This paper describes the quantitative evaluation of inner branding. Inner branding is a concept that “changes the awareness of employees themselves”. For example, how employees work can be considered part of the brand. In other words, it is important that employees work with awareness of their own brand is an important factor in instilling the brand. Employee hospitality is also an inner branding. We also need a brand name and a logo, but one of the factors is the cooperation of our employees in order to build a good brand. Generally, with regard to branding, no research has been found on their quantitative evaluation regardless of inner or outer. With this background, the authors have attempted to derive evaluation items for quantitative evaluation of inner branding, and have proposed a quantitative evaluation method by CS analysis. Here, practical verification is performed to apply the CS analysis method to inner branding evaluation. We conducted a questionnaire survey on company managers and employees in the field. This paper describes the effectiveness of this evaluation method as a result of applying CS analysis to the data in the field.
To improve the safety of autonomous cars, their obstacle detection capability in bad weather must be substantially improved. Haze is a major factor that degrades outdoor images. Although various dehazing schemes have been proposed, a dehazing scheme designed to improve obstacle detection capability has not been reported. Hence, we present a dehazing algorithm that enhances the safety of an autonomous car. This algorithm should be able to work in real time, even using edge computers typically installed as car electronics. Furthermore, this algorithm should work on grayscale images, as systems dependent on color images are often unaffected by environmental color changes caused by factors such as a setting sun. The empirical results showed that our grayscale image-based proposed algorithm is comparable to the results of current cutting-edge methods, and operates in real time.