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An IoT routing based Local River Field Environment Management solution using Uzbekistan Testbed
Timur Khudaybergenov,박영기,임상일,배진호,양승윤,김진태,이성화,차대윤,우덕근,차재상 한국인터넷방송통신학회 2020 Journal of Advanced Smart Convergence Vol.9 No.3
Water consumption has grown at more than 2.5 times, comparing the past century. About 2.8 billion people live in river basins with some form of water deficit, because more than 75 % of the river flows are withdrawn for agriculture and other needs [1]. Challenges faced by more and more countries in their struggle for economic and social development are increasingly related to water [2]. This paper proposes a test of an effective local river field environment management solution. And describing a part of a pilot project for the ministry of water resources of Uzbekistan. Current work focused on direct action items of the existing project and describe an IoT routing based solution for local river field environment management solutions. Suggested technological decisions provided by needs and on-site testing results. The paper describes the backbone of IoT routing based river water resources management system.
Yan-Ming Cheng,Cheng Liu,Jing Wu,He-Miao Liu,Il-Kyoo Lee,Jing Niu,조주필,구경완,Min-Woo Lee,우덕근 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.6
This paper mainly focuses on the control strategy for phase-shifting full-bridge soft switching electrolytic silver power supply based on Zero Voltage Switching (ZVS) soft switching technology. Taking into consideration the low performance of traditional PID control for phase-shifting full-bridge soft-switching, this paper introduce a PID improved by Back Propagation (BP) neural network with one single learning rate which is used to calculate weights from the input layer to the hidden layer and weights from the hidden layer to the output layer. After testing, it is found that setting independent learning rate for calculation of weights from the input layer to the hidden layer and weights from the hidden layer to the output layer which will not have an adverse eff ect on the design of the controller. Instead, the learning rate can be set according to the respective characteristics of the weights between the two layers, which is called double learning rate BP neural network PID. The simulation results indicate that compared with the single learning rate BP neural network PID control, the double learning rate BP neural network control has higher response speed, less over-shoot, short time to enter the steady state and strong immunity.