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

8,377 篇 会议
50 篇 期刊文献
29 册 图书

馆藏范围

8,456 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,404 篇 工学
- 3,360 篇 计算机科学与技术...
- 2,243 篇 软件工程
- 1,092 篇 信息与通信工程
- 811 篇 控制科学与工程
- 760 篇 电气工程
- 356 篇 生物工程
- 329 篇 电子科学与技术（可...
- 254 篇 机械工程
- 248 篇 动力工程及工程热...
- 195 篇 生物医学工程（可授...
- 185 篇 网络空间安全
- 178 篇 仪器科学与技术
- 145 篇 交通运输工程
- 138 篇 光学工程
- 136 篇 安全科学与工程
- 115 篇 化学工程与技术
2,192 篇 理学
- 1,643 篇 数学
- 391 篇 生物学
- 364 篇 统计学（可授理学、...
- 329 篇 物理学
- 305 篇 系统科学
- 118 篇 化学
1,102 篇 管理学
- 799 篇 管理科学与工程(可...
- 365 篇 图书情报与档案管...
- 330 篇 工商管理
173 篇 医学
- 151 篇 临床医学
- 145 篇 基础医学(可授医学...
145 篇 法学
- 120 篇 社会学
132 篇 文学
- 128 篇 新闻传播学
111 篇 经济学
- 109 篇 应用经济学
56 篇 教育学
43 篇 农学
16 篇 军事学
9 篇 艺术学

主题

1,122 篇 convergence
444 篇 cloud computing
403 篇 optimization
338 篇 computational mo...
293 篇 genetic algorith...
230 篇 information tech...
200 篇 algorithm design...
196 篇 particle swarm o...
194 篇 neural networks
177 篇 resource managem...
177 篇 servers
173 篇 training
166 篇 internet of thin...
161 篇 computer archite...
148 篇 mobile computing
140 篇 wireless sensor ...
137 篇 educational inst...
135 篇 costs
133 篇 real-time system...
127 篇 routing

机构

37 篇 university of ch...
28 篇 state key labora...
25 篇 college of compu...
25 篇 chitkara univers...
22 篇 amrita vishwa vi...
18 篇 beijing key labo...
17 篇 school of comput...
15 篇 university of sc...
15 篇 shenyang institu...
15 篇 college of compu...
15 篇 school of inform...
14 篇 school of comput...
14 篇 department of co...
14 篇 college of infor...
13 篇 faculty of infor...
13 篇 college of compu...
13 篇 national univers...
13 篇 institute of com...
12 篇 communication un...
12 篇 state key labora...

作者

40 篇 jeong jongpil
24 篇 kim dong-seong
20 篇 lee jae-min
19 篇 yang yang
14 篇 wang hui
14 篇 pan jeng-shyang
13 篇 wang lei
12 篇 zhiwei ye
12 篇 dong-seong kim
11 篇 sampedro gabriel...
11 篇 peeta basa pati
11 篇 younghan kim
11 篇 jongpil jeong
10 篇 jongwon kim
10 篇 rajagopal shinu ...
10 篇 lee jae min
9 篇 jie yang
9 篇 li li
9 篇 liu yang
9 篇 jae-min lee

语言

8,302 篇 英文
127 篇 其他
37 篇 中文
1 篇 法文

检索条件"任意字段=International Conference on Computing and Convergence Technology"

共 8456 条记录，以下是41-50 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：

Crow Search Algorithm for Energy-Efficient UAV Path Planning in Large-Scale Aerial Surveys

Crow Search Algorithm for Energy-Efficient UAV Path Planning...

引用

Device Intelligence, computing and Communication Technologies, (DICCT), international conference on

作者： S. Amutha S.V. Ramanan Selvendran Ganesan B. Leena Akshya J Mani Deepak Choudhry M. Sundarrajan Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering PPG Institute of Technology Coimbatore Tamil Nadu India Department of Biomedical Engineering Mahendra Institute of Technology Namakkal Tamil Nadu India Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

UAVs have been extremely influential for applications such as remote/sensing, disaster response, surveillance, and infrastructure monitoring. They are valuable in modern-day operations because they can conduct high-precision aerial surveys autonomously. Most of the existing trajectory planning approaches are based on metaheuristic algorithms of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), which are capable of providing satisfactory results. Still, they are computationally expensive with slow convergence and sensitive to local optima. In this paper, the challenges raised above are addressed by an Optimized Trajectory Planning Framework based on the Crow Search Algorithm (CSA). It is based on a nature-inspired optimization technique that mimics the intelligent foraging behavior of crows. The proposed approach dynamically adjusts the UAV flight path by balancing the exploration and exploitation, to compute the optimal trajectory quickly and meanwhile to save energy and avoid the obstacle. Unlike other metaheuristic methods, the Crow Search Algorithm does not confront the premature convergence problem and can guarantee adaptive path optimization even under uncertain environmental conditions. Results show that the energy efficiency performance of the Crow Search Algorithm is 15 percent better, the performance in terms of coverage is 20 percent improved, and the speed of convergence is 30 percent faster compared to various state-of-the-art methods. These experimental findings demonstrate that integrating CSA into UAV trajectory planning offers high computational efficiency, obstacle avoidance, and mission endurance, rendering it a superior UAV trajectory planning algorithm for real-time aerial navigation and autonomous surveillance applications.

关键词： Surveys Trajectory planning Surveillance Heuristic algorithms Metaheuristics Autonomous aerial vehicles Energy efficiency Real-time systems Collision avoidance convergence

来源：评论

学校读者我要写书评

暂无评论

On Decentralized Learning with Stochastic Subspace Descent

On Decentralized Learning with Stochastic Subspace Descent

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shivangi Dubey Sharma Pranay Sharma Ketan Rajawat Electrical Engineering Indian Institute of Technology Kanpur Kanpur India Electrical and Computer Engineering Carnegie Mellon University Pittsburgh United States

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This work considers a high-dimensional decentralized optimization problem where computing the full gradient is prohibitively expensive. This is a common issue in Partial Difference Equation (PDE)-constrained optimization and some machine learning applications. Stochastic subspace descent (SSD) solves this problem in a single-agent setting by computing the projection of gradients on random low-dimensional subspaces. We study this problem in a more challenging decentralized setting, where individual agents can only access their local objective losses. We propose VR-DSSD-GT, a novel variance reductionbased extension of SSD, to overcome this issue. Variance reduction (VR) helps achieve accelerated convergence, while gradient tracking (GT) facilitates exact convergence to the global solution, regardless of the heterogeneity across local objectives. With smooth and strongly convex loss functions, our algorithm achieves linear convergence to the solution. Our results generalize the existing results for gradient-based methods to the broader class of subspace-based methods. Experimental results corroborate and complement our theoretical findings.

关键词： Difference equations Signal processing algorithms Machine learning Signal processing Convex functions Topology Computational efficiency Speech processing Optimization convergence

来源：评论

学校读者我要写书评

暂无评论

A Convolutional Recurrent Mixer Network For Radar Meteorological Image Super-Resolution

A Convolutional Recurrent Mixer Network For Radar Meteorolog...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Pires, Rafael Gonçalves Santos, Daniel F.S. Calheiros, Roberto V. Papa, João Paulo Lee, Ik Hyun Bakshi, Sambit Muhammad, Khan Department of Computing São Paulo State University Bauru Brazil Meteorological Research Institute São Paulo State University Bauru Brazil Dept. of Mechatronics Engineering Tech University of Korea IKLAB Inc. Korea Republic of Dept. of CS&E National Institute of Technology Rourkela India School of Convergence Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9798350368741

Image super-resolution (SR) focuses on reconstructing high-resolution images from their low-resolution counterparts, often affected by sensor limitations or environmental factors. Convolutional Neural Networks (CNNs) are state-of-the-art for SR tasks but computationally heavy. This paper introduces a novel CRMN (Convolutional Recurrent Mixer Network), a hybrid deep learning-based SR technique designed to address the complexity of CNNs, which is validated in the context of meteorological radar images. Experiments on public benchmark datasets (Berkley432 and T291) and our newly manually collected precipitation dataset from the Meteorological Research Institute (IPMET) show that our CRMN model provides competitive results compared to leading SR methods with significantly fewer parameters, making it a promising and practical solution for SR applications, particularly radar meteorology. © 2025 IEEE.

关键词： Convolutional Neural Networks Deep Learning Meteorology Recurrent Mixer Network Super Resolution

来源：评论

学校读者我要写书评

暂无评论

Air-Ground Edge Task Offloading Based on Multi-UAV Path Optimization and Resource Allocation 14th

Air-Ground Edge Task Offloading Based on Multi-UAV Path Opti...

引用

14th EAI international conference on Wireless and Satellite Systems, WiSATS 2024

作者： He, Chenguang Li, Jing Wei, Shouming Guo, Mengrui School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9783031862021

In recent years, space-air-ground integrated network(SAGIN) has been recognized as a promising area in 6G research. In the air-ground component of SAGIN, airborne device such as unmanned aerial vehicles (UAVs) can provide task offloading and computing services to ground devices. Edge servers are installed on UAVs to provide data offloading services for ground devices. The high mobility of UAVs, compared to base stations and edge computing devices on the ground, makes it better able to provide timely and effective services to the devices. Considering the needs of delay-sensitive devices, this paper jointly allocates computational resources and designs UAV trajectories to achieve the goal of minimizing delay. In this paper, we use non-orthogonal multiple access (NOMA) technique in the uplink channel, which allows a UAV to serve multiple ground devices simultaneously. Both the UAV and the ground devices are moving, and the ground devices need to re-establish their connection to the UAV every once in a while. Based on the traditional Deep Reinforcement Learning (DRL) algorithm, this study proposes the Multi-Agent DRL (MADRL) algorithm to jointly determine the optimal 3D trajectory and computational resource allocation of *** MADRL algorithm achieves complete ground cooperation among multiple UAVs as agents in optimizing the latency by co-training the neural network, simplifying the network structure, and improving the training efficiency. Numerical results show that the proposed MADRL algorithm can converge under the system quality of service (QoS) constraints, and the convergence speed is faster than that of the traditional deep Q network (DQN) algorithm. The average total delay of the system can also be effectively reduced and converged in a multi-UAV scenario. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Modified Grey Wolf Optimization using Refraction Learning and Differential Evolution Mutation

Modified Grey Wolf Optimization using Refraction Learning an...

引用

Emerging Systems and Intelligent computing (ESIC), international conference on

作者： Pranshu Bajaj Siddharth Dhingra Karan Aggarwal Preeti Kaur Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

An enhanced variant of the Grey Wolf Optimisation (GWO) technique is presented in this paper, addressing a number of issues, including reduced population diversity, slow convergence and vulnerability to local optima. The proposed variant incorporates innovative techniques, including Opposition Learning, Refraction Learning (RL), Differential Evolution Mutation and a non-linear time-varying hyperparameter tuning mechanism. Combining these strategies greatly enhances the standard GWO’s performance, leading to a more streamlined and effective search procedure. This enhancement leads to better performance in multi-modal landscapes, facilitates escaping local optima and allows the search space to be explored more extensively. Extensive testing across 23 benchmark functions demonstrates that the new variant outperforms the original GWO as well as other contemporary optimization methods. The results indicate substantial improvements in convergence speed, global search capability and solution accuracy, demonstrating how effective this method can be as an optimisation tool for solving challenging real-world issues.

关键词： Metaheuristics Diversity reception Optimization methods Benchmark testing Space exploration Particle swarm optimization Optimization Tuning Standards convergence

来源：评论

学校读者我要写书评

暂无评论

Solving the Balance Optimization Problem of Makespan and Total Computation Time for Cloud computing Scheduling Based on NSGA-II

Solving the Balance Optimization Problem of Makespan and Tot...

引用

Digital Analysis and Processing, Intelligent Computation (DAPIC), international conference on

作者： Can Wu Dan Liu College of Computer Science and Technology Changchun University of Science and Technology Changchun China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

In the context of industrial upgrading, the exponential growth of computational demands for big data processing has emerged as a critical challenge. Cloud computing, with its evolving technological capabilities, presents a viable solution to address these escalating computational requirements. This study focuses on optimizing resource allocation efficiency through the establishment of a multiobjective optimization framework for cloud computing systems. Specifically, we formulate mathematical representations of objective functions and constraints while detailing corresponding encoding/decoding methodologies. The research employs the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to resolve task scheduling dilemmas in cloud environments, simultaneously optimizing makespan and total computational time through innovative applications of nondominated sorting and congestion distance comparison. A series of multi-scale comparative experiments demonstrate the algorithm's superior performance over conventional scheduling approaches.

关键词： Cloud computing Job shop scheduling Processor scheduling Optimization models Linear programming Computational efficiency Resource management Optimization Sorting convergence

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Quantum Classification 4th

Hierarchical Quantum Classification

引用

4th international conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Gohel, Prashant Joshi, Manjunath HCL Technologies Karnataka Bengaluru India Dhirubhai Ambani Institute of Information and Communication Technology Gujarat Gandhinagar India

ISBN: (纸本)9789819757022

Quantum computing in machine learning gives us a new tool to address few complex problems in the domain of artificial intelligence (AI). In this work, we use the hybrid, i.e., a combination of quantum and classical Quantum convolution neural network (HQCNN) in a hierarchical way for image classification. A four class classification is obtained hierarchically using three HQCNNs. In phase 1, we perform binary classification using HQCNN1 to get the two class classification with each class a mix of two individual classes. We then further classify each of the images into their individual categories with the use of HQCNN2 and HQCNN3 in phase 2 by performing two more binary classifications. Two experiments are conducted to show the efficacy of our proposed approach. In the first experiment we use X-ray images of organs consisting of Chest, Hand, Ankle, and Knee images which are taken from UNIFESP dataset. In our second experiment we use Deer, Horse, Airplane, and Ship images drawn from CIFAR10. We implement our proposed approach on Pennylane Software Development Kit (SDK), a simulation tool that can be implemented on a classical machine. Comparison of results with the classical method clearly indicates that the training using the proposed approach takes fewer number of epochs with improvement in accuracy and the convergence is also stable when compared to the classical approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Uniting Digital Disciplines: Bridging Literacy, Ethics, and Security Among University Students

Uniting Digital Disciplines: Bridging Literacy, Ethics, and ...

引用

Advanced Research in computing (ICARC), international conference on

作者： Hansi Munasinghe Kaushalya Hewawasam Department of Computer Science and Statistics University of Kelaniya Sri Lanka Department of Criminology and Criminal Justice University of Sri Jayewardenepura Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

In an increasingly interconnected world, university students are at the forefront of digital engagement, relying on technology for academic and social interactions. However, the convergence of disciplines such as digital literacy, cybersecurity, and ethics remains underexplored, leaving gaps in students’ preparedness for navigating the digital landscape responsibly. This research identifies disparities: while students exhibit strong digital proficiency, significant weaknesses persist in their understanding of cybersecurity risks and ethical practices. These gaps expose them to threats such as identity theft, phishing, and cyberbullying, and compromise academic integrity. The findings emphasize the need for integrating cyber literacy, ethics, and security into the educational curricula to equip students with essential skills for responsible and secure digital engagement. This study advocates for a multidisciplinary framework, combining educational reforms, structured awareness campaigns, and institutional policies to create a culture of responsible digital citizenship. By bridging these domains, the research highlights the potential for uniting disciplines to address emerging academic digital challenges.

关键词： Ethics Navigation Phishing Identity theft Education Cyberbullying Pressing Information age Digital intelligence convergence

来源：评论

学校读者我要写书评

暂无评论

Towards Trustworthy Aircraft Safety: Explainable AI for Accurate Incident and Accident Predictions 20th

Towards Trustworthy Aircraft Safety: Explainable AI for Accu...

引用

20th international conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Amin, Maryam Noor, Umara Fatima, Manahil Rashid, Zahid Altmann, Jörn Department of Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad44000 Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul Korea Republic of College of Engineering Seoul National University Seoul Korea Republic of Integrated Major in Smart City Global Convergence Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031812255

Despite technological advancements, ensuring aircraft safety remains a challenge, however, Machine learning (ML)-based approaches for predicting future incidents play a crucial role in addressing flight safety. As ML models increase in complexity, their decision-making process becomes less transparent, posing significant challenges to trustworthiness. While simpler models demonstrate lower accuracy, more intricate models such as deep neural networks achieve higher accuracy but sacrifice interpretability. In this study, we enhance trustworthiness in aircraft safety prediction by leveraging a dataset of past accidents and incidents to prevent similar accidents from occurring in the future. To achieve this, we apply Random Forest and Extreme Gradient Boosting models to classify different categories of aircraft incidents. Additionally, we apply two powerful explainable artificial intelligence (XAI) techniques: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to provide insights into both local and global predictions made by the models. Notably, our results reveal high accuracy in these predictions while maintaining trustworthiness. This research contributes to the advancement of XAI and offers valuable insights for safety-critical applications and decision support systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aircraft accidents

来源：评论

学校读者我要写书评

暂无评论

HybridFL: Hybrid Approach Toward Privacy-Preserving Federated Learning 6th

HybridFL: Hybrid Approach Toward Privacy-Preserving Federate...

引用

6th international conference on Security and Privacy in New computing Environments, SPNCE 2023

作者： Ali, Sheraz Mamoon, Saqib Usman, Areeba Abidin, Zain ul Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Sargodha Sargodha Pakistan Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering University of Jinan Jinan250022 China

ISBN: (纸本)9783031736988

In this study, we introduce a novel Hybrid Federated Learning (HybridFL) approach aimed at enhancing privacy and accuracy in collaborative machine learning scenarios. Our methodology integrates Differential Privacy (DP) and secret sharing techniques to address inference risks during training and protect against information leakage in the output model. Drawing inspiration from recent advances, we present a HybridFL framework that combines the strengths of Homomorphic Encryption (HE) and Multi-Party Computation (MPC) to achieve secure computation without the computational overhead of pure HE methods. Our contributions include a privacy-preserving design for Federated Learning (FL) that ensures local data privacy through secret sharing while leveraging DP mechanisms for noise addition. The system offers resilience against unreliable participants and is evaluated using various machine learning models, including Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and linear regression. Furthermore, we address potential external threats by deploying predictive model outputs as robust services against inference attacks. Experimental results demonstrate improved accuracy and convergence speed, establishing the viability of HybridFL as an effective solution for collaborative machine learning with enhanced privacy guarantees. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Federated learning

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：