Computational Time Delay Research Articles

Far-focused pixel-based imaging of internal defects in multi-layered media based on attenuation compensation and circular coherence factor

ABSTRACT The multi-layered media composed of layers with different sound velocities exhibit anisotropy, strong attenuation, and interface reflection in terms of sound propagation, posing challenges for conventional ultrasound imaging methods, especially in detecting deep-seated and near-interface defects. In this study, an improved far-focused pixel-based (FPB) imaging method for detecting internal defects in multi-layered media is proposed. First, the time delay calculation in the FPB method is corrected using the shortest path searching method to determine the acoustic time-of-flight. Then, phase information is extracted from the echo signal to construct a circular coherence factor (CCF) that dynamically weights the time-corrected FPB images, addressing the issue of ineffective detection of near-interface defects caused by interface reflection. Finally, considering the sound attenuation in multi-layered media, the directivity coefficient, transmission coefficient, and divergence coefficient are determined through theoretical derivation to compensate for the echo amplitude. Experimental results demonstrate that the improved FPB method significantly enhances the reflection echo from deep-seated defects, reduces the maximum relative localisation error to 0.9%, and the quantisation error to 6.4%. The proposed method also greatly improves the lateral resolution and signal-to-noise ratio (SNR) of internal defect imaging compared to the total focusing method (TFM) and traditional FPB method.

Delayed non‐fragile robust control of civil structure considering structural uncertainties, actuator faults, and control force time delays

Active control is an effective strategy for suppressing unwanted structural vibrations. The uncertainties in computational models, actuator faults, and time delays in control forces are the potential threats for control performance improvements. These factors are interconnected and may lead to control system instability and poor control effectiveness. The paper presents a delayed non-fragile robust control (DNR) algorithm for uncertain systems with consideration of actuator faults and time delays. To this end, the existence conditions for the DNR multi-objective controller are derived by utilizing the Lyapunov stability proof and introducing a pole constraint inequality. Then, a dynamic output feedback DNR algorithm is further developed. Finally, two numerical simulation examples are discussed for several ground motions. The results show that the control system based on the proposed algorithm exhibits better stability, robustness, and fault tolerance than the linear quadratic Gaussian (LQG) algorithm. Furthermore, there is no need for additional control energy. Significantly, the control performance of the proposed algorithm can be further enhanced with the decentralized control strategy.

Multi-objective scheduling of cloud-edge cooperation in distributed manufacturing via multi-agent deep reinforcement learning

ABSTRACT Due to the limited computational power of edge devices in distributed manufacturing systems, the challenge of meeting real-time computing requirements for industrial big data arises. Additionally, the significant number of computational tasks results in considerable energy expenses. Therefore, it is crucial to effectively tackle the multi-objective cloud-edge collaborative task offloading problem (MOCECTOP). This paper focuses on two optimisation objectives: total computation time delay and computational energy consumption, which are related to promoting work efficiency and lowering carbon emissions. The weights of these two objectives are hard to determine at different production stages. The challenge is to get multiple models with various possible weight pairs of multiple objectives within a single training session and to achieve high-quality schedules for MOCECTOP in real-time production line control. We address this challenge by proposing a hierarchical parameter sharing (HPS) multi-objective optimisation framework based on multi-agent deep reinforcement learning. The network model comprises a task selection agent and a computing node selection agent. The task selection agent prioritises tasks for computation based on their state features, while the computing node selection agent allocates available computing devices to a selected task. Parameter sharing and collaborative training are employed to obtain a solution for the multi-objective problem when considering the variations in computational capabilities between the cloud and the edge. The HPS optimisation framework effectively fulfills the near real-time requirements for task computation in distributed manufacturing. Numerical experiments demonstrate that our HPS-based strategy quickly produces better schedules superior to those of existing multi-objective solution methods.

Time-Sensitive Network Simulation for In-Vehicle Ethernet Using SARSA Algorithm

In order to more accurately analyze the problem of time delay simulation and calculation in the time-sensitive network (TSN) of vehicular Ethernet, a TSN reservation class data delay analysis model improved based on the State–Action–Reward–State–Action (SARSA) reinforcement learning algorithm is proposed. Firstly, the TSN data queue forwarding delay model and reservation class data delay analysis intelligent body model are established, then the TSN traffic scheduling mechanism is improved by the SARSA reinforcement learning algorithm, and the improved TSN network reservation class data analysis model is established for the uncertainty of traffic scheduling in the network; finally, the fitting performance of the proposed method is verified by simulation and experimental validation. The results show that the deviation between the two is less than 5% under different BE loads, i.e., the established reservation class data delay analysis model is able to correctly fit the scheduling mechanism of the vehicle-mounted TSN network, which proves the reasonableness of the model simulation.

Evaluation of the corrections methodologies for improving the ignition delay time calculation in hydrogen/air mixtures using a design of experiments approach

Evaluation of the corrections methodologies for improving the ignition delay time calculation in hydrogen/air mixtures using a design of experiments approach

Data analysis solutions to improve blasting efficiency in mining

Purpose. To build an identification model to determine the appropriate explosion parameters value with reasonable cost. To optimize blasting works design at each blast site with the calculation of delay time based on the model used. Methodology. Blasting for mining is an issue of utilizing the most of explosive energy in order to achieve the highest smashing ability and the smallest level of vibration. In modern explosive techniques, the total amount of explosive is divided into parts to detonate after differential time intervals. This solution creates interference between stress waves causing the durability of rock structures to be reduced and the blasting efficiency to be improved. Although delay time plays an important role in this method, so far its value is still calculated empirically at the blast site due to the irregular characteristic of the rock environment. Technical design parameters for explosion including delay time has been also determined from smart analysis software and simulation models. However, their applicability is limited because of high payments and strict implementation conditions. The method proposed in the study overcomes this drawback and its effectiveness is proven by the process of analyzing experimental data at Nui Beo Mountain of Vietnam. Findings. An identification model is developed based on the information including: explosion delay time value; average propagation speed of the vibration wave; maximum amplitude of the vibration wave. Originality. Basic data analysis software and an artificial neural network model are used. A new data analysis algorithm is established to determine the optimal explosion delay time value. Practical value. A simple and reasonable-cost solution is formed for improving the efficiency of blasting in mining.

Experimental study on the ignition in a scramjet with a self-designed water plasma ignitor

In this paper, aiming at the problematic situation of igniting and improving combustion efficiency in a supersonic combustor, a self-designed water plasma ignitor is designed for ignition in supersonic combustors. For the self-designed water plasma ignitor, deionized water is selected as the coolant. During the ignitor operation, the water coolant cools the cathode and evaporates. Steam is ionized between the cathode and anode, generating a water plasma jet that is injected into the combustor for ignition. The self-designed ignitor has the advantages of long-time operation, repeatable startup, convenient coolant portability, and a low water consumption rate. The temperature of the water plasma jet exceeds 3500 K. The calculation of ignition delay time indicates that OH, H, and other free radicals in water plasma can significantly shorten the ignition delay time of kerosene and extend the ignition equivalence ratio limitation. The ignition experimental study was conducted using the water plasma ignitor in a thin strut equipped supersonic combustor whose design point corresponds to the cruise condition at a Mach number of 6. In the ignition experiment, a flame, which is self-sustaining, was established after the ignitor was activated for approximately 682 ms. The feasibility of the water plasma ignitor in a supersonic combustor has been verified.

Investigating the Lorentz invariance violation effect using different cosmological backgrounds

Familiar concepts in physics, such as Lorentz symmetry, are expected to be broken at energies approaching the Planck energy scale as predicted by several quantum-gravity theories. However, such very large energies are unreachable by current experiments on Earth. Current and future Cherenkov telescope facilities may have the capability to measure the accumulated deformation from Lorentz symmetry for photons traveling over large distances via energy-dependent time delays. One of the best natural laboratories to test Lorentz invariance violation (LIV) signatures are gamma-ray bursts. The calculation of time delays due to the LIV effect depends on the cosmic expansion history. In most of the previous works calculating time lags due to the LIV effect, the standard ΛCDM (or concordance) cosmological model is assumed. In this paper, we investigate whether the LIV signature is significantly different when assuming alternatives to the ΛCDM cosmological model. Specifically, we consider cosmological models with a non-trivial dark-energy equation of state (EoS) (), such as the standard Chevallier–Polarski–Linder parameterization, the quadratic parameterization of the dark-energy EoS, and the Pade parameterizations. We find that the relative difference in the predicted time lags is small, of the order of at most a few percent, and thus likely smaller than the systematic errors of possible measurements currently or in the near future.

Identification and extraction of lateral target signals in tunnel geological prediction with the Karhunen-Loéve beamforming method

Before the construction of a tunnel, effective and accurate advanced prospecting techniques are required to detect unexpected geological heterogeneity around the tunnel. However, because the data collected during tunnel advance prediction include waves reflected from different directions and a large amount of environmental noise, the seismic records collected are extremely complex, and it is difficult to extract the target signals from the lateral area of the tunnel effectively. The beamforming method can create a focused wave front through the stack of the wavefield, improving the data quality of the target signal. This paper incorporates the Karhunen-Loéve method in the beamforming procedure to extract the lateral signal of the tunnel. First, the test principle of Karhunen-Loéve beamforming (KLBF) based on the tunnel seismic prediction (TSP) observation system is discussed, and the corresponding delay parameters are estimated according to different application conditions. In addition, ray tracing and three-dimensional wave equation methods are used to carry out numerical simulation research on the models of faults, karst caves and underground rivers. Finally, the corresponding KLBF method can be used to improve the quality of effective signals by different delay time calculation methods, which can better identify and extract the lateral reflected signals.

Loss Modulated Deadbeat Control for Grid Connected Inverter System

Deadbeat controllers are commonly employed in grid connected inverter systems for their fast transient response. In this paper, a loss modulated deadbeat current control (DCC) is proposed for a grid connected inverter system. This control calculates the required modulation signal by considering the losses in the system at every switching cycle. The proposed method tracks the reference current with negligible error. The loss component used in the control method eliminates the effect of sensing and computational time delay and adapts to variation in system parameter. The proposed DCC is extended for deadbeat based direct power control (DDPC) without employing inner current control loop. Further, a modified DDPC is presented to eliminate the delay of one switching cycle which is inherently present for the grid current in DCC. The proposed control concepts are realized on a multi-source fed switched capacitor based multilevel inverter. The topology considered features more number of voltage levels with less component count, high gain, self capacitor charge balancing and increased source utilization. The steady-state and transient behaviour of the control methods are verified in simulation and experimental studies for a power rating of 500 W under various system conditions. The proposed algorithms are able to track the reference signals with fast transient response and negligible steady-state error.

Critical Node Identification of Multi-UUV Formation Based on Network Structure Entropy

In order to identify and attack the multi-UUV (unmanned underwater vehicle) groups, this paper proposes a method for identifying the critical nodes of multi-UUV formations. This method helps in combating multi-UUV formations by identifying the key nodes to attack them. Moreover, these multi-UUV formations are considered to have an unknown structure as the research object. Therefore, the network structure of the formation is reconstructed according to its space–time trajectory, and the importance of nodes is determined based on network structure entropy. As for the methodology, firstly, based on the swarm intelligence behavior method, the motion similarity of multi-UUV nodes in the formation is analyzed in pairs; furthermore, the leader–follower relationship and the network structure of the formation are calculated successively. Then, based on this network structure, the importance of the network nodes is further determined by the network structure entropy method. Finally, through simulation and experiments, it is verified that the algorithm can accurately construct the network structure of the unknown multi-UUV formation, and the accuracy of the calculated time delay data reaches 84.6%, and compared with the traditional information entropy algorithm, the ordering of the important nodes obtained by this algorithm is more in line with the underwater formation network.

Cement rotary kiln temperature prediction based on time-delay calculation and residual network and bidirectional novel gated recurrent unit multi-model fusion

China's cement production energy consumption accounts for more than 10% of the country's total energy consumption, and its emissions account for about 60% of the country's industrial emissions. The temperature of a cement rotary kiln directly affects the quality of the final product, the energy consumption of a plant, and emissions. Therefore, it is necessary to measure, to a high degree of accuracy, the temperature of a cement rotary kiln to reduce energy consumption, emissions, and improve product quality. However, the conditions in cement kilns are complex and variable; and there are large time-delays, while temperatures are affected by historical working conditions, making temperature measurement extremely difficult. In this paper, a modelling algorithm with a residual network and a bidirectional novel gated cycle unit is used to predict the clinker exit temperature of the rotary kiln. In addition, to solve the problem of large time-delays in cement kilns, a dynamic time-delay calculation algorithm based on Mutual information and adaptive sliding windows is proposed for time series data reconstruction, and finally a Gaussian-weighted multi-model fusion is applied to the prediction results for cement rotary kilns that produce a wide range of working conditions. To verify the advantages and feasibility of the proposed method, experimental validation is carried out using original production data from a large cement manufacturing enterprise in South China. The results demonstrate that the single step prediction time of the presented approach is 0.66 s, which can predict cement kiln temperatures quickly and accurately, compared to a single conventional forecasting algorithm, the average temperature difference dropped from 11.76 °C to 3.51 °C, R2 increased from 0.43 to 0.89, 94% of the depicted temperature errors are at ± 10 °C This will provide an effective solution for online decision-making during actual cement production.

The influences of finite aperture size in photoacoustic computed tomography

In photoacoustic computed tomography (PACT), the “finite aperture effect” is often characterized as a tangential resolution that increases proportionally with the distance from the rotation center. However, this conclusion is based on the inaccurate point-detector assumption used in image reconstruction. In this study, we appropriately modeled the finite size of the acoustic detector in the back-projection (BP) based image reconstruction to improve the accuracy of the time delay calculation and systematically investigated its effects. Our results showed that the main effect of the finite aperture size is the creation of a limited high-quality imaging region (HQIR) around the scanning center, due to the directional sensitivity of the detector. We also demonstrated that the “finite aperture effect” can reduce the optimal number of detectors required for spatial anti-aliasing. These new findings provide novel perspectives for optimizing PACT systems and corresponding reconstruction methods.

Machine learning driven damper for response control in vehicle–bridge interaction systems

The implementation of machine learning for the real-time prediction of the suitable value of the damping ratio of a semi-active tuned mass damper (SA-TMD) is investigated to ensure enhanced vibration control in vehicle–bridge interaction (VBI) problems. The response assessment of the uncontrolled, tuned mass damper (TMD)-controlled, and SA-TMD-controlled bridge models is performed under the Japanese Shinkansen (SKS) train model. The energy-based predictive (EBP) control algorithm is implemented for the bridge fitted with the SA-TMD. The EBP algorithm-controlled SA-TMD results in more effective suppression of the bridge vibration as compared to the passive TMD. However, the effectiveness of the EBP algorithm reduces for more complex VBI systems because of the increased computational time delay. To circumvent the effect of the delay, a control strategy is proposed based on the weighted random forest (WRF) algorithm. The WRF algorithm is trained based on the data obtained from the EBP algorithm-controlled bridge and implemented to suppress the vehicle-induced vibration of the bridge using SA-TMD. The results demonstrate that the implementation of the newly proposed WRF algorithm-based control strategy nullifies the effects of the computational time delay. Furthermore, it is established that the WRF algorithm suppresses the bridge vibration more effectively than the EBP algorithm.

Research on Winter Wheat Growth Stages Recognition Based on Mobile Edge Computing

The application of deep learning (DL) technology to the identification of crop growth processes will become the trend of smart agriculture. However, using DL to identify wheat growth stages on mobile devices requires high battery energy consumption, significantly reducing the device’s operating time. However, implementing a DL framework on a remote server may result in low-quality service and delays in the wireless network. Thus, the DL method should be suitable for detecting wheat growth stages and implementable on mobile devices. A lightweight DL-based wheat growth stage detection model with low computational complexity and a computing time delay is proposed; aiming at the shortcomings of high energy consumption and a long computing time, a wheat growth period recognition model and dynamic migration algorithm based on deep reinforcement learning is proposed. The experimental results show that the proposed dynamic migration algorithm has 128.4% lower energy consumption and 121.2% higher efficiency than the local implementation at a wireless network data transmission rate of 0–8 MB/s.

Merging black holes in dwarf galaxies: calculating binary black hole coalescence time-scales from simulations forLISAdetection

ABSTRACTSupermassive black holes (SMBHs) merging in dwarf galaxies will be detectable by the Laser Interferometer Space Antenna (LISA) in the mid-2030s. Previous cosmological hydrodynamic simulations have shown the prediction of massive BHs merging in dwarf galaxies, but these simulations are limited by their resolution and cannot follow BH pairs all the way to coalescence. We calculate the delay time between BH pairing and merger based on the properties of the BHs and their host galaxies, and use these properties to calculate gravitational wave strains for eleven different binary BHs that merge inside dwarf galaxies from eight cosmological simulations. This delay time calculation accounts for dynamical friction due to gas and stars, loss-cone scattering, and hardening of the binary due to gravitational radiation. Out of the eleven BH mergers in the simulations, five BH pairs will merge within 0.8–8 Gyr of forming a close pair and could be observed by LISA, and the remaining six are unresolved due to resolution limitations of the simulation. As all five of the resolved close pairs merge within a Hubble time, we make the broad estimate that close SMBH pairs in dwarf galaxies will merge and be detectable by LISA, but this estimate depends on either the presence of gas during orbital decay or a solution to the dynamical buoyancy problem in cored potentials.

Precision in Motion: Assessing the Accuracy of LiDAR Sensors for Delay Time Calculation at Signalized Intersections

Precision in Motion: Assessing the Accuracy of LiDAR Sensors for Delay Time Calculation at Signalized Intersections

Methodology for calculating the delay time in the management system infocommunication networks based on the theory of mass service

The article is devoted to the consideration of delay time calculation methods in the M/M/m service system, the main components of the model, such as information flows and the service device, are investigated, and the delay time distribution formula based on Little’s law is improved. The importance of this indicator in determining the effectiveness of information communication systems and optimizing their functioning is noted. The article provides examples of delay time calculations and considers factors that affect this indicator, such as the workload of the telecommunications system and the characteristics of the service device. In addition, possible ways to improve the system are explored, including the use of buffers and more complex models. M/M/m is a system with m service lines, a Poisson input flow, an indicative service time distribution, and a First-Come-First-Serve (FCFS) discipline, which means that requests are served in the order in which they are received. A detailed analysis of the M/M/m model and the study of its main components allow a deeper understanding of the processes in mass service systems. The delay time in the M/M/m system is reflected in its direct impact on the quality of service and satisfaction of users of such networks

Direct and accurate calculation of dwell times and time delays using quantum trajectories

Among the numerous concepts of time in quantum scattering, Smith's dwell time (Smith, 1960 [7]) and Eisenbud & Wigner's time delay (Wigner, 1955 [12]) are the most well established. The dwell time represents the amount of time spent by the particle inside a given coordinate range (typically a potential barrier interaction region), while the time delay measures the excess time spent in the interaction region because of the potential. In this paper, we use the exact trajectory-ensemble reformulation of quantum mechanics, recently proposed by one of the authors (Poirier), to study how tunneling and reflection unfold over time, in a one-dimensional rectangular potential barrier. Among other dynamical details, the quantum trajectory approach provides an extremely robust, accurate, and straightforward method for directly computing the dwell time and time delay, from a single quantum trajectory. The resultant numerical method is highly efficient, and in the case of the time delay, completely obviates the traditional need to energy-differentiate the scattering phase shift. In particular, the trajectory variables provide a simple expression for the time delay that disentangles the contribution of the self-interference delay. More generally, quantum trajectories provide interesting physical insight into the tunneling process.

Construction and validation of a novel phased array borehole probe for ultrasonic investigations at sealing structures in radioactive repositories

A new type of ultrasonic borehole probe is developed for the quality assurance of sealing structures in radioactive waste repositories using existing research boreholes. The goal is to examine the sealing structures made of salt concrete for possible cracks, delamination, and embedded objects. A prototype probe uses 12 individual dry point contact (DPC) horizontal shear wave transducers separated by equidistant transmitter/receiver arrays, each made up of six individual transducers. It is operated with a commercially available portable ultrasonic flaw detector used in the civil engineering industry. To increase the generated sound pressure of the borehole probe, the number of transducers in the novel probe is increased to 32. In addition, timed excitation of each probe is used to direct a focused sound beam to a specific angle and distance based on calculated time delays. Hence, the sensitive test volume is limited, and the signal-to-noise ratio of the received signals is improved. This paper presents the validation of the newly developed phased array borehole probe by beam computation in CIVA software and experimental investigations on a semi-cylindrical test specimen to investigate the directional characteristics. In combination with geophysical reconstruction techniques, an optimised radiation pattern of the probe is expected to improve the signal quality and thus increase the reliability of the imaging results. This is of great importance for the construction of safe sealing structures needed for the disposal of radioactive or toxic waste.