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:
Electric utilities worldwide are going through a process aimed at load balancing, reducing technical losses and improving the power quality. Network reconfiguration is one of several methods or applications to achieve these goals. It is featured with no investment costs where it is carried out through identifying the best location of the open point in the network. For this purpose, this paper is focused on applying the fuzzy clustering technique (FCT) on real distribution network in Mazoon Electricity Network (MEN) as a part of Oman distribution network. Supervisory Control and Data Acquisition (SCADA) system is used for measurement the performance of MEN for minimizing the power losses and improving the voltage profile. The FCT is applied to classify the distribution nodes based on their belonging to their feeder branch. The output of this FCT application is to create different valid reconfiguration scenarios which are simulated by ETAP to extract the optimal scenario that achieves the optimal network operation in terms of power losses reduction and voltage profile improvement
Advances in tech geared targeted at moving the society to a higher plain and sophistication with ease. The further integration of Internet to ease resource dissemination is attributed to its usage ease, ubiquity in its nature, low transaction cost and trust in channel. All these will continue to advance its popularity, usage ease and adoption. The rise thus, in adversaries threatens data integrity – and the task of detecting and use of countermeasures against intrusion remains a continuous task. Our study use a convolution neural network deep learning model to detect intrusion activities. Results shows model accurately detects malicious from genuine uncompromised packets traffic with confusion matrix yielding: TP = 43, TN = 3, FN = 5, FP = 9 for model sensitivity, specificity and accuracy. Model also displays a sensitivity of 93%, specificity of 25%, accuracy of 97% with a misclassification error rate of 4% for data inclusion that were not originally used to train model.
