Photocatalysts using hexa- and octa-titanates with different tunnel space for water decomposition

Photocatalytic activities for water decomposition were examined for photocatalysts using hexa- and octa-titanates and TiO2(B) with different tunnel space in the structure. Using RuO2 as promoter, M2Ti6O13 (M = Li, Na, K, Rb) showed the stoichiometric production of Hz and O₂ except for Li, whereas H₂Ti₈O₁₇ and TiO₂(B) had very low activity producing only hydrogen as a product. The effects of promoters on Na₂Ti₆O₁₃ showed that the activity increased in the order of RuO₂ > RuO₂ + IrO₂ > IrO₂ > RuO₂ + Pt > MnO₂. These effects along with other related ones are discussed: it emerges that the presence of the tunnels is important for the achievement of high photocatalytic activity.     

H∞ filtering for discrete-time systems subject to stochastic missing measurements: a decomposition approach

This paper deals with the problem of H_∞ filtering for discrete-time systems with stochastic missing measurements. A new missing measurement model is developed by decomposing the interval of the missing rate into several segments. The probability of the missing rate in each subsegment is governed by its corresponding random variables. We aim to design a linear full-order filter such that the estimation error converges to zero exponentially in the mean square with a less conservatism while the disturbance rejection attenuation is constrained to a given level by means of an H_∞ performance index. Based on Lyapunov theory, the reliable filter parameters are characterised in terms of the feasibility of a set of linear matrix inequalities. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed design approach.     

Stability analysis and controller synthesis for discrete uncertain singular fuzzy systems with distinct difference matrices in the rules

This paper mainly studies an extended discrete singular fuzzy model incorporating the multiple difference matrices in the rules and discusses its stability and design issues. By embracing additional algebraic constraint, traditional discrete Takagi-Sugeno (T-S) fuzzy model can be extended to a generalised discrete singular Takagi-Sugeno (GDST-S) model with individual difference matrices E_i in the locally singular models, where it can describe a larger class of physical or non-linear systems. Based on the linear matrix inequality (LMI) approach, we focus on deriving some explicit stability and design criteria expressed by the LMIs for the regarded system. Thus, the stability verification and controller synthesis can be performed by the current LMI tools. Finally, some illustrative examples are given to illustrate the effectiveness and validity of the proposed approach.     

Sliding mode control for Markov jump linear uncertain systems with partly unknown transition rates

In this paper, based on sliding mode control approach, the robust stabilisation problem for a class of continuous-time Markovian jump linear uncertain systems with partly unknown transition rates is investigated. The transition rate matrix under consideration covers completely known, boundary known and completely unknown elements. By making use of linear matrix inequalities technique, sufficient conditions are presented to derive the linear switching surface and guarantee the stochastic stability of sliding mode dynamics. Then a sliding mode control law is designed to drive the state trajectory of the closed-loop system to the specified linear switching surface in finite time in spite of the existing uncertainties and unknown transition rates. Finally, an example is given to verify the validity of the theoretical results.     

An alignment-free fingerprint bio-cryptosystem based on modified Voronoi neighbor structures

Bio-cryptography is an emerging security technology which combines cryptography with biometrics. A good bio-cryptosystem is required to protect the privacy of the relevant biometric data as well as achieving high recognition accuracy. Fingerprints have been widely used in bio-cryptosystem design. However, fingerprint uncertainty caused by distortion and rotation during the image capturing process makes it difficult to achieve a high recognition rate in most bio-cryptographic systems. Moreover, most existing bio-cryptosystems rely on the accurate detection of singular points for fingerprint image pre-alignment, which is very hard to achieve, and the image rotation transformation during the alignment process can cause significant singular point deviation and minutiae changes. In this paper, by taking full advantage of local Voronoi neighbor structures (VNSs), e.g. local structural stability and distortion insensitivity, we propose an alignment-free bio-cryptosystem based on fixed-length bit-string representations extracted from modified VNSs, which are rotation- and translation-invariant and distortion robust. The proposed alignment-free bio-cryptosystem is able to provide strong security while achieving good recognition performance. Experimental results in comparison with most existing alignment-free bio-cryptosystems using the publicly-available databases show the validity of the proposed scheme.     

End-to-end scene text recognition using tree-structured models

Detecting and recognizing text in natural images are quite challenging and have received much attention from the computer vision community in recent years. In this paper, we propose a robust end-to-end scene text recognition method, which utilizes tree-structured character models and normalized pictorial structured word models. For each category of characters, we build a part-based tree-structured model (TSM) so as to make use of the character-specific structure information as well as the local appearance information. The TSM could detect each part of the character and recognize the unique structure as well, seamlessly combining character detection and recognition together. As the TSMs could accurately detect characters from complex background, for text localization, we apply TSMs for all the characters on the coarse text detection regions to eliminate the false positives and search the possible missing characters as well. While for word recognition, we propose a normalized pictorial structure (PS) framework to deal with the bias caused by words of different lengths. Experimental results on a range of challenging public datasets (ICDAR 2003, ICDAR 2011, SVT) demonstrate that the proposed method outperforms state-of-the-art methods both for text localization and word recognition.     

甲醇制烯烃流化床内流化特性的多尺度CFD模拟

近些年，我国成功开发了以煤为原料的甲醇制烯烃(Methanol to Olefins, MTO)生产工艺和技术，带动煤制烯烃产业的快速发展，保障了国家能源安全。流化床式反应器是MTO工业生产的核心反应装置，通过计算流体力学(Computational Fluid Dynamics, CFD)方法深入认知MTO流化床内的流化特性规律具有重要的意义，它可以从理论上更加准确地指导MTO流化床的优化与放大。本工作采用基于宏观?亚网格层次的气泡EMMS曳力和传统TFM耦合计算的多尺度CFD方法，对工业尺度MTO流化床内的多相流化行为进行了三维数值模拟。模拟结果表明，该多尺度CFD方法考虑了气泡结构对气?固相间曳力的影响，能较准确地预测MTO流化床内轴向颗粒浓度的“S-型”分布规律，且得到实验数据的验证；所预测的径向颗粒浓度分布呈现出经典的“环?核”分布规律，气体/颗粒的轴向时均速度在径向上的分布也与实际情况相互佐证，表明该多尺度CFD方法显著改善了基于均匀曳力的传统TFM对于宏观流场的预测能力。下一步工作将多尺度CFD方法拓展应用于MTO流化床优化放大及反应特性的研究。     

机械镀锌-铝复合镀层的结构分析

采用金相显微镜、扫描电镜、能谱仪分析了混粉法机械镀锌-铝复合镀层的结构、形貌和化学成分分布。结果表明，机械镀锌-铝镀层主要是由尺寸不等的锌粉颗粒相互参杂、填充，形成致密结构体；锌粉颗粒之间的间隙填充着更小尺寸的锌粉和夹杂物，镀层中部分锌粉颗粒发生变形；片状铝粉在镀层中不均匀分布，铝粉和锌粉没有明显的包裹、缠绕；镀层中Fe，Sn，Zn的含量分布正常，且Al的含量比施镀前混粉中Al的含量低得多。     

Integrated Fisher linear discriminants: An empirical study

This paper studies Fisher linear discriminants (FLDs) based on classification accuracies for imbalanced datasets. An optimal threshold is found out from a series of empirical formulas developed, which is related not only to sample sizes but also to distribution regions. A mixed binary–decimal coding system is suggested to make the very dense datasets sparse and enlarge the class margins on condition that the neighborhood relationships of samples are nearly preserved. The within-class scatter matrices being or approximately singular should be moderately reduced in dimensionality but not added with tiny perturbations. The weight vectors can be further updated by a kind of epoch-limited (three at most) iterative learning strategy provided that the current training error rates come down accordingly. Putting the above ideas together, this paper proposes a type of integrated FLDs. The extensive experimental results over real-world datasets have demonstrated that the integrated FLDs have obvious advantages over the conventional FLDs in the aspects of learning and generalization performances for the imbalanced datasets.