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Effect Factor Analysis of Spraying Quality for Agricultural Chemicals
Wenfeng Sun,Qichao Li,Yongcun Fan,Yanhua Wan,Teng Wang,Baozhong Cong 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.11
As one of the most important parts of the agricultural work, agricultural chemicals spray is to protect the plants from weeds, pest and bacteria. With development of high efficient agricultural chemicals, the consumption of agricultural chemicals should be controlled within a couple of liter per hectare in theory. But it’s very hard to realize effective use of agricultural chemicals in fact. In this paper, the most effect factors of spray quality such as droplets size, environment temperature, droplets density, nozzle type and spray method were analyzed in agricultural chemicals spray operation. Droplet size effects drift and uniformity of droplet distribution directly. Environment temperature effects drift and uniformity of droplet distribution greatly. Character of nozzle affects spraying quality directly. All research of the paper can offer valuable reference to appropriate application of agricultural chemicals.
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Detection of Starch Content in Potato Based on Hyperspectral Imaging Technique
Wei Jiang,Junlong Fang,Shuwen Wang,Yongcun Fan 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.12
Detection of starch content in potato is studied applying hyperspectral imaging technique in the paper. The original and preprocessing spectra were processed with partial least square(PLS) method to build prediction model of starch content. The original spectra between 400 and 1000nm was preprocessed with smoothing, second derivation, and multiplicative scatter correction (MSC). Prediction model was built with preprocessing spectra by applying principal component analysis (PCA). Known from the result, the model based on the preprocessing spectra preprocessed with smoothing and PCA is the best of all prediction models built in research. The determination coefficient (R2)of calibration set and prediction set was 0.8234 and 0.9031 respectively. The root mean square error of calibration set (RMSEC) and root mean square error of validation set(RMSEV) was 0.5633 and 0.5025,respectively.It indicated that this method could be applied in detection of starch content in potato. The study could offer theoretical and practical reference for further study in the future.
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Xu, Wenhua,Wang, Shunli,Fernandez, Carlos,Yu, Chunmei,Fan, Yongcun,Cao, Wen The Korean Institute of Power Electronics 2020 JOURNAL OF POWER ELECTRONICS Vol.20 No.6
Accurate estimation of the lithium-ion battery state of charge plays an important role in the real-time monitoring and safety control of batteries. In order to solve the problems that the real-time estimation of the lithium-ion battery is difficult and the estimation accuracy is not high under various working conditions, a lithium-ion battery is taken as a research object, and the working characteristics of the lithium-ion battery are studied under various working conditions. In order to reduce the computational complexity of the traditional unscented Kalman algorithm, an improved unscented Kalman algorithm is proposed. Considering the importance of accurately estimating the initial state of charge for later estimation, the initial estimation value is calibrated by using the open-circuit voltage method. Then, the improved unscented Kalman filter algorithm based on a reduced-order model is used for assessing and tracking to realize real-time high-precision estimation of the state of charge of the lithium-ion battery. A simulation model is built and combined with a variety of working conditions data for performance analysis. The experimental results show that the convergence speed and tracking effect are good and that the estimation error control is within 0.8%. It is verified that the reduced order of the three-particle nonlinear transform unscented Kalman results in higher accuracy in the state-of-charge estimation of lithium-ion batteries.
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