In-situ synthesis of TiO2 nanostructures on Ti foil for enhanced and stable photocatalytic performance

TiO_2 nanostructures with strong interfacial adhesion and diverse morphologies have been in-situ grown on Ti foil substrate through a multiple-step method based on conventional plasma electrolytic oxidation(PEO) technology, hydrothermal reaction and ion exchange process. The PEO process is critical to the formation of TiO_2 seeding layer for the nucleation of Na_2Ti_3O_7 and H_2Ti_3O_7 mediates that are strongly attached to the Ti foil. An ion exchange reaction can finally lead to the formation of H_2Ti_3O_7 nanostructures with diverse morphologies and the calcination process can turn the H_2Ti_3O_7 nanostructures into TiO_2 nanostructures with enhanced crystallinity. The morphology of the TiO_2 nanostructures including nanoparticles(NP), nanowhiskers(NWK), nanowires(NW) and nanosheets(NS) can be easily tailored by controlling the NaOH concentration and reaction time during hydrothermal process. The morphology, composition and optical properties of TiO_2 photocatalysts were analyzed using scanning electron microscope(SEM), X-ray diffraction(XRD), photoluminescence(PL) spectroscopy and UV–vis absorption spectrum. Photocatalytic tests indicate that the TiO_2 nanosheets calcined at 500?C show good crystallization and the best capability of decomposing organic pollutants. The decoration of Ag cocatalyst can further improve the photocatalytic performance of the TiO_2 nanosheets as a result of the enhanced charger separation efficiency. Cyclic photocatalytic test using TiO_2 nanostructures grown on Ti foil substrate demonstrates the superior stability in the photodegradation of organic pollutant, suggesting the promising potential of in-situ growth technology for industrial application.     

权重自适应clonal选择算法及其应用研究

在求解两个目标以上的多目标优化问题时,基于Pareto支配的多目标进化算法多数需要较长的求解时间.基于固定权重的聚合函数方法求解速度快,但要确定一个适合待求解问题的合理权重是十分困难的,为了解决这一问题,将clonal选择算法与权重自适应方法相结合,提出了一种适用于多目标优化问题的权重自适应clonal选择算法.并将权...     

流水线前端资源分配及其性能影响研究

动态超标量处理器的性能取决于流水线各段资源的配置情况,通常认为资源越多对性能提升越有利。对流水线前端资源的分配进行研究,发现各类资源的合理搭配更重要,资源配置过多或搭配不当可能产生负面影响。分析各类资源的潜在互相干扰、影响性能的现象、过程及深层次原因,并给出一些参考建议。     

Correlation of direct piezoelectric effect on EAPap under ambient factors

Direct piezoelectricity of electro-active papers(EAPap)is analysed in this paper. The test setups for direct effect are designed and determined. Different ambient factors impacting the piezoelectricity of EAPap, such as temperature, humidity, and strain rate, are applied and analyzed. Strong piezoelectricity of EAPap is found on the basis of the test results and in comparison with polyvinylidene fluoride(PVDF)and lead zirconate titanate(PZT)-5H. The maximum piezoelectric constant is achieved to be 504 pC/N. The reason of strong piezoelectricity of EAPap is discussed in this paper. The potential of EAPap as a biomimetic actuator and sensor is also investigated.     

基于动态安全属性保护的可信固件

遵循TCG可信计算的固件通过可信度量和信任传递保证固件模块及OS Loader的完整性。这种可信固件当前实现的缺点在于只保护了系统的静态安全属性,而对动态安全属性保护无能为力。基于此,通过对固件代码和数据的分类研究,对固件代码和数据进行安全分级控制,提出固件动态安全属性保护模型,在EFI/UEFI可信固件的基础上实现动态安全属性的保护。实验证明,该方法简单有效,适合于固件代码尺寸小、启动速度快的要求。     

Single-machine scheduling jobs with exponential learning functions

In a manufacturing system workers are involved in doing the same job or activity repeatedly. Hence, the workers start learning more about the job or activity. Because of the learning, the time to complete the job or activity starts decreasing, which is known as “learning effect”. In this paper, an exponential sum-of-actual-processing-time based learning effect is introduced into single-machine scheduling. By the exponential sum-of-actual-processing-time based learning effect, we mean that the processing time of a job is defined by an exponential function of the sum-of-the-actual-processing-time of the already processed jobs. Under the proposed learning model, we show that under a sufficient condition, the makespan minimization problem, the sum of the θth (θ > 0) power of completion times minimization problem, and some special cases of the total weighted completion time minimization problem and the maximum lateness minimization problem remain polynomially solvable.     

Motion vector recovery for video error concealment based on the plane fitting

Multimedia Tools and Applications - In a wireless transmission, the compressed video stream can result in lower quality of image reconstruction with the motion vector mismatch. A motion vector...     

面向中文专利文献的有标记并列结构的统计分析

该文在中文专利语料的基础上,统计分析了中文专利文献中有标记并列结构的内部特征和外部特征。内部特征主要考察了中文专利文献中有标记并列结构的并列标记、并列结构内部分析和词性分布等。外部特征主要统计了可能的边界特征词,并分析了有标记并列结构在中文专利文献中出现的外部环境。     

A parsimony fuzzy rule-based classifier using axiomatic fuzzy set theory and support vector machines

This paper proposes a classification method that is based on easily interpretable fuzzy rules and fully capitalizes on the two key technologies, namely pruning the outliers in the training data by SVMs (support vector machines), i.e., eliminating the influence of outliers on the learning process; finding a fuzzy set with sound linguistic interpretation to describe each class based on AFS (axiomatic fuzzy set) theory. Compared with other fuzzy rule-based methods, the proposed models are usually more compact and easily understandable for the users since each class is described by much fewer rules. The proposed method also comes with two other advantages, namely, each rule obtained from the proposed algorithm is simply a conjunction of some linguistic terms, there are no parameters that are required to be tuned. The proposed classification method is compared with the previously published fuzzy rule-based classifiers by testing them on 16 UCI data sets. The results show that the fuzzy rule-based classifier presented in this paper, offers a compact, understandable and accurate classification scheme. A balance is achieved between the interpretability and the accuracy.     

Simultaneous tracking and action recognition for single actor human actions

This paper presents an approach to simultaneously tracking the pose and recognizing human actions in a video. This is achieved by combining a Dynamic Bayesian Action Network (DBAN) with 2D body part models. Existing DBAN implementation relies on fairly weak observation features, which affects the recognition accuracy. In this work, we use a 2D body part model for accurate pose alignment, which in turn improves both pose estimate and action recognition accuracy. To compensate for the additional time required for alignment, we use an action entropy-based scheme to determine the minimum number of states to be maintained in each frame while avoiding sample impoverishment. In addition, we also present an approach to automation of the keypose selection task for learning 3D action models from a few annotations. We demonstrate our approach on a hand gesture dataset with 500 action sequences, and we show that compared to DBAN our algorithm achieves 6% improvement in accuracy.