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| 1 | Application study of a correction method for a spacecraft thermal model with a Monte-Carlo hybrid algorithm显示文摘The correction of a thermal model for a thermally controlled satellite in ground test conditions is studied using a Monte Carlo hybrid algorithm.First,the global and local parameters are summarized according to sensitivity analyses on uncertain parameters,and then the model correction is treated as a parameter optimization problem to be solved with a hybrid algorithm.Finally,the correction of the thermal model is completed using a layered correction method.The sensitivity analysis showed that the effective emissivities across the multi-layer insulation (MLI) and the emissivities of the thermal control coating are global parameters,while the contact heat transfer coefficients are local parameters.After correction,the deviations between the calculated and test values were all within ± 3 ℃.The final results prove that the method in this study is superior to traditional methods and satisfies the requirements for thermal model correction. | CHENG WenLong LIU Na LI Zhi ZHONG Qi WANG AiMing ZHANG ZhiMin HE ZongBo | 2011 | Chinese Science Bulletin2011，56，13： | 2 |
| 2 | Quantification of Membrane Protein Dynamics and Interactions in Plant Cells by Fluorescence Correlation Spectroscopy显示文摘译解适当空间、时间的规模上的蛋白质和类脂化合物分子的动力学可以使蛋白质功能和膜组织清楚些。然而,传统的体积途径不能明白地确定在生活房间的蛋白质的极其多样的活动性和相互作用。荧光关联光谱学(FCS ) 是一种强大的技术描述发生在单个分子的水平并且在毫微秒上到第二 timescales 的事件;因此, FCS 能在膜系统的异构的组织上提供数据。FCS 能也与另外的显微镜学技术被相结合,例如超级决定的技术。更重要地, FCS 是最低限度地侵略的,它成为在开发期间检测关键蛋白质的异构的分发和动力学是一条理想的途径。在这评论,我们关于 FCS 的发展给简短介绍并且在理解膜蛋白质的植物房间膜和动力学和相互作用的组织总结 FCS 的重要贡献。我们也在植物生物学讨论这种技术的潜在的应用。 | Xiaojuan Li Jingjing Xing Zongbo Qiu Qihua He Jinxing Lin | 2016 | Molecular Plant2016，9，9： | 2 |
| 3 | Characterization of airborne individual particles collected in an urban area, a satellite city and a clean air area in Beijing, 2001显示文摘 | Zongbo Shi Longyi Shao T.P Jones A.G Whittaker Senlin Lu K.A Bérubé Taoe He R.J Richards | 2003 | Atmospheric Environment2003，，29： | 1 |
| 4 | Characterization of airborne individual particles collected in an urban area, a satellite city and a clean air area in Beijing, 2001 显示文摘 | Shi Zongbo Shao Longyi Jones T P Whittaker A G Lu Senlin Berube K A He Tao Richard R J | 2003 | Atmospheric Environment2003，37，： | 1 |
| 5 | A denoising-classification neural network for power transformer protection显示文摘Artificial intelligence(AI)can potentially improve the reliability of transformer protection by fusing multiple features.However,owing to the data scarcity of inrush current and internal fault,the existing methods face the problem of poor generalizability.In this paper,a denoising-classification neural network(DCNN)is proposed,one which inte-grates a convolutional auto-encoder(CAE)and a convolutional neural network(CNN),and is used to develop a reli-able transformer protection scheme by identifying the exciting voltage-differential current curve(VICur).In the DCNN,CAE shares its encoder part with the CNN,where the CNN combines the encoder and a classifier.Based on the inter-action of the CAE reconstruction process and the CNN classification process,the CAE regards the saturated features of the VICur as noise and removes them accurately.Consequently,it guides CNN to focus on the unsaturated features of the VICur.The unsaturated part of the VICur approximates an ellipse,and this significantly differentiates between a healthy and faulty transformer.Therefore,the unsaturated features extracted by the CNN help to decrease the data ergodicity requirement of AI and improve the generalizability.Finally,a CNN which is trained well by the DCNN is used to develop a protection scheme.PSCAD simulations and dynamic model experiments verify its superior performance. | Zongbo Li Zaibin Jiao Anyang He Nuo Xu | 2022 | Protection and Control of Modern Power Systems2022，7，1： | 0 |
| 6 | Knowledge-based Convolutional Neural Networks for Transformer Protection显示文摘Deep learning based transformer protection has attracted increasing attention.However,its poor generalization abilities hinder the application of deep learning in the power system owing to the limited training samples.In order to improve its generalization abilities,this paper proposes a knowledge-based convolutional neural network(CNN)for the transformer protection.In general,the power experts can reliably discriminate between faulty transformers and healthy transformers only through the unsaturated parts of equivalent magnetization curve(voltage of magnetizing branch-differential current curve)but deep learning intends to focus on the combined features of saturated and unsaturated parts.Inspired by the identification process of power experts,CNN adopted a specially designed loss function in this paper which is used to identify the running states of power transformers.Specifically,the presented Restrictive Weight Sparsity substitutes a special regularization term for the common LI regularization.The presented Adaptive Sample Weight Adjustment endows the softmax loss of each sample with the optimizable weight the softmax loss of each sample with the optimizable weights to increase the impact of more-difficult-to-identify cases on the training process.With the modified loss function,the knowledge is abstractly introduced into the training process of CNN so as to successfully imitate the identification process of power experts.Accordingly,the proposed knowledge-based CNN will pay more attention to the unsaturated parts of equivalent magnetization curve even if only limited samples are included in the training process.The results of simulations and dynamic model experiments reveal that the knowledge-based CNN exhibits an improved generalization ability and the knowledge-based deep learning algorithm is a promising research direction. | Zongbo Li Zaibin Jiao Anyang He | 2021 | CSEE Journal of Power and Energy Systems2021，7，2： | 0 |
| 7 | Enhancing growth rate of lettuce by mutating lettuce seeds with nuclear irradiation显示文摘To improve growth rate of hydroponics lettuce with CO_(2)aeration,lettuce seeds were mutated by nuclear irradiation(c rays from^(137)Cs)and domesticated with high concentration of CO_(2).When lettuce seeds were mutated with 30 Gy dosage,wet weight of lettuce increased by 22.3%(to 52.24 g)on the 30th day and grana volume in the chloroplasts of leaf cells increased by 244%.After the mutant(irradiated by 50 Gy dosage)was domesticated with elevated CO_(2)concentration,wet weight of lettuce increased by 74.6%(to 48.7 g)on the 24th day under 3 vol%CO_(2),compared with wet weight of lettuce under air condition.Results showed that mutagenesis by nuclear irradiation made it possible to improve CO_(2)fixation rate in lettuce in a gene-modified way. | Jun Cheng Hongxiang Lu Xin He Zongbo Yang Junhu Zhou Kefa Cen | 2018 | Carbon Resources Conversion2018，1，1： | 0 |
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