1. 2015-10-21 Study Effects that Factors Have On Human`s Vision Capability DOE JI Xiaocong Huang Fei Wang Mingqiang Mei Lin

2. 2015-10-22 Introduction DOE

3. 2015-10-23 We are thinking about… Under what condition can he see it clearly?

4. 2015-10-24 Measure the effects of aimed environmental factors on human’s vision – luminance, color, shape and contrast Objectives of the experiment preliminary work: Indentify the response factor: vision ability Indentify the degrees to which these factors contribute Reference: Based on National standard vision chart - the standard logarithmic visual acuity chart [GB 11533-1989]

5. 2015-10-25 Control variables – Screen luminance – Character color & Screen background color – Character shape – Screen contrast – Character shape Variables(1/3)

6. 2015-10-26 "held constant" variables – Subjects’ age – Subjects’ vision – Subjects’ health condition – Subjects’ environmental adaptability – Subjects’ visual angle – Illumination of the test environment – Testing time – The interval between vision table and subjects Variables(2/3)

7. 2015-10-27 Nuisance factor (units) – Gene – Gender – Psychological state Variables(3/3)

8. 2015-10-28 Focus on the environmental factors that affect the vision capability & Ignore the individual differences between anticipants Restricted by the experiment conditions, some of the control variables are difficult to adjust. Restrictions

9. 2015-10-29 Methodology DOE

10. 2015-10-210 Analysis of the factors(1/2) Color(latter) Color(background) Shape Contrast Luminance Two Level Two Level

11. 2015-10-211 Analysis of the factors(2/2) Color(latter): Purple and yellow Color(background):blue and red Shape: “C” and “E” Contrast:”30” and “70” Luminance:”50” and “90”

12. 2015-10-212 Analysis of the Response Based on National standard vision chart - the standard logarithmic visual acuity chart 【 GB 11533-1989 】 The ability to recognize the different layout (12 degrees )

13. 2015-10-213 Full Factorial VS Fractional Factorial Full factorial design – 5 factorials – 2^5=32 Full factorial design – 5 factorials – 2^5=32 Full factorial design – 4 factorials – 2^4=16 Full factorial design – 4 factorials – 2^4=16 Fractional Factorial – 5 factorials – 2^(5-1)=16 Fractional Factorial – 5 factorials – 2^(5-1)=16 Not easy to actualize Lead the tiredness Not easy to actualize Lead the tiredness All the factors is important Which one is better??

14. 2015-10-214 Fractional Factorial Design Alias Structure I + A*B*C*D*E Blocks = A + B*C*D*E B + A*C*D*E C + A*B*D*E D + A*B*C*E E + A*B*C*D Alias Structure I + A*B*C*D*E Blocks = A + B*C*D*E B + A*C*D*E C + A*B*D*E D + A*B*C*E E + A*B*C*D Blocks(Con’t)= A*B + C*D*E A*C + B*D*E A*D + B*C*E A*E + B*C*D B*C + A*D*E B*D + A*C*E B*E + A*C*D C*D + A*B*E C*E + A*B*D D*E + A*B*C Blocks(Con’t)= A*B + C*D*E A*C + B*D*E A*D + B*C*E A*E + B*C*D B*C + A*D*E B*D + A*C*E B*E + A*C*D C*D + A*B*E C*E + A*B*D D*E + A*B*C

15. 2015-10-215 We shall select candidates with almost the same eyesight level, and other gene conditions looking similar. We will make some preparation for the experiment to make candidates have similar mental conditions. Block ONE BLOCK ONE PERSON

16. 2015-10-216 Methodology sum-up Two levels fractional factorial design 5 factorials 2^(5-1)=16 one block one person FactorsLOW LEVELHIGH LEVEL Color(latter)PURPLEYELLOW Color(background)BLUERED ShapeCE Contrast3070 Luminance5090 Response12 DEGREES

17. 2015-10-217 Preparation & Performing DOE

18. 2015-10-218 Preparation Response variable Vision Capability Known Study Color Inverse colors increase the Vision Capability Colors with long wavelength decrease the VC

19. 2015-10-219 Preparation Known Study Shape Generally, Shape E is easier to identify than C. Contrast High contrast lead to high visual capacity. Luminance In a certain range, High luminance level increase the visual capacity.

20. 2015-10-220 Preparation Factor Level Code NO.FactorsActual LevelCoded Level 1Color (Foreground) Yellow Purple+1 2Color (Background) Red Blue+1 3ShapeC E+1 4Contrast30 70+1 5Luminance50 90+1

21. 2015-10-221 Preparation Held-constant factors Candidate’s Physical Conditions Distance from Vision Chart Light Color & Luminance etc.

22. 2015-10-222 Experiment Performing Experiment Environment – ZJ12#510A – Vision Chart Made by PowerPoint 8 Versions with different Foreground, Background color, and Symbol Shape Presented on the Computer Monitor in order to change the contrast and luminance – Distance from Vision Chart: 5 meters.

23. 2015-10-223 Data Analysis DOE

24. 2015-10-224 DATA ANALYSIS Fractional factorial experiment VarFactorsActual LevelCoded Level ALuminance50 90+1 BColor (Background) Red Blue+1 CColor (Foreground) Yellow Purple+1 DShapeC E+1 EContrast30 70+1

25. 2015-10-225 DATA ANALYSIS Fractional factorial experiment Normal probability plot

26. 2015-10-226 DATA ANALYSIS Fractional factorial experiment ANOVA Term Effect Coef SE Coef T P Constant 10.1719 0.05842 174.13 0.000 Block 1 0.0781 0.10118 0.77 0.444 Block 2 -0.1094 0.10118 -1.08 0.285 Block 3 -0.2969 0.10118 -2.93 0.005 A 0.2187 0.1094 0.05842 1.87 0.068 B -1.7813 -0.8906 0.05842 -15.25 0.000 C -0.9688 -0.4844 0.05842 -8.29 0.000 D 0.5938 0.2969 0.05842 5.08 0.000 E 0.0313 0.0156 0.05842 0.27 0.790 A*B -0.0313 -0.0156 0.05842 -0.27 0.790 A*C -0.0938 -0.0469 0.05842 -0.80 0.427 A*D -0.0312 -0.0156 0.05842 -0.27 0.790 A*E 0.0313 0.0156 0.05842 0.27 0.790 B*C -1.2188 -0.6094 0.05842 -10.43 0.000 B*D 0.0937 0.0469 0.05842 0.80 0.427 B*E -0.2187 -0.1094 0.05842 -1.87 0.068 C*D 0.1563 0.0781 0.05842 1.34 0.188 C*E -0.1562 -0.0781 0.05842 -1.34 0.188 D*E 0.0312 0.0156 0.05842 0.27 0.790 S = 0.467336 R-Sq = 91.15% R-Sq(adj) = 87.62%

27. 2015-10-227 DATA ANALYSIS Fractional factorial experiment Model check

28. 2015-10-228 DATA ANALYSIS Fractional factorial experiment Further analysis:  Factor A and E are not significant  Ignore A and E,  Three factor two level and two replicates and four blocks full factorial design: background color letter color letter shape four blocks: four people Full factorial experiment

29. 2015-10-229 DATA ANALYSIS Full factorial experiment

30. 2015-10-230 DATA ANALYSIS Full factorial experiment Term Effect Coef SE Coef T P Constant 10.1719 0.05936 171.35 0.000 Block 1 0.0781 0.10282 0.76 0.451 Block 2 -0.1094 0.10282 -1.06 0.292 Block 3 -0.2969 0.10282 -2.89 0.006 B -1.7813 -0.8906 0.05936 -15.00 0.000 C -0.9688 -0.4844 0.05936 -8.16 0.000 D 0.5938 0.2969 0.05936 5.00 0.000 B*C -1.2188 -0.6094 0.05936 -10.27 0.000 B*D 0.0937 0.0469 0.05936 0.79 0.433 C*D 0.1563 0.0781 0.05936 1.32 0.194 B*C*D 0.0312 0.0156 0.05936 0.26 0.793 S = 0.474901 R-Sq = 89.24% R-Sq(adj) = 87.21%

31. 2015-10-231 DATA ANALYSIS Full factorial experiment

32. 2015-10-232 DATA ANALYSIS Full factorial experiment

33. 2015-10-233 DATA ANALYSIS Full factorial experiment From the analysis, we get such conclusion:  For background color: warm color (red) will lead to better performance.  For letter color: warm color (yellow) will lead to better performance.  For color interaction: inverse color will to better performance blue/ yellow and red/ purple is better than blue/purple and red/yellow  For letter shape: E shape will lead to better performance than C shape.  For other factors: under given condition not significant.

34. 2015-10-234 DATA ANALYSIS Check list to improve vision capability: – Red background color, purple letter color, E shape – Inverse color pairs do better – Warm color improve performance

35. 2015-10-235 TO BE IMPROVED Why other factors are not significant? – luminance and contrast – caused by device Response level is not very sensitive. Design method – not linear, center points? – not serious.

36. 2015-10-236  对比度因素对彩色 CRT 视觉工效的影响 曹立人、朱祖祥 人类工效学 1995 第 1 卷第 1 期  VDT 界面颜色视觉工效：色调因素对视觉绩效的影响 张德乾 张智君 心理科学 2008 31(2)  标准化 LogMAR 视力表与 C 形对数视力表一致性与稳定性分 析 李刚等， 海军总医院学报 2007 20 （ 4 ）  影响视力检测的因素 刘建军，中国眼镜科技杂志 2005.7  正常人亮暗背景下不同对比度的视力变化 史胜 柳林， 第二军医大学学报 2007 28 （ 7 ）

37. 2015-10-237  Dr. Kaibo Wang  TA Song XU  People involved in the experiment  Other team who help us in project

38. 2015-10-238 Thank You !