Volume 24, No. 3 (2018)

THE RELATIONSHIP BETWEEN THE ANTHROPOMETRIC MEASURES AND RESPIRATORY FUNCTIONS OF TOBACCO WORKERS

PETER OLAITAN AIYEDUN(1), SALAMI OLASUNKANMI ISMAILA(*1), DAVID OJO(2), GIDEON OLABODE ADENIRAN(1)

The aim of this research was to establish the relationship between some anthropometric measurements and peak expiratory flow rate of tobacco factory workers. Eighty permanent workers who had been employed for at least three years with no history of cardiovascular disease were involved with the study. The measured anthropometric data (chest width and depth, height and weight) were analyzed using SPSS (V 16.0) to develop predictive models for PEFR of tobacco factory workers. It was concluded that the developed models could determine the PEFR of workers in a tobacco manufacturing company.

INFLUENCE OF DIMENSIONAL DEVIATIONS OF MECHANICAL ELEMENTS IN THE KINEMATIC STRUCTURE OF SCARA INDUSTRIAL ROBOTS ON POSITIONING PRECISION AND TRAJECTORY ACCURACY

ANDREI LUNCANU(*1), GHEORGHE STAN(1)

The purpose of this article is to present the research on the influence of the dimensional deviations of the mechanical elements in the kinematic structures of the SCARA industrial robots regarding the positioning precision and trajectory error. In this article the direct kinematics method is used to calculate the positioning of the end-effector and generate the trajectory simulation with and without dimensional deviations.

WIND SPEED ANALYSIS AT IKEJA, NIGERIA USING THE CONVENTIONAL PROBABILITY DENSITY FUNCTIONS

OLUSEYI OGUNSOLA(*1), OFURE OSAGIEDE(1)

The wind energy potential at Ikeja (Lat. 6.35 N; Long. 3.20 E), Nigeria was statistically analyzed using three of the mostly utilized conventional Probability Distribution Functions (PDFs) in order to determine which of these distributions would give the best means of analysis for wind in this particular location. The best fit test for these PDFs were determined from Akaike Information Criteria, Bayesian Information Criteria, Kolmogorov-Smirnov test, Cramer-von Mises statistics, Anderson-Darling Statistic, Mean Square Error and Chi-Square Test using Maximum Likelihood Estimation and Method of Moments as parameter estimates. The Weibull distribution gave the best fit in this location.

INTELLIGENT LOW-POWER SMART HOME ARCHITECTURE

CORNEL POPESCU(*1), GEORGE CULEA(2)

The Smart Home technology is a house that implements a variety of physical and digital well-integrated technologies, providing a number of functionalities like control of lighting, temperature, multimedia devices, flammable gas leakage, automatic plant irrigation or alarms security using a set of hardware components connected to a development board that can be accessed remotely through an Android application. First, is presented the context and a state-of-the-art for such of applications. Second, is made a description for the architecture hardware and software of a smart home application like custom. Finally, results are highlighted and some edifying conclusions.

ON THE PHOTOLUMINESCENCE AND OPTICAL PROPERTIES OF ZINC OXIDE THIN FILMS PREPARED BY THERMAL OXIDATION

DRAGOȘ-IOAN RUSU(*1)

Zinc oxide (ZnO) thin films were prepared by thermal oxidation of vacuum evaporated metallic zinc films. Some structural investigation, performed by X-ray diffraction technique, showed that films are polycrystalline and have a würtzite structure. We also determined some structural parameters of the films. The optical absorption was studied in the wavelength range from 300 nm to 1700 nm. Photoluminescence spectra at temperatures 293 K and 78 K have been analysed.

MODELING OF SOLAR RADIATION WITH A NEURAL NETWORK

VALENTIN STOYANOV(*1), IVAYLO STOYANOV(1), TEODOR ILIEV(1)

Modeling of solar radiation with neural network could be used for real-time calculations of the radiation on tilted surfaces with different orientations. In the artificial neural network (ANN), latitude, day of the year, slope, surface azimuth and average daily radiation on horizontal surface are inputs, and average daily radiation on tilted surface of definite orientation is output. The possible ANN structure, the size of training data set, the number of hidden neurons, and the type of training algorithms were analyzed in order to identify the most appropriate model. The same ANN structure was trained and tested using data generated from the Klein and Theilacker model and long-term measurements. Reasonable accuracy was obtained for all predictions for practical need.