Maximum Error Research Articles

A double-helix plane winding sensor with wire using transient plane source method

The transient plane source (TPS) method is widely used to measure thermophysical properties of various materials, and the high-precision double-helix sensor is a key technology. A novel sensor using an efficient, simple, and low-cost lacquered micron-wire-wound process is proposed to replace traditional complex micro- and nano-etching processes. The TPS test platform was built based on the Wheatstone Bridge theory. A new sensor sample of diameter 5.377 mm was wound with an enameled copper wire of diameter 0.063 mm. Owing to the characteristics of the process, a 1-mm round hole was bored inside the sensor. An analytical solution for the concentric ring heat transfer model of the sensor with a central circular hole structure was derived, and a two-dimensional concentric circle simulation model of the sensor was established. As the number of turns decreased, the average error between the analytical solution of the concentric ring model with a large center hole (LHAS) and that of the traditional concentric circle model (TAS), as well as LHAS and the simulation model, gradually increased. The maximum relative errors between LHAS and TAS, as well as between LHAS and the simulation model, were 23.69 % and 0.1 %, respectively, and the average errors of the absolute maximum were 11.86 % and −0.06 %, respectively. The LHAS exhibited excellent accuracy because of the structural characteristics of the novel sensor. The accuracy of LHAS has been validated by simulation and hot wire method. Temperature rise data of high-borosilicate glass and polytetrafluoroethylene samples at different powers were obtained. The thermal properties of the samples were determined by fitting LHAS and TAS data. The thermal conductivity errors measured by LHAS were smaller than those measured by TAS, the thermal conductivity and thermal diffusivity errors were less than 3.5 % at different powers, and the measured repeatability errors were less than 1 %, indicating the good application prospect of the proposed model.

Characterizing the hysteretic effects of water and salinity stresses on root-water-uptake

Characterizing the effects of previous water and salinity stresses is critical for the evaluation of plant water status, which, in turn, is essential for understanding soil-plant water relations and optimizing irrigation schemes. Recent research has found that hysteresis of plant response following water stress alone can be described by an exponential function of the stress degree on the previous day. To explore and quantify the effects of hysteresis concerning salinity stress and combined water-salinity stress, a hydroponic experiment and a soil column experiment on winter wheat, and a field experiment on cotton were conducted. Like water stress, previous salinity stress and combined water-salinity stress also resulted in hysteretic effects on root-water-uptake. Leaf stomatal conductance and plant transpiration rate of stressed crops could only recover gradually from a previous stressed status after re-watering. When stress was mild, compensatory recovery was found, while incomplete recovery occurred when stress was severe. Although the recovery process was closely related to stress history and type, a recovery coefficient was quantified universally with an exponential function of the stress extent on the previous day (with a coefficient of determination R2 ≥ 0.60). Consideration of hysteresis for water and salinity stresses with a mathematical model led to significant improvement in the simulation of both relative transpiration rate (R2 = 0.94, root mean squared error RMSE = 0.04, maximal absolute error MAE = 0.12) and soil water content (R2 = 0.90, RMSE = 0.01 cm3 cm–3, MAE = 0.03 cm3 cm–3), especially during the recovery periods severely affected by historical stress. Consideration of hysteresis is expected to benefit regulation of soil water and salinity and thus enhance water use efficiency. However, the mechanisms underlying hysteresis, especially the compensatory recovery mechanisms, still need to be further investigated.

A semi-analytical method based on the Green's function for the laser melting process of the metal materials

Thermal responses of the metal materials subjected to intense laser irradiation are complicated due to the phase change in the melting process. In order to simplify this problem, previous studies usually ignored the liquid phase or took the usage of finite element method, which limited the achievement of theoretical breakthroughs. In the present study, a one-dimensional heat transfer model with consideration of phase change was established to describe the melting process of the metal materials caused by a constant intense laser beam. The governing equations with three domains were constructed in the piecewise form: the liquid phase region (melted already), the solid phase region (unmelted yet) and the melting region. The limited discretization method was employed to describe the moving of the soli-liquid interface and the energy exchange. The piecewise governing equations in liquid and solid region were solved separately and analytically by means of the Green's function method, and the calculation result was verified by the finite element simulation. The results of the analytical model were in good agreement with those of the finite element simulation, and the maximal prediction errors of the temperature field prediction between the theoretical model and FE simulation is limited to 2.8 %. In addition, the influences of heat source intensity, material heat conductivity, latent heat on the melting process were discussed in detail. The analytical and simulation results reveal that the semi-analytical solution in piecewise for 1-D laser melting process can be successfully used to reflect the dynamic temperature response of the metal materials and the movement of the solid-liquid interface. This work is of guiding significance for the analytical solution of the multi-dimensional melting process of the metal materials under the fixed and continuous laser beams.

Construction of on-site DNA pre-treatment device and rapid electrochemical biosensor set for Escherichia coli detection in milk

Restaurants and cafeterias are constantly exposed to bacterial contamination. In such environments, it is highly needed the rapid bacteria detection system with simple pre-treatment device. To solve this problem, this study presented the pre-treatment device and Rapid electrochemical (REC) biosensor set for detecting Escherichia coli (E. coli) gene in milk sample. The pre-treatment device is consisted of tetraethyl orthosilicate (TEOS)-coated bead that operated with sonication method, and it can effectively remove the E. coli residues and extract E. coli DNA well. By analyzing the sequences of various foodborne pathogens, we selected the highly variable region sequences of E. coli DNA to use as the bioprobe for REC biosensor. The selected thiol-modified bioprobe was immobilized onto the fabricated biosensor electrode. For detecting the extracted E. coli gene fragment rapidly, the alternating current electrothermal flow (ACEF) technique was introduced to REC-biosensor. ACEF technique minimize the target-bioprobe binding time to 10 mins. Using the extracted E. coli DNA fragments with the pre-treatment device, the proposed REC-biosensor shows the limit of detection (LOD) in milk samples was 9.235 × 10-5 ng/μL, and the target detection accuracy was 92.5 %, with a maximal error rate up to 6.730 %. Moreover, the biosensor demonstrated high selectivity in electrochemical performance evaluation (Matrix effect = 7.425 %). The developed pre-treatment device provides simple and quick preprocessing time (within 20 min) of E. coli, and REC biosensor provides the rapid detection of E. coli (within 10 min). In conclusion, the proposed pre-treatment device and biosensor set can be easily applied to other types of bacteria, virus or germs in the near future.

Mean and covariance estimation for discretely observed high-dimensional functional data: Rates of convergence and division of observational regimes

Estimation of the mean and covariance parameters for functional data is a critical task, with local linear smoothing being a popular choice. In recent years, many scientific domains are producing multivariate functional data for which p, the number of curves per subject, is often much larger than the sample size n. In this setting of high-dimensional functional data, much of developed methodology relies on preliminary estimates of the unknown mean functions and the auto- and cross-covariance functions. This paper investigates the convergence rates of local linear estimators in terms of the maximal error across components and pairs of components for mean and covariance functions, respectively, in both L2 and uniform metrics. The local linear estimators utilize a generic weighting scheme that can adjust for differing numbers of discrete observations Nij across curves j and subjects i, where the Nij vary with n. Particular attention is given to the equal weight per observation (OBS) and equal weight per subject (SUBJ) weighting schemes. The theoretical results utilize novel applications of concentration inequalities for functional data and demonstrate that, similar to univariate functional data, the order of the Nij relative to p and n divides high-dimensional functional data into three regimes (sparse, dense, and ultra-dense), with the high-dimensional parametric convergence rate of log(p)/n1/2 being attainable in the latter two.

A method for accuracy of placement analysis on radiolucent polyether-ether-keton facial implants: A case series

Facial asymmetry is defined as a bilateral difference between facial components. Correction, often desired by the patient, can be performed with the aim of bone born patient-specific solid implants designed using 3D CAD software. This treatment is embedded in the daily practice of today's healthcare. However, an analysis of the implant's accuracy of placement has not been reported. This case series describes the accuracy analysis of bone born aesthetic facial implants manufactured out of polyether-ether-ketone (PEEK). The accuracy analysis was based on postoperative (cone beam) computed tomography ((CB)CT) data and preoperative 3D planning. The analysis showed a median entry point error of 0.7 mm (min: 0.1, max: 3.3, interquartile range: 0.78). The median maximal orientation error was 5.5° (min: 0.1, max: 36.8, interquartile range: 7.13). Both parameters showed an excellent intraobserver and interobserver agreement with an ICC above 0.84. The described cases show that the analysis method is an objective approach for determining the accuracy of PSI placement and indicates that these implants can be placed accurately on the osseous face.

Enhancing five-axis CNC toolpath smoothing: Overlap elimination with asymmetrical B-splines

In the realm of five-axis Computer Numerical Control (CNC) machining, the challenge of minimizing velocity, acceleration, and jerk fluctuations has prompted the development of various local corner smoothing methods. However, existing techniques often rely on symmetrical spline curves or impose pre-set transition length constraints, limiting their effectiveness in reducing curvature and maintaining velocity at critical corners. To comprehensively address these limitations comprehensively, a novel approach for local smoothing of five-axis linear toolpaths is presented in this paper. The proposed method introduces two asymmetrical B-splines at corners, effectively smoothing both the tool-tip position in the workpiece coordinate system and the tool orientation in the machine coordinate system. To fine-tune transition curve scales and minimize velocity disparities between adjacent corners, an overlap elimination scheme is employed. Furthermore, the two-step strategy emphasizes the synchronization of tool-tip position and tool orientation while considering maximal approximation error. The outcome is a blended five-axis toolpath that significantly reduces curvature extremes in the smoothed path, achieving reductions ranging from 36.28 % to 45.51 %. Additionally, the proposed method streamlines the interpolation process, resulting in time savings ranging from 5.68 % to 8.78 %, all the while adhering to the same geometric and kinematic constraints.

Dynamic modeling and vibration suppression evaluation of composite honeycomb hemispherical shell with functional gradient protective coating

The vibration reduction performance of composite honeycomb hemispherical shells (CHHSs) coated with functional gradient protection coating (FGPC) are investigated in this work. Using the first-order shear deformation theory and the power-law distribution rule, the virtual spring technique, the regional decomposition method, and the Newmark-Beta approach, etc., a dynamic model of the FGPCCHHSs under base excitation is formulated to solve the inherent characteristics and displacement responses in time and frequency domains. After a set of convergence analyses are completed to ascertain an appropriate segment number and the stiffness values of virtual springs employed in the predictive model, the forecasted vibration parameters are verified using the literature and experimental results that are performed on uncoated and coated shells. The maximal natural frequency errors of the current model compared to the experimental results are 3.8% and 4.8%, and the displacement response errors under different excitation amplitudes are less than 10.3% and 12.7%, respectively, which demonstrate the correctness of such a model. Finally, the impact of key structural and material parameters on the vibration behaviors of the FPGCCHHSs is evaluated. To improve their vibration suppression capability, it is recommended to choose a high gradient index of coating material and a large thickness ratio of the FGPC to the overall shell with a reasonable moduli ratio of Material A to Material B of the FGPC to improve vibration reduction capability. This study offers a practical model tool and several important design recommendations for vibration prediction and dynamic attenuation of honeycomb sandwich hemispherical shell structures in aerospace engineering.

Forest practitioners’ requirements for remote sensing-based canopy height, wood-volume, tree species, and disturbance products

Abstract Despite decades of development, the uptake of remote sensing-based information products in the forestry sector is still lagging behind in central and southern Europe. This may partly relate to a mismatch of the developed remote sensing products and the requirements of potential users. Here, we present the results of a questionnaire survey in which we questioned 355 forest practitioners from eight central and southern European countries. We aimed to learn about forest practitioners' technical requirements for four remote sensing-based information products, including information on tree species, canopy height, wood volume/biomass, and forest disturbances. We asked for practitioners’ preferences with respect to thematic and spatial detail as well as the maximal acceptable error and the temporal frequency with which the information layers would be needed. We then examined whether the education, age, and professional background affect the requirements. Preferences with respect to spatial and thematic detail were comparably diverse while more homogenous patterns could be observed for demands with respect to errors and temporal frequency. Our results indicate that for some information products such as canopy height maps, existing remote sensing technology, and workflows can match all demands of practitioners. Remotely sensed information on forest disturbances partly fulfils the demands of the practitioners while for products related to tree species and wood volume/biomass the level of thematic detail and the accuracy of the products demanded by practitioners in central and southern Europe is not yet fully matched. We found no statistically significant differences between the demographic groups examined. The findings of this study improve our understanding of matches and mismatches of the technical requirements of practitioners for remote sensing-based information products.

An I–V characteristic reconstruction-based partial shading diagnosis and quantitative evaluation for photovoltaic strings

The partial shading condition (PSC) is the most common abnormality occurred in photovoltaic (PV) systems. Accurate quantitative evaluation of the shaded area and the severity of the shading is of potential importance in optimizing the maintenance strategy for PV systems. In this paper, we propose a PSC diagnosis and quantitative evaluation method by analyzing the measured string current–voltage (I–V) characteristic obtained from the PV inverter with the I–V scanning function, which includes pre-diagnosis of the system abnormality based on the operational power deviation, the diagnosis of PSCs based on the derivatives characteristics of the PV string, and the quantitative evaluation based on the I–V characteristic reconstruction. The quantitative evaluation of PSCs is the main focus in this paper, where the I–V characteristics of the unshaded PV modules in the partially shaded PV string are reconstructed according to different mismatch levels, respectively. The number of shaded PV modules and the corresponding severity of the partial shadings are estimated according to the reconstructed I–V characteristics. The simulation and experimental results verify that both the proposed diagnosis and quantitative evaluation method is effective with decent accuracy, especially for severe mismatch conditions. Experimental results show that the maximal mean absolute error of the quantified shaded area and quantified shaded rate are approximately 1.4446 units of 1/3 PV modules and 0.026, respectively.

Back Propagation Artificial Neural Network Based DC Bus Capacitance Identification Method in Three-Phase PWM Rectifier for Charging System of EVs

In order to improve the performance and reliability of the three-phase PWM rectifier in electric vehicle (EV) charging system, it is important to focus on the identification of the DC bus capacitance. The existing methods have the disadvantages such as a low identification accuracy, a high hardware dependency, and high costs. Therefore, the development of new identification methodologies could be the way out of the aforementioned disadvantages, in terms of advanced algorithms. In this paper, a DC bus capacitance identification method based on back propagation (BP) artificial neural network (ANN) algorithm is proposed. The capacitance can be accurately identified in the diode rectification stage, by training the neural network to extract the potential laws in the data, which only includes the Phase-a voltage and current, as well as the DC bus voltage ripple. The regression response is 0.99991. Finally, the experimental results show the accuracy and effectiveness of the proposed method. A simple and effective BP ANN is designed, and it can identify the capacitance at different grid voltages and DC bus loads. It has a high identification accuracy that the maximum identification error is less than 4.5%.

Self-optimizing method and software for calibration and mapping of a laser system for laser machining

Short and ultrashort pulse lasers can ablate the vast majority of materials compared to other machining methods, making them the preferred tool in many niche applications. For the use in precision machining, such laser systems need to reach machining accuracy in the range of 1 µm. Usually, a galvanometric scanner is used in such laser systems to scan the laser beam across the scan field to mark or ablate the various materials. To achieve high accuracy, the overall laser system must be calibrated. However, such calibration is time-consuming and must be monitored at regular intervals and repeated if necessary. In this paper, a method and routine are introduced for self-optimizing calibration, monitoring and recalibration of a laser system. After calibration, the maximal deviation error of the laser system is 3.9 µm within the 30 mm x 20 mm scan field, and the optical axes are capable to match the positioning accuracy of the mechanical axes at 1.11 µm. A beam profiling camera is used to measure the beam position needed for calibration within the scan field. At the same, time other properties of the laser beam such as peak intensity, spot size and ellipticity are mapped within the scan field and is then used as a tool fingerprint of the laser beam across the scan field. Based on this, the influence of the position-dependent beam properties on the removal rate and surface quality is determined.

Joint reconstruction and segmentation in undersampled 3D knee MRI combining shape knowledge and deep learning

Objective. Task-adapted image reconstruction methods using end-to-end trainable neural networks (NNs) have been proposed to optimize reconstruction for subsequent processing tasks, such as segmentation. However, their training typically requires considerable hardware resources and thus, only relatively simple building blocks, e.g. U-Nets, are typically used, which, albeit powerful, do not integrate model-specific knowledge. Approach. In this work, we extend an end-to-end trainable task-adapted image reconstruction method for a clinically realistic reconstruction and segmentation problem of bone and cartilage in 3D knee MRI by incorporating statistical shape models (SSMs). The SSMs model the prior information and help to regularize the segmentation maps as a final post-processing step. We compare the proposed method to a simultaneous multitask learning approach for image reconstruction and segmentation (MTL) and to a complex SSMs-informed segmentation pipeline (SIS). Main results. Our experiments show that the combination of joint end-to-end training and SSMs to further regularize the segmentation maps obtained by MTL highly improves the results, especially in terms of mean and maximal surface errors. In particular, we achieve the segmentation quality of SIS and, at the same time, a substantial model reduction that yields a five-fold decimation in model parameters and a computational speedup of an order of magnitude. Significance. Remarkably, even for undersampling factors of up to R = 8, the obtained segmentation maps are of comparable quality to those obtained by SIS from ground-truth images.

Measurement of coating–substrate interface stiffness using a constructed ultrasonic echo phase derivative spectrum

Measurement of coating–substrate interface stiffness can indirectly characterize interface bonding quality. This paper proposes a noval quantitative inversion coating–substrate interface stiffness method based on the multi-resonance frequencies of a constructed ultrasonic echo phase derivative spectrum (UEPDS). The theoretical relationship between UEPDS resonance frequencies and interface stiffness is derived. The detection frequency and high-sensitivity interface stiffness range are optimized based on the sensitivity analyzed. Numerical simulation and experiment are implemented on a 0.48 mm aluminum layer/carbon steel substrate specimen to prove the validity of the proposed ultrasonic method. The simulation results show that the maximal relative error between the inversion and the preset interface stiffnesses is reduced from 23% to 8% compared with the traditional ultrasonic reflection coefficient amplitude spectrum-based (URCAS-based) method. The experiment results indicate that the UEPDS-based inversion interface stiffnesses have the same trend as the nominal contact pressures between the coating–substrate interface.

An improved Mickens’ solution for nonlinear vibrations

The Duffing equation and other nonlinear equations of motion of nonlinear vibrations are widely applied in the fields of engineering and science, especially mechanical and electrical engineering. The modified Mickens extended iteration method has been utilized in this article to investigate for further accurate solutions to the Duffing equation and an equation of motion of nonlinear vibrations. The third approximate frequency shows good agreement with the exact result for both oscillators. For simplicity in algebraic calculations, the increasing harmonic terms have been taken to the fifth order. In order to establish the reliability and effectiveness of the proposed approach, the results obtained through this method are compared with those available in the literature. The comparison of the obtained solutions with numerical solutions shows a remarkable accuracy. Also, it becomes evident that the modified Mickens extended iteration method is significantly simpler, more accurate, efficient, and straightforward than other existing methods. The highest percentage error in the third approximate frequency of an equation of motion of nonlinear vibrations is less than 0.0488, and the maximal percentage error in the third approximate frequency of the Duffing oscillator is less than 0.0065. The proposed technique is principally exhibited in nonlinear models where the nonlinear terms are strong, but it can also be broadly applicable to other problems arising in engineering. Mathematics Subject Codes34A34, 34C25, 37N15 (AMS Mathematics Subject Classification 2010)

METHOD OF HIGH-PRECISION NEURO-FUZZY SYSTEMS DEFUZZIFIERS’ AUTOMATIC CONSTRUCTION

Increasing the precision of neuro-fuzzy approximation systems is considered. Modification of defuzzifier, which allows producing multiple types of approximating functions is presented. RMSE (Root Mean Square Error), maximal error, training time and models’s size are used to evaluate the efficiency of proposed method. Post-processing functions were also considered as a tool to increase approximation precision. Common solution to this problem is a modification of model’s fuzzy sets (membership functions and their number), which significantly increases model’s size and complexity. Given approach uses search algorithms to find the configuration of defuzzification layer, which produces the best approximation precision for a given function and fuzzy sets configuration, since all possible configurations are checked. It is possible to model classic approximation methods and combine them. Possible modifications of presented search algorithms application are described. Search depth can be limited in order to reduce the size of produced model. The computing experiment, aimed at finding best approximation precision was carried out. Presented method provided lowering of RMSE without significant increasing of model’s size. Using Fourier series components in resulting expression can be used for precise periodic functions approximation or for general precision improvement. Using post-processing functions provided significant improvement in precision for multiplication approximation, which proves, that it can be used as another tool to be used to reduce errors in some specific cases.

Design of General Parametric Repetitive Control Using IIR Filter With Application to Piezo-Actuated Nanopositioning Stages

The achievable performance with repetitive control is limited due to its inherent sensitivity to the frequency shift away from the intended periodic frequencies and the undesired gain amplification of the aperiodic disturbances. To address these limitations, the paper proposed a general parametric repetitive control (GPRC) method based on the IIR filter with the features of low-order and excellent magnitude responses to result in better tracking performance in diverse applications. By analyzing its sensitivity function, it is found that the design of GPRC can be converted to the explicit parametric design of an IIR high pass filter. The controller design process and the stability analysis are presented in detail. To show the effectiveness of GPRC, comparative experiments are conducted via tracking sinusoids, triangular trajectories and other complex trajectories with multi-frequency components. The experimental results show that, in contrast with the conventional repetitive control (CRC) and a modified repetitive control (MRC), the GPRC exhibits excellent robustness against the frequency shift and advanced performance at the aperiodic frequencies. The tracking results of the sinusoids show that the maximum tracking error obtained with GPRC for a frequency shift of 3 Hz decreases from 0.0259 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu m$</tex-math> </inline-formula> (CRC) and 0.2207 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu m$</tex-math> </inline-formula> (MRC) to 0.0101 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu m$</tex-math> </inline-formula> at the nominal frequency of 1000 Hz, demonstrating the merits of the proposed GPRC. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —To enable automation systems, one of the crucial requirements is to track repetitive references with high precision. Although the normal repetitive control (RC) based schemes are successfully applied to improve the tracking accuracy of periodic trajectories, the existing RC schemes suffer from the problems of lower robustness against frequency shift and the unwanted gain amplification at the aperiodic frequencies due to Bode’s sensitivity integral. To overcome this problem, this paper proposes a novel general parametric repetitive control (GPRC) method via characterizing the loop properties quantitatively based on the IIR high pass filter design. Focusing on specific issues, the detailed variations to handle the errors at only the odd-and even-harmonics are also demonstrated. This framework leads to a flexible solution in practical implementations. The experimental validation on a piezo-actuated nanopositioning stage is comparatively presented in terms of tracking accuracy and rejection ability of the gain amplification at the aperiodic frequencies. With its flexibility and effectiveness, the proposed GPRC can be easily implemented in diverse applications.

Multifunctional Optical Tomography System With High-Fidelity Surface Extraction Based on a Single Programmable Scanner and Unified Pinhole Modeling.

Macroscopic optical tomography is a non-invasive method that can visualize the 3D distribution of intrinsic optical properties or exogenous fluorophores, making it highly attractive for small animal imaging. However, reconstructing the images requires prior knowledge of surface information. To address this, existing systems often use additional hardware components or integrate multimodal information, which is expensive and introduces new issues such as image registration. Our goal is to develop a multifunctional optical tomography system that can extract surface information using a concise hardware design. Our proposed system uses a single programmable scanner to implement both surface extraction and optical tomography functions. A unified pinhole model is used to describe both the illumination and detection procedures for capturing 3D point cloud. Line-shaped scanning is adopted to improve both spatial resolution and speed of surface extraction. Finally, we integrate the extracted surface information into the optical tomographic reconstruction to more accurately map the fluorescence distribution. Comprehensive phantom experiments with different levels of complexity were designed to evaluate the performance of surface extraction and fluorescence tomography. We also imaged the axillary lymph nodes in living mice after injection of fluorophore, demonstrating the proposed system facilitates more reliable fluorescence tomography. We have successfully developed a versatile optical tomography system by leveraging concise hardware design and unified pinhole modeling. Phantom validation demonstrates that our system provides high-precision surface information with a maximum error of 0.1 mm, while the surface-guided FMT reconstruction is more reliable than the blind reconstruction using simplified surface geometry, elevating several quantitative metrics including RMSE, CNR, and Dice. Our work explores the feasibility of obtaining additional surface information using existing components of standalone optical tomography. This makes the optical tomographic technique more accurate and more accessible to biomedical researchers.

Post-tensioning tendon force estimation of in-service prestressed concrete structure using cylindrical lamb waves

This paper proposes an ultrasonic-based force estimation technique for the post-tensioning (PT) tendon of an in-service prestressed concrete structure. First, three macro fiber composite transducers are installed on the surface of an in-service PT tendon subjected to an unknown tensile force for the generation and measurement of cylindrical Lamb waves. Then, the velocities of the longitudinal and shear waves are calculated using the measured cylindrical Lamb waves at two ultrasonic input frequencies. Subsequently, the Poisson’s ratio, elastic modulus, and strain induced by the PT tendon force are estimated from the longitudinal and shear wave velocities. Finally, the PT tendon force is obtained from the estimated mechanical property values. The experimental validation was performed using a mono-strand PT tendon under various temperature conditions, and the developed technique estimated the unknown PT tendon force in the range of 10–100 kN with a 6.22 kN maximum error and a 2.99 kN root-mean-square error. The uniqueness of this paper includes (1) no requirement for temperature compensation for field deployment, (2) PT tendon force estimation without calibration of the PT tendon force and cylindrical Lamb wave relationship, and (3) easy sensor installation and no interference with the PT construction process.

Self‐Powered Vibration Frequency Monitoring Device for the Grid Based on Triboelectric Nanogenerator and Micro Thermoelectric Generator

AbstractThe galloping of transmission lines (GTLs) has a significant impact on the development of smart grids. However, traditional vibration frequency monitoring devices for transmission lines suffer from issues such as low measurement accuracy, high environmental requirements, and the inability to achieve self‐powering. A self‐powered vibration frequency monitoring method is proposed based on frequency‐sensing triboelectric nanogenerator (F‐TENG) and micro thermoelectric generators (MTEG). Models for vibration frequency sensing based on F‐TENG and energy capture based on MTEG are established. A flexible self‐powered sensing prototype, integrating F‐TENG, MTEG, and a Signal processing and Energy harvesting Circuit (SEC), is fabricated. Additionally, an innovative solvothermal method for the preparation of MTEG materials is presented, resulting in Bi2Te3‐based thermoelectric materials with significantly high thermoelectric conversion performance. Experimental results demonstrate that within the range of 0.1–5.1 Hz, F‐TENG can precisely perceive the frequency of GTLs, with a maximum error of 1.274%. MTEG achieves a maximal open‐circuit voltage of 3.282 V. Finally, the SEC unit is designed to couple the outputs of F‐TENG and MTEG for frequency calculation and wireless transmission to a microcontroller. This device provides an efficient solution for monitoring the frequency of GTLs and offers robust support for the stability of the smart grid.