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:
Bioindicators are organisms or biological responses that indicate the entry of certain substances in the environment. Lichen (Moss Crust) is an indicator plant that is sensitive to air pollution. One of methods to determine the condition of pollution in an area is to look at the macroscopic appearance of Lichen (moss crust) attached to trees or rocks in an area. The aim of this research is to investigate the level of air pollution in Sukolilo District, Surabaya, Indonesia using a bioindicator (lichen). A total of 7 villages (Klampis Ngasem, Menur Pumpungan, Nginden Jangkungan, Gebang Putih, Semolowaru, Medokan Semampir, Keputih) in Sukolilo District were selected as sampling points. Two methods are used to determine air quality in Sukolilo District, namely by biomonitoring the presence of Lichen as well as by measuring the size of Lichen found. Data analysis was performed by identifying the results of both methods with the The Hawksworth and Rose Index indicator table to determine air quality. The results showed that there were two types of Lichen identified in Sukolilo District, namely Lichen Crustose and Folilose with an average size of Lichen 4-6 cm. So it can be concluded that air quality based on the presence of Lichen is classified as Poor and air quality based on Lichen's size is classified as moderate. Therefore, it is concluded that the level of air pollution can be measured by using a Lichen bioindicator
The transmission system is the connecting part of the power station and, distribution is capable of being forwarded to the load center. If there is a fault in the transmission line by interrupting the electricity supply to the load, then this will cause a loss for consumers. Therefore, another technique is needed to identify the fault in the electrical power distribution system accurately and quickly by reducing search time and speeding up the repair process. This study will present a method to identify fault by classifying and estimating the location of a fault in the 115 kV transmission system. This technique is performed by combining Discrete Wavelet Transformation (DWT) and Recurrent Neural Networks (RNNs) of Elman. DWT aimed at extracting information of transient signals for each phase current and zero sequence current during one cycle when the fault starts. Elman RNNs are classified to detect a fault in each phase and ground, while Elman RNNs are used to measure the location of the fault in the transmission line. Training and testing data be carried out for the simulation of short circuit fault under different fault resistance and varying starting angle. Short circuit fault applied in the transmission line to 115 kV bus LK to BK on 63km line lengths. The fault classification results obtained are the accuracy of 100%, and the estimated location of fault received the most significant average error value is 1.4%
