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Science and Technology Innovation (IAECST), International Academic Exchange conference on

作者： Xiao Fu Junyi Han Xinnuo Li Kuo Cao School of Information Management Shanghai Lixin University of Accounting and Finance Shanghai China Yingke Zhushu Network Technology Co. Ltd Chongqing China Golden Data Ltd London UK

In the context of Industry 4.0, machine learning algorithms have been commonly used to monitor the health state of wind turbine gearboxes to avoid catastrophic failure and reduce maintenance costs. However, due to the lack of a certain category of data (i.e., healthy or faulty) and the various working conditions of wind turbines, many existing methods may not provide reliable results in practical industrial applications. To solve this problem, we create an industrial internet of things (IIoT) platform, through which a machine learning-based adaptive fault detection method for wind turbine gearboxes is proposed. The features are extracted and adapted to fine-tune the pre-trained model on newly arriving samples from different wind turbines, components, or failure modes. The adaptation performance is evaluated with accuracy, false alarm rate, and fault detection rate. Case studies are then performed using highfrequency vibration signals acquired from two megawatts (MW) onshore wind turbines. The results show that the proposed adaptive method significantly improves the fault detection performance when class distribution is not balanced, and can be easily applied to the fault diagnosis of large numbers of wind turbines. This, integrated with the IIoT platform that alleviates the shortage of computational and storage capacity in wind farms and requires less user involvement, allows for a more effective condition monitoring system.

关键词： Adaptation models Fault detection Wind farms sensor phenomena and characterization sensor fusion Maintenance engineering Feature extraction

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Multispectral and panchromatic images fusion based on NSCT and GS transform 26

Multispectral and panchromatic images fusion based on NSCT a...

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conference on algorithms, Technologies, and applications for Multispectral and Hyperspectral Imagery XXVI

作者： Wang, Feifei Zhang, Junping Zou, Bin Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

The spatial and spectral resolution of remote sensing images are mutually restricted due to the limitation of sensor technology. Multispectral (MS) image has high spectral resolution, but low spatial resolution. While, panchromatic (PAN) image can provide high spatial resolution. fusion of MS and PAN images is to get MS image with high resolution, which is a hot research in the field of remote sensing image processing. In this paper, a fusion algorithm of MS and PAN images is presented based on non-subsampled contourlet transform (NSCT) and Gram-Schmidt (GS) transform. Firstly, the low-resolution PAN image is synthesized by weighing each band of MS image whose weight coefficients are obtained by least squares estimation. MS image is decomposed by GS transform with the first GS component of the synthetic low-resolution PAN image. Secondly, one-level and three-level NSCT decomposition is performed on the synthetic low-resolution PAN image and PAN image, respectively. Low-frequency coefficients of low-resolution PAN image are as ones of the generated PAN image. High-frequency coefficients of first level decomposition of low-resolution PAN image and PAN image are fused according to region energy. The other level high-frequency coefficients of PAN image are as ones of the generated PAN image. Thirdly, the generated PAN image is reconstructed by the inverse NSCT with these coefficients. Lastly, inverse GS transform is performed to gain improved MS image by replacing the first GS component with the generated PAN image. The experiments conducted on Quickbird satellite images show that the proposed method is superior to the other typical methods, which improves the spatial resolution and has smaller spectral distortion.

关键词： Image fusion multispectral image Gram-Schmidt (GS) transform non-subsampled contourlet transform (NSCT)

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2020 International conference on Innovation and Intelligence for Informatics, Computing and Technologies, 3ICT 2020

2020 International Conference on Innovation and Intelligence...

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2020 International conference on Innovation and Intelligence for Informatics, Computing and Technologies, 3ICT 2020

ISBN: (纸本)9781728196732

The proceedings contain 82 papers. The topics discussed include: improving software reuse prediction using feature selection algorithms;toward hybrid deep convolutional neural network architectures for medical image processing;multi-input multi-output DC-DC converter network for hybrid renewable energy applications;fraudulent transaction detection in FinTech using machine learning algorithms;collaborative data anonymization for privacy-preserving vehicular ad-hoc network;an experimental evaluation of the advanced encryption standard algorithm and its impact on wireless sensor energy consumption;Word2Vec duplicate bug records identification prediction using tensorflow;and a novel low-energy CNTFET-based ternary half-adder design using unary operators.

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2020 European Navigation conference, ENC 2020

2020 European Navigation Conference, ENC 2020

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2020 European Navigation conference, ENC 2020

ISBN: (纸本)9783944976273

The proceedings contain 108 papers. The topics discussed include: development of an error-state kalman filter for emergency maneuvering of trucks;EGNOS system evolutions in europe and within the international multi-SBAS context;ship traffic organization with moving havens: ship and shore perspective;global navigation satellite systems contributing to space weather services for civil aviation;a prototype for a multi-GNSS orbit combination;kalman filtering versus voting: which strategy is best for multi-sensor localization?;a promising distributed position and orientation system with flexible baseline for array SAR applications;need and approaches for norm-compliant qualification for satellite based train localization evaluation - assessment- certification – approval;and integrity concept for sensor fusion algorithms used in a prototype vehicle for automated driving.

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1st International conference on Smart Technologies, Systems and applications, SmartTech-IC 2019

1st International Conference on Smart Technologies, Systems ...

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1st International conference on Smart Technologies, Systems and applications, SmartTech-IC 2019

ISBN: (纸本)9783030467845

The proceedings contain 30 papers. The special focus in this conference is on Smart Technologies, Systems and applications. The topics include: Increasing K-Means Clustering Algorithm Effectivity for Using in Source Code Plagiarism Detection;using Machine Learning Techniques for Discovering Latent Topics in Twitter Colombian News;latent Semantic Index: A Microservices Architecture;factors that Affect i-Vectors Based Language Identification Systems;Incorporation of Language Discriminative Information into Recurrent Neural Networks Models to LID Tasks;a Content-Based Multi Label Classification Model to Suggest Tags for Posts in Stack Overflow;benchmarking of Classification algorithms for Psychological Diagnosis;Proposal for an Integral System for Massive Open Online Courses (ISMOOC);Security Mechanisms in NoSQL DBMS’s: A Technical Review;corrective Feedback Through Mobile Apps for English Learning: A Review;implementation of a Real-Time Monitoring and Analysis System for Luminous Flux Test in Integrating Sphere;Classification and Visualization of Web Attacks Using HTTP Headers and Machine Learning Techniques;fusion of 3D Radiomic Features from Multiparametric Magnetic Resonance Images for Breast Cancer Risk Classification;evaluation of Learning Approaches Based on Convolutional Neural Networks for Mammogram Classification;technological Platform for the Control of the Medication Supply to People with Diabetes;Emotion Recognition from Time-Frequency Analysis in EEG Signals Using a Deep Learning Strategy;automatic Exercise Recognition Based on Kinect sensor for Telerehabilitation;Hand Angle Estimation Based on sEMG and Inertial sensor fusion;estimation of Spatio-temporal Parameters of Gait Using an Inertial sensor Network;A Genetic Algorithm for BAP + QCAP with Imprecision in the Arrival of Vessels.

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Robust sensor fusion algorithms Against Voice Command Attacks in Autonomous Vehicles

Robust Sensor Fusion Algorithms Against Voice Command Attack...

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IEEE International conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Jiwei Guan Xi Zheng Chen Wang Yipeng Zhou Alireza Jolfaei Macquarie University Sydney Australia CSIRO Data61 SydneyAustralia

ISBN: (纸本)9781665416597

With recent advances in autonomous driving, voice control systems have become increasingly adopted as human-vehicle interaction methods. This technology enables drivers to use voice commands to control the vehicle and will be soon available in Advanced Driver Assistance Systems (ADAS). Prior work has shown that Siri, Alexa and Cortana, are highly vulnerable to inaudible command attacks. This could be extended to ADAS in real-world applications and such an inaudible command threat is difficult to detect due to microphone nonlinearities. In this paper, we aim to develop a more practical solution by using camera views to defend against inaudible command attacks where ADAS are capable of detecting their environment via multi-sensors. To this end, we propose a novel multimodal deep learning classification system to defend against inaudible command attacks. Our experimental results confirm the feasibility of the proposed defense methods and the best classification accuracy reaches 89.2%. Code is available at https://***/ITSEG-MQ/sensor-fusion-Against-VoiceCommand-Attacks.

关键词： Visualization Ultrasonic imaging Virtual assistants sensor fusion Cameras Reliability engineering Robustness

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A fusion of Wavelet-based and Unsupervised Machine Learning Method for Artifacts Removal in Electrodermal Activity Signal

A Fusion of Wavelet-based and Unsupervised Machine Learning ...

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Asilomar conference on Signals, Systems & Computers

作者： Lili Zhu Petros Spachos Konstantinos N. Plataniotis School of Engineering University of Guelph Guelph ON Canada Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada

The fast tempo of modern society has brought people a series of emotional changes and mental pressures. Therefore, research have emerged to help people pay attention to and regulate their mental health. Physiological signals are used in studies from different fields to monitor and detect emotional change and stress. Electrodermal activity (EDA) is such a physiological signal that can reflect changes in skin conductivity when people's emotions change. The nature of neuromodulation makes such changes not easily controlled by people subjectively so EDA is an ideal emotion and stress monitoring indicator. Especially with the popularity of wearable devices in the market, wearable devices with built-in EDA sensors will be more competitive for the functions that help people regulate their mental health. However, since the EDA sensor usually acquires signals through fingers, palms, or wrists, artifacts will inevitably be generated when users move their hands or arms, and the artifacts will affect the accuracy of emotional change detection. As a result, removing artifacts in EDA signals is a challenging and important topic. In this work, multiple signal processing methods are applied to realize the objective of removing artifacts in the EDA signals from the AMIGOS dataset. The results show that the proposed method is promising and has the potential to be utilized in real-time EDA signal processing and emotional change detection applications.

关键词： Wrist Wearable computers Signal processing algorithms Mental health Signal processing Skin Biomedical monitoring

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Hand Angle Estimation Based on sEMG and Inertial sensor fusion 1st

Hand Angle Estimation Based on sEMG and Inertial Sensor Fusi...

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1st International conference on Smart Technologies, Systems and applications, SmartTech-IC 2019

作者： Lobaina Delgado, Alfredo Quinayás, César Ruiz, Andrés F. da Rocha, Adson López Delis, Alberto Department of Biomedical Universidad de Oriente Santiago de Cuba Cuba Antonio Nariño University Bogotá Colombia Department Electrical Engineering University of Brasilia Brasilia Brazil Center of Medical Biophysics Universidad de Oriente Santiago de Cuba Cuba

ISBN: (纸本)9783030467845

Continuous kinematics estimation based systems from surface electromyographic (sEMG) signals can predict movement intention to drive upper-limb prostheses in a more natural and intuitive way. Recently studies about inertial sensors combined with this kind of biosignal have improved the regression algorithms performance significantly. The goal of this work is to propose and evaluate strategies to control prostheses that can be applied in a virtual hand training platform during the training phase of amputee patients. This work presented strategies based on MLP neural networks to estimate 18 DoFs across hand joint angles using sEMG and Inertial sensor fusion (ISF). The dataset used was obtained of the seventh Ninapro database. The regression algorithms applied are based on time-domain features, such as auto-regressive model using recursive least squares, slope-sign changes and waveform length. The sEMG and ISF based strategy showed equivalent performance compared to strategy based exclusively on sEMG signals. The results suggest the usability of inertial sensors as a source signal in multi-modal control of upper-limb prostheses. © Springer Nature Switzerland AG 2020.

关键词： Artificial limbs

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Dynamic probabilistic model checking for sensor validation in industry 4.0 applications 4

Dynamic probabilistic model checking for sensor validation i...

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4th IEEE International conference on Smart Internet of Things, SmartIoT 2020

作者： Xin, Xin Keoh, Sye Loong Sevegnani, Michele Saerbeck, Martin University of Glasgow School of Computing Science Glasgow United Kingdom Tüv Süd Asia Pacific Digital Service Centre of Excellence Singapore Singapore

ISBN: (纸本)9781728165141

Industry 4.0 adopts Internet of Things (IoT) and service-oriented architectures to integrate Cyber-Physical Systems and Enterprise Planning into manufacturing operations. This kind of integration consists of a combination of connected sensors and run-Time control algorithms. Consequential control decisions are driven by sensor-generated data. Hence, the trustworthiness of the sensor network readings is increasingly crucial to guarantee the performance and the quality of a manufacturing task. However, existing methodologies to test such systems often do not scale to the complexity and dynamic nature of today's sensor networks. This paper proposes a novel run-Time verification framework combining sensor-level fault detection and system-level probabilistic model checking. This framework can rigorously quantify the trustworthiness of sensor readings, hence enabling formal reasoning for system failure prediction. We evaluated our approach on an industrial turn-mill machine equipped with a sensor network to monitor its main components continuously. The results indicate that the proposed verification framework involving the quantified sensor's trustworthiness enhances the accuracy of the system failure prediction. © 2020 IEEE.

关键词： Industry 4.0

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Special Section on sensors applications Symposium

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2019年第7期68卷 2333-2334页

作者： Sen Gupta, Gourab Bilas, Vedran Massey Univ Sch Engn & Adv Technol Palmerston North New Zealand Massey Univ Sch Engn & Adv Technol Food & Engn Palmerston North New Zealand Univ Zagreb Fac Elect Engn & Comp Elect Engn Zagreb Croatia Univ Zagreb Fac Elect Engn & Comp Lab Intelligent Sensor Syst Zagreb Croatia

The IEEE sensors applications Symposium (SAS) 2018 was the 13th edition of this conference. SAS is a fertile ground for emerging and top-class researchers to mingle together to discuss latest advancements and bright ideas in the areas of sensor development, sensing methodologies, algorithms, and novel sensor applications. The conference has always maintained a strong focus on providing students and young researchers a conducive venue to interact with their peers and experienced researchers. A diverse range of subject areas were covered in SAS 2018 such as energy harvesting, energy conservation in sensor networks, smart homes and cities, smart agriculture, remote sensing, sensors for automotive and aerospace, flexible and wearable sensors, and sensor data fusion. In addition, the highly important topics such as assisted living for the aging population, the Internet of Things (IoT), and data visualization were also addressed.

关键词： Special issues and sections Meetings sensor systems and applications

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