Time Delay Research Articles

TDOA-AOA Localization Algorithm for 5G Intelligent Reflecting Surfaces

5G positioning technology has become deeply integrated into daily life. However, in wireless signal propagation environments, there may exist non-line-of-sight (NLOS) conditions, which lead to signal blockage and subsequently hinder the provision of positioning services. To address this issue, this paper proposes an intelligent reflecting surface (IRS) NLOS time difference of arrival–angle of arrival (TDOA-AOA) localization (INTAL) algorithm. First, the algorithm constructs a system model for 5G IRS localization, effectively overcoming the challenges of positioning in NLOS paths. Then, by applying the multiple signal classification algorithm to estimate the time delay and angle, and using the Chan algorithm to obtain the user’s estimated coordinates, an optimization problem is formulated to minimize the distance between the estimated and actual coordinates. The tent–snake optimization algorithm is employed to solve this optimization problem, thereby reducing localization errors. Finally, simulations demonstrate that the INTAL algorithm outperforms the snake optimization (SO) algorithm and the gray wolf optimization (GWO) algorithm under the same conditions, reducing the localization error by 56% and 60% on average, respectively. Additionally, when the signal-to-noise ratio is 30 dB, the localization error of the INTAL algorithm is only 0.2968 m, while the errors for the SO and GWO algorithms are 0.6733 m and 0.7398 m, respectively. This further proves the significant improvement of the algorithm in terms of localization accuracy.

Characteristics, Relationships, and Differences in Muscle Activity and Impact Load Attenuation During Tennis Forehand Stroke with Different Grips

In forehand strokes with different grips in tennis, the forearm muscle activities, the distribution and attenuation of the impact loads, and the effects of the muscles on the impact load attenuation exhibited different characteristics. This study aimed to explore these characteristics by analyzing electromyography (EMG) and acceleration data, and comparing the differences between the Eastern and Western grips. Fourteen level II or above tennis players (ten males, aged 22.4 ± 3.6 years; four females, aged 19.8 ± 2.0 years) were recruited and instructed to perform forehand strokes using the Eastern and Western grips, respectively. The EMG of eight forearm muscles and the acceleration data at the ulnar and radial sides of the wrist and elbow were collected. The root mean square (RMS), the peaks of the impact load, the amplitude of impact load attenuation (AC), and the jerk value (Jerk) were calculated. The cross-correlation coefficients and time delays of EMG–EMG, EMG–AC, and EMG–jerk were obtained using the cross-correlation method. The results showed that in the Eastern grip group (group E), the RMS of the flexor carpi ulnaris (FCU) was significantly greater than that in the Western grip group (group W). In group E, the peaks of impact load, AC, and Jerk on the Y axis of the wrist ulnar side were all significantly higher than those in group W. The activity of the extensor digitorum commonis (EDC) had significantly different effects on the amplitude and rate of impact load attenuation at specific locations in different grips, especially at the elbow (p < 0.05). The conclusion indicated that the FCU exhibited higher levels of EMG activity in the Eastern grip. This grip responded to greater impact loads with more substantial and rapid attenuation on the wrist ulnar side. Furthermore, the EDC appeared to contribute more to the amplitude of impact load attenuation in the Western grip and to have a more significant influence on the rate of impact load attenuation in the Eastern grip, especially at the elbow. These results suggest that tennis players and coaches should pay more attention to improving the strength of the EDC and FCU, which can improve sports performance and comfort, as well as prevent sports injuries.

Deep reinforcement learning for collision avoidance of autonomous ships in inland river

Due to the characteristics of large inertia, large time delay, and nonlinearity in ship motion, the maneuverability of ships is an important issue related to the safety of ship navigation. The manipulation of ships significantly affects the safety of ship navigation, and its importance will gradually increase with the development of autonomous ships. Collision accidents are common due to the complexity of inland waterways and density of the ships. Herein, an autonomous learning framework with deep reinforcement learning (DRL) is constructed for autonomous driving tasks. The state space, action space, reward function and neural network structure of DRL are designed based on the ship’s maneuvering characteristics and control requirements. A DDPG algorithm is then used to implement the controller. Finally, some representative route segments of the inland waterway is selected for simulation research based on the virtual simulation environment. The designed DRL controller can quickly converge from the training and learning process to meet the control requirements. By comparing with the previous experimental results, the effectiveness of this algorithm is verified. Therefore, this study provides a reference for future research on the collision avoidance technology of autonomous ships in inland rivers.

Linear parameter varying μ synthesis robust control of CDC semi‐active suspension system for nonlinearity and uncertainty

AbstractBecause the continuous damping control (CDC) semi‐active suspension system has complex nonlinear and uncertain characteristics that can significantly affect the performance of the system. And in the controller design process, achieving a balance of multiple performance objectives is often difficult. Therefore, this paper designs a linear parameter varying (LPV) μ synthesis robust controller for the vibration suppression and multi‐objective control of a 7‐degree‐of‐freedom (7‐DOF) vehicle suspension system equipped with CDC dampers. Firstly, the nonlinear force model of the solenoid valve CDC damper is developed based on the actual damping variation. Considering the nonlinear forces of the coil springs, the nonlinear model of the 7‐DOF suspension system is expressed as LPV form. Further, the linear fractional transformation (LFT) method is used to analyze and reconstruct the system model for multiple parameter uncertainties of the suspension. The actuator time delays are also simulated through a frequency domain transfer function, which is considered to be an unmodeled dynamic at the input of the system. Finally, an LPV‐μ synthesis robust controller based on nonlinearity and mixed uncertainty is designed to improve car ride comfort and achieve the best relationship between comfort and handling stability. Simulation and experimental results under random disturbance conditions show that the LPV‐μ synthesis controller has better anti‐jamming performance and controllability compared to the H∞ controller and μ synthesis controller.

Find the haystacks, then look for needles: The rate of strongly lensed supernovae in galaxy-galaxy strong gravitational lenses

Abstract The time delay between appearances of multiple images of a gravitationally lensed supernova (glSN) is sensitive to the Hubble constant, H0. As well as time delays, a lensed host galaxy is needed to enable precise inference of H0. In this work we investigate the connection between discoverable lensed transients and their host galaxies. We find that the Legacy Survey of Space and Time (LSST) will discover at least 90 glSNe per year, of which 54% will also have a strongly lensed host. The rates are uncertain by approximately 30 % depending primarily on the choice of the unlensed SN population and uncertainties in the redshift evolution of the deflector population, but the fraction of glSNe with a lensed host is consistently around a half. LSST will discover around 20 glSNe per year in systems that could plausibly have been identified by Euclid as galaxy-galaxy lenses before the discovery of the glSN. Such systems have preferentially longer time delays and therefore are well suited for cosmography. We define a golden sample of glSNe Ia with time delays over 10 days, image separations greater than 0.8 arcseconds, and a multiply imaged host. For this golden sample, we find $91\%$ occur in systems that should already be discoverable as galaxy-galaxy lenses in Euclid. For cosmology with glSNe, monitoring Euclid lenses is a plausible alternative to searching the entire LSST alert stream.

EXPRESS: To Dispose or Eat? the Impact of Perceived Healthiness on Consumption Decisions for About-to-Expire Foods

Perceived healthiness of food is generally regarded as a positive attribute in food choices as it positively impacts consumers’ preferences. The current research demonstrates that in contexts where there is a time delay between a food’s production and its consumption (referred to as “about-to-expire” food), strong perceptions of a food’s healthiness can be detrimental. This is because consumers hold a lay theory that healthy food expires more quickly. In eight studies ( N = 3,552), we find that merely portraying food as healthy increases the perception that it expires quickly and that this effect attenuates when consumers hold the lay theory weakly or have a high level of knowledge about food expiration. Importantly, this lay theory leads consumers to avoid consuming healthy (vs. non-healthy) about-to-expire food, resulting in increased disposal intentions and decreased preferences. In designing sales promotions for about-to-expire food, managers should consider the healthiness of food products, as consumers prefer different types of sales promotions and require different magnitudes of price discounts for healthy (vs. non-healthy) about-to-expire food. Finally, adding an expiration date label that provides unambiguous guidance (i.e., “consume by”) can effectively mitigate the detrimental effect of perceived healthiness on the consumption for about-to-expire food.

Optical Time-Domain Quantum State Tomography on a Subcycle Scale

Following recent progress in the experimental application of electro-optic sampling to the detection of the quantum fluctuations of the electromagnetic-field ground state and ultrabroadband squeezed states on a subcycle scale, we propose an approach to elevate broadband electro-optic sampling from a spectroscopic method to a full quantum tomography scheme, able to reconstruct a free-space quantum state directly in the time domain. By combining two recently developed methods to theoretically describe quantum electro-optic sampling, we analytically relate the photon-count probability distribution of the electro-optic signal to a transformed phase-space quasiprobability distribution of the sampled quantum state as a function of the time delay between the sampled midinfrared pulsed state and an ultrabroadband near-infrared probe pulse. We catalog and analyze sources of noise and show that in quantum electro-optic sampling with an ultrabroadband probe pulse one can expect to observe thermalization due to entanglement breaking. Mitigation of the thermalization noise enables a tomographic reconstruction of broadband quantum states while granting access to its dynamics on a subcycle scale. Published by the American Physical Society 2024

Medical image recognition based on multilayer neural network

As a common accidental injury, accurate identification of burns and scalded injuries is of great significance to the development of treatment programs and prognosis assessment. Traditional identification methods are subjective and inaccurate, this paper proposes a burns and scalds grade identification technique based on multilayer neural network. The three-degree classification standard of burns and scalds is described, and the structure and principle of multilayer neural network are introduced, including the network structure composed of input layer, hidden layer and output layer, forward propagation, back propagation and training process. The implementation steps of burns and scald grade recognition technology based on multi-layer neural network are discussed in detail, such as data acquisition, image pre-processing, feature extraction, classifier design, model training and evaluation. Through experiments, the model is tested using a dataset containing 362 burns and scalds images, and the model achieves more than 90% accuracy on the test set with high accuracy and reliability. The multi-layer neural network model is used to classify the burn and scald data, which improves the diagnostic accuracy, shortens the delay time of the disease, reduces the pressure of professional doctors, and helps patients have a preliminary understanding of the degree of burn.

Equilibrium Control in Uncertain Linear Quadratic Differential Games with V-Jumps and State Delays: A Case Study on Carbon Emission Reduction

Uncertainty, time delays, and jumps often coexist in dynamic game problems due to the complexity of the environment. To address such issues, we can utilize uncertain delay differential equations with jumps to depict the dynamic changes in differential game problems that involve uncertain noise, delays, and jumps. In this paper, we first examine a linear quadratic differential game optimistic value problem within an uncertain environment characterized by jumps and delays. By applying the Z(x,y) transform, we convert the infinite-dimensional problem into a finite-dimensional one. We then demonstrate that the condition for the existence of a Nash equilibrium strategy is equivalent to the existence of solutions to two cross-coupled matrix Riccati equations. Furthermore, we explore the saddle point equilibrium strategy of the linear quadratic differential game optimistic value model and derive the corresponding saddle point equilibrium solution. Finally, we apply our results to solve a carbon emission reduction game problem.

Limits to dynamic range in GHz-THz single-dish planetary spectra

Abstract When bright solar-system objects are observed by GHz-THz regime telescopes, off-axis signals bounce around locally and re-enter the signal path with a time delay, causing sinusoidal ripples in output spectra. Ripples that are unstable over time are challenging to remove. A typical detection limit for planetary spectral lines is a fraction ∼10−3 of continuum signal, restricting searches for minor atmospheric trace-gases. Modern wideband spectra of Venus demonstrate a plethora of effects, at three example telescopes spanning nearly a factor-of-50 in frequency. Characterisation of instrumental effects as families of pure sine-waves via Fourier-analysis is shown to improve dynamic-range by factors of a few. An example upper limit on sulphuric acid (H2SO4) vapour in Venus’ mesosphere, from fully-automated data-cleaning of a 3.5 GHz band containing 10 line components, goes as deep as the best previously-published limit. The most challenging cases are searches for single lines of width comparable to ripple periods. Traditional polynomial-fitting approaches can be deployed to test for false positives, to demonstrate robustness at a level of zero ”fake lines” in >1000 comparisons. Fourier-based data-cleaning avoids subjectivity and can be fully automated, and synthetic spectra can be injected before processing to test to what degree signals are lost in cleaning. An ideal robustness strategy is mitigation at the data-acquisition stage, e.g. using slow drifts in target-velocity with respect to the telescope to isolate a planetary line from a quasi-static instrumental ripple pattern.

Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks

Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage.

Intelligent Model-Free Control for Power Line Inspection Robots: Tackling Input Time Delays with Data-Driven Solutions

This article presents an innovative approach to model-free adaptive control designed for power line inspection robots facing challenges with input time delays. The strategy begins by employing a compact-form dynamic linearization technique to transform the original system into a data-driven model. Subsequently, utilizing real-time input and output information, the system’s pseudo-partial derivatives are assessed online. Leveraging these assessment parameters, a weighted one-step prediction control mechanism is designed, and a compact-form dynamic linearization model-free adaptive control framework is established. Moreover, the research incorporates compression mapping to thoroughly confirm the convergence of the algorithm, thereby ensuring its stability. Ultimately, the effectiveness and practicality of this control method are substantiated through a series of simulation experiments, demonstrating its robust performance.

Existence and stability of a weakly damped laminated beam with a nonlinear delay

AbstractA weakly damped laminated beam system with nonlinear time delay is studied. The existence and uniqueness are proved by Faedo–Galerkin approach. We prove that the system is stable under some specific conditions on the weight of the delay and the equal wave speeds of propagation. The general energy decay rate is established by using multiplier method and some properties of convex functions. This decay result is obtained without imposing any restrictive growth assumption on the damping term at the origin. In addition, our result improves and develops some existing results in the literature.

A Fast Chebyshev Collocation Method for Stability Analysis of a Robotic Machining System with Time Delay

A Fast Chebyshev Collocation Method for Stability Analysis of a Robotic Machining System with Time Delay

Retrieve water quality parameters of urban rivers from hyperspectral images through feature interaction based two-stage method fused with spectral unmixing and spatial relatedness

Monitoring water quality through efficient quantification of water quality parameters (WQPs) is of paramount importance to environmental management of urban rivers. Unmanned aerial vehicle (UAV) remote sensing techniques posed a great opportunity for visualizing spatial distributions of WQPs concentrations with higher flexibility and monitoring frequency compared to satellite remote sensing techniques, assisting to trace potential contamination sources and prevent water quality from degradation. However, current methods of water quality monitoring usually involved large masses of water samples as training data to keep calculation accuracy every time their study area changed, increasing financial cost and incurring time delay in monitoring and evaluating water quality. This study proposed a UAV-based two-stage multidirectional fusion with probabilistic matrix factorization (TSMF-PMF) method in unified framework, to effectively quantify WQPs including chemical oxygen demand (COD), biochemical oxygen demand (BOD), and turbidity from UAV hyperspectral images. TSMF-PMF established feature interaction and outlier feedback module, ensuring relatively high calculation stability with less training samples. The experimental results showed that TSMF-PMF achieved good performance on predicting concentrations of WQPs with coefficient of determination (R2) and mean absolute percentage error (MAPE) ranging from 0.82 to 0.91 and from 5.35 % to 10.23 %. This study established theoretical and technical foundation to optimize environmental management scheme of urban rivers and provided an application demonstration.

Synchronization of directly coupled complex networks with multiweights and multiple delays

This paper investigates the synchronization of directed and multiweighted dynamic networks (DMDNs) with different delay couplings. Time delays in this paper are assumed to exist between the communication process, so communication between different nodes can generate time delays, while communications between the same nodes do not need time delays. Using the normalized left eigenvectors (NLEigs) corresponding to the zero eigenvalue of the coupling matrix, we prove that if both the time delay and the Chebyshev distance between NLEigs are small enough, all coupling matrices can accelerate the synchronization. Moreover, we also investigate the consensus for directed and multiweighted multi-agents with arbitrary bounded/unbounded time delays. Furthermore, the corresponding pinning control synchronization and consensus problem are also investigated, and sufficient synchronization criteria are also obtained. Numerical simulations are presented to show the validity of obtained theoretical results.

Nonlinear second order plus time delay model identification and nonlinear PID controller tuning based on extended linearization method

PID control systems based on the first order plus time delay model (FOPTD), which approximate the full system dynamics, are well-accepted for a wide range of linear processes. While such controllers can be applied to overdamped nonlinear processes, they often experience excessive overshoots and oscillations for general nonlinear processes. To overcome this limitation, we propose a novel method to design a nonlinear PID controller based on the second order plus time delay (SOPTD) model. The system nonlinearity requires parameter adjustments of the linearized model across operational ranges. Hence, in this work, it is handled by the extended linearization method (ELM), ensuring local stability under the assumptions of slow and small changes in operating points. Importantly, the model achieves global input-to-output stability even without the above constraints, provided there are no structural and parametric errors. The resulting nonlinear SOPTD model can describe changes in process gain and two time constants as the operation point varies. We demonstrate the applicability of our approach with a polymerization reactor simulation and liquid-level control experiments.

Nanoripples evolution on tungsten surface induced by two-pulse configuration

Tuneable shapes and uniformity of the laser-induced periodic surface structures (LIPSS) attract interest because of their diverse applications in both scientific research and technological advancements. In this work, we investigate the progression of regular one-dimensional (1D) LIPSS on a tungsten surface, examining its evolution based on the time delay between two laser pulses that initiate the formation of nanoripples. 1D-LIPSS were formed in the case of single-beam laser ablation with approximately 84 laser pulses. Two-dimensional (2D) LIPSS, including triangle, hexagonal, and square shapes, were generated by employing two cross-polarized laser beams (with wavelengths of 1030 nm and pulse durations of 40 fs) with no delay between the pulses. However, introducing a time delay of 2 picoseconds (ps) between the two cross-polarized laser pulses resulted in the division of the initially cross-oriented 2D-LIPSS into square-shaped structures, particularly along the spatially overlapped region of the laser beams. The emergence of triangle- and hexagonal-shaped two-dimensional laser-induced periodic surface structures (2D-LIPSS) is analyzed within the framework of a self-organization model, particularly in the context of solidifying the molten layer under cold temperature conditions on the tungsten surface. We examine the formation mechanism of LIPSS, attributing it to a combination of the self-organization/hydrodynamic mechanism initially, which transitions to the electromagnetic mechanism as the effective number of pulses increases.

A Transparent AI-based Approach for Controlling Processes with Time Delays

A Transparent AI-based Approach for Controlling Processes with Time Delays

Treatment and delay control strategy for a non-linear rift valley fever epidemic model

Rift Valley Fever (RVF) is a viral disease affecting animals and humans, causing symptoms such as fever, liver damage, and bleeding, particularly prevalent in Africa. This study focuses on numerical solutions for a non-linear delayed dynamic epidemiological model of RVF. It extends a control problem incorporating the susceptible, infected, treated, recovered vector to analyze the impact of measures such as mosquito repellent and treatment. The goal is to examine how time delays in implementing control measures affect the dynamics of an epidemic. The model considers delay factors such as mosquito replication, hospitalization, travel restrictions, and isolation due to the lack of proper vaccination. The study explores the model’s aspects, including the reproduction number, equilibrium points, and stability. Local and global implications are examined using techniques such as the Lyapunov function and the Brauer-F lemma. Numerical analysis employs the non-standard finite difference method, establishing the local stability of the equilibrium through the effective reproduction number Rrvf and sensitivity analysis. The research highlights the importance of treatment and delay strategies in reducing RVF transmission, emphasizing the critical need for immunization and preventive measures.