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:
Interconnecting a PV inverter system with utility line has been widely adopted to process renewable energy and improve power factor. The PV system combined with the function of the active filter system called PV-AF can be useful for the application in the power distribution system. This paper presents a comparative analysis of performances between Series PV-AF and the proposal PV system under the hybrid structure PV-HF. The two configurations are based three level neutral point clamped inverter to keep a power raised without to oversize the switches and to associate structures in series. It has been observed from the simulation results that the performances of PV-HF are betters especially in the current and power quality
Long term diabetes could cause the diabetic retinopathy (DR) disease, if in long term is not treated appropriately, could affect in loss of vision and the effect is irreversible. Automated DR grading is important to help the ophthalmologist in DR treatment. Exudates detection is part of DR detection, but unfortunately, the exudates detection mostly focusses on binary grading (disease or no disease) rather than multi-class grading. Thus, in this paper, we proposed method in multiclass DR detection by using exudates candidates. Exudates candidates can be obtained by utilized CLAHE and wiener filter to enhanced the fundus images. Then to improve the candidates, region growing, segmentation and clustering methods which considering circularity, areas and eccentricity are utilized. Finally, Those candidates were extracted for features using statistical features and fed into ensemble learning process. The results concluded that our methods with XG-Boost as ensemble classifier is able to grade the multi-class DR severity level and comparable with other researcher which use MESSIDOR datasets