Measurement Science Review最新文献_第8页

A Study on Autonomous Integrity Monitoring of Multiple Atomic Clocks 多原子钟的自主完整性监测研究

IF 0.9 4区工程技术

Measurement Science Review Pub Date : 2022-08-05 DOI: 10.2478/msr-2022-0025

Bo Xiao, Ya Liu, Yanrong Xue, Xiaohui Li

引用次数: 0

Deep Learning Measurement Model to Segment the Nuchal Translucency Region for the Early Identification of Down Syndrome 用于早期识别唐氏综合症的颈部半透明区域分割的深度学习测量模型

IF 0.9 4区工程技术

Measurement Science Review Pub Date : 2022-05-14 DOI: 10.2478/msr-2022-0023

M. C. Thomas, S. Arjunan

引用次数: 4

Comparison of GUM and Monte Carlo Methods for Measurement Uncertainty Estimation of the Energy Performance Measurements of Gas Stoves 燃气炉具能源性能测量不确定度的GUM和Monte Carlo方法比较

IF 0.9 4区工程技术

Measurement Science Review Pub Date : 2022-05-14 DOI: 10.2478/msr-2022-0020

B. Utomo, N. Kusnandar, H. Firdaus, Intan Paramudita, I. Kasiyanto, Q. Lailiyah, W. Syam

{"title":"Comparison of GUM and Monte Carlo Methods for Measurement Uncertainty Estimation of the Energy Performance Measurements of Gas Stoves","authors":"B. Utomo, N. Kusnandar, H. Firdaus, Intan Paramudita, I. Kasiyanto, Q. Lailiyah, W. Syam","doi":"10.2478/msr-2022-0020","DOIUrl":"https://doi.org/10.2478/msr-2022-0020","url":null,"abstract":"Abstract The paper presents the comparison of uncertainty measurement estimations of the energy performances of gas stoves. The Guide to the Expression of Uncertainty in Measurement (GUM) framework and two Monte Carlo Simulation (MCM) approaches: ordinary and adaptive MCM were applied for the energy performance uncertainty: thermal energy and efficiency measurement uncertainties. The validation of the two MCMs is performed by comparing the MCM estimations to the GUM estimations for the thermal energy and efficiency measurement results. A test method designed in Indonesia National Standard SNI 7368:2011 was employed for the thermal energy and efficiency determinations. The results of the GUM and two MCM methods are in good agreement for the estimation of the thermal energy value. Significant differences of the uncertainty estimations for the thermal energy and efficiency results are observed for both GUM and MCM methods. Both the ordinary and adaptive MCM estimations give larger coverage interval compared to the GUM method. The adaptive MCM can give similar estimations with a much lower number of iterations compared to the ordinary MCM. From the estimation difference between the GUM and MCM methods, suggestions are needed for the improvement in measurement models for thermal energy and efficiency of the standard.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"160 - 169"},"PeriodicalIF":0.9,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41790794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stacked Auto-encoder Based Feature Transfer Learning and Optimized LSSVM-PSO Classifier in Bearing Fault Diagnosis 基于堆叠自编码器的特征迁移学习和优化LSSVM-PSO分类器在轴承故障诊断中的应用

IF 0.9 4区工程技术

Measurement Science Review Pub Date : 2022-05-14 DOI: 10.2478/msr-2022-0022

V. Nguyen, Junsheng Cheng, V. Thai

{"title":"Stacked Auto-encoder Based Feature Transfer Learning and Optimized LSSVM-PSO Classifier in Bearing Fault Diagnosis","authors":"V. Nguyen, Junsheng Cheng, V. Thai","doi":"10.2478/msr-2022-0022","DOIUrl":"https://doi.org/10.2478/msr-2022-0022","url":null,"abstract":"Abstract This paper proposes a new diagnosis technique for predicting the big data of roller bearing multi-level fault, which uses the deep learning method for the feature representation of the vibration signal and an optimized machine learning model. First, vibration feature extraction by stacked auto-encoders (VFE-SAE) with two layers in roller bearing fault signals is proposed. The unsupervised learning algorithm in VFE-SAE is used to reveal significant properties in the vibration data, such as nonlinear and non-stationary properties. The extracted features can provide good discriminability for fault diagnosis tasks. Second, a classifier model is optimized based on least squares support vector machine classification and particle swarm optimization (LSSVM-PSO). This model is used to perform supervised fine-tuning and classification; it is trained with the labelled features to identify the target data. Especially, using transfer learning, the performance of the bearing fault diagnosis technique can be fine-tuned. In other words, the features of the target vibration signal can be extracted by the learning of feature representation, which is dependent on the weight matrix of hidden layers of the VFE-SAE method. The experimental results (by analyzing the roller bearing vibration signals with multi-status fault) demonstrate that VFE-SAE based feature extraction in conjunction with the LSSVM-PSO classification is more accurate than other popular classifier models. The proposed VFE-SAE – LSSVMPSO method can effectively diagnose bearing faults with 97.76 % accuracy, even when using 80 % of the target data.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"177 - 186"},"PeriodicalIF":0.9,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43703324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Estimation of Energy Meter Accuracy using Remote Non-invasive Observation 利用远程无创观测估算电能表精度

IF 0.9 4区工程技术

Measurement Science Review Pub Date : 2022-05-14 DOI: 10.2478/msr-2022-0021

M. Saunoris, Ž. Nakutis, M. Knyva

引用次数: 0

Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network 基于MEMS IMU和BLE网络的可穿戴人体活动识别集成传感与计算

IF 0.9 4区工程技术

Measurement Science Review Pub Date : 2022-05-14 DOI: 10.2478/msr-2022-0024

Mingxing Zhang, Hongpeng Li, Tian Ge, Zhaozong Meng, N. Gao, Zonghua Zhang

{"title":"Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network","authors":"Mingxing Zhang, Hongpeng Li, Tian Ge, Zhaozong Meng, N. Gao, Zonghua Zhang","doi":"10.2478/msr-2022-0024","DOIUrl":"https://doi.org/10.2478/msr-2022-0024","url":null,"abstract":"Abstract The miniature sensor devices and power-efficient Body Area Networks (BANs) for Human Activity Recognition (HAR) have gained increasing interest in different fields, including Daily Life Assistants (DLAs), medical treatment, sports analysis, etc. The HAR systems normally collect data with wearable sensors and implement the computational tasks with a host machine, where real-time transmission and processing of sensor data raise a challenge for both the network and the host machine. This investigation focuses on the hardware/software co-design for optimized sensing and computing of wearable HAR sensor networks. The contributions include (1) design of a miniature wearable sensor node integrating a Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS IMU) with a Bluetooth Low Energy (BLE) in-built Micro-Control Unit (MCU) for unobtrusive wearable sensing; (2) task-centric optimization of the computation by shifting data pre-processing and feature extraction to sensor nodes for in-situ computing, which reduces data transmission and relieves the load of the host machine; (3) optimization and evaluation of classification algorithms Particle Swarm Optimization-based Support Vector Machine (PSO-SVM) and Cross Validation-based K-Nearest Neighbors (CV-KNN) for HAR with the presented techniques. Finally, experimental studies were conducted with two sensor nodes worn on the wrist and elbow to verify the effectiveness of the recognition of 10 virtual handwriting activities, where 10 recruited participants each repeated an activity 5 times. The results demonstrate that the proposed system can implement HAR tasks effectively with an accuracy of 99.20 %.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"193 - 201"},"PeriodicalIF":0.9,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46473263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Review of Measurement Techniques of Hydrocarbon Flame Equivalence Ratio and Applications of Machine Learning 碳氢化合物火焰当量比测量技术及机器学习应用综述

IF 0.9 4区工程技术

Measurement Science Review Pub Date : 2022-04-22 DOI: 10.2478/msr-2022-0016

Hao Yang, Yuwen Fu, Jiansheng Yang

引用次数: 1

Real-Time Instance Segmentation of Metal Screw Defects Based on Deep Learning Approach 基于深度学习方法的金属螺杆缺陷实时实例分割

IF 0.9 4区工程技术

Measurement Science Review Pub Date : 2022-04-22 DOI: 10.2478/msr-2022-0014

Wei-Yu Chen, Yu-Reng Tsao, Jin-Yi Lai, Ching-Jung Hung, Yu-Cheng Liu, Cheng-Yang Liu

引用次数: 2

An Integrated Testing Solution for Piezoelectric Sensors and Energy Harvesting Devices 压电传感器和能量采集设备的集成测试解决方案

IF 0.9 4区工程技术

Measurement Science Review Pub Date : 2022-04-22 DOI: 10.2478/msr-2022-0013

José Dias Pereira, M. Alves

{"title":"An Integrated Testing Solution for Piezoelectric Sensors and Energy Harvesting Devices","authors":"José Dias Pereira, M. Alves","doi":"10.2478/msr-2022-0013","DOIUrl":"https://doi.org/10.2478/msr-2022-0013","url":null,"abstract":"Abstract With the fast growth of wireless communications between nodes and sensor units and the increase of devices installed in remote places, and the development of IIoT applications, new requirements for power energy supply are needed to assure device functionality and data communication capabilities during extended periods of time. For these applications, energy harvesting takes place as a good solution to increase the autonomy of remote measuring solutions, since the usage of conventional power supply solutions has clear limitations in terms of equipment access and increased maintenance costs. In this context, regenerative energy sources such as thermoelectric, magnetic and piezoelectric based, as well as renewable energy sources, such as photovoltaic and wind based, among others, make the development of different powering solutions for remote sensing units possible. The main purpose of this paper is to present a flexible testing platform to characterize piezoelectric devices and to evaluate their performance in terms of harvesting energy. The power harvesting solutions are focused on converting the energy from mechanical vibrations, provided by different types of equipment and mechanical structures, to electrical energy. This study is carried out taking into account the power supply capabilities of piezoelectric devices as a function of the amplitude, frequency and spectral contents of the vibration stimulus. Several experimental results using, as an example, a specific piezoelectric module, are included in the paper.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"100 - 106"},"PeriodicalIF":0.9,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48524799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Complex Analysis of the Necessary Geometric Parameters of the Tested Component in the Ring-Core Evaluation Process 环芯评估过程中被测部件必要几何参数的复杂分析

IF 0.9 4区工程技术

Measurement Science Review Pub Date : 2022-04-22 DOI: 10.2478/msr-2022-0017

Patrik Šarga, A. Galajdová, M. Vagaš, F. Menda

引用次数: 0