Classification of Young Females' Body Shape in Jiaodong Area Based on 3D Morphological Characteristics

To improve the classification method of body type, 103 young female college students in Jiaodong area(Shandong, China) were measured by a 3 D body scanning system, and variables of upper body parts were selected and analyzed by SPSS software. According to the indices such as the chest ratio, the chest sagittal diameter ratio, and the shoulder angle, the tested population was quickly clustered into six categories by the classification method of “size feature+shape index+front and back indices”, which were divided into flat chest body, graceful body, breast augmentation body, normal body, convex back body, and flat body. The proportion of various body types and classification rules were obtained. According to the classification rules, 103 samples and 15 new females’ body data were analyzed and verified. Finally, according to the descriptive statistical analysis of upper body-related indicators of young female in this area, the height and the chest circumference were selected as independent variables, regression analysis was carried out on 11 related indicators, and the mapping relationship between height and chest circumference was studied, which provided a mathematical model for the design of fit clothing structure of young females in Jiaodong area.

Shallow Water Body Data Processing Based on the Seismic Oceanography

Physical properties of sea water,such as salinity,temperature,density and acoustic velocity,could be demarcated through degradation of energy caused by water absorption,attenuation and other factors.To overcome the challenging difficulties in the quick monitoring of these physical properties,we have explored the high resolution marine seismic survey to instantly characterize them.Based on the unique wavefield propagating in the sea water,we have developed a new approach to suppress the noise caused by the shallow sea water disturbance and obtain useful information for estimating the sea water structure.This approach improves seismic data with high signal-to-noise ratio and resolution.The seismic reflection imaging can map the sea water structure acoustically.Combined with the knowledge of local water body structure profile over years,the instant model for predicting the sea water properties could be built using the seismic data acquired from the specially designed high precision marine seismic acquisition.This model can also be updated with instant observation and the complete data processing system.The present study has the potential value to many applications,such as 3D sea water monitoring,engineering evaluation,geological disaster assessment and environmental assessment.

Recognition of abnormal body surface characteristics of oplegnathus punctatus

To identify the abnormal characteristics of the oplegnathus punctatus is great importance to the detection of iridovirus disease in the breeding environment.In this paper,an advanced neural network model to identify the characteristics of the oplegnathus puncta-tus and predict its different periods of suffering from iridovirus disease is proposed based on the establishment of a data set.First of all,a standard format data set of oplegnathus punctatus and an abnormal format date set are established in order to verify the effective-ness of the method in this paper.And then,the feature extraction fusion method is used for preprocessing in terms of the abnormal format data set,which combines the edge fea-tures extracted by the improved multi-template Sobel operator and the color features extracted by the HSV model.Finally,an improved VGG-GoogleNet network recognition model comes into being through the fusion and improvement of the VGG and GoogleNet neural network structure.The experiments results show that the prediction accuracy rate for oplegnathus punctatus suffering from iridovirus disease in the the abnormal format data set and the standard format data set are improved,which reach 98.55%and 69.18%.