Currently, yellow phosphor of Y3Al5O12:Ce3+ (YAG:Ce) fluorescent material is applied to a 450~480nm blue LED light source to implement a white LED device and it has a simple structure, can obtain sufficient luminance, and is economical. However, in this method, in terms of spectrum analysis, it is difficult to mass-produce white LEDs having the same color coordinates due to color separation cause by the wide wavelength gap between blue and yellow band. There is a disadvantage that it is difficult to control optical properties such as color stability and color rendering. In addition, this method does not emit purple light in the range of 380 to 420nm, so it is white without purple color that can not implement the spectrum of the entire visible light spectrum as like sunlight. Because of this, it is difficult to implement a color rendering index(CRI) of 90 or higher, and natural light characteristics such as sunlight can not be expected. For this, need for a method of implementing sunlight with one LED by using a method of combining phosphors with one light source, rather than a method of combining red, blue, and yellow LEDs. Using this method, the characteristics of an artificial sunlight LED device with a spectrum similar to that of sunlight were demonstrated by implementing LED devices of various color temperatures with high color rendering by injecting phosphors into a 405nm deep blue LED light source. In order to find the spectrum closest to sunlight, different combinations of phosphors were repeatedly fabricated and tested. In addition, reliability and mass productivity were verified through temperature and humidity tests and ink penetration tests.

The purpose of this research was to revive the bathochromic effects of wool fabrics by using natural dyestuffs with minimum heavy metallic mordants. The natural dyestuffs used in this research were the indigo plant, Phellodendron amurense, and Caesalpinia sappan. Sample no. 1 was pre-dyed five times with indigo. Sample no. 2 was pre-dyed five times with indigo and then once dyed with Phellodendron amurense. Following the same method as sample no. 2 with an additional Phellodendron, Sample no. 3 consisted of a pre-dye five times with indigo and twice with Phellodendron amurense. Sample no. 4 was pre-dyed six times with indigo and then once dyed with Caesalpinia sappan. Sample no. 5 followed the same method as no. 4 with an additional dye of Caesalpinia sappan. Sample no. 6 was pre-dyed five times with indigo and then once dyed with Phellodendron amurense and once dyed with Caesalpinia sappan. The results were as follows: first, all samples showed deeper colors. Second, according to the results of the surface K/S measurement, the surface K/S of wool fabrics was >20. Third, the results of lightfastness measurement showed superiority over grade 4 in samples no. 1, 2, 3, 5, and 6. However, sample no. 4 was grade 3. In the colorfastness to washing measurement, sample no. 2 showed greater superiority than grade 3—4, while samples no. 1 and 3 were grade 3. In addition, the colorfastness to dry cleaning for all samples was satisfactory or excellent by more than grade 3.

This study has suggested an image analysis system based on the Deep Learning for CCTV pedestrian detection and tracing improvement and did experiments for objective verification by designing study model and evaluation model. The study suggestion is that if someone’s face did not be recognized in crime scene CCTV footage, the same pedestrian would be traced and found in other image data from other CCTV by using Color Intensity Classification method for clothes colors as body features and body fragmentation technique into 7 parts (2 arms, 2 legs, 1 body, 1 head, and 1 total). If one of other CCTV footage has recorded its face, the identity of the person would be secured. It is not only detection but also search from stored bulk storage to prevent accidents or cope with them in advance by cost reduction of manpower and a fast response. Therefore, CIC7P(Color Intensity Classification 7 Part Base Model) had been suggested by learning device such as Machine Learning or Deep Learning to improve accuracy and speed for pedestrian detection and tracing. In addition, the study has proved that it is an advanced technique in the area of pedestrian detection through experimental proof.