IEEE Open Journal of Instrumentation and Measurement最新文献_第8页

Improved-Performance Vehicle’s State Estimator Under Uncertain Model Dynamics 不确定模型动态条件下的高性能车辆状态估计器

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-03-25 DOI: 10.1109/OJIM.2024.3379386

Mohammad Avzayesh;Wasim Al-Masri;Mamoun F. Abdel-Hafez;Mohammad AlShabi

{"title":"Improved-Performance Vehicle’s State Estimator Under Uncertain Model Dynamics","authors":"Mohammad Avzayesh;Wasim Al-Masri;Mamoun F. Abdel-Hafez;Mohammad AlShabi","doi":"10.1109/OJIM.2024.3379386","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3379386","url":null,"abstract":"This article proposes an enhanced fusion technique to improve the accuracy of the state estimation of a navigational system. The smooth variable structure filter (SVSF) is examined to estimate the system’s state under model uncertainty. Its combination with the unscented Kalman filter (UKF) to acquire better navigational accuracy while being robust to the system’s modeling uncertainty is investigated. The proposed hybrid method is compared with the extended Kalman filter (EKF), the UKF, and the SVSF. The proposed algorithms fuse an inertial measurement unit (IMU) with the Global Positioning Systems (GPS) measurements to obtain the vehicle’s state. Experimental results are compared to a commercial off-the-shelf (COTS) solution. It is shown that all filtering strategies have similar performance in the absence of large-magnitude noise and model uncertainties. When injecting modeling uncertainties, the performance of the UKF degrades, and that of the EKF goes out of bounds. On the other hand, increasing the covariances of the measurement and dynamics noise sequences causes the path of the SVSF to become nonsmooth and roughly oscillates around the true path. The proposed integrated UK-SVSF algorithm achieves the following objectives: first, using the Kaman-based filter enhances the optimality of the filter to GPS/IMU dynamics and measurements noise. Second, using the UKF reduces the estimation error by eliminating the first-order linearization step. Finally, using the SVSF enhances the estimate’s robustness to model uncertainty. Results reveal that, in the presence of both large-magnitude noise and model uncertainties, the UK-SVSF gives an enhanced estimation performance.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10477539","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140641614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Band-Pass Filter-Based PLL-Less Approach for Three-Phase Nonsinusoidal Grid Conditions 基于稳健带通滤波器的无 PLL 方法，适用于三相非正弦电网条件

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-03-10 DOI: 10.1109/OJIM.2024.3399250

Manish Kumar;Anant Kumar Verma;Claudio Burgos-Mellado;Raj Kumar Jarial;Ravinder Nath;Bhumaiah Jula;Diego Muñoz-Carpintero;Catalina González-Castaño;Pedro Roncero-Sánchez

{"title":"Robust Band-Pass Filter-Based PLL-Less Approach for Three-Phase Nonsinusoidal Grid Conditions","authors":"Manish Kumar;Anant Kumar Verma;Claudio Burgos-Mellado;Raj Kumar Jarial;Ravinder Nath;Bhumaiah Jula;Diego Muñoz-Carpintero;Catalina González-Castaño;Pedro Roncero-Sánchez","doi":"10.1109/OJIM.2024.3399250","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3399250","url":null,"abstract":"The performance enhancement of an inverter-based grid-connected system necessitates a fast and accurate dynamic response in terms of estimating three-phase grid voltage attributes. The synchronous reference frame phase-locked loop (PLL) and/or the frequency-locking (i.e., frequency-locked loop) approaches are widely used in practical applications. However, due to the phase/frequency feedback loops, the aforementioned parameter estimation schemes may experience instability and provide a slow dynamic response. This work presents a PLL-less grid synchronization solution for three-phase applications to counter the slower dynamic response and demonstrate better immunity against the nonideality of a three-phase grid. In order to remove even and odd-order harmonics and extract the fundamental frequency positive sequence (FFPS), the proposed method employs a combination of band pass filters (CBPFs). Additionally, a novel frequency estimation algorithm is developed, which accurately estimates the angular three-phase grid frequency. Furthermore, the phase angle and amplitude are adaptively estimated using an off-line error-resolving approach, which is derived from the transfer function of the proposed prefiltering solution. Finally, the experimental findings validate the robustness of the current proposal.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10529140","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141304054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pitting Detection and Characterization From Ultrasound Timelapse Images Using Convolutional Neural Networks 利用卷积神经网络从超声延时图像中检测点蚀并确定其特征

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-03-06 DOI: 10.1109/OJIM.2024.3396829

Magnus Wangensteen;Tonni Franke Johansen;Ali Fatemi;Erlend Magnus Viggen;Lars Eidissen Haugan

{"title":"Pitting Detection and Characterization From Ultrasound Timelapse Images Using Convolutional Neural Networks","authors":"Magnus Wangensteen;Tonni Franke Johansen;Ali Fatemi;Erlend Magnus Viggen;Lars Eidissen Haugan","doi":"10.1109/OJIM.2024.3396829","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3396829","url":null,"abstract":"Pitting corrosion, a localized form of corrosion leading to cavities and structural failure in metallic materials, requires early detection for effective mitigation. While ultrasonic inspection techniques can readily detect uniform wall thinning, they often struggle to identify pitting corrosion. This study proposes a time-lapse ultrasound inspection method to detect early-stage pitting using pulse-echo sensors. By recording multiple ultrasonic traces over time, 2-D timelapse images of ultrasonic reflectivity can be generated and fed into a trained neural network for pitting diagnostics. In general, training a machine-learning model requires a large training dataset. This work used data from a drilling experiment to generate a suitable dataset. Dataset construction by random time-ordered combinations of ultrasonic measurements was conducted to create a diverse set of time-lapse image samples to generalize the resulting machine-learning model adequately. A classification neural network was trained to detect the presence of drilled holes, and a separate regression network was trained to estimate the hole depth. Based on drilling data from an independently acquired test dataset, results demonstrate a mean absolute error of 0.163 mm for hole depth estimations. All holes are successfully detected when 0.1 mm deeper than the defined pitting threshold of 0.5 mm. This suggests that the proposed method generalizes well and can be deployed to any similar acquisition system.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10520720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pressure Detection With Mach–Zehnder Linearized Tunable Diode-Laser Absorption Spectroscopy 利用马赫-泽恩德线性化可调谐二极管激光吸收光谱进行压力检测

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-03-06 DOI: 10.1109/OJIM.2024.3396843

Raoul-Amadeus Lorbeer;Matthias Bittner;Oliver Kliebisch;Peter Mahnke

引用次数: 0

Optimization of Electrode Configuration for 3-D Brain EIT 优化三维脑 EIT 的电极配置

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-03-06 DOI: 10.1109/OJIM.2024.3390197

Siyuan Bai;Yitong Guo;Weichen Li;Lei Wang;Xuetao Shi

引用次数: 0

Microwave Characterization of Metal Powder in Additive Manufacturing (AM) 增材制造 (AM) 中金属粉末的微波表征

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-03-03 DOI: 10.1109/OJIM.2024.3396226

Joseph Filbert;Aaron Barvincak;Mohammad Tayeb Al Qaseer;Reza Zoughi

{"title":"Microwave Characterization of Metal Powder in Additive Manufacturing (AM)","authors":"Joseph Filbert;Aaron Barvincak;Mohammad Tayeb Al Qaseer;Reza Zoughi","doi":"10.1109/OJIM.2024.3396226","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3396226","url":null,"abstract":"Common additive manufacturing (AM) methods use metal powder feedstock. The properties of the metal powder, such as particle size distribution (PSD), morphology, and presence of surface oxides or other contaminants, significantly impact the quality of the built part. Microwave materials characterization techniques potentially offer effective means by which to evaluate such metal properties. To assess sensitivity of microwave signals to the properties of metal powder used in AM, different types of metal powder were incorporated into resin composite samples, whose dielectric and magnetic properties were then measured using the well-known completely filled-waveguide technique at the Ka-band (26.5–40 GHz) and V-band (50–67 GHz). These measurements revealed that microwave signals are sensitive to small (~0.5%) changes in the metal powder volume fraction. It was also found that the resin powder composites exhibited diamagnetic properties and could be accurately modeled using effective media theories which consider both the dielectric and magnetic properties. The findings open the door for future investigations by which optimized techniques can be devised to do the same in an in-line manner during the AM process.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10517939","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141164692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling and Analysis of Lung Water Content Using RF Sensor 利用射频传感器对肺水含量进行建模和分析

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-01-03 DOI: 10.1109/OJIM.2023.3348904

Prapti Ganguly;Shreyasi Das;Amlan Chakrabarti;Jawad Yaseen Siddiqui

{"title":"Modeling and Analysis of Lung Water Content Using RF Sensor","authors":"Prapti Ganguly;Shreyasi Das;Amlan Chakrabarti;Jawad Yaseen Siddiqui","doi":"10.1109/OJIM.2023.3348904","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3348904","url":null,"abstract":"Abnormal fluid buildup in the lungs, termed pulmonary edema (PE), is a result of congestive heart failure. It is a life-threatening condition, and early detection and prompt treatment can help save lives. In this article, we demonstrate the feasibility of using a microwave sensor to monitor changes in lung water content and hence detect PE. The research paper utilizes a combination of the Debye and Maxwell models, along with the Cole–Cole equation, to evaluate alterations in the dielectric properties and conductivity of lung tissue. By incorporating elements such as air and water found within the tissue, this dielectric model has been employed to foresee how lung tissues behave when subjected to different levels of hydration and inflation. A printed antenna resonating at 2.4 GHz was designed to work as a sensor. The static dielectric parameters of lung tissue at various water volume fractions were calculated at 2.4 GHz using the Debye–Maxwell model. These parameters were substituted in the Cole–Cole equation to calculate the dielectric constant of lung tissue for different levels of water in the lungs. These values were then substituted in the simulation environment, where the sensor is placed on blocks modeling the human thorax. This work is a first of its kind where the dielectric parameters at different levels of hydration have been previously estimated using mathematical models and substituted accordingly in the modeling environment to test the possibility of detection of PE with high precision. It was observed that the magnitude of the reflection coefficient values changes with increasing water volume fraction, making the microwave method of detection of PE feasible and a reliable technique.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10380229","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of an FPGA-Based High-Speed Data Acquisition System for Frequency Scanning Interferometry Long-Range Measurement 为频率扫描干涉仪远程测量设计基于 FPGA 的高速数据采集系统

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2023-12-26 DOI: 10.1109/OJIM.2023.3347268

Sivagunalan Sivanathan;Mohammed Ali Roula;Kang Li;Dun Qiao;Nigel Joseph Copner

{"title":"Design of an FPGA-Based High-Speed Data Acquisition System for Frequency Scanning Interferometry Long-Range Measurement","authors":"Sivagunalan Sivanathan;Mohammed Ali Roula;Kang Li;Dun Qiao;Nigel Joseph Copner","doi":"10.1109/OJIM.2023.3347268","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3347268","url":null,"abstract":"Frequency scanning interferometry (FSI) has become a popular method for long-range, targetbased, distance measurements. However, the cost of developing such systems, particularly the electronic components required for high-speed data acquisition (DAQ), remains a significant concern. In this article, we present a cost-effective, FPGA-based real-time DAQ system specifically designed for FSI, with a focus on long absolute distance measurements. Our design minimizes the use of third-party intellectual property (IP) and is fully compatible with the Xilinx FPGA 7 series families. The hardware employs a 160-MS/s, 16-bit dual-channel ADC interfaced to the FPGA via a low-voltage differential signaling (LVDS). The proposed system incorporates an external sampling clock, referred to as the K-clock, which linearizes the laser’s tuning rate, enabling optical measurements to be sampled at equal optical frequency intervals rather than equal time intervals. Additionally, we present the design of a high-speed, 160-MS/s ADC module for the front-end analog signal interface and the LVDS connection to the chosen FPGA. We demonstrate that the digitized data samples can be efficiently transmitted to a polarization controller (PC) application via a USB interface for further processing.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10374215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Message From the Incoming Editor-in-Chief 新任主编致辞

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2023-12-12 DOI: 10.1109/OJIM.2023.3336150

Reza Zoughi

引用次数: 0

Guest Editorial Special Section on Signal Processing and Machine Learning in Intelligent Instrumentation, IEEE Open Journal of Instrumentation and Measurement 智能仪器仪表中的信号处理和机器学习》特约编辑专栏，《IEEE 仪器仪表与测量开放式期刊》（IEEE Open Journal of Instrumentation and Measurement

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2023-12-08 DOI: 10.1109/OJIM.2023.3334827

Anirban Mukherjee;Rajarshi Gupta;Amitava Chatterjee

{"title":"Guest Editorial Special Section on Signal Processing and Machine Learning in Intelligent Instrumentation, IEEE Open Journal of Instrumentation and Measurement","authors":"Anirban Mukherjee;Rajarshi Gupta;Amitava Chatterjee","doi":"10.1109/OJIM.2023.3334827","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3334827","url":null,"abstract":"There has been tremendous interest in the development and deployment of Signal Processing and Machine Learning algorithms for almost all areas of instrumentation and measurement systems, starting from power systems, transportation, biomedical and healthcare, industrial measurements and automation, robotics and mechatronics, smart infrastructure, and facility management to aerospace and navigation. Their combination, signal processing and machine learning, is expected to dominate the next decade industrial developments. In order to embed the “intelligence” into the measurement, signal processing has been one of the ubiquitous techniques for quite some time. Machine learning methods make these intelligent methods “experienced.” Because machine learning has been around in recent years, signal processing software–hardware systems equipped with machine learning are relatively mature. In this Special Section, a call for paper included (but were not limited to) the following areas.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10352322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0