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:
Large-volume data is very difficult to find hidden patterns in the data. The complexity and computational time for analyzing large volumes of data to obtain important information are very dependent on the number of data and variables in a dataset. Big data intersects with incomplete data. This study aims to develop a method of data clustering that is sensitive to missing values in big data that is fast and efficient. This research develops data clustering using fuzzy c-means clustering methods. This method can accommodate the incompleteness of data by calculating the datum expertise in the dataset. Dimension reduction is applied to reduce dimensions in a data set while maintaining important information in the dataset. Core and Reduct which is one of the concepts in the rough set theory was chosen to reduce and leave only the core of a dataset. Core and Reduct are applied to look for core data patterns and select important variables in the data. The results showed that the application of Core and Reduct in the Fuzzy C-Means clustering could shorten the computational time and reduce the value of objective functions until the remaining 43.49%. At the same time, the quality of the clusters produced can be better with relatively unchanged purity and far better accuracy. The combined advantage of this method is that it has a better performance compared to the standard fuzzy c-means clustering
Enhancing the wet structure of the root zone under drip irrigation is one of the objectives of the irrigation designers and researchers. Improved operating standards such as application rate enable us to realize the full potential of drip irrigation technology. In this study, we applied two approaches to improve the wetting pattern; an experimental approach and a simulation approach. For simulation, we used HYDRUS 2D/3D model to simulate the application of four discharge rates. We performed our experiments in an open field at Chlef, Algeria with tomatoes cultivated. The objectives of this research work were to validate HYDRUS 2D/3D model, and to evaluate the application rate effect on soil moisture distribution, root water uptake and crop yield of tomato plant. Based on the measured data, the infiltration process and root absorption caused by the applied irrigation water under different parameters were analyzed and simulated using the HYDRUS 2D/3D model. We found satisfactory agreement between the simulated and experimental approaches. These results demonstrate the reliability of HYDRUS 2D/3D in the simulation of volumetric water content values (VWC) compared to those measured in the field. Additionally, the results indicate that under a discharge of 3 L.h-1 with 3 days’ frequency gives uniform moisture in the profile of the root zone. This strategy was tested in a field experiment on tomato cultivation and has been shown to have a significant effect on root extraction and crop yield