Large Errors Research Articles

Torque control of grasping force feedback using a series elastic actuator with an ultrasonic motor for a teleoperated surgical robot

In the context of teleoperated surgical robots, much research has been conducted into haptic feedback systems. Ultrasonic motors (USMs) are expected to be used as actuators for generating feedback torque because of their fast response and high torque–weight ratio. However, their nonlinear characteristics make control of the output torque difficult, leading to large spike-like torque errors during rotation reversal. One approach to counteracting this effect is to use series elastic actuators (SEAs). In this study, a force feedback system using an SEA with a USM was proposed. The spike-shaped errors in the output torque of the USM were reduced by up to 60 % by attaching an SEA to its output shaft. The dependence of the torque accuracy and the response time of the SEA on its spring constant were also examined. By introducing the SEA with a spring constant of 42.4 mN·m/°, it was indicated that the torque error of 14.8 mN·m and the response time of 9 ms could be simultaneously achieved. Finally, the force feedback system was applied to a teleoperated surgical robot, and the stiffness of the objects grasped by the robot was successfully fed back with the error of 0.65 % when grasping a sponge.

Precise orbit determination of the LuTan satellite using GPS, BDS-2, and BDS-3 signals

Abstract The LuTan satellite is the first formation-flying mission of China enabling synthetic aperture radar interferometry in the L-band. As a key mission requirement, the absolute position of each satellite must be determined with an accuracy of 5 cm in three dimensions (3D). To fulfill this demand, each satellite carries a Global Positioning System/Beidou Navigation Satellite System (GPS/BDS) dual-constellation Global Navigation Satellite System receiver that can simultaneously track the GPS L1 and L2 signals, the BDS-2 B1I and B3I signals (BD2), and the BDS-3 B1C and B2a signals (BD3). In this study, we assess the performances of precise orbit determination with different signals and their combinations. For that purpose, we produce a total of five sets of orbit solutions, i.e., GPS-based, BD2-based, BD3-based, GPS/BD2 combined, and GPS/BD3 combined solutions. To evaluate the orbit solutions, comparisons in the orbital overlaps and with the GPS-based orbit are adopted. The orbital overlap analysis shows that the GPS-based solution has the smallest root mean square (RMS) of overlap orbit differences than other single-constellation solutions with 3.7 mm in 3D. The BD2- and BD3-based orbits are suffered from large errors and the overlap RMS differences are 24.7 and 18.9 mm, respectively. Furthermore, all of the combined orbits (both 3.1 mm for the GPS/BD2 and GPS/BD3 combined orbits) based on the variance component estimation method can obtain improvement than GPS-based solutions. For orbit comparisons, the GPS-based solution is selected to serve as the reference. The RMS values of the orbit differences are 29.9 and 15.4 mm for the BD2- and BD3-based solutions, respectively. For the GPS/BD2 and GPS/BD3 combined solutions, the values are 4.1 and 4.8 mm. In general, the results agree with those obtained in the overlap analysis. These results indicate that the BD3-based orbit is superior to the BD2-based orbit, while both of them are inferior to the GPS-based orbit. We show that inaccurate ephemerides of the BDS satellites can partly explain the degraded performance of the BDS-derived orbits. Nevertheless, the orbits still fulfill the 5 cm accuracy demand and can be readily used for subsequent precise baseline determination.

Critical role of arterial constitutive model in predicting blood pressure from pulse wave velocity

BackgroundA promising approach to cuff-less, continuous blood pressure monitoring is to estimate blood pressure (BP) from Pulse Wave Velocity (PWV). However, most existing PWV-based methods rely on empirical BP-PWV relations and have large prediction errors, which may be caused by the implicit assumption of thin-walled, linear elastic arteries undergoing small deformations. Our objective is to understand the BP-PWV relationship in the absence of such limiting assumptions. MethodWe performed Fluid-Structure Interaction (FSI) simulations of the radial artery and the common carotid artery under physiological flow conditions. In these dynamic simulations, we employed two constitutive models for the arterial wall: the linear elastic model, implying a thin-walled linear elastic artery undergoing small deformations, and the Holzapfel-Gasser-Ogden (HGO) model, accounting for the nonlinear effects of collagen fibers and their orientations on the large arterial deformation. ResultsDespite the changing BP, the linear elastic model predicts a constant PWV throughout a cardiac cycle, which is not physiological. The HGO model correctly predicts a positive BP-PWV correlation by capturing the nonlinear deformation of the artery, showing up to 50 % variations of PWV in a cardiac cycle. ConclusionDynamic FSI simulations reveal that the BP-PWV relationship strongly depends on the arterial constitutive model, especially in the radial artery. To infer BP from PWV, one must account for the varying PWV, a consequence of the nonlinear arterial response due to collagen fibers. Future efforts should be directed towards robust measurement of time-varying PWV if it is to be used to predict BP.

An eddy current sensor for end-point detection in chemical mechanical planarization using composition signal filtration and a digital potentiometer

End-point detection (EPD) is important for controlling the chemical mechanical polishing process to prevent underpolishing and overpolishing. One EPD method using the relative current density on a Cu wafer involves an eddy current, which has advantages of noncontact and a fast response speed. However, the eddy current method has a large mean error at a low relative current density. To overcome the disadvantage, composition signal filtration uses a high mean error and long delay time using a moving average and 1-D Kalman filtration. After applying composition filtration, the mean error improved by approximately 94.7 %. Still, it is difficult to measure a thickness of 4000 Å or less with existing systems. A digital potentiometer was developed that changed the resistance in real time and improved the detection range. The step number for changing the resistance was 120 steps in the resistance range of 100–5000 Ω at approximately 40 Ω/step. When a digital potentiometer was applied, the EPD detection range improved by 85.8 % over the existing system. In other words, the developed system could measure a Cu wafer thickness of 586 Å.

Ideas of lattice-basis reduction theory for error-stable Bravais lattice determination and abinitio indexing.

In ab initio indexing, for a given diffraction/scattering pattern, the unit-cell parameters and the Miller indices assigned to reflections in the pattern are determined simultaneously. `Ab initio' means a process performed without any good prior information on the crystal lattice. Newly developed ab initio indexing software is frequently reported in crystallography. However, it is not widely recognized that use of a Bravais lattice determination method, which is tolerant of experimental errors, can simplify indexing algorithms and increase their success rates. One of the goals of this article is to collect information on the lattice-basis reduction theory and its applications. The main result is a Bravais lattice determination algorithm for 2D lattices, along with a mathematical proof that it works even for parameters containing large observational errors. It uses two lattice-basis reduction methods that seem to be optimal for different symmetries, similarly to the algorithm for 3D lattices implemented in the CONOGRAPH software. In indexing, a method for error-stable unit-cell identification is also required to exclude duplicate solutions. Several methods are introduced to measure the difference in unit cells known in crystallography and mathematics.

Extraction of amplitude-domain characteristics from dynamic electric signals that cause large errors in electricity metering

Abstract Recent studies have shown that the power and current of high-power dynamic loads exhibit dynamic behaviors that cause large errors in electrical energy meters. Aimed at fast variations and large fluctuations in dynamic electric signals, this study examines a characteristic extraction method for small-time granularity and the impact of these characteristics on electrical energy meters. First, we constructed quasi-steady and dynamic model parameters for the dual-modal modulation models of current and power signals. Second, based on the two sets of signals sampled from the electrified railway traction substation and steelmaking arc furnace substation, we extracted quasi-steady and dynamic signal model parameters from fundamental active power signals. Third, we proposed three novel characteristic parameters by mapping the model parameter method, and extracted the four important characteristics. Finally, we designed a set of on-off-keying test signal experiments to determine the impact of the proposed characteristic parameters on the electric energy meter. The experimental results show that the electrical energy meter errors are sensitive to the proposed characteristic parameters, wherein the maximum impulse coefficient is the most sensitive characteristic of the EEM, which causes an error as large as −81.28%. The proposed characteristic parameters can be used as the input characteristic information of dynamic test signals for type testing and updating the test standards for electrical energy meters.

Uncertainty and macroeconomic forecasts: Evidence from survey data

This paper examines the effects of macroeconomic and financial uncertainty on professional economic forecasts. Using the predictions of four variables sourced from the Bloomberg survey, we find that macroeconomic uncertainty, a proxy for the complexity of the forecasting task, is associated with high disagreement and low accuracy. By contrast, the results show that financial uncertainty is associated with low disagreement and high accuracy, which suggests that financial uncertainty encourages forecasters to adhere to the consensus to avoid large forecast errors. Furthermore, we find that the forecaster rank moderates the effect of macroeconomic uncertainty, indicating that high-ability forecasters can better navigate periods of high uncertainty. This study advances the understanding of forecasting behavior under uncertainty.

Photonic-assisted RF phase difference detector with full 0° to 360° phase detection capability

A microwave photonic structure for detecting the phase difference of two radio frequency (RF) signals is presented. It is capable of determining the RF signal phase difference unambiguously over the full 0° to 360° range, which overcomes the 0° to 180° phase detection range limitation in the reported structures. The proposed RF phase detector is designed to generate two DC voltages with amplitudes depending on the phase difference of the two input RF signals. An RF signal phase difference in the 0° to 360° range can be obtained from the ratio of the two system output DC voltages. The proposed RF phase detector has a wide operating frequency range, as it is free of microwave components. It overcomes the drawbacks of complex structures, large phase detection errors, limited phase detection range, and limited operating frequency range in the reported photonics-based RF phase detectors. Experimental results are presented that demonstrate 0° to 360° phase difference detection with errors below ±3∘ for different input RF signal frequencies.

Function observer based on model compensation control for a fixed-wing UAV

Fixed-wing unmanned aerial vehicle (UAV) is a nonlinear system characterized by strong coupling, high complexity, and sensitivity to external interference and model uncertainty. For the fixed-wing UAV having eight degrees of freedom, the uncertainty and wind disturbance parts of the model are clarified, and a model with lump disturbance is established through the mechanic analysis. To get the high performance of control, a highly accurate observer for the lump disturbance is necessary. A second-order compensation function observer (CFO) with feedforward is proposed with new structure. The CFO accuracy is much higher than the same order of the extended state observer (ESO) by convergence analysis. A hyperbolic tangent function is combined with the CFO for effectively reducing the large estimation error at the initial moment. A CFO-based model compensation control (MCC) algorithm is provided for controlling the longitudinal and lateral channels of the fixed-wing UAV. The MCC contains the known model, the compensation of unknown model via the CFO, feedback of error and the dynamic information of the given information. The MCC realizes the desired linear characteristics for the feedback system because it decouples the channels, restrains the lump disturbance. The stability of the closed-loop system is proved by Lyapunov theorem. The proposed CFO-based MCC is applied to the fixed-wing UAV, and compared with the traditional PID and ESO-based on sliding mode control (SMC) algorithm through simulation and the control test platform experiment based on Pixhawk, showing the superiority of the MCC and highly accuracy of the CFO.

Use of the inverse method to determine the thermal properties of liquid n-octadecane accounting for natural convection effect

This paper numerically makes use of the inverse method coupled with experimenal results obtained through the fluxmetric method in the Laboratoire de Génie Civil et géo-Environnement (LGCgE) to determine the specific heat, thermal conductivity and thermal expansion coefficient of liquid n-octadecane in an enclosure accounting for natural convection present in the experimental system. The impact of ommiting the presence of natural convection for the thermophysical propertie determination is studied and shows very little effect on the specific heat value but a large error (by a factor of two in this study) on the thermal conductivity value. The correct properties identified when accounting for natural convection (ρ = 2150 J/kg‧K; k = 0.16 W/m‧K; β = 9.38 × 10−4 K−1) are consistent with the literature. Additional direct numerical work was done to determine the impact of using the different thermal conductivity values, comparing numerical to additional expermental restuls where natural convection was stronger. The thermal conductivity value played a minial role when simulating the behavior of the systems rightly accounting for natural convection. Combining the numerical and experimental resutls allowed the calculation of encolsure specific Nusselt numbers for the various experimental conditions used and a comparison to known correlations. It was determine that the Nusselt number obtained in the liquid n-octadecane were larger by 10 to 48%.

Modified (n+1)D Laplacian for smooth pressure reconstruction based on time-resolved velocimetry (1): analysis and numerics

Abstract We analyze a smooth pressure solver based on the "modified Poisson equation": ∇2 p +ξ 2∂2 p/∂ t 2 = f(u(t)), where p is the pressure field, u (t) is the velocity field measured by time-resolved image velocimetry, and ξ 2 is a tunable parameter to control the solver's diffusive behavior in time. This modified Poisson equation aims at obtaining smooth pressure fields from potentially noisy image velocimetry measurements, and is a part of the current four-dimensional (4D) pressure solver (implemented in, for example, DaVis 10.2) by LaVision. This work focuses on investigating three aspects of the "modified Poisson equation": smoothing effect, error propagation, and drift in time. We first provide rigorous analysis and validate that this solver can sufficiently smooth the computed pressure field by setting a large enough ξ 2. However, a large value of ξ 2 may cause large errors in the reconstructed pressure fields. Then we introduce an upper bound on the error in the reconstructed pressure fields to quantify the error propagation dynamics. Finally, we discuss the potential drift due to the partitioning in time, which is an optional strategy used in LaVision's current 4D pressure solver to reduce computational costs. Our analysis and validation not only show that careful choice of the parameters (e.g., ξ 2) is needed for smooth and accurate pressure field reconstruction but provide theoretical guidelines for parameter tuning when similar pressure solvers are used for time-resolved image velocimetry data.

A single-axis force sensor based on a 3D-printed elastic ring

Force measurement is indispensable in vast engineering applications but conventional force sensors usually encounter large measurement errors or high costs. To address this problem, we develop a force sensor based on a 3D-printed elastic ring structure, which measures the external force by utilizing the displacement variation of the elastic ring. The nonlinear theory based on the minimum potential energy principle is proposed to describe the nonlinear relationship between the external force and the ring top displacement. The feasibility of the proposed force sensor scheme is validated through FEM simulations and experiments. The range of the force sensor can be adjusted by tuning the geometric parameters and the material parameters of the elastic ring, while the force sensor shows good measurement error at different measurement ranges. This force sensor possesses simple fabrication and low cost, showing good potential for various applications such as industrial automation, mechanical testing, and medical devices.

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder.

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.

A novel competitive color-tone change fluorescence immunochromatographic assay for the ultrasensitive detection of pesticide and veterinary drug residues

One important challenge faced by competitive immunoassay techniques is the inability to visually distinguish weak color changes in the T-line during the detection of trace analyte concentrations. In this study, we introduced multi-color fluorescence into the immunoassay system and developed a novel competitive dual-channel color-tone change fluorescent immunochromatographic assay (CFICA) for the visual and quantitative detection of kanamycin (KAN) and carbendazim (CBZ). At the same time, we have developed matching, portable fluorescence-reading equipment, which can analyse the spectral data of the RGB channel individually and can effectively solve the problems of the traditional single-fluorescence reader – large reading error and poor detection sensitivity. After optimization, our CFICA, combined with the corresponding instrument, achieved low limits of detection (LOD) for KAN and CBZ, as low as 1.98 and 12.82 pg/mL, respectively. We believe that this CFICA detection system, with its high accuracy, low error, and multi-color detection capabilities, holds tremendous potential for visual and multi-channel competitive point-of-care testing.

Height Control Strategy Design and Simulation of Electronic Control Air Suspension for Trucks

To address the large height error and attitude destabilization phenomenon in regulating the frame height of trucks with electronic control air suspension (ECAS), a height control strategy was designed. Firstly, the fundamental principles of height control were elucidated based on the single degree-of-freedom (DOF) vehicle model. The limitations of the classic non-linear mathematical model for the air spring were also highlighted. Thus, a dynamic model was constructed, consisting of an AEMSim model for the ECAS and a Simulink model for the truck. A frame height fuzzy controller was designed based on the fuzzy control theory to improve the height control accuracy and to solve the control conflict problem of the solenoid valves. Additionally, three typical control modes of the height and corresponding control strategies were proposed based on the practical requirements of usage scenarios for trucks. Finally, dynamic simulations were conducted under different modes. The results show that, compared to the existing switching control method, the proposed control approach can reduce height control errors by an order of magnitude and decrease the pitch angle by over 30%. The steady-state error remains nearly unchanged under the 30% variation of the sprung mass. The proposed control approach exhibits the superior control performance and robustness. It effectively reduces height errors and avoids the posture instability during the adjustment of the ECAS.

Saturated nucleate pool boiling of cryogenic fluids: Review of databases, assessment of existing models and correlations, and development of new universal correlation

The absence of a comprehensive and reliable nucleate pool boiling database for heat transfer coefficient (HTC) of cryogenic fluids is the driving force behind this study. Such a database is essential to evaluating the predictive accuracy of existing tools, including both models and correlations, and to pioneering the development of a new, more accurate predictive method. To address this need, a new Consolidated Cryogenic Nucleate Pool Boiling Database (comprised of 2908 data points) was compiled, with emphasis on HTC from flat horizontal and vertical surfaces, drawing from the broad literature resources available from across the globe. The database enabled a comprehensive assessment of prior models and correlations, and careful examination showed most of these tools yield unacceptably large errors in predicting the HTC data, especially for elevated pressures and large heat fluxes. Consequently, a new correlation is proposed for all cryogenic fluids combined, which is fine-tuned to outperform prior predictive tools, particularly for elevated pressures and large heat fluxes. The new correlation has a Mean Absolute Error (MAE) of 25.36 %, with 64.5 % of the predictions falling within ±30 % of the data and 87.2 % within ±50 %. This exceptional predictive capability positions the new correlation as a robust tool for both thermal design and performance assessment of a broad variety of devices and systems.

Monthly electricity generation forecast based on similar month screening and STL decomposition

In order to solve the problems of monthly electricity generation forecasting being limited by the lack of actual data source, and the large errors caused by the influence of various factors such as weather and holidays, and the limitations of the applicable scenarios of the existing research results, a monthly electricity generation forecasting model based on similar month screening and Seasonal and Trend decomposition using Loess (STL) was proposed in this paper. The complementary advantages of Multiple Linear Regression (MLR) and Improved Random Forest Regression (RFR) are utilized to achieve the monthly electricity generation prediction in the province. This prediction model does not require a large number of data to obtain a better prediction accuracy, and breaks through the limitations of the existing monthly electricity prediction model that are only suitable for a certain industry or a certain region. Experiments performed on an actual electric power generation series validate the efficiency of the proposed model.

Effects of Freezing Temperature Parameterization on Simulated Sea‐Ice Thickness Validated by MOSAiC Observations

AbstractFreezing temperature parameterization significantly impacts the heat balance at sea‐ice bottom and, consequently, the simulated sea‐ice thickness. Here, the single‐column model ICEPACK was used to investigate the impact of the freezing temperature parameterization on the simulated sea‐ice thermodynamic growth during the MOSAiC expedition from October 2019 to September 2020. It is shown that large model errors exist with the standard parameterization and that different formulations for calculating the freezing temperature impact the simulated sea‐ice thickness significantly. Considering the winter mixed layer temperature, a modified parameterization of the freezing point temperature based on Mushy scheme was developed. The mean absolute error (ratio) of simulating sea‐ice thickness for all buoys reduces from 7.4 cm (4.9%) with the “Millero” scheme, which performs the best among the existing schemes in the ICEPACK model, to 4.2 cm (2.9%) with the new developed scheme.

Analysis of Bell Pepper (Capsicum annuum L.) Leaf Spectral Properties and Photosynthesis According to Growth Period

This study analyzed the leaf spectral properties and photosynthesis rates of greenhouse-grown bell pepper leaves according to the growth period and leaf position to investigate the changes in carbon assimilation function according to leaf aging. Photosynthesis, growth, transpiration, stomatal conductance, light transmittance, and light reflectance were measured. As the plants’ growth progressed, the number of leaves, fresh weight, and dry weight increased, but the specific leaf area decreased, likely due to the increased distribution of assimilates to reproductive organs. The average photosynthesis rate, according to the measured dates, exhibited a high value despite a large standard error, which was likely influenced by measurement errors caused by external environmental factors. The reflectance and transmittance increased from the upper to the middle and bottom leaves, and the absorption ratio decreased in the same order. The green light spectrum (500–580 nm) had a lower absorption ratio than other spectra because the green coloration of the leaves increased the light reflectance of this spectrum. As the PPFD increased where the leaf was positioned higher, the photosynthesis rate, transpiration amount, and stomatal conductance also increased. The higher the leaf position, the higher the photosynthesis rate, the amount of transpiration, and the stomatal conductance. As the CO2 concentration increased, the photosynthesis rate increased, but the transpiration and stomatal conductance changed little, indicating that the gas exchange within leaves was hardly affected by CO2, but the light levels promoted photosynthesis. From the results of this study, the optical properties of the leaves indicate that they are consistent with Lambert–Beer’s law, which implies that the length of the optical path is linearly proportional to the number of molecules in the absorption layer. We obtained the light saturation point and CO2 saturation point of bell peppers grown in a greenhouse and were able to determine the physiological changes in the leaves with increasing leaf age. Therefore, based on this information, it appears that a leaf removal model based on the productivity of bell pepper leaves could be developed.

Hydrogen consumption estimation of fuel cell vehicle based on vehicle energy transfer

The hydrogen consumption of fuel cell vehicles (FCVs) can serve as both a measure of vehicle economy and a real-time notification of the driver’s fuel surplus status while driving. The traditional estimation method has poor real-time performance and a large error in accuracy. This paper proposes a new method for predicting hydrogen consumption based on vehicle energy transfer. This method extracts relative parameters based on the models of various components during the hydrogen conversion process and then trains the hydrogen consumption prediction model using the Informer algorithm. The training results show that the model has high prediction accuracy. Finally, the accuracy of this method was compared with the LSTM algorithm under urban and high-speed conditions. The results show that the root mean square error (RMSE) and mean absolute error (MAE) are optimized by 35 % and 26 % under urban conditions, while the RMSE and MAE are optimized by 41 % and 36 % under high-speed conditions. Its maximum error remains below 4 g.