High-velocity oxyfuel reactive spraying of mechanically alloyed Ni-Ti-C powders

Recently, there has been considerable interest in producing cermet coatings with nanoscale carbide grains in the size range 50 to 500 nm. In this article, the production of nanoscale TiC grains in a Ni-based alloy matrix by reactive high-velocity oxyfuel (HVOF) spraying of metastable Ni-Ti-C powder is reported. Mechanical alloying of a Ni(Cr) prealloyed powder and Ti and C elemental powders was performed in a planar-type ball mill, and materials were characterized in detail using x-ray diffraction (XRD) and scanning electron micros-copy (SEM). Phase changes were correlated with milling time and other processing conditions. Results show that, by the selection of appropriate conditions, a metastable Ni-Ti-C powder could be obtained with the nominal composition 50wt.%Ni-40wt.%Ti-10wt.%C. Following sieving and classification, powder was produced with a particle size range of −38 to 8 μm, which is suitable for HVOF spraying. Coatings, approximately 250 μm thick, were deposited by HVOF spraying onto mild steel substrates, and the microstructures formed were investigated. XRD showed that a self-propagating high-temperature synthesis (SHS) reaction had occurred in the powder particles during spraying and that the principal phases present in the coating were TiC and a Ni-rich solid solution; small quantities of NiTi, TiO₂, and NiTiO₃ were also present. SEM revealed that the coatings had a characteristic, splatlike morphology and that TiC formed as a nanoscale dispersion, with a size range of ∼50 to 200 nm, within solidified splats. The microstructures of these reactively sprayed Ni-TiC coatings are briefly compared with those observed in HVOF-sprayed coatings deposited using prereacted SHS powder. The original version of this paper was published as part of the ASM Proceedings, Thermal Spray 2003: Advancing the Science and Applying the Technology, International Thermal Spray Conference (Orlando, FL), May 5–8, 2003, Basil R. Marple and Christian Moreau, Ed., ASM International, 2003.     

Dynamic recrystallization by rapid heating followed by compression for a 17Ni–0.2C martensite steel

A 17Ni–0.2C martensite steel was rapidly heated below the austenite formation temperature and deformed in compression. Continuous dynamic recrystallization was observed. The behavior is similar to that in a ferrite–pearlite steel but the onset strain is much smaller. Sub-micron ferrite grains were obtained through the dynamic recrystallization.     

水管式沉降仪产品优化研究

水管式沉降仪是土石坝安全监测的主要仪器之一,水管式沉降仪测量误差主要是由管路系统的结构、传感器稳定性和综合精度等造成。目前,水管式沉降仪在工程现场的实际应用中出现一些问题：水管沉降仪安装复杂,主要表现在控制箱、各组件之间的链接;传感器的测量方式,不是直接测得观测管内的水位高度,需要多次转换;在连接的管路中空气得不到有效排出。本文主要针对以上几种情况对水管沉降仪在实际应用中呈现的问题进行优化改进。     

Greedily computing associative aggregations on sliding windows

We present an algorithm for combining the elements of subsequences of a sequence with an associative operator. The subsequences are given by a sliding window of varying size. Our algorithm is greedy and computes the result with the minimal number of operator applications.     

一种基于蚁群算法的呼叫中心人力需求计算方法

针对呼叫中心人力需求优化这一离散约束问题,基于Dantzig提出的集合覆盖模型,建立了单技能呼叫中心的人力需求计算线性规划模型,并提出一种基于改进蚁群算法的求解方法.在该方法中,对算法的信息素更新规则进行了修改,并基于MATLAB编程针对实例进行仿真分析.选取统计学指标将算法的仿真结果与遗传算法进行对比,结果表明:基于蚁群算法的方法计算复杂度可行;能够节约人力,且话务员匹配度可满足实际运营需求;为智能算法在呼叫中心人力需求计算问题上的应用研究提供了一种新的解决思路.     

Consensus of a class of second‐order nonlinear heterogeneous multi‐agent systems with uncertainty and communication delay

In this paper, a consensus problem is studied for a group of second‐order nonlinear heterogeneous agents with non‐uniform time delay in communication links and uncertainty in agent dynamics. We design a class of novel decentralized control protocols for the consensus problem whose solvability is converted into stability analysis of an associated closed‐loop system with uncertainty and time delay. Using an explicitly constructed Lyapunov functional, the stability conditions or the solvability conditions of the consensus problem are given in terms of a set of linear matrix inequalities apart from a small number of scalar parameters that appear nonlinearly. Furthermore, the linear matrix inequalities are theoretically verified to be solvable when the communication delay is sufficiently small. The effectiveness of the proposed control protocol is illustrated by numerical examples. Copyright © 2016 John Wiley & Sons, Ltd.     

Extensions of principle component analysis with applications on vision based computing

This paper mainly focuses on the principle component analysis (PCA) and its applications on vision based computing. The underlying mechanism of PCA given and several significant factors, involved with subspace training are discussed theoretically in detail including principle components energy, residuals assessment, and decomposition computation. The typical extensions, including probabilistic PCA (PPCA), kernel PCA (KPCA), multi-dimensional PCA and robust PCA (RPCA), have been presented with critical analysis on both mechanisms and computations. Combining with the studies on, such as, image compression, visual tracking, image recognition and super-resolution image reconstruction, PCA and its extensions applied to computer vision are critically reviewed and evaluated on the targeted issues in each application as well as the role they played at specific tasks to the characteristics, cost and suitable situations of each PCA based vision processing technique.     

Multi-label classification via learning a unified object-label graph with sparse representation

Automatic annotation is an essential technique for effectively handling and organizing Web objects (e.g., Web pages), which have experienced an unprecedented growth over the last few years. Automatic annotation is usually formulated as a multi-label classification problem. Unfortunately, labeled data are often time-consuming and expensive to obtain. Web data also accommodate much richer feature space. This calls for new semi-supervised approaches that are less demanding on labeled data to be effective in classification. In this paper, we propose a graph-based semi-supervised learning approach that leverages random walks and ? ₁ sparse reconstruction on a mixed object-label graph with both attribute and structure information for effective multi-label classification. The mixed graph contains an object-affinity subgraph, a label-correlation subgraph, and object-label edges with adaptive weight assignments indicating the assignment relationships. The object-affinity subgraph is constructed using ? ₁ sparse graph reconstruction with extracted structural meta-text, while the label-correlation subgraph captures pairwise correlations among labels via linear combination of their co-occurrence similarity and kernel-based similarity. A random walk with adaptive weight assignment is then performed on the constructed mixed graph to infer probabilistic assignment relationships between labels and objects. Extensive experiments on real Yahoo! Web datasets demonstrate the effectiveness of our approach.     

Gaming Social Capital: Exploring Civic Value in Multiplayer Video Games

Recent research suggests that social interactions in video games may lead to the development of community bonding and prosocial attitudes. Building on this line of research, a national survey of U.S. adults finds that gamers who develop ties with a community of fellow gamers possess gaming social capital, a new gaming‐related community construct that is shown to be a positive antecedent in predicting both face‐to‐face social capital and civic participation.