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Chinese Automation Congress (CAC)

作者： Huiyan Li Ming Zhang Guang Li Ruifen Hu Bixuan Zhang Institute of Cyber Systems and Control Zhejiang University State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou

By extracting electrical signals on the surface of the speech-related muscles, which are, surface electromyography signals, information of the brain can be traced back to achieve silent speech recognition. Preprocessing is a crucial step for silent speech recognition and subsequent phases. In preprocessing of mime speech signals, baseline drift and active segment detection play an important role, but efforts in the direction are very limited. In this paper, the quadratic variation reduction separation baseline drift was introduced. On this basis, the three-parameter double threshold method based on autocorrelation function, band standard deviation, and short-time energy were used to detect the active segment of the surface electromyography signals, and good results were obtained. Finally, by extracting features, silent speech recognition was classified by the random forest algorithm. Experiments showed that the method described in this paper obtains 85% correctness in the data set of 9 words.

关键词： Muscles Electromyography Feature extraction Speech recognition Correlation Electrodes Standards

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THE METHOD OF PROCESSING DIGITAL PCR IMAGES BASED ON MASK R-CNN MODEL FOR ISMM 2019

THE METHOD OF PROCESSING DIGITAL PCR IMAGES BASED ON MASK R-...

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中国化学会第十二届全国微全分析系统学术会议、第七届全国微纳尺度生物分离分析学术会议、第七届国际微流控学学术论坛、The 11th International Symposium on Microchemistry and Microsystems

作者： Zhen-Ming Hu Tong Gou Ying Mu Research Center for Analytical Instrumentation Institute of Cyber-Systems and ControlState Key Laboratory of Industrial Control TechnologyZhejiang University

INTRODUCTION Digital polymerase chain reaction(dPCR) using fluorescence images for collecting quantitative information needs efficient software tools to automate the image analysis process. However,due to the broad range of dPCR fluorescence image characteristics,such as uneven fluorescence intensity distribution,irregular structure of microarray,existing tools failed to extract signals from dPCR images properly which hindering the full exportation of dPCR technology's precision. In this paper,a deep learning method was used for image processing to achieve more accurate quantification of nucleic acids in both microarray and droplet dPCR METHODS This Mask R-CNN[1] based method uses massive dPCR fluorescence image data to train a model that has the ability to recognize target signals in dPCR images precisely and automatically,regardless of the non-uniform luminosity or spot impurities appearing in dPCR *** CONCLUSIONS In this paper,we improved a Mask R-CNN model for processing the images of both chip-based and droplet dPCR. The method has higher precision and stronger robustness than the traditional threshold segmentation method in dealing with non-uniform light images and impure images,and will benefit for popularizing dPCR in molecular diagnosis and biological research,as well as has potential for other types of digital detection.

关键词： Deep learning Digital polymerase chain reaction Image process Data analysis

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PASS3D: Precise and accelerated semantic segmentation for 3D point cloud

arXiv

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arXiv 2019年

作者： Kong, Xin Zhai, Guangyao Zhong, Baoquan Liu, Yong Institute of Cyber-Systems and Control Zhejiang University Zhejiang310027 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang310027 China

In this paper, we propose PASS3D to achieve point-wise semantic segmentation for 3D point cloud. Our framework combines the efficiency of traditional geometric methods with robustness of deep learning methods, consisting of two stages: At stage-1, our accelerated cluster proposal algorithm will generate refined cluster proposals by segmenting point clouds without ground, capable of generating less redundant proposals with higher recall in an extremely short time;stage-2 we will amplify and further process these proposals by a neural network to estimate semantic label for each point and meanwhile propose a novel data augmentation method to enhance the network's recognition capability for all categories especially for non-rigid objects. Evaluated on KITTI raw dataset, PASS3D stands out against the state-of-the-art on some results, making itself competent to 3D perception in autonomous driving system. Our source code will be open-sourced. A video demonstration is available at https://***/ watch?v=cukEqDuP_Qw. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Model Predictive control Based on Bayesian Forecasting for a Nitrobenzene Rectifying Tower

Model Predictive Control Based on Bayesian Forecasting for a...

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第三十八届中国控制会议

作者： Bingqiang Huang Biniyam Anteneh Ayalew Xinjian Xiang Zhiyuan He Yong Gu School of Automation and Electrical Engineering Zhejiang University of Science and Technology State Key Laboratory of Industrial Control Industrial Technology Institute of Cyber-Systems and Control Zhejiang University

Step test method is a commonly used system identification method in the plant advanced process control application. Bayesian forecasting method is a kind of method based on subjective experience information and historical data. In this paper, a model identification method based on Bayesian forecasting is proposed. Combining the operators experience information and historical model data, the Bayesian method is used to predict the process model, and the model monitoring is used to abandon the abnormal data. Finally, the MPC controller is designed and the practical application of the top temperature control of the nitrobenzene rectifying tower proves the effectiveness of this method.

关键词： Model predictive control Bayesian dynamic linear model Nitrobenzene rectifying tower

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain technology for Al-Najaf/Iraq Oil Refinery

Secure and Privacy-preserving Data-sharing Framework based o...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Samir M. Umran SongFeng Lu Zaid Ameen Abduljabbar Zhi Lu Bingyan Feng Lu Zheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Ministry of Industry and Minerals Iraqi Cement State Company Baghdad Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Education for Pure Sciences University of Basrah Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Huazhong Numerical Control Co. Ltd Industrial Internet Research Institute Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

The industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.

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Towards navigation without precise localization: Weakly supervised learning of goal-directed navigation cost map

arXiv

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arXiv 2019年

作者： Ma, Huifang Wang, Yue Tang, Li Kodagoda, Sarath Xiong, Rong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang China Centre for Autonomous Systems University of Technology Sydney Australia

Autonomous navigation based on precise localization has been widely developed in both academic research and practical applications. The high demand for localization accuracy has been essential for safe robot planing and navigation while it makes the current geometric solutions less robust to environmental changes. Recent research on end-to-end methods handle raw sensory data with forms of navigation instructions and directly output the command for robot control. However, the lack of intermediate semantics makes the system more rigid and unstable for practical use. To explore these issues, this paper proposes an innovate navigation framework based on the GPS-level localization, which takes the raw perception data with publicly accessible navigation maps to produce an intermediate navigation cost map that allows subsequent flexible motion planning. A deterministic conditional adversarial network is adopted in our method to generate visual goal-directed paths under diverse navigation conditions. The adversarial loss avoids the pixel-level annotation and enables a weakly supervised training strategy to implicitly learn both of the traffic semantics in image perceptions and the planning intentions in navigation instructions. The navigation cost map is then rendered from the goal-directed path and the concurrently collected laser data, indicating the way towards the destination. Comprehensive experiments have been conducted with a real vehicle running in our campus and the results have verified the robustness to localization error of the proposed navigation system. Copyright © 2019, The Authors. All rights reserved.

关键词： Motion planning

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Weakly-supervised road affordances inference and learning in scenes without traffic signs

arXiv

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arXiv 2019年

作者： Ma, Huifang Wang, Yue Xiong, Rong Kodagoda, Sarath Luo, Qianhui State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang China Centre for Autonomous Systems University of Technology Sydney Australia

Road attributes understanding is extensively researched to support vehicle’s action for autonomous driving, whereas current works mainly focus on urban road nets and rely much on traffic signs. This paper generalizes the same issue to the scenes with little or without traffic signs, such as campuses and residential areas. These scenes face much more individually diverse appearances while few annotated datasets. To explore these challenges, a weakly-supervised framework is proposed to infer and learn road affordances without manual annotation, which includes three attributes of drivable direction, driving attention center and remaining distance. The method consists of two steps: affordances inference from trajectory and learning from partially labeled data. The first step analyzes vehicle trajectories to get partial affordances annotation on image, and the second step implements a weakly-supervised network to learn partial annotation and predict complete road affordances while testing. Real-world datasets are collected to validate the proposed method which achieves 88.2%/80.9% accuracy on direction-level and 74.3% /66.7% accuracy on image-level in familiar and unfamiliar scenes respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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A wireless acoustic wave sensor modified with biocompatible polymer for glucose monitoring

A wireless acoustic wave sensor modified with biocompatible ...

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IET Doctoral Forum on Biomedical Engineering, Healthcare, Robotics and Artificial Intelligence 2018, BRAIN 2018

作者： Li, Huiyan Sha, Pengfei Hu, Ruifen Li, Guang State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781839530838

The dynamic monitoring of vivo blood glucose acts an important role for diabetic patients to ensure timely healthcare and help to prevent from multiple complications caused by diabetes. This report describes a new non-enzyme glucose sensor based on wireless quartz crystal microbalance (QCM) that can be adapted for use in implanted devices. The chitosan-based enzyme-free boronic-acid glucose sensor improves biocompatibility considering for future vivo implanting. The optimal response of the sensor is in chitosan: 4-Formylphenylboronic acid (4-FPBA) =3:2. In the case of pH 7.4, the sensor has good linear response to glucose and selectivity of sensor to fructose, mannose, urea, and lactic acid performs well under physiological conditions. At physiological temperature, the linear response was also repeated and the average response time was about 4 min. The characteristics of this sensor indicate that it can be applied to dynamic monitoring of implantable blood glucose concentrations. © 2018 Institution of Engineering and technology. All rights reserved.

关键词： Glucose sensors

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Output Regulation of Multivariable Nonlinear systems Using Extra Outputs

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IFAC-PapersOnLine 2018年第13期51卷 13-18页

作者： Wang, Lei Marconi, Lorenzo Wen, Changyun Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China CASY - DEI University of Bologna Italy School of Electrical and Electronic Engineering Nanyang Technological University Singapore

We consider the output regulation problem for a class of nonlinear multivariable systems in the case, besides the regulated error, also additional measurements not necessarily zero in steady state are available. To offset the steady state value of the extra measurements and make the latter effective for stabilisation purposes, we propose a control structure in which those measurements are filtered by a post-processor specifically designed to block their steady state value. The specific scenario we have in mind is the one of nonlinear systems that are nonminimum-phase between the input and the regulated error and for which extra measurements are necessary, or simply desirable, to succeed in robust output feedback stabilisation. © 2018

关键词： Nonlinear systems Multivariable systems Stabilization Internal modeling Multivariable nonlinear systems Nonlinear multivariable systems Normal form Output regulation Output regulation problem Robust output feedbacks Steady state values

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Multi Mask Learning of Stone Segmentation for Auto-Monitoring System in TBM Construction

Multi Mask Learning of Stone Segmentation for Auto-Monitorin...

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第三十八届中国控制会议

作者： Zhenfeng Xue Lianhui Jia Wei Sun Fulong Lin Zhitao Liu Weijie Mao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China Railway Engineering Equipment Group Co. LTD

The tunnel boring machine(TBM) control parameters are usually hard to measure due to the complexity of geological structure and dynamic model of TBM. In order to improve the TBM operation performance, the information of the rock particles can be given back to the TBM driver. In this paper, the size distribution of produced rock particles during tunnelling is chosen as an index. However, as detecting the sizes of rock particles in our task is quite difficult, semantic segmentation technique in deep learning is applied to solve the problem. A new dataset with annotation tools is first taken, then two semantic segmentation networks are trained to extract rock object mask and rock contour mask, separately. Finally, two masks are mixed to get the prediction of rock area. The results show that the proposed method is effective and has good performance.

关键词： TBM size detection semantic segmentation multi mask learning

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