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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wu, Penghao Tao, Hongfeng Tian, Engang Yin, Tangwen Chen, Hongtian Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214122 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350361674

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods. © 2024 IEEE.

关键词： Variational mode decomposition

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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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Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Fu, Lijiang Xia, Qian Tang, Hao Chen, Junqing Tan, Jinglu Guo, Ya Jiangnan University Ministry of Education Key Laboratory of Advanced Process Control for Light Industry Wuxi China Changshu Institute of Technology School of Electrical Engineering and Automation Changshu China University of Missouri Department of Chemical and Biomedical Engineering ColumbiaMO United States

Long-term reconstructed solar-induced chlorophyll fluorescence (SIF) derived from raw gridded SIF has been used for the estimation of gross primary production (GPP), but the robustness of the spatial relationship may vary from location to location. We examined the often-used linear relationship between GPP and SIF in terms of R2 values for varied locations globally using three GPP datasets (FLUXCOM, VPM, PML) and three long-term reconstructed monthly SIF datasets (CSIF, SIF005, and RTSIF). The results show that the R2 value is a concave function of vegetation greenness level (NDVI) on an annual or seasonal basis. The average R2 is over 0.8 in areas where the annual average NDVI is in the range of 0.4 to 0.6, whereas the R2 is much lower where the annual average NDVI is less than 0.2 or greater than 0.8. Prediction of GPP or SIF by three methods from five major environmental variables revealed greater uncertainties in GPP and/or SIF at low or high greenness levels as an apparent cause of the low R2. The results offer useful insights into how global GPP may be effectively estimated from multi-satellite measured SIF. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Optical correlation

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Unscented Kalman Filter for State of Charge Estimation of Lithium Titanate Battery

Unscented Kalman Filter for State of Charge Estimation of Li...

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International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Joshua Chun-Ken Dardchuntuk David Banjerdpongchai Department of Electrical Engineering Center of Excellence in Intelligent Control Automation of Process Systems Faculty of Engineering Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok Thailand

An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO’s electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems.

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Performance Comparison of Lightweight CNN Models for Drone Collision Avoidance Dataset

Performance Comparison of Lightweight CNN Models for Drone C...

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International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Rifqi Nabila Zufar David Banjerdpongchai Department of Electrical Engineering Faculty of Engineering Center of Excellence in Intelligent Control Automation of Process Systems Chulalongkorn University254 Phayathai Road Pathumwan Bangkok Thailand

Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain information, process information, and estimate whether the object has a risk of collision. Convolution Neural Network (CNN) is one of the methods that can be employed for collision prediction. However, CNN is a method that needs a large data in the training. Dario Pedro et al. provided a dataset called the CoLANet dataset that consists of VDOs of collision drones. Subsequently, they proposed a new algorithm called Neural Network Pipeline which has a Convolution Neural Network (CNN) part to extract the feature from a couple of images. CNN extracts images by using MobileNetV2 as a pre-trained model. They chose MobileNetV2 due to training performance from another dataset. This paper aims to assess the performance of lightweight CNN models using the CoLANet dataset. The models will be trained on the Keras library with parameters of fewer than ten million. The models will be validated by Confusion Matrix and Receiver Operating Characteristics. In conclusion, we examine which pre-trained CNN model has the best performance and suggest ongoing work.

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Dynamic Graph Learning Soft Sensor in process Industry

Dynamic Graph Learning Soft Sensor in Process Industry

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Data Driven control and Learning Systems (DDCLS)

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

The development of an accurate soft sensor modeling method in the process industry remains a great challenge because the coupling relationship between variables is always intricate and difficult to model. In this work, a dynamic graph learning (DGL) soft sensor is proposed to alleviate this problem. The proposed model realizes the ability of the soft sensor to perceive the coupling relationship in real time by automatically learning the dynamic graph. Then, a causal convolutional mechanism and a multi-hop graph attention mechanism are used to systematically construct the dependencies of variables in the spatial-temporal dimension and model their variation patterns effectively. Finally, the proposed method is tested on the penicillin fermentation process and shown to be feasible and effective. The results showed that the change of the dynamic graph in the spatial-temporal dimension was in line with the process mechanism.

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Indistinguishability analysis and observer design for size-structured cell populations

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IFAC-PapersOnLine 2023年第2期56卷 581-586页

作者： P. Jerono J.J. Winkin A. Vande Wouwer A. Schaum Digital Process Engineering Group (DPE) Karlsruhe Institute of Technology Germany Department of Mathematics and Institute for Complex Systems (naXys) University of Namur Belgium Systems Estimation Control and Optimization (SECO) University of Mons Belgium Chair of Automation and Control (ACON) Kiel University Germany

The observability property of population balance equations with biomass measurements is addressed within the framework of indistinguishable trajectories. The population balance equation is described by a partial integro-differential equation which is coupled with an ordinary differential equation for the nutrient dynamics. For the semi-discretized model equations, the dynamics of indistinguishable trajectories are derived and evaluated for the special case of equal partitioning at cell division revealing that the observability property is guaranteed as long as the nutrient concentration is not depleted. Based on these results, an observer is designed and tested in simulation to estimate the cell population distribution using biomass measurements in a batch bioreactor for the case of a bi-structured population.

关键词： Observability indistinguishable trajectories cell population dynamics

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Multimodal Decoupling of Stationarity and Nonstationarity with Deep Cascaded Architecture for Varying-Condition process Monitoring

Multimodal Decoupling of Stationarity and Nonstationarity wi...

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第34届中国过程控制会议

作者： Pengyu Song Chunhui Zhao Jinliang Ding Furong Gao College of Control Science and Engineering Zhejiang University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Department of Chemical and Biological Engineering Hong Kong University of Science and Technology

Nonstationarity,i.e.,the time-varying property of the data distribution,is a typical process characteristic caused by operating condition switching,which can conceal stationarity describing time-invariant process mechanisms and thus bring challenges for explicit process *** achieve full decoupling of stationarity and nonstationarity,this study reveals the following facts:1) the coupling between stationarity and nonstationarity is not uniform but can present time-varying modes,defined as multimodal coupling here;2) diverse coupling modes possess intrinsic association since they essentially stem from definite nonstationary driving forces,namely changeable operating ***,this study proposes a multimodal decoupling representation method for learning explicit representations of stationarity and ***,to capture the basic coupling mode caused by nonstationary driving forces,an information refinement module(IRM) is designed in which a shortcut connection is incorporated into the auto-encoding *** the deep cascading of multiple IRMs,multimodal coupling modes can be fully ***,a compatibility learning algorithm is developed,which can balance the stationarity and reconstruction requirements to form a stationary feature learning module(SFLM).With compatibility learning,IRMs can be induced to capture and filter out nonstationarity and output stationary refined information for *** this way,multimodal decoupling of stationarity and nonstationarity can be fully *** effectiveness of the proposed method is illustrated through a numerical example and an actual industrial process.

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Semi-Supervised process Data Regression and Application Based on Latent Factor Analysis Model

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IEEE Transactions on Instrumentation and Measurement 2023年 72卷 1页

作者： Zheng, Junhua Liu, Yangxuan Liu, Yi Hou, Beiping Yao, Yuan Zhou, Le Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou310023 China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China National Tsing Hua University Department of Chemical Engineering Hsinchu30013 Taiwan

This article investigates the problem of modeling regression with labeled and unlabeled data samples commonly found in industrial processes. By incorporating additional information on unlabeled data samples, a new semi-supervised latent factor analysis model is developed. Compared with the purely supervised regression models which can only use the information of labeled dataset, the semi-supervised model can efficiently extract useful information for improvement of the regression performance. Furthermore, the proposed basic semi-supervised model has been extended to a mixture form, which is capable of describing data from more complex processes. For predictive modeling for key/mass variables, two soft sensors are constructed based on the semi-supervised models. Then, three case studies are used to evaluate the performance of the proposed soft sensing methods. © 2023 IEEE.

关键词： Regression analysis

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Privacy Preservation by Intermittent Transmission in Cooperative LQG control Systems

Privacy Preservation by Intermittent Transmission in Coopera...

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Asian control Conference

作者： Wenhao Lin Yuqing Ni Wen Yang Chao Yang Department of Automation Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, we study a cooperative linear quadratic Gaussian (LQG) control system with a single user and a server. In this system, the user runs a process and employs the server to meet the needs of computation. However, the user regards its state trajectories as privacy. Therefore, we propose a privacy scheme, in which the user sends data to the server intermittently. By this scheme, the server's received information of the user is reduced, and consequently the user's privacy is preserved. In this paper, we consider a periodic transmission scheme. We analyze the performance of privacy preservation and LQG control of different transmission periods. Under the given threshold of the control performance loss, a trade-off optimization problem is proposed. Finally, we give the solution to the optimization problem.

关键词： Measurement Privacy Data privacy Networked control systems Trajectory Servers Optimization

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