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:
Electricity can be described as flow of electric power or electric charge. Consumer will receive alternating current (AC) from power station, boosted by transformer to step up or step down, and distributed miles away to different location all over the world. To maintain electricity in stable condition, protection system must be clear with any faults. The protection system function as keeping the power system maintain in stable condition by isolating the components that under faults and leaving as much of network as possible in operation. Thus. This paper presents the events detection focusing on voltage sag and current swell during high fault resistance. The method is developed by applying time domain signal analysis using wavelet transform approach in MATLAB. This project analyzed real interrupted signal obtained from 11 KV distribution network in PSCAD software, comprise actual data form modelling of transmission line from Tenaga Nasional Berhad (TNB) distribution network. The signal will be decomposed through the wavelet mother. The lowers percentage different will prove the best detection event
Learning styles can be referred to the ways an individual gathers, processes, and organizes information. Several students prefer learning by doing and practicing, seeing and listening, memorizing and describing, or reasoning logically and intuitively. Learning style has an effect on the learning process and learners’ achievement. The collaborative way to identify learning styles is through a questionnaire or survey. Despite being reliable, these instruments have several shortcomings that hinder the learning style identification such as students are unmotivated to fill out a questionnaire and reluctant to provide information. Thus, to solve these problems, researchers have proposed several approaches to automatically detect learning styles. This paper identifies Felder-Silverman learning style model as a suitable model for learning style detection and proposes to use fuzzy rules to handle the uncertainty in the learning style detection. The evaluation has used the trapezoidal and triangular membership functions based fuzzy logic for 25 students and compared to their results from the Index of Learning Styles questionnaire. The proposed fuzzy inference system obtained 38% similar classification compared to Felder-Silverman learning styles
