检索结果-内蒙古大学图书馆

Chinese Control and Decision Conference, CCDC

作者： Yicheng Wang Shu Wang Kang Li College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The state and trend of flotation surface foam play a crucial role in flotation operations as they provide important indications of working conditions, index predictions, and operational volume adjustments. While researchers have focused on manual feature design of froth images or automatic extraction of depth features and their relationship to flotation conditions, the information contained in the flotation process variables and the importance of data pre-processing are usually overlooked. To address these shortcomings, this study proposes an integrated framework for flotation process data preprocessing and condition recognition. It includes a fast imputation algorithm based on multi-condition data center of mass (FCKNN) for overcoming the challenges of imputing missing values in on-line industrial processes, as well as a high-precision work condition recognition method that fuses deep visual features with key process variables. Finally, the framework was applied to the gold flotation process. The experimental results show that the proposed framework can perform work condition recognition with high accuracy and solve the challenge of online missing value imputation.

关键词： Employee welfare Visualization Image recognition Accuracy process control Manuals Feature extraction

来源：评论

学校读者我要写书评

暂无评论

TSDD-UB: A Texture Simplification-Based Denoising Diffusion Model for Unsupervised Defect Detection Under Ultrasonic B-Scan Signal

引用

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wang, Liwei Liu, Jinhai Zhong, Yuan Shen, Xiangkai Zhao, He Northeastern University School of Information Science and Engineering Shenyang110004 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries School of Information Science and Engineering Shenyang110004 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China

The ultrasonic B-scan signal is widely used in the detection of pipeline defects. However, most existing methods face challenges in achieving high accuracy due to the amplitude attenuation phenomenon and the complicated texture structure of the ultrasonic B-scan signal. To address the above issues, a texture simplification-based denoising diffusion model is proposed for unsupervised defect detection under an ultrasonic B-scan signal. This model closely follows the principles of ultrasonic propagation and incorporates meticulous designs in both data processing and model architecture, aiming to achieve high-precision defect detection while training exclusively on normal samples. First, an adaptive time gain compensation (ATGC) method is proposed to mitigate the amplitude attenuation phenomenon of the B-scan signal. Second, a novel texture simplification network is proposed to simplify texture structures while preserving the echo characteristics of the B-scan signal. Third, a double noise scale reconstruction (DNSR) method is designed, which utilizes the denoising diffusion model to reconstruct signals with two different levels of perturbation, aiming to overcome the issue of low reconstruction quality. Experimental results in real pipelines demonstrate that texture simplification-based denoising diffusion model for defect detection under ultrasonic B-Scan signal (TSDD-UB) achieves an image-level area under the receiver operator characteristic curve (AUROC) of 100% and a pixel-level AUROC of 98.6%, outperforming state-of-the-art methods and indicating significant potential for practical applications. © 1963-2012 IEEE.

关键词： Ultrasonic testing

来源：评论

学校读者我要写书评

暂无评论

An Optical Fiber Optofluidic Laser for Hydrogen Peroxide Detection 13

An Optical Fiber Optofluidic Laser for Hydrogen Peroxide Det...

引用

13th IEEE International Conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2023

作者： Hu, Jiachen Zhang, Hongrui Zhao, Yong Zhang, Ya-Nan College of Information Science and Engineering Northeastern University Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

ISBN: (纸本)9798350315196

Hydrogen peroxide (H2O2) is an environmentally friendly fungicide and an intermediate product in many biochemical reaction processes. In this paper, we proposed two kinds of H2O2 detection schemes based on optical fiber optofluidic laser (FOFL) to achieve highly-sensitive detection of H2O2 concentration. The H2O2 and rhodamine B (H2O2-RhB) reaction system was used in the first scheme. The reaction system was prepared by using RhB as the laser gain medium and the oxidation and fading effect of H2O2 on RhB under alkaline condition. Based on the FOFL system, a good detection linearity and a high detection sensitivity were achieved at the concentration of H2O2 from 1 mM to 10 mM. Furthermore, the H2O2, ferrous ion and rhodamine 6G (H2O2-Fe2+-Rh6G) reaction system was used in the second scheme. The reaction system was prepared in weak acid environment with Fe2+ as catalyst and Rh6G as laser gain medium. Based on the FOFL system, the experimental results showed good linearity and high sensitivity in the range of 0~270.4 µMR2O2 concentration. © 2023 IEEE.

关键词： Optical fibers

来源：评论

学校读者我要写书评

暂无评论

Performance improvement of multi-parameter measurement optical fiber sensors based on machine learning methods 21

Performance improvement of multi-parameter measurement optic...

引用

21st International Conference on Optical Communications and Networks, ICOCN 2023

作者： Zhou, Yi-Fan Han, Bo Jin, Po Zhang, Ya-Nan Zhao, Yong Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

A machine learning based spectral data analysis method is proposed to solve the problem of limited measurement range of optical fiber sensors. This method expands the measurement range of the sensor and improves measu... 详细信息

ISBN: (纸本)9798350343502

关键词： Spectrum analysis

来源：评论

学校读者我要写书评

暂无评论

Time-Attention Graph Convolutional Network Soft Sensor in Biochemical processes 4

Time-Attention Graph Convolutional Network Soft Sensor in Bi...

引用

4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Jia, Mingwei Xu, Danya Yang, Tao Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering 30013 Taiwan

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Most data-driven soft sensor methods can model nonlinear time-varying characteristics of biochemical processes. However, the intrinsic relationship between variables, which is helpful for understanding model behavior, has rarely been investigated in existing data-driven methods. In this work, a novel soft sensor model of time-attention graph convolutional network (TA-GCN) is proposed, which jointly leverages variable relationships and long-term temporal dependencies to improve interpretability and prediction accuracy. This model first uses the maximum information coefficient to construct a topology graph and trains edge strengths end-to-end. The data are then encoded in the spatial-temporal dimension based on GCN and attention mechanism. Finally, the empirical knowledge that analyzes the operating state of the process and graph are combined to explain the model behavior. In comparison to existing soft sensors, TA-GCN enables efficient and scalable training for long-term spatial-temporal dependencies. Experimental results on InPenSim dataset demonstrate that TA-GCN is competitive with state-of-the-art methods. © 2022 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Ti2C MXene-based highly sensitive optical fiber MZI salinity sensor 21

Ti2C MXene-based highly sensitive optical fiber MZI salinity...

引用

21st International Conference on Optical Communications and Networks, ICOCN 2023

作者： Jin, Po Zhou, Yi-Fan Han, Bo Zhang, Ya-Nan Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

This paper presents an optical fiber MZI sensor based on Ti2C-MXene nanosheet, which has a salinity sensitivity of -3.4360 nm%. The sensitivity is improved by 28% after coating with Ti2C-MXene nanosheet. © 2023 I... 详细信息

ISBN: (纸本)9798350343502

关键词： Refractive index

来源：评论

学校读者我要写书评

暂无评论

Topology-Informed Graph Convolutional Network for Fault Diagnosis 11

Topology-Informed Graph Convolutional Network for Fault Diag...

引用

11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022

作者： Jia, Mingwei Liu, Yi Xu, Danya Yang, Tao Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering 30013 Taiwan

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

The development of an accurate data-driven fault diagnosis model faces several obstacles. One significant challenge is how to combine the model with the process mechanism rationally and improve the model interpretability. In this work, a fault diagnosis method based on a topology-informed graph convolutional network (TIGCN) is proposed. This model first uses the empirical knowledge that analyzes the operating state of the process and combines the operating data to construct a signed directed graph. Then the operating data is divided into three modalities in the time dimension. The model utilizes a well-designed spatial-temporal convolutional layer and signed directed graph to construct different modalities separately to effectively detect fault types. The superior fault diagnosis performance of the proposed TIGCN model is demonstrated on the Tennessee-Eastman process as compared to traditional approaches. © 2022 IEEE.

关键词： Failure analysis

来源：评论

学校读者我要写书评

暂无评论

Bioinspired materials for droplet manipulation:Principles,methods and applications

引用

Droplet 2022年第1期1卷 11-37页

作者： Jinkai Xu Siyu Xiu Zhongxu Lian Huadong Yu Jiaji Cao Department of Mechanical Design Manufacturing and AutomationMinistry of Education Key Laboratory for Cross‐Scale Micro and Nano ManufacturingChangchun University of Science and TechnologyChangchunChina Department of Mechanical Materials and Process EngineeringSchool of EngineeringUniversity of WarwickCoventryUK Department of Mechanical and Electronic Engineering School of Mechanical and Aerospace EngineeringJilin UniversityChangchunChina

Droplet manipulation techniques such as transport and merging have been widely used in many fields including biology,chemistry,material and energy ***,droplet manipulation strategies have been extensively investigated and reviewed in terms of droplet placement on solid ***,less attention has been paid to the practice of droplet manipulation technology in other environments,limiting our understanding and the broadening of technology *** this article,we provided an overview of the recent progress in controlling droplets in various situations,including droplet manipulation on a surface mediated by the passive strategy(Laplace pressure and wettability gradients)and active strategy(electric field,magnetic field,light and heat).We also presented the principle of droplet manipulation and detailed the application of bionic surfaces in droplet manipulation,and the applications and prospects of droplet manipulation technology were summarized.

关键词： summarized limiting manipulation

来源：评论

学校读者我要写书评

暂无评论

Actuator Fault Estimation Based on Sliding Mode Observer for Continuous-Time Switched Systems

Actuator Fault Estimation Based on Sliding Mode Observer for...

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Mohamed Elouni Habib Hamdi Bouali Rabaoui Mickael Rodrigues Naceur BenHadj Braiek Advanced Systems Laboratory Polytechnic School of Tunisia University of Carthage La Marsa Tunisia Automation and Process Engineering Laboratory University of Lyon Lyon Villeurbanne France

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

In this paper, an actuator fault reconstruction scheme for a class of continuous-time switched systems with unknown external disturbances is investigated. The proposed fault estimation approach uses a switching Sliding Mode Ob-server (SMO) which allows achieving fast and exact fault information. Sufficient conditions are formulated in terms of Linear Matrix Inequalities (LMIs) optimization by using a multiple Lyapunov-Krasovskii functional while respecting the Mode-Dependent Average Dwell Time (MDADT) constraints in the objective to obtain less conservative results. Finally, a simulation study on a two-tank process is considered to illustrate the effectiveness of the proposed fault reconstruction approach.

关键词： Switched systems Performance evaluation Actuators Sufficient conditions Switching systems Estimation Switches

来源：评论

学校读者我要写书评

暂无评论

An Online Anomaly Detection Algorithm with Adaptive Concept Drift

An Online Anomaly Detection Algorithm with Adaptive Concept ...

引用

International Conference on Communications, Information System and Computer engineering (CISCE)

作者： Mengyuan Li Beijing Key Laboratory of Knowledge Automation for Industrial Process (University of Science and Technology Beijing) Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

In response to the existing offline anomaly detection algorithms performing well on static data, but struggle to meet the requirements of accuracy and real-time processing when the data distribution changes. This paper proposes an online anomaly detection algorithm with concept drift adaptation to adapt to continuously changing data distributions. Firstly, the data is subjected to offline training using a Long Short-Term Memory autoencoder to obtain anomaly scores. And unsupervised anomaly detection is performed based on dynamic thresholds. Then, an adaptive sliding window is employed for concept drift detection, with the model being updated and adjusted through concept drift adaptation. Finally, the improved Particle Swarm Optimization algorithm is utilized to optimize the hyperparameters that contribute more to the accuracy. Results indicate that the proposed algorithm effectively adapts to evolving data distributions, meeting the real-time processing and accuracy requirements essential for anomaly detection.

关键词： Training Adaptation models Accuracy Heuristic algorithms Concept drift Prediction algorithms Real-time systems

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：