Xin Liu

TL;DR: This paper aims to develop a method of analyzing equipment working condition based on the sensed data and building a prediction model for working status forecasting and designing a deep neural network model to predict equipment running data and improving the prediction accuracy by systematic feature engineering and optimal hyperparameter searching.

TL;DR: A practical massive video management platform using Hadoop, which can achieve a fast video processing (such as video summary, encoding, and decoding) using MapReduce, with good usability, performance, and availability.

TL;DR: This paper proposes a graph neural network-based modeling approach for IoT equipment (called GNNM-IoT), which considers both temporal and inner logic relations of data, in which vertices denote sensor data and edges denote relationships between vertices.

TL;DR: A new deep learning network - RCNN4SPTL (RCNN -based Foreign Object Detection for Securing Power Transmission lines), which is suitable for detecting foreign objects on power transmission lines.

TL;DR: Several kinds of speech features were extracted and combined in different ways to reflect the relationship between feature fusions and emotion recognition performance, and two methods were explored, namely, support vector machine (SVM) and deep belief networks (DBNs), to classify six emotion status.