Time Delay Characteristics Research Articles

Mining Sequential Alarm Pattern Based on the Incremental Causality PrefixSpan Algorithm

Alarm flood is the main reason for the alarm system failure in the modern industry, so suppressing the subsequent alarms caused by anomalous propagation during the alarm flooding is an important issue. An incremental causality prefixSpan malgorithm is proposed to mine the frequent and sequence alarm patterns from the alarm flood, and dynamically update the old alarm mode from the new database. First, the alarm event is re-defined and the detection of alarm flood is formulated. The frequent alarm pattern with causality is expressed with the orderly and time delay features of sequential alarms. Under the new definition of alarm pattern, causality prefixSpan algorithm is proposed by introducing the time constraints, i.e., the causality. Furthermore, a new incremental mining strategy is co-implemented on the improved algorithm. When new alarm flooding data occurs, the old alarm modes are quickly updated without re-scanning the entire changing database. The incremental strategy makes the proposed method easily implemented in the real operation. The effectiveness of the proposed method is verified with the synthetic data sets and Tennessee-Sterman (TE) process.

Time Delay Characteristics Analysis of UWB Diffraction Propagation in Indoor NLOS Environment

Time Delay Characteristics Analysis of UWB Diffraction Propagation in Indoor NLOS Environment

A Novel Model Prediction and Migration Method for Multi-Mode Nonlinear Time-Delay Processes

Most industrial processes have nonlinear and time-delay characteristics leading to difficulty in prediction modeling. In addition, the working conditions of most industrial processes are complex, which results in multiple modes. Testing and modeling for each mode is a waste of time and resources. Therefore, it is urgent to complete model migration between different modes. In this work, a new prediction model, a nonlinear autoregressive model with exogenous inputs and back propagation neural network (NARX-BP), is proposed for the nonlinear and time-delay processes, where the input data order of the model is determined by the feedforward neural network (FNN) method, and the nonlinear relation is realized by the BP neural network. For the multi-mode characteristic, a new migration optimization algorithm, input–output slope/bias correction and differential evolution (IOSBC-DE), is provided for using a small amount of data under a new mode to correct the slope and bias of the relationship between the input and output variables through DE. The modeling and migration methods are applied to a wind tunnel system, and the simulation result shows the effectiveness of the proposed method.

In-Vehicle Network Average Response Time Analysis for CAN-FD and Automotive Ethernet

In-vehicle network (IVN) technologies are changing corresponding to the rapidly increasing demand in autonomous driving and vehicle electrification. Due to the increase in real-time data generated from the control modules and sensors, controller area networks (CANs) have a heavier burden to satisfy the strict delay bounds. Therefore, CAN with flexible data rate (CAN-FD) and Automotive Ethernet (AE) have drawn attention as promising IVNs. This paper provides an overview of the IVN transmission protocols, time delay characteristics, and analysis methods. Most of the existing IVN timing analysis studies assume the worst case and consider only the occurrence of periodic or aperiodic messages, however for more accurate control, the average time delay needs to be accurately estimated. In this paper, CAN, CAN-FD, and AE analytical models based on queueing analysis are proposed to estimate the average response time for mixed traffic. For CAN and CAN-FD systems, the network synchronization statuses are considered and AE with 1-level preemption to support deterministic delays is analyzed. The improved accuracy of the newly derived analytical models is verified based on comparison with existing CAN queueing models through simulation experiments.

Optimal Control of VAV Air Conditioning Terminal in the Polar Cruise Public Area

As a key system to ensure residential comfort, the control mode of the polar cruise air conditioning system directly relates to the comfort of the cabin environment and the economy of ship operation. Aiming at the characteristics of uncertainty and regulation time delay in the load change of the VAV air conditioning system in the public area of polar cruise ships, this paper models the mechanism of the VAV terminal system and conducts simulation experiments on the Matlab/Simulink simulation platform. By comparing the difference between the traditional PID control strategy and the fuzzy PID control strategy in the control effect, it provides a reference for the optimal control of the polar cruise air conditioning system.

Intelligent train stopping control for railways: A deep learning approach

AbstractStation parking accuracy is an important indicator for the automatic control of high‐speed trains; however, it is subject to many influencing factors, such as the characteristics of high nonlinearity and large time delays in the train control model, time‐variant humidity, and uncertain weather conditions, leading to unsatisfying performance with existing feedback control algorithms. This study first proposes an intelligent train stopping control approach based on deep learning for high‐speed railways. By collecting a large amount of historical train operation data from the Beijing–Shenyang high‐speed railway, three data‐driven models are developed for train stopping control. The first model is based on a deep‐layered feedforward neural network (DNN), which predicts the exact train stopping position with dynamic train running states (position, velocity etc.) as input. Taking advantage of the physical train control models used in practice, the DNN to a convolutional neural network (CNN) is then improved by customizing the convolutional layers of the CNN. To overcome the issues arising from the incompleteness of data samples, a few‐shot convolutional neural network (FSCNN) is further developed to enhance the prediction performance of the CNN. Compared with that of the current method used in practice, the simulation experiments show that the train station parking error can be decreased by 38.6%, 42.8%, and 49.2% by our developed DNN, CNN, and FSCNN, respectively.

Resilience assessment methodology of natural gas network system under random leakage

Natural gas accounts for more than 20% of global primary energy demand. Potential natural or man-made disasters can cause natural gas supply shortages, with severe economic and social consequences. System resilience is difficult to achieve because of many factors affecting the natural gas network system (NGNS) and the uncertainty of the timing, location, and shock strengths. A new methodology for evaluating the resilience of NGNS under random leakage conditions is developed by combining Monte Carlo simulation and hydraulic analysis of an unsteady flow. A resilience metric based on gas supply capacity and reliability is proposed. The extensive time delay and nonlinear dynamic characteristics of the system caused by the compressibility of the natural gas are considered in combination with hydraulic analysis. System uncertainty in damage and recovery states is resolved by batch simulation of the random leakage. The resilience value is calculated by integrating the performance curve. The proposed methodology is demonstrated on the NGNS of Shandong Province, China, and results show that it can effectively assess the resilience of the NGNS under random leakage conditions.

Resilient Load Frequency Control of Power Systems to Compensate Random Time Delays and Time-Delay Attacks

Load frequency control (LFC) of modern power systems tends to employ open communication networks to transmit measurement/control signals, which makes the LFC scheme more vulnerable to random time delays and time delay attacks. In this paper, a resilient and active time-delay-compensation-based LFC scheme is proposed to compensate the random time delays and time delay attacks. At first, a state observer is employed to estimate the state of the LFC system. Then a networked predictive control method is used to predict the control signals of the system at the future moments. Next, an evaluation and compensation scheme for random time delays and time delay attacks is constructed in the actuator side of the LFC scheme based on the updating period of the actuator and the timestamp technique. Due to the stochastic characteristics of the random time delays or TDAs, the stability condition of the proposed scheme is developed with the aid of the mean square stability theory. Moreover, a dual-loop open communication is employed in the proposed scheme to improve the reliability and resilience. At last, simulation and experiment tests are undertaken to demonstrate the effectiveness of the proposed scheme.

Intelligent Temperature Control of Pressure Gauge Calibration Based on ANT-LION Neural Network

Aiming at the actual industrial requirements of high accuracy and high efficiency in the industrial process of pressure gauge calibration, this paper studies the intelligent temperature control of the pressure gauge calibration system. Based on the physical mechanism of the calibration system, the controlled object model is established. According to the characteristics of large time delay and nonlinearity of the calibration industrial process, a neural network PID control method based on ANT-LION optimization algorithm is proposed. The experimental results show that the method is effective and reliable, which improves the speed and stability of the temperature control of the pressure gauge calibration system, and realizes the intelligent control of the temperature of the calibration system.

Research on the prediction model of agricultural drought hazard considering the time-delayed cumulative effect and system development characteristics

Agricultural drought hazard is a complex time-delayed system affected by multiple hazard factors. The ability to estimate agricultural drought hazard accurately is crucial for guaranteeing food security. A TDMGM(1,m,N) prediction model coupling the time-delayed cumulative driving effect of multi-factor and the development characteristics of multi-system is constructed by introducing the time-delayed driving term and simultaneous formula with the goal of solving the problem of multivariate time-delayed prediction modeling of agricultural drought hazard. The definition form and derivation form of the TDMGM(1,m,N) model are given under the two cases of small and large variations of relevant variables, and the nonlinear solutions of the optimal delay parameters are given by using the fmincon function in Matlab. The solution method for model parameter estimation is also provided. It is proved that GM(1,1), GM(1,N), time-delayed GM(1,N), MGM(1,m) and MGM(1,m,N) are all special forms of TDMGM(1,m,N) model. The effect of multiplier transformation on model parameters, simulation prediction value, and model accuracy is also investigated. Finally, the TDMGM(1,m,N) model is applied to predict agricultural drought hazard in Henan Province. The findings demonstrate that the model can address the prediction problem of multiple system characteristic variables when multiple relevant variables exhibit time-delayed properties with good fitting and prediction accuracy.

Time Lag of Stem Water Deficit in Response to Increased Vapor Pressure Deficit

Abstract Vapor pressure deficit (VPD) shows a gradually increasing trend that forces trees to produce ecological memory with time-delay characteristics to drought year by year. The reversible stem diameter variation of trees can serve as an indicator for tree water status. In the context of global warming, these variations can dynamically monitor the response of stem water to the change of environmental water, especially to VPD. In this study, stem diameter variations of Larix gmelinii Rupr. and environmental factors were measured at high temporal resolution. We analyzed the tree water deficit–induced stem shrinkage (TWD) response to VPD variations. The results showed that variation of TWD lagged behind that of VPD to a certain extent, by about 3–5.5 h. Environmental water supply of trees under natural conditions can be divided into water deficit and adequate water supply. The division of the two water conditions was helpful to study the correlation between TWD and VPD. Observing the response of changes of stem diameter to the time lag of VPD is helpful to accurately represent the relationship of changes between moisture in the tree body and moisture in the air and to identify the stress effect of the lag in a short period of time. Study Implications: Stem diameter changes with change in moisture in the environment and usually reversibly shrinks under water deficits. Such reversible change in stem diameter is closely related to change in vapor pressure deficit, which is an environmental factor. Tree water deficit–induced trunk shrinkage, an index reflecting the change in stem diameter affected by moisture, had a time lag correlation with vapor pressure deficit in terms of variation characteristics. The time lag of the tree water deficit–induced trunk shrinkage response to vapor pressure deficit was 3–5.5 h. Just as the phenomenon of ecological memory cannot be ignored in an observational study with the year as the time scale, the phenomenon of time lag should not be ignored during an observational study of moisture stress at small time scales.

Multi-Delay Identification of Rare Earth Extraction Process Based on Improved Time-Correlation Analysis.

The rare earth extraction process has significant time delay characteristics, making it challenging to identify the time delay and establish an accurate mathematical model. This paper proposes a multi-delay identification method based on improved time-correlation analysis. Firstly, the data are preprocessed by grey relational analysis, and the time delay sequence and time-correlation data matrix are constructed. The time-correlation analysis matrix is defined, and the H∞ norm quantifies the correlation degree of the data sequence. Thus the multi-delay identification problem is transformed into an integer optimization problem. Secondly, an improved discrete state transition algorithm is used for optimization to obtain multi-delay. Finally, based on an Neodymium (Nd) component content model constructed by a wavelet neural network, the performance of the proposed method is compared with the unimproved time delay identification method and the model without an identification method. The results show that the proposed algorithm improves optimization accuracy, convergence speed, and stability. The performance of the component content model after time delay identification is significantly improved using the proposed method, which verifies its effectiveness in the time delay identification of the rare earth extraction process.

Strategy optimization of emergency frequency control based on new load with time delay characteristics

With the development of modern communication technology and the large number of new controllable loads connected to the power grid, the new controllable loads with flexible regulation characteristics can participate in the emergency frequency stability control. However, the communication state differences and spatial distribution characteristics of controllable load will affect the actual effect of frequency control. In this paper, an emergency frequency control method based on deep reinforcement learning is proposed considering the response time of controllable load shedding. The proposed method evaluates response ability for emergency control of controlled loads through load response time, controllable load amount and controllable load buses. Then, the controllable load with smaller response time is cut out preferentially to ensure rapid control, and the Markov Decision Process (MDP) is used to model the emergency frequency control problem. Finally, Rainbow algorithm of Deep Reinforcement Learning (DRL) is used to optimize the emergency frequency stability control strategy involving controllable load resources. The formation of emergency load shedding instruction is directly driven by high-dimensional operation state data after power grid failure, so that, the aim of minimizing the economic cost is achieved under the constraint of system stability. The effectiveness of the proposed method is verified in the IEEE 39-bus system.

Efficient logic cell design for ultralow-energy subthreshold operation in wearable biomedical systems

This paper explores efficient logic cell design strategies for ultralow-energy subthreshold operation in wearable biomedical systems. Traditional logic cell design approaches often lead to suboptimal efficiency due to unbalanced Pull-Up (PU) and Pull-Down (PD) networks. Various techniques are examined, including balanced PU/PD network design using body biasing and statistical distribution analysis of drain-source current. Moreover, the reverse channel (RSC) effect is investigated for device optimization. Single-stage gates such as inverters, NAND, and NOR gates are designed with Mentor Graphics tools to minimize power dissipation and area. Multistage gates like XOR and XNOR gates are also optimized for reduced signal propagation delay. A detailed analysis of D-flip-flop designs with varying transistor counts is presented. Power dissipation, delay, rise time, and fall time characteristics are compared for flip-flop configurations comprising 18, 10, and 5 transistors. The results indicate significant energy optimization with the proposed designs, particularly with the 5-transistor configuration, demonstrating the effectiveness of the proposed methodologies in achieving superior energy efficiency and reduced signal noise in biomedical systems.

The Planning Method of Township Regional Low-carbon Energy System Based on Demand Side Response

To solve the problem of environmental pollution caused by rising energy demand and explore the impact of demand-side resources on system optimal scheduling, based on the EH model, it is of great significance to study the integrated demand response strategy, carbon trading mechanism and low-carbon energy uncertainty for the operation of IES low-carbon economy. Compared with the traditional load response model, this model has better flexibility with the adjustment response. According to the thermal inertia and certain time delay characteristics of the heat load, the demand response model of heat load is established. Then, according to the application of the multienergy price demand response theory in natural gas system, the optimization model of gas load response is constructed. Three different optimization scenarios are set for simulation and comparative analysis, which verifies that the township comprehensive demand response model considering electric and thermal gas load has a significant effect on improving the system economy and the absorption capacity of low-carbon energy.

Development mechanism of radial chain rockbursts in a deep tunnel excavated by TBM

In this work, the concept of radial chain rockbursts (RCRs) was proposed for the first time. As a case study, RCRs that occurred in a deep tunnel excavated by TBM was analyzed. The occurrence process and geological characteristics of the RCRs were described. Though analyzing the microseismic (MS) temporal and spatial evolution characteristics, stress and energy evolution characteristics and fracturing mechanism of the RCRs, the development mechanism of the RCRs was revealed. The following results were obtained: (1) The RCRs is prone to occur under geological conditions with alteration zone and lithologic interface. (2) The time interval of adjacent rockbursts gradually increased, and RCRs exhibited the characteristics of time and space delay. The time interval of adjacent rockbursts was mainly affected by the adjustment of the excavation and support measures, and the stress gradually developed to the deep surrounding rock mass. (3) The stress and energy in the development stage were distributed in the deep surrounding rock mass and were released in the surface layer of surrounding rock mass in the occurrence stage. The apparent stress of the rock mass in the occurrence stage was higher than that in the development stage, and the stress gradually developed to the deeper zone during both the occurrence stage and the development stage. The repeated occurrence of RCRs was mainly controlled by three factors: the mechanical properties of the surrounding rock, stress conditions and multiple local shear slips in the rock mass near the alteration zone that are caused by TBM excavation disturbance. (4) The first two rockbursts were strain rockburst, and the latter two rockbursts were strain-structure slip rockburst that occurred along the alteration zone. The research has a guiding role for construction under similar engineering geological conditions, and provides a basis for the research on warning and mitigation of RCRs.

On Prebreakdown Time Delays of Water Under Millisecond Pulses

Prebreakdown time delay is one of the key breakdown characteristics which can reflect the insulation and discharge performances of dielectric liquids. In this article, the investigation of the voltage-varying characteristics of prebreakdown time delays and the corresponding voltage polarity effects for underwater millisecond discharges have been carried out. The results indicated that the voltage polarity effect of total time delay is determined by formation time delay at low applied voltage; meanwhile, determined by propagation time delay at high applied voltage. It was further found that the liquid phase disturbance induced by Joule heat during the prebreakdown process has a remarkable influence on the streamer propagation. As a result, the abnormal voltage-varying characteristics and polarity effects variation of total and propagation time delays were observed for the first time. Finally, a simple prediction model of prebreakdown time delays was proposed and compared with the classical Martin fitting formula. The findings could give a new insight into the liquid breakdown phenomena under long pulses and provide a practical tool for the related engineering applications.

Influence of brake chamber pressure and effective cross-sectional area on time delay of pneumatic braking system

To face the problem of freight safety and reduce the traffic accidents, the influence of the response time of the pneumatic braking system on the braking performance should be investigated, which can improve the safety level of vehicle. The mathematical model of the pneumatic braking response time was established in this paper. The parameters of the pneumatic braking time and the time delay characteristics of the pneumatic braking system was discussed. The results show that the pressure and effective cross-sectional area of the brake chamber are the main factors affecting the time delay of the pneumatic braking system. When the effective cross-sectional area of the brake chamber is certain, the push rod stroke of the brake chamber increases significantly with the increase of air pressure; when the brake chamber pressure is constant, the larger the effective cross-sectional area of the brake chamber, the greater the output thrust of the brake chamber push rod.

A model predictive control regulation model for radiant air conditioning system based on delay time

Radiant air conditioning (RAC) systems are widely used because of the advantage of higher thermal comfort compared to convection systems. However, RAC systems exhibit considerable hysteresis and significant time delays in heat transmission from the terminal to the interior environment. Traditional feedback control methods do not adequately account for the system's delay impact while implementing dynamic control, resulting in a mismatch between supply and demand loads in time series, an inefficient impact of interior temperature control, and energy waste. According to this study, the time-delay characteristics of the RAC system are quantified by the startup-process delay time (SPDT) and operating-phase integrated delay time (IDT). The system startup time is set by the SPDT. Based on the IDT, the prediction interval of the model predictive control (MPC) and the step time of rolling optimization are determined. On this basis, a short-term forecasting model of air conditioning operating load and the regulation model of the system using MPC are established and verified by comparing with the conventional rule-based control of constant water temperature (CRBC) and proportionally-integral-differential (PID) control. The results for the RAC system with a large time delay indicate that the stability of indoor temperature under MPC controlling is 18.8% higher than the PID control and 63.5% higher than the CRBC method. Also, the indoor temperature controlled by the MPC is closest to the set value and shows the minimum fluctuation. Furthermore, the overall energy consumption of the system is estimated for reducing by 14.89% in comparison with the CRBC and by 8.51% in comparison with the PID.

Estimating energy-related CO2 emissions using a novel multivariable fuzzy grey model with time-delay and interaction effect characteristics

An objective and accurate forecast of carbon emissions can provide the government with an important baseline for the implementation of the Green Economic Development Strategy. This paper considers the time-lag effect and the interaction effect of the influencing factors on carbon emissions simultaneously and establishes a new grey multivariate coupled model (CTGM(1,N)) for carbon emission projection by introducing the Choquet fuzzy integral and grey multivariate delay model. To further promote the prediction performance, the time-lag number of each influencing factor is determined by time-delay grey correlation analysis, and the whale optimization algorithm is designed to acquire the optimal parameters and accuracy of the model. The new model is designed to fitting carbon emissions data in three countries and compare it to six reference models. The performance test shows that the CTGM(1,N) model has high stability. The results of the forecasts show that China's carbon emissions are expected to rise by 1.17% by 2025 from 2020 levels. Meanwhile, emissions will decrease by 5.08% (US) and 0.88% (Japan). The prediction results were consistent with the development status of the three countries. According to the results, we can grasp the development trend of carbon emissions and formulate targeted strategies to achieve sustainable development.