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Smart Power & Internet Energy Systems (SPIES), International Conference on

作者： Haonan Li Bozhen Jiang Zhengyang Ma Hua Geng Yi Liu Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Department of Automation Tsinghua University Beijing China

Accurate wind power forecasting plays an increasingly important role in the field of wind power forecasting but still intractable in practice. In order to improve the accuracy of wind power forecasting, a novel model based on squeeze and excitation network (SENet) embedded in a dual-channel is proposed. The dual-channel mechanism integrates convolutional neural network and gated recurrent unit, both of which can be used without interfering with each other. SENet can increase the attention of each important features, thus improving the network efficiency. Compared with a single model, the proposed model has better feature extraction performance with the root mean square error of 29.9. The test results of wind power data based on Kaggle platform show that the proposed method outperforms several deep learning forecasting methods.

关键词： Neural networks Wind power generation Predictive models Logic gates Feature extraction Internet Convolutional neural networks

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Analysis of Operational Data and Self-learning control Rules for Mining Mill Equipment

Analysis of Operational Data and Self-learning Control Rules...

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Asian control Conference

作者： Guicheng Wang Liang Yuan Zhicheng Bai Guoliang Cai Ning Hua Xiaobin Li School of Electrical and Electronics Engineering Shanghai Institute of Technology Shanghai China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process and Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Zijin Intelligent Control (Xiamen) Technology Co. Ltd. Xiamen China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The engineering application under industrial big-data analysis is of great significance for improving the optimization effect of enterprise production process control processes. By analyzing the operating principle of the semi-automatic mill and studying its control optimization strategy, based on the operational data of the SAG mill, industrial big-data analysis is adopted to self-learning its control optimization rules. Firstly, SPSS is used to preprocess the high-dimensional process data generated during the operation of the semi-automatic mill. Four parameters, namely power, axial pressure, grinding sound, and grinding concentration, are preliminarily selected. Then, PCA is used to screen and reduce the dimensionality of multidimensional data, and K-Means clustering algorithm is used to classify and identify the reduced dataset. After simulation research, the results show that PCA can reduce the redundancy of SAG mill operation data, and the control optimization rules formed by self-learning can serve as operational guidance for improving the efficiency and energy-saving of SAG mill operation. The effectiveness can be further verified in practical engineering applications in the later stage.

关键词： Dimensionality reduction Energy consumption Technological innovation Redundancy Force process control Clustering algorithms

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Supervised Dynamic Latent Variable Models for Fault Identification in Dynamic processes

Supervised Dynamic Latent Variable Models for Fault Identifi...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical processes, SAFEprocess 2021

作者： Niu, Qiqi Shen, Chengkai Lyu, Yuting Liu, Yi Zhou, Le Zhejiang University of Science Technology School of Automation and Electrical Engineering Zhejiang Hangzhou China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Zhejiang Hangzhou China

ISBN: (纸本)9781665401159

With an increasing application of the multivariate statistical process monitoring method, the fault identification becomes more and more crucial after the fault detection, which is benefited to fault troubleshooting effectively and quickly. To deal with the dynamic process and the impact of random noise on fault identification, a novel fault identification method based on supervised autoregressive dynamic latent variable is proposed in this paper. After the fault label structure is added on the unsupervised model, the relationship between fault information and dynamic latent feature is strengthened. Then, the fault cause is determined based on the fault relevance method. Finally, the validity of method is verified by a numerical example and an actual wastewater treatment case. © 2021 IEEE.

关键词： Fault detection

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A Koopman Operator-Based Finite Impulse Response Filter for Nonlinear Systems

A Koopman Operator-Based Finite Impulse Response Filter for ...

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IEEE Conference on Decision and control

作者： Zhichao Pan Biao Huang Fei Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi China department of chemical and materials engineering University of Alberta Edmonton Alberta Canada

This paper proposes a novel Koopman operator-based finite impulse response (KFIR) filter for nonlinear dynamic systems. This filter is generalized from the minimum variance unbiased (MVU) FIR filter for linear systems by using a global linear approximation of the nonlinear dynamics obtained from Koopman operator theory and the extended dynamic mode decomposition (EDMD) algorithm. Based on the recursive linear model, a reduced-order FIR filtering structure is proposed, and the optimal gain is derived to minimize the trace of the estimation error covariance. Unlike traditional methods, the KFIR filter requires no prior knowledge of the initial state and fully utilizes the data of a moving horizon. Simulation results show that the proposed filter has excellent robustness against unexpected modeling uncertainties and inaccurate noise information, making it suitable for real applications.

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th International Conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Adaptive Energy Management Strategy to Avoid Battery Temperature Peaks in Fuel Cell Electric Trucks

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IFAC-PapersOnLine 2022年第24期55卷 311-316页

作者： Alessandro Ferrara Matthias Hütter Christoph Hametner Institute of Mechanics and Mechatronics Division of Process Control and Automation TU Wien Vienna Austria AVL List GmbH Graz Austria Christian Doppler Laboratory for Innovative Control and Monitoring of Automotive Powertrain Systems TU Wien Vienna Austria

Thermal management is essential in electric vehicles to preserve battery life. In particular, avoiding temperature peaks is critical to prevent accelerated degradation. The battery thermal management problem is crucial in fuel cell electric trucks due to the heavy vehicle weight, especially on mountain or hilly roads. Therefore, this paper proposes an energy management strategy that reduces battery degradation by limiting its usage at high temperatures to allow its cooldown and avoid peaks. The energy management strategy is adaptive because the main control parameters for the fuel cell/battery power-split are adjusted depending on the battery temperature. The comparison between adaptive and non-adaptive strategies proves the effectiveness of the proposed formulation in avoiding temperature peaks without hindering fuel consumption or fuel cell degradation. The robustness of the proposed energy management strategy is validated with simulations of several real-world driving cycles with various speed and elevation profiles.

关键词： Energy Management Heavy-Duty Fuel Cell Vehicle Adaptive control Battery Temperature Fuel Cell Degradation Battery Thermal Management

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On Assignments Verification during the ICS Databases Lifecycle 14

On Assignments Verification during the ICS Databases Lifecyc...

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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Budynkova, Elizaveta Baybulatov, Artur Joint Stock Company «aLFA-BANK» Business Process Automation Directorate Moscow Russia Ivery Prangishvili Laboratory 31 'Distributed Data-Analysis and Control Systems' V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

ISBN: (纸本)9781665412308

Today, Industrial control System (ICS) databases are incredibly complicated with quite prolonged iterative lifecycle where every new iteration is driven by a new version of the database assignment. For the correct databases development, the assignments should be consistent and correspond to special requirements, but this is not always the case. Therefore, the checking, or verification, of the assignments is becoming especially important. However, the appropriate problem of databases assignments verification is not widely discussed, and available verification methods are not suitable enough for solving the problem. The article clarifies the place of the assignments verification in the ICS databases lifecycle and presents a novel algorithm for the assignments verification. The particular implementation of the algorithm is also considered. As an example, the results of the verification of the assignments for the NPP ICS databases are presented. © 2021 IEEE.

关键词： Database systems

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A parallel intelligent system for optimizing high-speed railway rescheduling by learning 1

A parallel intelligent system for optimizing high-speed rail...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Yang, Pengxin Cui, Dongliang Dai, Xuewu Lv, Yisheng Dong, Hairong Wang, Xinghao Liu, Ruiguang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing China

ISBN: (纸本)9781665433372

The railway system is a complex system because of many constraints, randomness and high security requirements, so it is difficult to establish an accurate mathematical model for it, which brings great challenges to railway scheduling. Parallel intelligence theory can effectively solve the modeling, analysis and control of complex systems, so it has a wide range of applications in the field of intelligent transportation. In this paper, we build an artificial system according to the actual system, and use the surrogate modelling to carry out the calculation experiments, and proposed a parallel scheduling system based on the parallel intelligence method. Select a case to design scenario and perform computational experiment, the scheduling strategy is given and the scheduling strategy is analyzed. © 2021 IEEE.

关键词： Intelligent systems

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Impeller Position in a Magnetically Levitated Rotodynamic Blood Pump and Its Impact on In-Silico Hemocompatibility

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ASAIO Journal 2025年 2025 Apr 3页

作者： Grujic, Marko Schoefbeck, Rosmarie Thamsen, Bente Aigner, Philipp Röhrich, Michael Jakubek, Stefan Zimpfer, Daniel Granegger, Marcus Christian Doppler Laboratory for Mechanical Circulatory Support Department of Cardiac Surgery Medical University of Vienna Vienna Austria Department of Anaesthesia Intensive Care Medicine and Pain Medicine Medical University of Vienna Vienna Austria Division of Control and Process Automation Institute of Mechanics and Mechatronics Vienna University of Technology Vienna Austria

In magnetically levitated rotodynamic blood pumps (RPBs), the impeller position depends on a balance of electromagnetic and fluid dynamic forces. The aim of this study was to describe the impeller position of the HeartMate 3 over a wide range of operating conditions and assess its potential impact on hemocompatibility. Three-dimensional impeller positions were measured using a transparent HeartMate 3 pump casing, laser distance measurements, and a high-speed camera. Accompanying computational fluid dynamic (CFD) hemocompatibility predictions of a displaced and centered impeller at a typical operating point were compared. Impeller positions vary substantially with different operating points with a maximum axial displacement of 223 µm at 7 L/min and 7,000 rpm and a maximum radial displacement of 145 µm at 0 L/min and 7,000 rpm. In CFD, a displaced impeller had only a minor influence on global pump parameters ( © ASAIO 2025.

关键词： Computational fluid dynamics

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A Norm Optimal Iterative Learning control Approach With Efficient Computation

A Norm Optimal Iterative Learning Control Approach With Effi...

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Data Driven control and Learning Systems (DDCLS)

作者： Luyuan Gao Zhihe Zhuang Hongfeng Tao Yiyang Chen Wojciech Paszke Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi PR China School of Mechanical and Electrical Engineering Soochow University Suzhou PR China Institute of Automation University of Zielona Gora Zielona Gora Poland

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Iterative learning control (ILC) is excellently applied to output tracking for systems that perform repetitive tasks. Classical norm optimal ILC (NOILC) approach yields optimal control inputs by minimizing a quadratic cost function. However, this approach requires the introduction of supervectors, i.e., lifting technique, for system description, which raises a high computational load problem. The contribution of this paper is to propose a non-lifted NOILC (N-NOILC) approach by redesigning a cost function that does not involve the lifting technique, achieving the goal of efficient computation. Therefore, the proposed approach is particularly suitable for large tracking tasks with long trial duration and high sampling frequency. Moreover, considering the model uncertainty in practice, robust convergence is analyzed. Numerical simulations are shown to demonstrate the properties and advantages of the proposed approach.

关键词： Uncertain systems Uncertainty Optimal control Cost function Numerical simulation Computational efficiency Numerical models

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