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检索条件"任意字段=Conference on Data Analysis and Modeling for Process Control"

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Transfer learning-based multi-fidelity modeling method for multimode process monitoring

Transfer learning-based multi-fidelity modeling method for m...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International conference on

作者： Baoyu Zhu Shaojun Ren Zihan Wei Fengqi Si Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education Southeast University Nanjing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

data-driven modeling techniques have been widely applied in industrial systems for process monitoring. However, these models heavily rely on data accuracy and completeness. Challenges emerge when the mode characteristics of the system alter due to equipment deterioration (such as heat exchanger fouling, component wear, catalyst deactivation) or after maintenance activities (like cleaning, repair, replacement, etc.). data collected from the old mode (before the mode change) no longer accurately reflects the characteristics of the new mode (after the mode change). This presents a significant challenge for multimode process modeling, as the new mode model cannot directly utilize old mode data when there is insufficient training data for the new mode. To address this issue, we propose a novel transfer learning-based multi-fidelity modeling (TL-MFM) method. The key innovation of this method lies in its fusion of limited high-fidelity data from the new mode with sufficient low-fidelity data from the old mode to construct a robust monitoring model. By leveraging a model transfer framework that optimizes the transfer of relevant information across fidelity levels, the TL-MFM method enhances the adaptability of the monitoring model. The effectiveness of the TL-MFM method is validated through a case study on a real-world condenser in a steam turbine generator set.

关键词： process monitoring data-driven modeling Adaptation models Technological innovation Accuracy Systematics Training data Predictive models Generators Turbines

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Adaptive Admission control Policy in Distributed Real Time database System 11

Adaptive Admission Control Policy in Distributed Real Time D...

引用

11th International conference on System modeling and Advancement in Research Trends, SMART 2022

作者： Chauhan, Nupa Ram Dwivedi, Rakesh Kumar CCSIT Tmu Computer Science and Engineering Moradabad India

ISBN: (纸本)9781665487320

Today's real time computers activities and applications required data services in the distributed platform where real time applications have been needed for databases, in which data and operations have special characteristics in terms of time constraints. It stores large amount of data with time-sensitive manner and process it within fix given time frame. The requirement for real time computers activities and its applications are increasing in distributed real time database system (DRTDBS) including advanced traffic control, global environment control, nationwide power grid control etc. The transaction work load in DRTDBS has not balanced and the transaction processing patterns have time varying and distorted. Due to the unbalanced computation workload on DRTDBS and unpredictable communication delay can lead to miss the large number of transaction deadline. Hence, an adaptive admission control architecture and protocol have required for DRTDBS. The Adaptive admission control Policy has fulfil to accessing and maintaining real time computers activities and application of data for satisfy timing constraints of transaction supported applications. This paper presents an adaptive admission control architecture and an efficient set of protocol. The local controller controls the local transaction request and global load balancer works global transaction request on distributed environment. The functionality of Adaptive Admission control Policy have increased the performance because minimizing the miss ratio and increasing throughput. The experiment results show that an adaptive admission control policy effectively balances the workload in distributed real time database system under tight transaction workload as compared to existing protocols. © 2022 IEEE.

关键词： Distributed database systems

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Probabilistic process Window: A new approach to focus-exposure analysis 36

Probabilistic Process Window: A new approach to focus-exposu...

引用

conference on Metrology, Inspection, and process control XXXVI Part of SPIE Advanced Lithography and Patterning conference

作者： Mack, Chris A. Yannuzzi, Jonathan Lorusso, Gian Zidan, Mohamed De Simone, Danilo Weldeslassie, Ataklti Vandenbroeck, Nadia Foubert, Philippe Beral, Christophe Charley, Anne-Laure Fractilia LLC Austin TX 78703 USA IMEC Leuven Belgium

ISBN: (纸本)9781510649828;9781510649811

Background: Focus-exposure process window measurement and analysis is an essential function in lithography, but the current geometric approach suffers from several significant deficiencies. Aim: By clearly identifying the problems with the Geometric process Window approach, a new process window measurement and analysis method will be proposed to address these problems. Approach: The Probabilistic process Window proposed here takes metrology uncertainty into account and rigorously calculates the expected fraction of in-spec features based on settings for best dose/focus and presumed random errors in dose and focus. Using the fraction of in-spec features thus calculated, a much more rigorous determination of the trade-off between exposure latitude and depth of focus can be performed. Results: The Probabilistic process Window approach is demonstrated on focus-exposure data generated from a standard extreme ultraviolet lithography process at three different pitches, showing the value of this method. Conclusions: The new Probabilistic process Window approach offers clear advantages in accuracy for both depth of focus determination and best dose/focus determination. Consequently, its use is preferred both for process development applications and high-volume manufacturing.

关键词： focus-exposure matrix FEM process window exposure latitude depth of focus DOF EL vs. DOF

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HVDC Commutation Failure analysis and Simulation after Removing the Three Phase Short Circuit Fault 5

HVDC Commutation Failure Analysis and Simulation after Remov...

引用

5th International conference on Energy, Electrical and Power Engineering, CEEPE 2022

作者： Liu, Zengxun Gao, Yiying Cheng, Jinmin Cheng, Baohua Wang, Weiyuan Liu, Tianyang State Grid Corporation of China Technology Department Beijing China China Electric Power Research Institute State Key Laboratory of Power Grid Safety and Energy Conservation Beijing China State Grid Jiangsu Electric Power CO. LTD Technology Department Nanjing China Tianjin Chengjian University School of Control and Mechanical Engineering Tianjin China

ISBN: (纸本)9781665479059

Aiming at the commutation failure problem after removing receiving end three phase short circuit fault of HVDC system, this paper expounds the power angle oscillation process and power angle oscillation characteristics after removing receiving end three phase short circuit fault from the perspective of mechanism, analyzes the voltage variation characteristics near the oscillation center, analyzes the risk of commutation failure near the oscillation center converter station and extends to any DC drop point of the line, summarizes the key influencing factors of reducing the risk of commutation failure by constant extinction angle control. Combined with the simulation, the correctness of the theoretical analysis in this paper is verified. © 2022 IEEE.

关键词： Analytical models HVDC transmission process control Inverters Stability analysis Circuit faults Integrated circuit modeling

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2nd International conference on Laser, Optics and Optoelectronic Technology, LOPET 2022

作者： Zang, Tengfei Chen, Xingyu Li, Lijuan Nanjing Tech University College of Electrical Engineering and Control Science Jiangsu Nanjing China Southeast University School of Instrument Science and Engineering Jiangsu Nanjing China

ISBN: (纸本)9781510657588

process controller performance evaluation systems have been widely used in modern industries with increasingly complex control objects. However, there is a prerequisite for their implementation: the models obtained from the system identification are all from the same stable operating conditions, because when the operating conditions are not unique, the performance evaluation strategy cannot determine whether the controller performance degradation is due to real uncertainties or changes in the operating conditions. Therefore, it is necessary to identify the corresponding model according to the specific operating conditions and use it to solve the optimization problem to obtain a specific performance benchmark for accurate performance evaluation. In this paper, we propose an online data modeling strategy based on an improved just-in-time learning algorithm, which proposes an integrated learning framework for similar sample selection using a variety of different similarity measures in the selection of local modeling neighborhoods. Then, the local prediction models are trained, and finally, the final results are obtained by integrating the strategy to synthesize multiple local prediction models. The effectiveness of the proposed algorithm in this paper is demonstrated by Wood-Berry simulation experiments. © 2022 SPIE.

关键词： process control

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Rapid parameter identification of BUCK converter using Hilbert phase compensation

Rapid parameter identification of BUCK converter using Hilbe...

引用

International conference on Power Electronics and Motion control (IPEMC)

作者： Hongzhi Liu Hui Li Changhao Zhu Xiangtan University China

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

This paper proposes a phase compensation method for rapid parameter identification aimed at addressing the hysteresis problem in the digital twin parameter identification of BUCK converters. Based on collected BUCK circuit data, this method performs Hilbert transformations on the data from both the physical model and the digital twin model. It calculates the phase difference between the two and mitigates the error caused by actual sampling data through phase compensation. This process reduces computational redundancy resulting from errors, enabling timely parameter updates in the digital twin model. Additionally, a BUCK converter digital twin platform is designed for experimental verification. The experimental results demonstrate that, while ensuring accuracy, this method effectively enhances the parameter identification speed of the digital twin model, facilitating subsequent field-circuit coupling analysis..

关键词： Parameter estimation Buck converters Computational modeling Redundancy Linear programming data models Power electronics

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DMF-MPC: A Dual-Stage and Multi-Step Forecasting Model Based Cooling System control Method for data Center Energy Cost Minimization 23

DMF-MPC: A Dual-Stage and Multi-Step Forecasting Model Based...

引用

23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on data Science and Systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： He, Xinrui Chen, Weiduo Dong, Xiaoshe Wang, Qiang Liu, Jialin Dong, Fan Su, Yuanqi School of Computer Science and Technology of Xi'an Jiaotong University Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9781665494571

Nowadays, surveys show that the cooling system accounts for 30% of the energy consumption of data centers. The challenge of designing an effective cooling control system to minimize data center energy consumption cost as much as feasible while maintaining normal operation is a pressing one to address. The traditional cooling control method produces excessive cooling energy consumption, which causes a large amount of energy waste and economic expenses;Moreover, the existing Model Predictive control (MPC) method based on the physical model has high modeling difficulty and model migration cost, and can only provide short-term control strategy. In this paper, we propose an MPC method based on a dual-stage and multi-step forecasting model (DMF-MPC), which can forecast the cooling system energy consumption in data centers accurately and provide a cost-saving control strategy of the cooling system for the next 24 hours on a combination of the peak-valley time-of-use (TOU) electricity price. We firstly propose a dual-stage and multi-step forecasting model (DMFM), a pure data-driven deep learning model, which adopts the historical server features of data centers, future weather factors, and cooling control to forecast the cooling system energy consumption for the multiple future periods, also a multi-step iterative training method is proposed in the training process to reduce the multi-step forecasting errors. In the control part of DMF-MPC, we introduce an optimal control model for the data center cooling system based on DMFM. The cost-saving control strategy for the next 24 hours under the peak-valley TOU electricity price standard can be obtained by solving the model through optimization algorithms. Finally, the effectiveness of the method is verified by validating DMF-MPC in a real campus high-performance computing (HPC) data center located in China. Our method achieves a 15%-25% cost reduction in the experiment. © 2021 IEEE.

关键词： Training data centers Energy consumption Costs Cooling Computational modeling Weather forecasting

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Research on data-Driven Fault Diagnosis of Aero-Engine Transmission Systems 42

Research on Data-Driven Fault Diagnosis of Aero-Engine Trans...

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42nd Chinese control conference, CCC 2023

作者： Ma, Song Yu, Xin-Meng Sun, Tao Zhao, Guang Guo, Mei Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Tsinghua University Department of Automation Beijing100084 China School of Energy and Power Engineering Dalian University of Technology Dalian116024 China Avic Shenyang Engine Design Institute Shenyang110066 China

ISBN: (纸本)9789887581543

In this brief paper, a data-based method on the fault diagnosis in aero-engine transmission systems is developed. Firstly, during the operation of splines, we acquire the acceleration vibration signal. We process the signal into time-frequency images using the short-time Fourier transform. Then, the improved convolutional neural network with channel attention is trained using time-frequency images of multiple fault signals. Finally, we implement fault prediction for spline wear and misalignment faults in aero-engine transmission systems. The result shows that the fault diagnosis using the proposed method has a high level of accuracy. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Failure analysis

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Enhanced Air Quality Prediction Using Optimized Sarimax Model for Coimbatore Region

Enhanced Air Quality Prediction Using Optimized Sarimax Mode...

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Science, Technology, Engineering and Management (ICSTEM), International conference on

作者： Bande Revanth Manoranjitham R Division of Computer Science and Engineering Karunnya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

This research delves into examining the forecast of air quality in Coimbatore, India, according to data gleaned from the Central Pollution control Board (CPCB) specific to the area. The gathered information underwent a preprocessing step, using interpolation to ensure uniformity and completeness. Forecasting methods such as Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) and Long Short-Term Memory (LSTM) models were utilized to predict air quality levels. The SARIMAX model was fine-tuned through a grid search process, while the LSTM underwent hyperparameter adjustments to improve air quality predictions. Performance evaluation of these models involved metrics like R-squared, Mean absolute error, and Root mean square error. Based on the selected evaluation criteria, the outcome shows that SARIMAX outperformed LSTM in Coimbatore when it came to predicting the air quality. This analysis improves our understanding of air quality patterns in Coimbatore. Highlights SARIMAX's effectiveness as a modeling tool for atmospheric science applications.

关键词： Interpolation Solid modeling Atmospheric modeling Pollution control Predictive models Air quality Solids

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Research on Abnormal Risk Identification of Safe Power Consumption for Active Customers Based on Cluster analysis 3

Research on Abnormal Risk Identification of Safe Power Consu...

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3rd International conference on Electrical Engineering and control Science, IC2ECS 2023

作者： Pan, Mingming Xu, Zishang Yuan, Jindou Li, Yongjun China Electric Power Research Institute Co. Ltd Department of Power Consumption & Energy Efficiency Beijing China

ISBN: (纸本)9798350382426

Aiming at the complexity of abnormal risk identification of active customers' safe power consumption, this paper proposed a clustering analysis method for abnormal risk identification of active customers' safe power consumption. Firstly, the basic composition of the proposed method is depicted which includes feature extraction and clustering recognition. Then, the specific three-step implementation of the proposed method is described, which is composed of obtaining typical features, normalizing typical features to map three-dimensional feature space, and classifying risk states by k-means cluster analysis. Finally, the case analysis is carried out by using daily-load curve data of charging-pile active customers as dataset. Compared with the comparative analytic hierarchy process algorithm, the experimental results illustrate that the proposed method can effectively identify the abnormal risk of safe power consumption of charging-pile active users which may further improve the risk management and control strategy of abnormal risk of safe power consumption for active customers. © 2023 IEEE.

关键词： active users clustering electricity safety risk identification

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