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

79 篇 会议
33 篇 期刊文献

馆藏范围

112 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

68 篇 工学
- 59 篇 计算机科学与技术...
- 46 篇 软件工程
- 17 篇 信息与通信工程
- 12 篇 电气工程
- 11 篇 控制科学与工程
- 8 篇 机械工程
- 8 篇 生物工程
- 6 篇 仪器科学与技术
- 6 篇 电子科学与技术（可...
- 5 篇 网络空间安全
- 4 篇 动力工程及工程热...
- 4 篇 化学工程与技术
- 3 篇 安全科学与工程
- 2 篇 力学（可授工学、理...
- 2 篇 航空宇航科学与技...
- 2 篇 环境科学与工程（可...
- 1 篇 建筑学
29 篇 理学
- 14 篇 数学
- 8 篇 生物学
- 7 篇 统计学（可授理学、...
- 5 篇 物理学
- 5 篇 化学
- 5 篇 系统科学
- 1 篇 大气科学
19 篇 管理学
- 11 篇 管理科学与工程(可...
- 9 篇 图书情报与档案管...
- 6 篇 工商管理
4 篇 法学
- 4 篇 社会学
3 篇 经济学
- 3 篇 应用经济学
1 篇 教育学
- 1 篇 教育学
1 篇 艺术学

主题

5 篇 computational mo...
5 篇 real-time system...
5 篇 semantics
5 篇 software
4 篇 internet of thin...
4 篇 computer archite...
4 篇 automata
4 篇 clocks
3 篇 reliability
3 篇 reinforcement le...
3 篇 distillation
3 篇 delays
3 篇 cyber physical s...
3 篇 optimization
3 篇 monitoring
3 篇 schedules
3 篇 software reliabi...
3 篇 software measure...
3 篇 feature extracti...
3 篇 probabilistic lo...

机构

21 篇 moe engineering ...
8 篇 software enginee...
8 篇 moe engineering ...
6 篇 moe engineering ...
5 篇 school of inform...
5 篇 school of comput...
5 篇 shanghai key lab...
5 篇 east china norma...
4 篇 school of comput...
4 篇 east china norma...
3 篇 moe research cen...
3 篇 east china norma...
3 篇 moe engineering ...
3 篇 moe engineering ...
2 篇 national mobile ...
2 篇 moe engineering ...
2 篇 beijing national...
2 篇 school of comput...
2 篇 yale university ...
2 篇 the moe engineer...

作者

16 篇 yixiang chen
11 篇 chen mingsong
10 篇 chen yixiang
10 篇 wang ting
9 篇 bo xiao
7 篇 xiao bo
7 篇 minghua zhu
7 篇 shi yuanming
6 篇 ting wang
5 篇 tao hongwei
5 篇 zhu minghua
4 篇 yuanrui zhang
4 篇 xinyu zhang
4 篇 wang jiali
4 篇 wenjie chen
4 篇 wu hengyang
3 篇 hengyang wu
3 篇 liang shi
3 篇 liu tian
3 篇 shi jianqi

语言

107 篇 英文
5 篇 其他

检索条件"机构=MoE Engineering Center for Software/Hardware Co-design Technology and its Application"

共 112 条记录，以下是1-10 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：

Joint computing Offloading and Resource Allocation in MEC-Enabled IoT: A Diffusion-Based Reinforcement Learning Approach

Joint Computing Offloading and Resource Allocation in MEC-En...

引用

2024 IEEE International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Cao, Huimin Xiao, Bo MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

ISBN: (纸本)9781665410205

The integration of the Internet of Things (IoT) with mobile edge computing (MEC) has come out to be a promising solution to address the requirements of high computing capabilities and low latency services, enabling user equipments(UE) to migrate the computation of tasks onto edge servers. This paper focuses on optimizing the performance of MEC-enabled IoT system by formulating a joint computing offloading and resource allocation problem. The objective is to minimize the total delay of the system consisting of multiple servers and multiple users. The denoising network of a diffusion model with capabilities of generation can be trained to obtain optimal solution given the changed environment conditions. Therefore, we propose the diffusion-based deep deterministic policy gradient (DiffDDPG) algorithm which utilizes a diffusion model as the policy to learn optimal decisions jointly. Simulation results exhibits the superior performance of the DiffDDPG algorithm. © 2024 IEEE.

关键词： computation offloading

来源：评论

学校读者我要写书评

暂无评论

MACEdge: Real-Time Video Analytics Based on Multi-Access collaborative Edge computing

MACEdge: Real-Time Video Analytics Based on Multi-Access Col...

引用

2023 IEEE International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhong, Dian Zhu, Minghua Huang, Binbin East China Normal University Moe Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350337020

Video analysis typically requires a significant amount of computing resources and energy. Traditional cloud-based video analysis relies on concentrating computing resources in the cloud, which puts a tremendous load on network bandwidth and introduces poor user experience due to latency. Edge computing offers a solution by offloading a portion of the video analysis tasks to the edge, significantly reducing the pressure on bandwidth and latency. However, the fixed video configurations (resolution, frame rate, etc.) uploaded to the edge may not be optimal for specific situations. Moreover, using a low video configuration makes it difficult for edge video analysis systems to recognize environmental changes, while using a high video configuration poses challenges to latency and bandwidth. Therefore, in this paper, we propose the MACEdge-an edge video analysis framework by leveraging the multi-access capability of the edge gateway to enhance edge perception by accessing heterogeneous sensors. We offer an algorithm based on Q Learning for dynamic adaptive adjustment of sensor threshold and video configurations. In experiments using real-world data, our collaborative solution improved accuracy and reduced high bandwidth costs compared to other video configuration adjustment benchmarks. © 2023 IEEE.

关键词： Edge computation Multi-access collaboration Video analysis

来源：评论

学校读者我要写书评

暂无评论

LWSA: A Learning-Based Workflow Scheduling Algorithm for Energy-Efficient UAV Delivery System 29

LWSA: A Learning-Based Workflow Scheduling Algorithm for Ene...

引用

29th IEEE International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Jiali Ye, Yutong Wang, Ting Chen, Mingsong East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350330717

Due to their fast speed and easy deployment, Unmanned Aerial Vehicles (UAVs) have been widely used across various sectors, such as earthquake rescue, medical assistance, and smart agriculture. However, UAVs in delivery networks face significant challenges due to limited battery life and computational capabilities, particularly for tasks that entail intensive computing workflows. In this context, Multi-access Edge computing (MEC), which provides computing resources in close proximity to mobile terminal devices, has emerged as a promising solution. UAVs can offload computing tasks to MEC resources across diverse Internet of Things (IoT) environments. Although task offloading can enhance their task processing capability, it simultaneously brings additional costs, encompassing data transmission time and energy consumption. To address these issues, this paper proposes a novel workflow scheduling method based on the Proximal Policy Optimization (PPO) algorithm, aimed at optimizing UAV energy consumption within MEC environments. The proposed approach establishes a learning-based workflow scheduling strategy harnessing the adaptability of the PPO algorithm to manage dynamic and intricate scenarios, which facilitates efficient task allocation to optimal computational resources while accounting for flight time constraints. Extensive experiments conducted on various well-known scientific workflow benchmarks in real-world UAV delivery networks validate the effectiveness of our method. compared with state-of-the-art methods, our approach significantly reduces UAV energy consumption and task completion time, simultaneously increasing UAV's effective payload capacity. © 2023 IEEE.

关键词： Energy Efficiency Multi-Access Edge computing Reinforcement Learning Workflow Scheduling

来源：评论

学校读者我要写书评

暂无评论

QueryEdge: Real-Time Muti-Video Query in Edge-Cloud collaborative System

QueryEdge: Real-Time Muti-Video Query in Edge-Cloud Collabor...

引用

2023 IEEE International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhong, Jihua Niu, Yannian Zhu, Minghua East China Normal University MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350337020

The real-time query of surveillance video plays a significant role in many fields such as public safety, smart city, and abnormality monitoring. However, with the exponential growth of surveillance video data, traditional cloud-based intelligent video processing faces significant challenges in terms of latency and bandwidth, while the pure edge computing approach is deficient in query accuracy due to its lack of computational power. Existing edge cloud collaboration approaches, such as SurveilEdge, focus on real-time target queries within a single video stream and do not show promising results during target queries across multiple video streams. For this reason, this paper proposes Query Edge, an edge-cloud collaborative real-time query system for multiple video streams. Specifically, we design a real-time query system based on an edge-cloud collaboration framework to achieve highly accurate and low-latency target query services in multiple video streams. In addition, we introduce a prioritization mechanism and a load-balancing strategy in the query task scheduling process to further improve query efficiency. The evaluation proves that QueryEdge has a significant improvement in query latency and bandwidth consumption compared with pure cloud computing, pure edge computing, and SurveilEdge. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Slim-MY: A Lightweight Network for Power Line Defect Detection with FPGA Acceleration 29

Slim-MY: A Lightweight Network for Power Line Defect Detecti...

引用

29th IEEE International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhang, Xiaoyang Chen, Wenjie Zhou, Zihao Duan, Zhuowen East China Normal University MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350330717

The power line is a crucial component of the power system. However, long-term exposure to the natural environment can lead to various defects, such as burrs, corrosion, and corona. These defects seriously threaten the safe operation of the power system. Traditional manual inspections and image processing methods are time-consuming, labor-intensive, and have lower accuracy. In light of these, we propose a lightweight network called Slim-MY. It utilizes lightweight feature extraction and fusion modules, significantly reducing computational and parameter overhead while ensuring defect detection accuracy. Furthermore, to accelerate the post-processing process, we propose the FP-NMS algorithm. It parallelizes the sorting and overlapping box removal parts of the NMS algorithm, making it more suitable for deployment on devices that support parallel computing, such as FPGA. By testing on our dataset of power line defects, the Slim-MY network achieves a detection accuracy and computation of 92.7% and 1.1 GFLOPs respectively, which outperforms most other networks. By accelerating on the KV260 FPGA, the Slim-MY network achieves an inference speed of 94 fps, and the FP-NMS algorithm's execution speed is 14.8 times that of the GTX 1080Ti. It indicates the feasibility and efficiency of the method we propose in power line defect detection tasks. © 2023 IEEE.

关键词： Deep Learning Defect Detection FPGA Parallel computing

来源：评论

学校读者我要写书评

暂无评论

Hybrid Forwarding Strategy Pressure-based Routing for Underwater Wireless Sensor Network 3

Hybrid Forwarding Strategy Pressure-based Routing for Underw...

引用

3rd International conference on Green communication, Network, and Internet of Things, CNIoT 2023

作者： Yan, Xianyi Xiao, Bo MOE Research Center for Software/Hardware Co-Design Engineering and Application East China Normal University China

ISBN: (纸本)9781510668867

Due to the harsh underwater communication environment, the design of unique routing protocols is essential for underwater sensor networks. This paper proposes a Hybrid Forwarding Strategy Pressure-Based Routing (HFS-PBR) protocol for the Underwater Wireless Sensor Network (UWSN). The protocol categorises forwarding nodes for every sending node into three different types, each of which employs a distinct routing forwarding strategy. Nodes in HFS-PBR can gather neighbour information by exchanging information regularly, which enables the protocol to mitigate the problem of void node and make more optimal routing decisions. Through extensive simulation experiments, our proposed routing protocol demonstrates lower end-to-end delay and improves packet delivery probability compared to various routing protocols. These advantages are particularly prominent in sparse network topologies. © 2023 SPIE.

关键词： Routing protocols

来源：评论

学校读者我要写书评

暂无评论

A Range/Angle-based Localization Framework in WSN: A Clustering Approach 2

A Range/Angle-based Localization Framework in WSN: A Cluster...

引用

2nd International conference on Sensing, Measurement, communication and Internet of Things Technologies, SMC-IoT 2023

作者： Kong, Sijie Xiao, Bo East China Normal University Moe Research Center for Software Hardware Co-Design Engineering and Application Software Engineering Institute Shanghai China

ISBN: (纸本)9798350373257

In this paper, we propose a localization algorithm framework for wireless sensor networks (WSNs) based on cluster implementation. The algorithm addresses the issue of information locality in traditional distributed localization algorithms by utilizing clustering. In contrast to centralized algorithms, this algorithm achieves a more balanced node energy consumption without the need for additional special nodes. Through clustering, nodes pass appropriate information to their targets, and through optimizations within and between clusters, relatively accurate location information is obtained. After multiple rounds of clustering-localization iterations, the accuracy of node localization significantly improves. Importantly, simulation results demonstrate that the algorithm can adapt to various deployment scenarios by adjusting clustering parameters to balance energy consumption and localization accuracy. © 2023 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Green Federated Learning over Cloud-RAN with Limited Fronthaul and Quantized Neural Networks 3

Green Federated Learning over Cloud-RAN with Limited Frontha...

引用

3rd IEEE International Mediterranean conference on communications and Networking, Meditcom 2023

作者： Wang, Jiali Mao, Yijie Wang, Ting Shi, Yuanming Shanghai Key Lab. of Trustworthy Computing East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application China China

ISBN: (纸本)9798350333732

In this paper, we investigate a green federated learning (FL) framework over cloud radio access network (Cloud-RAN) system that comprises a server, multiple devices and remote radio heads (RRHs). Each device utilizes quantized neural networks (QNNs) and sends quantized model parameters to the server to save energy consumption via RRHs. The server aggregates all the signals to update the global model parameters and broadcasts the updated parameters to the selected devices. In this context, we propose an energy consumption model for the QNN training and communication model over Cloud-RAN. We develop an energy minimization problem based on the proposed energy model. We jointly design fronthaul rate allocation, device transmit power, and precision level of QNNs while ensuring target accuracy, transmit power budget and limited fronthaul capacity. Guided by the convergence analysis, we adopt alternative optimization method to solve the energy minimization problem. The simulation outcomes demonstrate that the FL framework suggested can considerably diminish energy usage in comparison to other traditional methods. This has immense potential in realizing a sustainable and eco-friendly FL over Cloud-RAN. © 2023 IEEE.

关键词： Radio access networks

来源：评论

学校读者我要写书评

暂无评论

Using Multi-feature Embedding towards Accurate Knowledge Tracing 34

Using Multi-feature Embedding towards Accurate Knowledge Tra...

引用

34th International conference on software engineering and Knowledge engineering, SEKE 2022

作者： Yu, Yang Huang, Caidie Chen, Liangyu Chen, Mingsong MoE Engineering Research Center of Software Hardware Co-Design Technology and Application East China Normal University Shanghai China

ISBN: (纸本)1891706543

Knowledge tracing is a crucial task in intelligent tutoring systems. Aiming at the shortcomings of traditional knowledge tracing technology such as low prediction accuracy, overfitting and low utilization of multi-features, this paper proposes a knowledge tracing model SRGCA-M using multi-feature embedding with stacked residual GRU network. compared with the traditional methods that only use the historical record of answering exercises, our approach utilizes a variety of features in the learning process of students to deep characterize students' learning. We increase the layers number of GRU network to expand the capacity of sequence learning and use residual connections to solve the problems of network degradation and vanishing gradient. We use the auto-encoder to solve the problem that the cross-feature encoding will rapidly increase the dimension of the input data. comprehensive experimental results demonstrate that compared with various advanced techniques, our approach can not only achieve better performance of tracking knowledge changes of students but also fully utilize multi-feature information of students in the learning process. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Throughput-Efficient communication Device Driver for IoT Gateways

Throughput-Efficient Communication Device Driver for IoT Gat...

引用

2022 IEEE International conference on Systems, Man, and Cybernetics, SMC 2022

作者： Niu, Yannian Zhu, Minghua East China Normal University MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

As bridges for data exchange between end devices and cloud servers, gateways play an important role in IoT and cloud computing fields. However, with the dramatic increase of data generated by the end devices in recent years, the limited data forwarding capability of the gateway has become the bottleneck of the performance of the IoT cloud-based architectures. To efficiently improve the data throughput of links between end devices and cloud servers, this paper proposes a user-space driver for gateways that places the packet-forwarding behavior in the user space of the gateway OS and achieves high throughput by bypassing the kernel and simplifying the packet delivery path. In addition, to obtain more significant data throughput in small packet-intensive scenarios, we integrate packet assembling methods for the driver, which assembles received small packets into a large one before forwarding it. Our evaluation shows that the user-space driver has tens of times throughput compared to the kernel driver. Furthermore, our driver cooperated with the packet assembling method shows a significant advantage compared to the latest related work in small packet-intensive scenarios. © 2022 IEEE.

关键词： Cloud computing

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12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：