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:
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
The cost of a gearbox depends on many parameters, including its weight. Besides, the weight of the gearbox is highly dependent on the gear ratios of the gearbox. Therefore, to minimize the cost of a gearbox, it is necessary to determine the optimum partial gear ratios. This study is aimed to find the minimum gear ratios to minimize the weight of a two-stage helical gearbox with the second stage double-gear set. To do that, a simulation experiment was designed and performed. Also, nine design factors were chosen for the investigation of their effect on the optimum gear ratios. Besides, regression models to determine the optimum gear ratios were proposed
Outliers appear as extreme values but often contain information that is very important so it needs to be examined first whether the data is still used or issued. Outlier detection is a hot topic for research. Improved new technologies and a variety of applications cause an increase in the need for outlier detection. The outlier method was successfully applied in various fields, such as education, economics, business, health, space, geology, and credit cards. This study aims to discuss the design of outlier detection methods using the KMeans Clustering method. This study uses four different types of identification tests, there are Extreme Standard Deviation (ESD), Shapiro-Wilk W Test, Dixon-Type Test, and Boxplot-Rule. The results showed that the design of outlier detection methods can be used to compare and find which is more effective in solving problems in the grouping of national exam absorption data in Mathematics. Grouping is done by using 40 indicators of competency achievement on 5 materials tested: Algebra, Geometry and Trigonometry, calculus, and Statistics. Further research can be carried out at the implementation and evaluation stages of the design of this outlier detection method