Error Characteristics Research Articles

Probability density function based adaptive ensemble learning with global convergence for wind power prediction

Accurate wind power prediction is highly significant to the safety, stability, and economic operation of power grids. Currently, typical ensemble methods for wind power forecasting are widely designed based on the mean square error (MSE) loss, which are very suitable for the assumption that the error distribution obeys the Gaussian distribution. However, the complex nonlinear conversion of wind energy into wind power may change the statistical characteristics of errors, thus the prediction model based on the traditional MSE loss may lead to the performance degradation of the forecasting model. To address these problems, a probability density function based adaptive ensemble learning with global convergence is proposed for wind power prediction, which comprises three modules: a data preprocessing module, a prediction module, and a combination module. Firstly, an effective feature generation mechanism is employed to extract the multi-mode characteristics of wind data. Then, an auxiliary error based adaptive global convergence model is developed as benchmark predictor in the prediction module, where an adaptive updating algorithm is derived based on the Lyapunov approach to ensure the global convergence of the model weights. Moreover, considering the asymmetric characteristic of modeling error, a probability density function (PDF) based ensemble learning is created to integrate the results of benchmark predictors. Specifically, the ensemble model parameter updating is transformed into the shape control for the modeling error PDF, which can break through the limitation of MSE loss capturing only the second moment information, and emphasize the spatial distribution of errors to make an unbiased estimate of wind power. Experimental results show that the proposed ensemble model has significant advantages over other models involved in this study.

Study on the Feasibility and Performance Evaluation of High-Orbit Spacecraft Orbit Determination Based on GNSS/SLR/VLBI

Deep space exploration utilizing high-orbit vehicles is a vital approach for extending beyond near-Earth space, with orbit information serving as the foundation for all functional capabilities. The performance of orbit determination is primarily influenced by observation types, errors, geometrical structures, and physical perturbations. Currently, research on orbit determination for high-orbit spacecraft predominantly focuses on single observation methods, error characteristics, multi-source fusion techniques, and algorithms. However, these approaches often suffer from low observation accuracy and increased costs. This paper advocates for the comprehensive utilization of existing multi-source observation methods, such as GNSS (Global Navigation Satellite System), SLR (Satellite Laser Ranging), and VLBI (Very Long Baseline Interferometry), in research. The decoupled Kalman filter reveals a positive correlation between measurement positioning accuracy and orbit determination accuracy, and it derives a simple orbit performance evaluation model that considers the influence of observation value types and geometric configurations, without the need to introduce complex dynamic models. Simulations are then employed to verify and analyze antenna gain, observation values, and performance evaluation. The results indicate the following: (1) Under simulated conditions, the optimal strategy involves employing the SLR/VLBI dual system during periods when VLBI orbit determination is feasible, yielding an average Weighted Position Dilution of Precision (WPDOP) of 26.79. (2) For periods when VLBI orbit determination is not feasible, the optimal approach is to utilize the GNSS/SLR/VLBI triple system, resulting in an average WPDOP of 16.32. (3) The orbit determination performance of the triple system is not significantly impacted by the use of global SLR stations compared to using only Chinese SLR stations. However, the global network enables continuous, round-the-clock orbit determination capabilities.

Characteristics of R2019 Processing of MODIS Sea Surface Temperature at High Latitudes

Satellite remote sensing is the best way to derive sea surface skin temperature (SSTskin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SSTskin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SSTskin fields are currently derived using latitudinally and monthly dependent algorithm coefficients, including an additional band above 60°N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SSTskin still has some unrevealed error characteristics. This study uses 21 years (2002–2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 μm and 12 μm BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrieval algorithm lose its effectiveness. Moreover, the statistics of the MODIS SSTskin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrieval algorithms to obtain more accurate MODIS SSTskin data to study surface ocean processes and climate change in the Arctic.

An interval prediction approach for quantifying the thermal error uncertainty of ball screw system

The ball screw system is one of the most core components of CNC machine tool, and its thermal positioning error directly affects the machining accuracy of parts. Establishing a data-based thermal error model is currently a popular method to predict this thermal error. Uncertain disturbances are unavoidable in reality. However, most of the existing thermal error models are deterministic, which cannot consider the uncertainty and generally can only provide the predictive value. To quantify the uncertainty of thermal error from the ball screw system, this paper proposes an interval prediction method, in which gated recurrent unit (GRU) neural network is embedded into the model and prediction interval (PI) evaluation indexes are used to determine the model parameters. Both the temporal and spatial characteristics of thermal error in the ball screw system are taken into account by the proposed method. The experimental data has verified that the proposed method can effectively generate PIs for the thermal error of ball screw system.

Evaluation of satellite-derived precipitation datasets: a case study in the Ten Tributaries region of the Yellow River Basin

ABSTRACT Satellite-derived precipitation datasets are essential components of hydrological simulations, particularly in data-scarce regions of western China. However, a comprehensive assessment of their accuracy and reliability is required. Here, the accuracy of two high-resolution satellite-derived precipitation datasets, Integrated Multi-satellite Retrievals for GPM – Final (IMERG-F) and Gauge-Adjusted Global Satellite Mapping of Precipitation (GSMaP-Gauge), was evaluated across the Ten Tributaries region of the Yellow River Basin in western China using four quantitative metrics and three categorical scoring indicators. This evaluation sought to ascertain the retrieval accuracy of these products on both the daily scale and hourly scale of heavy precipitation events, and investigated their inversion error characteristics across various spatiotemporal scales. Both datasets effectively captured the spatiotemporal patterns of annual average precipitation within the study area. Notably, the daily-scale accuracy of these satellite-derived precipitation products surpassed their hourly and half-hourly counterparts. Both GPM-IMERG and GSMaP-Gauge adeptly reproduced most precipitation events in the Ten Tributaries region, with peak detection performance observed in the central and southern zones, providing a reliable data source for drought monitoring and hydrological modeling. Overall, compared with GPM-IMERG, GSMaP-Gauge displayed superior inversion accuracy across diverse spatiotemporal scales.

Toward Process-Oriented, Dimensional Approaches for Diagnosis and Treatment of Speech Sound Disorders in Children: Position Statement and Future Perspectives.

Children with speech sound disorders (SSD) form a heterogeneous group, with respect to severity, etiology, proximal causes, speech error characteristics, and response to treatment. Infants develop speech and language in interaction with neurological maturation and general perceptual, motoric, and cognitive skills in a social-emotional context. After a brief introduction into psycholinguistic models of speech production and levels of causation, in this review article, we present an in-depth overview of mechanisms and processes, and the dynamics thereof, which are crucial in typical speech development. These basic mechanisms and processes are: (a) neurophysiological motor refinement, that is, the maturational articulatory mechanisms that drive babbling and the more differentiated production of larger speech patterns; (b) sensorimotor integration, which forms the steering function from phonetics to phonology; and (c) motor hierarchy and articulatory phonology describing the gestural organization of syllables, which underlie fluent speech production. These dynamics have consequences for the diagnosis and further analysis of SSD in children. We argue that current diagnostic classification systems do not do justice to the multilevel, multifactorial, and interactive character of the underlying mechanisms and processes. This is illustrated by a recent Dutch study yielding distinct performance profiles among children with SSD, which allows for a dimensional interpretation of underlying processing deficits. Analyses of mainstream treatments with respect to the treatment goals and the speech mechanisms addressed show that treatment programs are quite transparent in their aims and approach and how they contribute to remediating specific deficits or mechanisms. Recent studies into clinical reasoning reveal that the clinical challenge for speech-language pathologists is how to select the most appropriate treatment at the most appropriate time for each individual child with SSD. We argue that a process-oriented approach has merits as compared to categorical diagnostics as a toolbox to aid in the interpretation of the speech profile in terms of underlying deficits and to connect these to a specific intervention approach and treatment target.

Comprehensive analysis of zenith tropospheric dealy and precipitable water vapor retrieved from BDS-3 B1C and B2a signals

This study presents a comprehensive analysis of ZTD and PWV estimated from BDS-3 new B1C/B2a signal combination. ZTD are firstly estimated using B1C and B2a signals and evaluated with the IGS final products. The average convergence time of ZTD was 1980 s, which was 12.0 % shorter than that from the legacy B1I/B3I signals. This indicated their superiority for real-time operational meteorology. The average RMS value of ZTD were 5.9 mm for all stations, corresponding to an ∼ 7.8 % improvement with respect to the legacy B1I/B3I signal combinations. ZTD error characteristics analysis revealed relationships with station latitudes and seasons, mainly due to the more active water vapor variation in equator region and summer. Afterwards, the converted PWV was assessed using multiple inter-technique datasets. For radiosonde-derived PWV, the RMS for GPS L1W/L2W, BDS-3 legacy B1I/B3I, and BDS-3 new B1C/B2a signals were 2.5, 2.4, and 2.5 mm, respectively, whereas those for the remotely-sensed PWV from MODIS were 4.9, 5.0, and 4.9 mm, respectively. Besides, evaluation with ERA5 showed the average RMS were all 1.7 mm for different GNSS signal combinations. These results prove the feasibility of PWV retrieval using BDS-3 new B1C/B2a signals, which are expected to provide scientific references for further applications.

UWB distance estimation errors in (non-)line of sight situations within the context of 3D analysis of human movement

Abstract Integrated Ultrawideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensors are becoming popular for indoor localization applications, as a higher accuracy can be achieved than with just MIMU sensors. These integrated sensors could extend stability and accuracy in the field of 3D analysis of human movement (3D AHM) if they can deliver position estimates with an accuracy close to 1 cm. Achieving this high accuracy of 1 cm remains challenging, with most studies reporting position estimation errors around 5 cm, often due to Non-Line of Sight (NLOS) conditions and systematic UWB sensor distance estimation errors. Studying the distance error characteristic of UWB in situations of 3D AHM is essential to deal with these errors. While research on UWB distance errors in Line of Sight (LOS) and NLOS situations exists, few studies focus on the NLOS errors caused by the human body and were not in the relevant scenarios of 3D AHM. Therefore, this article examines UWB sensor performance and distance error characteristics in LOS and NLOS situations typical for the 3D AHM. Both the LOS and NLOS situations were studied in the typical 3D AHM distance range of 0.2 m to 2 m. The NLOS situations were studied first with a human subject as NLOS causing object and then with simulated human body segments (PVC pipes filled with water) of varying diameters. In LOS situations, consistent systematic bias errors were observed, along with incidental errors at specific positions in the room. In NLOS scenarios caused by the human and simulated body segments, a consistent and reproducible overestimation of distances was found. The reproducibility of these errors based on relative node and object positions suggests that systematic mitigation methods could significantly reduce errors, enabling more accurate and reproducible 3D human movement analysis.

Methods to mitigate the impact of work environment on the measurement of building surface temperature by unmanned aerial vehicle onboard thermal imaging system

With the widespread adoption of UAVs and compact infrared cameras, UAV infrared remote sensing technology has extensive applications in qualitative research fields such as finding thermal bridges and air tightness detection in construction projects, but less in quantitative research, mainly due to its large temperature measurement error. When measuring temperature with UAV-mounted thermal imaging cameras, factors such as operational wind conditions and operation methods, in addition to the inherent accuracy of the instrument, can introduce measurement errors. To investigate the impact of operational wind conditions on temperature measurements by UAV-mounted thermal imaging cameras, both field and laboratory experiments were conducted to analyze the error characteristics caused by wind conditions. Devices were designed to mitigate the impact of wind on temperature measurements, and their effectiveness was validated through experiments. Based on these findings, methods were developed for reducing the influence of wind conditions on temperature measurements in UAV operations. The results indicate that wind condition errors primarily affect the stability of temperature data. Under the influence of wind conditions, the temperature data recorded by thermal infrared cameras exhibit periodic and significant fluctuations, with a temperature difference of approximately 4.7 °C between the highest and lowest points within a single cycle, constituting the main source of measurement error. However, by following the prescribed measurement procedures, the temperature measurement error can be reduced by 32 %, from ±5 °C to ±1.6 °C, approaching the accuracy of tripod-mounted thermal imaging cameras. This establishes a foundation for quantitative research in low-altitude infrared remote sensing.

A Simulation Study of the Effects of Additive, Multiplicative, Correlated, and Uncorrelated Errors on Principal Component Analysis

ABSTRACTMeasurement errors are ubiquitous in all experimental sciences. Depending on the particular experimental platform used to acquire data, different types of errors are introduced, amounting to an admixture of additive and multiplicative error components that can be uncorrelated or correlated. In this paper, we investigate the effect of different types of experimental error on the recovery of the subspace with principal component analysis (PCA) using numerical simulations. Specifically, we assessed how different error characteristics (variance, correlation, and correlation structure), loading structures, and data distributions influence the accuracy to estimate an error‐free (true) subspace from sampled data with PCA. Quality was assessed in terms of the mean squared reconstruction error and the congruence to the error‐free loadings, using the pseudorank and adjusting for rotational ambiguity. Analysis of variance reveals that the error variance, error correlation structure, and their interaction with the loading structure are the factors mostly affecting quality of loading estimation from sampled data. We advocate for the need to characterize and assess the nature of measurement error and the need to adapt formulations of PCA that can explicitly take into account error structures in the model fitting.

Extraction and analysis of short-term variation characteristics of code multipath error

Multipath errors (MP) can seriously affect positioning accuracy. Extracting and analyzing the variation characteristics of MP can provide a basis for mitigating it, but the current studies primarily focus on the characteristics of long-term variation of multipath errors while ignoring its short-term variation, which leads to incomplete understanding of the MP. Code and carrier phase dual-frequency observation combination and moving average method are combined to achieve accurate extraction of short-term code multipath error variation (MPvar), and different moving average strategies are adopted to satisfy the needs of real-time and after-the-fact extraction. The variation characteristics of MPvar between sea and land, among different GNSS systems, among different orbits of the BDS system are compared and analyzed. Study indicates that the carrier-to-noise ratio (C/N0) at sea is low and fluctuates greatly compared with the C/N0 at land, but the MPvar at sea is much smoother. There are differences in the magnitude of MPvar for each GNSS system, but they are all correlated with the elevation angle. For BDS GEO satellites, although the elevation angle variations are minimal, the MPvar has significant variations. Therefore, this study suggests that the MP variations of the BDS GEO satellites cannot be regarded as a smooth process when MP sources exist in the vicinity of the static observation stations. The extraction method and analysis results in this study helps to provide ideas for mitigating MP from a perspective of short-term variation.

Variational All‐Sky Assimilation Framework for MWHS‐II With Hydrometeors Control Variables and Its Impacts on Analysis and Forecast of Typhoon Cases

AbstractAll‐sky radiance assimilation has been extensively developed to provide additional information for numerical weather prediction under cloudy conditions. Microwave radiances are particularly sensitive to hydrometeors, which can be used to initialize hydrometeor directly if the hydrometeor control variables (HCVs) are available. However, the effects of HCVs statistical structure and their multivariate correlation on all‐sky radiance assimilation remain unclear. In this study, five HCVs are introduced into the variational assimilation system. The characteristics of hydrometeor background errors are analyzed, and the combined effect with the observation operator is discussed. Then a 3D Variational all‐sky assimilation framework with HCVs is modified to assimilate Fengyun‐3C/D Microwave Humidity Sounder‐II radiance. It is shown that hydrometeors are initialized by radiance directly, and the thermodynamic fields are adjusted accordingly. The characteristics of multi‐variables increments are associated with both the characteristics of HCVs in background error and the Jacobians in observation operator. Furthermore, cycle assimilation and forecast experiments for three typhoon cases are conducted. It is found that the difference between observed and analyzed brightness temperatures decreases when HCVs are activated, and the hydrometeors analysis fields are more consistent with observations. Additionally, the typhoon intensity forecasts are improved with enhanced double warm‐core and the secondary circulation. This paper analyzes the characteristics of variational all‐sky assimilation framework with HCVs, and demonstrates the potential value of HCVs for variational all‐sky radiance assimilation.

Preanalytical Errors in Hematology: Insights From a Tertiary Care Hospital.

Introduction Errors occur in the laboratory at any level of the testing process. Recognizing these errors may cause patient distress by delaying diagnosis and management until results are released. This study aims to assess preanalytical errors in the laboratory and suggests methods to prevent them to improve accuracy and efficiency in the hematology department of a tertiary care hospital. The background emphasizes the critical role of preanalytical procedures in ensuring accurate hematological diagnoses and highlights the frequent mistakes that occur during specimen collection, handling, and transportation. Staff training, process standardization, using suitable collecting and transport equipment, putting quality control measures in place, and using automation technologies are some strategies for reducing errors and speeding up turnaround times. To increase diagnostic accuracy, patient care outcomes, and laboratory efficiency, hematology laboratories should address preanalytical mistakes and employ fostering methods. Aims and objectives The objective of this research is to evaluate the frequency and characteristics of preanalytical errors within the hematology laboratory of a tertiary care hospital. The study seeks to gauge the scope of the problem, identify key factors contributing to these errors, such as issues in specimen collection, handling, and transportation, and highlight areas where improvements can be made. Also, it intends to assess how preanalytical errors affect hematological accuracy and turnaround times, with a focus on patient care outcomes, and recommend techniques to reduce preanalytical errors and improve the accuracy of hematological results. Materials and methods This study was conducted at the hematology laboratory of our hospital from January 2023 to June 2024 after getting proper approval from the Institutional Review Board (IRB approval number 294/08/2024/PG/SRB/SMCH). It is a retrospective analytical study, and the study population comprised samples of patients from the emergency, inpatient, and outpatient departments, which included 51,155 complete blood count (CBC) and 5,449 peripheral smear(PS) samples. The study included only test samples sent specifically for hematological analysis, while those sent for cytological, biochemical, or microbiological testing were excluded. The distribution frequency of the samples and the number of rejected samples were analyzed, and the results were then compared and correlated. Results During the study period, a total of 56,604 samples were processed in the hematology laboratory. Of these, 886 samples (1.3%) were rejected due to preanalytical errors. The most frequent error was submitting an insufficient sample (54.17%), while the least frequent was the use of an empty or defective tube (0.4%). In the emergency department, the primary issues were insufficient and clotted samples, while in pediatric cases, errors were mainly due to inadequate or diluted samples. Conclusion Preanalytical errors in hematology laboratories, though often overlooked, can significantly impact diagnostic accuracy and patient care. Our study highlights that a substantial portion of errors arise from inadequate sample collection and handling, particularly in emergency and pediatric cases. Adherence to standard laboratory techniques can dramatically reduce preanalytical errors.

Longitudinal refractive errors over 36 months in Hispanic and Black children.

This study brings awareness of racial/ethnic difference of refractive error characteristics in clinics. This study aimed to assess longitudinal change in refractive errors over a 36-month period in Hispanic and Black children. Children (2.4 to 15 years old) were studied. Cycloplegic refraction was measured annually. Spherical equivalent was calculated. Astigmatism was evaluated by magnitude of cylinder and power vector (J0 and J45). Absolute value of interocular spherical equivalent difference was used to calculate anisometropia. Mixed-linear model was used to analyze longitudinal annual change in spherical equivalent, cylinder, J0, and J45 over 36 months. A total of 485 participants (310 Black, 175 Hispanic) met the criteria. At the baseline examination, prevalence of myopia, emmetropia, and hyperopia was 39% (n = 187), 31% (n = 150), and 30% (n = 148), respectively. Spherical equivalent of Black children was not significantly different from that in Hispanic children (0.10 ± 2.92 vs. -0.37 ± 2.05 D, p=0.06); however, the Hispanic children had a significantly higher cylinder compared with Black children (Hispanic: 1.46 ± 1.57 D vs. Black: 0.92 ± 1.07 D; p<0.001). Both J0 (p<0.001) and J45 (p=0.01) were significantly different between two groups; the Hispanic children had more with-the-rule astigmatism and oblique astigmatism than the Black children. Prevalence of anisometropia (≥1 D) was higher in Black children (14%) compared with Hispanic children (5%, p=0.006). Over 36 months, spherical equivalent significantly decreased an average of 0.69 D (0.23 D/y, p<0.001) for both groups; neither astigmatism nor anisometropia changed significantly (p>0.05). Astigmatism in the Hispanic children was significantly higher than in Black children. However, the Black children had a higher prevalence and degree of anisometropia than the Hispanic children.

On the Use of SuperDARN Ground Backscatter Measurements for Ionospheric Propagation Model Validation

AbstractPrior to use in operational systems, it is essential to validate ionospheric models in a manner relevant to their intended application to ensure satisfactory performance. For Over‐the‐Horizon radars (OTHR) operating in the high‐frequency (HF) band (3–30 MHz), the problem of model validation is severe when used in Coordinate Registration (CR) and Frequency Management Systems (FMS). It is imperative that the full error characteristics of models is well understood in these applications due to the critical relationship they impose on system performance. To better understand model performance in the context of OTHR, we introduce an ionospheric model validation technique using the oblique ground backscatter measurements in soundings from the Super Dual Auroral Radar Network (SuperDARN). Analysis is performed in terms of the F‐region leading edge (LE) errors and assessment of range‐elevation distributions using calibrated interferometer data. This technique is demonstrated by validating the International Reference Ionosphere (IRI) 2016 for January and June in both 2014 and 2018. LE RMS errors of 100–400 km and 400–800 km are observed for winter and summer months, respectively. Evening errors regularly exceeding 1,000 km across all months are identified. Ionosonde driven corrections to the IRI‐2016 peak parameters provide improvements of 200–800 km to the LE, with the greatest improvements observed during the nighttime. Diagnostics of echo distributions indicate consistent underestimates in model NmF2 during the daytime hours of June 2014 due to offsets of −8° being observed in modeled elevation angles at 18:00 and 21:00 UT.

HEAD: High-Speed Approximate HEterogeneous ADder for Error-Resilient Applications

Media processing applications can tolerate error up to a certain limit due to the perceptual limitations of the human eye. Since adders are ubiquitous in power-hungry media processing applications, they can be approximated without much compromise in quality. An adder with better error characteristics without significant compromise in area and power is required to meet the future demands of ever-increasing computational needs. In order to achieve the same, a novel segmentation technique has been proposed. By varying the number of bits in each segment, three different designs are proposed, each with its advantage. Each proposed adder is segmented into three portions, namely Most Significant Portion (MSP), Intermediate Significant Portion (ISP) and Least Significant Portion (LSP). The sub-adder’s sum in each portion is computed concurrently to reduce the critical path delay. The proposed heterogeneous adder (HEAD) designs achieve better accuracy than the state-of-the-art designs. Synthesis results show that HEAD consumes less area up to 41.8% and consumes less power ranging from 0.8% to 51%, than existing adders. Exhaustive simulations are carried out on benchmarking image sharpening application to prove that the proposed adders obtain a better quality-effort tradeoff than the existing designs.

UWB/MEMS IMU integrated positioning method based on NLOS angle discrimination and MAP constraints

A dynamic nonline-of-sight (NLOS) angle discrimination method is proposed to address the insufficiency of current research on the NLOS error characteristics of ultrawideband (UWB) signals in dynamic environments as well as the problem that UWB signals frequently suffer from occlusion, leading to poor or impossible localization. The experimental results indicate that the degree of UWB signal occlusion increases as the horizontal angle decreases, and when the horizontal angle is less than 167°, the UWB ranging error is so large that no ranging value is available. On this basis, a tightly integrated UWB/MEMS IMU positioning algorithm incorporating NLOS angle discrimination and map constraints is proposed; it employs horizontal angles to discriminate UWB ranging values in NLOS environments in accordance with the dynamic NLOS characteristics of UWB signals to assign better weights to UWB observations. Through comparative analysis of the results from both groups of experiments, the algorithm achieved northward, eastward, and planar positioning errors of 0.189 m and 0.126 m, 0.119 m and 0.134 m, 0.243 m and 0.211 m, respectively. Compared to the Robust Adaptive Kalman Filtering algorithm, the positional accuracy in the plane improved by 22.9% and 28.5%, respectively.

Enhanced Kalman filter methods for end pose measurement of parallel kinematic machine considering the error sensitivity

For robust accuracy, this paper proposes a parallel measurement method for parallel kinematic machine (PKM) combining indirect measurement using grating ruler with direct measurement using monocular vision. However, due to differing error sensitivity of two methods, the general Kalman filter (KF) cannot integrate them under the assumption of equal data weighting. Therefore, a novel Enhanced KF data fusion method designed to account for varying data sensitivities is developed.The paper first outlines the principles of parallel measurement approach for PKMs, followed by modeling the error sensitivities associated with each measurement source. Subsequently, an enhanced KF data fusion model is developed, treating these sensitivities as noise, and validated. Compared to the conventional KF method, the root mean squared (RMS) position error is significantly reduced from 0.569 mm to 0.293 mm. This enhanced KF method presents an innovative approach to multi-source data fusion, tailored to the error characteristics of different measurement sources.

What Determines Civil Servants’ Error Response? Evidence From a Conjoint Experiment

To err is human and learning from mistakes is essential for finding viable solutions to grand societal challenges through development and innovation. Yet, public organizations often exhibit a punitive zero-error culture, and public employees are stereotyped as error and risk-averse. Little is known about the underlying behavioral mechanisms that determine civil servants’ likelihood of handling errors positively, namely reporting and correcting them instead of ignoring and hiding them to avoid blame. Based on the transactional theory of stress coping, we argue that individuals’ error-handling strategies relate to both rational and emotional evaluations of error-specific and consequential contextual factors. Using a conjoint survey experiment conducted with N = 276 civil servants in Germany ( Obs. = 1,104), this study disentangles the effects of error-related, individual, and organization-cultural factors as decisive drivers of individuals’ error response. We find that error characteristics (type and harmfulness) determine error-handling behavior, which is revealed to be independent from organizational error culture and individual error orientation, providing important and novel insights for theory and practice.

Extended Kalman filter-based robust roll angle estimation method for spinning vehicles

Attitude measurement is a basic technique for monitoring vehicle motion states and safety. The spin motion of a vehicle couples the attitude angles with each other, which has an impact on the navigation and control of the vehicle. Global navigation satellite system (GNSS) signals-based roll angle measurement methods are important for vehicle attitude measurement. Most of existing studies use continuous signal power, but the case of loop lock loss leading to discontinuous power reception has not been considered. A robust estimation method for the roll angle based on the Tukey weight function is proposed to improve the measurement accuracy in cases of discontinuous reception. The characteristics of the GNSS signals, the geometric relationship between the signal power and roll angle of the vehicle are discussed. By installing a GNSS receiver with a single patched antenna on a rotating platform with a controllable rolling speed, the proposed method was verified by experiments. The robust estimation errors of different weight functions are analyzed. According to the characteristics of the gross measurement errors, a robust estimation method of multisatellite power observations is proposed to obtain a high-precision and stable estimation of the vehicle roll angle. The results show that the proposed algorithm can improve the accuracy of roll angle estimation even with gross measurement errors. As a result of the experiments, the estimation errors of the algorithm are 6.57° at a confidence level of 68 % and 15.49°at the confidence level of 95 %. In contrast, they are 11.38° and 37.31° for the traditional LS method. Moreover, the estimation accuracy of the algorithm is not significantly correlated with the vehicle rotational speed. Therefore, the vehicle roll angle can be estimated with high accuracy under a variety of rotational speeds.