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Search Results: 1 - 10 of 103771 matches for " 洪瑞江SHEN Guosheng "
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中频磁控溅射法制备掺氢氮化硅减反/钝化复合功能薄膜的研究
Silicon nitride thin films with passivation and anti-reflection properties prepared by mid-frequency magnetron sputtering

沈国晟,陈文理,李仲,
SHEN Guosheng
,CHEN Wenli,LI Zhong,HONG Ruijiang

- , 2016,
Abstract: 使用中频磁控溅射法制备了具有光学减反射与电学钝化的复合功能的氮化硅(SiNx)薄膜,并对其结构和性能进行了综合研究。结果表明:在常规制绒硅片上沉积的两种不同折射率的单层SiNx减反膜表现出优异的光学性能,其在300~1 100 nm波段的平均反射率由镀膜前的14.86%下降到镀膜后的5.50%和6.58%;若采用多层的氮化硅(m-SiNx)+ 氮氧化硅(SiOxNy)薄膜作为减反层,则其平均反射率进一步下降到4.03%。同时,优化工艺制备得到的掺氢氮化硅(SiNx∶H)薄膜,表现出良好的电学钝化特性。试验中分别制备了两种复合结构的薄膜,即SiNx∶H(厚度15 nm) + m-SiNx+ SiOxNy与SiNx∶H(厚度30 nm) + m-SiNx+ SiOxNy复合薄膜,其平均反射率分别为5.88%和5.43%; 把这两种薄膜用于晶体硅太阳电池上,其开路电压则都达到了575 mV,表现出良好的性能
青藏高原近30年来现代冰川的演化特征
,,赵福岳
国土资源遥感 , 2010, DOI: 10.6046/gtzyyg.2010.S1.12
Abstract: ?青藏高原现代冰川总的演化特征是面积在不断缩小,厚度在不断减薄,冰储量在不断降低。冰川演化具有阶段性、地域性不同的特点。从20世纪60年代末期至80年代末期,青藏高原现代冰川的面积稍有增加,因为增加的冰川很少。从20世纪80年代末期开始,青藏高原现代冰川面积明显减少,减少的速度也在加快,尤其是毗邻塔里木盆地的冰川分布区及喜马拉雅山地区。青藏高原不同山系现代冰川的演化也不同——帕米尔高原现代冰川面积的减少最为明显,其次是喜马拉雅山和祁连山等地;羌塘高原和昆仑山地区现代冰川的面积减少较小;其他山系现代冰川面积的减少介于二者之间。
高温退火对物理提纯多晶硅位错密度及其电学性能的影响
徐华毕,,沈辉
科学通报 , 2010,
Abstract: 对纯度约为99.999%的物理提纯多晶硅片进行不同高温退火工艺热处理,经机械抛光和表面刻蚀后再用扫描电子显微镜(SEM)观察硅片内部位错密度变化情况,并通过WT2000少子寿命测试仪和双电四探针测试仪测试其少子寿命和电阻率变化情况.结果表明,在1100~1400℃之间退火6h的情况下,随着退火温度的升高,物理提纯多晶硅片内部位错密度逐渐减小甚至消失,然而硅片少子寿命和电阻率等电学性能不但没有随着位错密度的减小而提高,反而呈现逐渐降低的趋势.这一现象说明对于杂质含量高的低纯度物理提纯多晶硅片来说,位错密度并不是影响材料对载流子复合性能高低的决定性因素,高含量的杂质以及杂质在晶体内部造成的微缺陷(包括间隙态或替位态杂质以及纳米级杂质沉淀)才是决定其少子寿命等电学性能的主要因素.
动态分区原理及其应用
,康重庆,夏清,健健
中国电机工程学报 , 2005,
Abstract:
橙皮苷减轻大鼠心肌缺血/再灌注损伤所致的炎症反应 Hesperidin Reduces Inflammatory Response During Rat Myocardial Ischemia/Reperfusion Injury
李雪飞,,胡笑容,
- , 2016,
Abstract: 目的:探讨橙皮苷能否通过其抗炎作用减轻大鼠心肌缺血/再灌注损伤。方法:24只雄性SD大鼠随机分为假手术组、缺血/再灌注组(I/R组)及橙皮苷组,每组8只。结扎左冠状动脉前降支30min再灌注4h建立大鼠心肌缺血再灌注损伤模型。HE染色法光镜下观察大鼠心肌组织形态改变,量子点免疫荧光法荧光镜下观察心肌组织高迁移率族蛋白1(HMGB1)的表达,全自动生化分析仪测定血清中肌酸激酶(CK)和乳酸脱氢酶(LDH)的水平,ELISA法和Westen blot法分别检测心肌组织中白介素6(IL-6)、肿瘤坏死因子α(TNF-α)的水平及HMGB1的表达。结果:与I/R组相比,橙皮苷能减轻心肌组织损伤程度,降低心肌酶CK及LDH的水平,降低心肌组织炎症指标TNF-α和IL-6的含量及HMGB1的表达(P<0.05)。结论:橙皮苷能减轻心肌缺血/再灌注损伤,其机制与其抑制HMGB1的表达相关
Optimizing Ranking Measures for Compact Binary Code Learning
Guosheng Lin,Chunhua Shen,Jianxin Wu
Computer Science , 2014,
Abstract: Hashing has proven a valuable tool for large-scale information retrieval. Despite much success, existing hashing methods optimize over simple objectives such as the reconstruction error or graph Laplacian related loss functions, instead of the performance evaluation criteria of interest---multivariate performance measures such as the AUC and NDCG. Here we present a general framework (termed StructHash) that allows one to directly optimize multivariate performance measures. The resulting optimization problem can involve exponentially or infinitely many variables and constraints, which is more challenging than standard structured output learning. To solve the StructHash optimization problem, we use a combination of column generation and cutting-plane techniques. We demonstrate the generality of StructHash by applying it to ranking prediction and image retrieval, and show that it outperforms a few state-of-the-art hashing methods.
Deep Convolutional Neural Fields for Depth Estimation from a Single Image
Fayao Liu,Chunhua Shen,Guosheng Lin
Computer Science , 2014,
Abstract: We consider the problem of depth estimation from a single monocular image in this work. It is a challenging task as no reliable depth cues are available, e.g., stereo correspondences, motions, etc. Previous efforts have been focusing on exploiting geometric priors or additional sources of information, with all using hand-crafted features. Recently, there is mounting evidence that features from deep convolutional neural networks (CNN) are setting new records for various vision applications. On the other hand, considering the continuous characteristic of the depth values, depth estimations can be naturally formulated into a continuous conditional random field (CRF) learning problem. Therefore, we in this paper present a deep convolutional neural field model for estimating depths from a single image, aiming to jointly explore the capacity of deep CNN and continuous CRF. Specifically, we propose a deep structured learning scheme which learns the unary and pairwise potentials of continuous CRF in a unified deep CNN framework. The proposed method can be used for depth estimations of general scenes with no geometric priors nor any extra information injected. In our case, the integral of the partition function can be analytically calculated, thus we can exactly solve the log-likelihood optimization. Moreover, solving the MAP problem for predicting depths of a new image is highly efficient as closed-form solutions exist. We experimentally demonstrate that the proposed method outperforms state-of-the-art depth estimation methods on both indoor and outdoor scene datasets.
CRF Learning with CNN Features for Image Segmentation
Fayao Liu,Guosheng Lin,Chunhua Shen
Computer Science , 2015,
Abstract: Conditional Random Rields (CRF) have been widely applied in image segmentations. While most studies rely on hand-crafted features, we here propose to exploit a pre-trained large convolutional neural network (CNN) to generate deep features for CRF learning. The deep CNN is trained on the ImageNet dataset and transferred to image segmentations here for constructing potentials of superpixels. Then the CRF parameters are learnt using a structured support vector machine (SSVM). To fully exploit context information in inference, we construct spatially related co-occurrence pairwise potentials and incorporate them into the energy function. This prefers labelling of object pairs that frequently co-occur in a certain spatial layout and at the same time avoids implausible labellings during the inference. Extensive experiments on binary and multi-class segmentation benchmarks demonstrate the promise of the proposed method. We thus provide new baselines for the segmentation performance on the Weizmann horse, Graz-02, MSRC-21, Stanford Background and PASCAL VOC 2011 datasets.
溶解氧对输水管道生物膜微生物群落结构及出水水质影响
,,,阎力君,李圭白,梁恒
- , 2016, DOI: 10.11918/j.issn.0367-6234.2016.08.004
Abstract: 为提高微污染原水输水安全性,采用生物膜环状反应器(biofilm annular reactor,BAR)模拟原水输水管网,研究溶解氧(DO)对出水水质及生物膜微生物的影响,运用454-高通量测序技术对生物膜的微生物多样性和丰度进行分析.结果表明:随着溶解氧浓度的提高,反应器出水浊度、总铁、氨氮大幅度下降,该过程对总氮和CODMn去除的影响并不明显;生物膜中的铁细菌、硫酸盐还原菌的量均随溶解氧浓度的提高而降低,而微生物多样性和丰度(如硝化细菌)随溶解氧质量浓度的提高而升高.提高溶解氧能够缓解管道腐蚀,提高管壁生物膜的净水作用.
To improve the safety of distributing raw water, a BAR(Biofilm Annular Reactor)was constructed to simulate the distribution system and the effect of dissolved oxygen (DO) concentration on the effluent water quality and biofilm was studied. The 454-pyrosequencing technology was employed to analyze the diversity of biofilm in the reactor. Experimental results showed that the turbidity and the concentration of total iron, ammonia nitrogen decreased obviously with DO concentration increasing, while the concentrations of total nitrogen and CODMn changed slightly. The numbers of iron bacteria and sulfate-reducing bacteria reduced significantly, while the richness and diversity of the biofilm related bacteria (such as nitrifying bacteria) improved. So, increasing DO concentration can alleviate the pipeline corrosion and develop biofilm purifying water role.
青藏高原近30年现代雪线遥感调查
,赵福岳,,曾福年
国土资源遥感 , 2010, DOI: 10.6046/gtzyyg.2010.S1.14
Abstract: 通过对青藏高原现代冰川分布区ETM遥感图像的分析研究,提出了利用遥感图像确定现代雪线高度的方法;基本查明了青藏高原各山系的现代雪线高度,以及近30a来现代雪线高度的变化状况。调查结果显示:青藏高原现代雪线高度在4000~6000m,并呈现自东向西逐渐升高的分布规律;青藏高原现代雪线有上升、下降和基本不变3种变化方式,以上升为主,且与现代冰川的演变关系密切。
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