Tunable filters Social networks Internet Visual process modeling Artificial intelligence Feature extraction Optical filters Data transmission Information visualization Virtual reality

来源：评论

学校读者我要写书评

暂无评论

Stereo Vision and UWB based Multi-sensor fusion SLAM 5

Stereo Vision and UWB based Multi-sensor fusion SLAM

引用

5th international conference on Signal processing and Computer Science, SPCS 2024

作者： Li, Tongyang Huang, Yuqi School of Computer Science Hubei University of Technology Hubei Wuhan430068 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Accurate and robust localization is crucial for indoor mobile robots, where traditional vision-based systems often struggle with precision and reliability. this paper presents a SLAM/UWB fusion localization algorithm designed to overcome these challenges. By building on the ORB-SLAM3 stereo-inertial framework and integrating Ultra-Wideband (UWB) positioning data using an Extended Kalman Filter (EKF), the method introduces time and distance thresholds to filter outliers and improve data accuracy. Experimental results show that this approach outperforms ORB-SLAM3 stereo visual-inertial localization, achieving superior positioning accuracy and enhanced robustness in both the Euroc dataset and real-world indoor environments. © 2025 SPIE.

关键词： Extended Kalman filters

来源：评论

学校读者我要写书评

暂无评论

Intelligent Agent Based Clustering and Optimal Multipath Routing for Energy-Efficient Wireless sensor networks in Smart City Applications: A Distributed AI-Driven Approach 5th

Intelligent Agent Based Clustering and Optimal Multipath Rou...

引用

5th international conference on Computing Science, Communication and Security

作者： Patra, Binaya Kumar Mishra, Sarojananda Patra, Sanjay Kumar Mallik, Ashutosh Roy, Souveek Indira Gandhi Inst Technol Dept Comp Sci Engn & Applicat Sarang 759146 Odisha India GIET Univ Dept Comp Sci Engn Gunupur Odisha India

ISBN: (纸本)9783031751691;9783031751707

the study proposes an innovative approach to enhance energy efficiency in Wireless sensor networks (WSNs) for smart city applications. the primary focus is on leveraging distributed artificial intelligence (AI) and multipath routing techniques to address challenges such as unequal clustering, poor cluster head selection, and excessive power consumption within WSNs. the approach uses agent-based clustering, where autonomous AI agents dynamically form clusters of sensor nodes based on real-time data characteristics. these clusters are then used for multipath routing, optimizing energy consumption, reliability, and congestion reduction. the distributed nature of AI agents allows for adaptive cluster formations. this algorithm aims to address issues related to uneven clustering, inefficient cluster head selection, and excessive power consumption. Additionally, the integration of agent-based clustering is proposed, involving the deployment of autonomous AI agents that dynamically cluster sensor nodes based on real-time data properties. these AI agents facilitate self-organization and adaptability, ensuring that clusters accurately reflect the evolving data landscape in urban environments. the approach also employs sophisticated energy management strategies at the sensor node level, such as duty cycling, adaptive transmission power control, and sleep-wake scheduling. Simulations in a smart city environment show significant improvements in energy efficiency, prolonging the network's operational lifespan and improving service quality by mitigating data loss and latency issues. this approach contributes to the sustainable development and performance optimization of smart city infrastructure.

关键词： Wireless sensor networks Smart Cities Clustering Distributed Artificial Intelligence Multipath Routing Energy Efficiency

来源：评论

学校读者我要写书评

暂无评论

SVM-based malware classification through deep learning method 5

SVM-based malware classification through deep learning metho...

引用

5th international conference on Signal processing and Computer Science, SPCS 2024

作者： Shi, Jiawei Wang, Wancheng Zhang, Meng Yao, Qinbo Cheng, Shiyuan Zhan, Zhifei College of Artificial Intelligence and Automation Hohai University Nanjing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Various daily applications, including image categorization, natural language comprehension, and voice identification, heavily rely on fully connected and convolutional neural networks. When tackling classification problems, conventional deep learning architectures typically employ softmax activation functions to normalize outputs and minimize the model's cross-entropy loss. In this study, we present a novel malware classification model that integrates hybrid support vector machines with neural networks. Notably, this model substitutes the conventional softmax layer in neural networks with a support vector machine, thereby shifting the learning process towards minimizing margin losses instead of cross-entropy losses. this modification enhances the precision of malware classification for both standard machine learning algorithms and several prevalent deep learning models. To validate our proposed model, we employed the Malimg dataset, comprising malware images derived from binary malware samples. Subsequently, we trained an DL-SVM model to assign scores to each distinct malware family. Experimental outcomes revealed that the malware classification model achieved an accuracy of 96.31%, a precision rate of 97.00%, a recall rate of 97.35%, and an F1 score of 97.16%, respectively. © 2025 SPIE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Utilizing Deep Belief networks for Power System State Estimation and Anomaly Detection 5

Utilizing Deep Belief Networks for Power System State Estima...

引用

5th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Liu, Jiali Qiao, Meng Du, Lanna Zhang, Wei Wang, Mingming Jin, Kun Inner Mongolia Electric Power Economics and Technology Research Institute Inner Mongolia Hohhot China

ISBN: (纸本)9798331533694

the state estimation and anomaly detection of power systems highly rely on accurate and comprehensive real-Time data. However, many power systems face data quality issues such as sensor failures, data loss, or noise pollution. therefore, this study proposes a power system fault detection and recovery time prediction model based on deep belief network (DBN). Firstly, various fault feature extraction methods have been proposed for power system operation data, including electrical quantity features (such as voltage and current), time-domain features (such as power fluctuations, frequency fluctuations, etc.), and normalization methods have been used to preprocess the raw data to eliminate outliers, missing values, and redundant data. then, deep belief networks are used for pre training of fault features, and unsupervised learning is used to train each layer of restricted Boltzmann machines (RBM) to extract potential features of power system data. this process optimizes the weights by comparing the divergence (CD) algorithm, and further trains the backpropagation algorithm during the fine-Tuning stage to improve the accuracy of fault detection. In terms of recovery time prediction, by establishing the relationship between recovery time and fault type, combined with the feature extraction ability of DBN, the recovery time of different fault types can be predicted. the experimental results show that the model exhibits good performance in fault detection and recovery time prediction, which can effectively improve the stability and fault response speed of the power system. © 2025 IEEE.

关键词： deep belief network fault detection feature extraction power system recovery time prediction

来源：评论

学校读者我要写书评

暂无评论

Prediction of sea fog based on a bidirectional temporal convolutional and long short-term memory networks 5

Prediction of sea fog based on a bidirectional temporal conv...

引用

5th international conference on Signal processing and Computer Science, SPCS 2024

作者： Qin, Xiaobao Huang, Mengxing Xu, Bo School of Information and Communication Engineering Hainan University Haikou570228 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Sea fog is a common weather phenomenon at sea, which reduces visibility and poses a great threat to marine traffic and operations. Traditional satellite remote sensing sea fog monitoring algorithms need to be improved in terms of accuracy, portability and automation. In this paper, a sea fog prediction model based on a Bidirectional Temporal Convolutional and Long Short-Term Memory networks is proposed (BiTCN-LSTM). the BiTCN-LSTM introduces a network of causal convolutional and bidirectional gated recurrent cells, which improves the network's attention to sea fog features of important channels by learning forward and backward convolutional features of the input sea fog sequence. In addition, residual multi-scale feature fusion is employed to obtain multiscale information of sea fog, allowing the model to extract and fuse features progressively at different levels for better prediction of relative humidity and visibility time series of sea fog. Experiments show that the proposed BiTCN-LSTM has excellent performance in terms of long-term sea fog humidity and visibility prediction. © 2025 SPIE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

5th international conference on Computing and Network Communications, CoCoNet 2023

5th International Conference on Computing and Network Commun...

引用

5th international conference on Computing and Network Communications, CoCoNet 2023

ISBN: (纸本)9789819747108

the proceedings contain 44 papers. the special focus in this conference is on Computing and Network Communications. the topics include: Maximizing Efficiency: Unveiling the Potential of Kubernetes Metrics;resource-Aware Learning Automata for Low-Latency Task Scheduling in Fog Computing;Design of High Gain Wideband Petal-Shaped Magneto-Electric Dipole Metal Antenna for 5G Sub-6 GHz and Wi-Fi Applications;A Novel Billhook-Shaped 2-Element MIMO Array for Sub-6 GHz Millimeter Wave Application;energy-Efficient Transmission in Wireless sensor Network Using Compressive Sensing;smart City Solutions for Traffic Management;an IoT-Assisted Real-Time Urban Air Pollution Data Collection System;real-Time Stock Price Prediction Using Apache Kafka;RSS-Based Localization Using GRU and BiGRU Deep Learning Models in LoRaWAN-IoT networks;locating and Routing for IoT-Based Fire Evacuation System;an Energy-Efficient Data Management Approach Using Sparse Compression in Future-Ready IoT networks;a Framework for Developing IoT and Edge Computing-Based Smart Transportation Applications;a Mathematical Model for Data Traffic Management in Next-Generation IoT networks;crowdsourced Mobile Applications for Disaster Relief: A Case Study of Landslide Tracker and Amrita Kripa App Deployments in Sikkim Schools;design and Modeling of Modular Wheelchair for therapeutic Exercises;analyzing Data Traffic in IoT networks: A Novel Graph-Based Architecture;A Lightweight Privacy-Preserving and Authentication Model (LiPAM-FSG) for Fog-Based Smart Grids;time Series Analysis and Rule Mining for Detecting Industrial Control System Data Injection Attacks;Radius-Based Authentication: UFrames’ Novel Approach to Prevent Account Sharing;deep Neural Network-Based Secure Energy-Efficient Power Allocation in an Interference Network;An Adaptive Time Quantum-Based Efficient Round-Robin Algorithm for CPU Scheduling;low-Cost Advanced Undetectable Hardware-Based Malicious Keyboard;constructing a Blind Digital Signature Scheme

关键词：

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500页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：