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Wearing helmets while riding motorcycles is compulsory in Vietnam since 2007. The goal of this study is to build finite element models of two of the most common type of helmet to be worn by Vietnamese. Based-on observation, half helmet makes up the majority followed by open face helmet. The viability of the two helmet is studied using finite element method. The open face helmet and the half helmet was designed by the Solidwork CAD tool and modeled by LS- Prepost with different thickness of the impact absorbing liner. Among with the modified V-dummy, these helmets have been conducted drop test following the European helmet standard ECE R22.05 at four impact position. The result has pointed out that the helmet models created a significant decrease of multiple injury index comparing with non-helmet model. Nevertheless, even with a thicker liner, the half helmet failed more tests than the open face helmet due to ECE 22-05. Moreover, the study also points out that traditional helmets have limited effect on rotational-based brain injury.

In this research presents the vehicle body surface design and analysis of vehicle aerodynamic using flow simulation. The purpose of this paper is to develop a method for surface design of vehicle body model in SOLIDWORK software by using 2D real car images. Then this body structure is utilized for simulation and aerodynamic analysis using flow air. In this article, flow simulation/SOLIDWORK was used to analyze and to make the vehicle model to achieve more realistic simulation results. The model used in the simulation of this air flow has the dimensions as well as the mass, materials and properties that are almost equivalent to real life to evaluate the results of the air currents acting on the vehicle, at each other level of speed from 40km/h to 120 km/h. This paper is intended to provide some experience and familiarity with flow analysis and simulation.

Nowadays, the quality of classification systems depends on the presentation of the dataset, a process that takes time to use in-depth knowledge to produce specific characteristics. Meanwhile, deep learning can extract features from a dataset, without having to design feature extractors. Convolutional Neural Network (CNN) is a special type of deep learning that achieves many accomplishments in speech recognition, image recognition and classification. In this paper, we use CNN to classify and recognize breast cancer images from public BreakHis dataset. This dataset includes 7,909 breast cancer (BC) histopathology images with four benign subclasses and four malignant subclasses. Our new task with this dataset is the automated multi-classification of these breast cancer images in eight classes, which can help reducing death rates and saving people’s lives in the world. Our method based on the resizing original images for building CNN model and classifying breast cancer classes.