This paper concerns the RNG based algebraic turbulence model. This model has characteristics to capture transitional process from laminar to turbulent flow. This is determined by the argument of the Heaviside function, which becomes a threshold for the occurrence of turbulence. It is supposed that proper modeling of this argument will lead to correctly capture transition location. In the present paper, this argument is modeled in such a way that the form of cubic equation for the turbulent kinematic viscosity be maintained. Moreover, the length scale which is required to calculate the turbulent kinematic viscosity is newly proposed, taking into account the freestream pressure gradient. The validation is performed by comparing the calculated results with the empirical expressions as well as the experimental data. This model can simulate the streamwise intermittency effect, by which a sudden increase of skin friction is prevented. Moreover, the transition location can be predicted within reasonable accuracy compared with the experimental data. 相似文献
The concerns on visual privacy have been increasingly raised along with the dramatic growth in image and video capture and sharing. Meanwhile, with the recent breakthrough in deep learning technologies, visual data can now be easily gathered and processed to infer sensitive information. Therefore, visual privacy in the context of deep learning is now an important and challenging topic. However, there has been no systematic study on this topic to date. In this survey, we discuss algorithms of visual privacy attacks and the corresponding defense mechanisms in deep learning. We analyze the privacy issues in both visual data and visual deep learning systems. We show that deep learning can be used as a powerful privacy attack tool as well as preservation techniques with great potential. We also point out the possible direction and suggestions for future work. By thoroughly investigating the relationship of visual privacy and deep learning, this article sheds insights on incorporating privacy requirements in the deep learning era.
The interaction of a living organism with external foreign agents is a central issue for its survival and adaptation to the environment. Nanosafety should be considered within this perspective, and it should be examined that how different organisms interact with engineered nanomaterials (NM) by either mounting a defensive response or by physiologically adapting to them. Herein, the interaction of NM with one of the major biological systems deputed to recognition of and response to foreign challenges, i.e., the immune system, is specifically addressed. The main focus is innate immunity, the only type of immunity in plants, invertebrates, and lower vertebrates, and that coexists with adaptive immunity in higher vertebrates. Because of their presence in the majority of eukaryotic living organisms, innate immune responses can be viewed in a comparative context. In the majority of cases, the interaction of NM with living organisms results in innate immune reactions that eliminate the possible danger with mechanisms that do not lead to damage. While in some cases such interaction may lead to pathological consequences, in some other cases beneficial effects can be identified. 相似文献