Domain Parameters Research Articles

Cross-subject and cross-experimental classification of mental fatigue based on two-stream self-attention network

Mental fatigue detection based on Electroencephalogram (EEG) is an objective and effective detection method. However, individual variability and variability in mental fatigue experimental paradigms limit the generalizability of classification models across subjects and experiments. This paper proposes a Spatio-Temporal Transformer (STTransformer) architecture based on a two-stream attention network. We use datasets from three different mental fatigue experimental tasks and individuals. STTransformer has performed cross-task and cross-subject mental fatigue transfer learning and achieved promising results. This architecture is based on the idea of model migration, pre-training deep neural network parameters in the source domain to obtain prior knowledge, freezing some network parameters and migrating to the target domain containing similar samples for fine-tuning. This architecture achieves good transfer effects by using multiple attention mechanisms to capture common features between different individuals and experimental paradigms. Good performance was achieved in multiple individual and two mental fatigue experiments. We used the attention mechanism to visualize part of the feature maps, showing two characteristics of mental fatigue, and exploring deep learning interpretability.

Distribution and evolution of low-energy twin boundary density in time−space domain during isothermal compression for Ni80A superalloy

Adjusting the density of low-energy boundary, i.e., Σ3n twin boundary, in a thermal-plastic deformation process is a significant approach to enhance grain boundary-related properties for an alloy. In order to uncover the distribution and evolution of twinboundary density in a current-heating forming process, and their inner mechanisms dependent on grain size and stored-energy, an electrical−thermal−mechanical multi-field coupling and macro-micro multi-scale coupling finite element model was established. Simulation results of isothermal compression processes for Ni80A superalloy show that twin-boundary density keeps constant in the heating and holding stages, while it increases quickly and then intends to decrease in the compressing stage. Its distribution gets more homogeneous under high temperature and medium strain rate. The processing parameter domains corresponding to finer grain size and higher stored-energy, as well as higher dynamic recrystallization degree, contribute to promoting the twinboundary density.

Identification method for unsteady friction of tool flank for high efficiency milling cutter

Under the cutting loads of high efficiency and intermittent, the friction status between the tool flank and transition surface has time-varying characteristics, which directly affects friction and wear performance of the milling cutter. The dynamic distribution of instantaneous friction on tool flank under milling vibration needs to be revealed. In this work, the effect of milling vibration on instantaneous cutting contact relationship between milling cutter and workpiece was researched, a calculation method for the instantaneous friction velocity and friction energy consumption on tool flank was proposed. The variation of instantaneous friction between tool flank and transition surface under milling vibration was identified and evaluated by power spectral entropy and cross-correlation function. A method for identifying the unsteady friction of tool flank was proposed. The response analysis and experimental verification were conducted. The results showed that the frequency domain parameter distribution of friction energy consumption on tool flank had a strong correlation with the wear depth distribution, and was obviously sensitive to the milling parameters. The above models and methods could be used to reveal the influence of key milling process variables on unsteady friction and wear process of milling cutter’s multiple tool flank.

Refractive index sensing near exceptional point of a system of triple microcavity

Degeneracies of non-Hermitian Hamiltonian i.e., exceptional points (EPs) of parity-time (PT)-symmetric systems have received considerable research attention due to their various possible applications in optical devices. At EPs, at least two eigenvalues as well as their eigenvector coalesce. Recently, the effect of the eigenfrequency splitting on transfer function near EP was studied for an optical system consisting of two micro ring resonators, which led to complex splitting in PT‐symmetric and real splitting in anti‐PT‐symmetric sensors. In present work, we propose a simple system of three coupled ring resonators to show real splitting in both PT-symmetric and anti-PT-symmetric parameter domains by exploiting higher-order EPs. We indirectly couple two rings with equal amount of gain and loss via an intermediate neutral ring. This system is then tested for refractive index (RI) sensing by modulating the cladding index and we numerically show a huge enhancement in sensitivity as compared to those reported in previous studies of micro ring resonators. Importantly, the enhancement is found to be of the order of 108. Further, we have found that the order of EP can be tuned by perturbating the cladding. The outcomes of this study may set up a wide range of applications in non-Hermitian triplet cavity systems.

Sensitivity Study of Surface Roughness Process Parameters in Belt Grinding Titanium Alloys

In order to obtain the optimum range of process parameters for abrasive belt grinding of titanium alloys to achieve a surface roughness within a given range, titanium alloy TC4 was selected as the research object, and experiments on abrasive belt grinding surface roughness were conducted. Firstly, an empirical formula for the surface roughness of titanium alloys after abrasive belt grinding was constructed based on the balanced weight analysis of the process parameters for titanium alloy surface roughness. Sensitivity analysis was carried out to identify the process parameters with the greatest effect on surface roughness, and the stable and unstable domains of the process parameters were determined. Combined with range analysis in orthogonal experiments, the influence curves of the process parameters on surface roughness were obtained, and the optimal parameter ranges were selected. The research results showed that surface roughness is the most sensitive to changes in abrasive grain size and the least sensitive to changes in abrasive belt linear speed. The optimal ranges of abrasive grain size, abrasive belt linear speed, and grinding pressure were determined to be 120# to 150#, 15 m/s to 20 m/s, and 10 N to 15 N, respectively. This study provides a theoretical method and experimental basis for the control of surface roughness in abrasive belt grinding of titanium alloys.

A Novel Method for the Analysis and Optimization of End Face Stress in Cycloidal Gears Based on Conformal Mapping

In the realm of precision engineering, the evaluation of stress distributions in complex geometries, such as the end face of gears, is paramount for determining performance characteristics, including the fatigue life of transmission devices. This research introduces a methodology that combines the method of conformal mapping for multiply connected regions with a proposed global scaling factor. This combination facilitates the transformation of stress computations from intricate geometries to a unit circle. By employing this method as a foundation for surrogate models, and by incorporating the Bayesian-enhanced least squares genetic algorithm optimization technique, this study refines the end face stress distribution, particularly considering variations induced by tooth profile modification errors. The results delineate a design parameter domain that is conducive to achieving uniform stress distribution, offering a refined perspective and methodology for the design of cycloidal wheels.

Quasi-static shock absorber friction at low velocity reciprocating sliding conditions

The characteristics of vehicle shock absorbers have a major significance regarding vehicle dynamics, thus affecting vehicle ride comfort, handling and safety. Although much research focuses particularly on the hydraulic damping properties of this vital suspension component, friction properties are not considered in great detail. However, for driving scenarios of low excitation and velocity of the shock absorber due to moderate roughness of the road, the friction force generated by the shock absorbers and the remaining components is particularly important regarding the transfer of vibration from the road to the passenger. Hence, this study investigates the sensitivity of shock absorber friction to various parameters in the quasi-static domain. The effects of side force, temperature, excitation amplitude and waveform are determined for multiple shock absorbers. The results show no significant influence of the amplitude and waveform used in the conducted tests. Contrarily, the friction force strongly depends on the side force, which represents the variable load perpendicular to the shock absorber’s main axis arising during various driving events for MacPherson suspensions. An increased temperature reduces the side force effect.

Cardiac autonomic dysfunction in adult congenital heart disease

PurposeDue to recent advances in diagnosis and treatment, the number of adults with congenital heart disease (ACHD) has substantially increased. This achievement is mitigated by rhythm disorders. Here, we sought to determine alterations in heart rate variability (HRV) and their prognostic value in ACHD.MethodsNinety seven ACHD patients (39.2 ± 14.1 years, 51.5% female) and 19 controls (39.7 ± 15.0 years, 47.4% female) underwent 24-h Holter monitoring.ResultsAs compared to controls, ACHD patients offered a significantly higher burden of premature ventricular contractions (p = 0.02) and decreased HRV indices (natural logarithmic transformation of very low frequency (lnVLF): 7.46 ± 0.76 ms2 vs. 7.91 ± 0.92ms2, p = 0.03; natural logarithmic transformation of low frequency (lnLF): 6.39 ± 0.95ms2 vs. 7.01 ± 1.07ms2, p = 0.01; natural logarithmic transformation of the ratio of low to high frequency spectra (lnLF/HF): 0.81 ± 0.74 vs. 1.17 ± 0.51, p = 0.04). No differences in HRV measures were observed across ACHD lesion groups. NT-proBNP levels were significantly related to both time and frequency domain indices (natural logarithmic transformation of the standard deviation of NN intervals (lnSDNN): Spearman´s rho = -0.32, p = 0.001; natural logarithmic transformation of the standard deviation of the average NN intervals for each 5-min segment of a 24-h Holter monitoring (lnSDANN): Spearman´s rho: -0.33, p = 0.001; natural logarithmic transformation of the total power (lnTP): Spearman´s rho: -0.25, p = 0.01; lnVLF: Spearman´s rho: -0.33, p = 0.001; lnLF: Spearman´s rho: -0.35, p < 0.001; lnLF/HF: Spearman´s rho: -0.34, p = 0.001).After a mean follow-up of 3.9 ± 0.7 years, 8 patients died and 3 patients survived sudden cardiac death (SCD). Several HRV parameters were significantly higher in event-free ACHD patients than in those who died or survived SCD (natural logarithmic transformation of the average of the standard deviations of NN intervals for each 5-min segment of a 24-h Holter monitoring (lnASDNN): p = 0.04; lnPNN30: p = 0.04; lnVFL: p = 0.03; lnLF: p < 0.01). On univariate Cox regression analysis, the time domain indices lnSDNN, lnASDNN and lnPNN30, as well as the frequency domain parameters lnTP, lnVLF and lnLF were associated with death and survived cardiac arrest.ConclusionACHD is accompanied by HRV impairment that carries prognostic implications on ACHD mortality and survived SCD.

Effect Of 12 Weeks Yoga Training And 12 Weeks Detraining on Heart Rate Variability on Subjects with Hypertension

Background: Healthy lifestyle modifications can prevent hypertension, an early predictor of cardiovascular disease. Yoga is an effective way to manage stress-induced disorders, such as hypertension. Objective: This study investigated the effects of yoga training on hypertensive individuals receiving routine treatment over a 12-week period, comparing and analyzing heart rate variability (HRV) parameters at and after the 12-week yoga training period (the 12th week) with hypertensives receiving routine treatment (the non-yoga group). Materials and methods: A total of 132 hypertensive subjects between the ages of 25 and 45 were randomly assigned to control or experimental groups (N=66), and 66 of these subjects were exposed to yoga therapy (N=66). Before, after, and during the study period, HRV parameters were obtained. Results: RM ANOVA was used to compare time domain and frequency domain parameters of HRV of the control group at pre, post, and detrain, and it was found that no significant difference existed between the groups. Test group mean values show a significant increase in time domain parameters compared with pre-training values after training. Test group post-training and detrain values significantly increased when compared to control group post-training and detrain values. Test group detrain values decreased significantly from post-training to test group detrain values. Conclusion: Yoga therapy increases the HRV values in essential hypertension at rest. An increase in vagal modulation, a reduction in sympathetic activity, and an increase in baroreflex sensitivity may be responsible for the harmonization of autonomic cardiovascular rhythms. The results of detraining from yoga did not result in an increase in HRV. As a result, the present study shows that regular yoga practice reduces hypertension, and deep rest keeps the body healthy.

An explainable decision model based on extended belief-rule-based systems to predict admission to the intensive care unit during COVID-19 breakout

Given the shortage of intensive care units (ICUs) due to the coronavirus disease (COVID-19), a prediction model is essential in ensuring the availability of ICU beds. However, several challenges, such as the importance of distinguishing indicators, efficiency of ICU admission records, and the explainability and effectiveness of the prediction model, hinder the effective prediction of ICU admissions. To mitigate these challenges, an explainable decision model that uses the extended belief rule-based system is introduced to predict ICU admission. First, an indicator extraction model is proposed to measure the importance of the various indicators and obtain representative indicators. Second, a Charnes, Cooper, and Rhodes (CCR) model is constructed to measure the efficiencies of the belief rules to achieve the compact structure of an extended belief rule base. Third, a new extended belief rule-based model, optimized by parameter optimization and domain division-based rule reduction, is developed to predict ICU admission. These procedures enable the explainable decision model to adapt to big data situation, offer explanations and realize high efficiency. Finally, a case study of ICU admission during a COVID-19 outbreak is conducted to demonstrate the implementation and effectiveness of the proposed model in a comparative analysis.

A Cautionary Note on the Use of Linear Regression for Hypothesis Testing

This note draws researchers’ attention to the use of linear regression for the purpose of conducting a hypothesis testing. Even when multiple explanatory variables are included in a regression equation to preclude the hazard of a simple regression of omitting other factors, multicollinearity unfortunately is inherent in multiple regression simply because the included explanatory variables can share some common parameter domain; that is, they co-vary. Here we shall show that however one transforms the variance-covariance matrix of the least-squares estimation to reduce the estimation errors, the procedure amounts to affine transformations of the explanatory variables so that despite the transformation they remain to co-vary, rendering the coefficient as a partial derivative invalid. The root of this problem originates from the fact that one receives the explanatory variables’ values by observation rather than predetermining their values and then collecting the corresponding dependent variable’ values. This situation becomes especially disconcerting when a transformed explanatory variable has its estimated coefficient enjoying an exceptional degree of confidence but of no mathematical status as a partial derivative, misleading engineering or medical prescriptions and public policies.

A model predictive control trajectory tracking lateral controller for autonomous vehicles combined with deep deterministic policy gradient

To solve the problem of trajectory tracking lateral control in autonomous driving technology, a model predictive control (MPC) controller trajectory tracking lateral control method combined with a deep deterministic policy gradient algorithm (DDPG) is proposed in this paper. This method inputs the real-time state of the vehicle into DDPG to achieve real-time automatic optimization of the prediction time domain and control time domain parameters of the MPC controller, and then affects the specific performance of the MPC controller in trajectory tracking lateral control. Specifically, the state space, action space, and reward function of DDPG are defined, and the automatic driving trajectory tracking lateral controller is designed in combination with the vehicle dynamics model. To reduce the exploration space of DDPG and improve the training efficiency of the entire model, the technique of advantage-disadvantage experience separation and extraction is introduced. Finally, the proposed method was trained and verified in various scenarios, and compared with two other lateral control methods for autonomous driving. The results showed that the learning and training time of the trajectory tracking lateral control method based on DDPG-MPC was shorter than that of the DDPG-based method, and the evaluation indicators in the trajectory tracking control process were better than those of the DDPG-based method and original MPC-based method.

Wrist-Worn Sensor Validation for Heart Rate Variability and Electrodermal Activity Detection in a Stressful Driving Environment.

Wearable sensors are widely used to gather psychophysiological data in the laboratory and real-world applications. However, the accuracy of these devices should be carefully assessed. The study focused on testing the accuracy of the Empatica 4 (E4) wristband for the detection of heart rate variability (HRV) and electrodermal activity (EDA) metrics in stress-inducing conditions and growing-risk driving scenarios. Fourteen healthy subjects were recruited for the experimental campaign, where HRV and EDA were recorded over six experimental conditions (Baseline, Video Clip, Scream, No-Risk Driving, Low-Risk Driving, and High-Risk Driving) and by means of two measurement systems: the E4 device and a gold standard system. The overall quality of the E4 data was investigated; agreement and reliability were assessed by performing a Bland-Altman analysis and by computing the Spearman's correlation coefficient. HRV time-domain parameters reported high reliability levels in Baseline (r > 0.72), Video Clip (r > 0.71), and No-Risk Driving (r > 0.67), while HRV frequency domain parameters were sufficient in Baseline (r > 0.58), Video Clip (r > 0.59), No-Risk (r > 0.51), and Low-Risk Driving (r > 0.52). As for the EDA parameters, no correlation was found. Further studies could enhance the HRV and EDA quality through further optimizations of the acquisition protocol and improvement of the processing algorithms.

Impact of a combination of pimobendan, furosemide, and enalapril on heart rate variability in naturally occurring, symptomatic, myxomatous mitral valve degeneration dogs

BackgroundPimobendan, diuretics, and an angiotensin-converting enzyme inhibitor (ACEi) are widely used for the management of chronic valvular heart disease in dogs; however, the effects of that combination on heart rate variability (HRV) are unknown. The purpose of this study was to assess the HRV of symptomatic myxomatous mitral valve degeneration (MMVD) dogs in response to therapy with a combination of pimobendan, diuretics, and ACEi.ResultsMMVD stage C (n = 17) dogs were enrolled and a 1-hour Holter recording together with echocardiography, blood pressure measurement, and blood chemistry profiles were obtained before and 1, 3, and 6 months after oral treatment with pimobendan (0.25 mg/kg), enalapril (0.5 mg/kg), and furosemide (2 mg/kg) twice daily. The results revealed that MMVD stage C dogs at the baseline had lower values of time-domain indices, low frequency (LF), high frequency (HF), and total power, as well as higher value of LF/HF. Triple therapy significantly increases these parameters in MMVD stage C dogs (P < 0.05). A positive moderate correlation was observed between time domain parameters and a left ventricular internal diastole diameter normalized to body weight (P < 0.05).ConclusionsIt can be concluded that MMVD stage C dogs possess low HRV due to either the withdrawal of parasympathetic tone or enhanced sympathetic activation, and a combination therapy was shown to enhance cardiac autonomic modulation inferred from the increased heart rate variability. Therefore, a combination therapy may be useful for restoring normal autonomic nervous system activity in dogs with MMVD stage C.

Automatic initialization of a Complex Nonlinear Least Squares algorithm for impedance battery frequential identification

This paper deals with the identification of battery impedance parameters in the frequency domain using Electrochemical Impedance Spectroscopy (EIS) measurements and fractional modeling. Unlike other classical models used for frequency identification algorithms, fractional modeling allows to perform simulations also in the time domain offering interesting perspectives for identification in the time domain in future works. The objective of the paper is to propose an automatic initialization of a Complex Nonlinear Least Squares algorithm based on fractional modeling and EIS measurements in order to accurately estimate impedance parameters whatever the kind of diffusion involved: Finite Length Warburg (FLW) or Finite Space Warburg (FSW) diffusion. Fractional models are used to approximate non-integer orders and are based on a simplified Randles equivalent circuit. When no sufficient low frequency measurements are available, diffusion can be modeled thanks to a single fractional integrator whose order is close to 0.5 (Warburg zone). Otherwise, dedicated fractional models are proposed to take into account FLW or FSW diffusion. The method is validated using not only simulation data but also EIS measurements performed on a commercial 3.5 Ah Li-ion cell and open source experimental data.

Experimental investigation for fault diagnosis of a single stage worm gearbox using response surface methodology

Gearbox transmission system is a fundamental factor in the industrial applications. If gearbox stop working due to different faults, it may break normal machine operation and cause a production loss. This research paper focused on fault analysis of combination of worm wheel and bearing by using root mean square (RMS) and response surface method (RSM). Faults considered on outer race and inner race of worm wheel bearing, on worm wheel tooth. These faults and load were considered as an important independent factor to understand their effects on RMS response of worm gearbox. Worm gearbox experimental setup is for laboratory experimentation and RSM for analysis. Twenty-seven experimental trial conducted for three level of parameters based on design of experiment (DOE). Vibration based response measured in frequency domain and root mean square parameter extracted for the fault analysis. Box behnken design RSM is implemented to investigate independent parameters effect on output parameter i.e. RMS. The result shows that effect of faulty inner race bearing is more on worm wheel as compared to faulty outer race bearing and fault parameter influence RMS than load parameter.

Modelling of spent fuel dissolution process for assessment of criticality safety margin

Criticality safety margin assessment is a challenging task for a large dissolution tank under uncertain process conditions. The Monte-Carlo based neutron transport code (MCkeff) was used to model geometry, material and process conditions of dissolution tank containing fissile material. This manuscript presents a sampling-based probabilistic technique for estimating single-sided tolerance limit on criticality safety parameter due to random variation of process parameters. Based on their effect on fissile mass, fissile concentration, neutron moderation and neutron reflection, potential uncertain parameters impacting the criticality safety margin were identified. By withdrawing random samples from uncertain domain of process parameter, a simulation test matrix was formed to study the variability of the neutron multiplication factor. The Wilk's formula, which corresponds to the 95th percentile and 95% confidence level, was used to estimate required sample size to minimise statistical error (i.e., Type-I error associated to the rejection of the genuine sample and Type-II error connected to the acceptance of the false sample). In order to cut down the computation costs associated with requirement of large computer runs, a meta-model for the criticality safety problem was also constructed using a linear regression model. Additionally, associated model uncertainty was quantified. The variation in neutron multiplication factor caused by changes in process variables like pellet diameter, linear pellet mass, temperature, etc. was also investigated as an input parameter uncertainty. It is found that model uncertainty plays significant role while assessing the criticality safety margin using meta-model.

Transient replica symmetry breaking in Brillouin random fiber lasers

Replica symmetry breaking (RSB), as a featured phase transition between paramagnetic and spin glass state in magnetic systems, has been predicted and validated among random laser-based complex systems, which involves numerous random modes interplayed via gain competition and exhibits disorder-induced frustration for glass behavior. However, the dynamics of RSB phase transition involving micro-state evolution of a photonic complex system have never been well investigated. Here, we report experimental evidence of transient RSB in a Brillouin random fiber laser (BRFL)-based photonic system through high-resolution unveiling of random laser mode landscape based on heterodyne technique. Thanks to the prolonged lifetime of activated random modes in BRFLs, an elaborated mapping of time-dependent statistics of the Parisi overlap parameter in both time and frequency domains was timely resolved, attributing to a compelling analogy between the transient RSB dynamics and the random mode evolution. These findings highlight that BRFL-based systems with the flexible harness of a customized photonic complex platform allow a superb opportunity for time-resolved transient RSB observation, opening new avenues in exploring fundamentals and application of complex systems and nonlinear phenomena.

GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs

Physics-Informed Neural Network (PINN) has proven itself a powerful tool to obtain the numerical solutions of nonlinear partial differential equations (PDEs) leveraging the expressivity of deep neural networks and the computing power of modern heterogeneous hardware. However, its training is still time-consuming, especially in the multi-query and real-time simulation settings, and its parameterization often overly excessive. In this paper, we propose the Generative Pre-Trained PINN (GPT-PINN) to mitigate both challenges in the setting of parametric PDEs. GPT-PINN represents a brand-new meta-learning paradigm for parametric systems. As a network of networks, its outer-/meta-network is hyper-reduced with only one hidden layer having significantly reduced number of neurons. Moreover, its activation function at each hidden neuron is a (full) PINN pre-trained at a judiciously selected system configuration. The meta-network adaptively “learns” the parametric dependence of the system and “grows” this hidden layer one neuron at a time. In the end, by encompassing a very small number of networks trained at this set of adaptively-selected parameter values, the meta-network is capable of generating surrogate solutions for the parametric system across the entire parameter domain accurately and efficiently.

A fast Fourier convolutional deep neural network for accurate and explainable discrimination of wheat yellow rust and nitrogen deficiency from Sentinel-2 time series data.

Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the existing models always face several challenges: 1) computational inefficiency and the misclassifications between the different stresses with similar symptoms; and 2) the poor interpretability of the host-stress interaction. In this work, we propose a novel fast Fourier Convolutional Neural Network (FFDNN) for accurate and explainable detection of two plant stresses with similar symptoms (i.e. Wheat Yellow Rust And Nitrogen Deficiency). Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress. In addition, in order to alleviate over-fitting, a photochemical vegetation indices-based filter is placed as pre-processing operator to remove the non-photochemical noises from the input Sentinel-2 time series. The proposed model has been evaluated with ground truth data under both controlled and natural conditions. The results demonstrate that the high-level vector features interpret the influence of the host-stress interaction/response and the proposed model achieves competitive advantages in the detection and discrimination of yellow rust and nitrogen deficiency on Sentinel-2 time series in terms of classification accuracy, robustness, and generalization.