Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
Experiment investigation of twisted tapes insert have been performed for flat plate solar collector in laminar regime to study its effect on solar collector performance. Heat transfer rate and friction factor experiments were carried out with using twisted made from aluminum materials by using curvature vortex generators and fixed twist ratio(Y=2). Twisted tapes insertion was, typical twisted tape (TT), twisted tape with curved vortex generation in facing flow (TTFF). In order to expand the experimental range, the solar collector was tested at four different values of mass flow rate, from 0.025kg/s to 0.117kg/s. at Reynolds number Re from 400 to 2000. The results obtained were compared with those obtained from plain tube published data the results clearly indicate the enhancement of the Nusselt number by insert device. The results show that the Nusselt increasing with (31.4%-54%) by using twisted tape with curvature vortex generator (TTFF)
Semiconductor including integrated circuit (IC) is an expensive and complicated process. The trend of semiconductor packaging is going towards better performance with lower power consumption packages. Thus, the single-die packaging trend has evolved into multi-die packaging. The evolution of multi- die packaging requires more tools and processing steps in the assembly process. Furthermore, any die is tested at Class, and detected faulty will cause the whole package to be scrapped. These factors cause a bigger loss in production yield to compare to the single-die packaging. A new framework is suggested for model training and evaluation for the application of machine learning in the semiconductor test. The proposed new framework will be able to provide a range of possible recall rates from minimum to maximum to identify which machine learning algorithms specifically