ABSTRACT To the national economy, increasing the forecasting accuracy of realised volatility (RV) on crude oil futures markets is of critical strategic importance. However, the RV of crude oil futures cannot be accurately predicted with a single model. For this study, we adopt a hybrid model which combines gated recurrent unit (GRU) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast the RV of crude oil futures. Moreover, back propagation neural networks (BP), Elman neural networks (Elman), support vector regression machine (SVR), autoregressive model (AR), heterogeneous autoregressive model (HAR), and their hybrid models with CEEMDAN are adopted as comparisons. In general, this article demonstrates the superiority of the CEEMDAN‐GRU model in RV forecasting from several aspects: for both evaluation criteria, CEEMDAN‐GRU achieves the highest RV forecasting accuracy in emerging and developed crude oil futures markets; furthermore, the empirical results are robust to alternative realised measures and training sets of different lengths.

Bayesian optimization (BO) is an efficient global optimization method for expensive black-box functions, but the expansion for high-dimensional problems and large sample budgets still remains a severe challenge. In order to extend BO for large-scale analog circuit synthesis, a novel computationally efficient parallel BO method, D 3 PBO, is proposed for high-dimensional problems in this work. We introduce the dynamic domain decomposition method based on maximum variance between clusters. The search space is decomposed into subdomains progressively to limit the maximal number of observations in each domain. The promising domain is explored by multi-trust region-based batch BO with the local Gaussian process (GP) model. As the domain decomposition progresses, the basin-shaped domain is identified using a GP-assisted quadratic regression method and exploited by the local search method BOBYQA to achieve a faster convergence rate. The time complexity of D 3 PBO is constant for each iteration. Experiments demonstrate that D 3 PBO obtains better results with significantly less runtime consumption compared to state-of-the-art methods. For the circuit optimization experiments, D 3 PBO achieves up to 10× runtime speedup compared to TuRBO with better solutions.

A constrained Voronoi tree-based domain decomposition method for high-dimensional Bayesian optimization is proposed to solve large scale analog circuit synthesis problems, which can be formulated as high-dimensional heterogeneous black-box optimization. Hierarchical Voronoi tree progressively breaks down the design space into partitions with implicit performance boundaries such that promising regions are efficiently explored. Fast exploitation is ensured in Voronoi nest via local Bayesian optimization with a few observations. A slice-enhanced Gibbs sampling method is proposed to sample acquisition function cMES in irregular polyhedrons with design constraints. Compared with state-of-the-art methods, cVTS achieves significant speed up without loss of accuracy.

This paper proposes a hybrid boost converter for $\gt100V$ LED lighting applications. The proposed boost converter uses one flying capacitor to reduce the switch voltage stress, average inductor current, and inductor current ripple, and also to prolong the duty cycle (D) for the same voltage conversion ratio (M). Therefore, the proposed topology has reduced switching, conduction, and core losses, exhibiting also an enhanced step-up capability. Hence, the proposed boost converter considerably improves the power conversion efficiency (PCE), with M=(2–D)/(1–D). Besides, the proposed boost converter automatically sets the voltage on the flying capacitor to VOUT– $\mathrm{V}_{IN}$. Here, we simulate both the conventional and the proposed boost converters in Cadence Spectre with commercial device models. The proposed boost converter improves the peak PCE from 96.8% to 97.4%, with ${V}_{IN}=24V, V_{OUT} =103.5V$, and IOUT=1 A. That is equivalent to 18.75% total loss reduction.