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RISS 인기검색어
- 검색키워드
- 저자 : Mingze Yuan (검색결과 3 건)
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The Cloud Terminal Online Monitoring System of UPS Battery Performance based on MSP430 MCU
Yuan Zhisheng,Mingze Yuan,Haiying Wang,Tianjun Sun 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.6
Aiming at some problems in the process of UPS battery real-time monitoring, on the basis of the cloud terminal technology is proposed in this paper, a new method of remote online monitoring, and based on MSP430 single chip microcomputer, combined with modular guiding ideology, in order to improve the UPS battery remote monitoring automation as the core, through the reasonable design of hardware structure, the implementation of UPS battery on-line real-time monitoring of voltage, current and temperature. At the same time, the system based on ADO database access technology and C++ programming to complete the data management, human-computer interaction, and system control, by the cloud terminal implementation monitoring system data transmission between lower place machine and super-ordination machine. Practice has proved that the technology security and stability, has the very good practical and promotional value.
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The New Intelligent Control Strategy for Inverted Plasma Cutting Power
Zhisheng Yuan,Mingze Yuan 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.5
This paper proposes a new intelligent control strategy to meet the tracking requirement of the power itself considering the coupling impacts of the plasma cutting technology. The dynamic intense coupling impacts of the technologies can’t be simulated, and a novel style of PID neural network (PID-NN) is applied to obtain closed-loop controller’s reference cutting current via its nonlinear mapping effect. The PID algorithm parameters are self-adjusted through fuzzy logic, whose inputs are both the reference current error and its variation rate. Simulation and testing experiments have verified the greater improvements of this project in steady precision, response speed and robustness, and the controller’s advantage in dealing with discrete events and multivariate decoupling problems.
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Dai Yuntao,Peng Lizhang,Juan Zhaobo,Liang Yuan,Shen Jihong,Wang Shujuan,Tan Sichao,Yu Hongyan,Sun Mingze 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.4
In the fault diagnosis problem, where sample data of fault cases are imbalanced, data generation and expansion are performed based on a generative adversarial network to obtain balanced data for training. Combining a gated recurrent neural network and an autoencoder model, the GRU-BEGAN model for generating multiple time series data is proposed for the intelligent fault diagnosis of imbalanced nuclear power plant data. To guarantee the consistency of the probability distribution between the generated data and real data, the K-L losses are included as a part of the loss function of the generator. At the same time, the potential feature vector of the real data obtained by the discriminator encoder is introduced as a hidden variable in the generator, and the similarity between the generated data and the real data is controlled by introducing the hidden variables according to the probability to make the generated data diverse. For the imbalanced fault dataset of the nuclear power plant thermal–hydraulic systems, the proposed GRU-BEGAN model is used to expand the original data to obtain a balanced state. Then, a 1D-CNN fault diagnosis model is established based on a convolutional neural network. The experimental results show that the fault diagnosis accuracy of the total test data is improved by 1.45% after data expansion, and the fault diagnosis accuracy of the minority sample is improved by 6.8% after data expansion.
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