检索结果-内蒙古大学图书馆

UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science 2023年第3期85卷 285-296页

作者： Kong, Bing Yu, Mei Ye, Song Xu, Xiaoyong Xiang, Rong School of Electronic Engineering Chaohu University Hefei Anhui China Graduate School Angeles University Foundation Manila Philippines School of Chemistry & Material Engineering Chaohu University Hefei Anhui China Industrial Process Control Optimization and Automation Engineering Research Center Chaohu University Hefei Anhui China Key Laboratory of Novel Ceramic and Powder Engineering Chaohu University Anhui China

Traditional methods cannot meet the requirements of real-time monitoring and scientific prevention and control of blue-green algae bloom. This paper uses multi-parameter sensors, LoRa module, 4G module, PLC and shore-based video acquisition device to build a monitoring platform to achieve real-time monitoring of multiple locations and real-time collection of video images;Aiming at the problem of image motion blur caused by shore-based video in water surface fluctuation, the Multi-scale Revolution Neural Network is used to effectively eliminate the visual artifacts of motion blur image, improve the monitoring accuracy of blue-green algae bloom, and meet the actual needs. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Eutrophication

来源：评论

学校读者我要写书评

暂无评论

Frequency-Based Estimation of the B-H Curve of Steel Strips in a Continuous Induction Furnace*

引用

IFAC-PapersOnLine 2023年第2期56卷 4627-4632页

作者： L. Jadachowski L. Marko A. Kugi A. Steinboeck Christian Doppler Laboratory for Intelligent Process Control for High-Quality Steel Products Automation and Control Institute TU Wien Gußhausstraße 27-29 1040 Vienna Austria Automation and Control Institute TU Wien Gußhausstraße 27-29 1040 Vienna Austria

A parameter estimation strategy for continuous inductive heating of ferromagnetic steel strips is developed and investigated. During strip processing in an induction furnace, the parameters of the magnetization curve (B-H curve) of the strip material are identified by a moving horizon estimator (MHE). This estimator uses measurements of the resonance frequency of the electric circuit. A tailored 2D harmonic model of the electromagnetic subsystem is used to calculate the complex coil impedance. Other model parameters are identified in a scenario, where the magnetization curve of the strip is known. The approach does not require a thermal model of the induction furnace in favor of the computing time and the achieved accuracy. For model validation, the simulated resonance frequency is compared with measurements. To avoid a high computational effort, the effective magnetization approach is applied to approximate the solution of the nonlinear Maxwell equations. The effective B-H curve is mathematically expressed in terms of a linear and an exponential function and parametrized by two parameters. The MHE is validated in a test scenario using a strip with a known magnetization curve.

关键词： Moving horizon estimator Parameter estimation B-H curve Induction heating Effective magnetization Nonlinear Maxwell equations

来源：评论

学校读者我要写书评

暂无评论

Streaming-Data-Driven Multi-Objective Optimal Operation for Ammonia-based Integrated Energy Chemical Systems: An Event-Triggered Scheme 42

Streaming-Data-Driven Multi-Objective Optimal Operation for ...

引用

42nd Chinese control Conference, CCC 2023

作者： Li, Longyan Ning, Chao Shanghai Jiao Tong University Department of Automation Shanghai200240 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9789887581543

As an important component in the industry sector, the integrated energy chemical system is inescapably responsible for carbon reduction in addition to economic and reliability performance. We propose a novel streaming-data-driven multi-objective optimal operation for ammonia-based integrated energy chemical systems (AIECS). In the day ahead scheduling, a detailed mathematical model for AIECS is presented, upon which a multi-objective optimization considering cost, CO2 emission and reliability is established. For the intra-day operation, an event-triggered mechanism is firstly developed in the real-time operation of AIECS, and it enables the AIECS to be risk-aware just in time. By monitoring the difference between the predicted state of charge and the capacity limitations of battery, the AIECS identifies whether it is in hazardous regime. Then, the intelligent switch between certainty equivalence model predictive control and online-learning based distributionally robust model predictive control is proposed, such that their respective advantages are leveraged. Case studies demonstrate that the proposed approach guarantees the system safety, and brings good revenue. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

RIS-Aided Localization Method Based on DNN-SSA Fusion in NLOS Scenarios

RIS-Aided Localization Method Based on DNN-SSA Fusion in NLO...

引用

Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Yujia Luo Qingsong Hu Yuanxun Cheng Shuya Yuan Yuansheng Zhang School of Information and Control Engineering China University of Mining and Technology Xuzhou China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

With the advent of the 6G era, precise positioning and reliable, energy-efficient data transmission have become critical for the Internet of Things. However, high-frequency signals like millimeter-wave and terahertz are easily obstructed, making traditional LOS-based methods ineffective in complex environments. To overcome this, NLOS localization methods and Reconfigurable Intelligent Surfaces offer promising solutions by dynamically modifying the wireless environment. This paper introduces a Direct Position Determination method that integrates Deep Neural Networks (DNN) with the Sparrow Search Algorithm (SSA) to tackle the challenges of high computational complexity and low localization accuracy in RIS-aided systems. An NLOS scenario with RIS is established, and a far-field channel model is derived. The method uses DNN to model the nonlinear relationship between the likelihood function and user position, and then employs SSA to optimize the DNN-based fitness function, efficiently solving the objective function. Experimental results show that the DNN-SSA method consistently achieves high localization accuracy across different scenarios, even at low SNR levels, demonstrating its effectiveness and potential for 6G high-precision localization.

关键词： Location awareness 6G mobile communication Wireless communication Accuracy Simulation Search methods Artificial neural networks Reconfigurable intelligent surfaces Nonlinear optics Signal to noise ratio

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

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

STBT: 1Spatio-temporal Belt Transformer Prediction Network for Belt Deviation

STBT: 1Spatio-temporal Belt Transformer Prediction Network f...

引用

2022 Jiangsu Annual Conference on automation, JACA 2022

作者： Chen, Pengju Niu, Dan Liu, Minghao Chen, Xisong Li, Qi Yang, Yanlan School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of CSE Ministry of Education Research Laboratory China Nanjing Sciyon Wisdom Technology Group Co. Ltd. Nanjing China Jiangsu Province Key Laboratory of Thermal Process Intelligent Control Nanjing China

ISBN: (纸本)9781839538476

Belt conveyor is an important equipment for the transportation of raw materials in ports, wharves and industrial sites. Whether it runs stably is directly related to whether the whole production process can operate normally, and belt deviation is the most prone type of failure of belt conveyor. This paper proposes a Spatio-temporal Belt Transformer (STBT) method based on deep learning and the Encoder-Decoder network based on Transformer Block. The continuous frame belt motion images fused with temporal and spatial features are input, and the fusion information of global features and local features is extracted by the multi-head self-attention algorithm based on sliding window, and the final output of the predicted continuous frame results is simple and efficient. The experimental results show that STBT can predict the future deviation state of the belt well. Copyright© JACA 2022 All rights reserved.

关键词： Belt conveyors

来源：评论

学校读者我要写书评

暂无评论

Transductive Transfer Broad Learning Soft Sensor for Multimode Quality Prediction

Transductive Transfer Broad Learning Soft Sensor for Multimo...

引用

International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Jialiang Zhu Hongying Deng Danya Xu Tao Yang Yi Liu Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

In chemical processes, reliable soft sensors are generally established by enough labeled data. However, in most multimode processes, the collection of sufficient labeled data is difficult due to the high cost and complexity. In this work, transductive transfer broad learning (TTBL) is proposed for multimode quality prediction. By transferring the useful information from the related domain, unlabeled data in the prediction domain is utilized for modeling. First, the data feature is extracted by the feature and enhancement nodes. The similarity information of current and related domain data is captured by the $k$ nearest-neighbor graph. Then, label information in the related domain can be transferred and similar information in the same domain can be retained by the manifold regularization framework. Finally, the output weight can be effectively calculated by the ridge regression algorithm. Experimental results on continuous stirred tank reactor datasets show the superiority of TTBL, compared with several common methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring photon blockade in a two-photon Jaynes-Cummings model with atom and cavity drivings

引用

Physical Review A 2024年第4期110卷 043707页

作者： Hai-Ji Li Li-Bao Fan Shan Ma Jie-Qiao Liao Chuan-Cun Shu Hunan Key Laboratory of Nanophotonics and Devices Hunan Key Laboratory of Super-Microstructure and Ultrafast Process and School of Physics School of Automation Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications

We explore a method for achieving photon blockade (PB) through a two-photon Jaynes-Cummings (JC) model that includes both cavity and atom drivings. Analytical expressions for the steady state are derived to calculate photon-number distribution and correlation functions. Based on this analysis, we establish the theoretical conditions required for generating PB by controlling both drivings and propose a scheme to identify conventional photon blockade (CPB) and unconventional photon blockade (UPB) by analyzing the second- and third-order correlation functions. We validate our findings through numerical simulations conducted in an artificial atom system. We discuss two cases, one in which the transition frequency of the two-level atom is in two-photon resonance with the cavity frequency and the other in which it is off-resonance. We observe a significant interplay between CPB and UPB effects in the resonant case. In the off-resonance case, we demonstrate that UPB can be achieved by manipulating the strengths of atom driving and cavity driving to satisfy a specific relationship while meeting the requirements for optimal detuning relations. This work provides essential insights into controlling the PB effect using the two-photon JC model with both the atom and cavity drivings.

关键词： Quantum control Quantum optics Single photon sources

来源：评论

学校读者我要写书评

暂无评论

Lateral-Longitudinal Prescribed-Time Cooperative control of Vehicles Subject to Wear and Road Disturbance

Lateral-Longitudinal Prescribed-Time Cooperative Control of ...

引用

第43届中国控制会议

作者： Qian Zhang Ge Guo College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation of Process Industries Northeastern University School of Control Engineering Northeastern University at Qinhuangdao

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper studies the lateral and longitudinal formation control issue of a collection of vehicles subject to wear and road disturbance.A novel prescribed-time disturbance observer is designed to estimate the unknown ***,based on the disturbance observer,an adaptive nonsingular vehicle formation control strategy is proposed to ensure the formation errors converge to a close neighborhood of origin in a prescribed time,which can be confirmed in advance by specifying several simple *** results validate the effectiveness of the presented control method.

关键词： Connected vehicles formation control disturbance observer prescribed time

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：