Subjects: Materials Science >> Materials Science (General) submitted time 2023-03-31 Cooperative journals: 《腐蚀科学与防护技术》
Abstract： Nanocomposite epoxy coatings were prepared with different proportion of epoxy resin and inhibitor- grafted montmorillonite. The effect of the inhibitor- grafted montmorillonite on the corrosion resistance of epoxy coating on hot dip galvanized steel sheet was investigated by using salt spray tests and electrochemical impedance spectroscopy (EIS). The results show that among the coatings tested, the one with 3% of the inhibitor-grafted MMT exhibits the most superior corrosion resistance with water diffusion coefficient of 9.89×10-11 cm2/s and porosity of 2.22×10-8 respectively; while its impedance values is above 109 Ω·cm2 during the whole immersion times.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Modeling psychological indexes (i.e., psych-modeling) is an emerging method that uses machine learning algorithms to identify psychological indexes based on big data. This paper reviews the feasibility of psych-modeling methods based on social media data in the field of psychometrics. Frequently used data types and machine learning algorithms are introduced. Then, we summarize psych-modeling's application to various scenarios together with its strengths and weaknesses. Compared with traditional self-reporting methods, psych-modeling has some advantages, including better performance in retrospective studies, greater ecological validity, and greater time-efficiency. However, psych-modeling has several limitations. For example, researchers need to spend extra time and effort to learn this new method and bear the inevitable cost of hardware. In future studies, researchers could investigate further how user's behavior on social media relates to psychological indexes. We also expect psych-modeling will be used in future psychological studies. By combining psychometrics and machine learning, we believe psych-modeling could make great contributions to psychology research and practice in the future.
Subjects: Psychology >> Applied Psychology submitted time 2020-11-05
Abstract： Modeling psychological indexes (i.e., psych-modeling) is an emerging method that uses machine learning algorithms to identify psychological indexes based on big data. This paper reviews the feasibility of psych-modeling methods based on social media data in the field of psychometrics. Frequently used data types and machine learning algorithms are introduced. Then, we summarize psych-modeling’s application to various scenarios together with its strengths and weaknesses. Compared with traditional self-reporting methods, psych-modeling has some advantages, including better performance in retrospective studies, greater ecological validity, and greater time-efficiency. However, psych-modeling has several limitations. For example, researchers need to spend extra time and effort to learn this new method and bear the inevitable cost of hardware. In future studies, researchers could investigate further how user’s behavior on social media relates to psychological indexes. We also expect psych-modeling will be used in future psychological studies. By combining psychometrics and machine learning, we believe psych-modeling could make great contributions to psychology research and practice in the future.
Peer Review Status:
Awaiting Review
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-20 Cooperative journals: 《计算机应用研究》
Abstract： In order to improve the recognition effect of convolutional neural networks(CNN) in image steganalysis, this paper constructed a new steganalysis-convolutional neural networks model （S-CNN）for steganalysis. The model reduced the number of layers of the convolution layer by using two layers of convolution layer and two layers of the whole connection layer. By adding the batch normalization layer to optimize the model before the activation function, to avoid the model in the training process into the over-fitting. The cancellation of the pool layer reduced the loss of embedded information, thereby improving the classification effect of the mode. The experimental results show that, compared with the traditional steganalysis methods, the proposed model reduces the steganalysis step and has higher steganalysis accuracy.
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