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In deep-sea mixed transmission mining systems, the mixed transmission hard pipe plays an important role in mineral transport. To speed up the process of connecting the hard pipe, a quick disassembly and assembly system was designed. By model analysis, the key focus of the rapid disassembly and assembly system was the clamp connector. On the basis of vibration theory and finite-element simulations, the connector underwent static and vibration analysis, and the fatigue life was predicted. Finally, laboratory and sea test tests were conducted on the rapid disassembly and assembly system. The quick disassembly and assembly system can realise quick disassembly and assembly under the target working conditions and maintained smooth operation during two 24-h cycle tests. [ABSTRACT FROM AUTHOR]

The appearance of tea leaves is the most intuitive factor in assessing their quality and is an important indicator of the price. The study utilizes machine vision and hyperspectral image technology to develop a rapid method for assessing the appearance quality of seven grades of Dianhong Congou black tea samples based on shape, color, texture, and feature fusion. Using the shape metrics of length, width, perimeter, narrowness, area, and rectangularity, 88 texture values, and nine color values as features, the effectiveness of intelligent recognition algorithms such as support vector machine, random forest, and least squares support vector machine (LSSVM) in classifying single features (shape, color, and texture) and fused data models of the categories of Dianhong tea samples was explored. The results showed that the accuracy of the feature fusion-based appearance quality evaluation models was all higher than the modeling results of the corresponding single features, and the accuracy of the LSSVM model based on the fused data was 100%. This study showed that feature data fusion reflects the external information of tea leaves more comprehensively and that intelligent classification algorithms combined with image analysis techniques were an effective strategy for the identification of regional black tea. [ABSTRACT FROM AUTHOR]

Near-infrared spectroscopy and back propagation-artificial neural network (BP-ANN) model in conjunction with synergy interval partial least squares (siPLS) algorithm were used to evaluate tea shoots quality. The near-infrared spectra regions relevant to tea quality (12493 cm−1to 11645 cm−1, 9087.5 cm−1to 8242.7 cm−1, 8238.9 cm−1to 7394.2 cm−1, and 6541.7 cm−1to 5697 cm−1) were selected using siPLS algorithm. The two principal components that explained 99.46% of the variability in this spectral data were then used to calibrate the BP-ANN quality index (QI) model. The performance of this model [the coefficient of determination for prediction (), 0.9680; root mean square error of prediction (RMSEP), 0.0178] was superior to those of the BP-ANN model (= 0.9332, RMSEP = 0.0285) and the siPLS model (= 0.9230, RMSEP = 0.0360). The predicted QI values of 25 samples highly correlated with the experimental values (= 0.9223, RMSEP = 0.0344). The QI model with the combined siPLS-BP-ANN algorithms accurately predicted the quality of tea shoots. [ABSTRACT FROM PUBLISHER]