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

63,067 篇 会议
449 册 图书
404 篇 期刊文献
1 篇 学位论文

馆藏范围

63,920 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

35,041 篇 工学
- 21,454 篇 计算机科学与技术...
- 14,426 篇 控制科学与工程
- 12,482 篇 软件工程
- 9,179 篇 信息与通信工程
- 8,850 篇 电气工程
- 3,777 篇 机械工程
- 2,898 篇 电子科学与技术（可...
- 2,280 篇 仪器科学与技术
- 1,941 篇 动力工程及工程热...
- 1,478 篇 生物工程
- 1,330 篇 交通运输工程
- 1,259 篇 生物医学工程（可授...
- 1,142 篇 安全科学与工程
- 975 篇 光学工程
- 872 篇 建筑学
- 843 篇 化学工程与技术
- 806 篇 土木工程
- 801 篇 网络空间安全
9,313 篇 理学
- 4,608 篇 数学
- 2,463 篇 物理学
- 1,895 篇 系统科学
- 1,638 篇 生物学
- 1,121 篇 统计学（可授理学、...
- 765 篇 化学
6,306 篇 管理学
- 5,127 篇 管理科学与工程(可...
- 1,946 篇 工商管理
- 1,460 篇 图书情报与档案管...
1,178 篇 医学
- 979 篇 临床医学
- 846 篇 基础医学(可授医学...
839 篇 法学
791 篇 经济学
- 787 篇 应用经济学
476 篇 农学
318 篇 教育学
221 篇 文学
161 篇 军事学
63 篇 艺术学
11 篇 历史学
8 篇 哲学

主题

3,736 篇 control systems
3,439 篇 automation
2,955 篇 communication sy...
2,602 篇 cloud computing
2,545 篇 wireless communi...
2,282 篇 protocols
1,944 篇 wireless sensor ...
1,746 篇 mobile communica...
1,614 篇 computational mo...
1,480 篇 real-time system...
1,431 篇 monitoring
1,417 篇 computer archite...
1,340 篇 internet of thin...
1,277 篇 process control
1,164 篇 automatic contro...
1,135 篇 mobile computing
1,071 篇 traffic control
976 篇 hardware
965 篇 bandwidth
917 篇 security

机构

114 篇 school of automa...
77 篇 school of mechat...
75 篇 shenyang institu...
60 篇 department of au...
59 篇 school of electr...
50 篇 maharishi school...
44 篇 jiangsu automati...
41 篇 karnataka bangal...
37 篇 university of ch...
36 篇 department of co...
33 篇 college of medic...
31 篇 state key labora...
31 篇 school of inform...
30 篇 school of automa...
30 篇 school of automa...
29 篇 school of electr...
28 篇 china electric p...
27 篇 school of inform...
27 篇 department of co...
27 篇 lovely professio...

作者

56 篇 singh bhim
45 篇 li li
38 篇 wang lei
32 篇 li yang
30 篇 jian wang
27 篇 jun wang
27 篇 yin zhenyu
26 篇 wang ping
24 篇 lei wang
24 篇 zhang lei
24 篇 wang jing
24 篇 valeriy vyatkin
22 篇 li peng
22 篇 yu wang
22 篇 yang jun
21 篇 alois zoitl
21 篇 li xin
19 篇 kastner wolfgang
18 篇 jun zhang
18 篇 jing zhang

语言

59,249 篇 英文
3,979 篇 其他
788 篇 中文
11 篇 西班牙文
11 篇 俄文
1 篇 法文
1 篇 葡萄牙文
1 篇 土耳其文

检索条件"任意字段=International Conference on Computing Communication Control and Automation"

共 63921 条记录，以下是1681-1690 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：

Tomato Leaf Disease Detection Using Machine Learning

Tomato Leaf Disease Detection Using Machine Learning

引用

2024 international conference on communication, computing and Internet of Things, IC3IoT 2024

作者： Jayanthi, G. Brindha, S. Vijayalakshmi, S. Dharshini, V. Angelin Freeda, J. Sahana, S. Sri Sairam Engineering College Department Of Instrumentation And Control Engineering India Sri Sairam Engineering College Department Of Electronics And Communication Engineering India R.M.K Engineering College Department Of Electronics And Communication Engineering India

ISBN: (纸本)9798350352689

Leaf diseases are a major danger to the world's food security and have a substantial impact on crop productivity. Effective plant disease management depends on the early identification and precise diagnosis of these illnesses. Recent developments in computer vision techniques and the accessibility of inexpensive hardware, such the Raspberry Pi, have created new avenues for the development of portable and reasonably priced leaf disease detection devices. An innovative Raspberry Pi method for leaf disease detection is shown in this abstract. The proposed approach automatically detects and classifies various leaf diseases using machine learning and image processing techniques. For on-site disease detection in agricultural settings, the Raspberry Pi platform is a perfect fit because of its small size, low power consumption, and computational capacity. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

SDN-Based Critical Infrastructure Resilience: A Smart Grid Perspective

SDN-Based Critical Infrastructure Resilience: A Smart Grid P...

引用

international conference on Computational Science and Computational Intelligence (CSCI)

作者： Chiu, Steve C. Idaho State Univ Dept Elect & Comp Engn Pocatello ID 83209 USA

ISBN: (纸本)9798350361513;9798350372304

Software-defined network (SDN) systems in their recent development has enabled innovative methods and systems to facilitate control of large and complex infrastructures critical to both regional and national interests. The advent of cloud computing allows the convergence of operation and information aspects of the traditional industrial control systems (ICS), forming the core functionalities of many critical infrastructures ranging from energy, transportation, communication to supply chains. Resilience and the mitigation of natural, manmade and cyber-based attacks are at the center of these infrastructures given the vast amount of data (both friendly and malicious) encountered in the systems. To address these issues, a holistic approach to examine the major subsystems of the infrastructure is thus essential. This paper provides a cross-sectional study of the cloud-based Smart Grid, which utilizes policy-based network management methods in runtime monitoring and operation at both the control and data-plane levels. We present an experimental 5G simulation platform that connects a network of SDN embedded 5G devices with a PLC network via the classical Ethernet as Internet of Things (IoT). Basic functionality of this experimental platform is verified for performance and scalability for future extension in system complexity.

关键词： Cloud computing critical infrastructure (CIP) big data software-defined network cyberattack SDN ICS IoT 5G communication systems policy-based network management

来源：评论

学校读者我要写书评

暂无评论

2023 11th international conference on control, Mechatronics and automation, ICCMA 2023

2023 11th International Conference on Control, Mechatronics ...

引用

11th international conference on control, Mechatronics and automation, ICCMA 2023

ISBN: (纸本)9798350315684

The proceedings contain 82 papers. The topics discussed include: banking in terms of deposit prediction based on machine learning and big data analytics;enhancing stability and performance of hybrid offshore wind platforms: a novel fuzzy logic control approach with computational machine learning;proposal of an instantaneous cognitive map acquisition method for visual information presentation considering work states and cognitive states in unmanned construction;industrial engineering with large language models: a case study of ChatGPT’s performance on oil & gas problems;reinforcement learning-based response shaping control of dynamical systems;EEG based emotion recognition using variational mode decomposition and convolutional neural network for affective computing interfaces;path planning and formation control for multiple quadrotor UAV leader-follower systems;and adaptive tracking controller with exponential restrictions for robotic manipulators.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Leveraging Industrial IoT Infrastructure for Remote Sensing and Edge computing in the Mining Sector 5

Leveraging Industrial IoT Infrastructure for Remote Sensing ...

引用

5th international conference in Electronic Engineering, Information Technology and Education, EEITE 2024

作者： Eleftherios, Emmanouil Papatsaroucha, Dimitra Markakis, Evangelos K. Hellenic Mediterranean University Electrical and Computer Engineering Heraklion Greece

ISBN: (纸本)9798350372878

Mining 4.0 signifies a paradigm shift within the mining sector and is defined as an all-encompassing strategy for industry revolution by optimizing mining processes through the utilization of digital technologies, automation, and data analytics to streamline processes. Mining 4.0 aims to develop intelligent and networked mining systems that enhance resource utilization, improve operational efficiency, reduce costs, and mitigate environmental harm, all while prioritizing the safety and welfare of workers. The process of mining digitization encompasses the methodical collection, management, and monitoring of data derived from diverse sources, including sensors, equipment, vehicles, and more. It permits remote, realtime monitoring of environmental and operational conditions, as well as the integration of control systems designed to optimize and automate processes. With digital transformation, the mining sector aspires to overcome conventional obstacles and adjust to changing market requirements, therefore facilitating a more sustainable and efficient future. A critical challenge in the process of digitizing the mining sector is the need to provide reliable routes for transmitting and communicating data between physical and digital components within mining environments. This paper examines the convergence of Mining 4.0 and mining digitization, with a specific emphasis on the pivotal role that remote sensing, edge computing, and decentralized communication systems play in data transmission and real-time monitoring in light of enhancing mining operations' safety and efficiency. The study performed puts forward a European solution based on open standards and tools, revolving around an Industrial Internet of Things (IIoT) Gateway that is a core component of the proposed communication system. This gateway will function as an intermediary between the edge and cloud-based platforms and will facilitate real-time monitoring through the establishment of resilient wireless communi

关键词： Gateways (computer networks)

来源：评论

学校读者我要写书评

暂无评论

YOLOv4-Powered Traffic Intelligence: From Detection to Optimization

YOLOv4-Powered Traffic Intelligence: From Detection to Optim...

引用

2024 IEEE international conference on Information Technology, Electronics and Intelligent communication Systems, ICITEICS 2024

作者： Sanjiv, Sinha Tharunn, E. Baskar, M. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India

ISBN: (纸本)9798350382693

The Traffic Analysis System utilizes machine learning for real-time traffic insights at a junction. It provides instant stats like vehicle count, density, and FPS, while summarizing average vehicle crossings, densities, stop times, and identifying peak minutes. Machine learning techniques include object detection, tracking, and lane segmentation. The system offers a user-friendly interface with graphical representations, aiding traffic management decisions. It not only ensures efficient traffic control but also allows for predictive analytics and potential integration with traffic light systems for adaptive signal adjustments. Continuous model training and future enhancements promise improved accuracy and adaptability to evolving traffic scenarios. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Smart Traffic Management System For Emergency Vehicles Using Deep Learning 15

Smart Traffic Management System For Emergency Vehicles Using...

引用

15th international conference on computing communication and Networking Technologies, ICCCNT 2024

作者： Nirmaladevi, P. Pavithra, M. Jp Sundar Tharma, S. Manoj, V. Hariprasath, A. Kongu Engineering College Electronics and Communication Engineering Erode Perundurai638060 India

ISBN: (纸本)9798350370249

Road congestion is the main cause for slower speed, long waiting time for the emergency vehicles. The project proposes a Smart Traffic Management System for ambulances to reduce road congestion, improve response time, and reduce waiting times. It uses modern communication, computation, and sensing technologies to minimize response time, travel time, and waiting time. The system uses Dijkstra's shortest path algorithm to calculate optimal routes based on real-time traffic conditions, road closures, and congestion, ensuring the ambulance is always on the most efficient route. The Smart Traffic Management System uses the YOLO deep learning algorithm to detect and track ambulances in real-time, enabling rapid and accurate vehicle recognition in complex traffic scenarios. Once detected, the system controls traffic signals along the ambulance's route, prioritizing green lights when approaching intersections. This intelligent traffic signal control balances the needs of the emergency vehicle with traffic flow, minimizing delays and improving response times. The system's combination of YOLO-based vehicle detection, Dijkstra path optimization, and intelligent traffic signal control makes it a valuable tool for modern urban emergency services, reducing ambulance response times. © 2024 IEEE.

关键词： Deep learning Dijkstra algorithm Shortest Path Traffic Management Yolo

来源：评论

学校读者我要写书评

暂无评论

Resource Savings in Wireless Sensor Networks by Replacing Classical control with Markovian Model-Based control 26

Resource Savings in Wireless Sensor Networks by Replacing Cl...

引用

2024 IEEE international conference on automation/26th Congress of the Chilean Association of Automatic control, ICA-ACCA 2024

作者： De Oliveira, Lucas W. Palma, Jonathan M. Morais, Cecilia F. Pontifícia Univ. Católica de Campinas Escola Politécnica SP Campinas Brazil Universidad de Talca Talca Facultad de Ingeniería Curicó Chile Fac. de Eng. Elétrica e de Computação SP Campinas Brazil

ISBN: (纸本)9798350378115

The main obstacle to implementing classic control projects over long distances is the high cost of installing and maintaining the infrastructure necessary to guarantee reliable communication, for instance, through wired networks. Wireless Sensor Networks (WSN) came as an alternative to the need for cabling. However, given the loss of information inherent to this type of transmission, to guarantee the stability of the closed-loop system, it is necessary to include several nodes (close to each other) throughout the communication network. In this context, this paper describes the possibility of performing remote control via WSN, aiming to improve the trade-off: good performance of the control system and reducing network expenses by allowing a decrease in the number of nodes. The proposed strategy consists of replacing the use of a classical control technique (Linear Quadratic Regulator - LQR) that requires full-reliable communication with a Markov model-based control technique, which provides theoretical guarantees of closed-loop system stability even when there is the possibility of packet loss (as is usual in WSN context). To illustrate the efficiency of the proposed approach, simulations of the dynamic behavior of a plant controlled through a wireless communication network are carried out using real statistical data obtained via experimental tests in a workbench emulation of a WSN with two nodes. © 2024 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Diagnosis of Plant Infection for Optimised Authomatic Health Monitoring System Using Vision Transformer Models 5

Diagnosis of Plant Infection for Optimised Authomatic Health...

引用

5th IEEE international conference on computing, Power, and communication Technologies, IC2PCT 2024

作者： Thapliyal, Nitin Aeri, Manisha Satyarthi, Abhishek Kukreja, Vinay Sharma, Rishabh Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Artificial Intelligence & Data Science Iimt College of Engineering Uttar Pradesh Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383522

Mosaic Virus is highly responsible for the crop loss, especially the pepper, which is considered part of food crop, an essential aspect of world's agriculture. This work aims at automating the classification of disease severity level-related the Pepper Leaf Mosaic Virus Disease threshold through vision transformer (ViT) model to ensure a critical job. The classification of the severity is more often accompanied by a fairly significant amount of workload and arbitrariness, resulting in equipment innovation in most of the locations. The work applies ViT-based approaches with sufficient precision to appropriately classify a dataset, which contains 10,800 meticulously selected photographs. The present model obtained a commendable average accuracy of 97.25%, which shows that the model was able to detect even smaller differences between the classes of severity. The operational criteria that include the tale of the precision, recall, and F1-score highlight the model's ability to minimize to Feggsor false Positives with its ability to discriminate cases of viruses at different levels of the spectrum. The more advanced comparison charts made hand methodologies stand at the pinnacle in plant pathology studies. This study is not only beneficial in terms of illuminating the understanding about the Pepper Leaf Mosaic Virus but also gives farmers and researchers a transformative tool that could change the way disease control methods are used. Technologies AI that power ViT model are helping the agriculture industries throughout the world to implement sustainability and resiliency of the systems by providing responses in the right time and place in addressing viral infections. © 2024 IEEE.

关键词： Agricultural automation Agricultural Technology Artificial Intelligence Automated Diagnosis Crop Health Disease Management Global Food Security Image Classification Machine Learning Pepper Leaf Mosaic Virus Plant Pathology Precision Agriculture Severity Classification Vision Transformer ViT Model

来源：评论

学校读者我要写书评

暂无评论

automation and control Mechanism for Textile Dyeing Process Using PLC and IoT

Automation and Control Mechanism for Textile Dyeing Process ...

引用

2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Thangatamilan, M. Suji Prasad, S.J. Sagana, C. Subikshaa, R. Kumar, D. Paveen Shimak, M. Mohammed Kongu Engineering College Electrnoics and Instrumentation Erode India Kongu Engineering College Computer Science Engineering Erode India

ISBN: (纸本)9798350333244

Industrial automation is a developing technology that has a sustainable growth in the modern environment. The industrial automation has several benefits like the lesser involvement of employees in the industry operation, and maintains the process with accuracy and precision. The purpose of the industrial automation project equipped with PLC, minimizes the overall operational cost of the plant operation. Dyeing industry operates on different stages of tedious processes that certainly require factory automation for efficient plant operation. By implementing factory automation systems the one big problem statement of paying wages to the employees at the time of no order period is being eliminated. The PLC is a control unit where all the machinery from the dyeing industry is connected and monitored through IoT. This way of factory automation is implemented by developing a prototype model for automation and control mechanism for the textile dyeing process using PLC and IoT. Dyeing is a process, where the fabric is rolled up in the winch dyeing machine and the gradual transfer of dyes to the fabric under optimum temperature is performed. The major machineries involved in the dyeing process are the winch dyeing machine and the boiler. Winch dyeing machine have a rotating rod that spins when the motor is energized, the feed water and hot water is passed to the winch machine when the solenoid valve is actuated based on the preset value. The work in this paper deals with the process of automated the whole system using PLC. The level of the dyebath in the winch dyeing machine is actively monitored using the level sensor and the real time values are taken to the cloud. This automation benefits the dyeing Industry with precision, accuracy and real time monitoring of plant. © 2023 IEEE.

关键词： Dyeing

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning for Optimal Robotic Arm control in Ball-Balancing Tasks

Reinforcement Learning for Optimal Robotic Arm Control in Ba...

引用

2024 international conference on communication, Computer Sciences and Engineering, IC3SE 2024

作者： Vadlamudi, Sandeep Lakshmi, Korupu Vijaya Velagapudi Ramakrishna Siddhartha Engineering College Department Of Eie Vijayawada India

ISBN: (纸本)9798350366846

Reinforcement learning (RL) stands out as a promising method for developing better control strategies in robotic systems, particularly in tasks involving complex dynamics such as ball-balancing in automated manufacturing. In automated manufacturing processes, efficient control of robotic arms is crucial for maintaining precision and productivity. This paper presents the implementation of the Deep Deterministic Policy Gradient (DDPG) algorithm for optimal control of a robotic arm in ball-balancing tasks. The efficacy of the proposed DDPG algorithm is demonstrated through comparison with the existing Soft Actor-Critic (SAC) algorithm. Through simulations, the results obtained from DDPG are analysed and contrasted with those from SAC. The smooth trajectory achieved with the DDPG algorithm showcases its ability to generate precise and consistent control commands, leading to stable and smooth motion of the robotic arm during ball balancing. This research contributes to advancing automation in manufacturing by presenting a robust RL framework for optimizing robotic arm control in complex tasks. © 2024 IEEE.

关键词： Reinforcement 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 << < 165 166 167 168 169 170 171 172 173 174 > >>

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：