Human activity recognition by using convolutional neural network

In recent years, many researchers have studied the HAR (Human Activity Recognition) system. HAR using smart home sensor is based on computing in smart environment, and intelligent surveillance system conducts intensive research on peripheral support life. The previous system studied in some of the activities is a fixed motion and the methodology is less accurate. In this paper, vision-based studies using thermal imaging cameras improve the accuracy of motion recognition in intelligent surveillance systems. We use one of the deep learning architectures widely used in image recognition systems called Convolutional Neural Networks (CNN). Therefore, we use CNN and thermal cameras to provide accuracy and many features through the proposed method

Numerical simulation on the impact of the bionic structure on aerodynamic noises of sidewindow regions in vehicles

The paper adopted a bionic hemispherical convex structure in the A pillar-rear view mirror regions according to actual requirements. Furthermore, impacts of the bionic structure on aerodynamic characteristics and noises in the region were studied. Friction resistance of airflows was greatly reduced, fluctuations and pulsation pressures of flow fields were also reduced, and characteristics of flow fields and sound fields were improved. The computational results were finally verified by the experimental test. Firstly, the aerodynamic lift force coefficient and drag force coefficient of the bionic model were computed, and they were obviously lower than those of the original model. The adhesive force between tires and ground during vehicle running was increased, and the danger degree of “waving” of high-speed vehicle running was weakened. In this way, stability of vehicle running could be improved. Secondly, flow fields of the bionic model were computed. Compared with the original model, an obvious vortex was behind the original model, while no vortexes were behind the bionic model. Therefore, convex structures of the bionic model had obvious impacts on flow fields behind the rear view mirror. Airflow separation situations were obvious improved at wheels, windshield and rear side windows of the bionic model. Due to blocking of convex structures of the A pillar and rear view mirror in the bionic model, airflows was hindered and obvious dragging phenomena were formed. Therefore, flow fields in the side window regions could be improved greatly. In addition, the flow field scope under the rear view mirror in the bionic model was also decreased. Ringed vortex structures appeared behind the rear view mirror in the bionic model. The ringed vortex structures were closely interlaced and then extended together backwards. Vortexes behind the rear view mirror in the original model were chaotic, where most of them were attached on the surface of side windows. In the original model, turbulent flows with certain strength were on the right upper corner of the side window region. In the bionic model, no turbulent flows were in the same regions. This result indicated that through using the bionic convex structures, airflows flowing through side windows could be combed and could move backwards towards upper and lower edges of the side windows. It could be predicted that pulsation pressures on the side window surface would surely decrease. Therefore, aerodynamic noises caused by pulsation pressures in side window regions would also be improved correspondingly. Especially in regions behind A pillar-rear view mirrors, the maximum noise reduction amplitude reached about 20 dB