IEEE Open Journal of Instrumentation and Measurement最新文献

{"title":"Monocular Vision Measurement for Drones Obstacle Avoidance and Autonomous Path Planning With Circle RRT*","authors":"Mingrui Yu;Junyu Lu;Henry Leung","doi":"10.1109/OJIM.2026.3712927","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3712927","url":null,"abstract":"Drones have garnered increasing attention for applications, such as disaster response, surveillance, and smart city monitoring, necessitating effective collision avoidance systems (CASs) to ensure safe navigation in complex environments. Traditional CAS often relies on multiple sensors, which bring cost, weight, and computational concerns, posing challenges for lightweight civilian drones with limited payload capacity. This article proposes a novel, cost-effective obstacle avoidance system that utilizes only a monocular camera, integrating traditional image processing techniques, background subtraction, and a deep learning (DL) method for robust obstacle sensing. Enhanced by a depth predictor for distance estimation, the system is paired with a new path planning algorithm, Circle Rapidly Exploring Random Tree (RRT*), which improves upon the RRT* algorithm by focusing computational efforts on targeted sectors, thereby enhancing efficiency and enabling real-time navigation. The experimental evaluations in both 2-D and 3-D environments demonstrate the system’s high accuracy in obstacle sensing and its ability to generate optimized, collision-free paths with reduced computational overhead. This lightweight, single-camera solution offers a practical and efficient approach for autonomous drones operations, addressing the limitations of multisensor systems while maintaining reliability and performance in dynamic environments.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"5000314-5000314"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11606493","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626348","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}

{"title":"Transformer-Based NILM With Embedded Convolutional Layers for Sequence-to-Point Energy Disaggregation","authors":"Simone Mari;Álvaro Hernández;Daniel Pizarro;Fabrizio Ciancetta;Laura de Diego-Otón;Víctor M. Navarro","doi":"10.1109/OJIM.2026.3697081","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3697081","url":null,"abstract":"Energy disaggregation represents a fundamental approach in energy management, allowing total electrical consumption to be analyzed and broken down into individual appliances without the need for intrusive installations. Nonintrusive load monitoring (NILM) emerges as an innovative methodology in this context, offering the possibility to monitor and optimize household energy consumption. This article presents an advanced convolutional transformer-based model for NILM implemented under a sequence-to-point (S2P) setting for energy disaggregation. Compared to previous proposals, the model introduces a compact convolutional transformer architecture for S2P NILM by combining positional embeddings, multihead attention, pointwise convolutions, and dilated convolutions. The research evaluates the generalization capability of the model using never-before-seen houses within the same domain through the REFIT dataset, demonstrating a higher performance compared to several state-of-the-art models. In addition, the study explores the generalization capability of the model by evaluating its adaptability to houses belonging to different contexts, specifically using the U.K.-DALE dataset, thus proving the alternative of deploying the trained model in different environments/houses. The results indicate that the proposed model not only excels in terms of disaggregation performance, achieving the lowest average mean absolute error (MAE) (13.37) and signal aggregate error (SAE) (12.46) across all appliances but also shows robust performance in diverse scenarios. Compared to the best-performing reference model (with MAE = 40.24 and SAE = 16.95), the proposed method reduces the average error by approximately 67% in MAE and 21% in SAE.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"9000116-9000116"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11535469","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171692","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}

{"title":"Reconstruction-Free Vibration Monitoring Using Random Pattern Reuse Sampling","authors":"Boon-Yaik Ooi;Xin-Yi Kh'ng;Woan-Lin Beh;Shervin Shirmohammadi","doi":"10.1109/OJIM.2026.3736120","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3736120","url":null,"abstract":"Continuous vibration monitoring requires sensor nodes to acquire and transmit high-rate data to the Internet-of-Things (IoT) endpoints, such as cloud platforms or on-premises servers, resulting in substantial communication overhead and computational burden. While compressive sampling (CS) has been widely adopted to alleviate bandwidth and storage constraints, most existing approaches rely on signal reconstruction prior to analysis. This reconstruction step introduces significant processing latency and limits real-time applicability. This work proposes a novel reconstruction-free, decision-centric CS vibration monitoring framework. The proposed method integrates random pattern reuse sampling (RPRS), where an optimal random sampling pattern is identified during an initialization phase in the digital domain, and the same set of sampling indices is reused during runtime. This strategy preserves the randomness required by CS while removing the need to transmit sampling timestamp information, thereby reducing communication overhead. Instead of reconstructing signals, machine state inference is performed directly on compressed measurements, enabling real-time decision-making with minimal computational cost. A hybrid analysis approach integrating the two-sample Kolmogorov–Smirnov (KS) test and sinusoidal curve fitting is employed to detect variations in amplitude distribution and dominant frequency components associated with different operational states. Experimental validation using real manufacturing vibration data demonstrates that the proposed framework achieves a 46.3% reduction in data transmission, approximately two orders of magnitude faster processing, and a classification accuracy of 94.43% <inline-formula> <tex-math>$pm ~0.77$ </tex-math></inline-formula>% without requiring CS reconstruction. These results highlight the effectiveness of the proposed framework as a low-latency, resource-efficient alternative to reconstruction-based CS methods, making it particularly suitable for real-time, resource-constrained IoT vibration monitoring systems.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"3000217-3000217"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11703684","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133789","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}

{"title":"Real-Time Analysis of Track Surface Defect Images Using WT-YOLO12","authors":"Guanlin Zhang;Ping Wang;Wenhao Lu;Lu Han;Nan Li","doi":"10.1109/OJIM.2026.3709524","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3709524","url":null,"abstract":"The rapid and precise defect detection of rail surface defects is critical for railway operational safety. The conventional inspection methods, however, are often labor-intensive, resource-consuming, and vulnerable to noise. To address these challenges, this article proposes a real-time defect segmentation and detection model called wavelet transform (WT)-YOLO12. First, this article introduces the multiscale convolutional attention (MSCA) mechanism to capture long-range contextual information, compensating for the limited receptive field of the YOLO12 backbone network. Second, this article enhances high-frequency defect features using WTs, significantly alleviating the bottleneck in fine-grained track surface defect segmentation with YOLO12. Extensive experiments on the Types I and II track surface defect datasets validate the effectiveness of the proposed segmentation algorithm. On the Type II railway surface defect datasets (RSDDs) dataset, WT-YOLO12 improves mask mean average precision (mAP) by 11% over the baseline YOLO12s, while maintaining a processing speed of 71.2 frames per second (FPS), which satisfies real-time application requirements. Furthermore, the comparative experiments on the railway dataset demonstrate the model’s strong generalization, yielding a 3.9% increase in mAP over the baseline YOLO12s.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"5000213-5000213"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11594111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507692","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}

{"title":"Vibration-Based Condition Classification of ASTM C93900 Bronze Plain-Bearing Bushings Using Deep Learning","authors":"J. V. Pelegrino;R. D. Dantas;M. R. Silva;D. A. Seixas;L. A. Willenshofer;C. M. P. Souza;W. C. Silva","doi":"10.1109/OJIM.2026.3720860","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3720860","url":null,"abstract":"Plain-bearing bushings are essential components in rotating machinery, directly influencing reliability, efficiency, and maintenance costs. While vibration-based condition monitoring has been widely applied to rolling-element bearings, experimental studies involving sliding bushings under real fault conditions remain limited. This work proposes an integrated experimental and deep learning framework for the vibration-based condition classification of ASTM C93900 bronze plain-bearing bushings. An experimental test bench was developed to simulate realistic operating conditions and acquire vibration signals under three scenarios: the normal condition, clearance variation, and geometric eccentricity. Data were collected using low-cost MEMS accelerometers and processed through signal segmentation, MinMax normalization, and a compact <inline-formula> <tex-math>$8times 8$ </tex-math></inline-formula> grayscale matrix representation for convolutional neural network (CNN) classification. The proposed framework was evaluated under multiple fault combinations and bearing positions and was compared with baseline dense neural network (DNN) models. Results showed classification accuracies above 90% in all evaluated scenarios, demonstrating the capability of the CNN to identify subtle vibration patterns associated with different fault conditions. Furthermore, the compact signal representation reduced computational complexity while preserving relevant diagnostic information. The proposed methodology demonstrates the potential of combining tribological experimentation, vibration monitoring, and artificial intelligence (AI) techniques for predictive maintenance applications, and it provides an experimental dataset for future research involving sliding bushings.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"2500614-2500614"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11643864","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148780959","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}

{"title":"Measurement Beyond Quantification: Toward a Framework for Classificatory Properties","authors":"Luca Mari;Dario Petri","doi":"10.1109/OJIM.2026.3718085","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3718085","url":null,"abstract":"Whether properties lacking a quantitative structure can be meaningfully measured is a subject of debate in measurement science. In particular, the metrological status of classificatory properties is not fully established yet, and existing positions diverge significantly, ranging from restrictive definitions that limit measurement to quantitative properties to more inclusive views that also admit qualitative ones. It is proposed here to characterize measurement not by the algebraic structure of the evaluated properties or the information produced, but rather through publicly and explicitly justified epistemic conditions, as usually conveyed in terms of measurement uncertainty. A preliminary distinction is made between types of empirical properties and types of scales, where a scale is understood as a representation of the empirical structure known of a property. On this basis, classificatory properties are characterized not as merely “nominal” properties, but as properties whose instances are partitionable into empirically justified equivalence classes. It is then shown that classificatory evaluations can satisfy the same epistemic requirements as measurements—as traditionally intended—despite conveying structurally weaker information. This perspective supports the development of a unified framework in which the distinction between “strongly defined” and “weakly defined” measurement reflects differences only in the structural richness of the produced information, rather than in its trustworthiness.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"1000310-1000310"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11627822","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148746914","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}

{"title":"Neural-Based Closed-Loop Framework for Pseudomeasurements Generation in Distribution System State Estimation","authors":"Manuela Pasella;Andrea Benigni;Barbara Cannas;Daniele Carta;Carlo Muscas;Paolo Attilio Pegoraro;Fabio Pisano;Carlo Sitzia","doi":"10.1109/OJIM.2026.3720895","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3720895","url":null,"abstract":"Structural changes in modern power distribution networks are having an increasingly significant impact on monitoring and control applications, including distribution system state estimation (DSSE). These changes introduce uncertainties that degrade the quality of the available information, including measurements and pseudomeasurements (PMs), thereby limiting DSSE performance. With reference to PMs, which are nonmeasured information typically derived from a priori knowledge or historical data, machine learning (ML) techniques have emerged as a promising way to mitigate these effects. This article evaluates the effectiveness of using a neural-based approach to predict active and reactive power injections in a closed-loop framework that includes the PMG and the DSSE procedure. The performance achievable with neural models, such as linear neural networks, multilayer perceptron (MLP), recurrent neural network (RNN) architectures-including gated recurrent unit (GRU) layers and long short-term memory (LSTM) layers, and random forests (RFs), is compared. An assessment of weighting strategies for these predictions is also carried out to evaluate how these weights should be adjusted according to the prediction accuracy of DSSE-based neural models. The artificial neural networks (ANNs) were trained and characterized using real consumption data from the Forschungszentrum Jülich (FZJ) campus, and the influence of the improved PMs quality on the DSSE was evaluated via numerical simulations. Simulation results, based on historical data, prove the validity of the proposed approach as a reliable monitoring tool for system operators.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"9000215-9000215"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11643860","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837255","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}

{"title":"Adaptive Local Oscillator Phase Drift Compensation via Real-Time RF Temperature Monitoring","authors":"Yuan Liu;Hua Luo;Jiancao Hou","doi":"10.1109/OJIM.2026.3709529","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3709529","url":null,"abstract":"This article presents an adaptive local oscillator (LO) phase drift compensation method for a digital bandwidth interleaving (DBI) architecture-based direct radio frequency (RF) sampling receiver via real-time RF hardware temperature monitoring. Such phase drift is difficult to track and introduces significant amplitude and phase bias, compromising the measurement accuracy of the system. To tackle this, we explore a novel data-driven approach that leverages RF hardware temperature as a proxy to track and estimate LO phase drift behavior. Using multiple linear regression (MLR), we first establish an offline model to precisely map temperature variations to phase differences between adjacent channels. In real-time operation, the system tracks temperature changes to predict phase drift and triggers dynamic phase compensation and model update when the drift exceeds a predetermined threshold. Experimental results demonstrate that our approach outperforms equal-time calibration methods by reducing phase error uncertainty and enhancing long-term measurement stability.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"1000207-1000207"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11594106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510822","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}

{"title":"Sensor Model-Based Fault Detection for Patient Support Tables","authors":"Dimitrios G. Fragkoulis;Fotis N. Koumboulis;Konstantinos G. Tzierakis","doi":"10.1109/OJIM.2026.3730554","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3730554","url":null,"abstract":"The vertical and horizontal axis subsystems of patient support tables (PSTs) in healthcare systems are studied in the Ramadge–Wonham framework. The analytic models of the devices in the vertical and horizontal axis subsystems of a PST will be expressed in discrete event system (DES) form. The case of sensor faults in the system is analyzed. Based on the DES models of the devices, a sensor fault detection mechanism is designed in the form of automata of fault diagnosers. Based on these diagnosers, a set of safe supervisors is developed, acting simultaneously in both axes’ subsystems. The proposed sensor fault detection systems and the safe supervisors are implemented in Python. Also, a fault alarm mechanism is developed in Python, using the diagnosers’ outputs. The implementation is offered for integration into the dedicated PC, using several software platforms. Particularly, an implementation scheme based on the Node-RED platform is developed. Event-based simulations and real measurements validate the efficiency of the present results. Finally, the reliability of the proposed diagnosers is evaluated, addressing key metrological issues in supervisor control theory (SCT).","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"3000116-3000116"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11677126","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033614","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}

{"title":"Design and Validation of a Compact Radio Frequency Measurement System for Volumetric Near-Field Scans","authors":"Nils Bade;Dominik Langer;Alexander Koelpin","doi":"10.1109/OJIM.2026.3728937","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3728937","url":null,"abstract":"This work presents a modular near-field (NF) measurement system derived from the open-source 3-D printer project RatRig V-Core 3.1. Rather than requiring individual solutions for specific measurement tasks, the system at hand serves as a centralized measurement platform for a wide range of planar and volumetric measurements. It enables automated measurements in a controlled environment with a working volume of <inline-formula> <tex-math>$510times 450times 910$ </tex-math></inline-formula>  mm. Concepts from Cartesian 3-D printer mechanics are leveraged to achieve high positional accuracy, with positioning errors below 20 <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>m. The overall cost is significantly reduced compared to conventional measurement systems, using off-the-shelf components. The modular architecture allows for pairing the setup with different measurement instruments. The influence of moving cables is investigated, and amplitude and phase stability better than 0.6  dB and <inline-formula> <tex-math>$5{mathrm {~ {^{circ}}}}$ </tex-math></inline-formula> at 60  GHz is maintained across the full scan volume. An exemplary NF measurement of a horn antenna in the V-band is conducted and compared to simulation, showing good agreement. The system is released as an open-source project (AntRig-Measurement-System) that provides mechanical design files, automation software, and instructions for assembly and use. The proposed measurement system combines a compact, portable, cost-effective, yet flexible implementation that is easy to replicate and use, and offers accuracy comparable to that of conventional solutions.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"8000209-8000209"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11672788","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188290","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}