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:
The mass, dimension, and size of a gearbox have strong effect on the gearbox cost. Consequently, in order to minimize the cost of a gearbox, the optimization of the partial gear ratios should be found. This study targets to find the optimum partial gear ration of two-stage bevel helical gearboxes base on the objective function of minimizing gearbox cost. Nine main process parameters are adopted to probe their influences on the output response. A plan of simulation experiment is conducted to serve for finding regression model. It is revealed that the total gearbox ratio parameter, ug, has strongest impact on the response u1 when compared to those of others. Besides, the predicted values given by regression model are significantly consisted to experiments
Along with the development of technology, space weather activity becomes a very important thing in science. It’s caused by whether activity that occurs in space can affect the activities of life on the earth. Therefore, it’s important to be able to detect whether events in space, including solar flares. We believe that there has not been a single solar flare prediction study that did a prediction using PROBA-2 SWAP data, because flares are difficult to catch at that frequency. Nearly all previous researches have been focused on SOHO / MDI and SDO / HMI satellite. If the two satellites can’t capture the image for some reason, then PROBA2 SWAP satellite imagery can be an alternative. This research is aimed to implement image processing and machine learning methods on SWAP PROBA2 satellite imagery to predict event numbers of solar flare. The machine learning algorithm used is random forest, while the segmentation algorithm used is seeded region growing. This solar flare prediction research using SWAP PROBA2 satellite imagery produced the best f-measure value of 0.897
