Weijie Yu

Cutting-edge technologies are promoting the manufacturing industry to a higher level through the integration of information communication technology (ICT) and advanced manufacturing technologies (AMT), which leads to the revolution of traditional industry to intelligent industry. As a key enabling technology, digital twin technologies can effectively solve the problem of information and physical integration. Digital twin has demonstrated significant potential across various industries, garnering considerable attention from both industrial and academic sectors. This study conducts a comprehensive bibliometric and systematic review of nearly 9,000 publications, reconstructing DT’s historical trajectory from 1969 to 2025 through a four-stage conceptual evolution framework: Static Unidirectional Mapping, High-Fidelity Modeling, Bidirectional Interaction & CPS Integration, and Dynamic Bidirectional Intelligence.We propose a novel functional classification of DT technologies, namely visualization, digitization, dynamic simulation, and feature simulation, which reflects their lifecycle progression and multi-technology integration. Typical industrial applications are synthesized, and actionable research pathways are identified, including real-time data integration, cross-domain model interoperability, and predictive operational optimization. It clarifies some concepts and technologies that are unclear in the literature, which instructs and guides people in various industrial and academic backgrounds to develop and use digital twin properly in future.

Unprecedented mismatch occur frequently in the photovoltaic (PV) array systems, challenging the classical monitoring systems. However, the study of artificial intelligence methods lacks some well‐designed experiments for a systematic verification because mismatch are influenced by many aspects such as the PV array, the DC–DC converter, the DC–AC inverter, the loads as well as the environmental variables. The objective has two folds. One is to design some identical apparatuses to emulate the ‘same’ real‐world solar power station that is operated under multiple mismatch for a controllable, repeatable and comparable experimental study. Another is to propose a novel condition monitoring strategy based on the backward propagation neural network and a decision‐making formula. Some controllable indoor experiments and uncontrollable outdoor experiments are carried out to verify the ideas. Such work has never been studied before. The experimental results show that the derived monitoring systems identify and classify accurately different mismatch in both indoor tests and outdoor experiments. Moreover, real‐time experimental results infer that the data‐driven approach with the self‐learning capabilities is adaptive to the environmental changes. Therefore, the supervised learning‐based condition monitoring strategy is promising in the solar power industry in terms of operation management and performance enhancement.

With the development of multimedia presentation technology, image acquisition technology and the Internet industry, long-distance communication methods have changed from the previous letter, the audio to the current audio/video. And the proportion of video in work, study and entertainment keeps increasing, high-definition video is getting more and more attention. Due to the limits of the network environment and storage capacity, the original video must be encoded to be efficiently transmitted and stored. High Efficient Video Coding (HEVC) requires a large amount of time to recursively traverse all possible quantization parameter values of the coding unit in the adaptive quantization process. The optimal quantization parameter is calculated by comparing the rate distortion cost. In this paper, we propose a fast decision method for HEVC quantization parameters selection based on convolutional neural network, which saves video’s encoding time.