Time-varying Weights Research Articles

Dynamics of Timoshenko system with time-varying weight and time-varying delay

<p style='text-indent:20px;'>This paper is concerned with the well-posedness of global solution and exponential stability to the Timoshenko system subject with time-varying weights and time-varying delay. We consider two problems: full and partially damped systems. We prove existence of global solution for both problems combining semigroup theory with the Kato's variable norm technique. To prove exponential stability, we apply the Energy Method. For partially damped system the exponential stability is proved under assumption of equal-speed wave propagation in the transversal and angular directions. For full damped system the exponential stability is obtained without the hypothesis of equal-speed wave propagation.</p>

Mean-field limit of collective dynamics with time-varying weights

<p style='text-indent:20px;'>In this paper, we derive the mean-field limit of a collective dynamics model with time-varying weights, for weight dynamics that preserve the total mass of the system as well as indistinguishability of the agents. The limit equation is a transport equation with source, where the (non-local) transport term corresponds to the position dynamics, and the (non-local) source term comes from the weight redistribution among the agents. We show existence and uniqueness of the solution for both microscopic and macroscopic models and introduce a new empirical measure taking into account the weights. We obtain the convergence of the microscopic model to the macroscopic one by showing continuity of the macroscopic solution with respect to the initial data, in the Wasserstein and Bounded Lipschitz topologies.</p>

A Novel Multifrequency GPR Data Fusion Algorithm Based on Time-Varying Weighting Strategy

Ground-Penetrating Radar (GPR) technology plays an important role in near-surface geophysical survey. Usually, the dataset acquired by higher frequency antenna has higher resolution but limited penetration depth; while the dataset collected by lower one has deeper penetration depth but lower resolution. It is necessary to fuse several different frequency datasets into a composite displayed profile to get a trade-off between penetration depth and resolution. In such case, we propose a novel multi-frequency GPR data fusion method and test it on the data acquired from a limestone quarry in the Taihang Mountains of China. In our algorithm, time-varying weighting factors are calculated by virtues of a sliding time window, i.e. designed with segmented signals. According to this approach, we can achieve better local characteristics of the signal as well as the smoothness of the signal waveform. The results show that the spectrum bandwidth of the fused signal is broadened, and the fusion profile significantly improves the imaging of internal structures for limestone on different scales.

A transmission problem for waves under time-varying delay and time-varying weights

A transmission problem for waves under time-varying delay and time-varying weights

Spatial Dynamic Panel Data Models With High-Order Time-Varying Endogenous Weights Matrices

Spatial Dynamic Panel Data Models With High-Order Time-Varying Endogenous Weights Matrices

On Data-Driven Log-Optimal Portfolio: A Sliding Window Approach

In this paper, we propose an online data-driven sliding window approach to solve a log-optimal portfolio problem. In contrast to many of the existing papers, this approach leads to a trading strategy with time-varying portfolio weights rather than fixed constant weights. We show, by conducting various empirical studies, that the approach with proper choice of window size possesses a superior trading performance to the classical log-optimal portfolio and mean-variance portfolio in the sense of having a higher cumulative rate of returns.

Distributed Support Vector Machines Over Dynamic Balanced Directed Networks

In this letter, we consider the binary classification problem via distributed Support Vector Machines (SVMs), where the idea is to train a network of agents, with limited share of data, to cooperatively learn the SVM classifier for the global database. Agents only share processed information regarding the classifier parameters and the gradient of the local loss functions instead of their raw data. In contrast to the existing work, we propose a continuous-time algorithm that incorporates network topology changes in discrete jumps. This hybrid nature allows us to remove chattering that arises because of the discretization of the underlying CT process. We show that the proposed algorithm converges to the SVM classifier over time-varying weight balanced directed graphs by using arguments from the matrix perturbation theory.

Periodic Variable Stars Modulated by Time-varying Parameters

Abstract Many astrophysical phenomena are time-varying, in the sense that their brightness changes over time. In the case of periodic stars, previous approaches assumed that changes in period, amplitude, and phase are well described by either parametric or piecewise-constant functions. With this paper, we introduce a new mathematical model for the description of the so-called modulated light curves, as found in periodic variable stars that exhibit smoothly time-varying parameters such as amplitude, frequency, and/or phase. Our model accounts for a smoothly time-varying trend and a harmonic sum with smoothly time-varying weights. In this sense, our approach is flexible because it avoids restrictive assumptions (parametric or piecewise-constant) about the functional form of the trend and amplitudes. We apply our methodology to the light curve of a pulsating RR Lyrae star characterized by the Blazhko effect. To estimate the time-varying parameters of our model, we develop a semi-parametric method for unequally spaced time series. The estimation of our time-varying curves translates into the estimation of time-invariant parameters that can be performed by ordinary least squares, with the following two advantages: modeling and forecasting can be implemented in a parametric fashion, and we are able to cope with missing observations. To detect serial correlation in the residuals of our fitted model, we derive the mathematical definition of the spectral density for unequally spaced time series. The proposed method is designed to estimate smoothly time-varying trends and amplitudes, as well as the spectral density function of the errors. We provide simulation results and applications to real data.

CRPS learning

Combination and aggregation techniques can significantly improve forecast accuracy. This also holds for probabilistic forecasting methods where predictive distributions are combined. There are several time-varying and adaptive weighting schemes such as Bayesian model averaging (BMA). However, the quality of different forecasts may vary not only over time but also within the distribution. For example, some distribution forecasts may be more accurate in the center of the distributions, while others are better at predicting the tails. Therefore, we introduce a new weighting method that considers the differences in performance over time and within the distribution. We discuss pointwise combination algorithms based on aggregation across quantiles that optimize with respect to the continuous ranked probability score (CRPS). After analyzing the theoretical properties of pointwise CRPS learning, we discuss B- and P-Spline-based estimation techniques for batch and online learning, based on quantile regression and prediction with expert advice. We prove that the proposed fully adaptive Bernstein online aggregation (BOA) method for pointwise CRPS online learning has optimal convergence properties. They are confirmed in simulations and a probabilistic forecasting study for European emission allowance (EUA) prices.

Lateral automatic landing guidance law based on risk-state model predictive control

A lateral automatic carrier landing system (ACLS) control law is proposed for carrier-based aircraft, which could suppress the landing risk and increase the control precision. The nonlinear lateral landing equation is transformed into a polytopic model with a serial of state bounds. The landing risk including approach and arresting risks is proposed, and a Kalman filter is used to integrate both risks to construct the landing risk model. The concept of virtual state deviations is adopted by the output augment, which is estimated by an observer. The risk-state model predictive control (MPC) is established on the basis of the landing risk gradient composed of virtual states and time-varying weights. A compared simulation is tested on a semi-physical platform, which verifies excellent performance of the proposed method.

Influence of longitudinal vibration on the friction and wear characteristics of multi-layer winding hoisting wire rope

The longitudinal vibration of the multi-layer winding hoisting wire rope is inevitable due to the time-varying dead weight and the fluctuation of hoisting acceleration. Longitudinal vibration will lead to relative sliding between the hoisting wire rope and the rope groove, resulting in friction and wear, and thus threaten the service life of the wire rope. In order to investigate the influence of longitudinal vibration on the tribological characteristics of the wire rope and its wear mechanism, the friction and wear tests between the sliding wire rope and the fixed wire rope groove under different longitudinal vibration amplitudes and frequencies were carried out with the help of a self-developed test rig. Results show that the coefficient of friction (CoF) of the wire rope has experienced three stages of rapid growth, “concave” transition and relative stability with the increase of sliding distance, which increases first and then decreases with increasing the amplitude in the relatively stable stage and has no obvious change with an increase in the frequency. Additionally, the maximum temperature rises at the wind-out region of the sliding wire rope and the middle of the contact wire ropes increase first and then decrease with the increase of the amplitude in the relatively stable stage, but increase gradually with an increase in the frequency. The vibration intensities in the wear regions of the wire ropes have a great increase in the rapid wear stage. Furthermore, the main vibration wear mechanisms of the wire rope are abrasive wear and adhesive wear, the content of O element in the furrows and the abrasive adhesion regions are higher, and the frequency has less effect on the oxidation degree of the worn surface of the wire rope than the amplitude.

Stabilization of swelling porous elastic soils with fluid saturation, time varying-delay and time-varying weights

Stabilization of swelling porous elastic soils with fluid saturation, time varying-delay and time-varying weights

General decay estimate for a two-dimensional plate equation with time-varying feedback and time-varying coefficient

In this paper, we consider a plate equation as a model for a suspension bridge with a general nonlinear internal feedback and time-varying weight. Under some conditions on the feedback and the coefficient functions, we establish a general decay estimate for the associated energy functional, from which the exponential and the polynomial, are particular cases.

Time-varying weight coefficients determination based on fuzzy soft set in combined prediction model for travel time

Precise prediction of travel time is a valuable reference for traffic participants to make travel choices and is important for the development of intelligent transportation systems (ITS). Combined prediction method for travel time can effectively improve prediction accuracy, but the uncertainty and fuzziness in determining the weight coefficients are inherent nature. A novel time-varying weights determination method based on fuzzy soft set (TVWDFSS11Time-varying weights determination method based on fuzzy soft set, abbreviated as TVWDFSS), that the uncertainty and fuzziness are considered, is presented, and the combined travel time prediction model with TVWDFSS is proposed. In this model, several forecasting methods are firstly selected as the component predictors, and the forecasting using these predictors are performed, respectively. Then the relative error of each predictor at the specific time point is calculated, and a tabular form of the fuzzy soft set that describes the actual time series data is constructed. Finally, the weight coefficients of all component predictors at each time point are determined according to their forecasting errors recorded in the tabular form of fuzzy soft set. In case study, three individual methods, Kalman Filter (KF), Back Propagation Neural Network (BPNN), and k Nearest Neighbor (k-NN) are selected as the component predictors of the combined travel time prediction model with TVWDFSS, and real-world traffic data is used to verify the validity and precision of this presented model. Comparing with the three individual methods and another combined approach that employs a constant weight determination method that is also based on fuzzy soft set (CWDFSS22Constant weight determination method based on fuzzy soft set, abbreviated as CWDFSS), the results confirm the validity and superiority of the proposed models.

Stability result of a suspension bridge Problem with time-varying delay and time-varying weight

In this paper, we study a plate equation as a model for a suspension bridge with time-varying delay and time-varying weights. Under some conditions on the delay and weight functions, we establish a stability result for the associated energy functional. The present work extends and generalizes some similar results in the case of wave or plate equations.

Mean-field and graph limits for collective dynamics models with time-varying weights

In this paper, we study a model for opinion dynamics where the influence weights of agents evolve in time via an equation which is coupled with the opinions' evolution. We explore the natural question of the large population limit with two approaches: the now classical mean-field limit and the more recent graph limit. After establishing the existence and uniqueness of solutions to the models that we will consider, we provide a rigorous mathematical justification for taking the graph limit in a general context. Then, establishing the key notion of indistinguishability , which is a necessary framework to consider the mean-field limit, we prove the subordination of the mean-field limit to the graph one in that context. This actually provides an alternative proof for the mean-field limit. We conclude by showing some numerical simulations to illustrate our results.

Risk assessment of dynamic disasters in deep coal mines based on multi-source, multi-parameter indexes, and engineering application

For the characteristics of high frequency and strong suddenness of dynamic disasters in deep coal mines, the traditional detection and evaluation techniques applied to shallow coal mine failed to accurately judge the risk degree of disasters. Therefore, it is of great significance to use advanced detection technologies and appropriate evaluation methods to improve the accuracy and efficiency of risk assessment in the process of coal mining. The present paper applies the rapid detection and multi-source dynamic detection technologies used in the field of mining with the purpose of improving the reliability of detection technologies for typical dynamic disaster. In this study, the data fusion technology was used to analyze data obtained from laboratory experiments, engineering survey, detection and historical data, so as to form the final dynamic and static indicators. Then, the new combined evaluation models with time series of coal and gas outburst as well as rock burst were established respectively to carry out the comprehensive risk evaluation using the least-squares method and the time-varying weight method. After the comprehensive analysis on the results of the above two evaluation models, the risk areas of the typical dynamic disasters were judged and classified. Finally, the evaluation models were coded to build an early-warning software platform that could achieve automatic evaluation and the actual 3D visualization of coal mining areas. The early-warning software platform was applied to risk assessment of dynamic disasters in Gengcun Coal Mine in Yima City, Henan Province, China. The results of the 6-month experiment showed that the risk assessment accuracy and reliability of the proposed evaluation models was 100% and 90% respectively, which indicates that the newly developed approach is reliable and can be recommended for applying in more coal mines to improve the process safety risk control.

Localization of Rolling Element Faults Using Improved Binary Particle Swarm Optimization Algorithm for Feature Selection Task

The accurate localization of the rolling element failure is very important to ensure the reliability of rotating machinery. This paper proposes an efficient and anti-noise fault diagnosis model for rolling elements. The proposed model is composed of feature extraction, feature selection and fault classification. Feature extraction is composed of signal processing and signal noise reduction. Signal processing is carried out by local mean decomposition (LMD), and signal noise reduction is performed by product function (PF) selection and wavelet packet decomposition (WPD). Through the steps of signal noise reduction, high-frequency noise can be effectively removed, and the fault information hidden under the noise can be extracted. To further improve the effectiveness of the diagnostic model, an improved binary particle swarm optimization (IBPSO) is proposed to find the most important features from the feature space. In IBPSO, cycling time-varying inertia weight is introduced to balance exploitation and exploration and improve the capability to escape from local solutions, and crossover and mutation operations are also introduced to improve exploration and exploitation capabilities, respectively. The main contributions of this research are briefly described as follows: (1) The feature extraction process applied in this research can effectively remove noise and establish a high-accuracy feature set. (2) The proposed feature selection algorithm has higher accuracy than the other state-of-the-art feature selection algorithms. (3) In a strong noise environment, the proposed rolling element fault diagnosis model is compared with the state-of-the-art fault diagnosis model in terms of classification accuracy. Experimental results show that the model can maintain high classification accuracy in a strong noise environment. Therefore, it can be proved that the fault diagnosis model proposed in this paper can be effectively applied to the fault diagnosis of rotating machinery.

Islamic stock markets and COVID-19-induced shocks: simulations with global VAR approach

PurposeThis paper aims to make the first attempt to study the transmission of COVID-19 pandemic-induced shocks to the global Islamic stock markets in the midst of the overall macroeconomic environment and cross-country trade linkages. This is made possible by constructing a global vector autoregressive (GVAR) model and with it the authors arrive at noteworthy conclusions.Design/methodology/approachThe paper estimates both fixed and time-varying weights GVAR models for 15 Islamic stock markets with 5,000 bootstrap replications and reports impulse response functions. It simulates four shocks associated with the pandemic: first, a standard error negative shock to oil price; second, a standard error negative shock to the global Islamic stock markets; third, a standard error positive shock to equity-based uncertainty index; and fourth, a standard error negative shock to economic activity (inflation).FindingsThe paper shows that the pandemic engenders immediate negative impacts on the Islamic stock markets with the biggest impacts borne by the USA and China and the least by markets in the Middle East. The study documents the magnitudes of the responses to the shocks and shows that the impacts of the pandemic will take about 20 months to wither completely.Originality/valueThe findings throw up diversification benefits for investors toward the UAE, Oman, Bahrain and other Middle East markets especially during crisis. It further reveals the need for counter-cyclical measures in all countries to curtail the negative impacts of the pandemic which could linger for up to 20 months.

Dynamic diversification benefits of Sukuk and conventional bonds for the financial performance of MENA region companies: empirical evidence from COVID-19 pandemic period

PurposeThis study aims to investigate the spillover between the Middle East and North Africa (MENA) stock index and several security indices, including Sukuk and conventional bond, and ultimately compare the hedge effectiveness of Sukuk and conventional bond.Design/methodology/approachThe study uses VAR (1)-asymmetric Baba, Engle, Kraft and Kroner-multivariate generalized autoregressive conditional heteroskedasticity (1,1) model to analyze the volatility and shock and asymmetric shock spillover between Sukuk index and several bond indices in the MENA region including, Bond, All Bond, High Yield Bond and Bond and Sukuk and MENA stock market index and ultimately compare the hedging capabilities of Sukuk and conventional bonds by calculating the optimal portfolio weights for securities indices and stock portfolios and hedge effectiveness of security indices.FindingsResults indicate that there is no shock, volatility and asymmetric shock spillover between the Sukuk index and MENA stock index, implying that Sukuk indices behave independently from MENA stock indices; however, there is shock and asymmetric shock spillover between MENA stock indices and security indices that include conventional bonds. The result of optimal portfolio weights and corresponding hedge effectiveness indicate that Sukuk is the most significant asset among other security indices in diversifying and hedging stock MENA portfolios. Moreover, the hedge effectiveness of Sukuk shows persistent trends during both the normal and crisis periods.Practical implicationsThe study suggests that MENA stock market investors and investment managers should add Sukuk instead of the conventional bond to their portfolio to hedge their portfolio against investment risks during both normal and crisis periods.Originality/valueAlthough many studies compare many aspects of Sukuk and conventional bonds, this is the first study that compares the hedge effectiveness of Sukuk and conventional bond based on the time-varying optimal portfolio weights strategy.