Search results

In this paper, the authors aim to consider the manufacturer's battery research and development (R&D) decision under subsidy. The supply chain includes two manufacturers, which produce substitutable electric vehicles, and a battery supplier. One of the manufacturers can choose to develop batteries or buy batteries. The authors assume consumers do not have enough trust in the manufacturer-made battery.

Stackelberg game is made use of to study the battery R&D strategy of the manufacturer under the incentive of government subsidies. This paper makes a comparative analysis on six situations, then the authors get some conclusions and give some managerial insights.

The results show that subsidy strategies do not necessarily reduce actual payments when the manufacturer does not research and develop batteries. The retail prices and actual payments are closely related to the substitutability and total cost advantage of product. The authors also find consumer trust positively affects the demand of the electric vehicles using the manufacturer-made batteries and then affects the manufacturer's battery R&D decision. When consumers have low trust in manufacturer-made battery, subsidy can bring greater sales and make R&D more profitable than procurement, so that the manufacturer chooses R&D. This study's findings also suggest consumer subsidy is always better for the government.

Distinguished from previous studies, the authors discuss the decision-making of component research, and introduce various government subsidy strategies and consumer trust to study their roles in the manufacturer's battery R&D choice.

This paper aims to propose a fast machine compression scheme, which can solve the problem of low-bandwidth transmission for underwater images.

This fast machine compression scheme mainly consists of three stages. Firstly, raw images are fed into the image pre-processing module, which is specially designed for underwater color images. Secondly, a divide-and-conquer (D&C) image compression framework is developed to divide the problem of image compression into a manageable size. And extreme learning machine (ELM) is introduced to substitute for principal component analysis (PCA), which is a traditional transform-based lossy compression algorithm. The execution time of ELM is very short, thus the authors can compress the images at a much faster speed. Finally, underwater color images can be recovered from the compressed images.

Experiment results show that the proposed scheme can not only compress the images at a much faster speed but also maintain the acceptable perceptual quality of reconstructed images.

This paper proposes a fast machine compression scheme, which combines the traditional PCA compression algorithm with the ELM algorithm. Moreover, a pre-processing module and a D&C image compression framework are specially designed for underwater images.