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Exploration on the Negative Effects of Sensor Shifts in Photoplethysmography-Based Gesture Recognition and a Solution Based on Transfer Learning

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ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷

作者： Fang, Chengfeng Ruan, Xin Zhang, Xu Chen, Xun Chen, Xiang Univ Sci & Technol China USTC Dept Elect Sci & Technol Hefei 230027 Peoples R China

In order to promote the development of gesture interaction on wearable devices, this article explores the negative effects of sensor shifts in photoplethysmography (PPG)-based gesture recognition technology and propose a solution based on transfer learning (TL) method. First, ten batches of PPG data with sensor shifts are collected for 14 gestures and ten participants. Then, the negative effects of sensor shifts is explored through experiments of feature visualization and gesture recognition based on single-batch data and multibatch data. Experimental results show that the negative effect of sensor shifts can significantly change the feature distribution of gesture PPG signals, resulting in a sharp drop (59.24%) in gesture recognition accuracy. Finally, with the goal of reducing user training burden in the presence of sensor shifts, a long short-term memory (LSTM)-based TL scheme is proposed and implemented. Compared to non-TL strategy, TL strategy can improve the accuracy of gesture recognition to a certain degree, especially, the improvement effect is more significant when the amount of data involved in model calibration is small. Using the proposed TL scheme, only a small amount of data is needed to calibrate the classier in the practical application of PPG gesture interaction. This study lays a good foundation for the realization of PPG-based gesture interaction applications on wearable devices.

关键词： Training Wearable computers Transfer learning data visualization Gesture recognition Photoplethysmography data models long short-term memory (LSTM) photoplethysmography (PPG) sensor shifts transfer learning (TL)

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Scalable, interactive and hierarchical visualization of virus taxonomic data

Scalable, interactive and hierarchical visualization of viru...

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ieee Workshop on Visual Analytics in Healthcare (VAHC)

作者： Kashyap Balakavi Rushitha Janga Ahmedur Rahman Shovon Don Dempsey Elliot Lefkowitz Sidharth Kumar Department of Computer Science University of Alabama at Birmingham Department of Microbiology University of Alabama at Birmingham

The International Committee on Taxonomy of Viruses (ICTV) (https://***) maintains a database of virus taxonomy, including records from 1971 up to the most recent release in 2022. Recent years have seen an increasing number of viruses, recording 11,273 distinct species in the latest report. Exploring this increasingly high number of species requires a custom data visualization approach that is both scalable and interactive. Our paper presents a structure-preserving, collapsible, node-link layout-based hierarchical visualization called the Visual Taxonomy Browser (https://***/taxonomy/visual-browser), for the virus taxonomy dataset. Developed in collaboration with microbiologists, our visualization improves over the existing Taxonomy Browser (https://***/taxonomy) which is a stacked bar-based approach that lacks scalability and does not preserve the underlying topological structure of the taxonomy. The Visual Taxonomy Browser has been deployed on the ICTV website and has been serving virologists from across the world.

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Research trends of machine learning in traditional medicine:a big-data based tenyear bibliometric analysis

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Traditional Medicine Research 2023年第7期8卷 1-10页

作者： Wen-Cai Liu Meng-Pan Li Hai-Yue Huang Jing-Jie Min Tao Liu Ming-Xuan Li Wei-Jie Liao Hui Ying Jun-Bo Tu Department of Orthopaedics Shanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghai 200233China Department of Orthopaedic Surgery the First Affiliated Hospital of Nanchang UniversityNanchang 330006China Department of Clinical Medicine the First Clinical Medical College of Nanchang UniversityNanchang 330006China School of Basic Medical Sciences Nanchang UniversityNanchang 330006China Department of Clinical Medicine Fuzhou Medical College of Nanchang UniversityFuzhou 344099China Department of Emergency Trauma Surgery Ganzhou People’s HospitalGanzhou 341000China Department of Orthopaedics Xinfeng County People’s HospitalXinfeng 341600China

Background:With the rapid development of the world’s technology,the connection and integration between traditional medicine and modern machine learning technology are increasingly *** this study,we aimed to analyze publications on machine learning in traditional medicine by using bibliometric methods and explore global trends in the ***:Relevant research on machine learning in traditional medicine extracted from the Web of science Core Collection *** analysis and visualization were performed using the Bibliometrix package in R *** trends,source journals,authorship,and thematic keywords analysis were performed in this ***:From 2012 to 2022,a total of 282 publications on machine learning in traditional medicine were identified and *** average annual growth rate of the publications was 13.35%.China had the largest contribution in this field(53.9%),followed by the United States(32.6%).ieee Access had the largest number of published articles,with a total of 15 *** Yu-Chian Chen,Xiao-juan Hu and Jue Wang were the main researchers in this *** University of Traditional Chinese Medicine and University of California,San Francisco were the main research ***:This study provides information on research trends in machine learning in traditional medicine to better understand research hotspots and future developments in this *** to current global trends,the number of publications in this field will gradually *** currently dominated the *** research of machine learning techniques may be the next hot topic in this field and deserves further attention.

关键词： bibliometric analysis machine learning traditional medicine Web of science research trends

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An Efficient Generative data Imputation Toolbox with Adversarial Learning

An Efficient Generative Data Imputation Toolbox with Adversa...

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International Conference on data Engineering

作者： Yangyang Wu Xiaoye Miao Zilinghan Li Shilan He Xinkai Yuan Jianwei Yin Center for Data Science Zhejiang University Hangzhou China College of Computer Science Zhejiang University Hangzhou China

The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications. In this demonstration, we propose a scalable and extendible data imputation toolbox, SEMI, to deal with large-scale incomplete data imputation efficiently and visually. SEMI consists of three modules: data preprocessing, data imputation, and post-imputation prediction. It is built upon SCIS, a scalable imputation system, to significantly speed up the training of generative adversarial imputation models under accuracy-guarantees for large-scale incomplete data. Using a public real-world large-scale incomplete weather dataset, we demonstrate that, SEMI is capable of assisting users to efficiently address real-life large-scale imputation issues, from the aspects of high-efficient imputation system, user-friendly performance visualization, and easy-to-use interaction operation.

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Development and Initial Testing of XR-Based Fence Diagrams for Polar science

Development and Initial Testing of XR-Based Fence Diagrams f...

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ieee International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Naomi Tack Nicholas Holschuh Sharad Sharma Rebecca Williams Don Engel University of Maryland Amherst College University of North Texas

Earth’s ice sheets are the largest contributor to sea level rise. For this reason, understanding the flow and topology of ice sheets is crucial for the development of accurate models and predictions. In order to aid in the generation of such models, ice penetrating radar is used to collect images of the ice sheet through both airborne and ground-based platforms. Glaciologists then take these images and visualize them in 3D fence diagrams on a flat 2D screen. We aim to consider the benefits that an XR visualization of these diagrams may provide to enable better data comprehension, annotation, and collaborative work. In this paper, we discuss our initial development and evaluation of such an XR system.

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Fault diagnosis for aero-engine gas path considering operating condition 5

Fault diagnosis for aero-engine gas path considering operati...

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5th International Conference on System Reliability and Safety Engineering, SRSE 2023

作者： Zhang, Shijie Zhu, Guibing University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu China University of Electronic Science and Technology of China School of Automation Engineering Chengdu China Nottingham Electrification Centre Ningbo China

ISBN: (纸本)9798350305944

There are various of failure modes and operating conditions of an aero-engine gas path system. They are correlated with each other, making the fault diagnosis for aero-engine gas path difficult. This paper proposes a fault diagnosis method for aero-engine gas path considering operating condition. First, we develop a multilayer perceptron (MLP) model to identify the operating condition based on Mach number and altitude. Then, we adopt a Convolutional Bidirectional Long Short-Term Memory (CNN-BiLSTM) model to diagnose the failure mode under the corresponding operating condition based on monitoring parameters of each cross-section state. Finally, the effectiveness of the proposed gas path fault diagnosis method was validated by the simulation data generated by gas turbine simulation program (GSP). © 2023 ieee.

关键词： Failure analysis

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Research on Disaster Identification Algorithm System of Key Technologies of Computer Urban Pipe Network Safety Monitoring and Early Warning

Research on Disaster Identification Algorithm System of Key ...

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Control, Electronics and Computer Technology (ICCECT), ieee International Conference on

作者： Guangdi Zhang Wei Liu Jingyun Zhou Qi He The NO.203 Institute of the Second Academy of China Aerospace Science & Industry Corp Beijing China

This paper studies the online monitoring and accident warning system design of pipe network. Intelligent online liquid level monitoring and warning system is a digital monitoring platform for urban drainage based on the Internet of Things technology. It has the functions of permanent data storage, online fast query and statistical analysis, dynamic alarm management and so on. The system will organically integrate the 3D visualization scene, pipeline model and monitoring results with the Super Map software system. Establish an intuitive and effective three-dimensional visualization scene of urban underground pipe network. The system can dynamically display and interactive query visual model information in real time. By setting thresholds for water quality monitoring indicators, the system can automatically identify abnormal monitoring indicators and push abnormal information to monitoring personnel to achieve monitoring and early warning. The system provides an intuitive and interactive information visualization management platform and monitoring and warning platform for the urban underground pipe network water quality monitoring.

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visualization of 3D Hyperspectral Soil Mapping data via Autoencoder-based Clustering

Visualization of 3D Hyperspectral Soil Mapping Data via Auto...

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ieee International Conference on Big data

作者： Jianxin Sun Xinyan Xie Yu Pan Yakub Islamov Yufeng Ge Hongfeng Yu University of Nebraska-Lincoln Lincoln NE USA

Soil measurement and evaluation are crucial to various aspects of agriculture, including agricultural productivity, nutrient management, water management, and pH Regulation. Hyperspectral imaging is an advanced technique used to capture and analyze a wide range of light wavelengths (or spectral bands) across the electromagnetic spectrum. Hyperspectral imaging in soil research involves the use of this advanced imaging technique to analyze the spectral properties of soils. It allows researchers to capture detailed information about the composition, texture, and conditions of soil across a wide range of wavelengths in the electromagnetic spectrum. This in-depth spectral analysis provides valuable insights for studying soil health, nutrient content, moisture levels, and other critical parameters. However, existing hyperspectral analysis of soil relies on using imaging systems to exclusively capture information from the soil surface. This yields a two-dimensional image in which each pixel represents a spectrum vector. In this paper, we provide a new 3D hyperspectral data capturing features deep into the soil where each voxel represents a spectrum vector. For effective analysis of this type of new hyperspectral data, we develop a 3D visualization tool to not only directly visualize individual spectrum of the soil volume but also provide a way to cluster such high dimensional data leveraging a deep learning-based method through autoencoder.

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visualization of Mobility Digital Twin: Framework Design, Case Study, and Future Challenges

Visualization of Mobility Digital Twin: Framework Design, Ca...

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ieee Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Yueyang Liu Xiaolong Tu Dawei Chen Kyungtae Han Onur Altintas Haoxin Wang Jiang Xie Department of Computer Science Georgia State University GA USA InfoTech Labs Toyota Motor North America R&D CA USA Department of Electrical and Computer Engineering The University of North Carolina at Charlotte NC USA

A Mobility Digital Twin is an emerging implementation of digital twin technology in the transportation domain, which creates digital replicas for various physical mobility entities, such as vehicles, drivers, and pedestrians. Although a few work have investigated the applications of mobility digital twin recently, the extent to which it can facilitate safer autonomous vehicles remains insufficiently explored. In this paper, we first propose visualization of mobility digital twin, which aims to augment the existing perception systems in connected and autonomous vehicles through twinning high-fidelity and manipulable geometry representations for causal traffic participants, such as surrounding pedestrians and vehicles, in the digital space. An end-to-end system framework, including image data crowdsourcing, preprocessing, offloading, and edge-assisted 3D geometry reconstruction, is designed to enable real-world development of the proposed visualization of mobility digital twin. We implement the proposed system framework and conduct a case study to assess the twinning fidelity and physical-to-digital synchronicity within different image sampling scenarios and wireless network conditions. Based on the case study, future challenges of the proposed visualization of mobility digital twin are discussed toward the end of the paper.

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Research on Computer Big data Industrial Structure Adaptation Simulation System

Research on Computer Big Data Industrial Structure Adaptatio...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Wang Chao Lai Weike Yin Xuxi Wang Zhe Guangzhou Huashang Vocational College Guangzhou China

This system is an educational big data industrial structure adaptation simulation system integrating big data technology, information technology, data visualization technology and automation technology. It is a computer science decision support system aiming at automatic collection, intelligent storage, scientific decision making and intelligent application. A two-level resource scheduling mechanism is constructed to decouple the extended heterogeneous application of teaching resource scheduling and teaching resource allocation. Encapsulate applications and isolate running environments through lightweight container technology to achieve dynamic application expansion and deployment management. It is found that the data collection and analysis of teaching resources of the whole big data industry structure ADAPTS simulation system based on open protocol.

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