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Flow control characteristics of the digital and mechanical redundancy control electric modulation valve

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2022年第8期23卷 599-609页

作者： He LIU Bin ZHAO Bo WANG Long QUAN Yun-xiao HAO Yun-wei LI Key Laboratory of Advanced Transducers and Intlligent Control System of Ministry of Education and Shanxi Province Taiyuan University of TechnologyTaiyuan 030024China Deparment of Electrical and Computer Engineering University of AlbertaEdmonton ABT6G1H9Canada

The electrical modulation valve can provide proportional output valve element displacement,flow,or pressure according to a continuously changing input electrical *** is the core component of electro-hydraulic proportional control *** remove the influence of pressure difference changes on the output flow,the traditional scheme is to use a pressure compensation valve,which increases the difficulty of both manufacturing and maintaining the *** solve this problem,a method of digital and mechanical redundancy control flow is *** sensors are installed at the inlet and outlet of the valve,and the controller adjusts the displacement of the valve element according to the pressure difference between the valve ports to realize high-precision control of the flow.A pressure compensation valve is installed in front of the valve,and a three-way solenoid valve is used to control the working of the compensation *** the case of sensor failure,the valve is switched to the mechanical compensation differential pressure mode,to control the flow and to achieve redundancy *** system security is thereby *** feasibility of this scheme is verified through simulation and *** results show that,both for digital compensation and mechanical compensation,the output flow can be kept constant when the pressure difference changes,and the system has good static and dynamic *** principle can be applied to the displacement-flow feedback type electrical modulation valve,and can realize accurate control of the flow of the pilot valve and,finally,accurate control of the flow in the main valve.

关键词： Electrical modulation valve control characteristics Co-simulation Redundancy control

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Defect Detection in Solar Panels Based on PV-Flow

Defect Detection in Solar Panels Based on PV-Flow

引用

WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Minghao Niu Jiaqi Li Mengyu Li Yifan Zhao Ying Liu Shuhuan Wen Key Laboratory of Industrial Computer Control Engineering of Hebei Province Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Yingli Energy Development Co. Baoding China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

With technological advancements, resource utilization has increased, making renewable energy sources like solar power essential. Effective defect detection for solar panels is crucial. However, manual inspection is currently inefficient and machine vision lacks accuracy. In order to improve defect detection in high-resolution images and overcome the limitations of existing methods, we propose an unsupervised deep learning model called PV-Flow, which combines a hybrid attentional mechanism and a multiscale feature extraction module of the flow model. Before defect detection we need to categorize the defects and make a dataset, after observing the features we classify the defects into black spots, broken grids, hidden cracks and fragmentations. The performance of the model while performing defect detection is evaluated by metrics of reconstructed images and visual comparisons. Experiments on real solar panel datasets show that our proposed PV-Flow model is able to accurately identify the location of broken gate defects and show more details, with even the smallest defects clearly labeled and magnified. This suggests that our proposed model is able to detect potential defects earlier, thus reducing production risks and costs. We also conducted comparative experiments to further validate the reliability of the model.

关键词： Deep learning Visualization Accuracy Costs Sensitivity Production Feature extraction

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Meta-ZSDETR: Zero-shot DETR with Meta-learning

arXiv

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arXiv 2023年

作者： Zhang, Lu Zhang, Chenbo Zhao, Jiajia Guan, Jihong Zhou, Shuigeng Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China Department of Computer Science & Technology Tongji University China

Zero-shot object detection aims to localize and recognize objects of unseen classes. Most of existing works face two problems: the low recall of RPN in unseen classes and the confusion of unseen classes with background. In this paper, we present the first method that combines DETR and meta-learning to perform zero-shot object detection, named Meta-ZSDETR, where model training is formalized as an individual episode based meta-learning task. Different from Faster R-CNN based methods that firstly generate class-agnostic proposals, and then classify them with visual-semantic alignment module, Meta-ZSDETR directly predict class-specific boxes with class-specific queries and further filter them with the predicted accuracy from classification head. The model is optimized with meta-contrastive learning, which contains a regression head to generate the coordinates of class-specific boxes, a classification head to predict the accuracy of generated boxes, and a contrastive head that utilizes the proposed contrastive-reconstruction loss to further separate different classes in visual space. We conduct extensive experiments on two benchmark datasets MS COCO and PASCAL VOC. Experimental results show that our method outperforms the existing ZSD methods by a large margin. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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A Survey of Fault Tolerance in Cloud Computing

A Survey of Fault Tolerance in Cloud Computing

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systems, Advanced Technologies and Knowledge (ISSATK), International Symposium of

作者： Om Kolthoum Elhajjej Nouha Alyaoui Samia Dardouri Karim Chabir Research Laboratory Modeling Aanalysis Control of System (MACS) LR16ES22 National engineering school of Gabes (ENIG) University of Gabes Tunisia Computer Science and Telecommunications Department Laboratory of Modeling Analysis and Control of Systems (MACS) Higher Institute of Applied Sciences and Technology Zarroug Gafsa Gafsa University Tunisia Department of Computer Science College of Computing and Information Technology Shaqra University Saudi Arabia InnoV’COM Laboratory-Sup’Com University of Carthage Tunisia

ISBN: (数字)9798331540173

ISBN: (纸本)9798331540180

This paper is designed to provide a comprehensive overview of the latest developments in fault tolerance methods for cloud computing. Maintaining high availability and reliability of cloud environments requires fault tolerance. This paper explores fault tolerance in the context of cloud computing and discusses recent challenges and innovations in the field. Moreover, it examines the ongoing research efforts to improve fault-tolerance architectures. At the end of the paper, the paper presents system-level metrics that are relevant to fault tolerance.

关键词： Measurement Surveys Cloud computing Fault tolerance Technological innovation Scalability Fault tolerant systems computer architecture Software Software reliability

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Scalable Distributed Organizational Structure and Self-Organization Mechanisms for Unmanned system Cluster 9

Scalable Distributed Organizational Structure and Self-Organ...

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9th IEEE International Conference on computer Science and Network Technology, ICCSNT 2021

作者： Liu, Haibo Lin, Yang Shen, Jing Wang, Xing Shi, Changting Zhang, Jiayu College of Computer Science And Technology Harbin Engineering University Harbin150001 China Science And Technology On Complex System Control Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9781665443647

Reasonable control organizational structure can help an unmanned system cluster cooperate more effectively to complete tasks. Previous research of existing organizational structures has problems implementing the task in a natural environment, such as the cluster's lack of scalability. This paper proposes a three-layer scalable distributed organizational structure and dynamic self-organization mechanisms, which overcomes the lack of scalability and ensures that the cluster can still perform stably after the cluster size changes. The simulation results show that the organizational structure realizes information sharing and cross-layer control, which enhance the flexibility and stability of unmanned system clusters. Comparison results show that our achievements can make a good performance, contributing to this area of future research. © 2021 IEEE.

关键词： Scalability

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Meta-ZSDETR: Zero-shot DETR with Meta-learning

Meta-ZSDETR: Zero-shot DETR with Meta-learning

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International Conference on computer Vision (ICCV)

作者： Lu Zhang Chenbo Zhang Jiajia Zhao Jihong Guan Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China Department of Computer Science & Technology Tongji University China

关键词：

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High Performance Distributed control Law for Large-Scale Linear systems: A Partitioned Distributed Observer Approach

arXiv

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arXiv 2024年

作者： Xu, Haotian Liu, Shuai Shi, Ling Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

In recent years, the distributed-observer-based distributed control law has shown powerful ability to arbitrarily approximate the centralized control performance. However, the traditional distributed observer requires each local observer to reconstruct the state information of the whole system, which is unrealistic for large-scale scenarios. To fill this gap, this paper develops a greedy-idea-based large-scale system partition algorithm, which can significantly reduce the dimension of local observers. Then, the partitioned distributed observer for large-scale systems is proposed to overcome the problem that the system dynamics are difficult to estimate due to the coupling between partitions. Furthermore, the two-layer Lyapunov analysis method is adopted and the dynamic transformation lemma of compact errors is proved, which solves the problem of analyzing stability of the error dynamic of the partitioned distributed observer. Finally, it is proved that the distributed control law based on the partitioned distributed observer can also arbitrarily approximate the control performance of the centralized control law, and the dimension of the local observer is greatly reduced compared with the traditional method. The simulation results show that when the similarity between the physical network and the communication network is about 80%, the local observer dimension is greatly reduced by 90% and the relative error between the performance of the distributed control law and that of the centralized control law is less than 1%. © 2024, CC BY.

关键词： Continuous time systems

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Synergistic Optimization of Power & Capacity Configuration Considering control Strategy for Flywheel Energy Storage Participating in Primary Frequency Regulation

SSRN

引用

SSRN 2024年

作者： Liang, Lu Hong, Feng Ji, Weiming Jiang, Hui Hao, Junhong Fang, Fang Liu, Jizhen The State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Key Laboratory of Power Station Energy Transfer Conversion and System Ministry of Education School of Energy Power China

The intermittent and stochastic nature of renewable energy sources poses stern challenges to the frequency stability of new power system. The flywheel energy storage system (FESS), with its rapid response and high power density, plays a pivotal role in mitigating frequency fluctuations. To improve the performance of the FESS assisting thermal power unit (TPU), a collaborative optimization methodology of the capacity configuration and control strategy for FESS is presented in this paper. Aiming to enhance the primary frequency control (PFC) performance and reduce the life-cycle cost of the FESS, this paper established a capacity configuration model based on a dynamic adaptive control strategy, in which FESS compensates for the lack of TPU frequency regulation capability and recovery SOC actively. A case study demonstrated that the optimization model proposed is suitable for different frequency scenarios. It has been verified that the control strategy which is based on the SOC of FESS and frequency regulation capability of the TPU is economically more attractive compared to traditional strategies. In addition, this study revealed that a FESS with a charge/discharge rate of 7.5 C and rated power of 7.6 MW was appropriate for assisting a 600MW TPU in PFC by the optimal capacity configuration and control strategy. © 2024, The Authors. All rights reserved.

关键词： Wheels

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images

arXiv

引用

arXiv 2025年

作者： Feng, Fuxiang Cong, Runmin Wei, Shoushui Zhang, Yipeng Li, Jun Kwong, Sam Zhang, Wei The School of Control Science and Engineering Shandong University Jinan250061 China The Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China The University of California Los Angeles United States The Hubei Key Laboratory of Intelligent Geo-Information Processing The School of Computer Science China University of Geo-sciences Wuhan430078 China The Lingnan University Hong Kong

Reconstructing Hyperspectral Images (HSI) from RGB images can yield high spatial resolution HSI at a lower cost, demonstrating significant application potential. This paper reveals that local correlation and global continuity of the spectral characteristics are crucial for HSI reconstruction tasks. Therefore, we fully explore these inter-spectral relationships and propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images. For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module, which efficiently establishes spectral band similarity within a localized range. For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module, which employs memory units to recursively model the progressive variation characteristics at the global level. In order to explore the inherent complementarity of these two modules, we design the Patch-wise Adaptive Fusion (PAF) module to efficiently integrate global continuity features into the spectral features in a patch-wise adaptive manner. These innovations enhance the quality of reconstructed HSI. We perform comprehensive comparison and ablation experiments on the mainstream datasets NTIRE2022 and NTIRE2020 for the spectral reconstruction task. Compared to the current advanced spectral reconstruction algorithms, our designed algorithm achieves State-Of-The-Art (SOTA) performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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