• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.299-305
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• 2019

This study was initiated to solve the difficulties of aged and female workers in agriculture society due to aging and demise of young people. In the case of the conventional elevated lift, the risk of exposure to uneven road or work environment, not the difficulty of professional qualification and operation, and the risk of exposure to the uneven road or working environment, were also studied based on previous researches so that women could easily and efficiently perform productive agriculture. First, the simulation was carried out through the prediction model of traction performance using the object of agricultural forklift, and the soil of the Kimhae city in Gyeongnam (34.125kPa, internal friction angle 35.294deg, external friction angle 13.620deg, Adhesion force 5.750 kPa, average cone index 0-15 cm cl, 1001.8 kPa). In the case of the forklift for simulation, the driving force and the kinetic resistance prediction modeling of the agricultural electric forklift are modeled. Based on this model, the motor control drive adopts the 1232E model, which is a drive dedicated to AC motor, and divides the two drivers into master and slave And the model for the simulation was designed to control motor drive, hydraulic drive, and various outputs on the main PCB. The simulation model is undergoing continuous simulation, modification and supplementation. Based on this research, we will continue research for development of safer and more efficient agricultural electric forklift.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.620-626
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• 2020

This research propose and simulate a solar power generation system monitoring system based on Modbus TCP communication using RaspberryPi, an IOT equipment, as a master and an inverter as a slave. In this model, various sensors are added to the RaspberryPi to add necessary information for monitoring solar power plants, and power generation prediction and monitoring information are transmitted to the smart phone through real-time power generation prediction. In addition, information that is continuously generated by the solar power plant is built on the server as big data, and a deep learning model for predicting power generation is trained and updated. As a result of the study, stable communication was possible based on Modbus TCP with the Raspberry Pi in the inverter, and real-time prediction was possible with the deep learning model learned in the Raspberry Pi. The server was able to train various deep learning models with big data, and it was confirmed that LSTM showed the best error with a learning error of 0.0069, a test error of 0.0075, and an RMSE of 0.0866. This model suggested that it is possible to implement a real-time monitoring system that is simpler, more convenient, and can predict the amount of power generation for inverters of various manufacturers.