Ting Cui, Xianfeng Zhou^*, Yufeng Huang, Yanting Guo, Yunrui Lin, Leyi Song, Jingcheng Zhang

Phyton-International Journal of Experimental Botany, Vol.92, No.9, pp. 2633-2648, 2023, DOI:10.32604/phyton.2023.029259

Abstract Accurate assessment of canopy carotenoid content (CC_x+cC) in crops is central to monitor physiological conditions in plants and vegetation stress, and consequently supporting agronomic decisions. However, due to the overlap of absorption peaks of carotenoid (C_x+c) and chlorophyll (C_a), accurate estimation of carotenoid using reflectance where carotenoid absorb is challenging. The objective of present study was to assess CC_x+cC in winter wheat (Triticum aestivum L.) with ground- and aircraft-based hyperspectral measurements in the visible and near-infrared spectrum. In-situ hyperspectral reflectance were measured and airborne hyperspectral data were acquired during major growth stages of winter wheat in five consecutive field experiments.… More >

Jiali Wang¹, Bing Li², Chengyu Qiu¹, Xinyun Zhang¹, Yuting Cheng¹, Peihua Wang¹, Ta Zhou³, Hong Ge², Yuanpeng Zhang^1,3,*, Jing Cai^3,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4843-4866, 2023, DOI:10.32604/cmc.2023.037457

Abstract Epilepsy is a central nervous system disorder in which brain activity becomes abnormal. Electroencephalogram (EEG) signals, as recordings of brain activity, have been widely used for epilepsy recognition. To study epileptic EEG signals and develop artificial intelligence (AI)-assist recognition, a multi-view transfer learning (MVTL-LSR) algorithm based on least squares regression is proposed in this study. Compared with most existing multi-view transfer learning algorithms, MVTL-LSR has two merits: (1) Since traditional transfer learning algorithms leverage knowledge from different sources, which poses a significant risk to data privacy. Therefore, we develop a knowledge transfer mechanism that can protect the security of source… More >

Songlin Yang^1,2, Xingjin Han^1,2, Chufeng Kuang^1,2, Weihua Fang³, Jianfei Zhang⁴, Tiantang Yu^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 49-72, 2022, DOI:10.32604/cmes.2022.018325

Abstract The deformation prediction models of Wuqiangxi concrete gravity dam are developed, including two statistical models and a deep learning model. In the statistical models, the reliable monitoring data are firstly determined with Lahitte criterion; then, the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data, and the factors of water pressure, temperature and time effect are considered in the models; finally, according to the monitoring data from 2006 to 2020 of five typical measuring points including J₂₃ (on dam section ), J₃₃ (on dam section… More >