Volume 62, Issue 10 will be published on 02 December 2020
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 Lahendong Unit 3 geothermal power plant (PLTP) is a PLTP type of dry steam power plant and the thermodynamic cycle that occurs in the system is the Rankine Cycle. The objectives of this study are: a) Analyzing the temperature of the influence of the main air cooler on the performance of the turbine unit in Lahendong Units 3, b) Analyzing environmental factors that affect changes in the temperature of the cooling air which serves to improve its ability to repair the turbine. The method used is a survey method. The data used in this study is PLTP operation data for one month which is used to find turbine solutions to compare with the temperature of the main cooling air and ambient temperature. Resulting from the temperature of the main cooling air (cooling tower outlet) increases, the performance of the turbine shows a low number, on the contrary the temperature of the main cooling air increases, the performance of the turbine will rise. Factors that influence the main air temperature, the temperature and the temperature of the cooling tower inlet or the temperature of the air coming out of the condenser
The automatic face recognition system has become one of the most important functions in the field of computer vision. The systems of recognition have attracted great attention in terms of different applications in human life. Facial recognition based on automatic methods to determine the identity of individuals by different changes of the face, as indicated in the database [ORL]. In this article, we compare two techniques for measuring the classification rate. The first one is based on the extraction of key points by both methods (PCA-SIFT and SURF), then the association by the RANSAC algorithm, finally we determine face authentication based on distance measurement (Euclidean and Manhattan). The second one is based on the extraction of local features by three local binary patches (TPLBP) and then classification by support vector machine (SVM). Simulation results show that the second technique gives a good result reaching 98% and increases the recognition rate by 1.468%