This article presents a novel approach for fault detection in a hydraulic actuation system. The fault of interest is the internal leakage of the actuator, which may often be caused by the wearing down of the piston seal. Bayesian classification and polyscale complexity measures are used in this article. Bayesian inference provides a probabilistic framework for classification that combines prior knowledge with observed data to update the probability distribution of the classification parameters. It results in a posterior distribution that reflects the updated knowledge. This allows for more accurate and reliable fault detection, especially in cases where the available data are uncertain or noisy. In order to extract features from the acquired signals, a polyscale measure known as variance fractal dimension (VFD) is employed. VFD measures are employed as features for Bayesian classification, allowing for distinguishing faulty conditions. The efficacy of the proposed method is demonstrated using experimental data, achieving an accuracy of 93.75%. Consequently, the proposed method is considered to be promising for fault detection in fluid power applications.
{"title":"Fault Detection in an Electro-Hydrostatic Actuator Using Polyscale Complexity Measures and Bayesian Classification","authors":"Soleiman Hosseinpour;Witold Kinsner;Saman Muthukumarana;Nariman Sepehri","doi":"10.1109/OJIM.2024.3487237","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3487237","url":null,"abstract":"This article presents a novel approach for fault detection in a hydraulic actuation system. The fault of interest is the internal leakage of the actuator, which may often be caused by the wearing down of the piston seal. Bayesian classification and polyscale complexity measures are used in this article. Bayesian inference provides a probabilistic framework for classification that combines prior knowledge with observed data to update the probability distribution of the classification parameters. It results in a posterior distribution that reflects the updated knowledge. This allows for more accurate and reliable fault detection, especially in cases where the available data are uncertain or noisy. In order to extract features from the acquired signals, a polyscale measure known as variance fractal dimension (VFD) is employed. VFD measures are employed as features for Bayesian classification, allowing for distinguishing faulty conditions. The efficacy of the proposed method is demonstrated using experimental data, achieving an accuracy of 93.75%. Consequently, the proposed method is considered to be promising for fault detection in fluid power applications.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10739666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672094","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 : 2024-10-28DOI: 10.1109/OJIM.2024.3487238
David A. Jack;Pruthul Kokkada Ravindranath;Khaled Matalgah;Trevor Fleck
This work investigates the detection and quantification of the damages incurred during the drilling process on a carbon fiber-reinforced polymer (CFRP) composite using nondestructive evaluation techniques of full waveform captured ultrasonic testing (UT) and comparing the damage quantification with X-ray micro-computed tomography ( $mu $ CT) scan data. A methodology is presented to extract the 3-D damage surface from the captured UT data obtained from a 20-MHz spherically focused transducer, which can be later used for a structural simulation for the load reduction due to the presence of delaminations. Three samples are fabricated to demonstrate the methodology as well as the damage profile from different fabrication methods. Two samples are prepared using a precision drill press, while the third sample is drilled with a programmable drilling machine with a controlled feed rate. An immersion inspection system utilizing a spherically focused ultrasound transducer is used to capture the full waveform data from which the maximum effective radius of the drilled hole as well as the drilling-induced damage is extracted. The effective damage radius is measured through the thickness of the sample and compared to the $mu $ CT data sets. A relation between the drilling practices as well as the sample quality to the average error between the analyzed UT and $mu $ CT data sets is presented, with an absolute relative error between 1.2% and 6.0%.
{"title":"Quantification of Drill Hole Damages in CFRP Laminates Using High-Resolution Ultrasonic Testing","authors":"David A. Jack;Pruthul Kokkada Ravindranath;Khaled Matalgah;Trevor Fleck","doi":"10.1109/OJIM.2024.3487238","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3487238","url":null,"abstract":"This work investigates the detection and quantification of the damages incurred during the drilling process on a carbon fiber-reinforced polymer (CFRP) composite using nondestructive evaluation techniques of full waveform captured ultrasonic testing (UT) and comparing the damage quantification with X-ray micro-computed tomography (\u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000CT) scan data. A methodology is presented to extract the 3-D damage surface from the captured UT data obtained from a 20-MHz spherically focused transducer, which can be later used for a structural simulation for the load reduction due to the presence of delaminations. Three samples are fabricated to demonstrate the methodology as well as the damage profile from different fabrication methods. Two samples are prepared using a precision drill press, while the third sample is drilled with a programmable drilling machine with a controlled feed rate. An immersion inspection system utilizing a spherically focused ultrasound transducer is used to capture the full waveform data from which the maximum effective radius of the drilled hole as well as the drilling-induced damage is extracted. The effective damage radius is measured through the thickness of the sample and compared to the \u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000CT data sets. A relation between the drilling practices as well as the sample quality to the average error between the analyzed UT and \u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000CT data sets is presented, with an absolute relative error between 1.2% and 6.0%.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737247","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777889","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 : 2024-10-28DOI: 10.1109/OJIM.2024.3487239
Mohsen Barzegar;Dario J. Pasadas;Artur L. Ribeiro;Helena G. Ramos
Damage imaging algorithms are crucial for evaluating the condition of critical structures such as adhesively bonded joints. Particularly during service, baseline-free structural health monitoring (SHM) is essential for robust and real-time evaluation. This article proposes and investigates the impact of the shape of the damage intensity distribution and damage index on the damage imaging of composite lap joints using a baseline-free SHM system. This system comprises a parallel array of piezoelectric transducers attached to both sides of the lap joint for generating and receiving ultrasonic-guided waves. Various features are extracted from the received signals to serve as damage indices, representing the peak amplitude and energy of the signals as well as the time of flight (ToF). Different shapes of damage intensity distribution, including elliptical, diamond, rectangular, and quadrilateral, are considered between pairs of sensors to investigate their effects on the total damage intensity distribution. To evaluate the impact of these parameters, a 2-D correlation coefficient was employed to compare the results obtained from the baseline-free SHM system with the image containing actual defects. The results reveal that the ToF was ineffective in providing high correlation and considering the signal’s energy with quadrilateral shape achieved the highest correlation.
{"title":"Baseline-Free Damage Imaging for Structural Health Monitoring of Composite Lap Joint Using Ultrasonic-Guided Waves","authors":"Mohsen Barzegar;Dario J. Pasadas;Artur L. Ribeiro;Helena G. Ramos","doi":"10.1109/OJIM.2024.3487239","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3487239","url":null,"abstract":"Damage imaging algorithms are crucial for evaluating the condition of critical structures such as adhesively bonded joints. Particularly during service, baseline-free structural health monitoring (SHM) is essential for robust and real-time evaluation. This article proposes and investigates the impact of the shape of the damage intensity distribution and damage index on the damage imaging of composite lap joints using a baseline-free SHM system. This system comprises a parallel array of piezoelectric transducers attached to both sides of the lap joint for generating and receiving ultrasonic-guided waves. Various features are extracted from the received signals to serve as damage indices, representing the peak amplitude and energy of the signals as well as the time of flight (ToF). Different shapes of damage intensity distribution, including elliptical, diamond, rectangular, and quadrilateral, are considered between pairs of sensors to investigate their effects on the total damage intensity distribution. To evaluate the impact of these parameters, a 2-D correlation coefficient was employed to compare the results obtained from the baseline-free SHM system with the image containing actual defects. The results reveal that the ToF was ineffective in providing high correlation and considering the signal’s energy with quadrilateral shape achieved the highest correlation.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600214","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 : 2024-10-24DOI: 10.1109/OJIM.2024.3485621
Patrick Grates
In the field of sensors and instrumentation used for navigation, rotation of the instruments and sensors has been used extensively in navigation systems to remove errors due to bias, bias instability, and noise. Microelectronic mechanical systems (MEMSs)-based inertial measurement units (IMUs) have been rotated at increasingly higher angular rates in the interest of managing and removing error from the system. The question becomes, “What is the ultimate rotation rate for a MEMS-based IMU to manage or remove error while retaining sensitivity for accurate measurements? This study delves into the nuances of IMU rotation rates, and what rotation rates are optimal for high-quality measurements. It explores the impact of rotation on the sensitivity of the accelerometers while obtaining stability in angular rate measurements from the gyros. Additionally, the study evaluates methods used for determining which rotation rates are best. The findings aim to enhance the performance of MEMS-based IMUs in dynamic environments and contribute to advancements in navigation systems used in autonomous vehicles and robots reliant on internal and independent systems.
{"title":"IMU Optimal Rotation Rates","authors":"Patrick Grates","doi":"10.1109/OJIM.2024.3485621","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3485621","url":null,"abstract":"In the field of sensors and instrumentation used for navigation, rotation of the instruments and sensors has been used extensively in navigation systems to remove errors due to bias, bias instability, and noise. Microelectronic mechanical systems (MEMSs)-based inertial measurement units (IMUs) have been rotated at increasingly higher angular rates in the interest of managing and removing error from the system. The question becomes, “What is the ultimate rotation rate for a MEMS-based IMU to manage or remove error while retaining sensitivity for accurate measurements? This study delves into the nuances of IMU rotation rates, and what rotation rates are optimal for high-quality measurements. It explores the impact of rotation on the sensitivity of the accelerometers while obtaining stability in angular rate measurements from the gyros. Additionally, the study evaluates methods used for determining which rotation rates are best. The findings aim to enhance the performance of MEMS-based IMUs in dynamic environments and contribute to advancements in navigation systems used in autonomous vehicles and robots reliant on internal and independent systems.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10734361","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600217","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 : 2024-10-24DOI: 10.1109/OJIM.2024.3485618
Federica Zonzini;Wenliang Xiang;Luca de Marchi
Acoustic emission (AE) is one of the most effective nondestructive testing (NDT) techniques for the identification and characterization of stress waves originated at the uprising of acoustic-related defects (e.g., cracks). To this end, the estimation of the time of arrival (ToA) is crucial. In this work, a novel processing flow which shifts the identification process from the time to the time-frequency domain via wavelet transform (WT) is proposed, allowing to better capture transient behaviors typical of the originated AE signals. More specifically, both the continuous and the discrete WT alternatives have been explored to find the best compromise between time-frequency resolution and computational complexity in view of extreme edge deployments. Furthermore, the event-driven capabilities of neuromorphic architectures (and spiking neural networks (SNNs) in particular) in processing spiky and sparse temporal information are exploited to retrieve ToA in a beyond state-of-the-art power-efficient manner and negligible loss of performance with respect to standard models. Therefore, we aim at combining the superior performances in ToA identification enabled by the WT operator with the unique energy saving disclosed by spiking hardware and software. Experimental tests executed on a metallic plate structure demonstrated that WT combined with SNN can achieve high precision (median values less than 5 cm) in ToA estimation and AE source localization even in the presence of relevant noise (signal-to-noise ratio down to 2 dB), while its deployment on dedicated neuromorphic architectures can reduce by six orders of magnitude the power expenditure per inference when compared to standard convolutional architectures.
{"title":"Spiking Neural Networks for Energy-Efficient Acoustic Emission-Based Monitoring","authors":"Federica Zonzini;Wenliang Xiang;Luca de Marchi","doi":"10.1109/OJIM.2024.3485618","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3485618","url":null,"abstract":"Acoustic emission (AE) is one of the most effective nondestructive testing (NDT) techniques for the identification and characterization of stress waves originated at the uprising of acoustic-related defects (e.g., cracks). To this end, the estimation of the time of arrival (ToA) is crucial. In this work, a novel processing flow which shifts the identification process from the time to the time-frequency domain via wavelet transform (WT) is proposed, allowing to better capture transient behaviors typical of the originated AE signals. More specifically, both the continuous and the discrete WT alternatives have been explored to find the best compromise between time-frequency resolution and computational complexity in view of extreme edge deployments. Furthermore, the event-driven capabilities of neuromorphic architectures (and spiking neural networks (SNNs) in particular) in processing spiky and sparse temporal information are exploited to retrieve ToA in a beyond state-of-the-art power-efficient manner and negligible loss of performance with respect to standard models. Therefore, we aim at combining the superior performances in ToA identification enabled by the WT operator with the unique energy saving disclosed by spiking hardware and software. Experimental tests executed on a metallic plate structure demonstrated that WT combined with SNN can achieve high precision (median values less than 5 cm) in ToA estimation and AE source localization even in the presence of relevant noise (signal-to-noise ratio down to 2 dB), while its deployment on dedicated neuromorphic architectures can reduce by six orders of magnitude the power expenditure per inference when compared to standard convolutional architectures.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10734354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600118","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 : 2024-10-24DOI: 10.1109/OJIM.2024.3485711
Nick Torenvliet;John S. Zelek
Diffusion partition consensus is a novel generative AI-based technique for time-series anomaly detection and data imputation in the presence of outliers. To illustrate the method, an implementation with design choices tailored for well-structured time series typical of single probe ultrasonic nondestructive evaluation (NDE) datasets is proposed. The technique relies on cross-talk between a conditional score-based diffusion model, and two well-chosen Savitzky-Golay filters. Testing and evaluation are performed on a series of progressively information rich synthetic datasets, and on real-world ultrasonic NDE datasets taken from a Canada Deuterium Uranium nuclear reactor pressure tube and calibration fixture. The iterative technique is a blend of stochastic and deterministic methods that uses confidence and consensus of target parameter estimates to update several data classifying partitions over the dataset, which in turn allows a new set of estimates and confidence measures to be established. Data classification induces a progressive bias in the training datasets allowing a diffusion model to identify the prevalent distribution. Methods for fault diagnosis support the efficacious inclusion of a human in the loop making the technique suitable for use in safety-critical applications. The main advantages of the technique are that it is unsupervised—in that it does not require labeled datasets or significant data preprocessing, does not rely on out-of-distribution generalization, provides means for fault diagnosis without recourse to ground truth, converges with stability, and naturally includes a human in the loop. The quality of results, the checks and balances provided by the fault diagnosis mechanism, and the opportunity to include a human in the loop, support the case for usage in safety-critical contexts such as NDE at a nuclear power facility.
{"title":"Diffusion Partition Consensus: Diffusion-Aided Time-of-Flight Estimates, Anomaly Detection, and Localization for Ultrasonic Nondestructive Evaluation Data","authors":"Nick Torenvliet;John S. Zelek","doi":"10.1109/OJIM.2024.3485711","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3485711","url":null,"abstract":"Diffusion partition consensus is a novel generative AI-based technique for time-series anomaly detection and data imputation in the presence of outliers. To illustrate the method, an implementation with design choices tailored for well-structured time series typical of single probe ultrasonic nondestructive evaluation (NDE) datasets is proposed. The technique relies on cross-talk between a conditional score-based diffusion model, and two well-chosen Savitzky-Golay filters. Testing and evaluation are performed on a series of progressively information rich synthetic datasets, and on real-world ultrasonic NDE datasets taken from a Canada Deuterium Uranium nuclear reactor pressure tube and calibration fixture. The iterative technique is a blend of stochastic and deterministic methods that uses confidence and consensus of target parameter estimates to update several data classifying partitions over the dataset, which in turn allows a new set of estimates and confidence measures to be established. Data classification induces a progressive bias in the training datasets allowing a diffusion model to identify the prevalent distribution. Methods for fault diagnosis support the efficacious inclusion of a human in the loop making the technique suitable for use in safety-critical applications. The main advantages of the technique are that it is unsupervised—in that it does not require labeled datasets or significant data preprocessing, does not rely on out-of-distribution generalization, provides means for fault diagnosis without recourse to ground truth, converges with stability, and naturally includes a human in the loop. The quality of results, the checks and balances provided by the fault diagnosis mechanism, and the opportunity to include a human in the loop, support the case for usage in safety-critical contexts such as NDE at a nuclear power facility.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10734664","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645430","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 : 2024-10-14DOI: 10.1109/OJIM.2024.3477571
Aqeel T. Fadhil;Glenn Washer;Anish Poudel;Kalpana Yadav;Survesh Shrestha
The research presented in this article investigated the effect of low temperatures on acoustic properties in coupling fluid and rail steel. The study focused on the effect of low-temperature conditions on ultrasonic attenuation and velocity. The work introduces practical considerations for improving the quality of ultrasonic testing (UT) performed in cold weather. The study investigated common coupling fluids used in rail detector cars equipped with liquid-filled tires that house ultrasonic transducers. Velocity measurements of longitudinal waves propagating through the fluid and reflecting from a steel disc target were conducted. Steel properties were studied by fabricating two specimens from the head and Web of two different 136RE rail sections. Velocity of longitudinal waves and mode-converted shear waves as well as attenuation measurements were conducted in rail specimens with side drilled holes (SDHs) at different depths. The tests were performed in an ultrasonic immersion tank integrated with a heat exchanger and chiller bath to obtain the targeted test temperatures ranging from $- 50~^{circ }$ C to ${+} 20~^{circ }$ C. The coupling fluid test results showed a linear increase in the ultrasonic velocity as the temperature decreased with a rate that ranged from −2.70 m/s/°C to −1.83 m/s/°C for the tested fluids. The test results also showed increased velocity in rail steel with decreasing temperatures with an average rate of −0.65 m/s/°C for longitudinal waves and an average rate of −0.33 m/s/°C for shear waves. These results indicate that temperature-dependent velocities must be used to obtain the desired refraction angle and adjustments to amplitude-based acceptance criteria may be needed to ensure uniform acceptance/rejection capabilities across all potential inspection temperatures.
{"title":"Ultrasonic Testing of Railroad Rails: Cold Temperature Effects and Considerations","authors":"Aqeel T. Fadhil;Glenn Washer;Anish Poudel;Kalpana Yadav;Survesh Shrestha","doi":"10.1109/OJIM.2024.3477571","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3477571","url":null,"abstract":"The research presented in this article investigated the effect of low temperatures on acoustic properties in coupling fluid and rail steel. The study focused on the effect of low-temperature conditions on ultrasonic attenuation and velocity. The work introduces practical considerations for improving the quality of ultrasonic testing (UT) performed in cold weather. The study investigated common coupling fluids used in rail detector cars equipped with liquid-filled tires that house ultrasonic transducers. Velocity measurements of longitudinal waves propagating through the fluid and reflecting from a steel disc target were conducted. Steel properties were studied by fabricating two specimens from the head and Web of two different 136RE rail sections. Velocity of longitudinal waves and mode-converted shear waves as well as attenuation measurements were conducted in rail specimens with side drilled holes (SDHs) at different depths. The tests were performed in an ultrasonic immersion tank integrated with a heat exchanger and chiller bath to obtain the targeted test temperatures ranging from \u0000<inline-formula> <tex-math>$- 50~^{circ }$ </tex-math></inline-formula>\u0000C to \u0000<inline-formula> <tex-math>${+} 20~^{circ }$ </tex-math></inline-formula>\u0000C. The coupling fluid test results showed a linear increase in the ultrasonic velocity as the temperature decreased with a rate that ranged from −2.70 m/s/°C to −1.83 m/s/°C for the tested fluids. The test results also showed increased velocity in rail steel with decreasing temperatures with an average rate of −0.65 m/s/°C for longitudinal waves and an average rate of −0.33 m/s/°C for shear waves. These results indicate that temperature-dependent velocities must be used to obtain the desired refraction angle and adjustments to amplitude-based acceptance criteria may be needed to ensure uniform acceptance/rejection capabilities across all potential inspection temperatures.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552004","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 : 2024-10-11DOI: 10.1109/OJIM.2024.3478314
Camille Raets;Chaïmae El Aisati;Amir L. Rifi;Mark De Ridder;Koen Putman;Johan De Mey;Alexandra Sermeus;Kurt Barbé
The growing popularity of artificial intelligence (AI) has increased its widespread adoption in medicine. However, the relationship between AI and medical experts’ opinions remains elusive. This study investigated the consistency between Random Forest’s prediction for rectal cancer regression grades and doctors’ opinion based on clinical data. We examined the impact of grading system subjectivity on the algorithm. Analyzing clinical parameters and medical notes from 85 rectal cancer patients, we identified patients with ambivalent grades, the “gray-zone patients,” and explored the algorithm’s difficulty in predicting their regression grade. We also introduced a regularization parameter to test if some patients could still correctly be predicted when some statistical information is suppressed. Our results demonstrated that the gray-zone patients were significantly more difficult to classify using the algorithm, suggesting that such patients should be reviewed twice to reduce errors. Additionally, we observed that the regularization parameter did not benefit gray-zone patients as much as others. Our findings emphasize the need for AI and clinical experts to work collaboratively since the algorithm cannot consider the subjectivity that medical experts can identify. Further research is necessary to incorporate subjectivity into AI algorithms to enhance their predictive capabilities and further bridge the gap between medicine and AI.
{"title":"Bridging the Gap Between Machine Learning and Medicine: A Critical Evaluation of the Dworak Regression Grade in Rectal Cancer","authors":"Camille Raets;Chaïmae El Aisati;Amir L. Rifi;Mark De Ridder;Koen Putman;Johan De Mey;Alexandra Sermeus;Kurt Barbé","doi":"10.1109/OJIM.2024.3478314","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3478314","url":null,"abstract":"The growing popularity of artificial intelligence (AI) has increased its widespread adoption in medicine. However, the relationship between AI and medical experts’ opinions remains elusive. This study investigated the consistency between Random Forest’s prediction for rectal cancer regression grades and doctors’ opinion based on clinical data. We examined the impact of grading system subjectivity on the algorithm. Analyzing clinical parameters and medical notes from 85 rectal cancer patients, we identified patients with ambivalent grades, the “gray-zone patients,” and explored the algorithm’s difficulty in predicting their regression grade. We also introduced a regularization parameter to test if some patients could still correctly be predicted when some statistical information is suppressed. Our results demonstrated that the gray-zone patients were significantly more difficult to classify using the algorithm, suggesting that such patients should be reviewed twice to reduce errors. Additionally, we observed that the regularization parameter did not benefit gray-zone patients as much as others. Our findings emphasize the need for AI and clinical experts to work collaboratively since the algorithm cannot consider the subjectivity that medical experts can identify. Further research is necessary to incorporate subjectivity into AI algorithms to enhance their predictive capabilities and further bridge the gap between medicine and AI.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10715590","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736589","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 : 2024-10-11DOI: 10.1109/OJIM.2024.3477572
Amit Nayak;Miodrag Bolic
In this short article, we present a new method to use a smartphone placed unattached on a subject’s chest in the supine position to obtain heartbeat and breathing signals and estimate heart and breathing rates, simultaneously. We collected 3-axis accelerometer, gyroscope, and magnetometer signals and performed sensor fusion to extract a user’s breathing signal and breathing rate. A hidden Markov model was used to segment the ballistocardiograph/seismocardiograph signals and extract the heart rate. The smartphone application was verified against breathing belt measurements and electrocardiogram measurements. We modified and proposed several suitable signal quality metrics for seismocardiograph signals. The overall results show that the application accurately estimated the breathing and heart rates, achieving a minimum mean percent error of 2.52% for breathing and 2.33% for heart rate. This work is a big step forward for vital sign estimation using inexpensive pervasive devices.
{"title":"Smartphone System for Heart Rate and Breathing Rate Estimation","authors":"Amit Nayak;Miodrag Bolic","doi":"10.1109/OJIM.2024.3477572","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3477572","url":null,"abstract":"In this short article, we present a new method to use a smartphone placed unattached on a subject’s chest in the supine position to obtain heartbeat and breathing signals and estimate heart and breathing rates, simultaneously. We collected 3-axis accelerometer, gyroscope, and magnetometer signals and performed sensor fusion to extract a user’s breathing signal and breathing rate. A hidden Markov model was used to segment the ballistocardiograph/seismocardiograph signals and extract the heart rate. The smartphone application was verified against breathing belt measurements and electrocardiogram measurements. We modified and proposed several suitable signal quality metrics for seismocardiograph signals. The overall results show that the application accurately estimated the breathing and heart rates, achieving a minimum mean percent error of 2.52% for breathing and 2.33% for heart rate. This work is a big step forward for vital sign estimation using inexpensive pervasive devices.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10714465","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600216","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 : 2024-10-10DOI: 10.1109/OJIM.2024.3477568
Huashen Guan;Qiushen Cai;Xiaobin Li;Guofu Sun
With the development of optics, light section method has become a feasible measurement for surface roughness, while the short sampling length is negative to the accuracy. To overcome this defect, this article proposed a measurement under ResNet-based roughness classification and light section with seam-driven image stitching (RCLS). First, the images were classified with ResNet neural network, then stitched and enhanced by scale invariant feature transform (SIFT) and optimized random sample consensus (RANSAC) algorithm for the best visual effect. After this, images were processed by Nobuyuki Otsu method and Freeman chain code tracking algorithm. Least square was also adopted to calculate the optical band edge curve and contour midline. Finally, the roughness contour arithmetic mean deviation model was established to evaluate the surface roughness. The experiments were conducted with vertical milled, planned, and turned samples that self-machined. The light section method had a reduction of 2.75% on the mean relative error compared to stylus and RCLS could further reduce the mean relative error by 1.42%, especially in planned sample. The RCLS could achieve a more accurate surface roughness by overcoming the disadvantages of small sample length and low precision of the light section method, and is more convenient than stylus.
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