The systematic strain measurement error in digital image correlation (DIC) induced by self-heating of digital CCD and CMOS cameras was extensively studied, and an experimental and data analysis procedure has been proposed and two parameters have been suggested to examine and evaluate this. Six digital cameras of four different types were tested to define the strain errors, and it was found that each camera needed between 1 and 2 h to reach a stable heat balance, with a measured temperature increase of around 10 °C. During the temperature increase, the virtual image expansion will cause a 70–230 µε strain error in the DIC measurement, which is large enough to be noticed in most DIC experiments and hence should be eliminated.
The effect of noise on the pattern selection in a regular network of Hodgkin-Huxley neurons is investigated, and the transition of pattern in the network is measured from subexcitable to excitable media. Extensive numerical results confirm that kinds of travelling wave such as spiral wave, circle wave and target wave could be developed and kept alive in the subexcitable network due to the noise. In the case of excitable media under noise, the developed spiral wave and target wave could coexist and new target-like wave is induced near to the border of media. The averaged membrane potentials over all neurons in the network are calculated to detect the periodicity of the time series and the generated traveling wave. Furthermore, the firing probabilities of neurons in networks are also calculated to analyze the collective behavior of networks.
Skin cancer, the most common cancer in the world, has many detection steps and the detection process is easy to make mistakes. A detection method based on convolutional neural network (CNN) is proposed to assist doctors in the detection. Based on the development of CNN in the classification and diagnosis of skin cancer in recent years, this paper compares and summarizes the development of each step in this process. After reviewing previous papers, it can be concluded that the classification process is roughly divided into four parts. In addition, the evaluation indicators of the model are further analyzed. AUC Sen and SPE are the most basic evaluation indicators in the model evaluation. As a skin classifier, CNN improves the accuracy of classification and diagnosis results to a great extent. CNN model has also made progress in "lightweight" and "concise". However, there are few evaluation indicators available for different CNN methods, and the evaluation latitude is relatively single. In the future, the evaluation indicators should develop in more aspects, it will enable to better understand the personality of a CNN model.
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