Terms Of Error Research Articles

Bias correction based on AR model in spurious regression

<abstract><p>The regression of mutually independent time series, whether stationary or non-stationary, will result in autocorrelation in the random error term. This leads to the over-rejection of the null hypothesis in the conventional t-test, causing spurious regression. We propose a new method to reduce spurious regression by applying the Cochrane-Orutt feasible generalized least squares method based on a bias-corrected method for a first-order autoregressive model in finite samples. This method eliminates the requirements for a kernel function and bandwidth selection, making it simpler to implement than the traditional heteroskedasticity and autocorrelation consistent method. A series of Monte Carlo simulations indicate that our method can decrease the probability of spurious regression among stationary, non-stationary, or trend-stationary series within a sample size of 10–50. We applied this proposed method to the actual data studied by Yule in 1926, and found that it can significantly minimize spurious regression. Thus, we deduce that there is no significant regressive relationship between the proportion of marriages in the Church of England and the mortality rate in England and Wales.</p></abstract>

Graph Joint Representation Clustering via Penalized Graph Contrastive Learning.

Graph clustering based on graph contrastive learning (GCL) is one of the dominant paradigms in the current graph clustering research field. However, those GCL-based methods often yield false negative samples, which can distort the learned representations and limit clustering performance. In order to alleviate this issue, we propose the idea of maintaining mutual information (MI) between the representations and the inputs to mitigate the loss of semantic information of false negative samples. We demonstrate the validity of this proposal through relevant experiments. Since maximizing MI can be approximately replaced by minimizing reconstruction error, we further propose a graph clustering method based on GCL penalized by reconstruction error, in which our carefully designed reconstruction decoder, as well as reconstruction error term, improve the clustering performance. In addition, we use a pseudo-label-guided strategy to improve the GCL process and further alleviate the problem of false negative samples. Our experiment results demonstrate the superiority and great potential of our proposed graph clustering method compared with state-of-the-art algorithms.

A note on conjugate Bayesian estimators of random effects model

The theoretical findings for the Bayes random effects model and the Bayes random effects model with linear constraints on the model coefficients are the contribution of this study. We take into account the random effect model, which includes both fixed and random effects in addition to the experimental error term. We sought to offer a detailed examination of some characteristics of the Bayes and restricted Bayes estimators of the model in addition to applying the Bayesian approach to draw conclusions about the model using a conjugate prior distribution.

SPD Manifold Deep Metric Learning for Image Set Classification.

By characterizing each image set as a nonsingular covariance matrix on the symmetric positive definite (SPD) manifold, the approaches of visual content classification with image sets have made impressive progress. However, the key challenge of unhelpfully large intraclass variability and interclass similarity of representations remains open to date. Although, several recent studies have mitigated the two problems by jointly learning the embedding mapping and the similarity metric on the original SPD manifold, their inherent shallow and linear feature transformation mechanism are not powerful enough to capture useful geometric features, especially in complex scenarios. To this end, this article explores a novel approach, termed SPD manifold deep metric learning (SMDML), for image set classification. Specifically, SMDML first selects a prevailing SPD manifold neural network (SPDNet) as the backbone (encoder) to derive an SPD matrix nonlinear representation. To counteract the degradation of structural information during multistage feature embedding, we construct a Riemannian decoder at the end of the encoder, trained by a reconstruction error term (RT), to induce the generated low-dimensional feature manifold of the hidden layer to capture the pivotal information about the visual data describing the imaged scene. We demonstrate through theory and experiments that it is feasible to replace the Riemannian metric with Euclidean distance in RT. Then, the ReCov layer is introduced into the established Riemannian network to regularize the local statistical information within each input feature matrix, which enhances the effectiveness of the learning process. The theoretical analysis of the activation function used in the ReCov layer in terms of continuity and conditions for generating positive definite matrices is beneficial for network design. Inspired by the fact that the single cross-entropy loss used for training is unable to effectively parse the geometric distribution of the deep representations, we finally endow the suggested model with a novel metric learning regularization term. By explicitly incorporating the encoding and processing of the data variations into the network learning process, this term can not only derive a powerful Riemannian representation but also train an effective classifier. The experimental results show the superiority of the proposed approach on three typical visual classification tasks.

Analysis of Repetitive Positioning Accuracy of Parallel Mechanisms Based on Monte Carlo Method

Based on the inverse kinematics model of the Stewart parallel mechanism, a motion platform pose error model was constructed through differential transformation. The error model shows that the motion platform pose error comes from the length error of 6 struts and the position error of 12 hinge centers, with a total of 42 error terms. Monte Carlo simulation was used to analyze the effects of struts length error on the pose error of the moving platform. The results show that when the length error of the struts follows a normal distribution, the posture error of the moving platform approximately follows a normal distribution. Monte Carlo simulation is a highly effective error analysis method that can analyze the error distribution pattern of the mechanism before processing and assembly, which provides a method for error analysis of Stewart parallel mechanisms.

Psychometric properties of the Perceived Stress Scale-4 (PSS-4) in a Colombian sample: Onefactor, two-factor, or method effects?

Introduction: The Perceived Stress Scale-4 (PSS-4) is an ultra-brief self-report measure to assess psychological stress. This study evaluated the psychometric properties of the PSS-4 in Colombia. Method: A total of 1,911 adult participants with a university education completed the PSS-4. The characteristics of the items and the subscales were explored. The dimensionality was assessed using principal component analysis and confirmatory factor analysis (CFA), incorporating an examination of invariance (configural, metric, scalar, and strict) across socio-demographic characteristics. Construct validity (convergent and discriminant), reliability indices, know-groups, and predictive validity were also computed. Results: CFA showed that the one-factor model (psychological stress) with method effects (correlated error terms on the negative-phrased items) was the most appropriate according to fit indices and parsimony considerations, being invariant across gender and age, but not across income level and work status. Internal consistency was adequate for the PSS-4. Significant positive correlations of the PSS-4 were identified with depression and anxiety, as well as significant negative correlations were identified with post-traumatic growth and resilience. The PSS-4 showed adequate capacity to predict potential depressive and anxiety symptoms, as well as protective factors such as resilience and post-traumatic growth. Higher scores on the PSS-4 were observed among young people, as well as among people with lower incomes and those who are unemployed. Conclusions: These findings suggest that the PSS-4 can be a reliable and valid tool for assessing psychological stress in Colombians with a university education.

Helping or Hindering: Inclusive Design of Automated Task Prompting for Workers with Cognitive Disabilities

Of the ∼8.8 million working-age adults with cognitive disabilities in the United States, only 28.6% are employed, contributing to a poverty rate (26.1%) for people with cognitive disabilities (PwCDs) that is more than twice that for people without disabilities. PwCDs who are employed are often still marginalized via reduced hours and pay, largely due to their more limited capability to perform work efficiently and independently. As evidence, warehouse and shelf stocking jobs that serve as employment for a significant percentage of PwCDs, often require frequent intervention from a job coach in the workplace, impacting the pay and self-esteem of these workers. This study's objective was to remove barriers to employment for PwCD through inclusive design of technology supports in warehouse and similar settings. Specifically, a nonlinear context-aware prompting system (NCAPS) was developed, iteratively refined, and tested. In a pilot crossover study, subjects with cognitive disabilities participated in simulated work sessions, picking orders in a small warehouse environment using the NCAPS and industry standard paper tickets. Their performance was assessed in terms of errors and productivity, and their insights and perspectives were gathered. The resulting NCAPS prevented or corrected all errors for all but one participant. It also increased productivity for participants with the poorest baseline (paper ticket) performance, but decreased productivity for those with high baseline performance. No significant difference was observed in system usability scale (SUS) scores between methods. Topics emerging from user input highlighted the need for technological supports for PwCD to be simple and flexible in operation, and reliable to maintain user trust. By prioritizing robustness and intuitiveness, flexible technology supports can be developed to empower workers with a broad range of abilities, including those with temporary and situational impairments. Such tools would reduce barriers to employment, including stigma, discrimination, and other barriers to equity.

Comparative Study of Iconnet Jabodetabek and Banten Using Linear Regression and Support Vector Regression

PT PLN Indonesia Comnets Plus has little information regarding future customer growth, making it difficult to take steps to meet customer needs. This research aims to predict customer growth in the future based on quantitative data from the previous year, where the output provided produces data in the form of numbers that are analyzed using statistical methods. The hope is to provide information to maximize customer growth with the minimum area or bandwidth used by the company. This research uses linear regression and support vector regression (SVR) algorithms using a company secondary dataset of 252 data points with 5 attributes. Data was collected during the last one-year period, from January to December 2021. The results of the research show that predictions using both algorithms have increased, customer growth when viewed from the number of customers, bandwidth, and regional data has increased significantly. This can be seen from the value of the number of customers, which continues to increase, while the highest number of customers falls in December, the most requested bandwidth is 20 mbps, and the largest customer area is in the Depok area. The results of the research show that the SVR algorithm is superior in terms of mean absolute percentage error (MAPE): 0.02% MAPE, 0.10 MAE, and 0.99 RMSE, while for linear regression, the MAPE values were 36.28%, MAE 201, and RMSE 0.80.

Effect of Blood Flow Restriction during the Rest Periods of Squats on Accuracy of Estimated Repetitions to Failure.

This study investigated the impact of resistance training with blood flow restriction during rest (BFRrest) on the accuracy of estimated repetitions to failure (ERF). It also explored associations between error in ERF and mean concentric velocity (MCV) along with physiological responses. In a randomised cross-over study, 18 male trainers (23.4 ± 2.7 years) performed three sets of squats at 70% of their one-repetition maximum until failure. One session integrated BFRrest, while another employed traditional passive inter-set rest (TRAD) during the 3 min inter-set rest intervals. Cardiorespiratory and metabolic measures were taken in the inter-set recovery periods. The results revealed no significant differences between BFRrest and TRAD in terms of ERF and error in ERF. A notable set effect for ERF was observed, with a greater ERF during set 1 compared to sets 2 and 3 (p < 0.001). Additionally, a lower error in ERF was observed during sets 2 and 3 compared to set 1 (p < 0.001). Error in ERF were strongly associated with the respiratory exchange ratio, and moderately associated with end-tidal carbon dioxide partial pressure, carbon dioxide output, and MCV variables. Notably, the precision of ERF seems to be predominantly influenced by indicators of physiological stress rather than the incorporation of BFRrest.

Three-dimensional transient heat transfer analysis of micro-plasma arc welding process using volumetric heat source models

Abstract The micro plasma arc welding process is associated with different physical phenomena simultaneously. This results in complexities to comprehend the actual mechanism involved during the process. Therefore, a robust numerical model that can compute the weld pool shape, temperature distribution, and thermal history needs to be addressed. Unlike, other arc welding processes, the micro plasma arc welding process utilizes thin sheets of thickness between 0.5 and 2 mm. However, joining thin sheets using a high-density arc welding process quickens the welding defects such as burn-through, thermal stresses, and welding-induced distortions. The incorporation of a surface heat source model for computational modeling of the high energy density welding process impedes heat transfer analysis. In that respect, researchers have developed numerous volumetric heat source models to examine the welding process holistically. Although, selecting volumetric heat source models for miniature welding is a significant task. The present work emphasis developing a rigorous yet efficient model to evaluate weld pool shape, temperature distribution, and thermal history of plasma arc welded Ti6Al4V sheets. The computational modeling is performed using a commercially available COMSOL Multiphysics 5.4 package with a finite element approach. Two different prominent thermal models, namely, Parabolic Gaussian and Conical power energy distribution models are used. A comparative analysis is carried out to determine the most suitable heat source model for evaluating temperature distribution, peak temperature, and thermal history. The analysis is done by juxtaposing the simulated half-cross-section weld macrographs with the published experimental results from independent literature. The numerical results showed that the proximity of top bead width magnitude was obtained using the Parabolic Gaussian heat source model for low heat input magnitude of 47.52 and high heat input magnitude of 65.47 J·mm−1, respectively. In terms of percentage error, the maximum top bead width percentage error for the Parabolic heat source model is 13.26%. However, the maximum top bead width percentage error for the Conical heat source model is 18.36%. Likewise, the maximum bottom bead width percentage error for the Parabolic heat source model and the Conical heat source model is 12.3 and 25.8%, respectively. Overall, it was observed that the Parabolic heat source model produces the least deviating outcomes when compared with the Conical distribution. It was assessed that the Parabolic Gaussian heat source model can be a viable heat source model for numerically evaluating micro-plasma arc welded Ti6Al4V alloy of thin sheets.

Predictive Analytics for Production Line Downtime: A Comprehensive Study Using Advanced Machine Learning Models

This study delves into predictive analysis, employing advanced machine learning models to predict downtime in production lines. The research follows a detailed methodology, starting by exploring production lines and their conditions. The focal point is Overall Equipment Effectiveness (OEE), which includes availability, performance, and quality, with a special focus on downtime impacts. Through an OEE tracking system, downtime is categorized into planned and unplanned, offering insights into critical components affecting production line availability. The datasets cover three years, including runtime, unplanned downtime due to equipment failures, and idle times, emphasizing instances prone to frequent equipment failures. &#x0D; &#x0D; Examining relationships through a heatmap reveals essential correlations crucial for accurate predictions. We outline the feature selection process and fundamental data preprocessing steps to maintain dataset integrity. Introducing machine learning models such as Xgboost, Prophet, Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM), each model is carefully configured with past window parameters and forecast horizons. To prevent overfitting, the study employs K-fold cross-validation and early stopping mechanisms, ensuring robust model performance. &#x0D; &#x0D; Two datasets, comprising runtime, equipment-induced downtime, and idle time, are utilized for analysis. Data preprocessing involves a heatmap for characteristic-target correlation and basic steps like mean-based null value imputation. Results, presented in terms of Mean Absolute Error (MAE), underscore the superior performance of LSTM. The discussion interprets these findings, confirming the potential of LSTM for downtime prediction, supported by visualizations comparing actual and predicted downtimes. Future directions include comprehensive model comparisons, expanding metrics, and increasing datasets, building on the solid foundations established by this study.

Time Series Prediction with Digital Twins in Public Transportation Systems

Classical traffic and transportation control centres are becoming insufficient with the rapid spread of electric, intelligent, autonomous, and software-defined vehicles. Existing traffic management strategies have significant drawbacks in public safety, predictive maintenance, tuning the core functionality of vehicles, and managing mobility. We can renovate this system with next-generation intelligent Digital Twin (DT) technologies. This research proposes a timeseries prediction system through Digital Twins to manage public transportation system with Facebook’s Prophet. This study presents a model framework to build Digital Twin application in Intelligent Public Transportation Systems and used a public data set to validate model with Facebook’s Prophet library by forecasting metro lines usage. Obtained results presented in the study and it is shown that forecasting error interms of Mean Absolute Percentage Error (MAPE) is 0.017 for 1 day horizon.

Sparse identification of dynamical systems by reweighted [formula omitted]-regularized least absolute deviation regression

This work proposes a generalized methodology for sparse identification of dynamical systems (SID), utilizing reweighted l1-regularized least absolute deviation (LAD) regression to recover the governing equation of dynamical systems. To programmatically implement the proposed method, a stabilized and efficient resolution scheme is developed by combining the solution method of the classical LAD-based least absolute shrinkage and selection operator (LAD-lasso) and the threshold iterative idea of the sequentially thresholded least-squares (STLS) method. The new method relaxes the assumption of normality for the error terms in the linear regression model employed by traditional SID techniques. Additionally, it overcomes the low identification accuracy deficiency of the recently proposed regularized least absolute deviation-based sparse identification of dynamics method (RLAD-SID) under some poor data conditions. The performance of the proposed approach is investigated by simulations of two well-known dynamical systems, that is, the Lorenz system and the Duffing system. The reliability of this approach in recovering the governing equation of dynamical systems from noise-contaminated datasets is firstly demonstrated. Furthermore, the advantage of the proposed method is illustrated by comparing it with four traditional techniques in relatively poor data environments. Finally, we display the robustness of the new method when the time derivative dataset is affected by different types of noise, and reveal its superiority compared to the traditional RLAD-SID.

Iterated Orthogonal Simplex Cubature Kalman Filter and Its Applications in Target Tracking

In order to increase a nonlinear system’s state estimate precision, an iterated orthogonal simplex cubature Kalman filter (IOSCKF) is presented in this study for target tracking. The Gaussian-weighted integral is decomposed into a spherical integral and a radial integral, which are approximated using the spherical simplex-radial rule and second-order Gauss–Laguerre quadrature rule, respectively, and result in the novel simplex cubature rule. To decrease the high-order error terms, cubature points with appropriate weights are taken from the cubature rule and processed using the provided orthogonal matrix. The structure supporting the nonlinear Kalman filter incorporates the altered points and weights and the calculation steps; from this, the updated time and measurement can be inferred. The Gauss–Newton iteration is employed repeatedly to adjust the measurement update until the termination condition is met and the IOSCKF is attained. The proposed algorithms are applied in target tracking, including CV target tracking and spacecraft orbit tracking, and the simulation results reveal that the IOSCKF can achieve higher accuracy compared to the CKF, SCKF, and OSCKF. In spacecraft orbit tracking simulation, compared with the SCKF, the position tracking accuracy and velocity tracking accuracy of the OSCKF are increased by 2.21% and 1.94%, respectively, which indicates that the orthogonal transformation can improve the tracking accuracy. Furthermore, compared with the OSCKF, the position tracking accuracy and velocity tracking accuracy of the IOSCKF are increased by 2.71% and 2.97%, respectively, which indicates that the tracking accuracy can be effectively improved by introducing iterative calculation into the measurement equation, thus verifying the effectiveness of the method presented in this paper.

Image Steganography Using Optimized Twin Attention-Based Convolutional Capsule Network

The secret information hiding inside the cover image is termed image steganography, in which the secret information may be either in visual or text format. The concealment of secret information inside the cover image is devised by converting the information into the standard form using conventional image steganography. Here, the cover image is usually systematically altered to carry the secret binary bits after the translation of secret data into binary bits. The cover image may get distorted due to overload, making the hidden information obvious. As a result, the conventional image steganography approaches have a limited ability to conceal. Hence, this research introduces a novel image steganography using the optimized deep learning technique. For novel image steganography, an improved archerfish hunting optimization-based twin attention convolution capsule network (ImAho-TACCNet) is introduced for image steganography. Here, the proposed ImAho is utilized for modifying the tunable parameters of the TACCNet to enhance the efficiency of the image steganography process in terms of minimum mean square error (MSE) and maximal peak signal-to-noise ratio (PSNR). Besides, secret information compression and recursive encryption techniques further enhance the security of secret information. The analysis of ImAho-TACCNet based on various assessment measures like PSNR, SSIM and MSE accomplished enhanced outcomes with the values of 62.37, 0.9923, and 0.0165 for the hidden network model and 64.23, 0.9989, and 0.0125 for the extraction network model.

Diseases diagnosis based on artificial intelligence and ensemble classification

BackgroundIn recent years, Computer Aided Diagnosis (CAD) has become an important research area that attracted a lot of researchers. In medical diagnostic systems, several attempts have been made to build and enhance CAD applications to avoid errors that can cause dangerously misleading medical treatments. The most exciting opportunity for promoting the performance of CAD system can be accomplished by integrating Artificial Intelligence (AI) in medicine. This allows the effective automation of traditional manual workflow, which is slow, inaccurate and affected by human errors. AimsThis paper aims to provide a complete Computer Aided Disease Diagnosis (CAD2) strategy based on Machine Learning (ML) techniques that can help clinicians to make better medical decisions. MethodsThe proposed CAD2 consists of three main sequential phases, namely; (i) Outlier Rejection Phase (ORP), (ii) Feature Selection Phase (FSP), and (iii) Classification Phase (CP). ORP is implemented to reject outliers using new Outlier Rejection Technique (ORT) that contains two sequential stages called Fast Outlier Rejection (FOR) and Accurate Outlier Rejection (AOR). The most informative features are selected through FSP using Hybrid Selection Technique (HST). HST includes two main stages called Quick Selection Stage (QS2) using fisher score as a filter method and Precise Selection Stage (PS2) using a Hybrid Bio-inspired Optimization (HBO) technique as a wrapper method. Finally, actual diagnose takes place through CP, which relies on Ensemble Classification Technique (ECT). ResultsThe proposed CAD2 has been tested experimentally against recent disease diagnostic strategies using two different datasets in which the first contains several diseases, while the second includes data for Covid-19 patients only. Experimental results have proven the high efficiency of the proposed CAD2 in terms of accuracy, error, precision, and recall compared with other competitors. Additionally, CAD2 strategy provides the best Wilcoxon signed rank test and Friedman test measurements against other strategies according to both datasets. ConclusionIt is concluded that CAD2 strategy based on ORP, FSP, and CP gave an accurate diagnosis compared to other strategies because it gave the highest accuracy and the lowest error and implementation time.

Graph attention network and radial basis function neural network-based hybrid framework for epileptic seizure detection from EEG signal

&lt;p&gt;Epileptic seizure is a neurological disorder characterized by recurrent, abrupt behavioral changes attributed to transient shifts in excessive electrical discharges within specific brain cell groups. Electroencephalogram (EEG) signals are the primary modality for capturing seizure activity, offering real-time, computer-assisted detection through long-term monitoring. Over the last decade, extensive experiments through deep learning techniques on EEG signal analysis, and automatic seizure detection. Nevertheless, realizing the full potential of deep neural networks in seizure detection remains a challenge, primarily due to limitations in model architecture design and their capacity to handle time series brain data. The fundamental drawback of current deep learning methods is their struggle to effectively represent physiological EEG recordings; as it is irregular and unstructured in nature, which is difficult to fit into matrix format in traditional methods. Because of this constraint, a significant research gap remains in this research field. In this context, we propose a novel approach to bridge this gap, leveraging the inherent relationships within EEG data. Graph neural networks (GNNs) offer a potential solution, capitalizing on their ability to naturally encapsulate relational data between variables. By representing interacting nodes as entities connected by edges with weights determined by either temporal associations or anatomical connections, GNNs have garnered substantial attention for their potential in configuring brain anatomical systems. In this paper, we introduce a hybrid framework for epileptic seizure detection, combining the Graph Attention Network (GAT) with the Radial Basis Function Neural Network (RBFN) to address the limitations of existing approaches. Unlike traditional graph-based networks, GAT automatically assigns weights to neighbouring nodes, capturing the significance of connections between nodes within the graph. The RBFN supports this by employing linear optimization techniques to provide a globally optimal solution for adjustable weights, optimizing the model in terms of the minimum mean square error (MSE). Power spectral density is used in the proposed method to analyze and extract features from electroencephalogram (EEG) signals because it is naturally simple to analyze, synthesize, and fit into the graph attention network (GAT), which aids in RBFN optimization. The proposed hybrid framework outperforms the state-of-the-art in seizure detection tasks, obtaining an accuracy of 98.74%, F1-score of 96.2%, and Area Under Curve (AUC) of 97.3% in a comprehensive experiment on the publicly available CHB-MIT EEG dataset.&lt;/p&gt;

Comparative Study of Risk in General-Finance and Production-Service Categories of NEPSE Market

Background: The study of risk in investment in the stock market has become a popular area of research since last few decades. The inherent nature of risk that is associated with volatile behavior of stock prices as well as of returns of investment makes investors to think more than once before investing in stock markets. In this regard, this research is focused on comparison of risk associated with investment in General-Finance and Production-Service groups of the NEPSE market. Objective: Primary objective of this research is to assess and compare risk present in the ‘General-Finance’ group and the ‘Production-Service’ group of the NEPSE market. Materials and Methods: Different companies enlisted in the NEPSE stock market are categorized into ten groups. Some of the groups are related to direct financial activities and they are placed in the ‘General-Finance’ group. Other groups which are related to production and service activities are placed in the ‘Production-Service’ group. Daily means of NEPSE indices for these groups, available from official website of NEPSE are used to fit ARIMA model to the indices of ‘General-Finance’ and to ‘Production-Service’ groups. It is attempted to compare risks in these two groups with respect to the variance of error terms of the fitted models. Moreover, GARCH models are employed to describe conditional heteroscedasticity present in observations. Results: It is observed that ARIMA (1, 1, 3) is the optimal model for daily indices of ‘General-Finance’ group with variance of errors terms observed to be 4636.1933. For the ‘Production-Service’ group the optimal model resulted is ARIMA (1, 1, 1) with variance of error terms being 1335.0058. Conditional heteroscedasticity and volatility clustering are observed to have GARCH (1, 1) model for both the groups. Conclusion: After observing variance of error terms of optimal ARIMA models for ‘General-Finance’ and ‘Production-Service’ group and coefficients of GARCH models, it is concluded that ‘General-Finance’ group owes more risk in investment than that in ‘Production-Service’ group.

Modeling and compensation of enhanced volumetric error of machine tools containing crosstalk errors

The error compensation method is an economical and effective method to improve the machining accuracy of machine tools. Conventional volumetric error modeling of machine tools is based on the rigid body kinematics model using 21 terms of geometric errors and assumes the geometric error of one axis is independent of other axes. However, if the error motion does not satisfy this assumption in serial machine tools, it may significantly influence the overall machine tool accuracy. This paper reveals the existence of mutual crosstalk of angular errors in coupling axes by experiments with equipping the five-degree-of-freedom (5-DOF) embedded sensor module in each axis. A new on-machine measurement method is proposed for detecting crosstalk errors of the coupling axes covering the whole coupling area. It is confirmed that the position-dependent geometric errors (PDGEs) of each axis should be modified by two-dimensional dependence. Therefore, extended from the normal volumetric error model (NVEM) that is commonly used nowadays, a novel enhanced volumetric error model (EVEM) that contains crosstalk's PDGEs is proposed. The real-time error compensation controller (RECC) embedded in CNC system kernel has been developed in collaboration with the vender by writing in the application function. The effectiveness and feasibility of the EVEM and RECC are verified by non-cutting and cutting experiments. Through comparison tests with laser interferometer on body diagonals, the accuracy improving rate of EVEM is about 10 % higher than NVEM. The real-cutting tests on the ISO10791 sample are verified by a CMM and the results show the accuracy improvement of EVEM is better than NVEM by about 20 %.

Load prediction of integrated energy systems for energy saving and carbon emission based on novel multi-scale fusion convolutional neural network

The integrated energy system plays an important role in the energy conservation, emission reduction and the resource-efficient utilization. Accurate load forecasting is a significant basis for the optimal scheduling of the integrated energy system. The integrated energy system has coupling interaction between different energy sources in production, distribution and storage. However, the traditional method cannot effectively extract multi-scale features and utilize the coupling information between the multivariate energy sources. In this paper, a novel multi-scale fusion convolutional neural network integrating the bi-directional long short-term memory network and multi-domains hierarchical decoding is proposed to extract and analyze multivariate load data coupling in the integrated energy system data. The multi-scale fusion convolutional neural network is constructed by the multi-dimension convolution layer to obtain multi-scale feature of the integrated energy system data. Meanwhile, the bi-directional long short-term memory network is applied to extract the time dependencies of the integrated energy system data. Finally, the multi-domains hierarchical decoding extracts the coupling characteristics of different domains to predict multiple domains values. Compared with the backpropagation neural network, the support vector machine, the short and long-time memory network, the bi-directional long short-term memory network, and the convolutional neural network - bi-directional long short-term memory network, the proposed method achieves state-of-the-art results in terms of the mean absolute percentage error with 0.365 %, the explained variance score with 99.987 % and the R2-score with 99.984 %, which proves the effectiveness of the proposed model in the load prediction of the integrated energy system. In addition, the proposed method can provide operational guidance for energy production and storage. Through the operation guidance of the proposed method, the carbon emissions are reduced by 238791.58 kg every week.