Time-varying Coefficients Research Articles

Optimal energy portfolio method for regulable hydropower plants under the spot market

The energy allocation method for regulable hydropower plants under the spot market significantly impacts their income. The available studies generally draw on the Conditional Value-at-Risk (CVaR) approach, which typically assumes a fixed risk aversion coefficient for generators. This assumption is based on the assumption that the total energy the power plant can allocate is constant during the decision period. However, the amount of energy that the regulable hydropower plant can generate will be affected by inflow and water level during the decision period, and the assumption of the fixed risk aversion coefficient is only partially consistent with the actual decision behavior of the hydropower plant. In this regard, the time-varying relative risk aversion (TVRRA) based method is proposed for the energy allocation of regulable hydropower plants. That method takes the change value of the hydropower plant’s energy generation as the basis for adjusting the time-varying relative risk aversion coefficient to make the energy allocation results more consistent with the actual decision-making needs of the hydropower plant. A two-layer optimal method is proposed to obtain the income-maximizing energy portfolio based on regulable hydropower plants’ time-varying relative risk aversion coefficient. The inner point method solves the optimal energy portfolio of income and risk in the upper layer. The time-varying relative risk aversion coefficient in the lower layer accurately describes the dynamic risk preference of hydropower plants for each period. The results and comparison show that the proposed method increases the income of the energy portfolio by 31%, and water disposal of regulated hydropower plants is reduced by 2%. The energy portfolio optimization method for regulable hydropower plants proposed in this paper not only improves the economic income of hydropower plants but also improves the utilization rate of hydro energy resources and enhances the market competitiveness of regulable hydropower plants.

A Dynamic Binary Probit Model with Time-Varying Parameters and Shrinkage Prior*

This article studies a time series binary probit model in which the underlying latent variable depends on its lag and exogenous regressors. The regression coefficients for the latent variable are allowed to vary over time to capture possible model instability. Bayesian shrinkage priors are applied to automatically differentiate fixed and truly time-varying coefficients and thus avoid unnecessary model complexity. I develop an MCMC algorithm for model estimation that exploits parameter blocking to boost sampling efficiency. An efficient Monte Carlo approximation based on the Kalman filter is developed to improve the numerical stability for computing the predictive likelihood of the binary outcome. Benefits of the proposed model are illustrated in a simulation study and an application to forecast economic recessions.

The Role of Wages in Trend Inflation: Back to the 1980s?

This paper examines whether the measurement of trend inflation can be improved by using wage data in a dynamic factor model of disaggregated prices and wages for the United States. The model features time-varying coefficients and stochastic volatility. An estimate of trend inflation is a time-varying distributed lag of prices and wages, where the weight on a series depends on its time-varying volatility, persistence, and comovement with other series. The results show that wages inform estimates of trend inflation. The weight on wages was highest around 1980, drifted down through the 2000s, and returned to its 1980s value by 2022. In addition, inflation in the 2020s appears to have unmoored moderately from the 2 percent range that prevailed for decades, as the role of the persistent component of inflation increased in recent year. However, accounting for wages lowers the model's view of the increase in the volatility of trend inflation.

A New Cascaded Adaptive Deadbeat Control Method for PMSM Drive

In this article, we propose a novel cascaded adaptive deadbeat (CADB) control method for permanent magnet synchronous motor drives. First, an adaptive deadbeat (DB) based current controller is proposed with a simplified first-order current loop dynamic model. The motor parameters are compressed into few identifiable coefficients, and an improved gradient method with an adjustable gain factor is employed to identify these time-varying coefficients. Therefore, there is no need to design an extra observer to obtain specific motor physical parameters. Next, on the basis of the differential equation solving method, a similar model of speed loop is presented and adopted for the design of an adaptive speed controller. A robust DB-based tracking control law and a feedback control law are applied to the proposed adaptive controller. The stability of the proposed adaptive controller is confirmed by using the Lyapunov theorem. Finally, the proposed CADB control system is experimentally carried out in the steady state and transient state. The test results indicate that the system has good dynamic performance and robustness to the disturbance of parameters and loads.

Study on the Characteristics and Model Optimization of Coal Particle Gas Emission Law.

In order to further clarify the transport mechanism of coalbed methane and to study the gas diffusion law in coal, this paper conducted an experimental study on the desorption law of coal particle gas. On the basis of experiment, the time-varying characteristics of the diffusion coefficient in the process of coal particle gas desorption were analyzed, and the gas diffusion calculation model considering the time-varying diffusion coefficient was obtained by experimental fitting. On this basis, the influence of coal particles with different shapes on the gas desorption law is studied, and the gas diffusion equations of flat and cylindrical coal particles based on time-varying diffusion coefficients are established. The corresponding analytical solutions are obtained by using mathematical and physical methods, and the results are verified. The results show that the larger the adsorption equilibrium pressure is, the larger the gas desorption capacity is. At the initial stage of desorption, the larger the particle size is, the smaller the desorption capacity and desorption speed are. However, after reaching the "limit particle size", the particle size has little influence on the desorption capacity and desorption speed. Under different temperature conditions, the gas desorption amount monotonously increases with time. The higher the temperature is, the greater the desorption amount is. The adsorption constant a value slightly increases with the increase of temperature and then decreases. The adsorption constant b value has a trend of decreasing with the increase of temperature. The research results have very important practical significance for the optimization of the testing method of underground coalbed methane content and the prediction of coal and gas outburst risk.

Transient analysis for offset-elliptical journal bearings under misalignment and dynamic loading

Although the offset-elliptical journal bearings (OEJBs) are widely used in industry, there are few researches on the OEJBs, especially under the conditions of journal misalignment and sudden load. To overcome such deficiency, the dynamic response of misaligned OEJBs subjected to the dynamic load has been studied in this paper. Based on the Reynolds equation, Euler equation, and kinetic equations, a hydrodynamic model is developed and validated to conduct the transient analysis for the OEJBs. Moreover, the modified film thickness of the misaligned OEJB is given. The partial derivative method and the finite difference method are adopted to solve the lubrication equations. The correlations of bearing characteristics such as the minimum film thickness, maximum film pressure, journal orbits, and time-varying dynamic coefficients with the misalignment and dynamic load are studied. In addition, the effects of the misalignment, rotating speed, and clearance ratio on the transient performance of OEJBs under the dynamic load are discussed in detail. The results indicate that the journal misalignment and dynamic load have a significant impact on the transient performance of OEJBs. The decrease of the film thickness caused by the journal misalignment can be aggravated by the suddenly applied load. The proposed model can provide theoretical fundament in the design and monitoring of the dynamically loaded bearing-rotor system.

Global regulation of feedforward nonlinear systems with unknown time-varying control coefficients and growth rate

In this paper, we consider a regulation problem for a class of feedforward nonlinear systems with unknown control coefficients and unknown growth rate. More specifically, the unknown control coefficients are assumed to be time-varying and belong to ranges with unknown upper and lower bounds. Due to the described control coefficients with uncertain feedfoward nonlinearities, our considered system is the natural extension of the related existing results. In solving our control problem, a new adaptive controller is derived by constructing a Lyapunov function in backstepping-like procedure and utilizing appropriate parameters based on the gain scaling technique and Nussbaum function. The uniquely designed exponents of a dynamic gain overcome the difficulties caused from the unknown sign and unknown ranges of the control coefficients and uncertain nonlinearities and thus play a key role in system regulation. We give the rigorous system analysis and simulation results of the numerical example to certify our control method.

Cosmic expansion of the universe characterized by anisotropic viscous fluid cosmological models

In this paper, the cosmic expansion of the universe filled with viscous fluid has been discussed in Bianchi type-I space-time. To solve Einstein’s field equations, a time-varying deceleration parameter, ( q( t)) yields a time variable scale factor [Formula: see text], with " α" and " n" are positive constants. We also consider a time-varying gravitational constant ( G) via the formulation 8π G = G0 a m , where " G0" and " m" are positive constants. Further, based on the third constraint, two different viscous cosmological models, one by considering the perfect gas equation of state p = ωρ and the other by considering time-varying bulk viscosity coefficient ( ξ( t)), have been constructed. It is mentioned here that this particular choice of scale factor generates a class of accelerating models for n < 1, while for n > 1, the transition of the universe from early deceleration to current acceleration takes place. An important feature of the models is decreasing dark energy candidate, i.e., cosmological constant (Λ) with time, fine-tuning with the results from recent supernovae Ia observations. The various physical parameters involved in the models have been discussed analytically and graphically and are found in good agreement with recent observations. For the physical acceptability of both the models, energy conditions are shown graphically. The models are found to be physically acceptable, particularly in identifying the universe’s transition. We have discussed the statefinder and observed that all the trajectories indicate dark energy candidates as Chaplygin gas and quintessence for different constraints ( α, n). We have also calculated the cosmographic parameters such as jerk, lerk, and maxout to analyze the physical behavior of the DE models.

Latency period of aristolochic acid-induced upper urinary tract urothelial carcinoma.

Aristolochic acid (AA) is a carcinogen in upper urinary tract urothelial carcinoma (UTUC). This study investigated the latency period between AA exposure and UTUC development. This population-based cohort study was designed using record linkage of the National Health Insurance Research Database (NHIRD), Taiwan Cancer Registry Dataset, and cause-of-death data in Taiwan. Those aged 40-79 years were enrolled in this study. Patients who died or had renal insufficiency or UTUC before 2005 were excluded. The doses of AA exposure and rates of comorbidities between 2000 and 2005 were obtained. The Cox proportion hazard model was used to estimate the risk of UTUC between 2005 and 2016. In addition, the Cox model with time-varying coefficient of AA was used to measure the latency period of UTUC. Of the 752,232 participants enrolled from the NHIRD, 520,871 (68.29%), 210,447 (27.59%), and 31,415 (4.12%) were exposed to cumulative AA doses of 0-1 mg, 1-150 mg, and >150 mg, respectively. A total of 1,147 (0.15%) patients were diagnosed with UTUC between 2005 and 2016. The latency periods of UTUC in middle-aged (40-59 years old) men with cumulative AA doses of 1-150 mg and middle-aged women with cumulative AA doses of 1-150 mg and >150 mg were 8, 9, and 7 years, respectively. Among the aged (60-79 years) individuals, no time-varying effect was observed, and the latency period could not be measured. A decreased risk of UTUC was observed after the ban on AA in Taiwan, especially in middle-aged women with moderate to high doses of AA exposure and men with moderate doses of AA exposure. The latency period of UTUC varies with age, the dose of AA exposure, and sex.

A hybrid approach of ANN and improved PSO for estimating soaked CBR of subgrade soils of heavy-haul railway corridor

ABSTRACT The determination of subgrade/subsoil strength is one of the most important pavement design factors in transportation engineering, particularly for railways, roadways, and airport runways. The California bearing ratio (CBR) is often used to measure the strength and stiffness modulus of subgrade materials. This study presents a novel machine learning solution as an alternate approach for estimating soil CBR in soaked conditions. The present approach is an integration of an artificial neural network (ANN) and improved particle swarm optimisation (IPSO). According to experimental results during the testing phase, the proposed hybrid model, ANN-IPSO has achieved the highest predictive precision with root mean square error, RMSE = 0.0711 and mean absolute error, MAE = 0.0546. The findings of the proposed model are far superior to those of employed models including the conventional ANN, support vector machine, and group method of data handling. Six additional hybrid models of ANN and standard PSO (SPSO), PSO with time-varying accelerator coefficients, modified PSO, Harris hawks optimisation, slime mould algorithm, and colony predation algorithm were also constructed for a detailed comparison. Based on the outcomes, the newly created ANN-IPSO has the potential to be a new tool to estimate soaked CBR of fine-grained soils in civil engineering projects.

Managing Mortality and Aging Risks with a Time-Varying Lee-Carter Model.

Influential existing research has suggested that rather than being static, mortality declines decelerate at young ages and accelerate at old ages. Without accounting for this feature, the forecast mortality rates of the popular Lee-Carter (LC) model are less reliable in the long run. To provide more accurate mortality forecasting, we introduce a time-varying coefficients extension of the LC model by adopting the effective kernel methods. With two frequently used kernel functions, Epanechnikov (LC-E) and Gaussian (LC-G), we demonstrate that the proposed extension is easy to implement, incorporates the rotating patterns of mortality decline and is straightforwardly extensible to multi-population cases. Using a large sample of 15 countries over 1950-2019, we show that LC-E and LC-G, as well as their multi-population counterparts, can consistently improve the forecasting accuracy of the competing LC and Li-Lee models in both single- and multi-population scenarios.

An Improved Bat Algorithm With Time-Varying Wavelet Perturbations for Cloud Computing Resources Scheduling

Resources scheduling is a major challenge in cloud computing because of its ability to provide many on-demand information technology services according to needs of customers. In order to acquire the best balance between speed of operation, average response time, and integrated system utilization in the resource allocation process in cloud computing, an improved bat algorithm with time-varying wavelet perturbations was proposed. The algorithm provided a perturbation strategy of time-varying Morlet wavelet with the waving property to prevent from local optimum greatly and improve the converging speed and accuracy through the guide of individual distribution to control diversity and time-varying coefficient of wavelets. The experiments showed the proposed could significantly upgrade the overall performance and the capability of resource scheduling in cloud service compared to similar algorithms.

A Novel GSMC-based ACV Trajectory Tracking Under Unknown Disturbance Upper Bound and Time-varying Model Coefficients

A Novel GSMC-based ACV Trajectory Tracking Under Unknown Disturbance Upper Bound and Time-varying Model Coefficients

Consensus Protocol for Discrete-Time Linear Multiagent Systems With Channel Fadings: A Dynamic Event-Based Approach

This article investigates the consensus problem of discrete-time linear multiagent systems with channel fadings over wireless sensor networks. The phenomena of channel fadings are described as the stochastic Rice fading model with random time-varying channel coefficients. By utilizing the local state information and constructing a dynamic auxiliary system, a novel control protocol with the dynamic discrete-time event-triggered scheme has been proposed. Sufficient conditions are obtained to guarantee the asymptotic consensus of the multiagent systems under the proposed protocol. Compared with the existing works related to discrete-time event-triggered consensus, the interevent time between any two successive events can be prolonged, and thus, the transmission data packages can be decreased immensely. Finally, the effectiveness of the proposed consensus protocol with a dynamic discrete-time event-triggered scheme is testified by numerical simulations.

Smooth Transition of Safety Controllers Filtered by Control Barrier Functions

Recently, control barrier functions (CBFs), are becoming popular and widely used as a safety-filter of the controller through quadratic programming(QP) optimization framework to ensure the forward invariance of the dynamic systems. However, for sequential safety tasks, the transitions of CBFs are required. To avoid control input discontinuities, firstly, a smooth and speed adjustable transition mechanism by adding a time-varying coefficient of different CBFs is proposed. Moreover, the value of the previous CBF constraint is fixed to reduce the change of the combined CBF constraint. Secondly, a method to integrate different sequential CBFs into a synthetic new one is presented. Through these methods, the smoothness of optimized control inputs can be ensured compared with existing formulations. Simulation results demonstrate that our algorithm provides a smoother transition mechanism to switch different CBFs, and improves the control performance consequently.

Dynamic relationships among changes in prices of beef, pork, and chicken in Japan: A Bayesian approach

In this paper, we investigate the interdependence among changes in the prices of beef, pork, and chicken in Japan using a time-varying coefficient vector autoregressive model. Our empirical analysis using monthly data from January 1990 to March 2014 shows that changes in beef prices have long-term influences on changes in pork and chicken prices. Moreover, current changes in the prices of beef, pork, and chicken are closely related to changes in their prices in the preceding two months. Additionally, we do not find that the bovine spongiform encephalopathy outbreak announced by the Japanese government in September 2001 had a long-term influence on the dynamic relationships among changes in the prices of beef, pork, and chicken in Japan.

Load Frequency Control of multi-area multi-source system with nonlinear structures using modified Grasshopper Optimization Algorithm

Nowadays, power systems are in direct contact with sources such as renewable energy sources, Electric Vehicles (EVs), distributed generation resources, etc. In other words, these sources impose additional imbalances on the system and disturb the balance between generation and consumption. Hence, this study aims to model multi-area multi-source systems with the presence of units such as thermal, hydro, diesel, and gas units, wind farms, EVs, and energy storage sources. Without a properly coordinated controller, they can expose the system to severe stress under unwanted disturbances and cause it to deviate from normal operating conditions. Therefore, after modeling the proposed system, a Proportional–Integral–Derivative (PID) controller based on a low-pass filter is presented as a widely used device in the industry. Since tuning coefficients of this controller based on trial and error were irrational, this issue turned to a frequency-based optimization problem with different operating points in unwanted operating conditions in order to have a robust design. In the previous articles, a fixed working point employed to design the controller. To cover this issue, modeling towards reality, and a wider range of unexpected conditions and events, ±30% design uncertainty and different loading conditions are considered in this study. Then, the developed Grasshopper Optimization Algorithm (GOA) with decreasing coefficients is applied to solve it. The time-varying coefficients can properly improve the local and global search capabilities. Different scenarios based on unwanted disturbances, uncertainties, and random load changes are considered to evaluate the system and algorithm performance. Numerical results obtained by analyzing comparative criteria on time and frequency domains indicated the proposed method had over 25% better performance compared to other methods, on average. The results obtained from the frequency and time domains properly show that the settlement and overshoot and undershoot times of the system equipped with the proposed controller are less compared to other designs in the literature, and better stability has been provided in the s-plane. Also, Nyquist and Bode analyses have shown that the system can ensure optimal performance in a wide range of working points.

Trajectory prediction of underwater launch equipment based on varying flow coefficient

In the underwater launch systems, a real-time prediction of the trajectory of the equipment out of the tube can provide accurate motion feedback for the closed-loop control. In this paper, using an analytical approach, a hydrodynamic mathematical model is developed for the open pipeline underwater launch system, along with an analysis of the coupling of pressure-flow variables influence mechanism on the trajectory. A time-varying coefficient-based prediction model for the equipment trajectory of the underwater launch process is developed for the compressible fluid and the strong transient characteristics of the fluid during the underwater launch process. Through the incorporation of the time-varying component of inertia into the flow coefficient, the interlinking relationship between the fluid, the pipe structure, and the equipment motion is clarified. This addresses the problem that the traditional steady-state hydrodynamic pipeline model cannot accurately describe the strong transient launch process. The experimental results show that, under typical firing conditions, the maximum error between the equipment motion velocity predicted by the mathematical model based on the time-varying flow coefficient and the measured velocity is 8.48%, with an error root mean square of 0.3035, proving the accuracy of the model based on the time-varying flow coefficient.

Kernel Estimation of Bivariate Time-Varying Coefficient Model for Longitudinal Data with Terminal Event

We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The time-varying coefficients capture the longitudinal trajectories of covariate effects along with both the followup time and the residual lifetime. The proposed model extends the parametric conditional approach given terminal event time in recent literature, and thus avoids potential model misspecification. We consider a kernel smoothing method for estimating regression coefficients in our model and use cross-validation for bandwidth selection, applying undersmoothing in the final analysis to eliminate the asymptotic bias of the kernel estimator. We show that the kernel estimates follow a finite-dimensional normal distribution asymptotically under mild regularity conditions, and provide an easily computed sandwich covariance matrix estimator. We conduct extensive simulations that show desirable performance of the proposed approach, and apply the method to analyzing the medical cost data for patients with end-stage renal disease. Supplementary materials for this article are available online.

Prediction of Packet Loss Rate in Non-Stationary Networks Based on Time-Varying Autoregressive Sequences

Currently, most of the existing link parameter prediction schemes assume that the link state remains constant during the measurement period, making it difficult to capture their time-varying characteristics. To solve this problem, this paper proposes a prediction problem for packet loss rate in a non-stationary network environment. The measurement period is divided into several adjacent time windows, and the packet loss rates measured passively in each time window are regarded as non-stationary time sequences for real-time tracking to obtain the changes in link packet loss rate at a small cost. By analyzing time-varying autoregressive (TVAR) sequences, a scheme for estimating the time-varying coefficient was presented. In addition, a prediction scheme for the packet loss rate in a non-stationary network was proposed based on TVAR sequences. Finally, this paper conducts experiments based on a non-stationary network simulation environment established by the improved Gilbert–Elliot model and a small wireless multi-hop network experiment platform built in reality. Simulation and experimental results show that the prediction scheme of the packet loss rate based on the TVAR sequence can accurately predict the packet loss rate.