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| 1 | Wing tip vortex structure behind an airfoil with flaps at the tip显示文摘In this work,the wing tip vortex structure behind a NACA 0015 airfoil with and without small flaps was studied using a Partical Image Velocimetry (PIV) system.The experiment was carried out in a low speed wind tunnel with a test section of 0.5 m × 0.5 m.The Reynolds number (Re),defined by the chord length of the wing (C),was 8.1×10 4.The angle of attack was fixed at 10°.The PIV measurements were made from 0 to 2C,measured from the trailing edge of the model.The dihedral angle of three flaps was 15°,0° and 15°,respectively.Compared with the clean airfoil,the one with three flaps significantly changed the wing tip vortex structure,the vorticity and the core of the wing tip vortex.The occurrence of three flaps decreased the gradient of pressure on the two sides of the wing tip,which depressed wing tip vortex formation to some extent.Vortices shed from three flaps influence the evolution of the wingtip vortex generated by the base airfoil.The interaction of those vortices resulted in a weakening of the wing tip vortex. | YANG Ke XU ShengJin | 2011 | Science China(Physics,Mechanics & Astronomy)2011，54，4： | 6 |
| 2 | A deep learning-integrated phenotyping pipeline for vascular bundle phenotypes and its application in evaluating sap flow in the maize stem显示文摘Plant vascular bundles are responsible for water and material transportation, and their quantitative and functional evaluation is desirable in plant research. At the single-plant level, the number, size, and distribution of vascular bundles vary widely, posing a challenge to automatically and accurately identifying and quantifying them. In this study, a deep learning-integrated phenotyping pipeline was developed to robustly and accurately detect vascular bundles in Computed Tomography(CT) images of stem internodes. Two semantic indicators were used to evaluate and identify a suitable feature extraction network for semantic segmentation models. The epidermis thickness of maize stem was evaluated for the first time and adjacent vascular bundles were improved using an adaptive watershed-based approach. The counting accuracy(R^(2)) of vascular bundles was 0.997 for all types of stem internodes, and the measured accuracy of size traits was over 0.98. Combining sap flow experiments, multiscale traits of vascular bundles were evaluated at the single-plant level, which provided an insight into the water use efficiency of the maize plant. | Jianjun Du Ying Zhang Xianju Lu Minggang Zhang Jinglu Wang Shengjin Liao Xinyu Guo Chunjiang Zhao | 2022 | The Crop Journal2022，10，5： | 3 |
| 3 | Incremental Face Clustering with Optimal Summary Learning Via Graph Convolutional Network显示文摘In this study, we address the problems encountered by incremental face clustering. Without the benefit of having observed the entire data distribution, incremental face clustering is more challenging than static dataset clustering. Conventional methods rely on the statistical information of previous clusters to improve the efficiency of incremental clustering;thus, error accumulation may occur. Therefore, this study proposes to predict the summaries of previous data directly from data distribution via supervised learning. Moreover, an efficient framework to cluster previous summaries with new data is explored. Although learning summaries from original data costs more than those from previous clusters, the entire framework consumes just a little bit more time because clustering current data and generating summaries for new data share most of the calculations. Experiments show that the proposed approach significantly outperforms the existing incremental face clustering methods, as evidenced by the improvement of average F-score from 0.644 to 0.762. Compared with state-of-the-art static face clustering methods, our method can yield comparable accuracy while consuming much less time. | Xuan Zhao Zhongdao Wang Lei Gao Yali Li Shengjin Wang | 2021 | Tsinghua Science and Technology2021，26，4： | 3 |
| 4 | Improving Semantic Part Features for Person Re-identification with Supervised Non-local Similarity显示文摘In person re-IDentification (re-ID) task,the learning of part-level features benefits from fine-grained information.To facilitate part alignment,which is a prerequisite for learning part-level features,a popular approach is to detect semantic parts with the use of human parsing or pose estimation.Such methods of semantic partition do offer cues to good part alignment but are prone to noisy part detection,especially when they are employed in an off-the-shelf manner.In response,this paper proposes a novel part feature learning method for re-ID,that suppresses the impact of noisy semantic part detection through Supervised Non-local Similarity (SNS) learning.Given several detected semantic parts,SNS first locates their center points on the convolutional feature maps for use as a set of anchors and then evaluates the similarity values between these anchors and each pixel on the feature maps.The non-local similarity learning is supervised such that:each anchor should be similar to itself and simultaneously dissimilar to any other anchors,thus yielding the SNS.Finally,each anchor absorbs features from all of the similar pixels on the convolutional feature maps to generate a corresponding part feature (SNS feature).We evaluate our method with extensive experiments conducted under both holistic and partial re-ID scenarios.Experimental results confirm that SNS consistently improves re-ID accuracy using human parsing or pose estimation,and that our results are on par with state-of-the-art methods. | Yifan Sun Zhaopeng Dou Yali Li Shengjin Wang | 2020 | Tsinghua Science and Technology2020，25，5： | 2 |
| 5 | Exploiting Effective Facial Patches for Robust Gender Recognition显示文摘Gender classification is an important task in automated face analysis. Most existing approaches for gender classification use only raw/aligned face images after face detection as input. These methods exhibit fair classification ability under constrained conditions, in which face images are acquired under similar illumination with similar poses. The performances of these methods may deteriorate when face images show drastic variances in poses and occlusion as routinely encountered in real-world data. The reduction in the performances of current gender classification methods may be attributed to the sensitiveness of features to image translations. This work proposes to alleviate this sensitivity by introducing a majority voting procedure that involves multiple face patches.Specifically, this work utilizes a deep learning method based on multiple large patches. Several Convolutional Neural Networks(CNN) are trained on individual, predefined patches that reflect various image resolutions and partial cropping. The decisions of each CNN are aggregated through majority voting to obtain the final gender classification accurately. Extensive experiments are conducted on four gender classification databases, including Labeled Face in-the-Wild(LFW), CelebA, ColorFeret, and All-Age Faces database, a novel database collected by our group. Each individual patch is evaluated, and complementary patches are selected for voting. We show that the classification accuracy of our method is comparable with that of state-of-the-art systems. This characteristic validates the effectiveness of our proposed method. | Jingchun Cheng Yali Li Jilong Wang Le Yu Shengjin Wang | 2019 | Tsinghua Science and Technology2019，24，3： | 2 |
| 6 | Effect of Elevated CO2 on the Growth and Macronutrient (N, P and K) Uptake of Annual Wormwood (Artemisia annua L.)显示文摘Annual wormwood(Artemisia annua L.) is the only viable source of artemisinin,an antimalarial drug.There is a pressing need to optimize production per cultivated area of this important medicinal plant;however,the effect of increasing atmospheric carbon dioxide(CO_2) concentration on its growth is still unclear.Therefore,a pot experiment was conducted in a free-air CO2 enrichment(FACE) facility in Yangzhou City,China.Two A.annua varieties,one wild and one cultivated,were grown under ambient(374μmol mol^(-1)) and elevated(577 μmol mol^(-1)) CO_2 levels to determine the dry matter accumulation and macronutrient uptake of aerial parts.The results showed that stem and leaf yields of both A.annua varieties increased significantly under elevated CO_2 due to the enhanced photosynthesis rate.Although nitrogen(N),phosphorus(P),and potassium(K) concentrations in leaves and stems of both varieties decreased under elevated CO_2,total shoot N,P,and K uptake of the two varieties were enhanced and the ratios among the concentrations of these nutrients(N:P,N:K,and P:K) were not affected by elevated CO_2.Overall,our results provided the evidence that elevated CO_2 increased biomass and shoot macronutrient uptake of two A.annua varieties. | ZHU Chunwu ZENG Qilong YU Hongyan LIU Shengjin DONG Gangqiang ZHU Jianguo | 2016 | Pedosphere2016，26，2： | 2 |
| 7 | Seismic assessment and strengthening method of existing RC buildings in response to code revision 显示文摘 | Chen Shuntyan Jeng Van i Chen Shengjin | 2001 | Earthquake Engineering and Engineering Seismology2001，3，1： | 1 |
| 8 | Theeffectoflowtemperatureonpollengerminabilityofdifferentpumpkininbredlines[J]显示文摘 | SHENGJin(盛金) FANZhicheng(樊治成) CHENFengzhen(陈凤真) etal | | ActaAgriculturaeBoreali-oceidentalisSinica0，，： | 1 |
| 9 | Cyclic Behavior of Low Yield Point Steel Shear Walls显示文摘 | Chen Shengjin Chyuan Jhang | 2006 | Thin Walled Structures2006，44，： | 1 |
| 10 | Safety of vegetables and the use of pesticides by farmers in China: evidence from Zhejiang Provence显示文摘 | Jie Hongzhou Shao Shengjin | 2009 | Food Control2009，20，6： | 1 |
| 11 | National coastal zone and coastal resources comprehensive survey in seven years显示文摘 | Su Shengjin | 1988 | Ocean Development and Management1988，，2： | 1 |
| 12 | Effect of BN content on microstructures,mechanical and dielectric properties of porous BN/Si3N4 composite ceramics prepared by gel casting显示文摘 | Wang Shengjin Jia Dechang | 2013 | Ceram Int2013，39，： | 1 |
| 13 | Exploiting Sparse Representation in the P300 Speller Paradigm显示文摘A Brain-Computer Interface(BCI) aims to produce a new way for people to communicate with computers.Brain signal classification is a challenging issue owing to the high-dimensional data and low Signal-to-Noise Ratio(SNR). In this paper, a novel method is proposed to cope with this problem through sparse representation for the P300 speller paradigm. This work is distinguished using two key contributions. First, we investigate sparse coding and its feasibility for brain signal classification. Training signals are used to learn the dictionaries and test signals are classified according to their sparse representation and reconstruction errors. Second, sample selection and a channel-aware dictionary are proposed to reduce the effect of noise, which can improve performance and enhance the computing efficiency simultaneously. A novel classification method from the sample set perspective is proposed to exploit channel correlations. Specifically, the brain signal of each channel is classified jointly using its spatially neighboring channels and a novel weighted regulation strategy is proposed to overcome outliers in the group. Experimental results have demonstrated that our methods are highly effective. We achieve a state-of-the-art recognition rate of 72.5%, 88.5%, and 98.5% at 5, 10, and 15 epochs, respectively, on BCI Competition Ⅲ Dataset Ⅱ. | Hongma Liu Yali Li Shengjin Wang | 2021 | Tsinghua Science and Technology2021，26，4： | 1 |
| 14 | Person Re-Identification with Effectively Designed Parts显示文摘Person re-IDentification(re-ID) is an important research topic in the computer vision community, with significance for a range of applications. Pedestrians are well-structured objects that can be partitioned, although detection errors cause slightly misaligned bounding boxes, which lead to mismatches. In this paper, we study the person re-identification performance of using variously designed pedestrian parts instead of the horizontal partitioning routine typically applied in previous hand-crafted part works, and thereby obtain more effective feature descriptors. Specifically, we benchmark the accuracy of individual part matching with discriminatively trained Convolutional Neural Network(CNN) descriptors on the Market-1501 dataset. We also investigate the complementarity among different parts using combination and ablation studies, and provide novel insights into this issue. Compared with the state-of-the-art, our method yields a competitive accuracy rate when the best part combination is used on two large-scale datasets(Market-1501 and CUHK03) and one small-scale dataset(VIPeR). | Yali Zhao Yali Li Shengjin Wang | 2020 | Tsinghua Science and Technology2020，25，3： | 1 |
| 15 | Extremal matching energy of bicyclic graphs 显示文摘 | Ji Shengjin Li Xueliang Shi Yongtang | 2013 | MATCH Commun Math Comput Chem2013，70，2： | 1 |
| 16 | The extremal matching energy of graphs 显示文摘 | Ji Shengjin Ma Hongping | 2014 | Ars Combinatoria2014，115，： | 1 |
| 17 | Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data显示文摘Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction. | WANG Fengfei TANG Shengjin SUN Xiaoyan LI Liang YU Chuanqiang SI Xiaosheng | 2023 | Journal of Systems Engineering and Electronics2023，34，1： | 1 |
| 18 | Fast image retrieval:Query pruning and early termination显示文摘 | ZHENG Liang WANG Shengjin LIU Z | 2015 | IEEE Transactions on Multimedia2015，17，5： | 1 |
| 19 | Rhodium-Catalyzed Defluorinative Vinylation of gem-Difluoroalkenes for the Synthesis of 2-Fluoro-1,3-dienes显示文摘Herein,we present a strategy for the formation of 2-fluoro-1,3-diene derivatives via rhodium-catalyzed direct C(sp2)-C(sp2) cross-coupling of gem-difluoroalkenes and acrylamides.By merging Rh(Ⅲ)-catalyzed C(sp2)-H bond activation and nucleophilic addition/F-elimination of gem-difluoroalkene,an efficient defluorinative vinylation reaction is uncovered,which leads to the generation of 2-fluoro-1,3-dienes in moderate to good yields with excellent stereoselectivity under mild conditions.Preliminary mechanistic study suggests unique effects of fluorine substituents which allow the reactivity profile not observed with the congeners bearing heavier halides. | Shengjin Song Huan Liu Lu Wang Chuan Zhu Teck-Peng Loh Chao Feng | 2019 | Chinese Journal of Chemistry2019，37，10： | 1 |
| 20 | Multi Features Combination for Pedestrian Detection显示文摘 | Hu Bin Wang Shengjin Ding Xiaoqing | 2010 | Journal of Multimedia2010，，1： | 1 |
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