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 >

Yanming Wang^1,2,3, Kebin Jia^1,2,3,*, Pengyu Liu^1,2,3

Journal on Internet of Things, Vol.2, No.3, pp. 109-120, 2020, DOI:10.32604/jiot.2020.010138

Abstract With the global climate change, the high-altitude detection is more and more important in the climate prediction, and the input-output characteristic curve of the air pressure sensor is offset due to the interference of the tested object and the environment under test, and the nonlinear error is generated. Aiming at the difficulty of nonlinear correction of pressure sensor and the low accuracy of correction results, depth neural network model was established based on wavelet function, and Levenberg-Marquardt algorithm is used to update network parameters to realize the nonlinear correction of pressure sensor. The experimental results show that compared with the… More >

Rajendran Muthuraj, Abhishek Sachan, Mickael Castro*, Jean-François Feller, Bastien Seantier*, Yves Grohens

Journal of Renewable Materials, Vol.6, No.3, pp. 277-287, 2018, DOI:10.7569/JRM.2017.634182

Abstract In this work, thermally insulating and electrically conductive aerogels were prepared from cellulose nanofibers (CNF) and carbon nanotubes (CNTs) by environmentally friendly freeze-drying process. The thermal conductivity of neat CNF aerogel is 24 mW/(m·K) with a density of 0.025 g/cm3. With the addition of CNTs into CNF aerogel, the electrical conductivity was significantly increased while the thermal conductivity was increased to 38 mW/(m·K). Due to these interesting properties, the Seebeck coefficient and the figure of merit (ZT) of the CNF/CNTs aerogels were measured and showed that CNF/CNTs aerogel thermoelectric properties can be improved. The compressibility and electrical resistance of the… More >