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Chinese Ecosystem Research Network - with Special Reference to the Study of Aquatic Ecosystems
Shidong, Zhao,Xiangfei, Huang 한국육수학회 1997 생태와 환경 Vol.30 No.5
The Chinese Ecosystem Research Network (CERN) founded in 1988, consists of 29 ecological research stations (16 for agriculture, 7 for forest, 2 for grassland, 2 for lake and 2 for marine ecosystem), 5 disciplinary centers (for the elements of water, soil, atmosphere and biology, and aquatic ecosystems) and a synthesis research center. Its mejor tacks include monitoring and research on structure, function and dynamics of selected terrestrial and aquatic ecosystems, and managerial demonstration of sustainable ecosystems, providing a scientific basis for the study of sustainable development and global change. This network has already become an important site of ecological studies in China and an important component of the global ecological monitoring and research network. The study activities of aquatic ecosystems for the coming years under CBRN will focus on the formation mechanisms of ecosystem productivity and its sustainability, especially on the function of picoplankton.
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Zhao Shidong,Che Yanbo,Li Hongfeng 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.1
The accurate identifcation of rotor position is a prerequisite for the closed-loop control of spherical motors. In this paper, the spherical interval of the rotor of the permanent magnet spherical motor was partitioned by the Quaternary Triangular Mesh (QTM). According to diferent levels of division, the sphere was partitioned by triangles of diferent sizes. After the partition, a Hall sensor was placed at the vertex of the spherical triangle obtained by the level 3 partition. After the rotor starts rotating, the triangular intervals where the rotor poles were located could be identifed according to the output of the sensor array. To achieve more precise identifcation of spherical intervals, the paper proposes a strategy that combines Deep Neural Networks (DNN) with rotor spherical interval identifcation. However, due to the small change rate of the magnetic feld near the rotor pole, it was easy to get the local optimal solution by the neural network algorithm. To solve this problem, the parameters of DNN were optimized by the Krill Herd (KH) algorithm. Through 50 simulations, the classifcation accuracy of KH-DNN reached 97.7%, and the average Mean Squared Error (MSE) value was 0.0165, which was better than the original DNN algorithm and the control group algorithm. Finally, the algorithm was verifed by a rotation experiment. The R2 value of the trajectory remained above 0.99, and high classifcation accuracy was guaranteed. The algorithm proposed in the paper provided a new research idea for the position identifcation of spherical motors.
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Effect of Low-Temperature Heat Treatment on PM2.5 Adsorption Properties of GO Films
Weiwu Zou,Baoshan Gu,Shiqing Sun,Shidong Wang,Xin Li,Haoqi Zhao,Peiyan Yang 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2019 NANO Vol.15 No.01
To explore the mechanism of GO acting on PM 2.5, a graphene oxide (GO) film was prepared via a spraying method for air purification. The effects of different media, temperature and heat treatment times on the adsorption of PM 2.5 on GO film were investigated. The morphology, composition and structure of GO materials were characterized by scanning electron microscopy (SEM), electron spectroscopy (EDS), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), infrared spectroscopy (FT-IR) and Raman spectroscopy. When the vacuum heat-treatment temperature is below 80 ℃ and the atmospheric heat-treatment temperature is below 100 ℃, the air purification performance of the film does not change significantly. With the increase in the vacuum heat-treatment temperature, the removal efficiency of PM 2.5 by GO film decreases gradually from 95% to 83%. At different times, the vacuum heat treatment increases with time, and the film removal rate shows a downward trend. As the heat-treatment temperature and time increase, a certain redox reaction occurs in the GO, and the air purification performance decreases. At a temperature of 120 ℃ and a time of 8 h, the removal rate drops to 81.68%. The adsorption of PM 2.5 by GO film mainly relies on the action of oxygen-containing functional groups.
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