IEEE Open Journal of Instrumentation and Measurement最新文献_第5页

Microwave NDT/NDE Through Differential Bayesian Compressive Sensing 通过差分贝叶斯压缩传感实现微波无损检测/无损探伤

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-06-11 DOI: 10.1109/OJIM.2024.3412205

Marco Salucci;Lorenzo Poli;Giorgio Gottardi;Giacomo Oliveri;Luca Tosi;Andrea Massa

引用次数: 0

LiDAR-Based Optimized Normal Distribution Transform Localization on 3-D Map for Autonomous Navigation 基于激光雷达的三维地图优化正态分布变换定位，用于自主导航

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-06-11 DOI: 10.1109/OJIM.2024.3412219

Abhishek Thakur;P. Rajalakshmi

{"title":"LiDAR-Based Optimized Normal Distribution Transform Localization on 3-D Map for Autonomous Navigation","authors":"Abhishek Thakur;P. Rajalakshmi","doi":"10.1109/OJIM.2024.3412219","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3412219","url":null,"abstract":"Autonomous navigation has become a topic of immense interest in robotics in recent years. Light detection and ranging (LiDAR) can perceive the environment in 3-D by creating the point cloud data that can be used in constructing a 3-D or high-definition (HD) map. Localization can be performed on the 3-D map created using a LiDAR sensor in real-time by matching the current point cloud data on the prebuilt map, which is useful in the GPS-denied areas. GPS data is inaccurate in indoor or obstructed environments, and achieving centimeter-level accuracy requires a costly real-time kinematic (RTK) connection in GPS. However, LiDAR produces bulky data with hundreds of thousands of points in a frame, making it computationally expensive to process. The localization algorithm must be very fast to ensure the smooth driving of autonomous vehicles. To make the localization faster, the point cloud is downsampled and filtered before matching, and subsequently, the Newton optimization is applied using the normal distribution transform to accelerate the convergence of the point cloud data on the map, achieving localization at 6 ms per frame, which is 16 times less than the data acquisition rate of LiDAR at 10 Hz (100ms per frame). The performance of optimized localization is also evaluated on the Kitti odometry benchmark dataset. With the same localization accuracy, the localization process is made five times faster. LiDAR map-based autonomous driving on an electric vehicle is tested in the TiHAN testbed at the IIT Hyderabad campus in real-time. The complete system runs on the robot operating system (ROS). The code will be released at \u0000<uri>https://github.com/abhishekt711/Localization-Nav</uri>\u0000.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10552773","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602511","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

OJIM 2023 Reviewer List OJIM 2023 审查员名单

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-06-07 DOI: 10.1109/OJIM.2024.3403319

引用次数: 0

Assessment of Lidar Point Cloud Simulation Using Phenomenological Range-Reflectivity Limits for Feature Validation 利用现象学范围-反射率极限对激光雷达点云模拟进行评估以验证特征

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-04-17 DOI: 10.1109/OJIM.2024.3390214

Relindis Rott;Selim Solmaz

{"title":"Assessment of Lidar Point Cloud Simulation Using Phenomenological Range-Reflectivity Limits for Feature Validation","authors":"Relindis Rott;Selim Solmaz","doi":"10.1109/OJIM.2024.3390214","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3390214","url":null,"abstract":"We present an assessment of simulated lidar point clouds based on different phenomenological range-reflectivity models. In sensor model development, the validation of individual model features is favorable. For lidar sensors, range limits depend on surface reflectivities. Two phenomenological feature models are derived from the lidar range equation, for clear and adverse weather conditions. The underlying parameters are the maximum ranges for best environment conditions, based on sensor datasheets, and a maximum range measurement for attenuation conditions. Furthermore, an assessment of different feature models is needed, similar to unit tests. Therefore, resulting point clouds are compared with respect to the total number of corresponding points and the number of points with no correspondences for pair-wise cloud comparison. Applications are presented using a point cloud lidar model. Results of the point cloud comparison are demonstrated for a single scene or time step and an entire scenario of 40 time steps. When a reference point cloud is provided by the sensor manufacturer, feature validation becomes possible.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895102","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

A Novel ON-State Resistance Estimation Technique for Online Condition Monitoring of Semiconductor Devices Under Noisy Conditions 用于噪声条件下半导体器件在线状态监测的新型导通态电阻估算技术

IEEE Open Journal of Instrumentation and Measurement Pub Date : 2024-03-27 DOI: 10.1109/OJIM.2024.3379414

Mohsen Asoodar;Mehrdad Nahalparvari;Simon Schneider;Iman Shafikhani;Gunnar Ingeström;Hans-Peter Nee

{"title":"A Novel ON-State Resistance Estimation Technique for Online Condition Monitoring of Semiconductor Devices Under Noisy Conditions","authors":"Mohsen Asoodar;Mehrdad Nahalparvari;Simon Schneider;Iman Shafikhani;Gunnar Ingeström;Hans-Peter Nee","doi":"10.1109/OJIM.2024.3379414","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3379414","url":null,"abstract":"This article presents a novel method for accurate online extraction of semiconductor ON-state resistance in the presence of measurement noise. In this method, the ON-state resistance value is extracted from the measured ON-state voltage of the semiconductors and the measured load current. The extracted ON-state resistance can be used for online condition monitoring of semiconductors. The proposed method is based on the extraction of selective harmonic content. The estimated values are further enhanced through an integral action that increases the signal-to-noise ratio, making the proposed method suitable in the presence of noisy measurements. The efficacy of the proposed method is verified through simulations in the MATLAB/Simulink environment, and experimentally. The estimated ON-state resistance values from the online setup are compared to offline measurements from an industrial curve tracer, where an overall estimation error of less than 1% is observed. The proposed solution maintains its estimation accuracy under variable load conditions and for different temperatures of the device under test.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10479961","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141439496","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

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