Electric field strength of different micro-environments

This dataset is used for machine learning. And the data set is collected in different micro-environments. In this project, ExpoM-RF 4 is used to measure the electric field strength. Four different typs of micro-environments are selected which are urban (6 high population density areas in Kuala Lumpur), suburban (7 low population density areas in Cyberjaya), park (3 park areas) and one indoor micro-environment. From the measurement campaigns, three machine learning (ML) techniques are simulated to model the Electric Field Strength in each micro-environment. The ML techniques are Fully connected neural network (FCNN), eXtreme Gradient Boosting (XG Boost), and Linear Regression (LR) to predict the RMS and Maximum radiation exposure.

本数据集用于机器学习任务。该数据集采集自多种不同的微环境。本研究中，采用ExpoM-RF 4设备测量电场强度。共选取四类不同的微环境：城市环境（吉隆坡的6个人口高密度区域）、郊区环境（赛城的7个人口低密度区域）、公园环境（3处公园区域）以及1处室内微环境。本次研究模拟了三种机器学习（Machine Learning, ML）技术，用于对各类微环境下的电场强度进行建模；上述技术分别为全连接神经网络（Fully connected neural network, FCNN）、极限梯度提升（eXtreme Gradient Boosting，XGBoost）与线性回归（Linear Regression, LR），可用于预测辐射暴露的均方根（Root Mean Square, RMS）值与最大值。