Existence and Construction of Orthogonal and Nearly Orthogonal Latin Hypercube Designs with Eight Columns | Chapter 4 | Mathematics and Computer Science: Research Updates Vol. 1

Latin hypercube designs are widely used in computer experiments to study complex processes. Orthogonality and space-filling are important criteria used to select good Latin hypercube designs. Orthogonality allows to study of the main effect of each factor independently when a regression model is fitted. Designs with better space-filling properties are used to estimate the meta-model more efficiently. In this chapter, we have solved the problem of the existence and construction of orthogonal Latin hypercube designs (OLHD) with eight columns. In particular, the proposed method is used to construct OLHDs (whenever exist) with eight factors for n=8k+s runs, where k≥1 is an odd integer and 0≤s≤7. In addition, nearly orthogonal Latin hypercube designs have been constructed for some values of n for which OLHD(n,8) do not exist. We have also shown that an OLHD(2ut,u) and an OLHD(2ut+1,u) can always be constructed for u=8,16,32,64,96,128,160,192, if a Hadamard matrix of order 4t exists, where t>1 is an integer. All the designs constructed in this chapter can be optimized in terms of discrepancy measures.

 

Author (s) Details

 

Poonam Singh
Department of Statistics, University of Delhi, New Delhi 110007, India.

 

Nilesh Kumar
Department of Statistics, University of Delhi, New Delhi 110007, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcsru/v1/3435

Driving Towards Sustainability: Examining Truck Emissions and Speed on National Roads | Chapter 14 | Contemporary Perspective on Science, Technology and Research Vol. 6

This research aims to determine exhaust emissions and speed of the truck while driving. Measurements are carried out using a mobile emissions analyzer that absorbs emissions from truck exhaust gases. Data collection is carried out by placing a mobile Emission Analyzer on the right side of the vehicle which is capable of absorbing 5 emissions in approximately 5 minutes. The relationship between exhaust emissions and speed uses a polynomial model of the average value of exhaust emissions and speed. The reliability of the mobile emission analyzer uses a multiplier factor to resolve data differences between the mobile emission analyzer and Bosowa equipment, where the CO2 correction factor is 0.666; CO correction factor of 0.243; NOx correction factor of 1.236; HC multiplier factor is 0.764. The research results show that CO2, NOx, smoke, CO and HC emissions form driving cycle patterns. This pattern shows a pattern following a parabolic trend, compound pattern of truck exhaust emissions on good road conditions and speeds of 0 km/h to 49.1 km/h, CO2 emissions 4%-16%, CO 0%-3%, smoke 0%-13%, NOx 0%-12%, HC 0%-11%, and with the truck running in damaged road conditions and speed 0 km/h to 35.8 km/h, CO2 emissions 3%-18%, CO 0%-5%, Smoke 0%-13%, NOx 0%-15%, and HC 5%-14%. Optimum speed values for Truck Exhaust Emission Driving Cycle Patterns on Damaged Road Conditions at CO2 19.16 Km/hour, CO 27.32 Km/hour, Smoke 25.9 Km/hour, NOx 19.7 Km/hour, and HC 27 .32 Km/h. The optimum speed values on a good road are CO2 23.22 Km/hour, CO 33.12 Km/hour, Smoke 33.12 Km/hour, NOx 31.98 Km/hour, and HC 36.82 Km/hour.

Author(s) Details:

Mukhtar Lutfie,
Muhammadiyah University, Luwuk 94711, Indonesia.

Sakti A. Adisasmita,
Hasanuddin University, Makassar 90245, Indonesia.

Muhammad I. Ramli,
Hasanuddin University, Makassar 90245, Indonesia.

Please see the link here: https://stm.bookpi.org/CPSTR-V6/article/view/13631