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

2,082 篇 会议
100 册 图书
15 篇 期刊文献

馆藏范围

2,197 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

951 篇 工学
- 572 篇 计算机科学与技术...
- 565 篇 控制科学与工程
- 368 篇 软件工程
- 168 篇 电气工程
- 125 篇 机械工程
- 82 篇 信息与通信工程
- 44 篇 电子科学与技术（可...
- 39 篇 仪器科学与技术
- 39 篇 生物工程
- 38 篇 交通运输工程
- 35 篇 动力工程及工程热...
- 35 篇 化学工程与技术
- 35 篇 生物医学工程（可授...
- 29 篇 建筑学
- 24 篇 光学工程
- 22 篇 土木工程
- 20 篇 环境科学与工程（可...
- 19 篇 冶金工程
327 篇 理学
- 206 篇 数学
- 75 篇 系统科学
- 57 篇 统计学（可授理学、...
- 55 篇 物理学
- 46 篇 生物学
- 25 篇 化学
194 篇 管理学
- 173 篇 管理科学与工程(可...
- 53 篇 工商管理
- 29 篇 图书情报与档案管...
29 篇 医学
- 23 篇 临床医学
- 20 篇 基础医学(可授医学...
21 篇 经济学
- 20 篇 应用经济学
15 篇 法学
6 篇 农学
4 篇 教育学
4 篇 军事学
2 篇 艺术学

主题

336 篇 computational in...
326 篇 computational mo...
245 篇 artificial intel...
234 篇 automation
146 篇 control systems
97 篇 automatic contro...
80 篇 humans
78 篇 neural networks
70 篇 mathematical mod...
63 篇 predictive model...
62 篇 process control
56 篇 fuzzy control
52 篇 robotics and aut...
50 篇 mobile robots
47 篇 fuzzy logic
47 篇 intelligent robo...
47 篇 robots
47 篇 competitive inte...
46 篇 computer archite...
46 篇 optimization

机构

17 篇 college of intel...
16 篇 institute of aut...
16 篇 state key labora...
14 篇 unmanned system ...
14 篇 nanjing universi...
13 篇 school of artifi...
13 篇 school of artifi...
12 篇 beijing key labo...
10 篇 school of automa...
10 篇 school of automa...
9 篇 school of automa...
9 篇 complex system a...
8 篇 college of elect...
7 篇 beijing hiwing s...
7 篇 college of autom...
7 篇 school of automa...
7 篇 shenyang institu...
7 篇 department of au...
7 篇 key laboratory o...
6 篇 guangdong univer...

作者

14 篇 yifeng niu
14 篇 wenxing fu
14 篇 dongbin zhao
14 篇 yi qu
11 篇 fei-yue wang
10 篇 wang fei-yue
9 篇 derong liu
8 篇 songmin jia
8 篇 lianqing liu
8 篇 xiong huang
8 篇 zeng-guang hou
7 篇 gong daoxiong
7 篇 song lin
7 篇 yu jianjun
7 篇 jianjun yu
7 篇 mancang gu
7 篇 jianqiang yi
7 篇 f. matsuno
6 篇 jun wang
6 篇 de-shuang huang

语言

2,165 篇 英文
24 篇 其他
10 篇 中文

检索条件"任意字段=International Conference on Computational Intelligence for Modelling, Control and Automation"

共 2197 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：

Prediction of Rolling Force in Hot Rolling Process Based on Rollformer Model 16th

Prediction of Rolling Force in Hot Rolling Process Based on ...

引用

16th international conference on modelling, Identification and control, ICMIC 2024

作者： Li, Qing Jiang, Pengyuan Yuan, Li School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China

ISBN: (纸本)9789819617760

In the steel manufacturing industry, the precise forecasting of rolling forces in the hot rolling process is vital for enhancing production efficiency and ensuring product quality. Traditional forecasting methods exhibit limitations when addressing the nonlinearity and variability of industrial data, and current deep learning models are slow to respond to real-time data, with room for improvement in predictive accuracy within dynamic production settings. This paper introduces the Rollformer deep learning model, which adeptly captures intricate nonlinear patterns through the self-attention mechanism's nonlinear transformation capabilities, and boosts sensitivity and adaptability to data variability via adaptive weight distribution. The Rollformer model also pioneers the Roll Self-attention mechanism, leveraging probabilistic sparsity to diminish computational complexity and accelerate the model’s response. Additionally, it employs a generative decoder for swift long-sequence predictions, thereby augmenting the model’s predictive precision. Validated with extensive real-world data from steel mills, the Rollformer model demonstrates superior predictive accuracy and response speed when compared to other Transformer-based deep learning models such as Autoformer and Conformer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

A Fully Sparse Detector with Cascade Heads for LiDAR-based 3D Object Detection 13

A Fully Sparse Detector with Cascade Heads for LiDAR-based 3...

引用

13th international conference on Intelligent control and Information Processing, ICICIP 2025

作者： Chen, Junnan Zhang, Gang Hu, Xiaolin Zeng, Zhigang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science and Technology Institute for AI BNRist THU-Bosch JCML Center Tsinghua Universigy Beijing China

ISBN: (纸本)9798331516147

LiDAR-based 3D object detection is a critical component of perception systems in autonomous driving. Existing detectors typically rely on dense feature maps for 3D object prediction. However, as the perception range expands, the computational overhead associated with dense feature maps increases quadratically, creating significant challenges for scaling to long-range detection scenarios. To address this issue, fully sparse detectors have recently been proposed, but they often suffer from suboptimal performance or excessively complex architectures. In this paper, we present CHNet, a fully sparse detector designed to overcome these limitations by introducing a cascade head module, which consists of two sequential heads. By generating coarse object predictions, creating new feature points based on centers of the predicted objects, and aggregating features of the entire object to these new center points, CHNet enhances the qualities of object feature representations, achieving higher detection performance. Notably, CHNet effectively addresses the center feature missing problem in LiDAR-based 3D object detection by adding at most a hundred new feature points, offering a potential avenue for future research. On the widely used Waymo Open and Argoverse2 datasets, CHNet achieves impressive results without any additional embellishments. © 2025 IEEE.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Target recognition technology for unmanned ships based on improved YOLOv5 4

Target recognition technology for unmanned ships based on im...

引用

2024 4th international conference on Intelligent Traffic Systems and Smart City, ITSSC 2024

作者： Yuan, Mingkai Liang, Bingnan Wang, Haoliang Dong, Yixuan School of Marine Engineering Dalian Maritime University Dalian116026 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Dalian Key Laboratory of Swarm Control and Electrical Technology for Intelligent Ships Dalian116026 China State Key Laboratory of Maritime Technology and Safety Dalian116026 China

ISBN: (纸本)9781510686304

To enable safe and efficient autonomous navigation of unmanned ships in complex marine environments, particularly when encountering overtaking, head-on, or crossing scenarios, it is crucial to accurately identify and detect unknown unmanned vessels. This paper proposes a lightweight object detection algorithm tailored to these challenges. The lightweight network Ghostnet is used to improve the backbone network of the YOLOv5s algorithm, achieving lightweight target detection. By introducing the ECA attention mechanism, the model can better focus on important information in the image, enhancing the ability of feature representation, and improving the model's capability for complex scene understanding and target recognition and localization. Finally, the MPDIoU is used as the bounding box loss function for the YOLOv5s algorithm, which not only improves convergence speed but also obtains more accurate regression results. Comparative experiments show that the proposed improved algorithm outperforms models such as YOLOv5-BiFPN, YOLOv5-CBAM, and YOLOv5-SE in terms of model size, computational complexity, parameter count, detection speed, and average precision. © 2025 SPIE.

关键词： Marine navigation

来源：评论

学校读者我要写书评

暂无评论

Enhancing Threat Detection Accuracy in IIoT Networks using AI-based Models

Enhancing Threat Detection Accuracy in IIoT Networks using A...

引用

international conference on ent Computing and control Systems (ICICCS)

作者： Omer Waseem Al-Zaidi Mohammed Al-Hubaishi Department of Software Engineering Halic University Istanbul Turkey Department of Computer Engineering Halic University Istanbul Turkey

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Software-Defined Networking (SDN) and Artificial intelligence (AI) have the potential to address challenges in Industrial Internet of Things (IIoT) networks, such as security, scalability, and network performance, and this paper proposes a robust framework that leverages SDN and AI to enhance network management, real-time monitoring, and efficient resource utilization. SDN decouples the control and data planes, providing centralized control and simplifying the management because AI techniques, including machine learning and reinforcement learning, enable intelligent threat detection, optimized routing, and data protection. The synergistic fusion of these technologies ensures timely and secure data transmission, reduces latency and prevents data loss. Thus, the proposed framework enhances IIoT deployments through automation, predictive analytics, and proactive threat detection and mitigation. This paper demonstrates the integration of SDN and AI to create secure, scalable, and seamless IIoT networks that meet the requirements of modern industrial operations and information security standards. Therefore, it highlights the potential of SDN and AI to revolutionize IIoT networks, providing a comprehensive framework for future IIoT deployments.

关键词： computational modeling Scalability Routing Threat assessment Real-time systems computational efficiency Artificial intelligence Software defined networking Standards Industrial Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal Autonomous System

Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal Auton...

引用

Electrical automation and Artificial intelligence (ICEAAI), international conference on

作者： Ariano Da Conceicao Lianfang Tian Qiliang Du Ling Yuan School of Automation Science and Engineering South China University of Technology Guangzhou China Research Institute of Modern Industrial Innovation South China University of Technology Guangzhou China Guangdong Engineering Research Center of Cloud-Edge-End Collaboration Technology for Smart City Guangzhou China Key Laboratory of Autonomous Systems and Network Control of Ministry of Education Guangzhou China Key Laboratory of Large-Model Embodied-Intelligent Humanoid Robot (2024KSYS004) Guangzhou China Sino-Singapore International Joint Research Institute Guangzhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Indoor mobile robots require reliable solutions for mapping, localization, and navigation tasks. This paper presents a mobile robot system that implements the ability to realize mapping using visual SLAM with ORB-SLAM3 and LiDAR-based mapping with Gmapping for indoor perception and navigation through a modular ROS architecture. The framework also implements autonomous navigation and an autonomous Rapidly-exploring Random Trees (RRT) exploration module with adaptive reset mechanisms. The system operates on a ROS-based platform equipped with an Intel RealSense D435i RGB-D camera and LDS-02 LiDAR scanner. Experimental validation in simulated indoor environments demonstrates the system's capabilities and the presented architecture achieves computational efficiency through selective utilization of mapping modules while maintaining mapping accuracy through complementary sensor modalities.

关键词： Visualization Simultaneous localization and mapping Accuracy Laser radar Navigation Service robots Heuristic algorithms Computer architecture Mobile robots Autonomous robots

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence in Robotics: Revolutionizing Retail Store automation and Beyond

Artificial Intelligence in Robotics: Revolutionizing Retail ...

引用

Learning and Technology conference (L&T)

作者： Khalufi Nasser Ali Department of Management and Marketing College of Business Jazan University Jazan Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

AI in robotics involves the application of efficient algorithm and computational techniques that allows robots to operate independently, learn from the environment and make efficient decisions. However, in current studies, mostly contact methods that cannot be learned and do not respond to change are used despite the progress made in this regard. These approaches often fail to respond in real-time to а certain issue, which can result in inefficiency and low overall operational performance. To overcome these shortcomings, this paper presents a new technique in which DRL (Deep Reinforcement Learning) is used for motion control of a robotic arm and limb in retail automation. DRL allows the robotic system to learn the best motion profiles from playing a game with its surroundings with reduced chances of a less optimum setting due to changing environmental conditions. The most important peculiarities of the proposed approach include integration of enhanced neural structures and realization of automatic learning and decision-making. The effectiveness of the DRL approach is shown to be considerably higher than that of traditional ones, with accuracy reaching 99.3%. Such outcomes speak to the prospects to transform robotics through DRL knowledge and skills to enhance real-time adaptation and increase effectiveness. Lastly, this research enhances the existing literature in AI & Robotics and accordingly establishes the foundation for further developments of intelligent automation systems in various fields.

关键词： Motion planning Intelligent automation Limbs Service robots Transforms Deep reinforcement learning Manipulators Real-time systems Artificial intelligence Robots

来源：评论

学校读者我要写书评

暂无评论

Adaptive Fractional Order PI-PD controllers Applied on a DC Modular Servo System

Adaptive Fractional Order PI-PD Controllers Applied on a DC ...

引用

international conference on computational Cybernetics (ICCC)

作者： Marcian D. Mihai Emanuel S. Coman Hegedus T. Erwin Isabela R. Birs Cristina I. Muresan Department of Automation Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

Fractional calculus extends traditional calculus and provides a comprehensive framework for modelling and control via fractional-order transfer functions (FOTFs). Fractional-order (FO) controllers, including FO-PI and FO-PD, enhance flexibility and performance through the inclusion of an additional tuning parameter, surpassing classical controllers. Despite these advantages, the robustness of FO controllers remains uncertain under varying operating conditions, particularly in nonlinear systems. Adaptive control methods, including Model-Reference Adaptive control (MRAC), effectively mitigate this limitation by facilitating real-time adjustments to variations in process parameters. This study presents a new control strategy that integrates MRAC with FO control techniques to improve the adaptability and robustness of DC servo motor systems. The DC servo motor, represented as a first-order system, provides the environment for experimental validation. This paper presents the mathematical relations used for tuning the FO-PI and the adaptive FO-PI controllers. The results indicate that this hybrid approach outperforms the classical approach. This research presents the integration of FO and adaptive control for industrial applications.

关键词： Adaptation models Process control Transfer functions Robustness Real-time systems Adaptive control Servomotors Nonlinear systems Tuning Testing

来源：评论

学校读者我要写书评

暂无评论

Intelligent control algorithms for industrial automation systems

Intelligent control algorithms for industrial automation sys...

引用

IEEE Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Sarathkumar D Anil. A. R J Sivadasan T. Jayakumar Murugesan Manivel Anandhakumar C Department of Electrical and Electronics Engineering Kongu Engineering College Tamil Nadu India Department of Computer Science &Engineering Sree Buddha College of Engineering Pattoor Alappuzha Kerala India Department of Electronics and Communication Engineering PSR Engineering College Sivakasi Tamil Nadu India Department of Electrical and Electronics Engineering Nandha Engineering College Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore Tamilnadu India Department of Electrical and Electronic Engineering Sri Ramakrishna Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem.

关键词： Fuzzy logic automation Machine learning algorithms Neural networks Prediction algorithms Real-time systems Optimization Intelligent control Industrial Internet of Things Predictive control

来源：评论

学校读者我要写书评

暂无评论

Collaborative Optimization Algorithm of Deep Learning and Compliant control in Online Trajectory Planning

Collaborative Optimization Algorithm of Deep Learning and Co...

引用

international conference on Communication Systems and Network Technologies (CSNT)

作者： Junting Liao Chunjian Zhao Hui Xie School of Computer and Artificial Intelligence Xiangnan University Chenzhou China China Telecom Hunan Chenzhou Branch Chenzhou China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

With the rapid development of robotics and automation, online trajectory planning faces problems such as poor adaptability to dynamic environments and low computational efficiency. This study aims to propose a collaborative optimization algorithm that combines deep learning and compliant control to improve the efficiency and accuracy of online trajectory planning. First, CNN is used for feature extraction and a prediction model is established. Then a compliant control strategy is designed to deal with dynamic obstacles to ensure the flexibility and stability of the robot in trajectory planning. The trained CNN model is then used to generate real-time trajectory predictions. The robot motion is adjusted through a fuzzy logic controller to optimize the relative position of the trajectory and obstacles. The average obstacle avoidance success rate of the algorithm in dynamic environments reaches 97%. The collaborative optimization algorithm of deep learning and compliant control effectively improves the real-time and accuracy of online trajectory planning, providing a new solution for the application of intelligent robots in complex environments.

关键词： Deep learning Trajectory planning Heuristic algorithms Collaboration Predictive models Prediction algorithms Real-time systems Trajectory Convolutional neural networks Optimization

来源：评论

学校读者我要写书评

暂无评论

Cloud-Based AI Solutions for Personal Finance Management

Cloud-Based AI Solutions for Personal Finance Management

引用

Pervasive computational Technologies (ICPCT), international conference on

作者： Gurpreet Kaur Chhabra Ashutosh Kumar Mishra Varsha Tyagi Chandra Mani Vani Tyagi Pramod Kumar Sagar Department of Management Management Education and Research Institute Delhi India Department of Management SRM Business School Lucknow UP India Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad UP India Computer Science & Engineering Mangalmay Institute of Engineering & Technology Greater Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Cloud-based AI answers for private finance control are out-of-the-field tools that leverage synthetic intelligence and cloud computing technologies to manage people's money higher. In this article, I have described various solutions with features ranging from budgeting to personalized financial planning and investment suggestions. Utilizing superior system learning algorithms and cloud-based AI solutions can help understand an individual's fiscal information, including expenses, profits, or savings transactions, for generating personalized insights and advice. This lets people learn about their spending habits and make more informed financial choices. Through the power of the cloud, these solutions can securely store and access financial data from multiple devices, which helps individuals, manage their finances on the go easily. It also enables the client to get real-time updates and notifications, which offers him more comprehensive knowledge regarding his existing financial scenario. These offerings also come with more automation features like reminders to pay payments and computerized enough transfers for a price range, keeping customers from spending further effort and time on their funds. They also offer financial advisors, tax planning tools, and wealth management.

关键词： Cloud computing Fintech Finance Market research Real-time systems Planning Pattern recognition Artificial intelligence Hypertext systems Investment

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220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：