An intelligent aerator algorithm inspired-by deep learning.

Aerator is an indispensable tool in aquaculture, and China is one of the largest aquaculture countries in the world. So, the intelligent control of the aerator is of great significance to energy conservation and environmental protection and the prevention of the deterioration of dissolved oxygen. There is no intelligent aerator related work in practice and research. In this paper, we mainly study the intelligent aerator control based on deep learning, and propose a dissolved oxygen prediction algorithm with long and short term memory network, referred as DopLSTM. The prediction results are used to the intelligent control design of the aerator. As a result, it is proved that the intelligent control of the aerator can effectively reduce the power consumption and prevent the deterioration of dissolved oxygen.

[Chemical Constituents of Armadillidium vulgare].

OBJECTIVE: To study the chemical constituents of Armadillidium vulgare. METHODS: Compounds were isolated and purified by various column chromatography, spectroscopic methods were used to identify their structures. RESULTS: Seven compounds were isolated from Armadillidium vulgare and identified as vulgarine A (1), o-hydroxylbenzoic acid (2), dipyrrolopiperazine-2,5-dione (3), 4(1-H)-quinolone (4), adenine (5), n-acetyltyramine (6) and 4-methyl-5-thiazoleethanol (7). CONCLUSION: Compound 1 is a new alkaloid, compounds 3-7 are all nitrogen containing substances, which are isolated from Armadillidae family for the first time.