Classical Error Research Articles

Liquid‐Metal‐Based Soft Pressure Sensor and Multidirectional Detection by Machine Learning

AbstractElectronic skin (e‐skin) is an emerging technology with promising applications in various fields, including human–machine interfaces, prosthetics, and robotics. Soft and flexible sensors are vital components for the e‐skin that can mimic human skin's sensing capabilities. Among soft sensors, liquid‐metal‐based sensors have gained attention owing to their unique properties, such as high electrical conductivity, stretchability, and elasticity. Herein, a novel approach is presented that enables multidirectional pressure sensing with a machine‐learning approach from the transient response of the liquid‐metal‐based soft pressure sensor for the e‐skins. In this study, a soft sensor is developed that utilizes liquid metal and has an array of microchannels on a dome‐shaped structure to detect pressures from multiple directions. The transient response from six microchannels of the sensor is used as the input for a convolutional neural network (CNN) to predict the direction (classification accuracy of 99.1%) and magnitude (regression error of 20.13%) of the applied pressures in real time. Finally, a potential application of the developed liquid‐metal‐based soft sensor as a human–machine interface device is demonstrated by using it to control an RC model car through multidirectional predictions (pressure direction and magnitude) through machine learning in real time.

Global Precipitation Nowcasting of Integrated Multi-satellitE Retrievals for GPM: A U-Net Convolutional LSTM Architecture

Abstract This paper presents a deep supervised learning architecture for 30 min global precipitation nowcasts with a 4-hour lead time. The architecture follows a U-Net structure with convolutional long short-term memory (ConvLSTM) cells empowered by ConvLSTM-based skip connections to reduce information loss due to the pooling operation. The training uses data from the Integrated MultisatellitE Retrievals for GPM (IMERG) and a few key drivers of precipitation from the Global Forecast System (GFS). The impacts of different training loss functions, including the mean-squared error (regression) and the focal-loss (classification), on the quality of precipitation nowcasts are studied. The results indicate that the regression network performs well in capturing light precipitation (<1.6 mm hr−1) while the classification network can outperform the regression counterpart for nowcasting of high-intensity precipitation (>8 mm hr−1), in terms of the critical success index (CSI). It is uncovered that including the forecast variables can improve precipitation nowcasting, especially at longer lead times in both networks. Taking IMERG as a relative reference, a multi-scale analysis, in terms of fractions skill score (FSS), shows that the nowcasting machine remains skillful for precipitation rate above 1 mm hr−1 at the resolution of 10 km compared to 50 km for GFS. For precipitation rates greater than 4 mm hr−1, only the classification network remains FSS-skillful on scales greater than 50 km within a 2-hour lead time.

International managerial skill and big Colombian big exporting firms' performance, 2006–2014

AbstractThis paper uses a sample of the biggest private Colombian exporting firms to propose and estimate a two‐step methodology for measuring international managerial skill and calculating its impact on international firm performance. The first step quantifies the managerial team's organisational capital contribution to rise firms' export proficiency through the average of a regression residuals group conformed by export unit value residuals for differentiated products (multiplying by −1 the price competition products' residuals) and export quantity residuals for homogeneous products. The second step results indicate that: (i) international managerial quality has a significant and robust positive effect on export value, (ii) better managers in the international market do not increase the number of products exported but upgrade export basket's quality and (iii) export value elasticity relative to international managerial quality is around 5 times larger than export value elasticity relative to exogenous global demand shocks.

Comparing Two Smoothing Approaches in Estimating Kinematic Parameters.

We compare two signal smoothing and differentiation approaches: a frequently used approach in the speech community of digital filtering with approximation of derivatives by finite differences and a spline smoothing approach widely used in other fields of human movement science. In particular, we compare the values of a classic set of kinematic parameters estimated by the two smoothing approaches and assess, via regressions, how well these reconstructed values conform to known laws about relations between the parameters. Substantially smaller regression errors were observed for the spline smoothing than for the filtering approach. This result is in broad agreement with reports from other fields of movement science and underpins the superiority of splines also in the domain of speech.

Genome-wide Association Studies of Novel Resilience Traits Identify Important Immune QTL Regions and Candidate Genes in Duroc Pigs

Resilience traits in pig populations allow animals to deal better with infectious disease and suboptimal production environments. The data on daily weight, feed intake and feed behaviors in pigs are collected in test period by automated feeding stations, which facilitate to evaluate the resilience traits. In this study, we adopted the mean square error roots (RMSE) of ordinary least squares (OLS) and the negative residuals of quantile regression (QR) to generate four different novel resilience traits using daily records of feed intake and feed duration between 90 and 180 days of age in a population of commercial Duroc pigs. The genome-wide association studies (GWAS) based on single- and two- trait mixed models were carried out on 550 pigs using 48,603 single nucleotide polymorphisms (SNPs) to identify genomic regions associated with resilience traits in growing pigs. We further focused on the GWAS signals to conduct gene annotation, colocalization with multi-tissue eQTL summary statistics of PigGTEx project and identification of enhancers and promoters using the publicly available data. The genomic heritabilities of four novel resilience traits ranged from 0.09 to 0.41. The pairwise genetic and phenotypic correlations ranged from 0.16 to 0.95 and from 0.05 to 0.36, respectively. Twenty-seven SNPs were identified to be significantly associated with these resilience traits. They were distributed on nine chromosomes (SSC1, SSC2, SSC6, SSC7, SSC8, SSC12, SSC14, SSC16, and SSC17). After annotation, 39 QTLs and 49 candidate genes were identified. Several of these are functionally relevant candidate genes including OTUD4, TIFA and CARD14, which are involved in the host immune response, disease susceptibility and signal transduction. Eight unique SNPs were found to be causal in both GWAS and eQTL analyses across 15 tissues. Notably, one SNP (rs80794541) was associated with eQTLs identified concurrently across seven tissues/cell types, including the macrophage cell type. Furthermore, four significant SNPs (rs81467127, rs81356029, rs80794541, and rs81305085) were linked to the function of the primed enhancer, active element, and poised promoter in five pig tissues. Using the porcine fibroblast HiC dataset, SNP (rs81356029) on SSC2 regulates the CARNS1 and SSH3, while SNP (rs80794541) on SSC7 regulates the H2AC6. In this study, we generated four novel resilience traits and identified SNPs significantly associated with these resilience traits in a Duroc pig population. GWAS signals were associated with candidate genes involving in the immune traits, and were linked to the crucial regulatory elements as well. Our findings will contribute to elucidating the genetic mechanism that can enhance genome-enabled breeding and inform further research on resilience in domestic pigs.

A form-grinding wheel optimization method for helical gears based on a PSO-SVM model

A gear form-grinding optimization method is proposed to obtain an optimal profile of a form-grinding wheel by solving a transcendental equation of the contact line between a form-grinding wheel and a helical gear workpiece. Since the equation of the contact line is transcendental, the relationship between the installation angle of the form-grinding wheel and the shape of the contact line cannot be represented by explicit functions, which makes it difficult to obtain the optimal profile of the form-grinding wheel. An optimization method is proposed with three evaluation parameters that are the overrun, the shift, and the offset. Some gear form-grinding performances, such as machining stroke, tooth deviation, and grinding chatter, can be quantitatively described using those evaluation parameters. Further, a method for solving evaluation parameters is proposed, which uses a particle swarm optimization-support vector machine (PSO-SVM) model with the advantage of small-sample robustness to solve evaluation parameters. The PSO-SVM model is trained with the installation angle of the form-grinding wheel as the input and the evaluation function as the output. The regression error results of the evaluation function indicate the PSO-SVM model can respond correctly. Gear form-grinding test results show the proposed method can effectively improve grinding accuracy.

Improved uniform error bounds on an exponential wave integrator method for the nonlinear Schrödinger equation with wave operator and weak nonlinearity

In this paper, we establish the improved uniform error bounds for the Lawson-type exponential wave integrator Fourier pseudo-spectral (LEWIFP) method for the nonlinear Schrödinger equation with wave operator (NLSW) and weak nonlinearity characterized by a small constant ε∈(0,1]. We first convert the NLSW to a coupled system and then consider the LEWIFP method for the coupled system. The LEWIFP method are proved to be time symmetric and mass-conservative which is very important from the point of view of numerical geometric integration. Through careful and rigorous convergence analysis, we establish improved uniform error bounds for the full-discretization at O(hm+ε2pτ2) in the long-time domain up to O(1/ε2p) where m is determined by the regularity conditions. These error bounds are much better than the classical error bounds O(hm+τ2) provided by the traditional error analysis. In error analysis, in addition to the classical tools such as mathematical induction and energy method, we adopt the regularity compensation oscillation (RCO) technique to analyze the accumulation of errors carefully. The numerical experiments support our error estimates and demonstrate the discrete mass-conservation. In addition, the numerical results show that the discrete energy is stable in the time domain up to O(1/ε2p).

Cognitive flexibility in a generalist raptor: a comparative analysis along an urbanization gradient

Abstract In this study, we analyzed the variation in cognitive flexibility in the Chimango Caracara (Milvago chimango), across areas with different levels of urbanization. To assess this, we utilized the reversal learning assay which measures the ability to adapt behavior in response to changes in environmental contingencies. We also investigated the impact of neophobia on this variation. All chimangos studied succeeded in acquiring a color-reward association and reverting this learned association when the contingencies changed. Urban chimangos were faster than their rural and suburban counterparts during the initial discrimination and reversal phases. The reversal phase proved to be the most challenging task. The analysis of the errors made during this phase revealed that acquiring a new association (i.e., regressive errors) was challenging for the individuals studied, in comparison to inhibiting a previously learned one (i.e., perseverative errors). Neophobia was found to be lower in urban individuals compared to suburban and rural raptors. Moreover, neophobia showed a correlation with regressive errors during the reversal phase among rural and suburban chimangos, while no such correlation was observed among city-dwelling chimangos. We suggest that neophobia acted as a regulating factor of cognitive flexibility, mainly for individuals expressing relatively high levels of this personality trait.

Machine-learning descriptor search on the density of states profile of bimetallic alloy systems and comparison with the d-band center theory.

In this study, the electronic density of states (DOSs) calculated with density functional theory (DFT) were analyzed by the machine-learning techniques. More than 400 pure metal and bimetallic alloy systems were calculated with DFT, and obtained the surface DOSs and the CH3 adsorption energy (Ead). By fitting the Gaussian functions to the DOS, multiple descriptors, such as the Gaussian peak positions, heights, and widths were extracted. Several regression methods, such as the least absolute shrinkage of selection operator (LASSO), random-forest, gradient-boosting, and extra-tree were used to find the relationship between these descriptors and the Ead. The results show that the energy position of the peaks in the d-projected DOS is the most important descriptor, in agreement with the previously known d-band center theory. It was also shown that the peak position in d-projected DOS improves the regression model in addition to the d-band center, since it reduces the regression error.

Privacy Protection Based on Federated Learning

With the development of artificial intelligence technology, more and more fields will collect relevant user data, and provide users with a better experience through data analysis. But there are also risks involved in the process of data collection, namely how to protect personal privacy data. To address this issue, this study combined differential privacy, convolutional neural networks, and federated averaging algorithms to construct a privacy protection model. The study first utilized the federated average algorithm to handle data imbalance, ensuring that each analyzed data is in a balanced state. Then, based on of data balancing, a new algorithm model was constructed using differential privacy and convolutional neural networks. Finally, it utilized a number of public datasets to verify the role of the model in privacy protection. The results showed that the model can achieve recognition accuracy of 97.27% and 93.15%, respectively , for data under the influence of privacy budget and relaxation factor. Meanwhile, the classification accuracy of the model for data can reached 95.31%, with a regression error of 9.03%. When the local iteration number of the device was 30, the testing accuracy can reached 95.28%. This indicates that methods on the grounds of federated averaging algorithm and differential privacy can maintain the accuracy of the model while protecting user privacy. The application research of models has strong practical significance.

A novel approach for quantitatively distinguishing between anthropogenic and natural effects on paleovegetation.

How to distinguish and quantify past human impacts on vegetation is a significant challenge in paleoecology. Here, we propose a novel method, the error inflection point-discriminant technique. It finds out the inflection points (IPs) of the regression errors of pollen-climate transfer functions using modern pollen spectra from vegetation with different values of the Human Influence Index (HII), which represent the HII threshold values of native/secondary and secondary/artificial vegetation systems. Our results show that the HII value at the native/secondary vegetation IPs is approximately 22 and globally uniform, whereas it varies regionally for the secondary/artificial vegetation IPs. In a case study of the Liangzhu archaeological site in the lower Yangtze River, discriminant functions for pollen spectra from three vegetation types and pollen-climate transfer functions of the native vegetation were established to reconstruct paleovegetation and paleoclimate over the past 6,600 years. Our study demonstrates this method's feasibility for quantitatively distinguishing human impacts on paleovegetation and assessing quantitative paleoclimate reconstructions using pollen data.

Towards Fine-Grained HBOE with Rendered Orientation Set and Laplace Smoothing

Human body orientation estimation (HBOE) aims to estimate the orientation of a human body relative to the camera’s frontal view. Despite recent advancements in this field, there still exist limitations in achieving fine-grained results. We identify certain defects and propose corresponding approaches as follows: 1). Existing datasets suffer from non-uniform angle distributions, resulting in sparse image data for certain angles. To provide comprehensive and high-quality data, we introduce RMOS (Rendered Model Orientation Set), a rendered dataset comprising 150K accurately labeled human instances with a wide range of orientations. 2). Directly using one-hot vector as labels may overlook the similarity between angle labels, leading to poor supervision. And converting the predictions from radians to degrees enlarges the regression error. To enhance supervision, we employ Laplace smoothing to vectorize the label, which contains more information. For fine-grained predictions, we adopt weighted Smooth-L1-loss to align predictions with the smoothed-label, thus providing robust supervision. 3). Previous works ignore body-part-specific information, resulting in coarse predictions. By employing local-window self-attention, our model could utilize different body part information for more precise orientation estimations. We validate the effectiveness of our method in the benchmarks with extensive experiments and show that our method outperforms state-of-the-art. Project is available at: https://github.com/Whalesong-zrs/Towards-Fine-grained-HBOE.

A Panel-corrected Standard Error (PCSE) Framework to Estimate Capital Structure and Banking Performance within the Tunisian Context

The objective of this article is to empirically examine the effect of financing structure on the market share of banks, and their performance in Tunisian banks. To this end, we gathered financial statements of ten commercial banks over the period 2012-2019, and we employed the panel-corrected standard error (PCSE) regression. The empirical results show that the bank capital structure measured by the equity to total assets ratio negatively affects bank performance while the debt to total assets ratio can be a robust and positive driver of bank performance. Through this research, we recommend to Tunisian commercial banks to reduce their operating costs through a better management of their resources, and to find cheaper sources of financing such as increasing equity. We also recommend to Tunisian commercial banks to diversify further their revenues in order to enhance their performance and to generate more profits.

GPS displacement dataset for the study of elastic surface mass variations

Abstract. Quantification of uncertainty in surface mass change signals derived from Global Positioning System (GPS) measurements poses challenges, especially when dealing with large datasets with continental or global coverage. We present a new GPS station displacement dataset that reflects surface mass load signals and their uncertainties. We assess the structure and quantify the uncertainty of vertical land displacement derived from 3045 GPS stations distributed across the continental US. Monthly means of daily positions are available for 15 years. We list the required corrections to isolate surface mass signals in GPS estimates and screen the data using GRACE(-FO) as external validation. Evaluation of GPS time series is a critical step, which identifies (a) corrections that were missed, (b) sites that contain non-elastic signals (e.g., close to aquifers), and (c) sites affected by background modeling errors (e.g., errors in the glacial isostatic model). Finally, we quantify uncertainty of GPS vertical displacement estimates through stochastic modeling and quantification of spatially correlated errors. Our aim is to assign weights to GPS estimates of vertical displacements, which will be used in a joint solution with GRACE(-FO). We prescribe white, colored, and spatially correlated noise. To quantify spatially correlated noise, we build on the common mode imaging approach by adding a geophysical constraint (i.e., surface hydrology) to derive an error estimate for the surface mass signal. We study the uncertainty of the GPS displacement time series and find an average noise level between 2 and 3 mm when white noise, flicker noise, and the root mean square (rms) of residuals about a seasonality and trend fit are used to describe uncertainty. Prescribing random walk noise increases the error level such that half of the stations have noise > 4 mm, which is systematic with the noise level derived through modeling of spatially correlated noise. The new dataset is available at https://doi.org/10.5281/zenodo.8184285 (Peidou et al., 2023) and is suitable for use in a future joint solution with GRACE(-FO)-like observations.

Trajectory Tracking and Disturbance Rejection Performance Analysis of Classical and Advanced Controllers for a SCORBOT Robot

This work presents the design and assessment of four control schemes for the monitoring and regulation of joint trajectories applied in the dynamic model of a SCORBOT-ER V plus robot, which includes the dynamics of the actuators, and the estimation of the friction forces present within the joints. The two classical control strategies calculated torque and PID, and the two advanced control strategies, fuzzy and predictive, are considered. In the latter case, a gravitational compensation stage is incorporated, as well as the inverse models of the motors and the transmissions of belt movement for each joint. Computational tests are performed by applying an external step-type disturbance to the third joint of the robot. Finally, an evaluation of the results obtained is presented through trajectory curves, joint errors, and the three performance indexes residual mean square, residual standard deviation, and index of agreement.

Design of Bayesian stochastic networks for numerical treatment of Williamson fluid stretching flow model with mixed convected heat generation

In the presented article, a stochastic network paradigm through Bayesian Regularization backpropagation neural network (BRB-NN) is designed to interpret the dynamics of the Williamson fluid stretching flow model with mixed convected heat generation (WF-SFM). The governing nonlinear PDEs system of WF-SFM is reduced to a set of ODEs by incorporating appropriate transformations. The reference datasets for the anticipated BRB-NN approach are created with Adams solver for numerical solutions of WF-SFM by varying material variable W e , buoyancy factor λ , temperature characteristic parameter ε , thermal relaxation variable γ , heat source factor δ , and Prandtl numbers P r . The knacks of artificial intelligence (AI) based BRB-NN procedure is then employed on the generated dataset for WF-SFM. The bias training, biased testing, and validation of BRB-NN are conducted to compute the approximate numerical results for WF-SFM for sundry scenarios, and outputs are in good agreement in reference data that validate the worthy performance of the proposed BRB-NN which is further justified by results of absolute error, mean squared error, error histogram illustrations and regression measures. The viable performance in terms of mean square error (MSE) is achieved at levels ranging from E − 11 to E − 13 , consistently for all scenarios of WF-SFM. The accuracy and the justification of performance are effectively proven by the low level of MSE, to optimal regression metric index as well as distribution of instances error on histograms with negligible magnitude.

An efficient and easy-to-implement recovery-based a posteriori error estimator for isogeometric analysis of the Stokes equation

In this article, we present an efficient recovery-based a posteriori error estimator for adaptive isogeometric analysis (IGA) of the Stokes equation, which is straightforward to implement. The increased regularity of splines in IGA versus Lagrange polynomials used in the classical finite element method plays a significant role for the success of the presented recovery technique. We consider LR B-splines (Johannessen et al., 2014) for the adaptive mesh refinement, and the isogeometric Taylor–Hood element, Sub-Grid element (Buffa et al., 2011), and Raviart–Thomas element (divergence-conforming) (Buffa et al., 2010; Evans and Hughes, 2013; Johannessen et al., 2015) for the mixed discretization of the Stokes equation. Three benchmark problems with analytical solutions are tested, and they demonstrate clearly the great performance of the recovery-based error estimator compared to classical residual-based error estimator.

Design automation of sustainable self-compacting concrete containing fly ash via data driven performance prediction

Self-compacting concrete (SCC) is a highly flowable and segregation-resistant material, effectively facilitating proper filling and ensuring exceptional structural performance in confined spaces. Incorporating fly ash as a supplementary cementitious material in SCC mixtures yields numerous benefits, including enhanced cost-effectiveness in construction and the advancement of environmental sustainability. Nevertheless, the addition of fly ash in SCC poses significant challenges in modelling and predicting the properties of SCC due to lack of understanding of its influence on material rheology and bonding. It is therefore desirable to develop more appropriate machine learning approach to compliment the large scale and costly laboratory-based experiments. This paper presents four well trained supervised machine learning models for the prediction of fresh and hardened properties of SCC containing fly ash: support vector machine (SVM), decision tree, random forest, and artificial neural network (ANN). Training datasets gathered from publicly available existing relevant literature, were analysed and processed prior to shape the required machine learning models. Optimization strategies of hyperparameters were also implemented for each model. To evaluate the performance of these machine learning models and to compare their accuracy, regression error characteristic curves and Taylor diagrams were utilized. The findings reveal that all models demonstrate promising results, with the random forest model outperforming the others in predicting SCC properties with higher accuracy. This underscores the potential of random forest algorithms in accurately modelling and predicting the properties of fly ash-infused SCC. Finally, a data driven implementation framework has been developed, thereby offering robust and logical strategy for experimental designs and guidance for developing sustainable SCC.

Influence of the scanning path on the accuracy of intraoral scanners in the implanted edentulous patient: an in vitro study.

The aim of this in vitro study was to investigate the influence of scan paths on the accuracy (trueness and precision) of intra-oral scanning of an implant impression on an edentulous patient. An epoxy resin maxillary model was made with 6 bone level implants (NobelParallel Conical Connection RP, NobelBiocare®). The implants were placed at the spot of the first incisor, the canine and the first molar. The trans gingival component (Multi-unit, NobelBiocare®) was screwed onto the implants. The scanbodies (IO 2C-A, Elos Accurate®) were then screwed onto the multi-units. The model was run through a coordinate measurement machine to obtain a control cast. Then, five different scanning paths, respectively the zigzag technique (ZZT), the zigzag technique with palatal (ZZTP), the wrap technique (WT), the wrap technique with palatal (WTP), and the big zigzag technique (BZZT), were applied by a single operator. Finally, each scan was compared to the control model. Results were assessed by one-way ANOVA and linear mixed effects models at P<0.05. The study showed that scan paths ZZT and ZZTP had significantly lower absolute positioning errors and residual mean square errors than the others (P<0.0001). For distances between consecutive implant axes and for absolute vertical errors, their superiority was borderline (P<0.10). Overall, techniques ZZT and ZZTP were equally performant and proved to be the most accurate. This in vitro experimental study demonstrates that the scan path can have an influence on the accuracy of the optical impression for full arch rehabilitation on implants.

Damage identification of wind turbine blades based on deep learning and ultrasonic testing

ABSTRACT Ultrasonic testing (UT) is an effective method for detecting internal damage in wind turbine blades. However, manual interpretation is time-consuming and subjective. To automate UT in the wind turbine industry, an image detection model based on deep learning called UCD-YOLO (Ultrasonic C-scan image Detection You Only Look Once) is proposed to detect internal damage such as stratified and lack of glue in wind turbine blades. In order to enhance the feature extraction ability under complex damage forms, the use of deformable convolutional networks and context augmentation module are proposed. Designing a lightweight cross stage partial spatial pyramid pool fast (LCSP_SPPF) module, which allows the model to learn more information and improve detection accuracy. The introduction of wise intersection over union (WIoU) improves the convergence speed while reducing the regression error. Using UT devices to collect damage images of wind turbine blades and conducting comprehensive evaluation experiments on the detection performance of the proposed method. Results show that compared to the YOLOv5, UCD-YOLO is not only lighter but also achieves a 7.5% increase in mean average precision (mAP50). And compared with other detection methods, further verifying that the proposed method can quickly and effectively detect wind turbine blade internal damage, improving power generation efficiency.