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LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle

IFAC-PapersOnLine 2022年第35期55卷 37-42页

作者： Matthis H. de Lange Chris Verhoek Valentin Preda Roland Tóth Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven 5600 MB The Netherlands ESTEC European Space Agency Noordwijk 2200 AG The Netherlands Systems and Control Lab Institute for Computer Science and Control Budapest 1111 Hungary

Obtaining models that can be used for flight control is of outmost importance to ensure reliable guidance and navigation of spacecrafts, like a Generic Parafoil Return Vehicle (GPRV). In this paper, we convert an existing, high-fidelity nonlinear model of the atmospheric flight dynamics of a GPRV to a Linear Parameter-Varying (LPV) form that enables high-performance navigation control design. Application of existing systematic conversion methods for such complicated nonlinear models often result in complex LPV representations, which are not suitable for controller synthesis. We apply and compare state-of-the-art conversion techniques on the GPRV model, including learning based approaches, to optimize the complexity and conservatism of the resulting LPV embedding. The results show that we can obtain an LPV embedding that approximates the complex nonlinear dynamics sufficiently well, where the balance between complexity, conservatism and model performance is efficiently chosen.

关键词： linear parameter-varying systems scheduling reduction spacecraft modeling aerodynamics principle component analysis deep neural networks

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Track Prediction Based on Spatio-Temporal Attention 10th

Track Prediction Based on Spatio-Temporal Attention

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10th China Conference on Command and control, C2 2022

作者： Wang, Peng Zhang, Junyi Zhang, Lu Jin, Jianglin Fan, Yuqi The 54th Research Institute of China Electronics Technology Group Corporation Hubei Shijiazhuang050080 China Hebei Key Laboratory of Electromagnetic Spectrum Cognition and Control Hubei Shijiazhuang050080 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9789811960512

With the rapid development of aviation, the air traffic flow is increasing exponentially, leading to increasingly serious route congestion and route conflicts. Accurate prediction of aircraft trajectories is essential to ensure the safety of airspace. Trajectory data is a time-series sequence, and most of the existing methods for trajectory prediction are based on recurrent neural networks and attention mechanisms. However, these methods fail to effectively accumulate the correlation between the attributes of trajectory points and the time varying attribute features, and suffer from slow model training. In this paper, we propose STA-STCN, a novel track prediction model based on spatio-temporal dual attention (STA) and stacked temporal convolutional network (STCN). STA extracts the spatial and temporal features in parallel based on self-attention. The spatial feature characterizes the relationships between attributes of a track point, and the temporal feature represents the time varying characteristics of an attribute in track points. STA then fuses the extracted features to describe the accumulated time varying attributes correlation. STCN takes the fused features as the input to predict target track point attributes. We conduct experiments on the flight data provided by global flight data services website VariFight. Experimental results show that the model can reduce the root mean square error, mean absolute error, and track prediction model training time, indicating that model STA-STCN can achieve effective track prediction. © 2022, Chinese institute of Command and control.

关键词： Forecasting

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A Review of Intelligent Vision Enhancement technology for Battlefield

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Wu, Yufeng He, Yanjun Li, Ning Wang, Guowei Zhang, Longfei Zhang, Fuquan Zhu, Yifan The 15th Research Institute China Electronics Technology Group Corporation Beijing100083 China Key Laboratory of Digital Performance and Simulation Technology Beijing Institute of Technology Beijing100081 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Department of Computer Science and Technology Tsinghua University Beijing100081 China

This paper reviews the application of intelligent vision enhancement technology in the battlefield environment and explores new research directions. This paper mainly introduces three parts. First, we introduce a solution for enhancing the battlefield situation and using head-mounted displays for soldier combat. Second, we summarize three core technologies supporting intelligent vision enhancement technology: 3D environment reconstruction, tracking and registration, and situational awareness. Third, we summarize three application directions of intelligent vision enhancement technology on the battlefield. Finally, the problems and challenges in the research work are proposed, including current issues such as accurate battlefield situational awareness by augmented reality technology, multiperson collaborative management and data flow, as well as challenges in future battlefield situation enhancement and perception. The future development trend of intelligent vision enhancement technology on the battlefield has been prospected. Two meanings of this paper are as follows: the first is to review the research status of intelligent vision enhancement technology from the technical level and identify the key technical points that may restrict development in the future;the second is to analyze the advantages and disadvantages of intelligent vision enhancement technology from the level of battlefield application and the roles of users. In addition, this paper proposes how to take the lead and take initiative in future wars. © 2023 Yufeng Wu et al.

关键词： Augmented reality

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Fault-tolerant architecture for Cache: Summaries, Assessments and Trends 4

Fault-tolerant architecture for Cache: Summaries, Assessment...

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2021 4th International Conference on Mechatronics and computer technology Engineering, MCTE 2021

作者： Wang, Xuru Gao, Xin Liang, Zongnan Nian, Jiawei Liu, Hongjin Beijing Institute of Control Engineering Beijing100090 China Beijing SunWise Space Technology Ltd Beijing100090 China School of Computer Science and Technology Xidian University Shanxi Xi'an710071 China

Fault-tolerant design of cache is a key aspect of highly reliable processor design. In this paper, based on the key metrics in Cache architecture design: reliability, power consumption, latency and area, we divided the related research into two categories: one is to maximize reliability with guaranteed latency, power consumption and area, the other is to minimize latency, power consumption and area loss while ensuring fault tolerance reliability. Based on the classification, by analyzing different studies of Data and Tag in Cache, this paper gives the characteristics of these methods and the future development trend. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Electric power utilization

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A New Deep Learning Model for Semi-supervised Soft-sensing of an Industrial Production Process

A New Deep Learning Model for Semi-supervised Soft-sensing o...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： XuDong Shi ChenYu Tian Qi Kang MengChu Zhou HanQiu Bao Jing An Department of Control Science and Engineering Tongji University Shanghai China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China School of Internet of Things Engineering Jiangnan University Wuxi China School of Software Dalian University of Technology Liaoning China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Soft sensing offers a promising solution for predicting key quality variables in various production industries. One of the major challenges in developing an effective data-driven soft sensor is the scarcity of labeled data and the obstacle of extracting useful information from unlabeled samples. To address this issue, this work proposes a new deep learning-based soft sensor model called a spatiotemporal deep learning network. It leverages an encoder-decoder structure to explicitly exploit the spatial and temporal information in both labeled and unlabeled data, enabling efficient utilization of the latter to facilitate prediction performance. The encoder realizes more detailed spatiotemporal dependencies extraction by the proposed gated recurrent unit-based attention mechanism and the channel-calibration attention-based one. Finally, the extracted spatiotemporal features are fed to a multi-layer perceptron-based prediction head for soft sensor modeling. A mixed form loss is employed in the decoder to train our proposed model which incorporates both labeled and unlabeled data. Experiments are conducted on a real-life industrial process, demonstrating the feasibility and effectiveness of the proposed method.

关键词： Deep learning Attention mechanisms Accuracy Soft sensors Production Predictive models Feature extraction Data models Spatiotemporal phenomena Data mining

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Partial Discharge Classification With 1D Convolutional Neural Network

Partial Discharge Classification With 1D Convolutional Neura...

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International Conference on Properties and Applications of Properties and Applications of Dielectric Materials

作者： Thepjit Cheypoca Wiboon Promphanich Aung Ye Thway Angelina Phimpissada Hankae Siwakorn Jeenmuang Norasage Pattanadech Instrumentation and Control Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Computer Engineering Dept. School of EngineeringKing Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Electrical Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Mechatronics Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350374605

ISBN: (纸本)9798350386165

This paper introduces a novel approach for classifying with the 1D Convolutional Neural Network model for partial discharge patterns, that consists of corona discharge, surface discharge and internal discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The Artificial Neural Network for the classification model was constructed. Moreover, 2×1D CNN feature extraction was utilized to reduce the curse of dimensionality in the dense layer of the proposed PD classification model. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed.

关键词： Partial discharges Partial discharge measurement Surface discharges Neurons Training data Discharges (electric) Data models Convolutional neural networks Integrated circuit modeling Testing

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A Trusted Lesion-assessment Network for Interpretable Diagnosis of Coronary Artery Disease in Coronary CT Angiography 39

A Trusted Lesion-assessment Network for Interpretable Diagno...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ma, Xinghua Fang, Xinyan Zou, Mingye Luo, Gongning Wang, Wei Wang, Kuanquan Qiu, Zhaowen Gao, Xin Li, Shuo Faculty of Computing Harbin Institute of Technology Harbin China Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Faculty of Computing Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China Department of Computer and Data Science Case Western Reserve University Cleveland United States Department of Biomedical Engineering Case Western Reserve University Cleveland United States

ISBN: (纸本)157735897X

Coronary Artery Disease (CAD) poses a significant threat to cardiovascular patients worldwide, underscoring the critical importance of automated CAD diagnostic technologies in clinical practice. Previous technologies for lesion assessment in Coronary CT Angiography (CCTA) images have been insufficient in terms of interpretability, resulting in solutions that lack clinical reliability in both network architecture and prediction outcomes, even when diagnoses are accurate. To address the limitation of interpretability, we introduce the Trusted Lesion-Assessment Network (TLA-Net), which provides a clinically reliable solution for multi-view CAD diagnosis: (1) The causality-informed evidence collection constructs a causal graph for the diagnostic process and implements causal interventions, preventing confounders’ interference and enhancing the transparency of the network architecture. (2) The clinically-aligned uncertainty integration hierarchically combines Dirichlet distributions from various views based on clinical priors, offering confidence coefficients for prediction outcomes that align with physicians’ image analysis procedures. Experimental results on a dataset of 2,618 lesions demonstrate that TLA-Net, supported by its interpretable methodological design, exhibits superior performance with outstanding generalization, domain adaptability, and robustness. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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MHNNSL: EEG-Based Emotion Recognized by Multi-task Hypergraph Neural Network combined with Self-supervised Learning

MHNNSL: EEG-Based Emotion Recognized by Multi-task Hypergrap...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Xirong Xu Haochen Li Pengjie Wen Lin Li Zelin Wang Li Zhu Computer Science and Technology Dalian University of Technology Dalian P.R. China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen P.R. China Surgery Division Epilepsy Center Shenzhen Children’s Hospital Shenzhen P.R. China Computer Technology Beihang University Beijing P.R. China Control Science and Technology Dalian University of Technology Dalian P.R. China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

In recent years, the use of electroencephalography (EEG) for emotion recognition has attracted widespread attention in the fields of human-computer interaction and cognitive science. However, how to establish a brain network model to simulate the dynamic processes of the brain during various cognitive activities, thereby revealing the complex interrelationships between brain regions and completing emotion recognition tasks has become a focus of attention. This paper proposes an EEG emotion recognition model based on Multi-task Hypergraph Neural Network combined with Self-supervised Learning (MHNNSL). Firstly, we perform a hypergraph modeling of the brain network according to the structure of brain regions. Then we use a hypergraph neural network to learn the corresponding features. Moreover, by integrating multiple self-supervised tasks (including spatial transformation task, frequency transformation task, and contrastive learning task), more general representations are learned. Finally, emotion recognition is achieved through a multi-layer perceptron (MLP). To verify the effectiveness of the proposed method, comparative experiments were conducted on the publicly available SEED dataset with existing methods. Our proposed model achieved the highest accuracy of 85.17% and successfully reflected the response level of various brain regions to emotional tasks. Meanwhile, ablation experiments have demonstrated the effectiveness of our proposed model.

关键词： Human computer interaction Emotion recognition Network topology Brain modeling Multitasking Electroencephalography Topology

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On discretization of continuous-time LPV control solutions

arXiv

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arXiv 2022年

作者： Broens, Yorick Butler, Hans Tóth, Roland Control Systems Group Eindhoven University of Technology Eindhoven5600 MB Netherlands ASML Veldhoven5504 DR Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In recent years, the Linear Parameter-Varying (LPV) framework has become increasingly useful for analysis and control of time-varying systems. Generally, LPV control synthesis is performed in the continuous-time (CT) domain due to significantly less complex stability and performance requirements, see [1]. The main complication of CT synthesis approaches is implementation of the CT LPV control solutions on physical hardware. In literature, several discretization methods have been explored for LPV systems, see [2]. However, these approaches necessitate for heavy nonlinear operations introduced by the discretization of these time-varying matrices, thereby severely limiting implementation capabilities of the CT LPV control solutions. Alternatively, the w0 discretization approach illustrates to be a promising research direction for LPV systems, since it allows for preservation of the CT system matrices in the LTI case, see [3]. Moreover, this manuscript aims at extending the w0 discretization approach towards LPV systems, such that implementation of CT LPV control solutions on physical hardware is *** Codes 37M15 © 2022, CC BY.

关键词： Continuous time systems

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

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Robotics, Engineering, Science, and technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

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