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Hierarchical controller Synthesis Under Linear Temporal Logic Specifications Using Dynamic Quantization

IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2082-2098页

作者： Wei Ren Zhuo-Rui Pan Weiguo Xia Xi-Ming Sun the Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology the School of Control Science and Engineering Dalian University of TechnologyDalian 116024China

Linear temporal logic(LTL)is an intuitive and expressive language to specify complex control tasks,and how to design an efficient control strategy for LTL specification is still a *** this paper,we implement the dynamic quantization technique to propose a novel hierarchical control strategy for nonlinear control systems under LTL *** on the regions of interest involved in the LTL formula,an accepting path is derived first to provide a high-level solution for the controller synthesis ***,we develop a dynamic quantization based approach to verify the realization of the accepting *** realization verification results in the necessity of the controller design and a sequence of quantization regions for the controller ***,the techniques of dynamic quantization and abstraction-based control are combined together to establish the local-to-global control *** abstraction construction and controller design are local and dynamic,thereby resulting in the potential reduction of the computational *** each quantization region can be considered locally and individually,the proposed hierarchical mechanism is more efficient and can solve much larger problems than many existing ***,the proposed control strategy is illustrated via two examples from the path planning and tracking problems of mobile robots.

关键词： Abstraction-based control design dynamic quantization formal methods linear temporal logic(LTL)

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QTFN:A General End-to-End Time-Frequency Network to Reveal the Time-Varying Signatures of the Time Series

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Big Data Mining and Analytics 2024年第3期7卷 905-919页

作者： Tao Chen Yang Jiao Lei Xie Hongye Su State Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China Shenzhen Key Laboratory of Intelligent Bioinformatics and College of Computer Science and Control EngineeringShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonstationary time series are ubiquitous in almost all natural and engineering *** the time-varying signatures from nonstationary time series is still a challenging problem for data *** Time-Frequency Distribution(TFD)provides a powerful tool to analyze these ***,they suffer from Cross-Term(CT)issues that impair the readability of ***,to achieve high-resolution and CT-free TFDs,an end-to-end architecture termed Quadratic TF-Net(QTFN)is proposed in this *** by classic TFD theory,the design of this deep learning architecture is heuristic,which firstly generates various basis functions through ***,more comprehensive TF features can be extracted by these basis ***,to balance the results of various basis functions adaptively,the Efficient Channel Attention(ECA)block is also embedded into ***,a new structure called Muti-scale Residual Encoder-Decoder(MRED)is also proposed to improve the learning ability of the model by highly integrating the multi-scale learning and encoder-decoder ***,although the model is only trained by synthetic signals,both synthetic and real-world signals are tested to validate the generalization capability and superiority of the proposed QTFN.

关键词： Time-Frequency Analysis(TFA) Multi-scale Residual Encoder-Decoder(MRED) quadratic Time-Frequency Distribution(TFD)

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Nanogenerator-based bidirectional pressure sensor array and its demonstration in underwater invasive species detection

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Nano Research 2023年第9期16卷 11822-11831页

作者： Yunqi Cao Hongyang Shi Xiaobo Tan Nelson Sepúlveda State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMichigan 48824USA

Lakes areas,which cause catastrophic damages in both commercial fishery and ecological ***,current assessment strategies may pose challenges for lake-wide abundance estimation and non-target anadromous species ***,we demonstrate an efficacious species-specific non-destructive sensing system based on porous ferroelectret nanogenerator for in-situ monitoring of lamprey spawning migration using their unique suction *** show that the porous structure enables a redistribution of surface charges under bidirectional deformations,which allows the detection of both positive and negative *** quasi-piezoelectric effect is further validated by quantitative analysis in a wide pressure range of−50 to 60 kPa,providing detailed insights into transduction working *** reliable lamprey detection,a 4×4-pixel sensor array is developed and integrated with a complementary metal-oxide-semiconductor(CMOS)based signal processing array thus constituting a sensing panel capable of recording oral suction patterns in an underwater environment.

关键词： bidirectional pressure sensing nanogenerators self-powered sensor array ferroelectret invasive species detection

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Addressing Spatial-Temporal Heterogeneity: General Mixed Time Series Analysis via Latent Continuity Recovery and Alignment 38

Addressing Spatial-Temporal Heterogeneity: General Mixed Tim...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Jiawei Zhao, Chunhui State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University China

Mixed time series (MiTS) comprising both continuous variables (CVs) and discrete variables (DVs) are frequently encountered yet under-explored in time series analysis. Essentially, CVs and DVs exhibit different temporal patterns and distribution types. Overlooking these heterogeneities would lead to insufficient and imbalanced representation learning, bringing biased results. This paper addresses the problem with two insights: 1) DVs may originate from intrinsic latent continuous variables (LCVs), which lose fine-grained information due to extrinsic discretization;2) LCVs and CVs share similar temporal patterns and interact spatially. Considering these similarities and interactions, we propose a general MiTS analysis framework MiTSformer, which recovers LCVs behind DVs for sufficient and balanced spatial-temporal modeling by designing two essential inductive biases: 1) hierarchically aggregating multi-scale temporal context information to enrich the information granularity of DVs;2) adaptively learning the aggregation processes via the adversarial guidance from CVs. Subsequently, MiTSformer captures complete spatial-temporal dependencies within and across LCVs and CVs via cascaded self- and cross-attention blocks. Empirically, MiTSformer achieves consistent SOTA on five mixed time series analysis tasks, including classification, extrinsic regression, anomaly detection, imputation, and long-term forecasting. The code is available at https://***/chunhuiz/MiTSformer. © 2024 Neural information processing systems foundation. All rights reserved.

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A Method for Scattering Removal in Three-Dimensional Fluorescence Spectra Based on Physics-Informed Neural Networks

A Method for Scattering Removal in Three-Dimensional Fluores...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Lingke Yu, Jie Fan, Xingyu Cao, Shuai Zhang, Qiuhao Zhang, Guangxin State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

Three-dimensional fluorescence spectroscopy offers high-dimensional information and rapid sensitivity, making it advantageous in fields such as environmental monitoring and food testing. However, scattering effects can hinder analysis accuracy, and traditional de-scattering methods often lead to signal loss and distortion. To overcome these issues, a denoising method based on a physics-informed neural network is proposed. This method includes a physical information reconstruction module that generates theoretical values approximating actual scattering values. Integrated into a GAN framework, this module uses physical quantities to guide the generator in producing physically consistent images, thus preserving material integrity after scattering removal. Tests on a self-built dataset show that this method achieves higher physical consistency compared to traditional methods. © 2024 IEEE.

关键词： III-V semiconductors

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Development of a Compact Optical Layout for a Miniature Fluorescence Spectrometer: In-Situ Detection of Organic Pollutants in Aquatic Environment

Development of a Compact Optical Layout for a Miniature Fluo...

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2024 China Automation Congress, CAC 2024

作者： Fan, Xingyu Yu, Jie Zhang, Qiuhao Cao, Shuai Zhang, Lingke Xue, Fangjia State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

In recent years, the increasing presence of emerging organic pollutants in rivers, lakes, and other aquatic environments has posed significant threats to human health and ecological safety. Current monitoring technologies for these pollutants face several challenges, including the high cost of equipment and the scarcity of trained professionals. Furthermore, existing portable fluorescence sensors are limited by inadequate data, unsuitability for in-situ water environments, and the ability to detect only specific pollutants. This paper proposes a novel design for an in-situ miniature fluorescence spectrometer, utilizing a compact optical layout to monitor organic pollutants in water. The design features a high-power UV LED combined with a lens group to enhance the fluorescence excitation effect of low-concentration organic pollutants. The system components are integrated into a compact layout centered around a flow cell, significantly reducing the device's size. In experimental validation, vitamin B was used as a model organic pollutant to test the prototype's detection capabilities. Within the concentration range of 1 - 100 μg/L, the fluorescence intensity exhibited a strong linear correlation with the vitamin B concentration, achieving an R2 value of 0.9945, comparable to that of commercial spectrometers. This portable device demonstrates excellent performance in detecting organic matter and offers significant advantages for in-situ monitoring applications, making it well-suited for real-time pollutant detection in rivers, lakes, and other aquatic environments. © 2024 IEEE.

关键词： Lake pollution

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Relabeling Abnormal Videos via Intra-Video Label Propagation for Weakly Supervised Video Anomaly Detection 14

Relabeling Abnormal Videos via Intra-Video Label Propagation...

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14th Asian control Conference, ASCC 2024

作者： Zhou, Wenhao Li, Yingxuan Zhao, Jiancheng Zhao, Chunhui College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (纸本)9789887581598

Weakly supervised video anomaly detection is chal-lenging due to the lack of frame-level labels in abnormal videos. Previous work generally formulates it as a multiple instance learning (MIL) problem, in which abundant snippets especially including hard snippets in abnormal videos are underutilized, causing the training process to be dominated by redundant easy snippets. In this work, we introduce a semi-supervised learning perspective and aim to provide reliable snippet-level labels for abnormal videos to fully leverage them. Specifically, we propose a relabeling strategy by utilizing the Label Propagation Network (LPN) to propagate labels from confident snippets to hard snippets. Considering label propagation tends to be fragile and inaccurate in this case due to incorrect pseudo labels, we develop a feature relation module to enhance the intra-class compactness and inter-class dispersion, as well as a label separation module to enlarge the discrepancy of propagated labels. The overall model is designed to be concise and universal, which can be applied to existing weakly supervised methods to improve performance. Extensive experiments demonstrate that our method achieves state-of-the-art results on three benchmark datasets. © 2024 Asian control Association.

关键词： Self-supervised learning

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Identification of Mixed Organic Pollutants in River Water Based on Graph Convolutional Networks

Identification of Mixed Organic Pollutants in River Water Ba...

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2024 China Automation Congress, CAC 2024

作者： Cao, Shuai Yu, Jie Fan, Xingyu Zhang, Qiuhao Xue, Fangjia Hou, Dibo State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

In the application of three-dimensional (3-D) fluorescence spectroscopy for river water pollutant detection, traditional feature extraction models often neglect wavelength information and complex intermolecular interrelationships. Moreover, experiments are frequently limited to single-substance classifications. This research addresses these limitations by proposing a novel model for organic pollutant recognition based on graph neural networks (GNNs). The proposed method begins by using the excitation-emission matrix and the wavelength information corresponding to each fluorescence intensity value as inputs. A convolutional neural network (CNN) is initially employed to extract preliminary features from the three-dimensional fluorescence spectra. Subsequently, the initial spectral features obtained during the pre-training stage are used to construct a graph neural network model. This model represents each category of organic pollutants as nodes within the graph, capturing the intrinsic connections between different pollution samples through node interactions. Finally, the model outputs a score vector for each category to classify the pollutants. To validate the proposed model, datasets comprising twelve common organic pollutants, both as single substances and mixtures, were collected. Comparative analysis with other methods demonstrates that the proposed approach achieves a high accuracy rate. ©2024 IEEE.

关键词： Graph neural networks

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A general Boolean semantic modelling approach for complex and intelligent industrial systems in the framework of DES

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Journal of Systems engineering and Electronics 2024年第5期35卷 1219-1230页

作者： XU Changyi WANG Yun DUAN Yiman ZHANG Chao Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and EngineeringDalian University of TechnologyDalian 116024China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical EngineeringZhejiang UniversityHangzhou 310027China

Discrete event system(DES)models promote system engineering,including system design,verification,and *** advancement in manufacturing technology has endowed us to fabricate complex industrial ***,the adoption of advanced modeling methodologies adept at handling complexity and scalability is ***,industrial systems are no longer quiescent,thus the intelligent operations of the systems should be dynamically specified in the *** this paper,the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model,and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent operations in the *** traditional modeling approaches,the change or addition of specifications always necessitates the complete resubmission of the system model,a resource-consuming and error-prone *** with traditional approaches,our approach has three remarkable advantages:(i)an established Boolean semantic can be fitful for all kinds of systems;(ii)there is no need to resubmit the system model whenever there is a change or addition of the operations;(iii)multiple specifying tasks can be easily achieved by continuously adding a new ***,this general modeling approach has wide potential for future complex and intelligent industrial systems.

关键词： industrial complex system operation specifying Boolean semantic discrete event system(DES)theory intelligent operation

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An improved high-fidelity adaptive model for integrated inlet-engine-nozzle based on mechanismdata fusion

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Chinese Journal of Aeronautics 2024年第8期37卷 190-207页

作者： Chen WANG Ziyang YU Xian DU Ximing SUN School of Control Science and Engineering Dalian University of TechnologyDalian 116024China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of EducationDalian University of TechnologyDalian 116024China

Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion *** component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model,failing to meet the accuracy requirements of supersonic *** paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental ***,a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm(SSA-ELM)is *** study reveals the inlet model’s efficacy in reflecting installed performance,flow matching,and mitigating pressure distortion,while the nozzle model accurately predicts flow coefficients and thrust coefficients,and identifies various operational *** model’s output closely aligns with typical experimental *** combining offline optimization and online adaptive correction,the mechanismdata fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation,and effectively handles gradual degradation within a single flight ***,the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location *** approach significantly curbs performance deviations in supersonic *** example,at Ma=2.0,the system error impressively drops from 34.17%to merely 6.54%,while errors for other flight conditions consistently stay below the 2.95%*** findings underscore the clear superiority of the proposed method.

关键词： Aero-propulsion system Integrated inlet-enginenozzle Component-level model On-board adaptive model Mechanism-data fusion Extreme learning machine

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