Suchergebnisse

In many developing countries, the existence of the uncertified recycler seriously hinders the healthy development of the waste electrical and electronic equipment (WEEE or e-waste) recycling industry. As a result, how the government can regulate the uncertified recycler to improve environment and public health during the recycling processes has become a critical issue. To help tackle this issue, we build an evolutionary game model to study the interactions between the government and the uncertified recycler. We conduct stability analysis of each participant and obtain four asymptotically stable states. Furthermore, we conduct numerical simulations for comparative analysis based on the current situation of the Chinese e-waste recycling industry. Our results are as follows. First, there exist multiple asymptotically stable states for the government and the uncertified recycler, namely (no-governance, maintaining status quo), (governance, maintaining status quo), (governance, industrial upgrading), and (no-governance, industrial upgrading). Then, we verify the validity of the evolutionary game model through numerical simulations and find that penalty, supervision cost, additional investment cost, and financial subsidy can significantly influence the behavioral strategy of the government and the uncertified recycler. Finally, we find that the government should adopt the reward-penalty-supervision mechanism to promote the healthy development of the e-waste recycling industry and protect the environment and public health. Specifically, first, the government's subsidy for the uncertified recycler has upper and lower limits. Exceeding the upper limit will result in an excessive financial burden to the government, while falling below the lower limit will hinder the uncertified recycler from technology upgrading. Second, the government should strengthen the supervision of the uncertified recycler and increase the punishment for violations. Third, the government should focus on controlling the supervision cost. Fourth, ...

Inferior finger vein images would seriously alter the completion of recognition systems. A modern finger-vein recognition technique combined with image quality assessment is developed to overcome those drawbacks. By the quality assessment, this article can discard the inferior images and retain the superior images which are then transferred to the recognition system. Different from previous methods, this article assesses the quality features of the image for the purpose of distinguishing whether the image contains rich and stable vein characteristics. In light of this purpose, the quality assessment is implemented: first, the finger vein image is automatically annotated; second, the finger vein image is cut into image blocks to expand the training set; third, the average quality score of multiple image blocks from an image is the final quality score of the image in the course of testing. Next, the Histogram of Oriented Gradients (HOG) features are extracted from the four transformed high-quality sub-images, whose features are cascaded into the multi-scale HOG feature of an image. Finally, two modules, the quality assessment module using Convolutional Neural Networks (CNN) and finger vein recognition module which make full use of multi-scale HOG, are perfectly combined in this article. The test results have demonstrated that light-CNN can identifies inferior and superior images accurately and the multi-scale HOG is feasible and effective. What's more, this article can see the robustness of this combined method in this article.