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:
Regeneration of local cultural heritage is one way to strengthen the uniqueness and identity of the city in the era of globalization, that frequently seen as a threat to the existence of city identity. It is a very relevant issue for the corridor of Jalan Masjid Jamik, as a part of the historic downtown area in the city of Pangkalpinang. Hence, the purpose of this study is to investigate the phenomenon of local character transformation in the corridor of Jalan Masjid Jamik. In this descriptive study, the identification and classification of architectural style of the facade elements of 62 buildings, as the research sample, was conducted by the author objectively and by subjective confirmation from the building owner. The results of the study exhibited that the city corridor of Jalan Masjid Jamik still has a strong Malay identity or character, because buildings with Malay architectural facade designs were observed predominantly. This local character appears alongside the global character dominated by Art Deco and Modern architectural style. The scale of trading and services conducted is one of the factors affecting the transformation of local character in the study area
Automatic License Plate Detection and Recognition (ALPD-R) is used in traffic, security and parking management systems. In this paper, deep semantic segmentation methods are used for license plate detection. Three different deep segmentation methods are considered in license plate segmentation. They are SegNet, Fully Convolutional Network (FCN) and Densely-Connected and Concatenated-Multi-Encoder-Decoder (DCCMED) network. These models are further trained on the same training data set and tested accordingly. Visual and numeric evaluations are used to validate the performance of the proposed work. Recall, precision and F-measure values are calculated. According to the obtained results, FCN outperforms the recall, precision and F-measure values are 0.6410, 0.9043 and 0.7491, respectively. The DCCMED produces the second best evaluation scores where obtained scores are 0.4955, 0.9188 and 0.6493. Finally, the worse segmentation scores are produced by SegNet architecture. Its achievements are 0.4916, 0.7704 and 0.5913, respectively. Hence, the DCCMED’s precision value is higher than the SegNet’s and FCN’s precision value