检索结果-内蒙古大学图书馆

26th International Conference on Industrial Technology, ICIT 2025

作者： Ma, Tianyu Du, Sheng Fan, Haipeng Huang, Cheng 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

ISBN: (纸本)9798331521950

Geological drilling process, owing to complex geological environment and harsh downhole conditions, generates data including characteristics such as pressure, rotational speed, and depth, which are frequently high-dimensional and noisy. These characteristics make real-time monitoring more complex. Existing methods in the geological drilling process, such as rule-based systems and threshold techniques, struggle to handle the complexity and high dimensionality of drilling data, leading to high false alarm rates and low detection accuracy. This paper develops an integrated temporal dictionary learning with isometric mapping method for monitoring geological drilling process. Specifically, Isometric Mapping is employed to perform dimensionality reduction on high-dimensional data, thereby retaining the structural features in the lower-dimensional space. Subsequently, Lasso regularization is applied for sparse coding to extract essential features from the reduced data. To address the fluctuations arising from the iterative dictionary learning process, a temporal smoothing term is incorporated to ensure the stability of the dictionary across different time steps. After that, the reconstruction errors were adopted to achieve comprehensive statistical indicators. Then the overall monitoring was realized for the plant-wide process. The effectiveness and robustness of the proposed method are demonstrated through case studies on the Tennessee-Eastman process and the actual geothermal drilling process. © 2025 IEEE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

An Informer Based Alarm Early Prediction Method Over Consecutive Alarm Monitoring Periods

An Informer Based Alarm Early Prediction Method Over Consecu...

引用

IEEE International Conference on Industrial Informatics (INDIN)

作者： Wenbin Jiang Wenkai Hu Zhe Liu Fei Wang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China SINOPEC-SK (WUHAN) Petrochemical Company Limited Wuhan China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Alarm systems serve as the first layer of protection for modern process industries to monitor industrial processes and ensure operational safety. However, the presence of alarm floods is common in alarm systems and may distract operators from critical alarms. To provide decision supports to operators during alarm flood situations, alarm prediction becomes a potential effective solution. The difficulty of alarm prediction lies in how to achieve high prediction accuracy over long-term consecutive alarm monitoring periods, including situations with either high or low alarm rates. Consequently, this paper presents an industrial alarm prediction method for both alarm flood periods and non-alarm flood periods. Specifically, a strategy of combining fixed-length and fixed-time sliding windows is designed. Further, an alarm prediction model based on Informer is proposed for alarm prediction under different alarm rates. A case study is presented to prove the validity of the proposed alarm prediction method.

关键词： Industries Adaptation models Analytical models Accuracy Predictive models Alarm systems Floods Protection Informatics Monitoring

来源：评论

学校读者我要写书评

暂无评论

Coverage control of Unmanned Aerial Vehicles for Periodical Monitoring of Geohazards

Coverage Control of Unmanned Aerial Vehicles for Periodical ...

引用

2021 IEEE International Conference on Unmanned systems, ICUS 2021

作者： Zhang, Wei Zhai, Chao School of Automation China University of Geosciences Research Center of Intelligent Technology for Geo-Exploration Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Ministry of Education Wuhan430074 China

ISBN: (纸本)9780738146577

Landslides have become one of the major hazards endangering the lives and property of people all over the world. In order to improve the early warning of geohazards, a cooperative coverage control algorithm of unmanned aerial vehicles (UAV) is developed in this paper. The proposed control algorithm is integrated with the airborne radar to periodically monitor the surface deformation, which is closely related to the emergence of geohazards. A Lyapunov function is constructed to ensure the stability of UAVs driven by the proposed control algorithm. Numerical simulations are conducted to substantiate the coverage control approach for UAVs in the uncertain environment. This work provides new insight into the effective monitoring and early warning of geohazards with the assistance of UAVs. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Automatic Target Recognition in GPR Images Based on Time-Frequency Texture Features and Support Vector Machine

Automatic Target Recognition in GPR Images Based on Time-Fre...

引用

2021 china automation Congress, CAC 2021

作者： Chen, Kehui Xue, Wei Wang, Zexi Ma, Shaopeng School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665426473

Ground penetrating radar (GPR) images are easily disturbed by noise, which causes a lot of difficulties for target recognition. To improve the target recognition performance in GPR images, a novel recognition method based on time-frequency texture features and support vector machine (SVM) is proposed. The proposed method first extracts texture features from the gray co-occurrence matrix of S transform time-frequency images for the A-scan data, then establishes the SVM model and realizes the target recognition. The proposed method is assessed using synthetic GPR images and compared with other two time-frequency analysis methods, combined with SVM. The expermental results show that the proposed method can achieve higher recognition accuracies than the other two methods in heavily noisy environment. © 2021 IEEE

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

引用

13th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2021

作者： Liu, Chenliang Zhu, Yuan Zhang, Houwang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9781450389655

Single cell RNA sequencing (scRNA-seq) technology can study gene expression in single cell resolution and solve cell heterogeneity that cannot be solved by the traditional RNA sequencing (Bulk RNA-seq) technology. It provides a powerful analytical tool for in-depth study of the immune, regulatory, replication, and other activities of cells and explanations of life activities rules. However, single cell sequencing experiments are hindered by several technical issues, which cause output scRNA-seq with a high percentage of zeros (dropout events), impacting the reliability of downstream analyses. Therefore, a great number of computational methods have been proposed to address the increased sparsity observed in scRNA-seq data. In this paper, motivated by the image denoising method, we propose an imputation algorithm CSI based on the similarity between cells. The cell similarity is considered as the weight to average the gene expression values to impute the missing values in the data. CSI achieves better accuracy than other the 7 publicly notable scRNA-seq imputation methods on some real scRNA-seq data, especially gains better performance in some downstream analysis such as cluster analysis and pseudo trajectory inference. © 2021 ACM.

关键词： RNA

来源：评论

学校读者我要写书评

暂无评论

A Novel Rate of Penetration Fusion Modeling Method based on Improved Dung Beetle Optimization Algorithm and Support Vector Regression 26

A Novel Rate of Penetration Fusion Modeling Method based on ...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Zhou, Yang Zhang, Zihan Chen, Shipeng Wu, Min Chen, Xin 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 School of Future Technology China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798331521950

The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. However, ROP modeling in deep geological drilling is complicated by nonlinearity, diverse working conditions, and high-dimensional variations. To overcome these challenges, a fusion modeling approach for ROP is proposed. First, the fuzzy C-means clustering method is applied to classify drilling data into different working con-ditions. Based on this classification, support vector regression is employed to develop ROP sub-models, effectively addressing nonlinearity. To further enhance model accuracy, an improved dung beetle optimization algorithm (IDBO) is designed to deter-mine optimal model parameters and integrate the sub-models, thereby resolving issues related to multiple working conditions and high-dimensional variations. The IDBO incorporates four key enhancements, average weight, chaos disturbance, modified local search, and re-updating of the best solution, to strength-en its global search capability. Comparative results using the IEEE CEC2017 benchmark test functions demonstrate that the proposed algorithm outperforms others in 12 test functions, highlighting its strong global optimization ability. Additionally, results from real-world drilling data validate the effectiveness of the proposed modeling approach in practical applications. © 2025 IEEE.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Incomplete Multi-view Clustering for Single cell RNA Sequencing Data

Incomplete Multi-view Clustering for Single cell RNA Sequenc...

引用

2021 china automation Congress, CAC 2021

作者： Zhu, Tijian Zhu, Yuan Zhang, Chuan-Ke School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9781665426473

With the rapid development of sequencing technology, researchers can obtain a large number of single cell RNA sequencing (scRNA-seq) data which is useful for analysis of cell fate decision and growth process at individual cell resolution. But due to the limitations of sequencing technology, the data acquired has dropouts which may affect the results of down-steam analysis. Therefore, many algorithms have been proposed to impute the data before clustering, here in, imputation and clustering are considered as two separate processing stage. In this paper, we adopt a clustering algorithm-Incomplete Multiple Kernel k-means Clustering with Mutual Kernel Completion (MKKM-IK-MKC) to analyze scRNA-seq data. It unifies imputation and clustering into a process. Comparing with some existing "two stage" (imputation +clustering) algorithms, the experimental results on five scRNA-seq datasets from various species demonstrate the effective performance of our new proposed method. © 2021 IEEE

关键词： RNA

来源：评论

学校读者我要写书评

暂无评论

Distributed control Strategy for Layered Barrier Coverage of Multi-Agent systems in Uncertain Environments

arXiv

引用

arXiv 2023年

作者： Fan, Pengyang Zhai, Chao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and Engineering Research Center of Intelligent Technology for Geo-exploration Ministry of Education Wuhan430074 China

This paper presents a distributed multi-layer ring barrier coverage algorithm. In order to achieve single-layer ring barrier coverage, a distributed single-layer ring barrier coverage algorithm that maximises the probability of monitoring is proposed. Considering the security risks of single-layer barrier coverage, a distributed adjustment mechanism between multiple layers of barriers is designed and combined with the single-layer ring barrier coverage algorithm to propose a distributed multi-layer ring barrier coverage algorithm. Furthermore, we present a theoretical analysis of the proposed algorithm to demonstrate its effectiveness and necessity. Finally, our algorithm is verified by numerical simulation and experiment. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Optimizing Bounding Box Regression in complex Backgrounds

Optimizing Bounding Box Regression in Complex Backgrounds

引用

Chinese automation Congress (CAC)

作者： Jun Chen Haiyan Zhang Anjun Yu Yiwei Wang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China Jiangxi Ganyue Expressway Co. Ltd. Nanchang China School of Automation China University of Geosciences Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In the context of electrical power operation sites, the automated and accurate detection of whether workers are properly equipped with safety gear, such as safety clothing, helmets, and safety ropes for high-altitude operations, is crucial for ensuring the safety of personnel. Traditional object detection models often struggle to accurately identify these critical pieces of safety equipment in complex environments, due to variable object sizes, overlapping objects, and substantial background noise. To specifically address these challenges, we propose a novel loss function-Enhanced Alignment Intersection over Union (EAIoU) loss. This loss function is designed to enhance the model's ability to discriminate between closely spaced and overlapping safety gear in real-time, significantly improving robustness against scale variations and complex backgrounds. In experiments based on the YOLOv8s architecture, our model achieved a mean Average Precision (mAP) of 92.1 % on a dataset specific to electrical power operations, markedly outperforming traditional IoU loss functions. Additionally, EAIoU demonstrated improved performance on the COCO dataset, indicating its generalization capability across various complex scenarios. This research not only enhances the accuracy and real-time performance of safety equipment detection at electric power operation sites but also opens new avenues for advanced object detection technology in complex, multi-object environments.

关键词： Accuracy Gears Shape Object detection Robustness Real-time systems Safety Power systems Personnel Object recognition

来源：评论

学校读者我要写书评

暂无评论

Multi-Modal Based Fuzzy Atmosfield in Human-Robot Interaction

Multi-Modal Based Fuzzy Atmosfield in Human-Robot Interactio...

引用

Chinese control Conference (CCC)

作者： Xin-Heng Li Zhen-Tao Liu School of Automation China University of Geosciences (Wuhan) Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Recently, the emotional robot has basic functions of perceiving and expressing emotions, but it still hard to communicate naturally between humans and robots. One major reason is that communication atmosphere is seldom considered in Human-robot Interaction (HRI). We propose Multi-modal (i.e., music background, speech, and semantics) Based Fuzzy Atmosfield (FA), which can not only realize the dynamic adjustment of FA but also dynamically regulate human emotions. In the experiment, a Pepper robot is used and one hundred volunteers are invited for HRI, and soothing piano pieces are used as background music. Questionnaires were filled by the volunteers after the experiments, from which the results show that 90% of the volunteers felt the dynamic changes in the communication atmosphere and 77% of the volunteers felt significant emotional regulation, which demonstrates the effectiveness of our method.

关键词：

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：