Pengyu Liu^1,2,3,*, Wenjing Zhang^1,2,3, Tao Wang^1,2,3, Xiaowei Jia⁴, Ying Ma⁵, Kebin Jia^1,2,3, Yanming Wang^1,2,3

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 855-864, 2022, DOI:10.32604/iasc.2022.022380

Abstract The development of upper-air meteorological detection is contingent upon the improvement of detection instruments. Air pressure sensors play a key role in high altitude meteorological measurement, but they can be frequently affected by temperature fluctutations, resulting in less accurate measurement data. The need to address this limitation has served as the core problem for meteorological detection and drawn great attention from the community. In this paper, we propose a calibration model for the DF-RBF air pressure sensor. The proposed method decomposes the detection process and corrects the measurements by fitting the residuals to true pressure values. In particular, we first… More >

Jingtang Luo¹, Jingru Liao², Chenlin Zhang³, Ziqi Wang⁴, Yuhang Zhang², Jie Xu^2,*, Zhengwen Huang⁵

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1225-1239, 2022, DOI:10.32604/iasc.2022.022812

Abstract Accurate estimation of communication bandwidth is critical for the sensing and controlling applications of smart grid. Different from public network, the bandwidth requirements of smart grid communication network must be accurately estimated in prior to the deployment of applications or even the building of communication network. However, existing methods for smart grid usually model communication nodes in coarse-grained ways, so their estimations become inaccurate in scenarios where the same type of nodes have very different bandwidth requirements. To solve this issue, we propose a fine-grained estimation method based on multivariate nonlinear fitting. Firstly, we use linear fitting to calculate the… More >