A note on octonionic support vector regression

This note presents an analysis of the octonionic form of the division algebraic support vector regressor (SVR) first introduced by Shilton A detailed derivation of the dual form is given, and three conditions under which it is analogous to the quaternionic case are exhibited. It is shown that, in the general case of an octonionic-valued feature map, the usual "kernel trick" breaks down. The cause of this (and its interpretation) is discussed in some detail, along with potential ways of extending kernel methods to take advantage of the distinct features present in the general case. Finally, the octonionic SVR is applied to an example gait analysis problem, and its performance is compared to that of the least squares SVR, the Clifford SVR, and the multidimensional SVR.     

A scalable multi-attribute hybrid overlay for range queries on the cloud

Cloud computing has become a promising paradigm as next generation computing model, by providing computation, software, data access, and storage services that do not need to know the location of physical resources interconnected across the globe providing such services. In such an environment, important issues as information sharing and resource/service discovery arise. In order to overcome critical limitations in centralized approaches for information sharing and resource/service discovery, this paper proposes a framework of a scalable multi-attribute hybrid overlay featured with decentralized information sharing, flexible resource/service discovery, fault tolerance and load balancing. Additionally, the proposed hybrid overlay integrates a structured P2P system with an unstructured one to support complex queries. Mechanisms such as load balancing and fault tolerance implemented in our proposed system to improve the overall system performance are also discussed. Experimental results show that the performance of the proposed approach is feasible and stable, as the proposed hybrid overlay improves system performance by reducing the number of routing hops and balancing the load by migrating requests.     

Fuzzy local maximal marginal embedding for feature extraction

In graph-based linear dimensionality reduction algorithms, it is crucial to construct a neighbor graph that can correctly reflect the relationship between samples. This paper presents an improved algorithm called fuzzy local maximal marginal embedding (FLMME) for linear dimensionality reduction. Significantly differing from the existing graph-based algorithms is that two novel fuzzy gradual graphs are constructed in FLMME, which help to pull the near neighbor samples in same class nearer and nearer and repel the far neighbor samples of margin between different classes farther and farther when they are projected to feature subspace. Through the fuzzy gradual graphs, FLMME algorithm has lower sensitivities to the sample variations caused by varying illumination, expression, viewing conditions and shapes. The proposed FLMME algorithm is evaluated through experiments by using the WINE database, the Yale and ORL face image databases and the USPS handwriting digital databases. The results show that the FLMME outperforms PCA, LDA, LPP and local maximal marginal embedding.     

An SVD-based image watermarking in wavelet domain using SVR and PSO

The paper presents a novel blind watermarking scheme for image copyright protection, which is developed in the discrete wavelet transform (DWT) and is based on the singular value decomposition (SVD) and the support vector regression (SVR). Its embedding algorithm hides a watermark bit in the low–low (LL) subband of a target non-overlap block of the host image by modifying a coefficient of U component on SVD version of the block. A blind watermark-extraction is designed using a trained SVR to estimate original coefficients. Subsequently, the watermark bit can be computed using the watermarked coefficient and its corresponding estimate coefficient. Additionally, the particle swarm optimization (PSO) is further utilized to optimize the proposed scheme. Experimental results show the proposed scheme possesses significant improvements in both transparency and robustness, and is superior to existing methods under consideration here.     

基于物联网的教育与教学设备监控的研究与应用

新的现代化技术服务于教育是高职院校现代教育发展的必然趋势,文章系统地叙述了高职院校为进一步推动物联网在教育与教学设备监控的应用,做了不断的探索和实践,以期对高职院校的教育与教学设备监控建设工作提供一些借鉴。     

基于VMD和样本熵的电磁式电流互感器故障诊断

针对电磁式电流互感器故障诊断效率低、准确率不高问题,提出一种变分模态分解(VMD)和样本熵相结合的故障诊断方法.将原始故障信号通过VMD分解成一系列本征模函数(IMF)并进行优选,计算其样本熵作为新的互感器特征提取对象的特征值,与常见时频域特征指标组合成新的特征向量输入K近邻分类器进行训练.Matlab仿真实验结果表明,该方法中新的特征指标用于低压电流互感器故障诊断是有效可行的,可为电磁式电流互感器故障诊断提供参考.     

国内轴承钢的生产现状及发展

介绍了2002年国内高碳铬轴承钢的产量及品种结构，评述了国内轴承钢生产工艺装备、质量状况和发展方向。     

基于虚拟仪器的水电机组在线状态监测系统的研究*

随着水电机组状态检修的不断开展, 需要实现对机组的状态监测, 及时了解机组的运行情况并能及时处理。考虑到水电机组状态监测的多样性和复杂性, 结合实际, 提出水电机组在线状态监测的项目和基于PXI 总线仪器和LabVIEW 的具体实施方案。状态监测系统通过对水电机组状态量的采集、处理和分析, 来实现水电机组运行状态在线实时监测, 极大地提高了机组利用率。该系统在水电机组的状态检修中得到了实际应用。     

基于洁净煤燃烧技术的燃煤锅炉运行能耗率调试方法研究

针对燃煤锅炉运行能耗率传统调试方法存在能耗率损失分析性能较差导致锅炉热效率波动较大且运行成本较高的问题,提出基于洁净煤燃烧技术的燃煤锅炉运行能耗率调试方法.设定燃煤锅炉运行能耗率测试内容及仪器,完成燃煤锅炉运行能耗率损失的测试分析工作;利用小波变换技术对数据预处理,并设定燃煤锅炉清洁因子;根据清洁因子取值范围以及能耗率损失分析结果,引入多目标优化算法,对锅炉运行能耗率损失完成多目标寻优,实现锅炉运行能耗率调试过程.试验结果表明,应用所提方法下的锅炉热效率稳定,锅炉运行成本较低,可实现对锅炉运行能耗率的调试.     

一起发电机转子绕组动态匝间短路故障的诊断与分析

转子绕组是汽轮发电机的重要组成部分,运行状态下受机、电、热等多种因素影响,故障判断相对复杂.针对一起660 MW汽轮发电机运行中转子绕组动态匝间短路引发的振动异常,从不同角度及状态进行分析,确定了机组振动与转子绕组动态匝间短路的相关性,并对转子绕组动态匝间短路的诊断方法提出了建议.