检索结果-内蒙古大学图书馆

26th International Conference on Industrial Technology, ICIT 2025

作者： Wu, Jiajun Lu, Chengda Li, Hao Wei, Hongchao Wu, Min School of Future Technology China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an710077 China China Coal Research Institute Beijing100013 China

ISBN: (纸本)9798331521950

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the H∞ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment. © 2025 IEEE.

关键词： Predictive control systems

来源：评论

学校读者我要写书评

暂无评论

Fault diagnosis method for ship power-system DC–DC converter using augmented WPD and multi-branch CNN-BiLSTM network

引用

Journal of the Franklin Institute 2025年第9期362卷

作者： Ren, Yuanjie Zhang, Lanyong Shi, Peng Peng, Huimin College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China School of Electrical and Mechanical Engineering The University of Adelaide SA5000 Australia State Key Laboratory of Smart Grid Protection and Control Nari Group Corporation Jiangsu Nanjing211106 China

Direct current (DC) –DC converters are indispensable energy conversion devices in medium-voltage DC power systems of ships. Herein, the fault diagnosis problem of bidirectional DC–DC converters is investigated. Considering that the fault diagnosis of bidirectional DC–DC converters requires the effective combination of information from multiple diagnostic signals and differences in characteristic data in different time scales, a parallel stacking of multiple branches based on enhanced wavelet packet decomposition technology is proposed as a hybrid model of a convolutional neural network and bidirectional long short-term memory network. First, a multi-input method is used to extract effective features in the sequence data. Second, a multi-model fusion processing framework is used to perform fault diagnosis analysis on the DC–DC converter. Finally, the squeeze and excitation attention mechanism is used to weight all types of fault features and adaptively obtain them. The important weights of the classification feature maps learned by different branches generate fault classification results. Experimental results reveal that the proposed method can extract highly discriminative features from raw data and exhibits robustness in noisy environments. © 2025 The Franklin Institute

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive control

Trajectory Tracking for Directional Drilling in Underground ...

引用

IEEE International Conference on Industrial Technology (ICIT)

作者： Jiajun Wu Chengda Lu Hao Li Hongchao Wei Min Wu School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Automation China University of Geosciences Wuhan China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an China China Coal Research Institute Beijing China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the $H_{\infty}$ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment.

关键词： Drilling Uncertainty Trajectory tracking Stability criteria Predictive models Taylor series Coal mining Trajectory Thermal stability Predictive control

来源：评论

学校读者我要写书评

暂无评论

AI Enabled Automatic Mobile Robot intelligent Navigation in Construction with Obstacle Awareness

引用

IEEE Transactions on Automation Science and engineering 2025年

作者： Zhang, Yinlong Liu, Yuanhao Cui, Yunge Zeng, Ziming Liang, Wei Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China University of Chinese Academy of Sciences Beijing101408 China Zeekr Group Zeekr Intelligent Driving Department Shanghai110078 China Shenzhen Polytechnic University School of Automotive and Transportation Engineering Shenzhen518055 China

In the construction industry, the integration of artificial intelligence (AI) and robotics has led to significant advancements in automating various tasks. One critical aspect is the intelligent navigation of automatic mobile robot (AMR) within construction sites, where dynamic environments pose challenges such as inaccurate robot state estimation, irregular and textureless obstacle detection. To solve these issues, this paper designs an AI enabled obstacle-aware AMR intelligent navigation approach while ensuring robot safety and efficiency. Specifically, the robot is equipped with the complementary RGB camera, inertial measurement unit (IMU) and wheel encoder to estimate the states (i.e., positions, orientations and velocities), which have been tightly fused in the optimization framework. Furthermore, the RGB-Depth images are jointly fed into the AI model. It combines of Mask-RCNN and multi-scale attention network, to detect and segment the obstacles, and estimate the corresponding relative depth. It should be noted that the system incorporates obstacle awareness mechanisms to dynamically switch the robot's velocities in response to obstacles in the environment, ensuring smooth and collision-free movement. Experimental results demonstrate the effectiveness and robustness of the proposed AI-enabled navigation system in real-world construction scenarios, showcasing its potential to enhance productivity and safety in construction automation. © 2004-2012 IEEE.

关键词： Construction industry

来源：评论

学校读者我要写书评

暂无评论

The combined effects of microplastics and their additives on mangrove system: From the sinks to the sources of carbon

引用

Science of the Total Environment 2025年 959卷 178336页

作者： Guo, Junru Yang, Mingqing Huang, Ruohan Yu, Junyi Peng, Kaiming Cai, Chen Huang, Xiangfeng Wu, Qiaofeng Liu, Jia College of Environmental Science and Engineering Tongji University State Key Laboratory of Pollution Control and Resource Reuse Ministry of Education Key Laboratory of Yangtze River Water Environment Shanghai200092 China Fuzhou Urban and Rural Construction Group Company Limited Fuzhou350007 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Institute of Carbon Neutrality Tongji University Shanghai200092 China Fuzhou City Construction Investment Group Company Limited Fuzhou350014 China

Mangrove ecosystems, a type of blue carbon ecosystems (BCEs), are vital to the global carbon cycle. However, the combined effects of microplastics (MPs) and plastic additives on carbon sequestration (CS) in mangroves remain unclear. Here, we comprehensively review the sources, occurrence, and environmental behaviors of MPs and representative plastic additives in mangrove ecosystems, including flame retardants, such as polybrominated diphenyl ethers (PBDEs), and plasticizers, such as phthalate esters (PAEs). Mangrove ecosystems have a complex influence on the behaviors of MPs and additives. Under the action of natural and unnatural factors, these pollutants exhibit complex behaviors including migration, interception, deposition and transformation, that are closely linked to those of particulate carbon, particularly carbon sequestration processes. MPs and additives hinder the CS function of mangroves by harming the growth of flora and fauna, influencing microbial nitrogen and sulfur cycles, and enhancing the degradation of organic matter in the sediment. The increasing accumulation and widespread occurrence of MPs and additives will greatly influence the carbon cycle. Future work is encouraged on systematic investigation of new alternatives to plastics and additives, and research methods to uncover the impact mechanisms of MPs and additives on BCEs. The developments of management measures and engineering technologies are also required to enhance pollutant control and mangrove CS. © 2024 Elsevier B.V.

关键词： Carbon sequestration

来源：评论

学校读者我要写书评

暂无评论

exitCDSS: A framework for a workflow-based CBR for interventional CDSS application to Transcatheter Aortic Valve Implantation (TAVI)

eXiTCDSS: A framework for a workflow-based CBR for intervent...

引用

International Conference on Health Informatics, HEALTHINF 2013

作者： El-Fakdi, Andres Gamero, Francisco Meléndez, Joaquim Petite, Roberto Control Engineering and Intelligent Systems Group eXIT University of Girona Campus Montilivi Building PIV 17071 Girona Spain

ISBN: (纸本)9789898565372

The work presented in this paper presents a workflow-based Clinical Decision Support System (CDSS) designed to give case-specific assessment to clinicians during complex surgery or Minimally Invasive Surgerys (MISs). Following a perioperative workflow, the designed software will use a Case-Based Reasoning (CBR) methodology to retrieve similar past cases from a case base to give case-specific advice at any particular point of the process. The graphical user interface allows easy navigation through the whole support progress, from the initial configuration steps to the final results organized as sets of experiments easily visualized in a user-friendly way. The tool is presented giving advice to an example application, a complex minimally invasive surgery which is receiving growing attention lately, the Transcatheter Aortic Valve Implantation (TAVI).

关键词： Case based reasoning

来源：评论

学校读者我要写书评

暂无评论

A proposal of a workflow-based CBR for interventional CDSS: Application to Transcatheter Aortic Valve Implantation (TAVI)

A proposal of a workflow-based CBR for interventional CDSS: ...

引用

HEALTHINF 2012 - Proceedings of the International Conference on Health Informatics

作者： El-Fakdi, Andres Meléndez, Joaquim Petite, Roberto Control Engineering and Intelligent Systems Group eXIT University of Girona Campus Montilivi Building PIV 17071 Girona Spain

ISBN: (纸本)9789898425881

A Clinical Decision Support System (CDSS) is an interactive software designed to assist physicians, and other health professionals, with health decision-making tasks through an inference process that associates observations with conclusions supported by objective and expert knowledge on a specific application domain. As complexity of surgical processes arises, there is a demand from the clinicians to extend the capabilities from a pure research database towards a clinically integrated decision support system. The work presented in this paper details a proposal of a CDSS designed to to assess clinicians during interventions by retrieving and reusing past similar cases. Following an operative workflow, the designed software will use a Case-Based Reasoning (CBR) methodology to retrieve similar past cases from a case base to give case-specific advice at any particular point of the process, specially during preoperative and intraoperative stages. The proposal intends to develop a monitoring mechanism capable to recognize relevant data to every stage in the intervention workflow. Thus, measures and observations generated during the intervention will be used to retrieve the most similar cases occurred in previous occasions. The paper proposes an example application of the CBR strategy, where it is adapted to a recent complex minimally invasive intervention which is receiving growing attention lately, the Transcatheter Aortic Valve Implantation (TAVI).

关键词： Case based reasoning

来源：评论

学校读者我要写书评

暂无评论

Calculation of resonant frequency for an equilateral triangular microstrip antenna with the use of artificial neural networks

引用

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 1997年第2期14卷 89-93页

作者： Sagiroglu, S Guney, K Intelligent Systems Research Group Department of Control and Computer Engineering Erciyes University 38039 Kayseri Turkey Department of Electronic Engineering Erciyes University 38039 Kayseri Turkey

new method for calculating the resonant frequency of an equilateral triangular microstrip patch antenna, based on artificial neural networks, is presented The back-propagation algorithm is used to train the networks. The theoretical resonant frequency results obtained by using this method are in very good agreement with the experimental results reported elsewhere. (C) 1997 John Wiley & Sons, Inc.

关键词： triangular microstrip antennas resonant frequency artificial neural networks multilayer perceptron back propagation

来源：评论

学校读者我要写书评

暂无评论

Comparison of Principal Component Analysis Techniques for PMU Data Event Detection

Comparison of Principal Component Analysis Techniques for PM...

引用

2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Souto, L. Melendez, J. Herraiz, S. Universitat de Girona Intelligent Systems and Control Engineering Group Girona Spain

ISBN: (纸本)9781728155081

Principal component analysis (PCA) is a dimensionality reduction technique often applied to process and detect events in large amounts of data collected by phasor measurement units (PMU) at transmission and distribution level. This article considers five different approaches to select an appropriate number of principal components, builds the statistical model of the PMU data online over a sliding window of 10 seconds and 1 minute, and evaluates the computation times and the accuracy of correct event detections with use of two statistical tests in a 1-hour data file from the UT-Austin Independent Texas Synchrophasor Network with phasor quantities collected at different PMU substations. © 2020 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

PSO optimized PID parameters for coupled HVDC control

PSO optimized PID parameters for coupled HVDC control

引用

IET Irish Signals and systems Conference, ISSC 2010

作者： McNamara, P. Lightbody, G. Control and Intelligent Systems Group Department of Electronic Engineering University College Ireland Cork Ireland

ISBN: (纸本)9781849192521

High Voltage Direct Current (HVDC) systems are used in power systems for the efficient transfer of energy over long distances and can improve the stability and performance of electrical networks. Using the internal node representation, the dynamics of a system with HVDC links can be represented in terms of the internal currents of the generators of the system and this representation can be used to control the system frequency. Here the control of a multiple HVDC link system, modelled using this representation, is improved using Particle Swarm Optimization, PSO.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：