Polymeric absorbent for water sorption based on chemically crosslinked poly (acrylamide/2-acrylamido-2-methyl-1-propanesulfonic acid sodium salt) hydrogels

Summary Swelling equilibrium of polyelectrolyte copolymer gels containing of acrylamide (AAm) and 2-acrylamido-2-methyl-1-propanesulfonic acid sodium salt (AMPS) have been studied as a function of copolymer composition. AAm/AMPS hydrogels were prepared by free radical solution polymerization in aqueous solution of AAm with AMPS as anionic comonomer and two multifunctional crosslinkers such as ethylene glycol dimethacrylate (EGDMA) and trimethylolpropane triacrylate (TMPTA). Swelling experiments were performed in water at 25 °C, gravimetrically. The influence of AMPS content in hydrogels was examined. Swelling of AAm/AMPS hydrogels was increased up to 1018% (for containing 2% AMPS and crosslinked by EGDMA) 15246% (for containing 8% AMPS and crosslinked by TMPTA), while AAm hydrogels swelled up to 804% (crosslinked by TMPTA)–770% (crosslinked by EGDMA). The values of equilibrium water content of the hydrogels are 0.8851–0.9935. Diffusion behavior was investigated. Water diffusion into hydrogels was found to be non-Fickian in character.     

Viscoelastic and Processing Effects on the Fiber-Matrix Interphase Strength. Part I. The Effects of Loading Rate, Test Temperature, Fiber Sizing and Global Strain Level

The effects of loading rate, fiber sizing, test temperature and global strain level on the adhesion strength between carbon fibers and a thermosetting epoxy (Epon 815) are studied using the single fiber fragmentation test procedure. Analytical methodology describing the viscoelastic behavior observed is also presented. The possibility of rate-temperature-interphase thickness superposition for the interfacial strength function is illustrated based on the analytical models discussed. Experimental data are discussed using Weibull statistics and also presented in the form of percent relative frequency histograms for the fiber fragments in a collective fashion. The use of histograms allows for interpretation of the skewness in the data population.     

基于信道状态特征的手势动作识别技术研究

手势识别作为人和机器之间重要的交互手段,在日常生活中具有广泛的应用场景。基于无线信号特别是WiFi的手势识别由于其无接触、成本低等优点成为当前热门的方式。为解决传统基于无线信号手势识别算法没有充分利用信号相位特征的缺点,本文提出利用WiFi信道状态信息幅值和相位结合的方式进行手势识别。通过子载波降维和动作曲线提取对接收的WiFi信号进行处理,并将信号的幅值和相位结合,利用机器学习算法对数据进行训练和分类,实现了单手向前、单手向后、单手向左、单手向右、单手向上和单手向下六种手势的识别。实验结果显示,本文算法在近距离和远距离下的精确度分别为96%和92%。     

Design of Lu2O3-reinforced Cf/SiC-ZrB2-ZrC ultra-high temperature ceramic matrix composites: Wetting and interfacial reactivity by ZrSi2 based alloys

The wettability and infiltration of molten ZrSi₂ and ZrSi₂-Lu₂O₃ alloys into C_f/SiC and B₄C-infiltrated C_f/SiC composites were investigated to understand the interfacial interactions that occur during the development of C_f/SiC-ZrC and C_f/SiC-ZrB₂-ZrC-Lu₂O₃ materials. A significant evaporation of Si from the liquid affected the wetting behaviour of the alloy when tested in a vacuum at 1670 °C. The better wetting and spreading of the alloy over the surface was observed for the composites with lower overall porosity (12 %). On the other hand, the formation of an outer dense layer, followed up by the uniform infiltrated region up to ～ 1 mm was observed for the C_f/SiC with higher porosity (21 %). The infiltrated alloy reacted with SiC matrix to form ZrC or with B₄C-infiltrated SiC matrix to form ZrB₂-ZrC-SiC. The Lu₂O₃ particles were not wetted by the melt, and were pushed away of the reaction zone by the solidification front.     

SONIC: streaming overlapping community detection

A community within a graph can be broadly defined as a set of vertices that exhibit high cohesiveness (relatively high number of edges within the set) and low conductance (relatively low number of edges leaving the set). Community detection is a fundamental graph processing analytic that can be applied to several application domains, including social networks. In this context, communities are often overlapping, as a person can be involved in more than one community (e.g., friends, and family); and evolving, since the structure of the network changes. We address the problem of streaming overlapping community detection, where the goal is to maintain communities in the presence of streaming updates. This way, the communities can be updated more efficiently. To this end, we introduce SONIC—a find-and-merge type of community detection algorithm that can efficiently handle streaming updates. SONIC first detects when graph updates yield significant community changes. Upon the detection, it updates the communities via an incremental merge procedure. The SONIC algorithm incorporates two additional techniques to speed-up the incremental merge; min-hashing and inverted indexes. Results show that SONIC can provide high quality overlapping communities, while handling streaming updates several orders of magnitude faster than the alternatives performing from-scratch computation.     

Signal‐to‐noise ratios for measuring saliency of features extracted by eigenvector methods from ophthalmic arterial Doppler signals

Abstract: Features are used to represent patterns with minimal loss of important information. The feature vector, which is composed of the set of all features used to describe a pattern, is a reduced‐dimensional representation of that pattern. Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal‐to‐noise ratio saliency measure was employed to determine the saliency of input features of recurrent neural networks (RNNs) used in classification of ophthalmic arterial Doppler signals. Eigenvector methods were used to extract features representing the ophthalmic arterial Doppler signals. The RNNs used in the ophthalmic arterial Doppler signal classification were trained for the signal‐to‐noise ratio screening method. The application results of the signal‐to‐noise ratio screening method to the ophthalmic arterial Doppler signals demonstrated that classification accuracies of RNNs with salient input features are higher than those of RNNs with salient and non‐salient input features.     

A performance study of multiprocessor task scheduling algorithms

Multiprocessor task scheduling is an important and computationally difficult problem. A large number of algorithms were proposed which represent various tradeoffs between the quality of the solution and the computational complexity and scalability of the algorithm. Previous comparison studies have frequently operated with simplifying assumptions, such as independent tasks, artificially generated problems or the assumption of zero communication delay. In this paper, we propose a comparison study with realistic assumptions. Our target problems are two well known problems of linear algebra: LU decomposition and Gauss–Jordan elimination. Both algorithms are naturally parallelizable but have heavy data dependencies. The communication delay will be explicitly considered in the comparisons. In our study, we consider nine scheduling algorithms which are frequently used to the best of our knowledge: min–min, chaining, A^*, genetic algorithms, simulated annealing, tabu search, HLFET, ISH, and DSH with task duplication. Based on experimental results, we present a detailed analysis of the scalability, advantages and disadvantages of each algorithm.

适于深层次CNN的配电网过电压数据集建立方法

随着人工智能算法的快速发展,在配网过电压识别中层数较少的卷积神经网络已得到应用。深层次网络有较高的识别率,但需要大量数据样本,目前已有数据集数据量不足,不能满足深层级网络训练所需。为此提出了一种满足深层次网络训练所需的配电网过电压数据集建立方法。首先利用电磁暂态仿真软件EMTPworks仿真10 kV配网5种典型的过电压并且编辑相应的JavaScript脚本,通过改变故障初相角、过渡电阻、线路长度等参数产生16 272个数据。然后将三相过电压一维信号进行连续小波变换,得到相应二维时频图,并根据原始信号的特征自动标记二维时频图,从而建立了完整的配电网过电压数据集。最后利用卷积神经网络（CNN）对5类过电压信号数据的有效性进行验证。结果表明,构造的数据集数据规模大,有效性高,能够满足深层次网络需要。