Time-varying Weights Research Articles

Multi-Objective Optimization Operation of CCHP System Under Continuous Shock Load Using Time-Varying Weighting Factors

Improving the energy efficiency and maintaining the energy balance of integrated energy systems is a common concern. This study investigated the optimal operation of combined cooling, heating, and power (CCHP) systems under conventional and impact load scenarios. For the conventional operation, a time-varying weight factor was proposed to calculate the multi-objective function to optimize the system scheduling under off-grid and grid-connected modes. For the impact load scenario, the characteristics of the energy storage device was reasonably utilized to stabilize the impact of power. The simulation results revealed that the establishment of the time-varying weight factor optimized the energy economy level of the CCHP system and significantly improved its energy efficiency. Particularly, the energy storage device can cut off the impact peak when the system is under continuous impact load, and improve the energy supply reliability of the system.

Lyapunov Optimization in Online Battery Energy Storage System Control for Commercial Buildings

The high instantaneous discharging capability of battery energy storage systems (BESSs) make them ideal candidates for reducing peak loads in commercial buildings. An efficient online BESS control algorithm can be beneficial for reducing the monthly electricity bill of individual commercial buildings. Conventional model-based BESS control algorithms rely heavily on accurate long-horizon net load forecast, which is difficult to obtain. To address this problem, we develop a Lyapunov optimization-based online BESS control algorithm and derive its theoretical performance bound. Comprehensive numerical study results using real world commercial building smart meter data in southern California show that our proposed Lyapunov optimization-based online control algorithm with time-varying weighting parameters yields higher savings in electricity cost and requires less computation time in comparison to the state-of-the-art baseline algorithms.

Bayesian SAR Model with Stochastic Volatility and Multiple Time-Varying Weights

Bayesian SAR Model with Stochastic Volatility and Multiple Time-Varying Weights

Complex modified projective synchronization of fractional-order complex-valued dynamic network with time-varying coupling and parameters estimation

The problem of Complex Modified Projective Synchronization (CMPS) for fractional-order complex-valued dynamic networks (FOCDNs) is investigated in this paper. Compared with real-valued dynamic networks, complex-valued dynamic networks have expansive application space. First, coupling weight has been generally considered as static constant in most situations, we consider time-varying coupling weight and the unknown parameters in the FOCDNs. Meanwhile, several parameter update laws are designed to estimate unknown parameters and an adaptive coupling weight is defined to adjust coupling nodes in order to solve CMPS problem of the network. Second, some effective criteria for CMPS of FOCDNs are derived by fractional calculus and Lyapunov stability theory. According to the theoretical analysis, FOCDNs with time-varying delay can achieve CMPS via adaptive controller. Finally, the effectiveness of the modified projective strategy for fractional-order complex networks is illustrated via two numerical examples in the complex-valued space.

Transient response of Bouc–Wen hysteretic system under random excitation via RBFNN method

Hysteresis normally exhibited by mechanical systems and materials is so prevalent that its response prediction under random excitation has been extensively investigated for decades. Nevertheless, the transient solution of the response, which is crucial for assessing the system’s reliability, is still a challenging topic that requires additional development. To this regard, this work proposes a semi-analytical method using the radial basis function neural network (RBFNN) to attain the transient probability density distribution of the randomly excited Bouc–Wen system. Specifically, the trial solution of the corresponding FPK equation is configured as the RBFNN with undetermined time-varying weight coefficients. By discretizing the time derivative with the Euler difference method, a loss function with time recurrence is derived and minimized to yield the time-varying optimal weight coefficients through the optimization method. Additionally, an optimized sampling strategy is adopted to reduce the burden of calculation. Finally, the Bouc–Wen hysteretic systems with softening and hardening nonlinearity are considered to investigate the performance of the adopted technique. The numerical results have shown that the evaluation process of the probability density functions(PDFs) can be captured well with sufficient accuracy and efficiency. The proposed efficient sampling technique can provide considerable efficiency improvement for the medium dimension system. The work of the paper will contribute to the reliability design of hysteretic structures in engineering.

A layered roll stability control strategy for commercial vehicles based on adaptive model predictive control

Due to the high centre of mass, commercial vehicles are prone to ultimate rollover under extreme steering conditions. The rollover of commercial vehicles leads to serious traffic accidents and poses a threat to road traffic. In this paper, the mechanism of vehicle rollover is revealed by static equilibrium and phase plane analysis. Meanwhile, a robust roll angle observer is designed by combining Kalman filter and sliding mode observation. Considering the influence of wheel lifting on roll dynamics, a novel layered control strategy is proposed by employing engine torque limit, differential braking and active front-wheel steering. Moreover, the layered control of roll stability is realised by adaptive model predictive control with time-varying weights and constraints. To verify the validity and reliability of the proposed algorithm, the hardware-in-loop tests under typical driving conditions have been carried out. The results show that the proposed method can effectively restrain the divergence of roll state and prevent vehicle rollover.

Penalized time-varying model averaging

This paper proposes a new penalized time-varying model averaging method to determine optimal time-varying combination weights for candidate models, which avoids over-fitting and yields sparseness from various potential predictive variables. The asymptotic optimality and convergence rate of the selected weights are derived even when all candidate models are misspecified, and the consistency and normality of the proposed time-varying model averaging estimator are obtained when the true model is included in the candidate models. Simulation studies and empirical applications to inflation forecasting highlight the merits of the proposed method.

Linear Quadratic Regulator Weighting Matrices for Output Covariance Assignment in Nonlinear Systems

A time-varying output covariance assignment problem in the presence of a stochastic disturbance is solved using finite-horizon optimal control formulation. It is shown that an assignment of time-varying output error covariance is possible in the presence of model error by utilizing a time-varying linear quadratic regulator controller with a class of output and control weighting sequences. This paper develops a systematic algorithm to calculate the sequence of such time-varying output weights that are further shown to be the Lagrange multipliers associated with the covariance constraints. A short horizon attitude control problem with stringent covariance constraints and a more nonlinear example concerning a low-thrust interplanetary maneuver are solved to demonstrate the utility of the proposed approach. Numerical results offer a degree of optimism about the broad applicability of the time-varying covariance assignment approach to solve guidance and control problems associated with nonlinear dynamic systems.

Synthesis of Low Sidelobe Pattern with Enhanced Axial Radiation for Sparse Conformal Arrays Based on MCDE Algorithm

A hybrid optimization method for the synthesis of a sparse conformal array with the verification of a truncated cone antenna array is proposed in this manuscript. This array synthesis is studied aiming at enhancing axial radiation and reducing the peak sidelobe level (PSLL) by figuring out the optimal antenna element arrangement and corresponding feeding scheme. A multi-agent composite differential evolution (MCDE) algorithm is established by integrating a multi-agent system (MAS) with a differential evolution (DE) algorithm. In addition, a hybrid strategy method and a time-varying weighting factor are added to the mutation operator to accelerate convergence. Two examples of 64-element and 900-element truncated cone antenna arrays were synthesized. After forming a sparse antenna array out of the original full array, the number of antennas was decreased to 80% and 56.8%, respectively. The results show that the main beam of the sparse conformal antenna array is accurately fixed to the axial direction with the PSLL less than −20 dB at both the φ = 0° and φ = 90° planes, which proves the effectiveness of this method in conformal sparse array synthesis.

Dynamic Deadband Event-Triggered Strategy for Distributed Adaptive Consensus Control With Applications to Circuit Systems

This paper focuses on the distributed consensus seeking of multi-agent systems (MASs) with discrete-time control updating and intermittent communications among agents. Compared with existing linearly coupled protocols, a nonlinear coupled Zeno-free event-triggered controller is first proposed, which is further to project the static and dynamic triggering mechanisms exploited by using the deadband control method. Then, the node-based nonlinear coupled adaptive event-triggered controller with online self-tuning of time-varying coupling weight and its corresponding to static and dynamic deadband-based event-triggered mechanisms are designed, respectively. The exploited adaptive event-triggered controller does not rely on any global information of interaction structure and is implemented in a fully distributed fashion. In addition, two dynamic proposals not only cover existing static strategies as special cases, but also show that the minimal inter-execution time of dynamic one is not smaller than that of static one. Theoretical analysis shows that the proposed static and dynamic deadband-based event-triggered mechanisms can not only ensure the average consensus with Zeno-freeness, but also achieve the data reduction of communication and control. Finally, the proposed algorithms applied to circuit implementation are corroborated to prove its practical merits and validity.

Distributed Adaptive Finite-Time Bipartite Containment Control of Linear Multi-Agent Systems

Predefined the problem of finite-time bipartite containment control for heterogeneous linear multi-agent systems under directed topokogy. By designing a distributed bipartite compensator, the problem of bipartite containment can be considered as the problem of cooperative output regulation. Adaptive protocols are put forward by allocating a time-varying coupling weight to each follower. We thereby raise the distributed dynamic state feedback protocols combined with adaptive control, by which followers are driven to move to convex hull spanned by leaders in finite time. Furthermore, by employing the method of Lyapunov function, some plentitude criteria are inferred to pledge adaptive finite-time bipartite containment of heterogeneous agents. The final simulation examples proof the feasibility of the desired theoretical results.

A Survey of Alternative Measures of Macroeconomic Uncertainty: Which Measures Forecast Real Variables and Explain Fluctuations in Asset Volatilities Better?

In the past 20 years, measures of economic uncertainty have been developed that are purely market price based; structural model based, using data on real fundamentals and asset prices; text based; or survey based. We compare the performance of these uncertainty measures in forecasting three real variables with irreversibilities—investment, hiring, and credit creation—as well as in explaining fluctuations in stock market and Treasury bond market volatility. In general, we find that structural model–based measures do better than measures constructed using other approaches, with a model of stock market volatility by David and Veronesi performing the best on several (but not all) dimensions. Their learning-based model's volatility places time-varying weights on inflation, earnings, and consumption news, as agents in the economy assess the impact that inflation has on the stability of real economic growth.

Fuzzy Entropy for Frequency Diverse Array Beampattern Synthesis

Frequency diverse array (FDA) can generate a range-angle-dependent beampattern due to the introduction of the tiny frequency offsets across array elements. However, the range-angle-coupled beampattern is detrimental to the estimation of the target parameter, and even the normal function of the FDA. In fact, the main purpose of the FDA beampattern synthesis is to achieve beampattern decoupling, mainlobe attenuation avoidance, and sidelobe suppression. Furthermore, these conditions can be summarized as the chaotic degree modulation of the beampattern. In this communication, the 2-D fuzzy entropy is first employed in the FDA beampattern synthesis. The proposed FDA can generate a dot-shaped beampattern with high fuzzy entropy in the near-target field and low fuzzy entropy in the far-target field. In addition, by introducing time-variant weights, the time modulation is suppressed in the fuzzy entropy-based FDA (FEBFDA). The results of the numerical simulation demonstrate the superiority of the proposed beampattern synthesis method.

Fault Detection of an Actuator with Dual Type Motors and One Common Motion Sensor

In urban aerial mobility (UAM), the blade pitch angle is adjusted for flight control. Electro-mechanical actuators based on redundant motors have been used for controlling the blade pitch angle. This study addresses the problem of detecting faults in such redundant motors. In particular, the system is composed of two redundant motors using one common sensor for acquiring rotation-related information. In this study, the authors do not use current signals that have been widely used for fault detection in previous studies. Instead, we propose a method of modulating control signals with time-varying weights for determining a faulty motor. The proposed method is verified through simulation examples and experiments.

Reliability-centered prediction methods of clock battery low-output-voltage event for smart electricity meters

Smart electricity meter failures triggered by low-output-voltage accounted for 79 % of on-site meter failures. It is significant to predict the event of low-output-voltage of the clock battery. Still, it is also challengeable due to the complexity of the failure causes and the diversity of component reliability data. In this paper, multi-causes of battery low-output-voltage failure were analyzed and incorporated to develop a system reliability model for the clock battery circuit. The lifetime distribution of the clock battery circuit was computed using time-varying weights to integrate diverse forms of component data, including degradation data, lifetime distribution data, lifetime observation data, and failure rate data from datasheet. Finally, an engineering example is used to verify the effectiveness of the developed method. The developed reliability model of clock-battery systems can also be applied in other systems where power is supplied from batteries.

A Simulation Study Comparing the Performance of Time-Varying Inverse Probability Weighting and G-Computation in Survival Analysis.

Inverse probability weighting (IPW) and g-computation are commonly used in time-varying analyses. To inform decisions on which to use, we compared these methods using a plasmode simulation based on data from the Effects of Aspirin in Gestation and Reproduction (EAGeR) Trial (June 15, 2007-July 15, 2011). In our main analysis, we simulated a cohort study of 1,226 individuals followed for up to 10 weeks. The exposure was weekly exercise, and the outcome was time to pregnancy. We controlled for 6 confounding factors: 4 baseline confounders (race, ever smoking, age, and body mass index) and 2 time-varying confounders (compliance with assigned treatment and nausea). We sought to estimate the average causal risk difference by 10 weeks, using IPW and g-computation implemented using a Monte Carlo estimator and iterated conditional expectations (ICE). Across 500 simulations, we compared the bias, empirical standard error (ESE), average standard error, standard error ratio, and 95% confidence interval coverage of each approach. IPW (bias = 0.02; ESE = 0.04; coverage = 92.6%) and Monte Carlo g-computation (bias = -0.01; ESE = 0.03; coverage = 94.2%) performed similarly. ICE g-computation was the least biased but least precise estimator (bias = 0.01; ESE = 0.06; coverage = 93.4%). When choosing an estimator, one should consider factors like the research question, the prevalences of the exposure and outcome, and the number of time points being analyzed.

Improved runoff forecasting based on time-varying model averaging method and deep learning.

In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constructed. The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff. The results demonstrate that the current monthly runoff, the runoff of the previous month, the current monthly temperature, the temperature of the previous month and the current monthly rainfall were the variables suitable for runoff prediction. The results of runoff prediction show that the TV-DMA model has the highest prediction accuracy (with 0.97 Nash-efficiency coefficient (NSE)) and low uncertainty. The interval band of uncertainty was 33.3%-65.5% lower than single model. And the prediction performance of the single model and TV-DMA model in flood season is obviously lower than that in non-flood season. In addition, this study indicate that the current monthly runoff, rainfall and temperature are the important factor affecting the runoff prediction, which should be paid special attention in the runoff prediction.

A spatio-temporal decomposition framework for dynamic functional connectivity in the human brain

Much recent attention has been directed toward investigating the spatial and temporal organization of brain dynamics, but the rules which constrain the variation of spatio-temporal organization in functional connectivity under different brain states remain unclear. Here, we developed a novel computational approach based on tensor decomposition and regularization to represent dynamic functional connectivity as a linear combination of dynamic modules and time-varying weights. In this approach, dynamic modules represent co-activating functional connectivity patterns, and time-varying weights represent the temporal expression of dynamic modules. We applied this dynamic decomposition model (DDM) on a resting-state fMRI dataset and found that whole-brain dynamic functional connectivity can be decomposed as a linear combination of eight dynamic modules which we summarize as ‘high order modules’ and ‘primary–high order modules’, according to their spatial attributes and correspondence with existing intrinsic functional brain networks. By clustering the time-varying weights, we identified five brain states including three major states and two minor states. We found that state transitions mainly occurred between the three major states, and that temporal variation of dynamic modules may contribute to brain state transitions. We then conceptualized the variability of weights as the flexibility of the corresponding dynamic modules and found that different dynamic modules exhibit different amounts of flexibility and contribute to different cognitive measures. Finally, we applied DDM to a schizophrenia resting-state fMRI dataset and found that atypical flexibility of dynamic modules correlates with impaired cognitive flexibility in schizophrenia. Overall, this work provides a quantitative framework that characterizes temporal variation in the topology of dynamic functional connectivity.

Time-Varying Intuitionistic Fuzzy Integral for Emergency Materials Demand Prediction With Case-Based Reasoning

Recently, frequent emergencies seriously impact people’s life and production. The timely emergency material can save more people. Hence, emergency material demand prediction is a core of emergency rescue. However, emergency is a continuous, uncertain evolution process. The emergency of different periods has various situations. The time-varying and uncertain characteristics of emergency have made this representation and prediction work much more complex. In order to address this concern, this article proposes a time-varying intuitionistic fuzzy set (TVIFS) to precisely depict the time-varying scenario and the uncertainty of emergencies. For utilizing the TVIFS of different time frames, we deeply explore the integral aggregation operator of TVIFS, which can take full account of decision hesitation and possible information. Due to the different time importance of different time frames, we investigate the integral of TVIFS with constant period weight in advance. Considering the time-varying period weight, the TVIFS integral with time-varying period weight is investigated. For the sake of calculation feasibility, inspired by the relationship between continuity and dispersion, we translate the TVIFS integral with variable period weight into the TVIFS integral with constant period weight. Furthermore, we develop time-varying intuitionistic fuzzy case-based reasoning (CBR) model to forecast the emergency materials demand by designing hybrid similarity between cases. Finally, we elaborate the application of time-varying intuitionistic fuzzy CBR model by using an example of forest fire and verify the validity of our proposed method.

A dynamic ensemble learning with multi-objective optimization for oil prices prediction

Accurately predicting oil prices is a challenging task since its complex fluctuation characteristics. This paper innovatively introduces the “metabolism” mechanism and sliding window technology and proposes a dynamic time-varying weight ensemble prediction model with multi-objective programming to ameliorate the oil price's prediction performance. This paper first adopts the random forest to select and generate the best feature sets. Second, different individual models are selected to build a heterogeneous ensemble prediction framework. Then, a multi-objective weight generation model is established by considering horizontal and directional accuracy. Moreover, the nondominated sorting genetic algorithm-II is utilized to compute the prediction errors of a single model at different stages and achieve model optimization selection and ensemble weight generation. Finally, we take Brent and WTI oil prices as the prediction objects to verify the effectiveness and superiority of the proposed model. The experimental results reveal that the dynamic time-varying weight ensemble forecasting model has excellent prediction capability for oil prices and can become an effective forecasting tool.