Wenbin Dai¹, Lina Wang^1,2,*, Binrui Wang¹, Xiaohong Cui¹, Xue Li¹

Phyton-International Journal of Experimental Botany, Vol.91, No.10, pp. 2283-2296, 2022, DOI:10.32604/phyton.2022.021096

Abstract Temperature in agricultural production has a direct impact on the growth of crops. The emergence of greenhouses has improved the impact of the original unpredictable changes in temperature, but the temperature modeling of greenhouses is still the main direction at present. Neural network modeling relies on sufficient actual data to model greenhouses, but there is a widening gap in the application of different neural networks. This paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm (GA-WNN). With the simple network structure and the nonlinear adaptability of the wavelet basis function, wavelet neural network (WNN)… More >

Zhengyu Wu¹, Kaiqiang Li¹, Lin Yuan², Jingcheng Zhang¹, Xianfeng Zhou^1,*, Dongmei Chen^1,*, Kaihua Wei¹

Phyton-International Journal of Experimental Botany, Vol.91, No.10, pp. 2269-2282, 2022, DOI:10.32604/phyton.2022.022607

Abstract As the source and main producing area of tea in the world, China has formed unique tea culture, and achieved remarkable economic benefits. However, frequent meteorological disasters, particularly low temperature frost damage in late spring has seriously threatened the growth status of tea trees and caused quality and yield reduction of tea industry. Thus, timely and accurate early warning of frost damage occurrence in specific tea garden is very important for tea plantation management and economic values. Aiming at the problems existing in current meteorological disaster forecasting methods, such as difficulty in obtaining massive meteorological data, large amount of calculation… More >

Qi He¹, Wenlong Li¹, Zengzhou Hao², Guohua Liu³, Dongmei Huang¹, Wei Song^1,*, Huifang Xu⁴, Fayez Alqahtani⁵, Jeong-Uk Kim⁶

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 51-67, 2022, DOI:10.32604/cmc.2022.026771

Abstract Sea surface temperature (SST) is closely related to global climate change, ocean ecosystem, and ocean disaster. Accurate prediction of SST is an urgent and challenging task. With a vast amount of ocean monitoring data are continually collected, data-driven methods for SST time-series prediction show promising results. However, they are limited by neglecting complex interactions between SST and other ocean environmental factors, such as air temperature and wind speed. This paper uses multi-factor time series SST data to propose a sequence-to-sequence network with two-module attention (TMA-Seq2seq) for long-term time series SST prediction. Specifically, TMA-Seq2seq is an LSTM-based encoder-decoder architecture facilitated by… More >

Qihui Ling^1,2,*, Wei Zhang², Qiancheng Zhao^1,2, Juchuan Dai^1,2

Energy Engineering, Vol.119, No.1, pp. 287-300, 2022, DOI:10.32604/EE.2022.014718

Abstract The inverter is one of the key components of wind turbine, and it is a complex circuit composed of a series of components such as a variety of electronic components and power devices. Therefore, it is difficult to accurately identify the operation states of inverter and some problems regarding its own circuit, especially in the early stages of failure. However, if the inverter temperature prediction model can be established, the early states can be identified through the judgment of the output temperature. Accordingly, considering whether the inverter heats up normally from the perspective of heat dissipation, a method for the… More >