China Cotton Machinery and Equipment Co.

Beijing, China

China Cotton Machinery and Equipment Co.

Beijing, China
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Gao G.,China Agricultural University | Zhao H.,China Agricultural University | Li D.,China Agricultural University | Hu B.,China Cotton Machinery and Equipment Co. | Wang G.,China Agricultural University
Sensor Letters | Year: 2013

A new approach for evaluating cotton flow velocity in a rectangular pipeline, which was used in a cotton inspection machine, was proposed in this paper. A mathematical model for the cotton flow in the pipeline was developed. Firstly, governing equations were found based on motion characteristics of the cotton flow. Then the rectangular pipeline was meshed and appropriate boundary conditions were selected. At last, the cotton flow velocity at the feature locations in the rectangular pipeline was simulated by the Fluent software. In addition, the cotton flow velocity in a cross-section of the pipeline was actually measured, and its experimental curve has been done. In the same crosssection, the cotton flow velocity was simulated, and its simulated curve has also been done. The relative error between the measured and simulated velocity values has been calculated and source of the error has been analyzed. In fact, the measured values and the simulated values were quite similar. The experimental results indicated that the turbulent model was reasonable when the cotton density in the pipeline was low and the numerical simulation method was feasible. Copyright © 2013 American Scientific Publishers.

Yang W.,Hebei University | Yang W.,China Agricultural University | Li D.,China Agricultural University | Wei X.,Jiangsu University | And 2 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery | Year: 2010

An optimal feature subset selection method based on improved genetic algorithm (IGA) was presented. A novel scheme named segmented chromosome management was adopted in IGA. This scheme encodes the chromosome in binary as a whole while separates it logically into three segments for local management. These three segments are segment C for color feature, segment S for shape feature and segment T for texture feature separately. A segmented crossover operator and a segmented mutation operator are designed to operate on these segments to generate new chromosomes. These two operators avoid invalid chromosomes, thus improve the search efficiency extremely. The probabilities of crossover and mutation are adjusted automatically according to the generation number and the fitness value. By this way, the IGA could obtain strong search ability at the beginning of the evolution and achieve accelerated convergence along evolution. The experiment results indicate that IGA has stronger search ability and faster convergence speed than the simple genetic algorithm (SGA). The optimal feature subset that the IGA obtained has much smaller size than that of the SGA did, so it is more suitable for the online classification of foreign fibers.

Wang G.,China Agricultural University | Gao G.,China Agricultural University | Li D.,China Agricultural University | Hu B.,China Cotton Machinery and Equipment Co. | Liu J.,China Agricultural University
Applied Mechanics and Materials | Year: 2013

Cotton processing technology in an automatic production line was presented. A pipeline used to set an apparatus for removing the foreign fiber was chosen in the cotton processing technology. The distribution and velocity of the cotton in five positions of the central plane of the pipeline were simulated by the Fluent software. The simulated curves of the cotton distribution and velocity were obtained. According to the curves, there is the optimal length of the cotton conveyed pipeline with a certain cross-section area to set the apparatus for removing the foreign fiber. There is no the steady zone if the pipeline length is shorter than the optimal value, and if longer than it, the pipeline will waste the material and space. The steady zone is determined by the forces. In the certain position of the straight pipeline away from the curved pipeline, the various forces, which act on the cotton or foreign fiber, are in the dynamic equilibrium, and the distribution and velocity of the cotton are steady. For the rectangular pipeline with the cross-section area 2960 ́120mm, its total length should be 7000mm, the steady zone length of the cotton distribution is about 2500mm, and the steady zone of its velocity is 1000mm. In the common part of their steady zones the apparatus for removing the foreign fiber may be setting rationally. © (2013) Trans Tech Publications, Switzerland.

Guo J.-X.,Zhejiang University | Guo J.-X.,Xinjiang Agricultural University | Rao X.-Q.,Zhejiang University | Cheng F.,Zhejiang University | And 3 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2010

Near infrared (NIR) spectroscopy was investigated to predict trash content and classify types of ginned cotton by using a fiber-optic in diffuse reflectance mode. Different spectra preprocessing methods were compared, and partial least-squares (PLS) regression was established to predict the trash content of ginned cotton. Discriminant analysis (DA) was used to classify various types of lint and content level of trash. The correlation coefficient r was 0.906 for optimal PLS model using three factors based on first-order derivative spectra, and RMSEC and RMSEP was 0.440 and 0.823 respectively. To classify ginned cotton with and without plant trash, the accuracy rate reached 95.4% using 15 principal components (PCs) via DA, whereas the prediction accuracy rate was only 80.9% for the classification of sample types due to containing foreign fiber, and the classification result for the content level of trash in lint was not good for the samples without any preprocessing. The result indicated that the NIR spectra of sample can be used to predict trash content in ginned cotton, which is often disturbed by type, content and distribution of foreign matters, and the accuracy of some prediction model is unsatisfactory. In order to improve the prediction accuracy, some methods would be applied in future research, such as pretreatment according to acquisition request of solid sample, or using transmission mode.

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