Effect Of Time Delay Research Articles

Forecasting the output of high-tech industry in China: A novel nonlinear grey time-delay multivariable model with variable lag parameters

Under the rapidly developing economy in China, accurate forecasting holds vital significance for policymaking and operational planning within the high-tech industry. However, the influencing factors affecting the output, accompanied by the time-delay effect, could be nonlinear, and uncertain. Thereby, this paper proposes a new nonlinear grey multivariable model with time-varying lag parameters. To be specific, the newly designed time delay function and power exponent are introduced, which can significantly enhance the adaptability and flexibility of the proposed method. The Grey Wolf Optimization algorithm is utilized to calculate the dynamic time lag parameters and power exponent to improve the prediction reliability. Furthermore, this new approach is applied to predict the high-tech industry’s output in China, Shanghai Municipality, and the Eastern Region, with due consideration given to the time-delay effect between input factors and outputs. To assess its efficacy, some leading models are selected for comparison to the proposed model. Furthermore, the utilization of Monte-Carlo simulation, the Probability Density Analysis, and the simulations are used to demonstrate the robustness and stability of this new method. The findings show that the proposed model is a feasible and applicable approach for prediction, exhibiting outstanding accuracy.

Effects of Waveform, Time Delay, and Vibration Axis on the Perception of Vibrotactile Compliance Illusions on Smartphone Touchscreens

A hard surface feels soft or elastic when carefully controlled vibrations are provided to a pressing finger. This compliance illusion method may be employed to augment graphical objects on smartphone screens with compliant tactile properties. However, product engineers encounter practical questions when attempting to use the compliance illusion method for their products. Should the waveform be sinusoidal? How much time delay is acceptable? Should the vibration axis be perpendicular to the surface? A series of experiments was conducted to address these questions. The first experiment revealed that the waveform could not be distinguished in the context of the compliance illusion method when applied to typical smartphones. The second experiment demonstrated that time delays greater than 25 ms and the use of the three vibration axes could be distinguished in the same context. The third experiment explored how the perceptual qualities of the compliance illusion change with variations in time delay and the axis of vibration. Time delay had significant effects on softness, smoothness, elasticity, and unpleasantness, while the vibration axis had significant effects on softness, smoothness, and elasticity.

Energy-dependent intrinsic time delay of gamma-ray bursts on testing Lorentz invariance violation

High-energy photons of gamma-ray bursts (GRBs) might be emitted at different intrinsic times with energy dependence at the source. In this letter, we expand the model from previous works on testing the Lorentz Invariance Violation (LV) with the observed GRB data from the Fermi Gamma-ray Space Telescope. We reanalyze the previous data with the full Bayesian parameter estimation method and get consistent results by assuming that the time delays are due to an LV term and a constant intrinsic time delay term. Subsequently, we neglect the LV effect and only consider the intrinsic time delay effect. We assume a common intrinsic time delay term along with a source energy correlated time delay of high-energy photons. We find that the energy-dependent emission times can also explain the observed GRB data of high-energy photon events. Finally, we integrate these two physical mechanisms into a unified model to distinguish and evaluate their respective contributions using the observed GRB data.

A long-term Doppler wind LiDAR study of heavy pollution episodes in western Yangtze River Delta region, China

Previous studies are primarily based on surface air pollution for a single event, leaving research on the temporal characteristics of aerosols and wind fields in the atmospheric boundary layer (ABL) and their associations unclear due to a lack of long-term vertical profile data. This study was the first to conduct long-term Doppler wind LiDAR measurements from Sep. 2019 to Aug. 2022 in Hefei in western Yangtze River Delta (YRD) region, China. The spatiotemporal characteristics of retrieved aerosol backscatter coefficient (β) and wind profiles were analyzed from the perspective of long-term statistics and typical heavy pollution episodes (HPEs). The seasonal profile of β showed a peak in the near-surface layer meanwhile the overall β profile (<0.5 km) was the highest in winter but lowest in summer. Combined with ground-based meteorological and air quality observations, 12 HPEs were identified and classified into dust-related events and fog-haze episodes. The results showed a consistency between hourly variations in PM10 (particulate matter smaller than 10 μm) concentration and β retrieved at 300 m during a high PM10 concentration (>150 μg/m3) period. Different roles of horizontal wind at different altitudes and its associated time delay effect on surface PM10 pollution were evaluated based on correlation coefficients (Ф) and air pollution diffusion conditions. Significant positive Ф values were noticed below 0.5 km during the entire dust-related EP4 and EP6, indicating that the higher wind speeds could exacerbate PM10 pollution. In contrast, large negative Ф values meant the removal of PM10 pollutants by strong winds below 0.8 km during 24 h after peak in EP3. Combining with backward trajectory analysis and meteorological condition, notable transboundary pollution with positive contributions from upper winds (>1.5 km) was discovered in dust-related EP1, EP4, EP7, EP9, and EP10. Time delay effect (1-h/2-h lag) of upper winds (>0.8 km) on surface PM10 pollution was explored in EP5, EP6, EP7, EP8, and EP9. In spring dust-related HPEs, surface PM10 pollution was mostly contributed by long-range transport of aerosol particles at higher altitudes (>1.5 km) from the northwest direction, driven by the Mongolian cyclone and the cold front system. Transboundary aerosols originated from the northern part of Anhui province and the YRD region in the middle altitudes (∼0.5 km) was the main contributor to fog-haze HPEs. The findings demonstrated the ability of the real-time Doppler wind LiDAR system to monitor transboundary air pollution and provided a scientific reference for policy makers.

Study of self-actuated time-controlled tower microvalves

Abstract Time-controlled microvalves are vital components of microfluidic systems, primarily used to regulate the sequential or precise introduction of reagents. However, existing time-controlled microvalves face certain limitations. For instance, those driven by magneto-thermal mechanisms require additional control elements, and fabricating some 3D time-controlled microvalves can be complex. To address these challenges, a self-driven time-controlled tower microvalve was designed and fabricated. This study investigates how structural parameters of the time-delay unit affect bubble generation and the time-delay effect. Experimental testing confirmed that the delay time of the time-controlled microvalve increases and then decreases with variations in the trapezoidal aspect ratio (a) and the upper and lower width ratios (b). Additionally, the delay time of a single unit extends as the width of the delay unit increases. The optimal parameters for the time-controlled microvalve were determined as follows: W1 =1, 000 μm, W2 =500 μm, and H=800 μm, achieving an overall delay time of 16.7±0.8 s.

Rapid kilonova evolution: Recombination and reverberation effects

Kilonovae (KNe) are one of the fastest types of optical transients known, cooling rapidly in the first few days following their neutron-star merger origin. We show here that KN spectral features go through rapid recombination transitions, with features due to elements in the new ionisation state emerging quickly. Due to time-delay effects of the rapidly expanding KN, a ‘wave’ of these new features passing though the ejecta should be a detectable phenomenon. In particular, isolated line features will emerge as blueshifted absorption features first, gradually evolving into P Cygni features and then pure emission features. In this analysis, we present the evolution of individual exposures of the KN AT2017gfo observed with VLT/X-shooter, which together comprise X-shooter’s first epoch spectrum (1.43 days post-merger). The spectra of these ‘sub-epochs’ show a significant evolution across the roughly one hour of observations, including a decrease in the blackbody temperature and photospheric velocity. The early cooling is even more rapid than that inferred from later photospheric epochs and suggests that a fixed power-law relation between the temperature and time does not describe the data. The cooling constrains the recombination wave, where a Sr II interpretation of the AT2017gfo ∼1 μm feature predicts both a specific timing for the feature emergence and its early spectral shape, including the very weak emission component observed at about 1.43 days. This empirically indicates a strong correspondence between the radiation temperature of the blackbody and the ejecta’s electron temperature. Furthermore, this reverberation analysis suggests that temporal modelling is important for interpreting individual spectra and that higher-cadence spectral series, especially when concentrated at specific times, can provide strong constraints on KN line identifications and the ejecta physics. Given the use of such short-timescale information, we lay out improved observing strategies for future KN monitoring.

Dynamics and stability analysis of enzymatic cooperative chemical reactions in biological systems with time-delayed effects

The mathematical modeling and dynamic analysis of time-delayed enzymatic chemical reactions in biological systems are presented in this research. The objective is to examine the function of time lags in these reactions and to get a complete knowledge of the behavior of biological systems in a reaction to modifications in the quantity present of reactants and products. The model, which is based on delay differential equations, includes a time delay term to account for the lag between changes in the concentration of reactants, reaction rate constants and product responses. The findings give insight into how enzymatic processes behave dynamically and how stability is impacted by time lags, oscillation and general efficiency of the system. These results have significant importance for our comprehension of how biological processes are regulated and for the creation of biological control structures.

Effect of time delay on performance and timing control in dyadic rhythm coordination using finger tapping

In musical ensembles, people synchronise with each other despite the presence of time delays such as those related to sound transmission. However, the ways in which time delays in synchronisation are overcome remain unclear. This study aimed to investigate the basic characteristics and mechanism of synchronisation with time delays using a dyadic synchronisation-continuation finger-tapping task with time delays ranging from 0 to 240 ms. The results reveal that synchronisation performance improved under time delays of 40–160 ms compared with in the other conditions. This tolerance to the time delay could have been because such a delay allowed both participants in each pair to tap before receiving the stimuli from their partner, as seen in synchronisation with a constant-tempo metronome. In addition, the dependency of the timing control on the partner’s previous inter-tap interval decreased at a time delay of 80 ms, relating to the fact that the acceleration and deceleration of the tapping tempo reduced under certain time delays, while the synchronisation performance improved. Uncertainty in the timing of the partner’s stimulus could induce greater anticipatory responses, making it possible to tolerate longer time delays in dyadic finger-tapping tasks.

Prediction method of particle size qualification rate of high-pressure roller mill based on end-edge-cloud synergy

The pass rate of granules is an essential indicator during the high-pressure grinding process, as it accurately reflects the processing quality. Currently, the pass rate of granules is detected primarily based on manual experience judgments or offline inspections. Hence, this article presents a methodology for predicting the pass rate of granularity via an optimized support vector regression approach improved through genetic algorithms. Initially, a time-delay analysis method based on the particle swarm optimization algorithm is applied to mitigate the effects of time delays between the granularity pass rate and other data, thus aligning the dataset on a temporal scale. Subsequently, the feature data were selected using the maximum information coefficient analysis technique, which identified the most significant variables for inclusion in the training and testing sets of the predictive model. Predictions are then made using a support vector machine model that has been enhanced via genetic algorithm optimization. Furthermore, an online prediction model has been established, enabling real-time forecasting of the granularity pass rate and online model updates through root mean square propagation gradient descent optimization algorithm. This method leverages end-edge-cloud collaboration to provide a smart detection mechanism for the throughput rate of particles in high-pressure grinding mills. Experimental results demonstrate that, compared to traditional time-delay analysis, the improved time-delay analysis method proposed in this study is more effective and accurate. Simultaneously, the ɛ-GASVR granularity pass-rate prediction model proposed in this article achieved an R2 of 0.89.

Effects of Fractional Time Delay as a Low-Power True Time Delay Digital Beamforming Architecture

Effects of Fractional Time Delay as a Low-Power True Time Delay Digital Beamforming Architecture

Gaussian and Lévy noises excited delayed tumor growth model: first-passage behavior and stochastic resonance

In this paper, we analyze the dynamical behavior of a delayed tumor growth model under the joint effect of Gaussian white noise and Lévy noise by studying the mean first passage time (MFPT) and stochastic resonance (SR). Firstly, the tumor growth model under the joint effect of Gaussian white noise, Lévy noise and time delay is introduced. Then, the Lévy noise sequence is simulated by Janicki-Weron algorithm, and the MFPT and signal-to-noise ratio(SNR) of the system are simulated by using fourth-order stochastic Runge–Kutta algorithm. The effects of noise parameters, time delay and periodic signal parameters on MFPT, SR are discussed in detail, respectively. In addition, we find the phenomenon of noise enhanced stability. The results of the study can help to select the optimal regulatory parameters in the tumor growth model and promote the treatment of tumors.

The effect of path curvature on the stability of reversing truck–semitrailer in the presence of feedback delay

Stability analysis of the low-speed reverse motion along clothoid curve is presented for the truck–semitrailer combination. The single-track kinematic model supplemented with the steering system and the lower-level controller is applied. A higher-level controller is designed to stabilise the reverse motion of the vehicle. Linear state feedback with time delay is used with feedforward term to force the vehicle to the desired path. The linear stability analysis is done by semi-discretization and D-subdivision methods to determine the optimal control gain setup. The effects of path curvature and time delay on the stability are investigated and verified via numerical simulations. Results are validated via experiments using a small-scale test rig.

Effects of Time Interval and Interference Manipulations on Word-Level Repetition and Naming Performance in Persons with Aphasia

Objectives: The purpose of this paper is to investigate the impact on the repetition and naming ability of Korean aphasia patients by controlling their working memory load through time intervals and interference effects using the Temple Assessment of Language and verbal Short-term memory in Aphasia (TALSA) protocol. In addition, we examined the phonological and lexical-meaning processing abilities of persons with aphasia through word repetition tasks, nonword repetition tasks, and picture naming tasks to examine phonological and lexical-meaning production abilities. Methods: Thirteen native Korean speakers with aphasia participated in the study, along with twelve control group participants. The word and nonword repetition tasks were conducted under three conditions to examine the time delay and interference effects. Each condition entailed listening to each ten words of two and three syllables. Similarly, the picture naming tasks were carried out under three conditions involving viewing ten pictures of two and three syllables each. Results: This study examined how the repetition and naming ability of persons with aphasia, i.e., the ability to producing phonemes and lexical-meaning of persons with aphasia, varies when adjusting the working memory load under time interval and interference effect conditions. This study found that the difference in performance between groups was significant for both tasks. In particular, the larger the time interval, the greater the interference effect; that is, the greater the load of working memory, the lower the performance of aphasia subjects compared to the control group. Conclusion: Since the aphasia subjects have poor working memory ability, the results of this study indicate that as the cognitive load increases, they are more affected by the ability to process phonological and semantic activations.

Dynamic learning of flue gas desulfurization process using deep LSTMs neural network

SO2 emissions are known to pose great harm to both human health and atmospheric air, and flue gas generated from coal-fueled power plant is the prime source of sulfur dioxide. For this reason, flue gas desulfurization (FGD) technology has found wide applications in most coal-fired power stations. Correctly describe the dynamic behavior of an FGD process is the precondition of controlling it effectively. However, FGD process modeling is by no means an easy task, as the underlying process dynamics are highly nonlinear in nature, meanwhile time-delay effect is significant therein. Long short-term memory (LSTM) network possesses remarkable long-term memory capability, hence it is anticipated to have a powerful identification capability. In this paper, the connection between deep learning and system identification is established, further a unidirectional/bidirectional LSTM deep network is designed and employed to identify a real FGD process. Simulation results clearly demonstrate the effectiveness of deep learning-based identification approach, and the superiority of deep LSTMs over other conventional identification models is also verified.

Modeling, prediction and analysis of natural gas consumption in China using a novel dynamic nonlinear multivariable grey delay model

Accurately predicting natural gas consumption is essential for developing a clean, low-carbon, safe, and efficient energy system. Therefore, this paper integrates the idea of the extended logistic model into multivariable grey prediction theory. Considering the delay effect and nonlinear characteristics of the system, a novel dynamic nonlinear multivariate grey delay prediction model is constructed. Meanwhile, the algorithmic framework for solving the parameters of the new model is provided to identify and optimize the parameters of the time-delay effect of the model. The genetic algorithm was identified as the best matching optimization algorithm for the new model. Furthermore, the new model is applied to fitting and forecasting China's natural gas consumption, and the validity of the model is fully verified. Compared with other forecasting models, the new model has a lower average relative error in the fitting and forecasting stage, and, in particular, achieves a better forecasting effect with the lowest MAPEpre value (0.001 % for one-step ahead, 0.005 % for two-step ahead, and 0.746 % for three-step ahead). Moreover, the model is employed to forecast China's natural gas consumption for the next five years, providing a robust foundation for government policies related to the supply of natural gas.

A fast data-driven fault detection and location method for unknown distributed thermal processes

The distributed thermal processes are widely present in various industrial operations, but the presence of time delay effect and unknown model information make achieving fast and timely fault detection challenging. Furthermore, the involvement of spatiotemporal coupled data in measurement processes further exacerbates the situation. To solve these problems, this paper proposes a data-driven fault detection and location method for thermal processes described by distributed parameter systems (DPS). Firstly, a Time/Space separation is employed to decoupling the spatio-temporal data into spatial basis functions and temporal coefficients. Subsequently, dynamic partial least squares (DPLS) is employed to obtain the temporal information of the model with fault-free data. Monitoring statistics in both the temporal and spatial domains are then constructed, and their thresholds are estimated by kernel density function. Finally, an online strategy for fault detection and location is presented. The method was validated on the catalytic rod, and an experimental snap curing oven.

Stability of twisted states in power-law-coupled Kuramoto oscillators on a circle with and without time delay.

Other than the fully synchronized state, a twisted state can also be an equilibrium solution in the Kuramoto model and its variations. In the present work, we explore the stability of the twisted state in Kuramoto oscillators put on a rim of a planar circle in the two-dimensional space in the presence of power-law decaying interaction strength (∼r^{-α} with the distance r) and time delays due to a finite speed of information transfer. For example, our model can phenomenologically mimic a large sports stadium where many people try to sing or clap their hands in unison; the sound intensity decays with the distance and there can exist a time delay proportional to the distance due to the finiteness of sound speed. We first consider the case without the time delay effect and numerically find that stable twisted states emerge when the exponent α exceeds a critical value of α_{c}≈2. In other words, for α<α_{c}, the fully synchronized state, not the twisted state, is the only stable fixed point of the dynamics. In our analytic approach, we also derive an equation for α_{c} and discuss its solutions. In the presence of time delay, we find that it is possible that the synchronized state becomes unstable while twisted states are stable.

A master-slave teleoperation system control model based on model predictive control with fast dynamic response

Robot technology is advancing with constantly expanding applications. Teleoperation system demonstrates its potential to extend human capabilities in unstructured and hazardous environments as an important branch of robotics. However, there are still issues involving insufficient position tracking accuracy and dynamic response capabilities. This article proposes a master-slave teleoperation system control model based on model predictive control (MPC) with a real-time dynamic adaptive parameter adjustment algorithm. We establish a model based on the integral barrier Lyapunov function and construct a dynamic optimization model based on MPC. Then we carry out rolling optimization and feedback correction at each moment to feed the predicted values generated by MPC back to correct the actual tracking trajectories in real-time. Additionally, we utilize an adaptive neural network and robust term compensation to adjust dynamic parameters and eliminate the effects of time delays, external disturbances, and model uncertainties. Theoretical simulation experiments are conducted on the Simulink platform, analyzing position tracking error and dynamic response capabilities of the master-slave robot respectively to verify the proposed model. The results indicate a reduction in the position tracking errors of the master with 10% and slave with 11%, while also showing an improvement in their dynamic response capabilities. The speed error ranges of the master and slave are shortened with 6% and 7% respectively.

Stochastic response and stability analysis of nonlinear vehicle energy-regenerative suspension system with time delay

With the rapid development of new energy vehicles, a novel vehicle energy-regenerative suspension for simultaneous vibration suppression and energy harvesting has become one of the key development directions of the next generation of vehicle design. In order to improve the performance of suspension system, the time-delayed feedback control is applied to the novel suspension system subjected to different external excitation. At the same time, there are few studies related to the stochastic dynamics of nonlinear vehicle energy-regenerative suspension system with time delay. This paper studies stochastic response and stability of nonlinear vehicle energy-regenerative suspension with time-delayed feedback control under the excitation of Gaussian white noise. First of all, a stochastic dynamic model of the nonlinear vehicle energy-regenerative suspension with time delay system is established. Secondly, using the stochastic averaging method based on generalized harmonic function, the steady-state probability density function (PDF) of amplitude is obtained. The accuracy of the presented approach is verified through the comparison of analytical and numerical solutions. In addition, through combining the stochastic averaging method with the Khasminskii method, the top Lyapunov exponent (TLE) of the system is obtained, and then the influence of time delay and system parameters on the asymptotic Lyapunov stability is discussed. Finally, the effects of time delay and system parameters on system performance are also studied.

On fractional ring neural networks with multiple time delays: Stability and Hopf bifurcation analysis

Stability and Hopf bifurcation analysis of neural networks have become popular topics in recent years. In this paper, a large-scale fractional ring neural network with multiple time delays is considered. The model comprises three rings that share a single node. The effects of time delay and fractional order on its stability and Hopf bifurcation have been investigated. Firstly, the characteristic equation is obtained by using the Laplace transform. Then, the time delay and fractional order are set as a bifurcation parameter respectively. Correspondingly, the local stability and Hopf bifurcation conditions are derived by analyzing the characteristic equations. Finally, two examples are given to support the theoretical analysis. In fact, this article provides an effective method for analyzing the stability and Hopf bifurcation of fractional ring neural networks.