Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

With the assistance of powerful machine learning algorithms, data collecting and processing
efficiency of wearable electromechanical sensors are highly improved. Meanwhile, the …

Human–robot collaboration (HRC) has attracted strong interests from researchers and
engineers for improved operational flexibility and efficiency towards mass personalization …

Accurate and reliable remaining useful life (RUL) assessment result provides decision-
makers valuable information to take suitable maintenance strategy to maximize the …

In this paper, we present an extensive review on deep learning methods for image
restoration tasks. Deep learning techniques, led by convolutional neural networks, have …

Accurate and timely solar forecasts play an increasingly critical role in power systems.
Compared to longer forecasting timescales, very short-term solar forecasting has lagged …

In industrial steel plate production, process parameters and steel grade composition
significantly influence the microstructure and mechanical properties of the steel produced …

We develop a method to construct distribution-free prediction intervals for dynamic time-
series, called\Verb| EnbPI| that wraps around any bootstrap ensemble estimator to construct …

RSD.(triangle inequality) We first introduce the concept of weak majorization: Majorization is
a preorder on vectors of real numbers. For a vector a∈ Rd, we denote by a↓∈ Rd the …

Data-driven models based on deep learning have led to tremendous breakthroughs in
classical computer vision tasks and have recently made their way into natural sciences …