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Study of Pavement Identification Approach Based on Wavelet Analysis Renyun Sun 1, a, Yumei Shan 1,b and Bo Wang 1,c 1 School of Transportation and Automobile Engineering, Xihua University, Chengdu, 610039, China a sunry2004@163.com, b294816078 @qq.com, c 310641294@qq.com Key words: Wavelet analysis, Pavement identification, Electronic control brake system.
(10) From the multi-resolution analysis, that is[1,5,6]:
The data in Fig.1(c) and Fig.2(c) are de-noised with soft threshold wavelet analysis.
[3] Ergun Er&celebi: Computers in Biology and Medicine(2004),pp.479–493
[11] S.Abbasion, A.Rafsanjani, A.Farshidianfar and N.Irani: Mechanical Systems and Signal Processing (2007), pp.1-13

and aging treatment was shown in Fig.1, 2 and 3.
References [1] J.
Koo, Materials Chemistry and Physics, Vol. 94(1), (2005)131-140
Chang, Materials Science & Engineering A, Vol. 420(1-2) ,(2006) 155-164
Koo, Materials Science & Engineering A, Vol. 398(1-2), (2005) 113-127

Fig. 1.
In the case the filler thermal conductivity value considerable higher than binder one (λp>>λm) thermal conductivity could be predicted with a following equation: λλm=1(1-φ)3(1-α)(1+2α) (4) The third model was suggested by Hamilton R.L. and Crosser O.K. [16, 17].
Effective thermal conductivity could be calculated according to the formula: λλm=γ+n-1-(n-1)(1-γ)φγ+n-1+(1-γ)φ (5) where γ is thermal conductivity ratio of particles to binder (matrix) thermal conductivity γ = λp/λm, n – particle shape factor (3 or 6).
References [1] D.D.L.
A, 4 (50) (2008) 471-479.

Modeling Crash Frequency Using Crash and Geometric Data at Freeways Abdulrauf Khetrish1,a*, Muamer Abuzwidah2,b and Samer Barakat3,c 1-3Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, UAE au19104466@sharjah.ac.ae, bmabuzwidah@sharjah.ac.ae, csbarakat@sharjah.ac.ae Keywords: Crash data analysis, Crash Frequency, Poisson Regression, Negative Binomial Regression.
Figure 1: Trend of crashes Table 1: Total number of crashes per year year Number of crashes Increase 2014 1260 ------- 2015 1435 13.89 2016 1401 -2.37 2017 1421 1.43 2018 1917 34.90 Table 2: Summary of the Data N Minimum Maximum Mean Std.
Table 5: Parameter estimate for Poisson regression Parameter B Wald Chi-Square df P-value Exp(B) Segment length [miles] 2.894 6499.066 1 .000 18.074 AADT 7.988E-6 160.893 1 .000 1.000 Speed limit [mph] -.244 358.961 1 .000 .784 Number of lanes .008 .586 1 .444 1.008 Right shoulder width [ft] .013 6.886 1 .009 1.013 Left shoulder width [ft] .005 1.995 1 .158 1.005 Median Width [ft] -.003 11.702 1 .001 .997 In contrast to the Poisson model, as shown in Table 6 negative binomial model show significance only in three variables e segment length, AADT, and the speed limit, the rest are not significant based on p-value, similar to Poisson the coefficient estimate B sign for the speed was negative.
Exp(B) Segment length [miles] 5.869 510.982 1 .000 353.825 AADT 7.836E-6 20.429 1 .000 1.000 Speed limit [mph] -.175 37.562 1 .000 .840 Number of lanes -.020 .502 1 .479 .980 Right shoulder width [ft] .005 .196 1 .658 1.005 Left shoulder width [ft] -.002 .044 1 .834 .998 Median Width [ft] -.003 2.665 1 .103 .997 Conclusion In this paper , two widely used statistical models were utilized to analyze crash data from the North Carolina I-40 highway these models are Poisson regression and Negative Binomial, both models showed the importance of exposure factors the segment length and AADT, it was also concluded from the models that the increase of the speed decrease the number of crashes, although statistical advanced over the years there is still many limitations and issues associated with them for example in dealing with time-varying variables, unobserved heterogony, and the bias of under-reporting, so these limitations could be an opportunity to future research to utilize artificial techniques
References [1] P.P.

Welded specimens Fig. 1.
(a) Experimental setup Fig. 1.
Rank 1 is TSN and Rank 2 is WC and Rank 3 is DNW.
[12] Filippo Montevecchi, Niccolò Grossi, Hisataka Takagi, Antonio Scippa, Hiroyuki Sasahara, Gianni Campatelli, Cutting Forces Analysis in Additive Manufactured AISI H13 Alloy, Procedia CIRP, 46 (2016) 476-479
Sādhanā 47, (2022)1-14.

The detector of the computed tomography (CT) system was a flat-panel type with 1 024 × 1 024 detector elements and 1 080 projections.
References [1] W.
Nucl Eng Des 2002; 212(1-3):221-231
Cement Concrete Comp 2001; 23(6): 479-484
Industrial Construction 2005; 35(1): 5-7

ER308L-Si wire of 1 mm diameter was used as filler and 100% CO2 was used as shielding gas.
From the welded plates, ASTM E8M-04 standard tensile and ASTM D5045 standard impact toughness specimens were prepared by electrical discharge machine as shown in Fig. 1(b) and Fig. 1(c).
Table 1: DOE, yield strength, absorbed impact energy, calculated FT and failure locations Exp.
References [1] L.M.
Charact. 59, 4 (2008) 479-483

The components of chemical polishing solutions are shown in Table 1.
Fig. 1 The photography of CMP machine Results and discussion Corrosion on the SiC surface.
But the concentration of ionized Fe2+ ions is very low due to the slow ionization process of Eq.(1).
It can be found that the best polishing surface is obtained using Fe powder as catalyst of Fenton reaction, and the surface roughness reaches Ra0.479 nm and Rq0.658 nm, but some shallow scratches exist on the polished surface (see Fig. 6a).
References [1] B.Z.

Results and discussion Fig. 1(a) shows XRD patterns of the starting ilmenite mineral, as-synthesized nanofibers and calcined nanofibers at 300-1,000 ºC for 2 h.
Fig. 1 XRD patterns of (a) the starting ilmenite mineral, the as-synthesized nanofibers and the calcined nanofibers for 2 h at 100-500 ºC and (b) the calcined nanofibers for 2 h at 600-1,000 ºC.
Table 1.
References [1] B.
Energy Conv. 3 (1963) 455-479

FRGS/1/2019/TK10/UTP/02/1.
References [1] C.
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