Multivariate time series (MTS) forecasting plays an essential role in the automation and optimization process of intelligent applications. However, capturing correlations and dependencies among variables in MTS data remains a major challenge for ...

In recent years, significant achievements have been made in time series forecasting using deep learning methods, particularly the Long Short-Term Memory Network (LSTM). However, time series often exhibit complex patterns and relationships like ...

• A new Deep Implicit Memory Gaussian Network (DIMG) is proposed for TSF.
• A bidirectional deep memory kernel process is designed to improve prediction effect.
• The implicit features enhancement is introduced to obtain important ...

Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing ...

A GPGPU-based collision detection algorithm is proposed. Firstly, the information of OBB hierarchy tree and triangles of tested objects are mapped into some data textures designed for GPGPU-based calculation, such as triangle vertex textures, bounding ...

VR-based technology is a feasible idea for remote distance operation. Aiming the present material handling machine status, which is widely used in mine, port, train station, etc al, in this paper, the scheme of VR-based Material Handling Remote Operating ...