Time Delay Prediction Research Articles

A Machine Learning Approach to Predict Time Delays in Marine Construction Projects

A Machine Learning Approach to Predict Time Delays in Marine Construction Projects

Prediction of dynamical systems from time-delayed measurements with self-intersections

In the context of predicting the behaviour of chaotic systems, Schroer, Sauer, Ott and Yorke conjectured in 1998 that if a dynamical system defined by a smooth diffeomorphism T of a Riemannian manifold X admits an attractor with a natural measure μ of information dimension smaller than k, then k time-delayed measurements of a one-dimensional observable h are generically sufficient for μ-almost sure prediction of future measurements of h. In a previous paper we established this conjecture in the setup of injective Lipschitz transformations T of a compact set X in Euclidean space with an ergodic T-invariant Borel probability measure μ. In this paper we prove the conjecture for all (also non-invertible) Lipschitz systems on compact sets with an arbitrary Borel probability measure, and establish an upper bound for the decay rate of the measure of the set of points where the prediction is subpar. This partially confirms a second conjecture by Schroer, Sauer, Ott and Yorke related to empirical prediction algorithms as well as algorithms estimating the dimension and number of required delayed measurements (the so-called embedding dimension) of an observed system. We also prove general time-delay prediction theorems for locally Lipschitz or Hölder systems on Borel sets in Euclidean space.

Parameter-free Hubble constant from the quadruply lensed quasar SDSS J1004+4112

We present a free-form lens model for the multiply lensed quasar in the galaxy cluster SDSS J1004+4112. Our lens model draws minimal assumptions on the distribution of mass in the lens plane. We have paid particular attention to the model uncertainties on the predicted time delay originating from the particular configuration of model variables. Taking into account this uncertainty, we obtained a value of the Hubble constant of H0 = 74−13+9 km s−1 Mpc−1, which is consistent with recent independent estimates. The predicted time delay between the central image E and image C (the first to arrive) is ΔTE−C = 3200 ± 200 days for the estimated Hubble constant. Future measurements of ΔTE−C will allow for a tighter constraint to be imposed on H0 in this cluster-QSO system.

Dynamic Switching Control of Power-Split Hybrid Electric Vehicles Based on Time Delay Prediction and Interference Compensation

The deterioration of vehicle dynamics caused by the simultaneous action of time delay and interference is a key issue for the switching control system in a power-split hybrid electric vehicle (PS-HEV). This paper presents an innovative compound control method based on extended state observer (ESO) incorporating time delay during the mode transition process. First, the coupling effect of time-varying delay and external disturbance during the typical mode switching process is analyzed in depth, and the double deterioration mechanism of the two coupling effects on vehicle dynamics and ride comfort is revealed. To accurately estimate the system disturbance under the influence of time delay, an unconventional ESO is designed further by adopting the Markov chain time delay prediction model, and the main tool for interference observation is selected by comparing the practicability, reachable bandwidth and observation accuracy. Moreover, the stable mode switching performance and interference suppression are thoroughly investigated, and the smooth transmission of multi-power is realized through the multi-module fusion design of the compound coordinated controller. Finally, the hardware-in-the-loop test is implemented to validate the effectiveness of the control strategy, which also provides a novel idea and lays a theoretical foundation for the optimal design of the PS-HEV controller.

Gear Up for the Action Replay: Leveraging Lensing for Enhanced Gravitational-wave Early Warning

Premerger gravitational-wave (GW) sky localization of binary neutron star (BNS) and neutron star–black hole (NSBH) coalescence events would enable telescopes to capture precursors and electromagnetic (EM) emissions around the time of the merger. We propose an astrophysical scenario that could provide early-warning times of hours to days before coalescence with subarcsecond localization, provided that these events are gravitationally lensed. The key idea is that if the BNS/NSBH is lensed, then so is the host galaxy identified via the EM counterpart. From the angular separation of the lensed host galaxy images, as well as its redshift and the (foreground) lens redshift, we demonstrate that, for galaxy-scale lenses, we can predict the time delays/arrival time differences assuming a standard lens model. We further assess the feasibility and benefits of lensing as a tool for early warning in various GW observing runs of the LIGO–Virgo–Kagra network, including Voyager and the third-generation network. To that end, we study the effect of the limited angular resolution of the telescopes on our ability to predict the time delays. We find that with an angular resolution of 0.05″ we can predict time delays of >1 day with 1σ error bar of (hours) at best. We also construct realistic time delay distributions of detectable lensed BNSs/NSBHs to forecast the early-warning times we might expect in the observing scenarios we consider.

COOL-LAMPS. Discovery of COOL J0335−1927, a Gravitationally Lensed Quasar at z = 3.27 with an Image Separation of 23.″3

We report the discovery of COOL J0335−1927, a quasar at z = 3.27 lensed into three images with a maximum separation of 23.″3 by a galaxy cluster at z = 0.4178. To date, this is the highest redshift wide-separation lensed quasar known. In addition, COOL J0335−1927 shows several strong intervening absorbers visible in the spectra of all three quasar images with varying equivalent widths. The quasar also shows mini-broad line absorption. We construct a parametric strong gravitational lens model using ground-based imaging, constrained by the redshift and positions of the quasar images as well as the positions of three other multiply imaged background galaxies. Using our best-fit lens model, we calculate the predicted time delays between the three quasar images to be Δt AB = (stat) and Δt AC = (stat) days. Folding in systematic uncertainties, the model-predicted time delays are within the ranges 240 < Δt AB < 700 and −300 < Δt AC < −30. We also present g-band photometry from archival Dark Energy Camera Legacy Survey and Pan-STARRS imaging, and new multi-epoch observations obtained between 2022 September 18 UT and 2023 February 22 UT, which demonstrate significant variability in the quasar and will eventually enable the measurement of the time delay between the three quasar images. The currently available light curves are consistent with the model-predicted time delays. This is the fifth paper from the COOL-LAMPS collaboration.

Prediction and Control of Small Deviation in the Time-Delay of the Image Tracker in an Intelligent Electro-Optical Detection System

A small deviation in the time-delay of the image tracker is essential for improving the tracking precision of an electro-optical system, and for future advances in actuator technology. The core goal of this manuscript is to address issues such as tracking the controller time-delay compensation and the precision of an electro-optical detection system using an advanced filter design, a fire control modeling, and an anti-occlusion target detection system. To address this problem, a small deviation in the time-delay prediction and control method of the image tracker is proposed based on the principle of linear motion transformation. The time-delay error formation is analyzed in detail to reveal the scientific mechanism between the tracking controller feedback and the line-of-sight position correction. An advanced N-step Kalman filtering controller model is established by combining a line-of-sight firing control judgment and a single-sample training anti-occlusion DSST target tracking strategy. Finally, an actuator platform with three degrees of freedom is used to test the optical mechatronics system. The results show that the distribution probability of the line-of-sight measuring error in a circle with a radius of 0.15 mrad is 72%. Compared with the traditional control method, the tracking precision of the optimal method is improved by 58.3%.

A Time Delay Prediction Model of 5G Users Based on the BiLSTM Neural Network Optimized by APSO-SD

To address the problems of 5G network planning and optimization, a 5G user time delay prediction model based on the BiLSTM neural network optimized by APSO-SD is proposed. First, a channel generative model based on the ray-tracing model and the statistical channel model is constructed to obtain a large amount of time delay data, and a 5G user ray data feature model based on three-dimensional stereo mapping is proposed for input feature extraction. Then, an adaptive particle swarm optimization algorithm based on a search perturbation mechanism and differential enhancement strategy (APSO-SD) is proposed for the parameters’ optimization of BiLSTM neural networks. Finally, the APSO-SD-BiLSTM model is proposed to predict the time delay of 5G users. The experimental results show that the APSO-SD has a better convergence performance and optimization performance in benchmark function optimization compared with the other PSO algorithms, and the APSO-SD-BiLSTM model has better user time delay prediction accuracy in different scenarios.

High telepresence control for the relay communication based space teleoperation

This paper studies the high telepresence control for the relay communication based space teleoperation. High telepresence requires real-time, accurate force perception and good position tracking, but all of them are destroyed or affected by time delay. To eliminate the influence of time delay, direct recursive generalized predictive control (DR-GPC) is quoted into this paper to realize position tracking. As for the other two properties, the pseudo position-force (Pseudo-PF) feedback strategy (composed of parameter identification and force reconstruction) is a good way as it can provide real-time and precise force feedback when environment change slowly and time delay is relatively small. However, complex and changeable environment and serious and complex time delay in space teleoperation make the environment force parameter available to force reconstruction is outdated and cannot realize accurate force rebuilding. To solve this problem, we propose a dual regression Pseudo-PF feedback strategy. Compared with the traditional one, its biggest change is the introduction of dual regression that consists of time delay prediction and parameter regression. The former predicts time delay and provides it to the latter to determine the forward prediction steps, while the latter regresses the actual latest and precise environment force parameter according to the forward prediction steps. With the cooperation of time delay prediction and parameter regression, the influence of fast changing environment or serious and complex time delay on force reconstruction can be effectively overcome, so as to realize real-time and accurate force feedback. Finally, DR-GPC and dual regression Pseudo-PF feedback strategies cooperate to achieve high telepresence control, and simulation is given to show its effectiveness.

TDCOSMO

When strong gravitational lenses are to be used as an astrophysical or cosmological probe, models of their mass distributions are often needed. We present a new, time-efficient automation code for the uniform modeling of strongly lensed quasars withGLEE, a lens-modeling software for multiband data. By using the observed positions of the lensed quasars and the spatially extended surface brightness distribution of the host galaxy of the lensed quasar, we obtain a model of the mass distribution of the lens galaxy. We applied this uniform modeling pipeline to a sample of nine strongly lensed quasars for which images were obtained with the Wide Field Camera 3 of theHubbleSpace Telescope. The models show well-reconstructed light components and a good alignment between mass and light centroids in most cases. We find that the automated modeling code significantly reduces the input time during the modeling process for the user. The time for preparing the required input files is reduced by a factor of 3 from ~3 h to about one hour. The active input time during the modeling process for the user is reduced by a factor of 10 from ~ 10 h to about one hour per lens system. This automated uniform modeling pipeline can efficiently produce uniform models of extensive lens-system samples that can be used for further cosmological analysis. A blind test that compared our results with those of an independent automated modeling pipeline based on the modeling softwareLenstronomyrevealed important lessons. Quantities such as Einstein radius, astrometry, mass flattening, and position angle are generally robustly determined. Other quantities, such as the radial slope of the mass density profile and predicted time delays, depend crucially on the quality of the data and on the accuracy with which the point spread function is reconstructed. Better data and/or a more detailed analysis are necessary to elevate our automated models to cosmography grade. Nevertheless, our pipeline enables the quick selection of lenses for follow-up and further modeling, which significantly speeds up the construction of cosmography-grade models. This important step forward will help us to take advantage of the increase in the number of lenses that is expected in the coming decade, which is an increase of several orders of magnitude.

A VLA monitoring study of JVAS B1422+231: investigation of time delays and detection of extrinsic variability

ABSTRACTWe present an analysis of two seasons of archival, multifrequency VLA monitoring of the quad lens system JVAS B1422+231, the 15-GHz data of which have previously been published. The 8.4- and 15-GHz variability curves show significant variability, especially in polarization, but lack features on short time-scales that would be necessary for an accurate measurement of the very short predicted time delays ($\lesssim$1 d) between the three bright images. Time delays can only realistically be measured to the very faint image D and for the first time we detect its long-term variability and determine its polarization properties. However, image-dependent (extrinsic) variability (including variations on time-scales of hours) is present in multiple images and the magnitude of this is largest in image D at 15 GHz (±10 per cent). As the variations appear to increase in amplitude with frequency, we suggest that the most likely cause is microlensing by compact objects in the lensing galaxy. Combining the monitoring data allows us to detect a faint arc of emission lying between images B and C and the jets responsible for this are imaged using archival VLBA data. Finally, we have also measured the rotation measure of the three bright images and detected the polarization properties of image D.

Prediction of ignition time delay in a diesel engine when using composite fuel

BACKGROUND: Analytical determination of ignition time delay (ITD) which would give a prediction of ignition in diesel engines of various types using composite fuel (CF) of various kinds and compositions depending on loading and speed operation modes is a relevant issue.&#x0D; AIMS: Determination of the ITD values for a diesel engine at all loading and speed operation modes with using various kinds and compositions of CF.&#x0D; METHODS: In order to determine ITD, theoretical and experimental studies of the D-245.582 tractor diesel engine of 4ChN 11.0/12.5 size with using compositions of CF including diesel oil (DO) and rapeseed oil (RO) or ethanol (E) were carried out. As a result of the studies, the diesel engine ITD predictive indicators determination method considering dependence on loading and speed operation modes and CF kind and composition has been developed.&#x0D; RESULTS: Calculated values of the ITD numerical relationships revealed that the load (pe) increase from 0.2 to 1.0 MPa, the engine speed (n) decrease from 1800 to 1400 min-1 and decrease of mass fraction of rapeseed oil (RO) and ethanol (E) in CF from 40 to 20% lead to diesel engine ITD decrease from 7.69 to 4.82 degrees of crankshaft rotation and from 11.70 to 7.92 degrees of crankshaft rotation respectively. It was experimentally found that the load (pe) increase from 0.2 to 1.0 MPa, the engine speed (n) decrease from 1800 to 1400 min-1 and decrease of mass fraction of rapeseed oil (RO) and ethanol (E) in CF from 40 to 20% lead to diesel engine ITD decrease from 7.59 to 4.22 degrees of crankshaft rotation and from 11.20 to 7.72 degrees of crankshaft rotation respectively. The degree of convergence of the experimental data with the calculated values, assessed with a statistical processing method and experimental error calculation, is 94.04%.&#x0D; CONCLUSIONS: Practical value of the study lies in prediction of diesel engine ITD with using composite fuels of various kinds and compositions at all loading and speed operation modes.

Modeling time delays from two reprocessors in active galactic nuclei

Context. Continuum time delays from accretion disks in active galactic nuclei (AGNs) have long been proposed as a tool for measuring distances to monitored sources. However, the method faces serious problems as a number of effects must be taken into account, including the contribution from the broad line region (BLR). Aims. In this paper, we model the expected time delays when both the disk reprocessing of the incident X-ray flux and further reprocessing by the BLR are included, with the aim to see whether the two effects can be disentangled. Methods. We used a simple response function for the accretion disk, without relativistic effects, and we used a parametric description to account for the BLR contribution. We included only the scattering of the disk emission by the BLR inter-cloud medium. We also used artificial light curves with one-day samplings to check whether the effects are likely to be seen in real data. Results. We show that the effect of the BLR scattering on the predicted time delay is very similar to the effect of the rising height of the X-ray source, without any BLR contribution. This brings additional degeneracy for potential applications in the future, when attempting to recover the parameters of the system from the observed time delays in a specific object. Both effects, however, modify the slope of the delay-versus-wavelength curve when plotted in log space, which opens a way to obtaining bare disk time delay needed for cosmology. In addition, when the disk irradiation is strong, the modification of the predicted delay by the BLR scattering and by X-ray source height become considerably different. In this regard, simulations of the expected bias are also presented.

Networked Control System Based on PSO-RBF Neural Network Time-Delay Prediction Model

To satisfy the requirement of real-time and accurate control of the system, a time-delay prediction control system based on the PSO-RBF neural network model is established to solve the effect of time delay on the control system’s performance. Firstly, a network control model with a time delay is established to predict the control system’s output to solve the uncertainty of the output time delay. Secondly, an improved offline prediction model of RBF networks is proposed to solve the problem of the low accuracy of time-delay prediction in PSO-RBF networks. To solve the problem that the PSO algorithm is prone to fall into local optimality, a nonlinear adjustment formula for the parameters of the PSO algorithm based on the number of iterations is proposed, and the TS algorithm is used to make the optimal global solution. Finally, in order to compensate for the problem of time delay, an online RBF network prediction controller is designed, the parameters of the online RBF network are adjusted by the gradient descent method, and a target function with the differential component is proposed to evaluate the optimization effect of the rolling optimization stage. The results from the true-time simulation platform show that the delay prediction control system based on the PSO-RBF network model proposed in this paper improves the IAE by 59.9% and 31.7%, respectively, compared to the traditional PID controller and fuzzy PID control under the influence of uncertainty disturbances. Therefore, the time-delay prediction control system proposed in this paper has good control capability for the time-delay compensation problem and system output.

The Cosmic Telescope That Lenses the Sunburst Arc, PSZ1 G311.65–18.48: Strong Gravitational Lensing Model and Source Plane Analysis* *Based on observations made with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with programs GO-15101,

We present a strong-lensing analysis of the cluster PSZ1 G311.65−18.48, based on Hubble Space Telescope imaging, archival VLT/MUSE spectroscopy, and Chandra X-ray data. This cool-core cluster (z = 0.443) lenses the brightest lensed galaxy known, dubbed the “Sunburst Arc” (z = 2.3703), a Lyman continuum (LyC) emitting galaxy multiply imaged 12 times. We identify in this field 14 additional strongly lensed galaxies to constrain a strong-lens model and report secure spectroscopic redshifts of four of them. We measure a projected cluster core mass of M(<250 kpc) = M ⊙. The two least magnified but complete images of the Sunburst Arc’s source galaxy are magnified by ∼13×, while the LyC clump is magnified by ∼4–80×. We present time delay predictions and conclusive evidence that a discrepant clump in the Sunburst Arc, previously claimed to be a transient, is not variable, thus strengthening the hypothesis that it results from an exceptionally high magnification. A source plane reconstruction and analysis of the Sunburst Arc finds its physical size to be 1 × 2 kpc and that it is resolved in three distinct directions in the source plane, 0°, 40°, and 75° (east of north). We place an upper limit of r ≲ 50 pc on the source plane size of unresolved clumps and r ≲ 32 pc for the LyC clump. Finally, we report that the Sunburst Arc is likely in a system of two or more galaxies separated by ≲6 kpc in projection. Their interaction may drive star formation and could play a role in the mechanism responsible for the leaking LyC radiation.

TDCOSMO

The importance of alternative methods for measuring the Hubble constant, such as time-delay cosmography, is highlighted by the recent Hubble tension. It is paramount to thoroughly investigate and rule out systematic biases in all measurement methods before we can accept new physics as the source of this tension. In this study, we perform a check for systematic biases in the lens modelling procedure of time-delay cosmography by comparing independent and blind time-delay predictions of the system WGD 2038−4008 from two teams using two different software programs: GLEE and LENSTRONOMY. The predicted time delays from the two teams incorporate the stellar kinematics of the deflector and the external convergence from line-of-sight structures. The un-blinded time-delay predictions from the two teams agree within 1.2σ, implying that once the time delay is measured the inferred Hubble constant will also be mutually consistent. However, there is a ∼4σ discrepancy between the power-law model slope and external shear, which is a significant discrepancy at the level of lens models before the stellar kinematics and the external convergence are incorporated. We identify the difference in the reconstructed point spread function (PSF) to be the source of this discrepancy. When the same reconstructed PSF was used by both teams, we achieved excellent agreement, within ∼0.6σ, indicating that potential systematics stemming from source reconstruction algorithms and investigator choices are well under control. We recommend that future studies supersample the PSF as needed and marginalize over multiple algorithms or realizations for the PSF reconstruction to mitigate the systematics associated with the PSF. A future study will measure the time delays of the system WGD 2038−4008 and infer the Hubble constant based on our mass models.

An improved grey multivariable time-delay prediction model with application to the value of high-tech industry

To analyse the time lag effects between independent variables and dependent variables, we propose a discrete time-delay grey multivariable modelDMTGM1,Nτ. There are three improvements in this new model compared to the existing models. First, the time lag parameters are assigned different values for each independent variable. A linear correction term expands the new model. Second, with the given time lag, the least square method can be used to calculate the parameter vector. The time response function of DMTGM1,Nτ is generated, which has the advantage of eliminating the jumping errors between discrete and continuous functions over the existing grey forecasting models. Third, all of the feasible combinations of the time lag parameters are compared by using a traversal algorithm to identify the best values with the minimized mean absolute percentage error (MAPE). In three different case studies, the performance of the new model is evaluated and compared to that of a number of mainstream grey models as well as non-grey models. According to the findings, the newly designed model performs significantly better than the compared models.

Digital Twin Based Network Latency Prediction in Vehicular Networks

Network latency is a crucial factor affecting the quality of communications networks due to the irregularity of vehicular traffic. To address the problem of performance degradation or instability caused by latency in vehicular networks, this paper proposes a time delay prediction algorithm, in which digital twin technology is employed to obtain a large quantity of actual time delay data for vehicular networks and to verify autocorrelation. Subsequently, to meet the prediction conditions of the ARMA time series model, two neural networks, i.e., Radial basis function (RBF) and Elman networks, were employed to construct a time delay prediction model. The experimental results show that the average relative error of the RBF is 7.6%, whereas that of the Elman-NN is 14.2%. This indicates that the RBF has a better prediction performance, and a better real-time performance than the Elman-NN.

Space-Air-Ground Integrated Network Resource Allocation Based on Service Function Chain

Future communication networksrequire higher bandwidth, greater coverage, and better throughput. The Space-Air-Ground Integrated Network (SAGIN) has the advantage of wide-area coverage and can cover global communications. It can meet the needs of network resources for maritime activities and remote mountainous areas, which is of great significance to the realization of a new generation of communications networks. With the development of the network and the emergence of delay-sensitive applications such as the Internet of Things, improving the delay performance of the system has received extensive attention. SAGIN involves multiple networks and is more complex than other networks. If there is no reasonable management between different networks, it is easy to lead to difficult link deployment and high time delay. On the basis of research and customization of SFC technology, it can provide a wide range of services and other advantages. It has significant performance in application scenarios involving multi network integration. In order to solve the above problems, this paper studies the SAGIN architecture of SFC based on business types. A service function chain mapping method based on delays prediction is proposed. Calculate the delay of the deployment path and select the path with the lowest delay as the SFC mapping path. The service model is constructed according to the mapping path, and the network slices are divided based on the service type. The simulation results show that the SFC mapping algorithm based on time delay prediction is compared with the traditional SFC mapping scheme. The algorithm does not affect other indicators, the CPU resource utilization rate is 27.8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> higher, and the link resource utilization rate is 22.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> higher. The service acceptance rate increased by 21.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , the latency performance increased by 38.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , and the total resource consumption is reduced by 25.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

URLLC occasional large time delay prediction based on unbalanced regression and LSTM

Occasional large time delay is one of the bottlenecks for 5G applied to the industrial field. Accurate measurement, analysis, and prediction of communication network latency are of great significance. Due to the strong randomness and small number of samples in ultra-reliable and low latency communication (URLLC), the prediction task of occasional large time delay is very challenging. This paper proposes an URLLC occasional large time delay prediction method based on unbalanced regression algorithms and long–short term memory (LSTM). We first decompose the original sequence by using the variational mode decomposition (VMD) to obtain relatively stable component sequences. The parameters of the VMD are automatically optimized by grasshopper optimization algorithm (GOA). In order to improve the prediction effect of URLLC occasional large time delay, we introduce unbalanced regression algorithms. Before the VMD decomposition of data samples, the SMOGN is introduced to balance the number of large-time delay samples and common-time delay samples. Next we calculate the weight of each sample based on the rarity of each sample point and our proposed LDSWeight. Finally, LSTM is used to complete cost-sensitive learning. The experimental results show that the URLLC occasional large time delay prediction method proposed in this paper has better prediction accuracy than other methods.