ABSTRACT
Lithium-sulfur batteries with high theoretical energy density and cheap cost can meet people's need for efficient energy storage, and have become a focus of the research on lithium-ion batteries. However, owing to their poor conductivity and "shuttle effect", lithium-sulfur batteries are difficult to commercialize. In order to solve this problem, herein a polyhedral hollow structure of cobalt selenide (CoSe2) was synthesized by a simple one-step carbonization and selenization method using metal-organic bone MOFs (ZIF-67) as template and precursor. CoSe2 is coated with conductive polymer polypyrrole (PPy) to settle the matter of poor electroconductibility of the composite and limit the outflow of polysulfide compounds. The prepared CoSe2@PPy-S composite cathode shows reversible capacities of 341 mAh g-1 at 3 C, and good cycle stability with a small capacity attenuation rate of 0.072% per cycle. The structure of CoSe2 can have certain adsorption and conversion effects on polysulfide compounds, increase the conductivity after coating PPy, and further enhance the electrochemical property of lithium-sulfur cathode material.
ABSTRACT
Diseases not only bring troubles to people's body functions and mind but also influence the appearances and behaviours of human beings. Similarly, we can analyse the diseases from people's appearances and behaviours and use the personal medical history for human identification. In this article, medical indicators presented in abnormal changes of human appearances and behaviours caused by physiological or psychological diseases were introduced, and were applied in the field of forensic identification of human images, which we called medical forensic identification of human images (mFIHI). The proposed method analysed the people's medical signs by studying the appearance and behaviour characteristics depicted in images or videos, and made a comparative examination between the medical indicators of the questioned human images and the corresponding signs or medical history of suspects. Through a conformity and difference analysis on medical indicators and their indicated diseases, it would provide an important information for human identification from images or videos. A case study was carried out to demonstrate and verify the feasibility of the proposed method of mFIHI, and our results showed that it would be important contents and angles for forensic expert manual examination in forensic human image identification.
ABSTRACT
Face recognition technology has become an important quantitative examination method in the field of forensic identification of human images. However, face image quality affects the recognition performance of face recognition systems. Existing research on the effects of face image denoising and enhancement methods on the face recognition performance are typically based on facial images with manually synthesized noises rather than the noises under natural environmental corruption, and their studied face recognition techniques are limited on the traditional face recognition algorithms rather than state-of-the-art convolutional neural network based face recognition methods. In this work, face image materials from 33 real cases in forensic identification of human images were collected for quantitative analysis of the effects of face image denoising and enhancement methods on the deep face recognition performance of the MXNet system architecture based face recognition system. The results show that face image quality has a significant effect on the recognition performance of the face recognition system, and the image processing techniques can enhance the quality of face images, and then improve the recognition precision of the face recognition system. In addition, the effects of the Gaussian filtering are better than the self-snake model based image enhancement method, which indicates that the image denoising methods are more suitable for performance improvement of the deep face recognition system rather than the image enhancement techniques under the application of the practical cases.
