Calibration Errors Research Articles

A Review on the Inertial Measurement Unit Array of Microelectromechanical Systems

In recent years, microelectromechanical systems (MEMS) technology has developed rapidly, and low precision inertial devices have achieved small volume, light weight, and mass production. Under this background, array technology has emerged to achieve high precision inertial measurement under the premise of low cost. This paper reviews the development of MEMS inertial measurement unit (IMU) array technology. First, the different types of common inertial measurement unit arrays are introduced and the basic principles are explained. Secondly, IMU array’s development status is summarized by analyzing the research results over the years. Then, the key technologies and corresponding development status of IMU array are described, respectively, including error analysis modeling and calibration, data fusion technology, fault detection, and isolation technology. Finally, the characteristics and shortcomings of the past research results are summarized, the future research direction is discussed, and some thoughts are put forward to further improve the accuracy of the IMU array.

ESR Essentials: radiomics-practice recommendations by the European Society of Medical Imaging Informatics.

Radiomics is a method to extract detailed information from diagnostic images that cannot be perceived by the naked eye. Although radiomics research carries great potential to improve clinical decision-making, its inherent methodological complexities make it difficult to comprehend every step of the analysis, often causing reproducibility and generalizability issues that hinder clinical adoption. Critical steps in the radiomics analysis and model development pipeline-such as image, application of image filters, and selection of feature extraction parameters-can greatly affect the values of radiomic features. Moreover, common errors in data partitioning, model comparison, fine-tuning, assessment, and calibration can reduce reproducibility and impede clinical translation. Clinical adoption of radiomics also requires a deep understanding of model explainability and the development of intuitive interpretations of radiomic features. To address these challenges, it is essential for radiomics model developers and clinicians to be well-versed in current best practices. Proper knowledge and application of these practices is crucial for accurate radiomics feature extraction, robust model development, and thorough assessment, ultimately increasing reproducibility, generalizability, and the likelihood of successful clinical translation. In this article, we have provided researchers with our recommendations along with practical examples to facilitate good research practices in radiomics. KEY POINTS: Radiomics' inherent methodological complexity should be understood to ensure rigorous radiomic model development to improve clinical decision-making. Adherence to radiomics-specific checklists and quality assessment tools ensures methodological rigor. Use of standardized radiomics tools and best practices enhances clinical translation of radiomics models.

Neural networks pipeline for quality management in IVF laboratory

This study introduces a novel neural network-based pipeline for predicting clinical pregnancy rates in IVF treatments, integrating both clinical and laboratory data. We developed a metamodel combining deep neural networks and Kolmogorov-Arnold networks, leveraging their complementary strengths to enhance predictive accuracy and interpretability. The metamodel achieved robust performance metrics after training and fitting on 11500 clinical cases: accuracy = 0.72, AUC = 0.75, F1 score = 0.60, and Matthews Correlation Coefficient of 0.42. According to morpho-kinetical embryo evaluation, our model’s PRC of 0.66 significantly improves over existing time-lapse systems for pregnancy prediction, demonstrating better handling of imbalanced clinical data. The metamodel’s calibration metrics (Brier score = 0.20, expected calibration error = 0.06, maximum calibration error = 0.12, Hosmer-Lemeshow test p-value = 0.06) indicate robust reliability in predicting clinical pregnancy outcomes. We validated the model’s reproducibility using an independent dataset of 665 treatment cycles, showing close alignment between predicted and actual pregnancy rates (58.9% vs. 59.1%). With the Bayesian method, we proposed a robust framework for integrating historical data with real-time predictions from neural networks, enabling a transition from retrospective to prospective analysis. Our approach extends beyond conventional embryo selection, incorporating post-analytical phase evaluation in the IVF laboratory. This comprehensive framework enables detailed analysis across different patient subpopulations and time periods, facilitating the identification of systemic issues and IVF protocol optimization. The model’s ability to track pregnancy probabilities over time and staff members allows for both outcome prediction and retrospective and prospective assessment of IVF treatment efficacy, providing a data-driven strategy for continuous improvement in assisted reproductive technology.

Two-parametric prescan calibration of gradient-induced sampling errors for rosette MRI.

The aim of this study was to develop a simple, robust, and easy-to-use calibration procedure for correcting misalignments in rosette MRI k-space sampling, with the objective of producing images with minimal artifacts. Quick automatic calibration scans were proposed for the beginning of the measurement to collect information on the time course of the rosette acquisition trajectory. A two-parameter model was devised to match the measured time-varying readout gradient delays and approximate the actual rosette sampling trajectory. The proposed calibration approach was implemented, and performance assessment was conducted on both phantoms and human subjects. The fidelity of phantom and in vivo images exhibited significant improvement compared with uncorrected rosette data. The two-parameter calibration approach also demonstrated enhanced precision and reliability, as evidenced by quantitative relaxometry analyses. Adequate correction of data sampling is a crucial step in rosette MRI. The presented experimental results underscore the robustness, ease of implementation, and suitability for routine experimental use of the proposed two-parameter rosette trajectory calibration approach.

Visual Servoing for Aerial Vegetation Sampling Systems

This research describes a vision-based control strategy that employs deep learning for an aerial manipulation system developed for vegetation sampling in remote, dangerous environments. Vegetation sampling in such places presents considerable technical challenges such as equipment failures and exposure to hazardous elements. Controlling aerial manipulation in unstructured areas such as forests remains a significant challenge because of uncertainty, complex dynamics, and the possibility of collisions. To overcome these issues, we offer a new image-based visual servoing (IBVS) method that uses knowledge distillation to provide robust, accurate, and adaptive control of the aerial vegetation sampler. A convolutional neural network (CNN) from a previous study is used to detect the grasp point, giving critical feedback for the visual servoing process. The suggested method improves the precision of visual servoing for sampling by using a learning-based approach to grip point selection and camera calibration error handling. Simulation results indicate the system can track and sample tree branches with minimum error, demonstrating that it has the potential to improve the safety and efficiency of aerial vegetation sampling.

Industrial robot calibration optimization method based on R-optimal criterion and improved IOOPS algorithm

Abstract In this paper, a novel kinematic parameter calibration method based on the R-optimal criterion and the improved iterative one-by-one pose search (IOOPS) algorithm is proposed to improve the end-effector positioning accuracy of industrial robots. First, a novel industrial robot error calibration model based on the product of exponential model and generalized error model is established, and the ideal pose transformation matrix from the robot base coordinate system to the laser tracker measurement coordinate system is obtained using the least-squares method, thereby obtaining the initial parameter vector of the robot calibration system. Second, various joint angle values at different positions within the robot’s workspace and the corresponding end-effector coordinate data, namely calibration sampling point data, are collected. Subsequently, the improved IOOPS algorithm combined with the observability index O R proposed by the R-optimal criterion is used to select the optimized calibration sampling points. Finally, the optimized sampling point data are combined with the Levenberg–Marquardt algorithm to optimize the parameters and obtain the optimal kinematic parameters. The experimental results show that the average end-effector error of the robot decreases from 0.5822 to 0.2492 mm after calibration using this method, demonstrating the effectiveness of the optimized sampling points in the calibration process achieved through the improved algorithm and the new observability index.

Research on the simulation method of a BP neural network PID control for stellar spectrum

This study investigated the multiple correlations among spectral simulation units based on digital micromirror device (DMD) spectral simulation, which leads to the problem that conventional spectral simulation methods such as PID control exhibit a low fitting accuracy or long fitting time in the spectral simulation of various targets. In this paper, a method of stellar spectrum simulation based on back propagation neural network-based PID (BP-PID) control is proposed to achieve high efficiency and high precision simulation of various spectral targets. The topology of the BP neural network was constructed based on the spectral modulation model of a DMD stellar spectrum simulation system, and the algorithm of the BP-PID control was designed. Finally, an experimental platform was built to verify the performance and spectral simulation accuracy of the BP-PID control algorithm. The results show that the overshoot and response time of the BP-PID control algorithm decreased by 79.01% and 30%, respectively compared with those of the PID control algorithm. The maximum spectral simulation accuracies of 2000K, 7000K, and 12000K color temperature increased by a factor of 2.311, 1.871, and 2.254, respectively, and the standard deviations of the spectral simulation error decreased by 56%, 41%, and 54%, respectively. In the range of 2000-12000K color temperature, the spectral simulation error of the BP-PID control algorithm is better than ±3.495%, and the standard deviation of the spectral simulation error is between 1.8255 and 2.2358. The proposed method can improve the spectral simulation accuracy and simulation efficiency of a star simulator, reduce the magnitude and spectrum calibration errors caused by the differential response, improve the star feature recognition accuracy of the orbiting star sensor, and hence, provide a theoretical and technical basis for the development of high-precision star sensors.

Digital background calibration algorithm for pipelined ADC based on time-delay neural network with genetic algorithm feature selection

This paper presents a novel background calibration method for pipelined analog-to-digital converters (ADCs) using a time-delay neural network (TDNN), which is optimized through genetic algorithm (GA) techniques. The proposed technique leverages TDNN to create enhanced feature sets, significantly improving the calibration of nonlinear errors exhibiting memory effects. It harnesses the GA's global optimization capabilities for feature selection, effectively reducing the feature dimension and consequently alleviating the NN's computational burden. A parallel pipeline architecture is devised for the calibration circuit, with its implementation realized on FPGA to facilitate forward inference processing. The inference circuit is synthesized using TSMC's 90 nm CMOS process, achieving a power consumption of 40.11 mW and an area of 0.45 mm2. Simulations based on MATLAB for a 14-bit Pipelined ADC demonstrate that the proposed calibration method significantly improves the SFDR from 59.77 dB to 165.52 dB, and ENOB from 8.79 bits to 19.23 bits, surpassing the target ADC's specifications. Moreover, the dimensionality of features is effectively reduced by up to 34 % without compromising the calibration performance.

Quantitative Raman analysis of microplastics in water using peak area ratios for concentration determination

This study presents a novel analytical method for the quantitative and qualitative analysis of microplastics (MPs) in deionized (DI) water using Raman spectroscopy. Raman peak area ratios of 1295 cm−1 for polyethylene (PE) and 637 cm−1 for polyvinylchloride (PVC) to the broad H2O peak were utilized to establish a calibration model for the concentration of MPs dispersed in DI water at 0.1 wt% to 1.0 wt%. The calibration model demonstrated R2 values of 0.98537 for PE and 0.99511 for PVC, indicating high linearity between the peak area ratio and concentration. The calibration model was validated using mixed PE and PVC samples to confirm its applicability to real-world water bodies, where multiple types of MPs are present. The calculated standard error of calibration (SEC) and relative standard error of calibration (%RSEC) values further confirmed the accuracy of the predictions, providing a robust approach for detecting and quantifying MPs in aquatic environments.

Mitigating calibration errors from mutual coupling with time-domain filtering of 21 cm cosmological radio observations

Abstract The 21 cm transition from neutral Hydrogen promises to be the best observational probe of the Epoch of Reionisation (EoR). This has led to the construction of low-frequency radio interferometric arrays, such as the Hydrogen Epoch of Reionization Array (HERA), aimed at systematically mapping this emission for the first time. Precision calibration, however, is a requirement in 21 cm radio observations. Due to the spatial compactness of HERA, the array is prone to the effects of mutual coupling, which inevitably lead to non-smooth calibration errors that contaminate the data. When unsmooth gains are used in calibration, intrinsically spectrally-smooth foreground emission begins to contaminate the data in a way that can prohibit a clean detection of the cosmological EoR signal. In this paper, we show that the effects of mutual coupling on calibration quality can be reduced by applying custom time-domain filters to the data prior to calibration. We find that more robust calibration solutions are derived when filtering in this way, which reduces the observed foreground power leakage. Specifically, we find a reduction of foreground power leakage by 2 orders of magnitude at k∥ ≈ 0.5 h Mpc−1.

Deep Conformal Supervision: Leveraging Intermediate Features for Robust Uncertainty Quantification.

Trustworthiness is crucial for artificial intelligence (AI) models in clinical settings, and a fundamental aspect of trustworthy AI is uncertainty quantification (UQ). Conformal prediction as a robust uncertainty quantification (UQ) framework has been receiving increasing attention as a valuable tool in improving model trustworthiness. An area of active research is the method of non-conformity score calculation for conformal prediction. We propose deep conformal supervision (DCS), which leverages the intermediate outputs of deep supervision for non-conformity score calculation, via weighted averaging based on the inverse of mean calibration error for each stage. We benchmarked our method on two publicly available datasets focused on medical image classification: a pneumonia chest radiography dataset and a preprocessed version of the 2019 RSNA Intracranial Hemorrhage dataset. Our method achieved mean coverage errors of 16e-4 (CI: 1e-4, 41e-4) and 5e-4 (CI: 1e-4, 10e-4) compared to baseline mean coverage errors of 28e-4 (CI: 2e-4, 64e-4) and 21e-4 (CI: 8e-4, 3e-4) on the two datasets, respectively (p < 0.001 on both datasets). Based on our findings, the baseline results of conformal prediction already exhibit small coverage errors. However, our method shows a significant improvement on coverage error, particularly noticeable in scenarios involving smaller datasets or when considering smaller acceptable error levels, which are crucial in developing UQ frameworks for healthcare AI applications.

Improving Effectiveness by Reducing Overconfidence in Large Catalogue Sequential Recommendation with gBCE loss

A large catalogue size is one of the central challenges in training recommendation models: a large number of items makes them memory and computationally inefficient to compute scores for all items during training, forcing these models to deploy negative sampling. However, negative sampling increases the proportion of positive interactions in the training data, and therefore, models trained with negative sampling tend to overestimate the probabilities of positive interactions – a phenomenon we call overconfidence . While the absolute values of the predicted scores/probabilities are not important for the ranking of retrieved recommendations, overconfident models may fail to estimate nuanced differences in the top-ranked items, resulting in degraded performance. In this paper, we show that overconfidence explains why the popular SASRec model underperforms when compared to BERT4Rec. This is contrary to the BERT4Rec authors’ explanation that the difference in performance is due to the bi-directional attention mechanism. To mitigate overconfidence, we propose a novel Generalised Binary Cross-Entropy Loss function (gBCE) and theoretically prove that it can mitigate overconfidence. We further propose the gSASRec model, an improvement over SASRec that deploys an increased number of negatives and the gBCE loss. Through detailed experiments on three datasets, we show that gSASRec does not exhibit the overconfidence problem. As a result, gSASRec can outperform BERT4Rec (e.g. +9.47% NDCG on the MovieLens-1M dataset), while requiring less training time (e.g. -73% training time on MovieLens-1M). Moreover, in contrast to BERT4Rec, gSASRec is suitable for large datasets that contain more than 1 million items. Finally, we show how addressing overconfidence can improve model calibration – the ability of a model to predict actual interaction probabilities accurately. By applying gBCE to the SASRec model on MovieLens-1M dataset, we reduce the models’ expected calibration error by 98.9% (from 0.966 to 0.01).

Indoor Test and Evaluation of Asphalt Pavement Compaction Measurements Using Ground-Penetrating Radar-Based Density Profiling System

A density profiling system (DPS) based on ground-penetrating radar was tested simulating the in-place DPS measurement in an indoor setting. The test investigated two types of asphalt mixture commonly used in Japan. Eight 15 cm (6 in.) diameter gyratory pucks were made for mix calibrations, and ten 50 × 50 × 5 cm (20 × 20 × 2 in.) wheel tracking slab specimens were fabricated for simulating the DPS measurement at the job site. Observation of the data showed that puck heterogeneity might cause calibration errors when compaction is too low. Furthermore, the test illustrated that the surface texture influences the measurement, and a correction may need to be applied when it is significant. With careful treatment of data and the surface texture correction, the DPS achieved a mean error of approximately 0.1% or less in air void content with respect to that based on bulk specific gravity obtained from cores using the saturated surface dry method.

CoNglyPred: Accurate Prediction of N-Linked Glycosylation Sites Using ESM-2 and Structural Features With Graph Network and Co-Attention.

N-Linked glycosylation is crucial for various biological processes such as protein folding, immune response, and cellular transport. Traditional experimental methods for determining N-linked glycosylation sites entail substantial time and labor investment, which has led to the development of computational approaches as a more efficient alternative. However, due to the limited availability of 3D structural data, existing prediction methods often struggle to fully utilize structural information and fall short in integrating sequence and structural information effectively. Motivated by the progress of protein pretrained language models (pLMs) and the breakthrough in protein structure prediction, we introduced a high-accuracy model called CoNglyPred. Having compared various pLMs, we opt for the large-scale pLM ESM-2 to extract sequence embeddings, thus mitigating certain limitations associated with manual feature extraction. Meanwhile, our approach employs a graph transformer network to process the 3D protein structures predicted by AlphaFold2. The final graph output and ESM-2 embedding are intricately integrated through a co-attention mechanism. Among a series of comprehensive experiments on the independent test dataset, CoNglyPred outperforms state-of-the-art models and demonstrates exceptional performance in case study. In addition, we are the first to report the uncertainty of N-linked glycosylation predictors using expected calibration error and expected uncertainty calibration error.

Comparative Analysis and Optimal Selection of Calibration Functions in Pure Rotational Raman Lidar Technique

The pure rotational Raman (PRR) lidar technique relies on calibration functions (CFs) to extract temperature information from raw detection data. The choice of CF significantly impacts the accuracy of the retrieved temperature. In this study, we propose a method that combines multiple Monte Carlo simulation experiments with a statistical analysis, and we first conduct simulated comparisons of the calibration effects of different CFs while considering the impact of noise. We categorized ten common CFs into four groups based on their functional form and the number of calibration coefficients. Based on functional form, specifically, we defined 1/T = f(lnQ) as a forward calibration function (FCF) and lnQ = g(1/T) as a backward calibration function (BCF). Here, T denotes temperature, and Q denotes the signal intensity ratio. Their performance within and outside the calibration interval is compared across different integration times, smoothing methods, and reference temperature ranges. The results indicate that CFs of the same category exhibit similar calibration effects, while those of different categories exhibit notable differences. Within the calibration interval, the FCF performs better, especially with more coefficients. However, outside the calibration interval, the linear calibration function (which can be considered a two-coefficient FCF) has an obvious advantage. Conclusions based on the simulation results are validated with actual data, and the factors influencing calibration errors are discussed. Utilizing these findings to guide CF selection can enhance the accuracy and stability of PRR lidar detection.

Design and realization of multifunctional energy meter calibration station under extreme conditions

Abstract This paper designs a multifunctional calibration station that can be installed inside the temperature box in order to adapt to some of the special test requirements, which can be used for the testing of most types of energy meters on the market. Extreme environments such as high and low temperatures can easily cause cracks in equipment welds, aging of material insulation, and functionally cause sensitive components to drift affecting the accuracy of testing during the life cycle of the calibrator. This multifunctional calibration station designed and implemented in this paper is capable of maintaining its functionality without being destroyed under repeated experiences of extreme conditions. In this paper, a multifunctional calibration station is designed and realised. In order to verify the influence of the calibrator on the calibration error of the energy meter after repeatedly experiencing extreme conditions, a comparison test of 40 cycles was carried out by comparing the calibrator with the one placed at room temperature, and the difference between the two calibrators’ measurement results did not show a regular growth trend with the increase in the number of measurement cycles, which proves that the calibrator is able to keep its function undamaged under the repeated experience of extreme conditions.

Curriculum Design and Sim2Real Transfer for Reinforcement Learning in Robotic Dual-Arm Assembly

Robotic systems are crucial in modern manufacturing. Complex assembly tasks require the collaboration of multiple robots. Their orchestration is challenging due to tight tolerances and precision requirements. In this work, we set up two Franka Panda robots performing a peg-in-hole insertion task of 1 mm clearance. We structure the control system hierarchically, planning the robots’ feedback-based trajectories with a central policy trained with reinforcement learning. These trajectories are executed by a low-level impedance controller on each robot. To enhance training convergence, we use reverse curriculum learning, novel for such a two-armed control task, iteratively structured with a minimum requirements and fine-tuning phase. We incorporate domain randomization, varying initial joint configurations of the task for generalization of the applicability. After training, we test the system in a simulation, discovering the impact of curriculum parameters on the emerging process time and its variance. Finally, we transfer the trained model to the real-world, resulting in a small decrease in task duration. Comparing our approach to classical path planning and control shows a decrease in process time, but higher robustness towards calibration errors.

Extrinsic parameters optimization for fringe projection system based on standard components

Fringe projection profilometry (FPP) is a wildly used three-dimension reconstruction method, where the reconstruction accuracy is closely related to the calibration precision. However, various sources of error in the calibration process can result in inaccurate calibration results. In particular, the calibration results of extrinsic parameters are more likely to deviate from the true values, which in turn leads to low-frequency errors in the reconstruction results. In order to obtain the extrinsic parameters closer to the true values, a simple but effective method is proposed in this paper. The proposed method utilizes the standard plane and the dumbbell bat, which are commonly used in measurements, to optimize the extrinsic parameters. First, the standard plane and dumbbell bat are reconstructed using the initial calibration results to transfer the errors in the extrinsic parameters to the plane fitting residuals, as well as to the fitting residuals for the radius and the center distance of dumbbell bat. The residuals and errors are then minimized using nonlinear optimization, resulting in the more accurate extrinsic parameters. Experimental results demonstrate that the proposed method can effectively compensate the calibration error of the extrinsic parameters. The root mean square (RMS) error of planar reconstruction is reduced from 0.0291 mm to 0.0106 mm, which is reduced by about 63.6 %. The RMS error of spherical reconstruction is reduced from 0.0358 mm to 0.0166 mm, which is about 53.6 %. The measurement accuracy of the FPP system is further improved.

An evaluation of source-blending impact on the calibration of SKA EoR experiments

ABSTRACT Twenty-one-centimetre signals from the Epoch of Reionization (EoR) are expected to be detected in the low-frequency radio window by the next-generation interferometers, particularly the Square Kilometre Array (SKA). However, precision data analysis pipelines are required to minimize the systematics within an infinitesimal error budget. Consequently, there is a growing need to characterize the sources of errors in EoR analysis. In this study, we identify one such error origin, namely source blending, which is introduced by the overlap of objects in the densely populated observing sky under SKA1-Low’s unprecedented sensitivity and resolution, and evaluate its two-fold impact in both the spatial and frequency domains using a novel hybrid evaluation (HEVAL) pipeline combining end-to-end simulation with an analytic method to mimic EoR analysis pipelines. Sky models corrupted by source blending induce small but severe frequency-dependent calibration errors when coupled with astronomical foregrounds, impeding EoR parameter inference with strong additive residuals in the two-dimensional power spectrum space. We report that additive residuals from poor calibration against sky models with blending ratios of 5 and 0.5 per cent significantly contaminate the EoR window. In contrast, the sky model with a 0.05 per cent blending ratio leaves little residual imprint within the EoR window, therefore identifying a blending tolerance at approximately 0.05 per cent. Given that the SKA observing sky is estimated to suffer from an extended level of blending, strategies involving de-blending, frequency-dependent error mitigation, or a combination of both, are required to effectively attenuate the calibration impact of source-blending defects.

Robot Calibration Sampling Data Optimization Method Based on Improved Robot Observability Metrics and Binary Simulated Annealing Algorithm

As the societal demand for precision in industrial robot operations increases, calibration can enhance the end-effector positioning accuracy of robots. Sampling data optimization plays an important role in improving the calibration effect. In this study, a robot calibration sampling point optimization method based on improved robot observability metrics and a Binary Simulated Annealing Algorithm is proposed. Initially, a robot kinematic model based on the Product of Exponentials (POE) model and a generalized error model is established. By utilizing the least squares method, the ideal pose transformation relationship between the robot’s base coordinate system and the laser tracker measurement coordinate system is derived, resulting in an error calibration model based on spatial single points. An improved robot observability metric combined with the Binary Simulated Annealing Algorithm (BSAA) is introduced to optimize the selection of calibration sampling data. Finally, the robot’s parameter errors are obtained using a nonlinear least squares method. Experimental results demonstrate that the average end-effector positioning error of the robot after calibration can be reduced from 0.356 mm to 0.254 mm using this method.