An easy-to-fabricate, full circle range (0°–360°), planar coil-based variable reluctance (VR) angle transducer with enhanced linearity is presented in this article. The proposed sensor system aims to mitigate the limitations of the existing VR angle sensors, particularly their limited accuracy and nonlinearity, resulting from the inherent sensor output characteristics. By carefully designing the coil geometry to achieve uniform flux distribution and implementing a simple semicircular-shaped rotor, the sensor system offers enhanced performance and linearity. The proposed sensor employs a semicircular-shaped rotor plate (RP) placed between two printed circuit board (PCBs) with four coils each. These coils are strategically designed to ensure a linear variation of inductance with respect to the RP position, resulting in improved linearity in the sensor output. After validating the sensor design through analytical methods and finite-element analysis (FEA), a suitable algorithm was developed for accurately estimating the rotor angle. A sensor prototype was manufactured to evaluate the performance of the sensor system. The prototype showed an excellent linearity with a worst case error of 0.31% and a resolution of 0.11°. The sensor shows negligible sensitivity to axial misalignment of the shaft and the presence of external magnetic objects, highlighting the practical usefulness of the system.
本文介绍了一种易于制造、全圆范围（0°-360°）、基于平面线圈的可变磁阻（VR）角度传感器，具有更高的线性度。拟议的传感器系统旨在缓解现有 VR 角度传感器的局限性，特别是其固有的传感器输出特性所导致的有限精度和非线性。通过精心设计线圈的几何形状以实现均匀的磁通量分布，并采用简单的半圆形转子，该传感器系统的性能和线性度都得到了提高。拟议的传感器采用了一个半圆形转子板（RP），置于两块印刷电路板（PCB）之间，每块印刷电路板有四个线圈。这些线圈经过精心设计，可确保电感随 RP 位置的线性变化，从而提高传感器输出的线性度。通过分析方法和有限元分析（FEA）对传感器设计进行验证后，开发出一种合适的算法，用于准确估算转子角度。为评估传感器系统的性能，制造了一个传感器原型。原型显示出极佳的线性度，最坏情况下误差为 0.31%，分辨率为 0.11°。传感器对轴的轴向偏差和外部磁性物体的灵敏度几乎可以忽略不计，突出了该系统的实用性。
{"title":"A Variable Reluctance-Based Planar Dual-Coil Angle Sensor With Enhanced Linearity","authors":"Anil Kumar Appukuttan Nair Syamala Amma;P.P. Narayanan;Jeshma Thalapil Vaheeda;Sreenath Vijayakumar","doi":"10.1109/TIM.2024.3451596","DOIUrl":"https://doi.org/10.1109/TIM.2024.3451596","url":null,"abstract":"An easy-to-fabricate, full circle range (0°–360°), planar coil-based variable reluctance (VR) angle transducer with enhanced linearity is presented in this article. The proposed sensor system aims to mitigate the limitations of the existing VR angle sensors, particularly their limited accuracy and nonlinearity, resulting from the inherent sensor output characteristics. By carefully designing the coil geometry to achieve uniform flux distribution and implementing a simple semicircular-shaped rotor, the sensor system offers enhanced performance and linearity. The proposed sensor employs a semicircular-shaped rotor plate (RP) placed between two printed circuit board (PCBs) with four coils each. These coils are strategically designed to ensure a linear variation of inductance with respect to the RP position, resulting in improved linearity in the sensor output. After validating the sensor design through analytical methods and finite-element analysis (FEA), a suitable algorithm was developed for accurately estimating the rotor angle. A sensor prototype was manufactured to evaluate the performance of the sensor system. The prototype showed an excellent linearity with a worst case error of 0.31% and a resolution of 0.11°. The sensor shows negligible sensitivity to axial misalignment of the shaft and the presence of external magnetic objects, highlighting the practical usefulness of the system.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1109/TIM.2024.3481591
Xiangyang Yang;Haitao Hu;Donghua Xiao;Haidong Tao;Yitong Song;Zhengyou He
Accurate impedance measurements of the train and traction network are crucial for small-signal stability analysis of railway train-network system (RTNS). Although impedance measurement methods for four-quadrant converters (4QCs) in electric trains based on harmonic voltage disturbance injection have been proposed, few studies have investigated the impact of integrating a harmonic generator on RTNS stability. To address this issue, this article proposes a wideband harmonic disturbance generator (WHDG) and evaluates its impact on RTNS stability. The WHDG primarily comprises the back-to-back converter-based cascaded H-bridge (CHB) structure and a wideband coupling transformer. This generator can produce multifrequency perturbations with uniformly distributed spectrum energy. Subsequently, an accurate output impedance model is established based on the detailed topology and parameters of the WHDG. The model accounts for the impact of the dc impedance of the front-stage rectifier on the post-stage inverter. The close alignment between the modeling and simulation results demonstrates the accuracy of the deduced impedance model. Furthermore, an impedance-matching analysis of the RTNS with integrated WHDG is performed, indicating that the internal impedance of the WHDG weakens the stability of the tested RTNS. Finally, the effectiveness of the proposed WHDG is validated via a hardware-in-the-loop (HIL) experimental platform, and the impedance-matching analysis results are verified.
{"title":"Impedance-Matching Analysis of Wideband Harmonic Disturbance Generator for Railway Train-Network System","authors":"Xiangyang Yang;Haitao Hu;Donghua Xiao;Haidong Tao;Yitong Song;Zhengyou He","doi":"10.1109/TIM.2024.3481591","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481591","url":null,"abstract":"Accurate impedance measurements of the train and traction network are crucial for small-signal stability analysis of railway train-network system (RTNS). Although impedance measurement methods for four-quadrant converters (4QCs) in electric trains based on harmonic voltage disturbance injection have been proposed, few studies have investigated the impact of integrating a harmonic generator on RTNS stability. To address this issue, this article proposes a wideband harmonic disturbance generator (WHDG) and evaluates its impact on RTNS stability. The WHDG primarily comprises the back-to-back converter-based cascaded H-bridge (CHB) structure and a wideband coupling transformer. This generator can produce multifrequency perturbations with uniformly distributed spectrum energy. Subsequently, an accurate output impedance model is established based on the detailed topology and parameters of the WHDG. The model accounts for the impact of the dc impedance of the front-stage rectifier on the post-stage inverter. The close alignment between the modeling and simulation results demonstrates the accuracy of the deduced impedance model. Furthermore, an impedance-matching analysis of the RTNS with integrated WHDG is performed, indicating that the internal impedance of the WHDG weakens the stability of the tested RTNS. Finally, the effectiveness of the proposed WHDG is validated via a hardware-in-the-loop (HIL) experimental platform, and the impedance-matching analysis results are verified.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1109/TIM.2024.3484531
Weihao Zhang;Cai Yi;Lei Yan;Qi Liu;Qiuyang Zhou;Pengfei He;Le Ran;Yunzhi Lin
It has been demonstrated that fast convolutional sparse dictionary learning (FCSDL) is a useful instrument for diagnosing rolling bearing faults and can recover rolling bearing fault shocks unaffected by random slippage. However, although FCSDL is not impacted by random fluctuations and can rapidly reconstruct fault shock without truncating the signal, its performance for repetitive fault shock reconstruction is not optimal when dealing with strong noise vibration signals. Therefore, this article proposes cyclostationary convolutional sparse dictionary learning (CCSDL), which is guided by fault features (cyclostationarity) to achieve the greatest signal reconstruction performance. First, the proposed method is based on the rotation frequency, and various frequency-band-covering components in the vibration signal are reconstructed successively. In the meanwhile, the harmonic significance index (HSI), which can indicate the cyclostationarity of the fault shock, evaluates the fault characteristics of each reconstruction result and finally obtains the most significant reconstruction result. Compared with FCSDL and variational mode decomposition (VMD), the proposed method performs far superior in signal reconstruction when processing low SNR vibration data.
{"title":"Dictionary Learning Method for Cyclostationarity Maximization and Its Application to Bearing Fault Feature Extraction","authors":"Weihao Zhang;Cai Yi;Lei Yan;Qi Liu;Qiuyang Zhou;Pengfei He;Le Ran;Yunzhi Lin","doi":"10.1109/TIM.2024.3484531","DOIUrl":"https://doi.org/10.1109/TIM.2024.3484531","url":null,"abstract":"It has been demonstrated that fast convolutional sparse dictionary learning (FCSDL) is a useful instrument for diagnosing rolling bearing faults and can recover rolling bearing fault shocks unaffected by random slippage. However, although FCSDL is not impacted by random fluctuations and can rapidly reconstruct fault shock without truncating the signal, its performance for repetitive fault shock reconstruction is not optimal when dealing with strong noise vibration signals. Therefore, this article proposes cyclostationary convolutional sparse dictionary learning (CCSDL), which is guided by fault features (cyclostationarity) to achieve the greatest signal reconstruction performance. First, the proposed method is based on the rotation frequency, and various frequency-band-covering components in the vibration signal are reconstructed successively. In the meanwhile, the harmonic significance index (HSI), which can indicate the cyclostationarity of the fault shock, evaluates the fault characteristics of each reconstruction result and finally obtains the most significant reconstruction result. Compared with FCSDL and variational mode decomposition (VMD), the proposed method performs far superior in signal reconstruction when processing low SNR vibration data.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1109/TIM.2024.3485452
Chaozheng Xue;Tao Li;Yongzhao Li;Yuhan Ruan;Rui Zhang;Octavia A. Dobre
Radio frequency fingerprint (RFF) identification is a promising technique that exploits hardware impairment-induced features to achieve specific device identification. Among RFF features, carrier frequency offset (CFO) as a hotspot feature has received widespread attention. Since CFO is time-variant, existing research suggests compensating for its drift; however, this article emphasizes using the drift of CFO. Correspondingly, a novel RFF feature, named cyclic similarity (cyc-similarity), is proposed to depict the oscillator drift. Simply combining the cyc-similarity feature with a K-nearest neighbor (KNN) classifier, the system can achieve superior temporal and receiver generalization performance. On a public dataset of WiFi devices, the proposed method outperforms the existing methods.
射频指纹（RFF）识别是一种很有前途的技术，它利用硬件损伤引起的特征来实现特定设备的识别。在射频指纹特征中，载波频率偏移（CFO）作为一种热点特征受到广泛关注。由于载波频率偏移是时变的，现有研究建议对其漂移进行补偿；但本文强调利用载波频率偏移的漂移。因此，本文提出了一种名为 "循环相似性（cyc-similarity）"的新型 RFF 特征来描述振荡器漂移。只需将循环相似性特征与 K 近邻（KNN）分类器相结合，系统就能实现卓越的时间和接收器泛化性能。在一个公开的 WiFi 设备数据集上，所提出的方法优于现有的方法。
{"title":"Radio Frequency Fingerprinting for WiFi Devices Using Oscillator Drifts","authors":"Chaozheng Xue;Tao Li;Yongzhao Li;Yuhan Ruan;Rui Zhang;Octavia A. Dobre","doi":"10.1109/TIM.2024.3485452","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485452","url":null,"abstract":"Radio frequency fingerprint (RFF) identification is a promising technique that exploits hardware impairment-induced features to achieve specific device identification. Among RFF features, carrier frequency offset (CFO) as a hotspot feature has received widespread attention. Since CFO is time-variant, existing research suggests compensating for its drift; however, this article emphasizes using the drift of CFO. Correspondingly, a novel RFF feature, named cyclic similarity (cyc-similarity), is proposed to depict the oscillator drift. Simply combining the cyc-similarity feature with a K-nearest neighbor (KNN) classifier, the system can achieve superior temporal and receiver generalization performance. On a public dataset of WiFi devices, the proposed method outperforms the existing methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-31DOI: 10.1109/TIM.2024.3451569
Zhongliang Jiang;Xuesong Li;Xiangyu Chu;Angelos Karlas;Yuan Bi;Yingsheng Cheng;K. W. Samuel Au;Nassir Navab
Ultrasound-guided percutaneous needle insertion is a standard procedure employed in both biopsy and ablation in clinical practices. However, due to the complex interaction between tissue and instrument, the needle may deviate from the in-plane view, resulting in a lack of close monitoring of the percutaneous needle. To address this challenge, we introduce a robot-assisted ultrasound (US) imaging system designed to seamlessly monitor the insertion process and autonomously restore the visibility of the inserted instrument when misalignment happens. To this end, the adversarial structure is presented to encourage the generation of segmentation masks that align consistently with the ground truth in high-order space. This study also systematically investigates the effects on segmentation performance by exploring various training loss functions and their combinations. When misalignment between the probe and the percutaneous needle is detected, the robot is triggered to perform transverse searching to optimize the positional and rotational adjustment to restore needle visibility. The experimental results on ex-vivo porcine samples demonstrate that the proposed method can precisely segment the percutaneous needle (with a tip error of $0.37pm 0.29$ mm and an angle error of $1.19pm 0.29$ °). Furthermore, the needle appearance can be successfully restored under the repositioned probe pose in all 45 trials, with repositioning errors of $1.51pm 0.95~text {mm}$ and $1.25pm 0.79$ °.
{"title":"Needle Segmentation Using GAN: Restoring Thin Instrument Visibility in Robotic Ultrasound","authors":"Zhongliang Jiang;Xuesong Li;Xiangyu Chu;Angelos Karlas;Yuan Bi;Yingsheng Cheng;K. W. Samuel Au;Nassir Navab","doi":"10.1109/TIM.2024.3451569","DOIUrl":"https://doi.org/10.1109/TIM.2024.3451569","url":null,"abstract":"Ultrasound-guided percutaneous needle insertion is a standard procedure employed in both biopsy and ablation in clinical practices. However, due to the complex interaction between tissue and instrument, the needle may deviate from the in-plane view, resulting in a lack of close monitoring of the percutaneous needle. To address this challenge, we introduce a robot-assisted ultrasound (US) imaging system designed to seamlessly monitor the insertion process and autonomously restore the visibility of the inserted instrument when misalignment happens. To this end, the adversarial structure is presented to encourage the generation of segmentation masks that align consistently with the ground truth in high-order space. This study also systematically investigates the effects on segmentation performance by exploring various training loss functions and their combinations. When misalignment between the probe and the percutaneous needle is detected, the robot is triggered to perform transverse searching to optimize the positional and rotational adjustment to restore needle visibility. The experimental results on ex-vivo porcine samples demonstrate that the proposed method can precisely segment the percutaneous needle (with a tip error of \u0000<inline-formula> <tex-math>$0.37pm 0.29$ </tex-math></inline-formula>\u0000 mm and an angle error of \u0000<inline-formula> <tex-math>$1.19pm 0.29$ </tex-math></inline-formula>\u0000°). Furthermore, the needle appearance can be successfully restored under the repositioned probe pose in all 45 trials, with repositioning errors of \u0000<inline-formula> <tex-math>$1.51pm 0.95~text {mm}$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$1.25pm 0.79$ </tex-math></inline-formula>\u0000°.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liquid nitrogen (LN2) typically acts as a coolant in ice-assisted electron-beam lithography (iEBL) systems, so that the cryostage temperature cannot be lower than 77 K. To condense more gaseous precursors, such as carbon dioxide (CO2) in a high vacuum environment, a cooling system that does not rely on LN2 is necessary. In this article, we integrate a Gifford-McMahon (GM) cryocooler into the iEBL system, which can cool down samples from room temperature to 21 K in 2.25 h. The cold head and sample holder reach minimum temperatures of $5.37~pm ~0.012$ K and $19.14~pm ~0.009$ K, respectively, which lies within the temperature zone of liquid hydrogen. Furthermore, a gas-gap isolation system and discrete rotary valve are employed to minimize the vibration effects on the scanning electron microscope (SEM), with the vibration being limited to about 30 nm. Finally, CO2 has been investigated as the precursor, revealing itself as the second positive resist in iEBL, with a critical dose one order of magnitude less than water ice. Gold nanostructures are also successfully fabricated using such a resist. Our system achieves the lowest temperature in iEBL system to date, substantially expanding the range of precursors that can be used in iEBL.
{"title":"Liquid Hydrogen Temperature Cryostage for Ice-Assisted Electron-Beam Lithography","authors":"Rui Zheng;Limin Qi;Sizhuo Li;Zhihua Gan;Ding Zhao;Min Qiu","doi":"10.1109/TIM.2024.3485441","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485441","url":null,"abstract":"Liquid nitrogen (LN2) typically acts as a coolant in ice-assisted electron-beam lithography (iEBL) systems, so that the cryostage temperature cannot be lower than 77 K. To condense more gaseous precursors, such as carbon dioxide (CO2) in a high vacuum environment, a cooling system that does not rely on LN2 is necessary. In this article, we integrate a Gifford-McMahon (GM) cryocooler into the iEBL system, which can cool down samples from room temperature to 21 K in 2.25 h. The cold head and sample holder reach minimum temperatures of \u0000<inline-formula> <tex-math>$5.37~pm ~0.012$ </tex-math></inline-formula>\u0000 K and \u0000<inline-formula> <tex-math>$19.14~pm ~0.009$ </tex-math></inline-formula>\u0000 K, respectively, which lies within the temperature zone of liquid hydrogen. Furthermore, a gas-gap isolation system and discrete rotary valve are employed to minimize the vibration effects on the scanning electron microscope (SEM), with the vibration being limited to about 30 nm. Finally, CO2 has been investigated as the precursor, revealing itself as the second positive resist in iEBL, with a critical dose one order of magnitude less than water ice. Gold nanostructures are also successfully fabricated using such a resist. Our system achieves the lowest temperature in iEBL system to date, substantially expanding the range of precursors that can be used in iEBL.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1109/TIM.2024.3488150
Shaowen Ji;Chunxi Zhang;Longjie Tian;Longjun Ran;Yanqiang Yang
Two key factors for calculating the trajectory of a wellbore are the attitude and interval of the survey stations. A measurement while drilling (MWD) system that relies on magnetometers and accelerometers may fail to measure the wellbore attitude in an environment with magnetic anomalies. In addition, in the case of a wellbore trajectory with large variations in the attitude, the commonly used minimum curvature method (MCM) can result in large deviations from the planned wellbore trajectory. In this study, a novel dead reckoning (DR) method was developed that is constrained by drill pipe length subdivisions for improved tracking accuracy of wellbore trajectories with a large variety of attitudes. A gyro MWD system based on fiber optic gyroscopes (FOGs) and accelerometers is utilized to continuously calculate the wellbore attitude. The variations in the attitude angle within the wellbore can be tracked by using the proportional relationship between increments in the attitude and drill pipe length subdivisions, which can be synchronized with the gyro MWD update frequency. Simulations and experiments were performed to verify that the proposed method could accurately track wellbore trajectories with large variations in the attitude. In the simulation, the proposed method demonstrated a mean trajectory deviation of less than 0.5 m over a distance of 30 m, which was markedly lower than the mean deviation of 2.5 m by the MCM. In the slope experiment, the proposed method demonstrated substantially better tracking accuracy of the wellbore trajectory than the MCM. Measurements from an actual wellbore with large variations in the attitude confirmed that the proposed method reduced the tracking error by up to 3 m compared to the MCM.
{"title":"Dead Reckoning Method for Tracking Wellbore Trajectories Constrained by the Drill Pipe Length","authors":"Shaowen Ji;Chunxi Zhang;Longjie Tian;Longjun Ran;Yanqiang Yang","doi":"10.1109/TIM.2024.3488150","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488150","url":null,"abstract":"Two key factors for calculating the trajectory of a wellbore are the attitude and interval of the survey stations. A measurement while drilling (MWD) system that relies on magnetometers and accelerometers may fail to measure the wellbore attitude in an environment with magnetic anomalies. In addition, in the case of a wellbore trajectory with large variations in the attitude, the commonly used minimum curvature method (MCM) can result in large deviations from the planned wellbore trajectory. In this study, a novel dead reckoning (DR) method was developed that is constrained by drill pipe length subdivisions for improved tracking accuracy of wellbore trajectories with a large variety of attitudes. A gyro MWD system based on fiber optic gyroscopes (FOGs) and accelerometers is utilized to continuously calculate the wellbore attitude. The variations in the attitude angle within the wellbore can be tracked by using the proportional relationship between increments in the attitude and drill pipe length subdivisions, which can be synchronized with the gyro MWD update frequency. Simulations and experiments were performed to verify that the proposed method could accurately track wellbore trajectories with large variations in the attitude. In the simulation, the proposed method demonstrated a mean trajectory deviation of less than 0.5 m over a distance of 30 m, which was markedly lower than the mean deviation of 2.5 m by the MCM. In the slope experiment, the proposed method demonstrated substantially better tracking accuracy of the wellbore trajectory than the MCM. Measurements from an actual wellbore with large variations in the attitude confirmed that the proposed method reduced the tracking error by up to 3 m compared to the MCM.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1109/TIM.2024.3485405
Yinhao Ren;Kecheng Yuan;Guofang Xu;Chunyou Ye;Feng Liu;Bensheng Qiu;Xiang Nan;Jijun Han
This study aimed to improve the accuracy of electrical properties tomography (EPT) by proposing a fat-water quantification-based EPT (FW-EPT) using the Dixon technique and provided a feasible approach for obtaining electrical properties (EPs) from current clinical routing modalities. Nine human liver-mimicking phantoms were built with varying fat-water (FW) content at 64 MHz. The EPs were measured using the open-ended coaxial probe method, and an FW signal was obtained through Dixon scanning. Subsequently, three sets of fit models were established: F-EPs, considering only fat information; W-EPs, considering only water information; and FW-EPs, considering both fat and water information. To assess the accuracy of these models, FW-EPT experiments were conducted on two healthy subjects, and the results were evaluated using literature values as a reference benchmark. Experiments showed that the FW-EPs fitted model offered the best accuracy. Compared with the literature values, the average relative errors for human liver conductivity and relative permittivity at 1.5T magnetic resonance imaging (MRI) were lower than 2.89% and 5.37%, respectively. The scanning time for clinical human magnetic resonance (MR) experiments was approximately 22 s. FW-EPT enabled faster, higher resolution, and more precise imaging of EPs in human liver tissue. The findings of this study offered new insights for clinical EPT.
{"title":"Fat-Water Signal-Based Electrical Properties Tomography Using the Dixon Technique","authors":"Yinhao Ren;Kecheng Yuan;Guofang Xu;Chunyou Ye;Feng Liu;Bensheng Qiu;Xiang Nan;Jijun Han","doi":"10.1109/TIM.2024.3485405","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485405","url":null,"abstract":"This study aimed to improve the accuracy of electrical properties tomography (EPT) by proposing a fat-water quantification-based EPT (FW-EPT) using the Dixon technique and provided a feasible approach for obtaining electrical properties (EPs) from current clinical routing modalities. Nine human liver-mimicking phantoms were built with varying fat-water (FW) content at 64 MHz. The EPs were measured using the open-ended coaxial probe method, and an FW signal was obtained through Dixon scanning. Subsequently, three sets of fit models were established: F-EPs, considering only fat information; W-EPs, considering only water information; and FW-EPs, considering both fat and water information. To assess the accuracy of these models, FW-EPT experiments were conducted on two healthy subjects, and the results were evaluated using literature values as a reference benchmark. Experiments showed that the FW-EPs fitted model offered the best accuracy. Compared with the literature values, the average relative errors for human liver conductivity and relative permittivity at 1.5T magnetic resonance imaging (MRI) were lower than 2.89% and 5.37%, respectively. The scanning time for clinical human magnetic resonance (MR) experiments was approximately 22 s. FW-EPT enabled faster, higher resolution, and more precise imaging of EPs in human liver tissue. The findings of this study offered new insights for clinical EPT.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Camera decoration is an important part of smartphone. To achieve fully automated production, a dependable, efficient, and automatic method is required for camera decoration surface defect detection. This article presents a detection scheme based on computer vision to improve the efficiency of screening defective products. Since there is no available dataset for method designing in camera decoration field, we establish a camera decoration defect dataset CD3 including 9417 samples with four types of defects. To increase sample size and alleviate category imbalance of CD3, we provide a dataset enhancing framework including a defect copy method and a background reuse method to generate an enhanced dataset CD3_En containing 39649 samples. Besides, a feature fusion and mask-guided network (FMN) including a feature prefusion (FPF) module and a multistage fusion (MSF) module is proposed to screen the defective products. The FPF is constructed by receptive field blocks (RFBs) and information diffusions (IDs), and it can achieve data volume reduction and context enhancement after being embedded between the BoneNet and Neck. The MSF is used as the Neck to realize a two-step feature fusion for predicting the bounding boxes of defects and their masks. The experimental results on the CD3_En dataset demonstrate the superiority of the proposed method compared with other 11 classic object detection methods.
{"title":"A Feature Prefusion and Mask-Guided Network for Camera Decoration Defect Detection","authors":"Hui Wang;Yuqian Zhao;Fan Zhang;Gui Gui;Qiwu Luo;Chunhua Yang;Weihua Gui","doi":"10.1109/TIM.2024.3485445","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485445","url":null,"abstract":"Camera decoration is an important part of smartphone. To achieve fully automated production, a dependable, efficient, and automatic method is required for camera decoration surface defect detection. This article presents a detection scheme based on computer vision to improve the efficiency of screening defective products. Since there is no available dataset for method designing in camera decoration field, we establish a camera decoration defect dataset CD3 including 9417 samples with four types of defects. To increase sample size and alleviate category imbalance of CD3, we provide a dataset enhancing framework including a defect copy method and a background reuse method to generate an enhanced dataset CD3_En containing 39649 samples. Besides, a feature fusion and mask-guided network (FMN) including a feature prefusion (FPF) module and a multistage fusion (MSF) module is proposed to screen the defective products. The FPF is constructed by receptive field blocks (RFBs) and information diffusions (IDs), and it can achieve data volume reduction and context enhancement after being embedded between the BoneNet and Neck. The MSF is used as the Neck to realize a two-step feature fusion for predicting the bounding boxes of defects and their masks. The experimental results on the CD3_En dataset demonstrate the superiority of the proposed method compared with other 11 classic object detection methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1109/TIM.2024.3488136
Xiaohua Huang;Jiahao Zhu;Ying Huo
In the manufacturing process of hot-rolled steel strips, various mechanical forces, and environmental conditions can cause surface defects, making their detection crucial for ensuring high-quality product production and preventing significant economic losses in the industry. However, existing models within the you only look once (YOLO) family, commonly employed for steel surface defect detection, have exhibited limited effectiveness. In this article, we propose an improved version of YOLO, namely, YOLO enhanced by a convolution squeeze-and-excitation (CSE) module, Conv2d-BatchNorm-SiLU (CBS) with Swin transformer (CST) module, and adaptive spatial feature fusion (ASFF) detection head module, i.e., SSA-YOLO, specifically tailored for end-to-end surface defect detection. Our approach incorporates several key modifications aimed at improving performance. First, we integrate a channel attention mechanism module into the shallow convolutional network module of the backbone. This enhancement focuses on channel information to improve feature extraction related to small defects while reducing redundant information in candidate boxes. In addition, we fuse a Swin transformer (Swin-T) module into the neck to enhance feature representation for detecting diverse and multiscale defects. Finally, the ASFF is introduced in YOLO to increase cross-interaction between high and low levels in the feature pyramid network (FPN). Experimental results demonstrate the superior performance and effectiveness of our SSA-YOLO model compared to other state-of-the-art models. Our approach achieves higher accuracy and sensitivity in detecting surface defects, offering significant advancements in steel strip production quality control. The code is available at https://github.com/MIPIT-Team/SSA-YOLO .
{"title":"SSA-YOLO: An Improved YOLO for Hot-Rolled Strip Steel Surface Defect Detection","authors":"Xiaohua Huang;Jiahao Zhu;Ying Huo","doi":"10.1109/TIM.2024.3488136","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488136","url":null,"abstract":"In the manufacturing process of hot-rolled steel strips, various mechanical forces, and environmental conditions can cause surface defects, making their detection crucial for ensuring high-quality product production and preventing significant economic losses in the industry. However, existing models within the you only look once (YOLO) family, commonly employed for steel surface defect detection, have exhibited limited effectiveness. In this article, we propose an improved version of YOLO, namely, YOLO enhanced by a convolution squeeze-and-excitation (CSE) module, Conv2d-BatchNorm-SiLU (CBS) with Swin transformer (CST) module, and adaptive spatial feature fusion (ASFF) detection head module, i.e., SSA-YOLO, specifically tailored for end-to-end surface defect detection. Our approach incorporates several key modifications aimed at improving performance. First, we integrate a channel attention mechanism module into the shallow convolutional network module of the backbone. This enhancement focuses on channel information to improve feature extraction related to small defects while reducing redundant information in candidate boxes. In addition, we fuse a Swin transformer (Swin-T) module into the neck to enhance feature representation for detecting diverse and multiscale defects. Finally, the ASFF is introduced in YOLO to increase cross-interaction between high and low levels in the feature pyramid network (FPN). Experimental results demonstrate the superior performance and effectiveness of our SSA-YOLO model compared to other state-of-the-art models. Our approach achieves higher accuracy and sensitivity in detecting surface defects, offering significant advancements in steel strip production quality control. The code is available at \u0000<uri>https://github.com/MIPIT-Team/SSA-YOLO</uri>\u0000.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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