Study on Magnetic Domains in Sm-Co Based Alloy

ＳｔｕｄｙｏｎＭａｇｎｅｔｉｃＤｏｍａｉｎｓｉｎＳｍ－ＣｏＢａｓｅｄＡｌｌｏｙＬｉｕＡｎｓｈｅｎｇ，ＳｈａｏＢｅｉｌｉｎｇ，ＷａｎｇＸｉａｏｈｕａａｎｄＷａｎｇＪｉｎｇ（刘安生）（邵贝羚）（王晓华）（王敬）（ＧｅｎｅｒａｌＲｅｓｅａｒｃｈＩｎｓｔｉｔ...     

钛合金等温锻造进展

等温锻造是一种先进的加工工艺。本文从钛合金高温变形特性、等温锻造参数选择、模具设计、润滑剂摩擦特性等诸方面全面地介绍了这种先进的锻造工艺，它的应用前景及发展方向。     

THERMAL STABILITY OF THE MECHANICALLY ALLOYED Al－10Ti NANOCRYSTALLINE ALLOY DURING CONSOLIDATION PROCESS

ＴＨＥＲＭＡＬＳＴＡＢＩＬＩＴＹＯＦＴＨＥＭＥＣＨＡＮＩＣＡＬＬＹＡＬＬＯＹＥＤＡｌ－１０ＴｉＮＡＮＯＣＲＹＳＴＡＬＬＩＮＥＡＬＬＯＹＤＵＲＩＮＧＣＯＮＳＯＬＩＤＡＴＩＯＮＰＲＯＣＥＳＳＬｉａｎｇＧｕｏｘｉａｎ；ＬｉＺｈｉｃｈａｏ；ＷａｎｇＥｒｄｅ（...     

稀土纳米磁致冷复合材料

采用急冷快淬，高能球磨及粉末包套包覆轧制方法制备出Ｇｄ－Ｙ，Ｇｄ－Ｚｎ和Ｇｄ－Ｔｈ的纳米固体复合磁致冷材料。实验测试结果表明：与大块状材料相比，纳米固体的居里温度明显降低，比热显著增加：Ｇｄ－Ｔｈ和Ｇｄ－Ｚｎ合金的磁热熵效应有所降低，但Ｇｄ－Ｙ系的磁热熵效应则显著增加。对制备工艺条件及若干影响磁质变熵效应的因素进行了较详细的分析。     

HfO2膜料中的杂质对薄膜损伤及性能的影响

通过对HfO2膜料中杂质元素的分析，找出了影响薄膜性能的主要杂质元素。结果表明：金属元素、吸收性介质元素的存在对薄膜的损毁有很大负面影响；在紫外波段，Zr元素含量大的薄膜吸收较大；并且提出负离子元素在膜料蒸发过程中形成气源中心，产生喷溅，从而使薄膜的损伤阈值降低。     

An improved NDVI-based threshold method for estimating land surface emissivity using MODIS satellite data

This work estimated the land surface emissivities (LSEs) for MODIS thermal infrared channels 29 (8.4–8.7 μm), 31 (10.78–11.28 μm), and 32 (11.77–12.27 μm) using an improved normalized difference vegetation index (NDVI)-based threshold method. The channel LSEs are expressed as functions of atmospherically corrected reflectance from the MODIS visible and near-infrared channels with wavelengths ranging from 0.4 to 2.2 μm for bare soil. To retain the angular information, the vegetation LSEs were explicitly expressed in the NDVI function. The results exhibited a root mean square error (RMSE) among the estimated LSEs using the improved method, and those calculated using spectral data from Johns Hopkins University (JHU) are below 0.01 for channels 31 and 32. The MODIS land surface temperature/emissivity (LST/E) products, MOD11_L2 with LSE derived via the classification-based method with 1 km resolution and MOD11C1 with LSE retrieved via the day/night LST retrieval method at 0.05° resolution, were used to validate the proposed method. The resultant variances and entropies for the LSEs estimated using the proposed method were larger than those extracted from MOD11_L2, which indicates that the proposed method better described the spectral variation for different land covers. In addition, comparing the estimated LSEs to those from MOD11C1 yielded RMSEs of approximately 0.02 for the three channels; however, more than 70% of pixels exhibited LSE differences within 0.01 for channels 31 and 32, which indicates that the proposed method feasibly depicts LSE variation for different land covers.     

飞腾平台中硬件数据压缩的驱动设计与实现

数据压缩是有效处理大数据的关键技术,在飞腾处理器平台上基于zlib库的数据压缩通常采用软件的方式实现,在数据处理量大而且实时性要求较高的情况下,已经难以满足需求。针对这个问题,通过研究zlib编程函数库,结合飞腾处理器的特性,完成了飞腾平台中硬件数据压缩的驱动设计与实现,其中提出了双向DMA传输技术和基于一致性内存的命令环机制,从而进一步提高了基于硬件的数据压缩的效率。通过实验证实了飞腾平台中采用硬件数据压缩改进的有效性。     

面向电子商务的秒杀系统设计与实现

根据互联网服务的特色,从可靠性、可扩展性和安全性的角度着手,提出了一套面向电子商务领域的网络秒杀系统的架构设计方案。同时分析了秒杀系统的业务特点,设计了一套相应的业务体系,并给出了主要业务流程的具体实现方法。基于该解决方案的秒杀系统可以达到高并发、高可靠、易扩展以及高安全性的设计目标。     

Extended minimum-squared error algorithm for robust face recognition via auxiliary mirror samples

The changes of face images with poses and polarized illuminations increase data uncertainty in face recognition. In fact, synthesized mirror samples can be recognized as representations of the left–right deflection of poses or illuminations of the face. Symmetrical face images generated from the original face images also provide more observations of the face which is useful for improving the accuracy of face recognition. In this paper, to the best of our knowledge, it is the first time that the well-known minimum squared error classification (MSEC) algorithm is used to perform face recognition on an extended face database using synthesized mirror training samples, which is titled as extended minimum squared error classification (EMSEC). By modifying the MSE classification rule, we append the mirror samples to the training set for gaining better classification performance. First, we merge original training samples and mirror samples synthesized from original training samples per subject as mixed training samples. Second, EMSEC algorithm exploits mixed training samples to obtain the projection matrix that can best transform the mixed training samples into predefined class labels. Third, the projection matrix is exploited to simultaneously obtain transform results of the test sample and its nearest neighbor from the mixed training sample set. Finally, we ultimately classify the test sample by combining the transform results of the test sample and the nearest neighbor. As an extension of MSEC, EMSEC reduces the uncertainty of the face observation by auxiliary mirror samples, so that it has better robustness classification performance than traditional MSEC. Experimental results on the ORL, GT, and FERET databases show that EMSEC has better generalization ability than traditional MSEC.