基于视差与分层渲染的立体显示效果评价研究

本文在充分分析国内外立体显示技术评价方法的基础上,提出了利用视差和分层渲染的方法进行立体显示效果的评价,结果表明其对于自由立体显示评价可以得到较明确的分析结果,最后对研究前景进行了必要的探讨。     

Modulation on the electrical and optical properties of Na and Rh co-doped Ruddlesden-Popper layered Ca3Ti2O7

Layered perovskite Ca_2.91Na_0.09Ti_2-xRh_xO₇ (x?=?0.00, 0.02, 0.04, 0.06) were synthesized by a conventional solid-state reaction. Room temperature ferroelectricity has been confirmed. The remanent polarization increases with an increase of Rh content, which is due to a larger oxygen octahedral distortion by Rh doping. The coercive field increases with Rh doping as the pinning effect of oxygen vacancies reduce the mobility of domain wall. Remanent polarization and coercive field are caused by different mechanisms, so it is possible to modulate them independently to meet the requirement of application in ferroelectric field. The concentration of oxygen vacancy increased with Rh doping, leading to the significant increase of leakage current density. The bandgap of samples doped with Rh drastically decrease and the visible light response of the sample was improved by Rh doping due to the formation of impurity energy levels within the band gap.

     

Revealing Microstructural,Textural, and Hardness Evolution of Ti–6Al–4V Sheet Cooled From Sub β-Transus Temperature at Different Rates

Metallurgical and Materials Transactions A - A hot-rolled Ti–6Al–4V sheet was annealed at 960 °C (sub β-transus temperature) and then subjected to different coolings [in water...     

等效岩体三维随机节理网络模型构建及其在两河口水电站中的应用

节理的存在对水电高陡岩质边坡的力学性质有重要影响,如何构建反映三维节理分布特征的等效岩体计算模型,是分析与评价岩体力学特性的关键。本文基于损伤力学和统计强度理论,在三维岩石破裂过程分析（RFPA3D）软件的基础上,提出了一种计算等效岩体三维随机节理网络模型的新方法。首先,基于Baecher模型和Monte-Carlo方法,在RFPA3D软件中实现了三维随机离散节理网络（Discrete fracture network,DFN）模型的重构。然后,利用RFPA3D软件内嵌DFN模型,赋予节理和岩石不同的力学参数,构建了工程尺度等效岩体三维随机节理网络模型,实现了三维随机节理岩体破裂过程、变形和强度等力学性质的分析。最后,以两河口水电站左岸边坡坝址区下游节理岩体为研究对象,验证了三维随机DFN模型的准确性,开展了研究区内节理岩体尺寸效应研究,并获得了研究区内岩体的表征单元体（Representative elementary volume,REV）和等效力学参数。该研究成果为等效岩体力学行为分析提供一种新方法。      

Optimizing ranking method using social annotations based on language model

Recent research has shown that more and more web users utilize social annotations to manage and organize their interested resources. Therefore, with the growing popularity of social annotations, it is becoming more and more important to utilize such social annotations to achieve effective web search. However, using a statistical model, there are no previous studies that examine the relationships between queries and social annotations. Motivated by this observation, we use social annotations to re-rank search results. We intend to optimize retrieval ranking method by using the ranking strategy of integrating the query-annotation similarity into query-document similarity. Specifically, we calculate the query-annotation similarity by using a statistical language model, which in a shorter form we call simply a language model. Then the initial search results are re-ranked according to the computational weighted score of the query-document similarity score and the query-annotation similarity score. Experimental results show that the proposed method can improve the NDCG score by 8.13%. We further conduct an empirical evaluation of the method by using a query set including about 300 popular social annotations and constructed phrases. More generally, the optimized results with social annotations based on a language model can be of significant benefit to web search.     

Increasing recommended effectiveness with markov chains and purchase intervals

Recommendation system is an important component of many websites and has brought huge economic benefits and challenges for online shoppers and e-commerce companies. Existing recommendation systems focus on producing a list of products which users may be interested to purchase, while overlooking the purchase chain and temporal diversity which may increase the likelihood of a purchase decision. In this paper, we propose to utilize the Markov chain to track the chain of users’ purchase behaviors and utilize the purchase intervals to improve the temporal diversity for e-commerce recommender. We design and implement several algorithms and integrate these into our recommendation model. We evaluate our system on a real-world e-commerce dataset. Experimental results demonstrate that our approach significantly improves the accuracy, conversion rate and temporal diversity compared to the state-of-the-art algorithms.     

低密度泡沫玻璃的研究与应用

低密度泡沫玻璃是利用碎玻璃制造的一种气孔率大于90%的隔热保温材料,它具有强度高、导热系数小、吸水率低、不吸湿、耐腐蚀等特性,在潮湿环境下抗冻性能好,是一种优良的低温、超低温隔热保温材料。该产品用途广泛,所用的原材料简单易得,但生产工艺过程比较复杂,生产过程控制精度要求较高,容易产生各种缺陷而影响产品的质量。本文对低密度泡沫玻璃的生产工艺过程进行详细的分析研究,提出相应的措施,确保产品的低密度和高成品率,提高产品的性能,扩大产品的使用范围。     

SiC晶须对Si3N4陶瓷基复合材料机械强度的影响

制备了含10vol％,20vol％及30vol％SiC晶须的Si_3N_4基复合材料,对其室温及高温机械强度的测试研究表明,SiC晶须对基体的高温机械性能有明显的增强作用。含20vol％SiC晶须的复合材料1200℃下强度达780MPa,比基体材料提高50％。通过SEM和TEM研究,讨论了SiC晶须增强的原因。     

连续式双膜分离塔的数学模型

本文建立了适用于连续式双膜分离塔的数学模型。用实验数据验证了数学模型的可行性和合理性。将本模型与已发表的模型进行了比较,结果表明本模型具有形式简单、计算方便和适用范围更宽广等优点。