报告题目：Doubly Smoothed Density Estimation with Application on Miners’ Unsafe Act Detection
报告人： 王汉生 教授
报告时间：2024年10月30日（周三）13:00-14:00
报告地点：藕舫楼629室，腾讯会议537-253-996
主持人： 曹春正 教授
报告摘要：The mining industry is one of the most dangerous industries worldwide. Typical accidents include but are not limited to gas explosions, floods, and derailing. Unfortunately, most of these accidents are associated with human errors, often due to miners' unsafe acts. Thus, timely monitoring and correcting miners' unsafe acts is of great importance for safety assurance. To this end, we develop here a double smoothing kernel estimation method. It takes high-resolution images as inputs and then detects miners in the images automatically. Compared with the classical kernel density estimator, the new method contains two layers of nonparametric kernel smoothing. We show theoretically that the resulting density estimator enjoys much improved statistical efficiency, but also suffers from high computational cost. To speed up the computation, a grid point approximation (GPA) method is further developed. Once a miner is detected in the image, a pre-trained MobileNet model (i.e., a classical deep learning model based on convolutional neural networks) can be used to extract feature vectors. Based on the extracted feature vectors, a standard logistic regression model can be trained to classify the miners' acts into safe or unsafe categories. The resulting out-of-sample prediction accuracy is excellent.
报告人简介：王汉生，北京大学光华管理学院商务统计与经济计量系，教授，博导。国家杰出青年基金获得者，教育部特聘教授，全国工业统计学教学研究会青年统计学家协会创始会长，美国数理统计协会（IMS）Fellow，美国统计学会（ASA）Fellow，国际统计协会（ISI）Elected Member。先后历任10个国际学术期刊副主编（Associate Editor / Editor）。国内外各种专业杂志上发表文章180+篇，并合著有英文专著共1本，（合）著中文教材4本。爱思唯尔中国高被引学者学者（数学类，2014—2019；应用经济学类：2020；统计学类：2021—2023）。
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数学与统计学院
江苏省应用数学（南京信息工程大学）中心
江苏省系统建模与数据分析国际合作联合实验室
江苏省统计科学研究基地
2024年10月28日
