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Chinese automation Congress (CAC)

作者： Shaopeng Ma Wei Xue Kehui Chen Zexi Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

To deal with noise interference in frequency modulated continuous wave (FMCW) radar vital signs and the interference of breathing harmonics on the heartbeat signal, a vital signs detection method based on variational mode decomposition (VMD) and wavelet transform is proposed. First, VMD is applied to decompose the vital signs into a series of intrinsic mode function (IMF) components. Then, the IMF components with spectral peaks in the frequency range of breathing and heartbeat are selected to reconstruct the breathing and heartbeat signals respectively. Finally, the wavelet transform threshold method is used to remove the noise in the breathing and heartbeat signals. The experimental results show the proposed method can overcome the influence of breathing harmonics, accurately extract the breathing and heartbeat signals, and effectively improve the signal-to-noise ratio and detection accuracy of heart rate.

关键词： Frequency modulation Continuous wavelet transforms automation Heart beat Radar detection Radar Interference

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Automatic Target Recognition in GPR Images Based on Time-Frequency Texture Features and Support Vector Machine

Automatic Target Recognition in GPR Images Based on Time-Fre...

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Chinese automation Congress (CAC)

作者： Kehui Chen Wei Xue Zexi Wang Shaopeng Ma School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

Ground penetrating radar (GPR) images are easily disturbed by noise, which causes a lot of difficulties for target recognition. To improve the target recognition performance in GPR images, a novel recognition method based on time-frequency texture features and support vector machine (SVM) is proposed. The proposed method first extracts texture features from the gray co-occurrence matrix of S transform time-frequency images for the A-scan data, then establishes the SVM model and realizes the target recognition. The proposed method is assessed using synthetic GPR images and compared with other two time-frequency analysis methods, combined with SVM. The expermental results show that the proposed method can achieve higher recognition accuracies than the other two methods in heavily noisy environment.

关键词： Support vector machines Time-frequency analysis Image recognition Target recognition Support vector machine classification Transforms Radar imaging

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Short-term power load forecasting during the COVID-19 pandemic using XGBoost and Copula theory

Short-term power load forecasting during the COVID-19 pandem...

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Chinese automation Congress (CAC)

作者： Bo-Wen Liu Da Xu Yong He School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

The global coronavirus disease (COVID-19) has brought great challenges to the power systems due to its limitations on social, economic and productive activities. This paper proposes a short-term load forecasting method during COVID-19 pandemic based on copula theory and eXtreme Gradient Boosting (XGBoost). In this method, the coupling relationship among the cross-domain meteorological, public health, and mobility time-series data are fully analyzed based on copula theory, which is used for the short-term power load forecasting based on multi-factor fusion XGBoost algorithm. The proposed method has been fully evaluated and benchmarked on available cross-domain open-access United States data to demonstrate its effectiveness and superiority on short-term load forecasting of COVID-19.

关键词： COVID-19 Support vector machines Economics Load forecasting Pandemics Human factors Social factors

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MADDPG Algorithm for Coordinated Welding of Multiple Robots

MADDPG Algorithm for Coordinated Welding of Multiple Robots

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作者： Weibin Chen Lei Hua Leixin Xu Benshun Zhang Mengmeng Li Tao Ma Yang-Yang Chen School of Automation Southeast University Robot Department Jiangsu Automation Research Institute School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

This paper deals with the coordinated welding problem of multi-robot systems by applying a deep reinforcement learning algorithm which is called multi-agent deep deterministic policy gradient(MADDPG).It is assumed that the states and actions of robots are continuous and each robot can only get local information of its neighbors.A novel reward composed of trajectory optimization,coordinated welding and collision avoidance is designed,which yield multi-robot systems to arrive the welding targets quickly,achieve the simultaneous welding for a weld line and avoid collision among *** results are provided to demonstrate the effectiveness of the proposed control algorithm.

关键词： Deep multi-agent reinforcement learning MADDPG Coordinated welding

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Novel assisted cough system based on simulating cough airflow dynamics

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Bio-Design and Manufacturing 2021年第3期4卷 479-489页

作者： Shuai Ren Jinglong Niu Maolin Cai Liming Hao Yan Shi Weiqing Xu Zujin Luo School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China North Automatic Control Technology Institute Taiyuan 030006China Department of Respiratory and Critical Care Medicine Beijing Engineering Research Center of Respiratory and Critical Care MedicineBeijing Institute of Respiratory MedicineBeijing Chao-Yang HospitalCapital Medical UniversityBeijing 100043China

Cough is a defensive behavior that protects the respiratory system from infection and clears airway *** airflow dynamics have been analyzed by a variety of mathematical and experimental *** this paper,the cough airflow dynamics of 42 subjects were obtained and *** identification model based on piecewise Gauss function for cough airflow dynamics is proposed through the dimensionless method,which could achieve over 90%identification ***,an assisted cough system based on pneumatic flow servo system is *** vacuum situation and feedback control are used to increase the simulated peak cough flow rate,which are important for airway secretion clearance and to avoid airway collapse,*** simulated cough peak flow could reach 5 L/s without the external assistance such as manual pressing,patient cooperation and other ***,the backstepping control is developed to generate a simulated cough airflow that closely mimics the natural cough airflow of *** assisted cough system opens up wide opportunities of practical application in airway secretion clearance for critically ill patients with COVID 2019 and other pulmonary diseases.

关键词： Airflow dynamics Pneumatic system Simulated cough Model identification Airway secretion clearance

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DAFNI: A computational platform to support infrastructure systems research

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Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2023年第3期176卷 108-116页

作者： Matthews, Brian Hall, Jim Batty, Michael Blainey, Simon Cassidy, Nigel Choudhary, Ruchi Coca, Daniel Hallett, Stephen Harou, Julien J James, Phil Lomax, Nik Oliver, Peter Sivakumar, Aruna Tryfonas, Theodoros Varga, Liz Scientific Computing Department Science and Technology Facilities Council Didcot United Kingdom School of Geography and the Environment University of Oxford Oxford United Kingdom Centre for Advanced Spatial Analysis University College London London United Kingdom Transportation Research Group Faculty of Engineering and Physical Sciences University of Southampton Southampton United Kingdom Department of Civil Engineering University of Birmingham Birmingham United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Centre for Environmental and Agricultural Informatics Cranfield University Cranfield United Kingdom Department of Mechanical Aerospace and Civil Engineering University of Manchester Manchester United Kingdom School of Engineering Newcastle University Newcastle United Kingdom School of Geography University of Leeds Leeds United Kingdom Department of Civil and Environmental Engineering Imperial College London London United Kingdom Department of Civil Engineering University of Bristol Bristol United Kingdom Department of Civil Environmental and Geomatic Engineering University College London London United Kingdom

research into the engineering of infrastructure systems is increasingly data intensive. researchers build computational models to explore scenarios such as investigating the merits of infrastructure plans, analysing historical data to inform system operations or assessing the impacts of infrastructure on the environment. Models are more complex, at higher resolution and with larger coverage. researchers also require a 'multi-systems' approach to explore interactions between systems, such as energy and water with urban development, and across scales, from buildings and streets to regions or nations. Consequently, researchers need enhanced computational resources to support cross-institutional collaboration and sharing at scale. The Data and Analytics Facility for National Infrastructure (DAFNI) is an emerging computational platform for infrastructure systems research. It provides high-throughput compute resources so larger data sets can be used, with a data repository to upload data and share these with collaborators. Users' models can also be uploaded and executed using modern containerisation techniques, giving platform independence, scaling and sharing. Further, models can be combined into workflows, supporting multi-systems modelling and generating visualisations to present results. DAFNI forms a central resource accessible to all infrastructure systems researchers in the UK, supporting collaboration and providing a legacy, keeping data and models available beyond the lifetime of a project. © 2023 Emerald Publishing Limited: All rights reserved.

关键词： Urban growth

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Semi-supervised Anomaly Detection and Location Based on Generative Adversarial Network

Semi-supervised Anomaly Detection and Location Based on Gene...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Wei Liu Mingqiang Gao Shuaidong Duan Longsheng Wei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo- Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665446006

With the development of artificial intelligence, the anomaly detection plays more and more important role in security monitoring field. Because it is difficult to label abnormal data, most of the supervised methods consumed a lot of manpower and obtained low performance and generality. Inspired by this motivation, this paper proposes a semi-supervised method for anomaly detection in video frames based on GAN (Generative Adversarial Network), in which only normal data was used as the training sample. The quality gap between the predicted frame and the ground truth is used as the basis to determine whether it is abnormal. Moreover, the mathematical morphology approach was adopted to locate the anomaly area in the frames. Experiments show that our method can successfully detect abnormal frames in video and can also locate the area where abnormal behavior occurs in frames.

关键词： Training Conferences Morphology Generative adversarial networks Feature extraction Security Artificial intelligence

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Incomplete Multi-view Clustering for Single cell RNA Sequencing Data

Incomplete Multi-view Clustering for Single cell RNA Sequenc...

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Chinese automation Congress (CAC)

作者： Tijian Zhu Yuan Zhu Chuan-Ke Zhang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (纸本)9781665426480

With the rapid development of sequencing technology, researchers can obtain a large number of single cell RNA sequencing (scRNA-seq) data which is useful for analysis of cell fate decision and growth process at individual cell resolution. But due to the limitations of sequencing technology, the data acquired has dropouts which may affect the results of down-steam analysis. Therefore, many algorithms have been proposed to impute the data before clustering, here in, imputation and clustering are considered as two separate processing stage. In this paper, we adopt a clustering algorithm—Incomplete Multiple Kernel k-means Clustering with Mutual Kernel Completion (MKKM-IK-MKC) to analyze scRNA-seq data. It unifies imputation and clustering into a process. Comparing with some existing "two stage" (imputation +clustering) algorithms, the experimental results on five scRNA-seq datasets from various species demonstrate the effective performance of our new proposed method.

关键词： Dimensionality reduction Sequential analysis automation RNA Clustering algorithms Optimization methods Iterative methods

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Nanopore Sensing for Proteins Involved in Disease

Nanopore Sensing for Proteins Involved in Disease

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E-Health and Bioengineering Conference (EHB)

作者： Iuliana Şoldănescu Andrei Lobiuc Mihai Dimian Integrated Center for Research Development and Innovation for Advanced Materials Nanotechnologies Manufacturing and Control Distributed Systems (MANSiD) Stefan cel Mare University of Suceava Suceava Romania Department of Biomedical Sciences Stefan cel Mare University of Suceava Suceava Romania Department of Computer Electronics and Automation Stefan cel Mare University of Suceava Suceava Romania

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

The development of fast, accurate and low-cost methods of analysis is increasingly necessary in the context of species evolution. Techniques based on the use of nanopores for the analysis of various disease-specific biomarkers have received attention from researchers. Studies have developed significantly, from the use of the $\alpha$ - HL protein pore to integrated devices such as the one developed by Oxford Nanopore Technology. The aim is to reach the full potential of using nanopores for clinical analysis, specifically to use them for various molecules regardless of their physicochemical properties (size, electric charge, concentration in solution). In this context, experimental protocols need to be adapted for each molecule to optimize biomarker analysis methods. Early and accurate diagnosis is essential for effective treatment leading to a higher quality of life. Changes at the structural level of proteins involved in various physiological processes cause dysfunctions and implicitly lead to the development of diseases such as Alzheimer's disease which is activated by the degradation of amyloid fibers. The challenge of analyzing proteins with nanopores consists precisely of their large structural size and non-uniform charge loading. The current paper aims to synthesize some of the knowledge in the field to aid in the challenges raised by the experimental part of nanopore sensing for proteins.

关键词： Proteins Sequential analysis Protocols Costs Biomarkers Physiology Sensors

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Adaptive Future Frame Prediction with Ensemble Network 25th

Adaptive Future Frame Prediction with Ensemble Network

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Kim, Wonjik Tanaka, Masayuki Okutomi, Masatoshi Sasaki, Yoko Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Meguto-ku Tokyo152-8550 Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Koto-ku Tokyo135-0064 Japan

ISBN: (纸本)9783030687861

Future frame prediction in videos is a challenging problem because videos include complicated movements and large appearance changes. Learning-based future frame prediction approaches have been proposed in kinds of literature. A common limitation of the existing learning-based approaches is a mismatch of training data and test data. In the future frame prediction task, we can obtain the ground truth data by just waiting for a few frames. It means we can update the prediction model online in the test phase. Then, we propose an adaptive update framework for the future frame prediction task. The proposed adaptive updating framework consists of a pre-trained prediction network, a continuous-updating prediction network, and a weight estimation network. We also show that our pre-trained prediction model achieves comparable performance to the existing state-of-the-art approaches. We demonstrate that our approach outperforms existing methods especially for dynamically changing scenes. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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