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- 저자 : Chen Sifan (검색결과 3 건)
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Chen Sifan,Chen Baihe,Shu Peng,Wang Zhensheng,Chen Chengbin 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.1
In recent years, unmanned aerial vehicle (UAV) autonomous flight technology has been applied in many fields. However, in the process of autonomous operation, the UAV may deviate from the set flight path due to various disturbance factors, which results in mission failure. In order to find the abnormal situation in time and take corresponding measures, it is necessary to monitor the operation state of the UAV. Predicting the UAV flight path is the main monitoring method at present; however, the accuracy and real-time of the existing prediction methods are limited. Therefore, this paper proposes an error compensation Bessel bidirectional long short-term memory real-time path prediction model deployed in ground stations. First, because of inconsistency of the units in all directions of the original positioning information provided by global positioning system, the Bessel geodetic coordinate transformation is introduced to unify the units of three-dimensional coordinate data, so as to improve the prediction accuracy. Second, considering the problems of poor data quality and data missing in the operation process, the least square fitting method is used to supplement and correct the positioning coordinate data to obtain more reliable and accurate path observation values as the model input. Finally, a deep learning path prediction model based on bi-directional long short-term memory (BiLSTM) network is constructed, and the appropriate network parameters are determined with the prediction accuracy and time as evaluation indicators. In order to further improve the prediction accuracy, a compensator based on proportional integral differential error control theory is designed according to the output characteristics of the BiLSTM network, which is used for providing compensation values for the prediction results of the model. The training and testing results using the actual flight data of UAV operation show that, under the experimental environment built, the model proposed in this paper can complete the UAV flight path prediction with root mean square error < 1 meter within 0.1 second, and has better performance and higher prediction accuracy than other neural network models.
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Song, Dongran,Fan, Xinyu,Yang, Jian,Liu, Anfeng,Chen, Sifan,Joo, Young Hoon Elsevier 2018 APPLIED ENERGY Vol.224 No.-
<P><B>Abstract</B></P> <P>To optimize the power extraction from the wind, horizontal-axis wind turbines are normally manipulated by the yaw control system to track the wind direction. How is the potential power extraction efficiency of such wind turbines related to the parameter optimization of a yaw control system? We intend to answer this question in this study. First, we develop two control systems, a direct measurement-based conventional logic control (Control system 1), and a soft measurement-based advanced model predictive control (Control system 2). Then, a multi-objective Particle Swarm Optimization-based method is introduced to optimize control parameters and search for the Pareto Front, which represents different potential performance. On this basis, result investigation and analysis are carried out on an electrical yaw system of China Ming Yang 1.5 MW wind turbines based on three wind directions with different variations. Experimental results show that, under a large wind direction variation and with a 14% yaw actuator usage, 0.32% and 0.8% more power extraction efficiency are gained by Control system 1 and 2, respectively, after optimization. The achievable power extraction efficiency for the two yaw control systems goes down when the allowable yaw actuator usage is reduced. For instance, when the yaw actuator usage is 14%, 4.9% and 2%, the efficiency is 97.19%, 96.76% and 96.37% for Control system 1, and is 97.73%, 96.76% and 95.45% for Control system 2, respectively. Therefore, Control system 2 takes precedence over Control system 1 for having higher efficiency when the allowable yaw actuator usage is more than 4.9%. We also find that the potential power extraction efficiency of the two control systems is significantly influenced by the wind direction variation, that is, the optimized efficiency under small wind direction variation is 1.5% higher than that under large wind direction variation. In addition, the parameters of Control system 1 need to be re-optimized according to the wind condition, whereas the ones of Control system 2 may not. Finally, a novel yaw control strategy employing the optimized parameters as the query tables is suggested for the real applications.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Two favorable yaw control systems are developed and optimized. </LI> <LI> Intelligent optimization method is proposed to optimize the potential performance. </LI> <LI> Power extraction efficiency is optimized by 0.32% and 0.8% for two control systems. </LI> <LI> Optimized efficiency under small wind variation is 1.5% more than the large variation one. </LI> <LI> Novel yaw control strategy employing optimized parameters is suggested. </LI> </UL> </P>
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Dongwei Yuan,Li Qing,Lu Xing,Lan Jianfeng,Qiu Zhidong,Wang Xuehong,Wang Junnan,Zheng Xiaojiao,Chen Sifan,Zhang Chong,Jin Junfei 생화학분자생물학회 2024 Experimental and molecular medicine Vol.56 No.-
Acute liver injury is the basis of the pathogenesis of diverse liver diseases. However, the mechanism underlying liver injury is complex and not completely understood. In our study, we revealed that CERK, which phosphorylates ceramide to produce ceramide-1-phosphate (C1P), was the sphingolipid pathway-related protein that had the most significantly upregulated expression during acute liver injury. A functional study confirmed that CERK and C1P attenuate hepatic injury both in vitro and in vivo through antioxidant effects. Mechanistic studies have shown that CERK and C1P positively regulate the protein expression of NRF2, which is a crucial protein that helps maintain redox homeostasis. Furthermore, our results indicated that C1P disrupted the interaction between NRF2 and KEAP1 by competitively binding to KEAP1, which allowed for the nuclear translocation of NRF2. In addition, pull-down assays and molecular docking analyses revealed that C1P binds to the DGR domain of KEAP1, which allows it to maintain its interaction with NRF2. Importantly, these findings were verified in human primary hepatocytes and a mouse model of hepatic ischemia‒reperfusion injury. Taken together, our findings demonstrated that CERK-mediated C1P metabolism attenuates acute liver injury via the binding of C1P to the DGR domain of KEAP1 and subsequently the release and nuclear translocation of NRF2, which activates the transcription of cytoprotective and antioxidant genes. Our study suggested that the upregulation of CERK and C1P expression may serve as a potential antioxidant strategy to alleviate acute liver injury.
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