Large Delay Research Articles

Enhanced feedback performance in off-resonance AFM modes through pulse train sampling.

Dynamic atomic force microscopy (AFM) modes that operate at frequencies far away from the resonance frequency of the cantilever (off-resonance tapping (ORT) modes) can provide high-resolution imaging of a wide range of sample types, including biological samples, soft polymers, and hard materials. These modes offer precise and stable control of vertical force, as well as reduced lateral force. Simultaneously, they enable mechanical property mapping of the sample. However, ORT modes have an intrinsic drawback: a low scan speed due to the limited ORT rate, generally in the low-kilohertz range. Here, we analyze how the conventional ORT control method limits the topography tracking quality and hence the imaging speed. The closed-loop controller in conventional ORT restricts the sampling rate to the ORT rate and introduces a large closed-loop delay. We present an alternative ORT control method in which the closed-loop controller samples and tracks the vertical force changes during a defined time window of the tip-sample interaction. Through this, we use multiple samples in the proximity of the maximum force for the feedback loop, rather than only one sample at the maximum force instant. This method leads to improved topography tracking at a given ORT rate and therefore enables higher scan rates while refining the mechanical property mapping.

Pyramid Dynamic Bayesian Networks for Key Performance Indicator Prediction in Long Time-Delay Industrial Processes

Building Dynamic Bayesian Networks (DBN) for time-delay industrial processes has always been tough, since the structure learning of DBN is a NP-hard problem. In this article, a pyramid DBN framework is proposed to speed up modeling and improve feature learning for industrial processes with large time delays. In the pyramid DBN framework, a sequence of small-sized DBNs are established, each of which is a progressively simpler representation of the previous layer, and the feature information learned by the DBN sequence corresponds to the layers in the pyramid. With information fusion of the feature pyramid and a further feature filtering with hill climbing based on Bayesian information criterion, we can restore the feature information of the time-delay industrial processes. Regression models are built based on the features learned by the pyramid DBN framework for further Key Performance Indicator (KPI) estimation. Advantages of the method have been effectively validated on two actual industrial cases. With multi-level feature learning and filtering, both modeling speed and prediction accuracy have been greatly improved. The proposed method outperforms other similar works on identifying important features in industrial processes and existing DBN-based soft sensors.

Enabling Delay-Guaranteed Congestion Control With One-Bit Feedback in Cellular Networks

Unexpected large packet delays are often observed in cellular networks due to huge network queuing caused by excessive traffic coming into the network. To deal with the large queue problem, many congestion control algorithms try to find out how much traffic the network can accommodate, either by measuring network performance or by directly providing explicit information. However, due to the nature of the control in which queue growth should be observed or the necessity to modify the overall network architecture, existing algorithms are experiencing difficulties in keeping queues within a strict bound. In this paper, we propose a novel congestion control algorithm based on simple feedback, ECLAT which can provide bounded queuing delay using only one-bit signaling already available in traditional network architecture. To do so, a base station or a router running ECLAT 1) calculates how many packets each flow should transmit and 2) analyzes when congestion feedback needs to be forwarded to adjust the flow’s packet transmission to the desired rate. Our extensive experiments in our testbed demonstrate that ECLAT achieves strict queuing delay bounds, even in the dynamic cellular network environment.

The dividend policy of business entities and the analogy of law

Author discovers uncertainty that generates ambiguous administrative and judicial law enforcement on the issue related to the limits of business entity freedom in the creation and implementation of dividend policy. Importance of such insufficiently clearly regulated by corporate legislation aspects of the dividend policy as the scope of corporation's powers and limitations in determining frequency of making decisions on dividend payments and timing of the actual distribution of profits among the corporation's participants is shown. Special relevance of the doctrinal study and regulatory clarification of the relevant legal parameters of the dividend policy of domestic corporations is associated by the author with the need to overcome the negative consequences of the sanctions impact of unfriendly foreign states. The presented author's approach is aimed at ensuring greater variability of corporate and legal interaction and increasing the efficiency of business entities. Special technical and legal tools were used in the study, including the method of analogy, as well as civilistic means of analysis and synthesis, comparison and generalization. The idea of the possibility and practicability of flexible negotiation of this uncertainty based on the normative construction of the analogy of law, on the basis of the general principles and meaning of civil legislation, as well as through the analogical use of explanations on understanding of the imperativeness and optionality of legal norms contained in the Resolution of the Russian Federation Supreme Arbitration Court Plenum dated March 14, 2014 No. 16 «On freedom of contract and its limits» is argued. As a result, conceptual admissibility of «nonmonotonic» dividend accruals, not linked to calendar quarters has been proved, as well as the possibility of large delays in the actual payment of declared dividends in comparison with the periods pre-established by norms. Main conditions for the implementation of such a measure of freedom in determining dividend policy are identified, namely its focus on achieving a balance of corporate interests and availability of an actually generated net profit for a business entity.

Methods for controlling heavy metals in environmental soils based on artificial neural networks

The problem of heavy metal pollution in soil has become a global environmental problem, and it is very important to predict and manage the heavy metals in the environmental soil in a timely manner. The changes in heavy metal content in soil have characteristics such as nonlinearity and large delay, making it difficult to predict heavy metals in soil using traditional methods. Traditional prediction methods are complex and cumbersome, which can lead to longer treatment time and easy secondary pollution. This article analyzed the Back Propagation neural network (BPNN) in artificial neural networks (ANN) and applied it to the prediction of heavy metals in environmental soils. BPNN has good nonlinear function approximation ability, so it can be well applied to complex problems such as soil heavy metal prediction. The methods of treating soil heavy metals include physical repair method, chemical repair method, microbial repair method, plant repair method, plant microbial combined repair method and so on. The use of BPNN can predict heavy metals in environmental soils through adaptive dynamic learning. However, the training time of the BPNN is relatively long and the convergence speed is relatively slow. Therefore, additional momentum terms were added to adjust the weights and thresholds of the network to improve the BPNN. In the experiment, the prediction performance of the improved BPNN was compared before and after the improvement. This article took 50 monitoring data of heavy metals in the same soil in a certain region in 2021 as sample data and predicted the content of heavy metals in the soil using improved and improved BPNN. Due to time constraints, this article only conducted experimental analysis on heavy metals such as lead and cadmium. In the first experiment, when the soil sample data was 50, the prediction accuracy of the BPNN for cadmium before and after improvement was 75.95% and 89.56%, respectively. In the second experiment, when the soil sample data was 50, the prediction accuracy of the BPNN for cadmium before and after improvement was 77.99% and 89.85%, respectively. The improved BPNN has good prediction accuracy and can effectively predict the status of heavy metals in soil. The analysis in this article can provide scientific basis for the comprehensive prevention and control of heavy metals in regional soil, and also provide reference for the development of pollution-free agriculture and ensuring food safety.

Disinfection scheduling in water distribution networks considering input time-delay uncertainty

Abstract A significant challenge when attempting to regulate the spatial-temporal concentration of a disinfectant in a water distribution network is the large and uncertain delay between the time that the chemical is injected at the input node and the time that the concentration is measured at the monitoring output nodes. Uncertain time delays are due to varying water flows, which depend mainly on consumer water demands. Existing approaches cannot guarantee that the concentration of the disinfectant will remain within a specified range at the output, even though bounds on time-delay uncertainty may be known. In this work, given bounded water-flow uncertainty, we use the input–output modeling approach to develop a disinfectant scheduling methodology that guarantees a bounded output disinfectant concentration. The proposed methodology creates an input–output model uncertainty characterization by utilizing estimated bounds on water-quality states using the backtracking approach. An optimization problem is formulated and solved to find an input schedule that keeps the disinfectant concentration within predefined bounds for a specified time horizon. Simulation results in two case studies where water demands varied between ±20% of their nominal value show that the proposed scheduler is able to avoid lower bound violations of disinfectant concentration.

High-Gain-Observer-Based Predictive Output Feedback for Nonlinear Systems With Large Input-Delays

Abstract The predictor feedback has been demonstrated to be quite effective in large delay compensation. However, few researches in the field of predictor feedback for large delays focused on output feedback control (OFC). This paper develops the previous work to design high-gain-observer-based predictive output feedback for nonlinear systems with large delays. Two methods are employed for large delay compensation: the backstepping-based partial differential equation (PDE) method and the reduction-based ordinary differential equation (ODE) method. It appears that, for continuous-time control, the first method leads to simpler linear matrix inequality (LMI) conditions and deal with larger delays, whereas the second method is easily exploited for sampled-data implementation under continuous-time measurement. Lyapunov–Krasovskii method is presented to guarantee the exponential stability of the nonlinear systems under predictor-based controllers. Through a simulation example of pendulum, the proposed methods are demonstrated to be efficient when the input delays are too large for the system to be stabilized without a predictor.

Speed Tracking and Anti-Slip Control for Heavy Freight Trains Considering the Conicity of the Wheel

To ensure the safe and punctual transportation of freight trains, it is crucial for the train to travel at the targeted speed on the track. This paper proposes a scheme for speed tracking and anti-slip control for freight trains. The speed tracking is implemented through predictive auto disturbance rejection control (PADRC), which includes a flexible Smith estimation module capable of accurately predicting the output of large time delay systems, such as freight trains. The key to anti-slip control relies on the precise observation of the radial velocity and slip rate. Therefore, an unscented Kalman filter observer is designed in this article, incorporating an adaptive parameter adjustment mechanism to enhance observation accuracy. The anti-slip parameters obtained from this observer can then be used to determine the anti-slip control scheme. The effectiveness of this scheme is demonstrated through simulations of the HXD1 electric traction locomotive’s driving process, using line data from the Geku line section in China. Compared to conventional active disturbance rejection control, PADRC reduces speed fluctuation by 55%, and freight trains under anti-slip control decrease the slip speed by 90%.

Dynamic Properties for Time-Delayed Grazing Ecosystem Driven by Lévy Noise and Gaussian Noise

In this paper, we establish a stochastic dynamical grazing ecosystem with time delays and fluctuations. The effects of time delay, Gaussian noise and Lévy noise on the stationary probability distribution (SPD), the mean first passage time (MFPT) and stochastic resonance (SR) are analyzed. Our research results show the following: (i) For small time delay, the increasing Gaussian noise intensity leads to catastrophic regime shift (CRS) from high vegetation state to low vegetation state, while the increasing Lévy noise intensity contributes to the recovery of these shifts. For large time delay, the increasing Gaussian noise intensity or Lévy noise intensity causes the CRS phenomenon, the larger the time delay, the more frequent the CRS phenomenon. (ii) The increasing Gaussian noise intensity can diminish the stability of the high vegetation state and low vegetation state, the increasing Lévy noise intensity and Lévy stability index can enhance the stability of the high vegetation state and diminish the stability of the low vegetation state. The increasing Lévy noise intensity can lead to noise-enhanced stability (NES) of the high vegetation state, and the larger the time delay, the more pronounced the NES phenomenon. (iii) The increase of time delay can weaken SR phenomenon when signal-to-noise ratio (SNR) is a function of Gaussian noise intensity and Lévy noise intensity.

Spatial diversity processing mechanism based on the distributed underwater acoustic communication system.

To address the problem of unreliable single-link underwater acoustic communication caused by large signal delays and strong multipath effects in shallow water environments, this paper proposes a distributed underwater acoustic diversity communication system (DUA-DCS). DUA-DCS employs a maneuverable distributed cross-medium buoy network to form multiple distributed, non-coherent, and parallel communication links. In the uplink, a receiving diversity processing mechanism of joint decision feedback equalizer embedded phase-locked loop and maximum signal-to-interference ratio combining (DFE-PLL-MSIRC) is proposed to achieve waveform-level diversity combining of underwater signals. A phase-locked loop module is embedded in each branch of the decision feedback equalizer to eliminate the residual frequency and phase errors after Doppler compensation. Meanwhile, the combining coefficients are determined based on the maximum signal-to-interference ratio criterion, taking into account the residual inter-symbol interference after equalization, resulting in efficient and accurate computation. Additionally, the combined decision values are fed back to the feedback filters in each branch to ensure more accurate feedback output. Simulation and lake experiment results demonstrate that, compared to the single-link communication system, DFE-PLL-MSIRC can achieve a diversity gain of more than 5.2 dB and obtain about 3 dB more diversity gain than the comparison algorithm. And the BER of DFE-PLL-MSIRC can be reduced by at least one order of magnitude, which is lower by at least 0.6 order of magnitude compared to the comparison algorithm. In the downlink, a transmitting diversity processing mechanism of complex orthogonal space-time block coding (COSTBC) is proposed. By utilizing a newly designed generalized complex orthogonal transmission matrix, complete transmission diversity can be achieved at the coding rate of 3/4. Compared to the single-link communication system, the system can achieve a diversity gain of more than 6 dB.

Parameter‐Uniform Convergent Numerical Approach for Time‐Fractional Singularly Perturbed Partial Differential Equations With Large Time Delay

In this study, we consider a parameter‐uniform convergent numerical approach for a class of time‐fractional singularly perturbed partial differential equations (TF‐SPDPDEs) with large delay in time that exhibits a regular exponential boundary layer on the right side of the spatial domain. An arbitrary very small parameter ε(0 < ε < <1) multiplies the highest‐order derivative term of these singularly perturbed problems. The time‐fractional derivative is considered in the Caputo sense with order α ∈ (0, 1). The numerical scheme comprises the L1 scheme and nonstandard finite difference method (FDM) for discretizing the time and space variables, respectively, on a uniform mesh. To show the parameter uniform convergence of the proposed method, the truncation error and stability analysis are discussed. The method is shown to be parameter‐uniform convergent of order O((Δt)2−α + Δx), where Δt and Δx are the step sizes in the time and space directions, respectively. In order to confirm the theoretical predictions, two numerical examples are presented, and the numerical results support the theoretical concepts discussed. Finally, to show the advantage of the proposed scheme, we made comparisons with the existing numerical methods in the literature, and the numerical results reveal that the present scheme is more accurate.

Exponential Extended Dissipativity Analysis of Discrete-Time Neural Networks With Large Delays

Exponential Extended Dissipativity Analysis of Discrete-Time Neural Networks With Large Delays

Leveraging Human Mobility Data for Efficient Parameter Estimation in Epidemic Models of COVID-19

Effectively predicting the evolution of COVID-19 is of great significance to contain the pandemic. Extensive previous studies proposed a great number of SIR variants, which are efficient to capture the transmission characteristics of COVID-19. However, the parameter estimation methods in previous studies are based on data from epidemiological investigations, which inevitably have caused a large delay. The popularity of digital trajectory data world-wide makes it possible to understand epidemic spreading from human mobility perspective. The major advantage of digital trajectory data lies in that the co-location level of a population is reflected at every moment, making it possible to forecast the evolution in advance. We showed that the mobility data contributed by mobile phone users could be exploited to estimate the contact probability between individuals, thus revealing the dynamic transmission of COVID-19. Specifically, we developed an estimation method to obtain human co-location levels and quantified the variations of human mobility during the epidemic. Then, we extended the infection rate with a real-time co-location level to further forecast the transmission of an epidemic, predicting the epidemic size much more accurately than conventional methods. Finally, the proposed method was applied to evaluate the quantitative effect of different non-pharmacological interventions by predicting the epidemic situations with various mobility characteristics. The empirical results and simulations corroborated our theoretical analysis, providing effective guidance to contain the pandemic.

Comparison of PI/PID/PIDD2 Controllers for Higher Order Processes

PI, PID and PIDD2 controllers are compared for higher order processes. Tuning rules with robustness constraints are first proposed in this paper for PI, PID and PIDD2 controllers. Then the tuned rules are applied to the benchmark higher order processes and a three-tank system. It is shown that higher order derivatives can improve the control performance for processes with small time delays, while it is of no significant performance improvement for processes with large delays.

Quasinormal forms for systems of two equations with large delay

A system of two equations with delay is considered. The purpose of this work is to study the local dynamics of this system under the assumption that the delay parameter is sufficiently large. Critical cases in the problem of stability of an equilibrium state are identified and it is shown that they have infinite dimension. Methods. The research is based on the use of special methods of infinite-dimensional normalization. Classical methods based on the application of the theory of invariant integral manifolds and normal forms turn out to be directly inapplicable. Results. As the main results, special nonlinear boundary value problems are constructed, which play the role of normal forms. Their nonlocal dynamics determine the behavior of all solutions of the original system in the vicinity of the equilibrium state.

Synthesis of high-quality nano-H-ZSM-5 single crystals zeolite from montmorillonite and MTO catalytic studies

Clay is a rich aluminosilicate resource with unique advantages as a raw material for zeolites production. However, synthesizing nanosized single-crystalline zeolites through a simple approach using clay remains a significant challenge. In this paper, acid-washed montmorillonite (MSR) was used to prepare nano-H-ZSM-5 single crystals with high crystallinity, uniform grain size and permeable straight pores under the induction of a small amount of organic template tetrapropylammonium hydroxide (TPAOH). Through temperature-staged treatment, the nucleation and growth process of zeolite were decoupled, ultimately achieving precise control over the size of the nano-single crystals. Mechanistic study showed that the low-temperature aging process produces smaller structural units which probably derived from the geopolymer topology of the clay, providing more active sites for zeolite nucleation, leading to the rapid crystallization during the high-temperature process. The porous structure promotes material diffusion, inhibits aromatic cycling and reduces carbon formation in the pores. The large specific surface area and nanoscale multilayer pore structure delay carbon accumulation and increase carbon holding capacity. These properties make nano-ZSM-5 single crystals an excellent choice for high olefin selectivity and long catalytic life in the methanol-to-olefin reaction. This synthetic route provides a general strategy for preparing nanosized zeolite single crystals from clay.

Surface hopping molecular dynamics simulation of ultrafast methyl iodide photodissociation mapped by Coulomb explosion imaging.

We present a highly efficient approach to directly and reliably simulate photodissociation followed by Coulomb explosion of methyl iodide. In order to achieve statistical reliability, more than 40 000 trajectories are calculated on accurate potential energy surfaces of both the neutral molecule and the doubly charged cation. Non-adiabatic effects during photodissociation are treated using a Landau-Zener surface hopping algorithm. The simulation is performed analogous to a recent pump-probe experiment using coincident ion momentum imaging [Ziaee et al., Phys. Chem. Chem. Phys., 2023, 25, 9999-10010]. At large pump-probe delays, the simulated delay-dependent kinetic energy release signals show overall good agreement with the experiment, with two major dissociation channels leading to I(2P3/2) and I*(2P1/2) products. At short pump-probe delays, the simulated kinetic energy release differs significantly from the values obtained by a purely Coulombic approximation or a one-dimensional description of the dicationic potential energy surfaces, and shows a clear bifurcation near 12 fs, owing to non-adiabatic transitions through a conical intersection. The proposed approach is particularly suitable and efficient in simulating processes that highly rely on statistics or for identifying rare reaction channels.

A performant PID autotuner based on a short relay test

Industrial applications encompass a wide range of fields, including chemical plants, biomedical engineering, vibration control, and mechanical systems, among others. Frequently, controller tuning is performed using a process model as a basis. Determining such a model can be a time-consuming and challenging task, requiring proper identification. Nonlinear processes often have a limited number of relevant operating points. Autotuning methods that collect pertinent process information at specific operating points and determine controller parameters automatically streamline the controller design process by eliminating the need for process modelling. This paper presents a novel relay based autotuning methodology for Proportional Integral Derivative (PID) controllers highly suitable for Industry 5.0 applications that is easily implemented. The methodology is successfully validated on two numerical examples consisting of a highly oscillatory process and a process with large time delay. Additionally, two experimental validations are presented for a mass-spring-damper system and a 2-tank process.

FLLF: A Fast-Lightweight Location Detection Framework for False Data Injection Attacks in Smart Grids

This paper proposes a fast-lightweight location detection framework (FLLF) for false data injection attacks (FDIAs). The location detection of false data injection attacks is traditionally realized by computationally intensive neural networks, which can cause large detection delays due to the limited computation power and storage resources of the detection device. By contrast, the proposed method is lightweight and can be deployed on low-cost devices such as embedded devices, significantly improving detection speed while reducing power consumption. The proposed method consists of an efficient FDIA location detection model and an automatic model search algorithm. The model is a lightweight model that enables fast and accurate attack location detection through a combination of the early exiting mechanism and mixed-precision quantization (EE-MPQ). Then, an automatic model search algorithm is designed to enable the EE-MPQ model to match the smart grids with different structures. Finally, it is shown that the proposed method can accurately detect and locate FDIAs through numerical analysis on the IEEE 14-bus and 118-bus power systems.

Event-Triggered Multiasynchronous H∞ Control for Markov Jump Systems With Transmission Delay.

In this article, the issue of event-triggered multiasynchronous H∞ control for Markov jump systems with transmission delay is concerned. In order to reduce sampling frequency, multiple event-triggered schemes (ETSs) are introduced. Then hidden Markov model (HMM) is employed to describe multiasynchronous jumps among subsystems, ETSs, and controller. Based on the HMM, the time-delay closed-loop model is constructed. In particular, when triggered data are transmitted over networks, a large transmission delay may cause disorder of transmission data such that the time-delay closed-loop model cannot be developed directly. To overcome this difficulty, a packet loss schedule is presented and the unified time-delay closed-loop system is obtained. By the use of the Lyapunov-Krasovskii functional method, sufficient conditions with the controller design are formulated for guaranteeing the H∞ performance of the time-delay closed-loop system. Finally, the effectiveness of the proposed control strategy is demonstrated by two numerical examples.