China is among the leaders in the rapidly advancing artificial intelligence field, and its broad range of cutting-edge research expertise is on display in this special issue on “Artificial Intelligence in China” of Big Data.
Co-Guest Editors Weiping Zhang, PhD, Zheijiang University (China) and Mohit Kumar, PhD, Rostock University (Germany) organized the unique and timely collection of articles in this special issue.
Featured in the special issue is the article entitled “Abnormal Data Region Discrimination and Cross-Monitoring Points Historical Correlation Repair of Water Intake Data,” coauthored by Huifeng Xue, Xi’an University of Technology and China Academy of Aerospace System Scientific and Engineering (Beijing), Qiaoyun Liu, Xi’an University of Technology, Junjie Hou, China Academy of Aerospace System Scientific and Engineering, and Yi Wan, Ministry of Water Resources (Beijing). The researchers analyze the characteristics of abnormal data distribution and show how the data from current monitoring points do not maximally correlate with historical data from corresponding points. They use sample data from recent years to demonstrate that application of the Abnormal Data Region Discrimination algorithm and the Cross Monitoring-Points Historical Correlation Repair method can correctly identify the abnormal data region and repair the abnormal data.
Yao Yu and Junhui Zhao, East China Jiaotong University (Nanchang) and Wu Lenan, Southeast University (Nanjing) collaborated on the article entitled “Multiple Targets Tracking with Big Data-Based Measurement for Extended Binary Phase Shift Keying Transceiver”. The researchers proposed using Doppler measurements of target velocity in combination with target range information to improve the ability to detect multiple targets accurately in a noisy environment with an extended-binary phase shift keying (EBSPK) transmit-receive system – a high-resolution radar tracking system. In a simulated experiment, they showed significant enhancement in the tracking performance of the big Doppler data association method. The target velocity measurements support the likelihood of the EBPSK transceiver-generated information, helping to distinguish actual targets from phony targets or clutter measurements.
Big Data Editor-in-Chief Zoran Obradovic, PhD, Carnell Professor of Data Analytics, Temple University, (Philadelphia, PA) states: “China’s spending on research has increased 8-fold since 2000. The overall results of this increased research activity are evident in many fields and are particularly impressive in the area of Artificial Intelligence and Big Data. This special issue provides an excellent opportunity to read about a range of ongoing AI-related developments across multiple big data-related fields in China.”