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:
In recent years research in the field of Artificial Intelligence (AI) has shown a fairly rapid and extensive development. The application of AI in the form of artificial neural networks in the field of Civil Engineering has also shown significant developments. In line with scientific advances in the field of numerical computing, artificial intelligence, also known as AI, has gained popularity in recent years (AI). One of the branches of AI is Machine Learning (ML). Machine Learning is a smart system that can learn and predict output based on learning done on the system. One form of ML is an artificial neural network (artificial neural network, ANN). ANN is a numerical computation model created by mimicking the workings of the human brain. ANN then developed extensively, with a greater number of neurons or by increasing the number of hidden layers. This development leads to a learning model known as Deep Learning (DL). This paper has an objective to describes how deep learning method, one of AI branch, can be used to predict the compressive strength of concrete, especially geopolymer concrete
Solar energy is a renewable energy that utilizes photovoltaic (PV) to generate electricity. Solar panels are used in automatic plant sprinklers, which are strongly influenced by irradiation from the sun (w / m2) to generate electrical power to activate the PLC device. The PLC device used in this study is the Zelio Smart Relay with type SR2B121JD which has 12 I / O and has a working voltage of 12 VDC, which functions to run the water pump in the watering system automatically. The experimental data collected in the form of solar panel power sourced from solar energy, the power that has been processed by the MPPT Controller, which supplies the PLC and water pump, PLC switching data and water discharge from the output of the solar pump. From the research that has been done, it is obtained direct power from PV with the highest average of 136.52 W and with the lowest average power of 9.25 W. The output power of the PV before becoming the power input for the PLC first passes through the controller so that the excessive or insufficient voltage does not cause damage to the PLC system. It produces the highest average power of127.97 W and the lowest average power of 104.44 W. Automatic plant sprinklers tests are carried out with a programmable control system on PLC memory and running the water pump for 180 seconds on the tests automatically