Pub Date : 2023-03-14DOI: 10.1109/OJIM.2023.3249419
{"title":"OJIM 2022 Reviewer List","authors":"","doi":"10.1109/OJIM.2023.3249419","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3249419","url":null,"abstract":"","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10070116.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50417433","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}
Pub Date : 2023-02-28DOI: 10.1109/OJIM.2023.3250280
Christian Laurano;Paolo Attilio Pegoraro;Carlo Sitzia;Antonio Vincenzo Solinas;Sara Sulis;Sergio Toscani
Nowadays, in modern management and control applications, line parameters need to be known more accurately than in the past to achieve a reliable operation of the distribution grids. Phasor measurement units (PMUs) may improve line parameter estimation processes, but the accuracy of the result is affected by all the elements of the PMU-based measurement chain, in particular by the instrument transformers. Current transformers (CTs) are nonlinear and, therefore, their behavior is not easily described: their models cannot be straightforwardly included in the estimation problem. In this regard, this article refines modeling and compensation of CT systematic errors in line parameter estimation processes, based on different methods to describe the transformer behavior under various operating conditions. As the main result, the systematic errors of CTs are remarkably identified and mitigated. Moreover, the estimation of shunt susceptance values is significantly improved.
{"title":"Refined Modeling and Compensation of Current Transformers Behavior for Line Parameters Estimation Based on Synchronized Measurements","authors":"Christian Laurano;Paolo Attilio Pegoraro;Carlo Sitzia;Antonio Vincenzo Solinas;Sara Sulis;Sergio Toscani","doi":"10.1109/OJIM.2023.3250280","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3250280","url":null,"abstract":"Nowadays, in modern management and control applications, line parameters need to be known more accurately than in the past to achieve a reliable operation of the distribution grids. Phasor measurement units (PMUs) may improve line parameter estimation processes, but the accuracy of the result is affected by all the elements of the PMU-based measurement chain, in particular by the instrument transformers. Current transformers (CTs) are nonlinear and, therefore, their behavior is not easily described: their models cannot be straightforwardly included in the estimation problem. In this regard, this article refines modeling and compensation of CT systematic errors in line parameter estimation processes, based on different methods to describe the transformer behavior under various operating conditions. As the main result, the systematic errors of CTs are remarkably identified and mitigated. Moreover, the estimation of shunt susceptance values is significantly improved.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10056410.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50416434","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}
Pub Date : 2023-01-26DOI: 10.1109/OJIM.2023.3239777
L. Lavanya;K. Shanti Swarup
With the growing emphasis on mitigating climate change, the power industry is moving toward renewable energy sources as an alternative to fossil fuel-based power plants. The transition to renewable energy has created numerous challenges, one of which is the low levels of inertia that impact the stability of power systems. Therefore, inertia monitoring has become an integral part of power system operation to dispatch renewable energy sources while maintaining frequency stability. This article presents an online method to continuously estimate the inertia of a power system. The inertia is computed from data provided by Phasor Measurement Units (PMUs) using small variations in frequency and power under ambient conditions. The method uses electrical and kinetic energy variations to compute inertia. In addition, a $Q$ -learning-based method is presented to identify mechanical power changes to discard invalid inertia estimates. The method is demonstrated using the IEEE-39 bus system to monitor the regional inertia of the test system.
{"title":"Continuous Real-Time Estimation of Power System Inertia Using Energy Variations and Q-Learning","authors":"L. Lavanya;K. Shanti Swarup","doi":"10.1109/OJIM.2023.3239777","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3239777","url":null,"abstract":"With the growing emphasis on mitigating climate change, the power industry is moving toward renewable energy sources as an alternative to fossil fuel-based power plants. The transition to renewable energy has created numerous challenges, one of which is the low levels of inertia that impact the stability of power systems. Therefore, inertia monitoring has become an integral part of power system operation to dispatch renewable energy sources while maintaining frequency stability. This article presents an online method to continuously estimate the inertia of a power system. The inertia is computed from data provided by Phasor Measurement Units (PMUs) using small variations in frequency and power under ambient conditions. The method uses electrical and kinetic energy variations to compute inertia. In addition, a \u0000<inline-formula> <tex-math>$Q$ </tex-math></inline-formula>\u0000-learning-based method is presented to identify mechanical power changes to discard invalid inertia estimates. The method is demonstrated using the IEEE-39 bus system to monitor the regional inertia of the test system.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10026331.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50417314","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}
Pub Date : 2023-01-01DOI: 10.1109/OJIM.2023.3341668
{"title":"IEEE Instrumentation and Measurement Society","authors":"","doi":"10.1109/OJIM.2023.3341668","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3341668","url":null,"abstract":"","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10402604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139494252","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}
Pub Date : 2023-01-01DOI: 10.1109/OJIM.2024.3359518
{"title":"2023 Index IEEE Open Journal of Instrumentation and Measurement Vol. 2","authors":"","doi":"10.1109/OJIM.2024.3359518","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3359518","url":null,"abstract":"","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139676374","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}
Pub Date : 2023-01-01DOI: 10.1109/OJIM.2023.3338782
Katie Brinker;Reza Zoughi
Nondestructive testing and evaluation (NDT&E) are diverse fields that span many areas of science and engineering disciplines, while also impacting many industries. The ever-increasing growth and development of complex materials and structures necessitates new and advanced NDT&E methods for manufacturing process control, and in-service flaw detection and evaluation.
{"title":"Guest Editorial for Nondestructive Testing and Evaluation (NDT&E) Special Section","authors":"Katie Brinker;Reza Zoughi","doi":"10.1109/OJIM.2023.3338782","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3338782","url":null,"abstract":"Nondestructive testing and evaluation (NDT&E) are diverse fields that span many areas of science and engineering disciplines, while also impacting many industries. The ever-increasing growth and development of complex materials and structures necessitates new and advanced NDT&E methods for manufacturing process control, and in-service flaw detection and evaluation.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10389176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139434716","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}
Pub Date : 2023-01-01DOI: 10.1109/OJIM.2023.3341667
{"title":"IEEE Instrumentation and Measurement Society","authors":"","doi":"10.1109/OJIM.2023.3341667","DOIUrl":"https://doi.org/10.1109/OJIM.2023.3341667","url":null,"abstract":"","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10403397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139494231","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}
Pub Date : 2022-12-30DOI: 10.1109/OJIM.2022.3232650
Susnata Bhattacharya;Biplob Ray;Ritesh Chugh;Steven Gordon
This article presents a novel log abstraction framework based on neural open information extraction (OpenIE) and dynamic word embedding principles. Though various log parsing frameworks are proposed in the literature, the existing frameworks are modeled on predefined heuristics or auto-regressive methodologies that work well in offline scenarios. However, these frameworks are less suitable for dynamic self-adaptive systems, such as the Internet of Things (IoT), where the log outputs have diverse contextual variations and disparate time irregularities. Therefore, it is essential to move away from these traditional approaches and develop a systematic model that can effectively analyze log outputs in real-time and increase the system up-time of IoT networks so that they are almost always available. To address these needs, the proposed framework used OpenIE along with term frequency/inverse document frequency (TF/IDF) vectorization for constructing a set of relational triples (aka triple-sets). Additionally, a dynamic pretrained encoder–decoder architecture is utilized to imbibe the positional and contextualized information in its resultant outputs. The adopted methodology has enabled the proposed framework to extract richer word representations with dynamic contextualization of time-sensitive event logs to enhance further downstream activities, such as failure prediction and prognostic analysis of IoT networks. The proposed framework is evaluated on the system event log traces accumulated from a long range wide-area network (LoRaWAN) IoT gateway to proactively determine the probable causes of its various failure scenarios. Additionally, the study also provided a comparative analysis of its mathematical representations with that of the current state-of-the-art (SOTA) approaches to project the advantages and benefits of the proposed model, particularly from its data analytics standpoint.
{"title":"An Online Parsing Framework for Semistructured Streaming System Logs of Internet of Things Systems","authors":"Susnata Bhattacharya;Biplob Ray;Ritesh Chugh;Steven Gordon","doi":"10.1109/OJIM.2022.3232650","DOIUrl":"https://doi.org/10.1109/OJIM.2022.3232650","url":null,"abstract":"This article presents a novel log abstraction framework based on neural open information extraction (OpenIE) and dynamic word embedding principles. Though various log parsing frameworks are proposed in the literature, the existing frameworks are modeled on predefined heuristics or auto-regressive methodologies that work well in offline scenarios. However, these frameworks are less suitable for dynamic self-adaptive systems, such as the Internet of Things (IoT), where the log outputs have diverse contextual variations and disparate time irregularities. Therefore, it is essential to move away from these traditional approaches and develop a systematic model that can effectively analyze log outputs in real-time and increase the system up-time of IoT networks so that they are almost always available. To address these needs, the proposed framework used OpenIE along with term frequency/inverse document frequency (TF/IDF) vectorization for constructing a set of relational triples (aka triple-sets). Additionally, a dynamic pretrained encoder–decoder architecture is utilized to imbibe the positional and contextualized information in its resultant outputs. The adopted methodology has enabled the proposed framework to extract richer word representations with dynamic contextualization of time-sensitive event logs to enhance further downstream activities, such as failure prediction and prognostic analysis of IoT networks. The proposed framework is evaluated on the system event log traces accumulated from a long range wide-area network (LoRaWAN) IoT gateway to proactively determine the probable causes of its various failure scenarios. Additionally, the study also provided a comparative analysis of its mathematical representations with that of the current state-of-the-art (SOTA) approaches to project the advantages and benefits of the proposed model, particularly from its data analytics standpoint.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"2 ","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/10025401/10004508.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50417560","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}
Pub Date : 2022-12-22DOI: 10.1109/OJIM.2022.3225888
Sabrina Grassini;Marco Parvis
Measurement is fundamental to medical research and clinical practice. Physicians, clinicians, and medical laboratory scientists must not only be able to detect and diagnose health issues but also have confidence in the results reported by their instruments and measurement methods in order to make the correct decision for their patients. Reliability, accuracy, and efficiency of the implemented methods and devices are, therefore, the main concerns of researchers working in the field of medical measurements and applications.
{"title":"Guest Editorial for Recent Advances in Medical, Biomedical, and Healthcare Measurements Special Section","authors":"Sabrina Grassini;Marco Parvis","doi":"10.1109/OJIM.2022.3225888","DOIUrl":"https://doi.org/10.1109/OJIM.2022.3225888","url":null,"abstract":"Measurement is fundamental to medical research and clinical practice. Physicians, clinicians, and medical laboratory scientists must not only be able to detect and diagnose health issues but also have confidence in the results reported by their instruments and measurement methods in order to make the correct decision for their patients. Reliability, accuracy, and efficiency of the implemented methods and devices are, therefore, the main concerns of researchers working in the field of medical measurements and applications.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"1 ","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/9687502/09997178.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50426836","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}
Pub Date : 2022-12-20DOI: 10.1109/OJIM.2022.3225890
Wuqiang Yang;Masahiro Takei;Yixin Ma
Initially, electrical tomography techniques were developed for industrial applications. Recently, electrical tomography for medical application has attracted strong interest and several research groups over the world are working on this topic. In this special session, four papers are about electrical impedance tomography (EIT) for medical application, including cell, thorax, and bladder. One paper is about electrical capacitance tomography (ECT) for industrial application, i.e., velocity profile measurement in fluidized beds. Because electrical tomography has been around for many years, hardware design of electrical tomography systems is relatively mature. In contrast, there is plenty room for research in image reconstruction algorithms. In this special session, most papers are about improvement in image reconstruction, rather than in hardware. We hope that this special session provides a good reference point for researchers, who are active in the relevant field and will serve as a catalyst to trigger further investigation.
{"title":"Guest Editorial for Advances in Tomography for Industrial and Medical Applications Special Section","authors":"Wuqiang Yang;Masahiro Takei;Yixin Ma","doi":"10.1109/OJIM.2022.3225890","DOIUrl":"https://doi.org/10.1109/OJIM.2022.3225890","url":null,"abstract":"Initially, electrical tomography techniques were developed for industrial applications. Recently, electrical tomography for medical application has attracted strong interest and several research groups over the world are working on this topic. In this special session, four papers are about electrical impedance tomography (EIT) for medical application, including cell, thorax, and bladder. One paper is about electrical capacitance tomography (ECT) for industrial application, i.e., velocity profile measurement in fluidized beds. Because electrical tomography has been around for many years, hardware design of electrical tomography systems is relatively mature. In contrast, there is plenty room for research in image reconstruction algorithms. In this special session, most papers are about improvement in image reconstruction, rather than in hardware. We hope that this special session provides a good reference point for researchers, who are active in the relevant field and will serve as a catalyst to trigger further investigation.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"1 ","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/9687502/09994236.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50426825","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}
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