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10th International conference on control, Decision and Information Technologies (CoDIT)

作者： Tang, Zhenhui Liu, Ru Lou, Kang Gao, Chaojian Wang, Jingcheng SJTU Sanya Yazhou Bay Inst Deepsea Sci & Technol Sanya 572024 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Shanghai Marine Equipment Res Inst Shanghai 200040 Peoples R China Shanghai Jiao Tong Univ Dept Automat Key Lab Syst Control & Informat Proc Minist Educ China Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Shanghai Engn Res Ctr Intelligent Control & Manag Shanghai 200240 Peoples R China Xian Technol Univ Autonomous Syst & Intelligent Control Int Joint R Xian 710021 Peoples R China

ISBN: (纸本)9798350373981;9798350373974

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although data-driven fault diagnosis method attains remarkable progress by learning fault features automatically, the existing data-driven fault diagnosis pipelines learn non-discriminative feature. To deal with these problems, a new data-driven fault diagnosis method with recurrent attentional module is proposed to find a sequence of informative features by localizing distinguishing time fragments about each individual faults iteratively. The contextual sequence dependency between the localized time fragments is enhanced. Then, the fault types are further predicted on localized time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on hydraulic system show that the proposed framework achieves superior diagnosis performance and computation efficiency.

关键词： Hydraulic equipment

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Improved YOLOv5 Algorithm for Special People Recognition in a New Guide Robot Dog for the Blind 13

Improved YOLOv5 Algorithm for Special People Recognition in ...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Liu, Shida Bi, Yu Ji, Honghai Wang, Li School of Electrical and Control Engineering North China University of Technology Beijing100000 China

ISBN: (纸本)9798350361674

Aiming at the problem of target recognition accuracy of the guide quadruped robot dog, this paper proposes a novel YOLO (SG-YOLOv5) detection and recognition algorithm based on changing the loss function and activation function. The proposed SG-YOLOv5 method replaces the activation function ReLU with SiLU and the loss function CIoU with GIoU in the YOLOv5 model. In addition, a database of special populations containing four categories, namely, blind family members, doctors, firefighters, and traffic police officers, is trained and applied to a real Unitree Go1 quadruped robot dog. The experimental results show that the recognition accuracy of the blind-guiding robot dog for special people reaches 94.3%, the check-all rate reaches 91.7%, and the average accuracy is as high as 95.1%, which verifies the effectiveness of the algorithm. © 2024 ieee.

关键词： Multipurpose robots

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Predictor-Based data-driven Model-Free Adaptive Predictive control of Power Converters Using Machine learning

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ieee TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第8期70卷 7591-7603页

作者： Liu, Xing Qiu, Lin Fang, Youtong Rodriguez, Jose Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Urbana Champaign Inst Univ Illinois Hangzhou 310027 Peoples R China Univ San Sebastian Fac Engn Santiago 8370146 Chile

In this article, a novel robust data-driven model-free predictive control framework based on the I/O data of the controlled plants, which is performed by incorporating the neural predictor-based model-free adaptive control and finite control-set model predictive control, is first proposed. The salient feature of the suggested framework is that the uncertainties, such as unmodeled dynamics and external disturbances, can be explicitly addressed in controlled systems. From a practical standpoint, however, the potential of this proposal is limited by a significantly increased online computational complexity, which makes it difficult to implement. To circumvent this limitation, a supervised imitation learning technique using data labeled is developed to imitate the known suggested controller, which the majority of the online computational burden can be transformed into offline computing by utilizing a trained artificial neural network subject to robustness characteristics. In particular, this development motivates a much simpler robust predictive control solution, which is convenient to implement in applications. Thus, by this proposal, the online implementation of much more complex predictive control strategies is made possible, and it explores a new possibility for future development of the complex control methodology. Finally, extensive simulative and experimental investigations for modular multilevel converter validate the interest and viability of the proposed design methodology.

关键词： Finite control-set (FCS) model predictive control machine learning model-free adaptive control (MFAC) modular multilevel converter predictor-based neural network (NN)

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Identification of linear data-driven models for large-scale power systems 10

Identification of linear data-driven models for large-scale ...

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10th International conference on control, Decision and Information Technologies (CoDIT)

作者： Zuccaro, Elia di Capaci, Riccardo Bacci Pannocchia, Gabriele Panosyan, Ara Univ Pisa Dept Informat Engn Pisa Italy Univ Pisa Dept Civil & Ind Engn Pisa Italy Siemens Energy Grid Consulting Erlangen Germany

ISBN: (纸本)9798350373981;9798350373974

The linearization of the full model of electrical power systems is of great significance for the adoption of linear analysis techniques to examine the system's dynamic characteristics, as well as for the design and tuning of practical controllers. Typically, the state-space model of the power system is first obtained from the time domain model. Linear analysis and controller tuning are then performed utilizing the linear state-space model. This approach however often has several practical limitations, such as the unavailability of a time domain model, when only simulation or measurement data is available, or the lack of linearization capability in the software tool in which the time domain model is available. Moreover, the linearization of the time domain models of large-scale power systems results in very high-dimension state-space models, which greatly complicates further analysis. To this aim, in this paper, suitable linear data-driven models of reduced order are identified for power systems to retain the most relevant modes of oscillations of the original system. A commercial rigorous software is used for the data generation and a well-established Python toolbox is used for the model identification: different models and techniques are applied and then compared in terms of accuracy and simplicity.

关键词： Process modeling power systems modal identification system identification

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Lube Based Oil Unit Anomaly Limit Prediction using Decision Tree Classification Model

Lube Based Oil Unit Anomaly Limit Prediction using Decision ...

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ieee International conference on Automatic control and Intelligent systems (I2CACIS)

作者： Abdullah, Nurul Izzaty Binti Bidin, Abu Huzaifah Bin Haji Omar, Madiah Binti Zaid, Hayyiratul Fatimah Binti Mohd Ibrahim, Rosdiazli Bingi, Kishore Univ Teknol PETRONAS Chem Engn Dept Seri Iskandar Perak Malaysia PETROLIAM Nasl Berhad Project Armaya Proc Engn Kuala Lumpur Malaysia Univ Teknol PETRONAS Elect & Elect Engg Dept Seri Iskandar Perak Malaysia

ISBN: (纸本)9798350372113;9798350372106

The replication of human thinking by technology, including computer systems, is known as artificial intelligence (AI). The rise of AI can certainly help plant operations. In the past, process engineers manually predicted anomalous conditions in the plant to ensure every process was dependable and safe. This extensive procedure calls for an experienced and skilled engineer to find anomalies. This makes the job more difficult for engineers at the entry-level. This pattern is observed throughout the industry, where process plants with newly hired engineers tend to have more accidents and unit upsets because the engineers fail to identify anomalies early on. Thanks to the development of artificial intelligence and machine learning, we can now use all this expertise to detect early normal on computers. We can now use all this expertise to detect early normal data on computers due to the development of artificial intelligence and machine learning from historical data. For this study, we have gathered data from plant engineers over the last five years on common or prior events that have happened in the past. A decision tree learning model has been trained with the knowledge gathered from five fault tree analyses. A high degree of accuracy (99.9%) was achieved using supervised machine learning training when the model was evaluated using several data sets. Now that it can be used in production, this model helps engineers find anomalies early on.

关键词： Artificial intelligence decision tree fault tree analysis machine learning plant production

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WF-Velo: A New Method for RNA Velocity Analysis Based on Single-Cell data 13

WF-Velo: A New Method for RNA Velocity Analysis Based on Sin...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Li, Cheng Cao, Wenjie Zhang, Zihao Tang, Wenbo Shi, Yazhou Zhang, Ben-Gong School of Mathematical & Physical Science Wuhan Textile University Wuhan430200 China Sun Yat-Sen University Guangzhou510275 China

ISBN: (纸本)9798350361674

The direction of cell differentiation is very important to study the process of cell differentiation. There was a strong hypothesis that the direction was fixed in previous studies until the RNA velocity method appearance. It is a powerful tool to analyze cell differentiation. However, there is still some problem of it due to the low expression of unspliced mRNA readings and high randomness. This paper intends to propose a new method called WF-velo which means the whole feature (WF) are used during the process of RNA velocity estimations, while the part feature was used by the traditional method, to overcome the problem of the existed methods. By using the characteristics of the gene in the whole transcription process, the RNA velocity of cells can be estimated more realistically, and the cell differentiation process is more in line with biological phenomena compared with the cellDancer, scvelo, Deepvelo and other RNA velocity estimation algorithms. The pancreatic endocrine development dataset and the mouse hippocampus development dataset are tested by WF-velo. The cell differentiation trajectory shows that it is a good method to estimate the RNA velocity. © 2024 ieee.

关键词： Transcription

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Deep Co-Training Partial Least Squares Model for Semi-Supervised Industrial Soft Sensing

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ieee Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3363-3371页

作者： Zheng, Junhua Zhou, Le Ye, Lingjian Ge, Zhiqiang Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou310023 China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou313000 China Southeast University School of Mathematics Nanjing210096 China

data-driven soft sensing has become quite popular in recent years, which can provide real-time estimations of key variables in industrial processes. While the introduction of deep learning does improve the prediction performance, it is highly restricted to the number of labeled training data, as well as large computational burden and cumbersome parameter tuning procedures. How to break through the bottleneck of data-drive models in terms of limited labeled data and high computational complexity should be one of the main recent focuses in the field of industrial soft sensing. In this article, a deep co-training PLS (deep CT-PLS) model is proposed to extend the ordinary PLS model to the semi-supervised deep form. While the deep model can efficiently extract inherent natures of process data, the co-training strategy makes lots of unlabeled data useful through a two-view cross training and annotation process. In this case, the performance restriction of the deep PLS model can be greatly relieved, with the incorporation of additional unlabeled data, while at the same time the designed model structure keeps in a low computational complexity. Based on the case study on a real industrial production process, the deep CT-PLS model can significantly improve the soft sensing performance. © 2013 ieee.

关键词： Self-supervised learning

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HFRRU-Net: Hybrid Features Ringed Residual U-Net for Image Splicing Forgery Detection 13

HFRRU-Net: Hybrid Features Ringed Residual U-Net for Image S...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Sun, Mengjing Zhu, Haijiang College of Information & Technology Beijing University of Chemical Technology Beijing100029 China

ISBN: (纸本)9798350361674

With the rapid development of computers and the internet, digital image forgery detection has become one of the important research hot topics in the field of computer vision. In this article, we propose a dual stream network to detect image tampering and locate forged areas. Our network includes the ringed residual U-Net, RGB stream and DCT stream, which is named as a hybrid feature ringed residual U-Net (HFRRU-Net) because of combining frequency domain features with RGB features. Compared to the existing works, HFRRU-Net is an end-to-end training approach without image preprocessing or post-processing, and this network may speed up the training and prediction process. In the experments, the HFRRU-Net is evaluated on five public image forgery datasets, CASIA v1.0, Columbia, In-The- Wild, Nist2016, and Realistic. The results indicate that the F1 scores of the HFRRU-Net outperforms other methods in four datasets. And visual comparisons further demonstrate the proposed method. © 2024 ieee.

关键词： data streams

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Escape with Self-adaptive Decision Radius based on Deep Deterministic Policy Gradient in Pursuit Games 13

Escape with Self-adaptive Decision Radius based on Deep Dete...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhou, Xiaojie Yang, Chunxi Wang, Wenbo Sun, Pengqi Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650500 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming650500 China

ISBN: (纸本)9798350361674

The Pursuit-Evasion (PE) game of Unmanned Surface Vehicles (USVs) is a classic antagonistic problem for the intelligent agent system. To enhance the escaping success rate of evaders with better effort, this paper proposes an escape strategy based on the geometrical characteristics of Apollonius circles. An improved self-adaptive escaping strategy for the evader utilizing the deep deterministic policy gradient algorithm is proposed. Then, the criteria for successful encirclement by pursuer are given. A DDPG algorithm-based framework is proposed on the basis of markov decision process formulation. Specifically, an action space based on adaptive decision radius of evader is designed. Our simulation shows the proposed method has more advantages in terms of escape distance and escape time. © 2024 ieee.

关键词： Unmanned surface vehicles

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Additive-state-decomposition-Based Event Triggered Spatial Repetitive control for Fast Tool Servo systems 13

Additive-state-decomposition-Based Event Triggered Spatial R...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Yang, Qin Zhou, Lan Xiao, Wenbin Li, Meiliu School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan411100 China

ISBN: (纸本)9798350361674

This paper presents an event triggered spatial repetitive controller (ETSRC) method for a fast tool servo (FTS) system with multiple disturbances to improve the tracking performance for position-dependent periodic signals, which is based on the additive state decomposition (ASD) technique and the disturbance observer (DOB) approach. First, the original tracking problem for a position-dependent periodic reference input of the disturbed FTS system is decomposed into two sub-problems, namely, the tracking problem of the linear time-invariant (LTI) primary system and the robust stabilization problem of the secondary system. For the LTI primary system, an ETSRC method is constructed to ensure the tracking capability of the system while saving the communication and computational resources. Secondly, in the secondary system, the available position information of the FTS system is used to design a DOB to estimate and compensate for multiple disturbances. Finally, comparative simulations are performed on the FTS system and the results show that the method outperforms the conventional DOB control method and other repetitive control methods. © 2024 ieee.

关键词： Disturbance rejection

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