受限节点的WSNs非均匀分簇算法应用研究

针对常规分簇路由算法不能有效解决节点位置、能量、频段受限的固态发酵温度检测无线传感器网络(WSNs)中节点过早死亡和能耗不均衡的问题,提出了一种基于粒子群优化(PSO)算法的非均匀分簇路由协议。首先,根据网络规模选择固定数目的簇首节点,然后,引入PSO算法和非均匀分簇机制,以簇首节点覆盖范围和簇内节点与簇首之间平均欧氏距离作为评价函数的影响因子,选取一组最优簇首。仿真实验结果表明:所提算法有效改善了受限节点无线测温网络"热区"效应,均衡了节点能耗,显著延长了网络生存周期。     

应力型微悬臂梁生化传感器响应机理研究进展

针对静态微悬臂梁表面特异性结合产生表面应力信号的响应机制问题,介绍了微悬臂梁生化传感器的工作原理,阐述了应力响应机制的简化模型,从纵向界面上和横向分子间2个方面对特异性吸附引起的悬臂梁表面应力的变化进行了剖析,讨论了界面能变化、位阻作用、静电力、氢键作用等与表面应力大小及方向之间的关系,总结了应力型微悬臂梁生化传感器的响应机理的研究。     

氯乙烯-丙烯腈共聚物乳液合成技术及其表征

介绍了氯乙烯-丙烯腈共聚物的乳液聚合方法,研究了合成过程中乳化剂、引发剂的加入方式及工艺条件对共聚物性能的影响,并对共聚物进行了表征。结果表明:乳化剂的最佳投加方式为(以乳化剂/VCM表示):先投加0.02,反应开始后流加0.02;引发剂的最佳投加方式为(以引发剂/VCM表示):先投加0.012,反应开始后流加0.012。该共聚物具有优良的阻燃性能。     

Graph regularized multiset canonical correlations with applications to joint feature extraction

Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms.     

Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition

Due to the noise disturbance and limited number of training samples, within-set and between-set sample covariance matrices in canonical correlation analysis (CCA) usually deviate from the true ones. In this paper, we re-estimate within-set and between-set covariance matrices to reduce the negative effect of this deviation. Specifically, we use the idea of fractional order to respectively correct the eigenvalues and singular values in the corresponding sample covariance matrices, and then construct fractional-order within-set and between-set scatter matrices which can obviously alleviate the problem of the deviation. On this basis, a new approach is proposed to reduce the dimensionality of multi-view data for classification tasks, called fractional-order embedding canonical correlation analysis (FECCA). The proposed method is evaluated on various handwritten numeral, face and object recognition problems. Extensive experimental results on the CENPARMI, UCI, AT&T, AR, and COIL-20 databases show that FECCA is very effective and obviously outperforms the existing joint dimensionality reduction or feature extraction methods in terms of classification accuracy. Moreover, its improvements for recognition rates are statistically significant on most cases below the significance level 0.05.     

A QM/MM study of the catalytic mechanism of aspartate ammonia lyase

Aspartate ammonia lyase (Asp) is one of three types of ammonia lyases specific for aspartate or its derivatives as substrates, which catalyzes the reversible reaction of l-aspartate to yield fumarate and ammonia. In this paper, the catalytic mechanism of Asp has been studied by using combined quantum-mechanical/molecular-mechanical (QM/MM) approach. The calculation results indicate that the overall reaction only contains two elementary steps. The first step is the abstraction of C_β proton of l-aspartate by Ser318, which is calculated to be rate limiting. The second step is the cleavage of C_αN bond of l-aspartate to form fumarate and ammonia. Ser318 functions as the catalytic base, whereas His188 is a dispensable residue, but its protonation state can influence the active site structure and the existing form of leaving amino group, thereby influences the activity of the enzyme, which can well explain the pH dependence of enzymatic activity. Mutation of His188 to Ala only changes the active site structure and slightly elongates the distance of C_β proton of substrate with Ser318, causing the enzyme to remain significant but reduced activity.     

Finite-time control for discrete-time Markovian jump systems with deterministic switching and time-delay

In this paper, the finite-time control problem is investigated for a class of discrete-time Markovian jump systems (MJLSs) with deterministic switching and time-delay. The considered systems are subject to a piecewise-constant transition probability (TP) matrix, which leads to both the deterministic switches and stochastic jumps. First, the stochastic finite-time boundedness (SFTB) and l ₂ gain analysis for the systems are studied by employing the average dwell time (ADT) approach. Note that a finite-time weighted l ₂ gain is obtained to measure the disturbance attenuation level. Then, the mode-dependent and variation-dependent controller is designed such that the resulting closed-loop systems are stochastically finite-time bounded and have a guaranteed disturbance attenuation level. Finally, a numerical example is given to verify the potential of the developed results.     

双层模糊系统融合中心约束型最小包含球

与传统的TSK模糊系统相比,改进的双层TSK模糊系统CTSK(Central TSK Fuzzy System)有如下优点:良好的可解释性、更好的鲁棒性、较强的逼近能力。但对于大样本或超大样本数据集,其时间复杂度和空间复杂度的开销都极大地限制了它的实用性。针对此不足,通过模糊系统融合中心约束型最小包含球(CCMEB)理论提出了CCMEB-CTSK(CCMEB-based CTSK)算法。该算法在继承CTSK优点的同时,又较好地实现了处理大样本和超大样本数据集的有效性和快速性。仿真实验研究分析了采用不同模糊规则数的CCMEB-CTSK的性能指标和运行时间的比较,以及训练样本不加噪声和加入噪声情况下CCMEB-CTSK泛化能力和鲁棒性能的测试。     

Bezier曲线到AH-Bezier曲线的升阶算法

关于曲线升阶,已有的结论往往限于同类曲线之间。为了突破这一限制,考虑不同类曲线间的升阶,关注代数多项式空间中的Bezier曲线到代数双曲多项式空间中的AH-Bezier曲线的升阶。研究从基函数入手,利用Bezier和AH-Bezier共有的求导降阶的特点,结合矩阵分块的思想,先给出AH-Bezier基到Bernstein基的转换矩阵,进而推出控制顶点的升阶公式,最后给出升阶算法。结果表明,任意n次Bezier曲线可以通过该算法升到n＋3阶（等同于n＋2次）的AH-Bezier曲线。算法实现了Bezier到AH-B＆#233;zier曲线模型的精确转换。     

浅析数字媒体艺术的技术特征

数字媒体艺术是一个以技术为基础平台,艺术为上层建筑,两者相结合的新兴学科。此门学科在创作方式、传播途径以及展示效果上,都呈现出与传统艺术不同的新特点。在创作和运用过程中,应该把握好数字媒体的技术特征,才能处理好数字媒体中技术和艺术的关系,充分发掘数字媒体的艺术价值。