Residual learning of the dynamics model for feeding system modelling based on dynamic nonlinear correlate factor analysis

Dynamic Factor Models with Control Variables

Understanding the hydrological functioning of tidally influenced floodplain forests is essential for advancing ecosystem protection and restoration goals in impacted systems. However, finding direct relationships between basic hydrological inputs and floodplain hydrology is hindered by complex interactions between surface water, groundwater, and atmospheric fluxes in a variably saturated matrix with heterogeneous soils, vegetation, and topography. Thus, an explanatory method for identifying common trends and causal factors is required. Dynamic factor analysis (DFA), a time series dimension reduction technique, models temporal variation in observed data as linear combinations of common trends, which represent unidentified common factors, and explanatory variables. In this work, DFA was applied to model water table elevation (WTE) in the floodplain of the Loxahatchee River (Florida, USA), where altered watershed hydrology has led to changing hydroperiod and salinity regimes and undesired vegetative changes in the floodplain forest. The technique proved to be a powerful tool for the study of interactions among 29 long‐term, nonstationary hydrological time series (12 WTE series and 17 candidate explanatory variables). Regional groundwater circulation, surface water elevations, and spatially variable net local recharge (cumulative rainfall – cumulative evapotranspiration) were found to be the main factors explaining groundwater profiles. The relative importance of these factors was spatially related to floodplain elevation, distance from the river channel, and distance upstream from the river mouth. The resulting dynamic factor model (DFM) simulated the WTE time series well (overall coefficient of efficiency,Ceff= 0.91) and is useful for assessing management scenarios for ecosystem restoration and predicted sea level rise.

BackgroundCervical cancer is the fifth most common cancer among women, which is the third leading cause of cancer death in women worldwide. Brachytherapy is the most effective treatment for cervical cancer. For brachytherapy, computed tomography (CT) imaging is necessary since it conveys tissue density information which can be used for dose planning. However, the metal artifacts caused by brachytherapy applicators remain a challenge for the automatic processing of image data for image-guided procedures or accurate dose calculations. Therefore, developing an effective metal artifact reduction (MAR) algorithm in cervical CT images is of high demand.MethodsA novel residual learning method based on convolutional neural network (RL-ARCNN) is proposed to reduce metal artifacts in cervical CT images. For MAR, a dataset is generated by simulating various metal artifacts in the first step, which will be applied to train the CNN. This dataset includes artifact-insert, artifact-free, and artifact-residual images. Numerous image patches are extracted from the dataset for training on deep residual learning artifact reduction based on CNN (RL-ARCNN). Afterwards, the trained model can be used for MAR on cervical CT images.ResultsThe proposed method provides a good MAR result with a PSNR of 38.09 on the test set of simulated artifact images. The PSNR of residual learning (38.09) is higher than that of ordinary learning (37.79) which shows that CNN-based residual images achieve favorable artifact reduction. Moreover, for a 512 × 512 image, the average removal artifact time is less than 1 s.ConclusionsThe RL-ARCNN indicates that residual learning of CNN remarkably reduces metal artifacts and improves critical structure visualization and confidence of radiation oncologists in target delineation. Metal artifacts are eliminated efficiently free of sinogram data and complicated post-processing procedure.

The tiltrotor drive system is a complex and coupled system with different torsional dynamics compared to conventional rotorcraft. It is designed to guarantee safety and reliability and tested for the effects of its dynamics and loads on component sizing. The epicyclic gearing system, the final reduction stage in most aircraft gearboxes, is most impacted by such dynamics. The components of this subsystem must withstand all peak loads due to novel control strategies and torsional dynamics. In this paper, a gear contact analysis model is coupled with a validated torsional dynamics model of a tiltrotor interconnected drive system. The coupled model predicts component-level design loads in the interconnected drive system originating from its torsional dynamic behavior. An accurate gear macro and micro geometry is generated in the gear contact model which is used to derive the time varying mesh stiffnesses at the sub-component interfaces. The variable mesh stiffnesses are applied in the gear dynamic model to simulate the dynamic responses of the gear system under load. Finally, a dynamic overload factor relating the peak dynamic and static loads is analytically assessed, and it is shown to exceed conventional predictions in the case of an asymmetrically loaded interconnected drive system. This dynamic factor can be implemented to inform design decisions during the development of future multi-rotor VTOL aircrafts to mitigate component damage and, in turn, increase the safety and extend the lifespan of critical components.

The kinematic joints of machine tool feed system are crucial for the dynamics modeling. It affects the dynamic characteristics of machine tool feed system, and then there will be a significant influence on machining quality of workpieces. Considering the gravity of spindle box components, a dynamic model of vertical axis feed system was established using lumped parameter method in this paper. The axial stiffness of the screw-nut and up/down bearings was calculated based on the dynamics model. The transmission stiffness of the feed system was obtained when the nut moved to the middle of the screw. Furthermore, the feed system's natural frequency and acceleration vibration response in transmission direction were analyzed. Taking the vertical axis ball screw spindle box feed system of a three-axis high-speed vertical machining center as an example for analysis, the results show that the stiffness of the screw-nut, the stiifness of the up bearing and the down bearing, the transmission stiffness of the feed system varied about 34. 8%, 10.0%, -12.5%, 12.5% respectively compared with the situation ignoring gravity effect. The natural frequency and the amplitude of acceleration vibration response varied about 6.3% and 7.0% respectively. The theoretical data of natural frequency varies about 4.7% compared with the experimental data while that of the amplitude of acceleration vibration response is about 18.0% lower than the experimental data. When the gravity effect is ignored, the analytic value of natural frequency changes about 10.4% compared with the experiemental value, while the analytic amplitude of acceleration vibration response drops about 23.4% than the experimental one. Thus, the dynamic model of vertical axis feed system considering the gravity effect presents higher analytical accuracy.

A system dynamics model of fire-tube shell boiler was developed. The derivation of the dynamics model started with a nonlinear time-variant dynamic modeling based on the transport phenomena in the fire-tube boiler. A linear time-invariant perturbed model around steady-state operating points was then derived. The identifiable parameters τmw, τwa, K, β, and τd were identified by using field test data and least-squares estimation method; the coefficients C’s were, meanwhile, directly predicted by using the small-perturbation relations. Empirical correlations of the identifiable parameters were further derived to account for the variation of parameters with operating conditions. The present perturbed model is thus semi-empirical and can describe the dynamic behaviour of fire-tube boilers over a wide range of operating conditions. The predictions of dynamic responses using the present model were shown to agree very well with the test results.

Decomposing the Co-Movement of the Business Cycle: A Time-Frequency Analysis of Growth Cycles in the Euro Area

The main objective of this paper is the development and application of multivariate time series models for forecasting aggregated wind power production in a country or region. Nowadays, in Spain, Denmark or Germany there is an increasing penetration of this kind of renewable energy, somehow to reduce energy dependence on the exterior, but always linked with the increase and uncertainty affecting the prices of fossil fuels. The disposal of accurate predictions of wind power generation is a crucial task both for the System Operator as well as for all the agents of the Market. However, the vast majority of works rarely consider forecasting horizons longer than 48 hours, although they are of interest for the system planning and operation. In this paper we use Dynamic Factor Analysis, adapting and modifying it conveniently, to reach our aim: the computation of accurate forecasts for the aggregated wind power production in a country for a forecasting horizon as long as possible, particularly up to 60 days (2 months). We illustrate this methodology and the results obtained for real data in the leading country in wind power production: Denmark.

To model processes we propose merging idiographic filter measurement with dynamic factor analysis. This involves testing whether or not the same latent dynamics (concurrent and lagged factor interrelations) can describe different individuals' observed multivariate time series. The methodology allows fitting, across different individuals, dynamic factor models that are invariant with respect to the latent dynamics, but not necessarily the factor loadings (measurement model). This methodology allows the same latent process to manifest differently from one individual to another, thus recognizing that the process is general but its realization in a given person is to some degree idiosyncratic. The approach is illustrated with empirical data.

Deep learning-based image recognition technology has significantly advanced the development of modern industrial intelligence. However, the issue of image adversarial examples that follows has gradually garnered the attention of researchers. By injecting adversarial disturbances that are difficult for humans to detect into the image, the deep learning model generates incorrect results, severely impacting the security of deep learning applications. To address this problem and improve the accuracy and robustness of deep learning image recognition models, a combined adversarial examples detection method based on residual learning and difference assessment has been proposed. To start, an image denoising module, based on residual learning and named ANDNet, is designed. This method incorporates a depth separable convolution structure and conducts step processing on the channel number of the hidden layer in ANDNet, which significantly reduces the model's memory consumption and improves its computational efficiency. In addition, the difference assessment involves computing the l<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm distance between the original image and the denoised image softmax output of the classification model, to measure the disparity between the input image before and after denoising. The obtained detection threshold from the training dataset is integrated with this difference evaluation to accomplish the task of testing the input image for adversarial detection. Both theoretical analysis and experimental results confirm the effectiveness of the ANDNet noise reduction network, as well as the efficacy of the adversarial examples detection scheme in identifying adversarial examples. The model exhibits exceptional performance in terms of detection accuracy and F1 value.

Silicon content ([Si]) of the molten metal is an important index reflecting the product quality and thermal status of the whole blast furnace (BF) ironmaking process. Since the direct online measure on this index is difficult and larger time lag exists in the offline assay procedure, quality modeling is required to achieve online estimation of [Si], which is an open problem for realizing BF automation. Focusing on this practical problem, this paper proposes a data-driven dynamic modeling method for [Si] prediction using extreme learning machine (ELM) with the help of principle component analysis (PCA). First, data-driven PCA is introduced to pick out the most pivotal variables from multitudinous factors that influence [Si] to serve as the secondary variables of modeling. Second, since this BF metallurgical process is nonlinearity dynamic system with severe time-varying characteristic, dynamic ELM modeling technology with good generalization performance and strong nonlinear mapping capability is proposed by applying the self-feedback structure on traditional ELM. The self-feedback connection enables ELM to overcome the static mapping limitation of its feedforward network structure so as can cope with dynamic time-series prediction problems very well. At last, industrial experiments and compared studies demonstrate that the constructed model has a better modeling and estimating accuracy as well as a faster learning speed when compared with different modeling method and different model structure.

The main objective of this paper is to detect the existence of financial contagion between the North American and European markets during the recent crises. To accomplish this, the relationships between the US and the Euro zone stock markets are considered, taking the daily equity prices of the Standard and Poor’s 500 as representative of the United States market and for the European market, the five most representative indexes. Time Series Factor Analysis (TSFA) procedure has allowed concentrating the information of the European indexes into a unique factor, which captures the underlying structure of the European return series. The relationship between the European factor and the US stock return series has been analyzed by means of the dynamic conditional correlation model (DCC). Once the DCC is estimated, the contagion between both markets is analyzed. Finally, in order to explain the sudden changes in dynamic US-EU correlation, a Markov switching model is fitted, using as input variables the macroeconomic ones associated with the monetary policies of the US as well as those related to uncertainty in the markets. The results show that there was contagion between the United States and European markets in the Subprime and Global Financial crises. The two-regime Markov switching model has helped to explain the variability of the pair-wise correlation. The first regime contains mostly the financially stable periods, and the dynamic correlations in this regime are explained by macroeconomic variables and other related with monetary policies in Europe and US. The second regime is explained mainly by the Federal Funds rate and the evolution of the Euro/US Exchange rate.

The prediction of the chemical oxygen demand (COD) and total nitrogen (TN) in integrated anaerobic–anoxic–oxic (A2O) and anoxic–oxic (AO) processes (i.e., A2O+AO process) was achieved using a dynamic ensemble model that reflects the dynamics of wastewater treatment plants (WWTPs). This model effectively captures the variability in the influent characteristics and fluctuations within each reactor of the A2O+AO process. By employing a time-lag approach based on the hydraulic retention time (HRT), artificial intelligence (AI) selects suitable input (i.e., pH, temperature, total dissolved solid (TDS), NH3-N, and NO3-N) and output (COD and TN) data pairs for training, minimizing the error between predicted and observed values. Data collected over two years from the actual A2O+AO process were utilized. The ensemble model adopted machine learning-based XGBoost for COD and TN predictions. The dynamic ensemble model outperformed the static ensemble model, with the mean absolute percentage error (MAPE) for the COD ranging from 9.5% to 15.2%, compared to the static ensemble model’s range of 11.4% to 16.9%. For the TN, the dynamic model’s errors ranged from 9.4% to 15.5%, while the static model showed lower errors in specific reactors, particularly in the anoxic and oxic stages due to their stable characteristics. These results indicate that the dynamic ensemble model is suitable for predicting water quality in WWTPs, especially as variability may increase due to external environmental factors in the future.

Lithium-ion batteries (LIBs) have found widespread application in energy storage due to their high energy density, low self-discharge and low maintenance characteristics. However, the stability and longevity of the batteries under extreme operating conditions is yet to be fully understood. Numerous experimental and simulation studies have been performed to elucidate the effect of temperature, current, depth of discharge, and the number of cycles [1,2,3] on the performance of LIBs. These studies show that the solid-electrolyte interface (SEI) layer and gas formation accelerate at high C-rates due to increased electrolyte decomposition [4,5]. Consequently, the cell's internal resistance increases, resulting in ever-increasing irreversible heat generation inside the cell [6]. This heat generation leads to high internal temperature and a corresponding reduction in cell capacity. The data of cell temperature for a wide range of ambient temperatures and cycling rates is limited because cell performance is typically reported at room temperature. The design of battery packs for electric vehicles(EVs) is also based on test data taken at room conditions. Given that the cell's capacity, specific energy, maximum power output is bound to depend on C-rate and ambient temperature, it is necessary to test and design battery packs based on these parameters.The thermal characteristics of a lithium-ion cell can be predicted using a physics-based thermal model or data-driven methods (DDM) relying on empirical data. Experimental determination of internal cell parameters required in physics-based thermal models is challenging and time-consuming for commercial cells. On the other hand, DDM requires only cell cycling data at specific operating conditions, thereby eliminating the need for internal parameter estimation. This model predicts the experimental input and output data pattern and fits the response surface to estimate the unknown output. In this study, surrogate modelling has been employed to estimate the surface temperature, capacity, energy, and average power of commercial lithium-ion cells for different discharge currents and ambient temperatures. Surrogate modelling, a popular data analysis and reduced-order modelling technique, aims to find a global minimum of a particular objective function using a few objective function evaluations [7]. The surrogate-based model divides experimental data into training and test data sets. The training data set is employed to train the algorithm. After that, the testing data set is used to validate the model's accuracy. Experiments were performed to develop a surrogate model, with the number of experiments decided using the design of experiments[8] for a current range of 0.5C to 3C-rate and ambient temperature range of 0°C to 45°C . The cycling of cells was performed using a high current battery cycler (Arbin), and the ambient temperature was maintained using a thermal chamber (Cincinnati). The surface temperature of commercial 18650 (NMC811) lithium-ion cells was recorded using T-type thermocouples and a National Instruments DAQ module. A polynomial response surface was fitted using surrogate modelling on experimentally obtained data. The response surface shown in figure 1. contains nine data points for the preliminary study, sub-divided into a set of seven training and two testing data. The prediction error sum of squares (PRESS) is currently bounded within 10% due to the limited training data set availability and is expected to be within a band of 1% after adding data from ongoing experiments. The estimation of temperature, capacity, and specific energy can be used to optimize and develop an improved thermal management system for electric vehicles that works under various operating conditions. References Waldmann, T., Wilka, M., Kasper, M., Fleischhammer, M., & Wohlfahrt-Mehrens, M. (2014). Journal of Power Sources, 262, 129-135.Guan, T., Sun, S., Yu, F., Gao, Y., Fan, P., Zuo, P., Du,C., & Yin, G. (2018). Electrochimica Acta, 279, 204-212.Simolka, M., Heger, J. F., Traub, N., Kaess, H., & Friedrich, K. A. (2020). Journal of The Electrochemical Society, 167(11), 110502.Xu, B., Diao, W., Wen, G., Choe, S. Y., Kim, J., & Pecht, M. (2021). Journal of Power Sources, 510, 230390.Rashid, M., & Gupta, A. (2017). Electrochimica Acta, 231, 171-184.Leng, F., Tan, C. M., & Pecht, M. (2015). Scientific reports, 5(1), 1-12.Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R., & Tucker, P. K. (2005). Progress in aerospace sciences, 41(1), 1-28.Li, W., Xiao, M., Peng, X., Garg, A., & Gao, L. (2019). Applied Thermal Engineering, 147, 90-100. Figure 1

Dynamic mortality factor model with conditional heteroskedasticity