Global existence of three-dimensional incompressible magneto-micropolar system with mixed partial dissipation,magnetic diffusion and angular viscosity

The magneto-micropolar fluid flows describe the motion of electrically conducting micropolar fluids in the presence of a magnetic field. The issue of whether the strong solution of magneto-micropolar equations in three-dimensional can exist globally in time with large initial data is still unknown. In this paper, we deal with the Cauchy problem of the three-dimensional magneto-micropolar system with mixed partial dissipation, magnetic diffusion and angular viscosity. More precisely, the global existence of smooth solutions to the three-dimensional incompressible magneto-micropolar fluid equations with mixed partial dissipation, magnetic diffusion and angular viscosity are obtained by energy method under the assumption that $H^1$-norm of the initial data $(u_0,b_0,w_0)$ sufficiently small, namely $6u_0,b_0,{\omega }_0{6}_{H^1\left({\mathbb{R}}^3\right)}^2\le \frac{\epsilon }{2}$, where $\epsilon$ is a sufficiently small positive number. This work follows the techniques in the paper of Cao and Wu (2011).     

Fractional Wishart processes and ε-fractional Wishart processes with applications

In this paper, we introduce two new matrix stochastic processes: fractional Wishart processes and $\epsilon$-fractional Wishart processes with integer indices which are based on the fractional Brownian motions and then extend $\epsilon$-fractional Wishart processes to the case with non-integer indices. Both processes include classic Wishart processes (if the Hurst index $H$ equals $\frac{1}{2}$) and present serial correlation of stochastic processes. Applying $\epsilon$-fractional Wishart processes to financial volatility theory, the financial models account for the stochastic volatilities of the assets and for the stochastic correlations not only between the underlying assets’ returns but also between their volatilities and for stochastic serial correlation of the relevant assets.     

对K-means及势函数聚类算法的研究与改进

在目前聚类方法中, k-means与势函数是最常用的算法,虽然两种算法有很多优点,但也存在自身的局限性。 k-means聚类算法：其聚类数目无法确定,需要提前进行预估,同时对初始聚类中心敏感,且容易受到异常点干扰;势函数聚类算法：其聚类区间范围有限,对多维数据进行聚类其效率低。针对以上两种算法的缺点,提出了一种基于 K-means 与势函数法的改进聚类算法。它首先采用势函数法确定聚类数目与初始中心,然后利用K-means法进行聚类,该改进算法具有势函数法“盲”特性及K-means法高效性的优点。实验对改进算法的有效性进行了验证,结果表明,改进算法在聚类精度及收敛速度方面有很大提高。     

Clustering based multiple branches deep networks for single image super-resolution

Since the limitation of optical sensors, it’s often hard to obtain an image with the ideal resolution. Image super-resolution (SR) technology can generate a high-resolution image from the corresponding low-resolution image. Recently, deep learning (DL) based SR methods draw much attention due to their satisfying reconstruction results. However, these methods often neglect the diversity of image patches. Therefore, the reconstruction effect is limited. To fully exploit the texture variability across different image patches, we propose a universal, flexible, and effective framework. The proposed framework can be adopted to any DL based methods. It can significantly improve the SR accuracy while maintaining the running time. In the proposed framework, K-means is employed to cluster image patches into different categories. Multiple CNN branches are designed for these different categories to reconstruct the SR image. Each branch is weighted in accordance with the Euclidean distance to the cluster centers. Experimental results demonstrate that by applying the proposed framework, performance of the DL based SR method can be significantly improved.

     

基于动态期望的航空器滑行路径更新模型仿真

滑行路径优化是提升机场场面运行效率与安全水平的有效途径。为研究管制意图、监控范围对航空器滑行路径优化的影响,基于实际管制规定,同时考虑冲突和拥堵影响,定义优先级反映滑行道通行能力,定义动态期望值量化管制员意图,设计航空器滑行路径变更机制及实施步骤,构造基于动态期望的航空器滑行路径变更模型。采用Visual C++及Mathlab混合编程仿真,得到航空器分布密度、监控范围等参数改变时,不同模型的仿真结果对比及关键统计分析值的变化趋势。结果表明,上述模型能对低密度下的滑行道变更进行抑制,能提高中密度下的滑行道变更比例,并减少滑行速度变化幅度。扩大监控范围能有效增加航空器滑行路径变更比例,减少滑行速度标准差,提高机场场面运行保障能力。     

Multi-objective colonial competitive algorithm for hybrid flowshop problem

This study analyses the multi-objective optimization in hybrid flowshop problem, in which two conflicting objectives, makespan and total weighted tardiness, are considered to be minimized simultaneously. The multi-objective version of Colonial Competitive Algorithm (CCA) for real world optimization problem is introduced and investigated. In contrast to multi-objective problems solved by CCA, presented in the literature, which used the combination of the objectives as single objective, the proposed algorithm is established on Pareto solutions concepts. Another novelty of this paper is estimating the power of each imperialist by a probabilistic criterion for this multi objective algorithm. Besides that, the variable neighborhood search is implemented as an assimilation strategy. Performance of the algorithm is finally compared with a famous algorithm for scheduling problem, NSGA-II, and the multi-objective form of CCA [28].     

Enabling real-time information service on telehealth system over cloud-based big data platform

A telehealth system covers both clinical and nonclinical uses, which not only provides store-and-forward data services to be offline studied by relevant specialists, but also monitors the real-time physiological data through ubiquitous sensors to support remote telemedicine. However, the current telehealth systems do not consider the velocity and veracity of the big-data system in the medical context. Emergency events generate a large amount of the real-time data, which should be stored in the data center, and forwarded to remote hospitals. Furthermore, patients’ information is scattered on the distributed data center, which cannot provide a high-efficient remote real-time service. In this paper, we proposes a probability-based bandwidth model in a telehealth cloud system, which helps cloud broker to provide a high performance allocation of computing nodes and links. This brokering mechanism considers the location protocol of Personal Health Record (PHR) in cloud and schedules the real-time signals with a low information transfer between different hosts. The broker uses several bandwidth evaluating methods to predict the near future usage of bandwidth in a telehealth context. The simulation results show that our model is effective at determining the best performing service, and the inserted service validates the utility of our approach.     

飞秒激光辅助制备熔石英表面的网格状微流体

报道了一种利用飞秒激光直写后再用化学腐蚀熔石英表面制备得到网格状微流体沟道,然后在其表面覆盖一层PDMS薄膜制备网格状半密闭微流体通道的方法。微流体沟道的形成机制可以定义为飞秒激光辐照导致基底材料改性,而改性区域对HF水溶液的溶解度增强。使用光学显微镜和扫描电子显微镜(SEM)表征了网格状微流体通道的形貌。此外,为了测试该微流体结构的流动特性,进行了液体注入实验。     

An improved vector quantization method using deep neural network

To address the challenging problem of vector quantization (VQ) for high dimensional vector using large coding bits, this work proposes a novel deep neural network (DNN) based VQ method. This method uses a k-means based vector quantizer as an encoder and a DNN as a decoder. The decoder is initialized by the decoder network of deep auto-encoder, fed with the codes provided by the k-means based vector quantizer, and trained to minimize the coding error of VQ system. Experiments on speech spectrogram coding demonstrate that, compared with the k-means based method and a recently introduced DNN-based method, the proposed method significantly reduces the coding error. Furthermore, in the experiments of coding multi-frame speech spectrogram, the proposed method achieves about 11% relative gain over the k-means based method in terms of segmental signal to noise ratio (SegSNR).