One of the factors that inhibits the development of the Chinese wine industry is that the sugar content ofthe grape feedstock is insufficient. In order to produce wine with better qualities using these materials,concentrating the grape juice could be a good alternative to adding sugars. In this study, suspension freezeconcentrationtechnology was applied to concentrating grape juice with a low sugar content. The freezeconcentratedgrape juice was made into red and white wines separately. In the control group, red andwhite wines were made from chaptalized (sugar-enriched) grape juice. The physical and chemical indexes,sensory evaluation results and polyphenolic content of the wine were analysed to evaluate the practicalityof applying the freeze-concentration technology in the wine industry. The results show that, after removingice every 30 min for approximately 14 h with a -18°C coolant, grape juice with an initial sugar content of14°Brix reached 23°Brix. Both the red wines and white wines made from freeze-concentrated grape juicewere of a higher quality than the wines made from chaptalised grape juice. Moreover, the phenolic contentwas concentrated, which may provide health benefits. Thus, suspension freeze-concentration technology isa promising alternative to traditional chaptalisation technology.
The Chinese fairy tale of Shennong, the god of agriculture in China, who tasted hundreds of grasses to find foods and herbs for humans during the legend period.
Abstract A ground segmentation method based on line fitting of adjacent points was proposed for accurate and real-time segmentation of non-ground information from the LiDAR point cloud. Firstly, the point cloud is divided into several ordered regions depending upon the distribution characteristics of the LiDAR’s concentric circles. Then, the Euclidean distance between adjacent points and the spatial geometric features of ground point clouds is used for adaptive line fitting of ground point clouds. Finally, the ground points are divided by the distance between the adjacent points and the outer points of the line. The experiment was conducted using a real car and the KITTI open-source dataset. The approach presented in this research substantially enhances the accuracy of ground segmentation while ensuring real-time performance.
Face recognition algorithm is a very promising technique in biometric authentication. However, the recognition precision can be affected by many factors, such as feature extraction method and classifier selection. In this paper, a novel algorithm for face recognition is presented according to the advances of the wavelet decomposition technique and the Support Vector Machines (SVM) model. The extracted features from human images by wavelet decomposition are less sensitive to facial expression variation. As a classifier, SVM provides high generation performance without transcendental knowledge. First, we detect the face region using an improved AdaBoost algorithm. Second, we extract the appropriate features of the face by wavelet decomposition, and compose the face feature vectors as input to SVM. Third, we train the SVM model by the face feature vectors, and then use the trained SVM model to classify the human face. In the training process, three different kernel functions are adopted: Radial basis function, Polynomial and Linear kernel function. Finally, we present a face recognition system that can achieve high recognition precision and fast recognition speed in practice. Experimental results indicate that the proposed method can achieve recognition precision of 96.78 percent based on 96 persons in Ren-FEdb database that is higher than other approaches.
