Spatial Error Analysis Research Articles

Research on novel hybrid structure AACMM based on 3-RPS parallel mechanism

Abstract The articulated arm coordinate measuring machine (AACMM), widely utilized in the field of industrial measurement, is assessed based on key performance indicators such as measurement accuracy, measurement space, and flexibility. To enhance these aspects of the AACMM, this paper introduces a novel type of AACMM. The new model is based on a 3-RPS (revolute joint R, prismatic joint P, spherical joint S) parallel mechanism (3-RPSPM). The working principle of the 3-RPSPM is detailed, and a corresponding computational model is established. An algorithm is proposed based on this mathematical model to determine the theoretical achievable accuracy of the 3-RPSPM. Additionally, a spatial error analysis of the 3-RPSPM is conducted to verify its compliance with AACMM accuracy requirements and to demonstrate a certain level of accuracy improvement. Through simulation analysis, the differences in measurement accuracy, measurement space, and flexibility between the new type of AACMM based on the 3-RPS parallel mechanism (3-RPSAACMM) and traditional AACMMs are compared. The results indicate that the 3-RPSAACMM, with its novel parallel structure free from series accumulation errors, not only maintains measurement flexibility but also achieves improvements in measurement accuracy and space. The hybrid structure adopted by the new 3-RPSAACMM offers a new direction for the future development of AACMMs, presenting significant research value and application prospects.

Modeling population distribution: A visual and quantitative analysis of gradient boosting and deep learning models for multi‐output spatial disaggregation

AbstractSpatially aggregated data on socio‐demographic groups often fail to capture the population's spatial heterogeneity in cities. This poses challenges for urban planning, particularly when addressing the needs of groups such as migrants or families with children. Moreover, the commonly provided aggregated units, such as census tracts, vary in size and across data sources. Existing literature on disaggregation typically handles individual subgroups separately, ignoring their interrelations in the downscaling process. This article explores the potentials of multi‐output regression models for simultaneous spatial downscaling of multiple groups and conducts a detailed spatial error analysis using individualized neighborhoods. We experiment with self‐training gradient‐boosting trees and fully convolutional neural networks, assessing the quality of results against ground truth data at the target resolution. We show that the evaluation of the disaggregated results at this detailed resolution requires unconventional methods. The methodology proves convenient and achieves high‐accuracy results using input datasets of building features.

Reconstructing 3D ocean subsurface salinity (OSS) from T–S mapping via a data-driven deep learning model

Despite the increasing volume of oceanographic data, the available ocean salinity data remains patently insufficient which limits studying ocean dynamics, climate change and the calculation of salinity-related ocean elements. Considering that traditional salinity reconstruction methods often suffer from various factors such as additional constraints, priori physical assumptions and large of specific regression coefficients, a generative adversarial networks (GANs) based deep learning (DL) framework is proposed to directly construct a near real-time, high-resolution daily three-dimensional ocean subsurface salinity (3D OSS) dataset from a data-driven perspective in this study. Four models with different structural combinations are designed in the China’s marginal seas. Experimental results demonstrate that the models can successfully reconstruct the high-precision and high-resolution 3D OSS on a daily scale in 12 depth levels (from 2 m to 200 m). The asymmetric inception-3DGAN model with all enhanced structures has the highest accuracy, the average root-mean-squared error (RMSE) is 0.135psu, the average coefficient of determination (R2) is 0.5641 and the percent bias is 0.436%. Comparing with model without enhanced structures, the average RMSE is decreased by 22.41% when adding all the enhancement structures. Besides, temporal and spatial error analysis are also conducted to evaluate the models’ performance from different aspects. Finally, the model results are used to analyze common ocean elements, such as water mass properties, dynamic fields and geostrophic velocity fields, demonstrating that the 3D OSS dataset construction approach proposed in this study not only provides some new insights into ocean observations, but also has high application values.

Sharp error estimates for spatial-temporal finite difference approximations to fractional sub-diffusion equation without regularity assumption on the exact solution

Finite difference method as a popular numerical method has been widely used to solve fractional diffusion equations. In the general spatial error analyses, an assumption $$u\in C^{4}({\bar{\varOmega }})$$ is needed to preserve $${\mathcal {O}}(h^{2})$$ convergence when using central finite difference scheme to solve fractional sub-diffusion equation with Laplace operator, but this assumption is somewhat strong, where u is the exact solution and h is the mesh size. In this paper, a novel analysis technique is proposed to show that the spatial convergence rate can reach $${\mathcal {O}}(h^{\min (\sigma +\frac{1}{2}-\epsilon ,2)})$$ in both $$l^{2}$$ -norm and $$l^{\infty }$$ -norm in one-dimensional domain when the initial value and source term are both in $${\dot{H}}^{\sigma }(\varOmega )$$ but without any regularity assumption on the exact solution, where $$\sigma \ge 0$$ and $$\epsilon >0$$ being arbitrarily small. After making slight modifications on the scheme, acting on the initial value and source term, the spatial convergence rate can be improved to $${\mathcal {O}}(h^{2})$$ in $$l^{2}$$ -norm and $${\mathcal {O}}(h^{\min (\sigma +\frac{3}{2}-\epsilon ,2)})$$ in $$l^{\infty }$$ -norm. It is worth mentioning that our spatial error analysis is applicable to high dimensional cube domain by using the properties of tensor product. Moreover, two kinds of averaged schemes are provided to approximate the Riemann–Liouville fractional derivative, and $${\mathcal {O}}(\tau ^{2})$$ convergence is obtained for all $$\alpha \in (0,1)$$ . Finally, some numerical experiments verify the effectiveness of the built theory.

Comprehensive evaluation of satellite-derived precipitation products considering spatial distribution difference of daily precipitation over eastern China

Study regionEight main basins at eastern China. Study focusThis study proposes a comprehensive framework using Shannon’s entropy-weighting method for assessing satellite precipitation data from spatial and temporal perspectives. First, three new spatial evaluation metrics, the false monitoring score (FMS), spatial coverage approximation (SCA), and precipitation spatial difference index (PSDI), were introduced based on spatial distribution differences of daily precipitation. Then, the error and performance of the five satellite precipitation datasets over eastern China were analyzed. New hydrological insights for the regionThe spatial metrics, SCA, PSDI, and fraction skill score (FSS), provide an effective quantitative evaluation of the daily spatial distribution of precipitation. CMADS and MSWEP appear to be superior at reproducing the spatial distribution of precipitation. Except for CHIRPS, all other four datasets have a tendency to underestimate precipitation measurements in the study area. According to the evaluation, the latest version of MSWEP may be a suitable alternative for use in water and climate research due to its relatively high accuracy in eastern China. The spatial error analysis method utilized in this study can be transferred to evaluating other gridded precipitation products. Meanwhile, the spatial error indices are also acceptable as optimization targets for future satellite precipitation data production.

Spatial-temporal aspects of continuous EEG-based neurorobotic control

Objective. The goal of this work is to identify the spatio-temporal facets of state-of-the-art electroencephalography (EEG)-based continuous neurorobotics that need to be addressed, prior to deployment in practical applications at home and in the clinic. Approach. Nine healthy human subjects participated in five sessions of one-dimensional (1D) horizontal (LR), 1D vertical (UD) and two-dimensional (2D) neural tracking from EEG. Users controlled a robotic arm and virtual cursor to continuously track a Gaussian random motion target using EEG sensorimotor rhythm modulation via motor imagery (MI) commands. Continuous control quality was analyzed in the temporal and spatial domains separately. Main results. Axis-specific errors during 2D tasks were significantly larger than during 1D counterparts. Fatigue rates were larger for control tasks with higher cognitive demand (LR, left- and right-hand MI) compared to those with lower cognitive demand (UD, both hands MI and rest). Additionally robotic arm and virtual cursor control exhibited equal tracking error during all tasks. However, further spatial error analysis of 2D control revealed a significant reduction in tracking quality that was dependent on the visual interference of the physical device. In fact, robotic arm performance was significantly greater than that of virtual cursor control when the users’ sightlines were not obstructed. Significance. This work emphasizes the need for practical interfaces to be designed around real-world tasks of increased complexity. Here, the dependence of control quality on cognitive task demand emphasizes the need for decoders that facilitate the translation of 1D task mastery to 2D control. When device footprint was accounted for, the introduction of a physical robotic arm improved control quality, likely due to increased user engagement. In general, this work demonstrates the need to consider both the physical footprint of devices, the complexity of training tasks, and the synergy of control strategies during the development of neurorobotic control.

Terrestrial structure-from-motion: Spatial error analysis of roughness and morphology

Structure-from-Motion (SfM) photogrammetry is rapidly becoming a key tool for morphological characterisation and change detection of the earth surface. This paper demonstrates the use of Terrestrial Structure-from-Motion (TSfM) photogrammetry to acquire morphology and roughness data at the reach-scale in an upland gravel-bed river. We quantify 1) spatially-distributed error in TSfM derived Digital Elevation Models (DEMs) and 2) identify differences in roughness populations acquired from TSfM photogrammetry versus TLS. We identify an association between local topographic variation and error in the TSfM DEM. On flatter surfaces (e.g. bar and terrace surfaces), the difference between the TSfM and TLS DEMs are generally less than ±0.1m. However, in areas of high topographic variability (>0.4m) such as berm or terrace edges, differences between the TSfM and TLS DEMs can be up to ±1m. Our results suggest that grain roughness estimates from the TSfM point cloud generate values twice those derived from the TLS point cloud on coarse berm areas, and up to four-fold those derived from the TLS point cloud over finer gravel bar surfaces. This finding has implications when using SfM data to derive roughness metrics for hydrodynamic modelling. Despite the use of standard filtering procedures, noise pertains in the SfM DEM and the time required for its reduction might partially outweigh the survey efficiency using SfM. Therefore, caution is needed when SfM surveys are employed for the assessment of surface roughness at a reach-scale.

Error analysis and correction of spatialization of crop yield in China – Different variables scales, partitioning schemes and error correction methods

Spatialization of crop yield is beneficial to comprehensive analysis between interdisciplinary data. Multivariable linear regression models are often applied to spatialization of attribute data. The variables scales and the partitioning of China should be considered when the model is constructed. Different variables scales and partitioning schemes will inevitably results in different spatialization errors. Spatialization errors can be reduced by error correction methods. Different methods have different influence on the accuracy of crop yield spatialization. In this study, three variables scales were selected including prefectural scale, county scale and grid cell (1 km × 1 km). Five partitioning schemes (no partition of China, 7 regions of China, 9 regions of China, 10 regions of China, partitions of China by province) were considered. A total of 28 kinds of multivariable linear regression models were constructed with area of different types of farmland as independent variables, crop yields as dependent variables. Then, seven kinds of error correction methods were used to correct crop yield spatialization results. Three error evaluation indicators were selected to investigate the influence of different variables scales, partitioning schemes and error correction methods on the precision of spatialization results. The conclusions can be drawn as follows: (a) Nine models with intercept based on variables at regional scale could not be used to spatialize crop yield, while the others can be used for spatialization of crop yield. (b) The precision of the spatialization result based on the model without intercept is higher than that based on the model with intercept. (c) For models without intercept, precision of spatialization results increased first and then decreased with the refinement of partitioning scheme. (d) For models without intercept, the precision of spatialization results improved with scaling down of the variables scale from prefectural scale to county scale and grid scale. (e) Among the seven kinds of error correction methods, average correction method, weight coefficient correction methodⅡ and weight coefficient correction method III can’t be used to correct initial spatialization results. (f) Proportional coefficient correction method, weight coefficient correction methodⅠ, weight coefficient correction method Ⅳ and weight coefficient correction method Ⅴ can be used to correct initial results of spatialization. (g) The precisions of corrected spatialization products based on error correction methods, which can improve the precision of initial spatialization products, are very closely. This research made up for the deficiency of spatial error analysis of crop yield, explored the relationship between different sample scales and partitioning schemes and spatial error, compared the pros and cons of different error correction methods. Meanwhile, it also provided valuable information for other types of social and economic statistical data.

Anticipatory scene representation in preschool children's recall and recognition memory

Behavioral and neuroscience research on boundary extension (false memory beyond the edges of a view of a scene) has provided new insights into the constructive nature of scene representation, and motivates questions about development. Early research with children (as young as 6-7years) was consistent with boundary extension, but relied on an analysis of spatial errors in drawings which are open to alternative explanations (e.g. drawing ability). Experiment 1 replicated and extended prior drawing results with 4-5-year-olds and adults. In Experiment 2, a new, forced-choice immediate recognition memory test was implemented with the same children. On each trial, a card (photograph of a simple scene) was immediately replaced by a test card (identical view and either a closer or more wide-angle view) and participants indicated which one matched the original view. Error patterns supported boundary extension; identical photographs were more frequently rejected when the closer view was the original view, than vice versa. This asymmetry was not attributable to a selection bias (guessing tasks; Experiments 3-5). In Experiment 4, working memory load was increased by presenting more expansive views of more complex scenes. Again, children exhibited boundary extension, but now adults did not, unless stimulus duration was reduced to 5s (limiting time to implement strategies; Experiment 5). We propose that like adults, children interpret photographs as views of places in the world; they extrapolate the anticipated continuation of the scene beyond the view and misattribute it to having been seen. Developmental differences in source attribution decision processes provide an explanation for the age-related differences observed.

Spatial Error Modeling and Analysis of the Glass Fillet Machine Based on the Multi-Body System Theory

Research on spatial error modeling of the glass fillet machine is introduced based on the basic principles of multi-body system kinematics. Firstly, the topological structure and the low-order body arrays are described on the basis of the mechanical structure. Then, the spatial error analysis is carried out based on the theory of multi-body system. At the same time, the transformation matrix between the adjacent typical bodies is written. After that, in order to illustrate the relationship between the grinding wheel and the grinding point, the space error model of the glass fillet machine is established, which explains the effect of spatial structure on machining error and the calculation method of machining error. This model is easy to analyze the error and embodies the superiority on the error analysis and correction. Thus it provides a kind of ideal technique for the precision analysis of the glass fillet machine.

공간효과분석을 이용한 건강보험 환자 관외 의료이용도와 관련된 요소분석

Background: The purpose of this study was to analyze the association between areas of Korea Train Express (KTX) region and external medical service use in Korean society using spatial statistical model. Methods: The data which was used in this study was extracted from 2011 regional health care utilization statistics and health insurance key statistics from National Health Insurance Corporation. A total spatial units of 229 districts (si-gun-gu) were included in this study and spatial area was all parts of the country excepted Jeju, Ulleungdo island. We conducted Kruskal-Wallis test, correlation, Moran's I and hot-spot analysis. And after, ordinary linear regression, spatial lag, spatial error analysis was performed in order to find factors which were associated with external medical service use. The data was processed by SAS ver. 9.1 and Geoda095i (windows). Results: Moran's I of health insurance patients' external medical service use was 0.644. Also, population density, Seoul region, doctor factors positively associated with health insurance patients' external medical service. In contrast, average age, health care organization per 100 thousand were negatively associated with health insurance patients' external medical service use. Conclusion: The finding of this study suggested that health insurance patient's external medical service use correlated for seoul region in korea. The study results imply the need for more attention medical needs in the region (si-gun-gu unit) for health insurance patients of seoul region. It is important to adapt strategy to activation of primary health care as well as enhancing public health institution for prevent leakage of patients to other areas.

On the Expected Value and Higher-Order Moments of the Euclidean Norm for Elliptical Normal Variates

We consider the Euclidean norm of a bivariate normal vector, equivalently, the amplitude or envelope of a complex Gaussian variable with correlated real and imaginary parts - known also as Hoyt or Nakagami-q distribution. Such a model, popular in fading and other communication contexts, is also relevant for the analysis of spatial errors in positioning-related applications, namely localization. In this paper an efficient expression in terms of complete elliptic integrals is given for the expected value, and good approximations in simple algebraic form are derived. Results are generalized to moments of any order.

A morphology alignment method for resampled heartbeat signals

A morphology alignment method for digital heartbeat signals of a person is proposed. In this method, a heartbeat is delineated into stable and flexible segments which are resampled at two different rates and then concatenated again. A resampling rate is decided based on the analysis of spatial error introduced by the interpolation method. Baseline shift of the resampled heartbeat is removed by amplitude normalization. The alignment of resampled heartbeat by this piecewise-uniform resampling method is then compared with those by uniform and non-uniform resampling methods. Four different morphological features are extracted from resampled heartbeats by each of three methods. Improvement of alignment is evaluated by to two metrics known as morphology alignment score and correlation coefficient. The method is tested on ECG signals obtained from two publicly available databases having different sampling frequencies. Statistical analyses suggest that the piecewise-uniform resampling method improves the alignment of morphology significantly. Computational time for the alignment is linear to the number of samples in a heartbeat and hence the method should be efficient enough for different practical applications using morphological features for automatic classification and decision making.

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

BackgroundAutoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature.MethodsField and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix.ResultsBy specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with An. arabiensis aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled An. arabiensis aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat.ConclusionAn autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific An. arabiensis aquatic habitats based on larval/pupal productivity.

Nature of perceptual encoding of hand positions according to subjects' perceptuomotor expertise.

We investigated whether the nature of the perceptual information encoded for the memorization of successive hand positions varied according to subjects' perceptuomotor expertise. The experimental task used perceptual conflicts, induced by prisms, between visual and proprioceptive afferences during the encoding of various static hand positions. Control subjects were compared with skilled fencers, whose expertise was derived from the optimization. required and developed with practice, of the perceptuomotor processes involved in movement control. The analysis of spatial errors observed in a remembered target location test indicated that control subjects were dependent on the use of static visual information for the encoding of hand positions, whereas skilled fencers were not. Results are discussed in the light of the role of expertise in sensory integration and perceptual dominance.

NATURE OF PERCEPTUAL ENCODING OF HAND POSITIONS ACCORDING TO SUBJECTS' PERCEPTUOMOTOR EXPERTISE

We investigated whether the nature of the perceptual information encoded for the memorization of successive hand positions varied according to subjects' perceptuomotor expertise. The experimental task used perceptual conflicts, induced by prisms, between visual and proprioceptive afferences during the encoding of various static hand positions. Control subjects were compared with skilled fencers, whose expertise was derived from the optimization, required and developed with practice, of the perceptuomotor processes involved in movement control. The analysis of spatial errors observed in a remembered target location test indicated that control subjects were dependent on the use of static visual information for the encoding of hand positions, whereas skilled fencers were not. Results are discussed in the light of the role of expertise in sensory integration and perceptual dominance.