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。     

Exploring the methods on improving CH4 delivery performance to surpass the Advanced Research Project Ageney-Energy target

CH_4 storage associated with adsorbed natural gas(ANG) technology is an issue attracting great concern.Following the Advanced Research Project Agency-Energy(ARPA-E) targeted deliverable capacity of 315 cm~3·cm~(-3)(STP), hundreds of thousands of materials have been experimentally or theoretically evaluated,while the best results still show a 35% gap from the target. Moreover, recent theoretical research reveals that the target is beyond the possibility that real materials can be designed. To get rid of the awkward situation, we make attempts on investigating the CH_4 delivery performance under other operation conditions. Methods of raising the discharge temperature(to infinite high) or elevating the storage pressure(to 25 MPa) have been proved to show limited effectiveness. In this work, it is found that the ARPA-E target can be achieved by using a decreasing storage temperature strategy. By taking 280 Co RE(computation-ready, experimental) COFs(covalent organic frameworks) as ANG materials, when reduce the storage temperature to 190.6 K, the highest deliverable capacity can reach 392 cm~3·cm~(-3)(STP), and16.1% Co RE COFs can surpass the target. The target is also achievable when storage at 220 K. Structure performance relationships study shows strong correlation between deliverable capacity and void fraction. Hence, 120 hypothetical COFs are generated to ascertain the optimum void fraction. In addition,the performance of 2 D-COFs can be greatly enhanced by increasing the interlayer spacings, e.g. CH_4 deliverable capacity(storage at 190.6 K) of ATFG-COF can be improved from 239 to 411 cm~3·cm~(-3)(STP) when interlayer spacing is enlarged to 1.65 nm.     

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.     

Interspecies Behavioral Variability of Medaka Fish Assessed by Comparative Phenomics

Recently, medaka has been used as a model organism in various research fields. However, even though it possesses several advantages over zebrafish, fewer studies were done in medaka compared to zebrafish, especially with regard to its behavior. Thus, to provide more information regarding its behavior and to demonstrate the behavioral differences between several species of medaka, we compared the behavioral performance and biomarker expression in the brain between four medaka fishes, Oryzias latipes, Oryzias dancena, Oryzias woworae, and Oryzias sinensis. We found that each medaka species explicitly exhibited different behaviors to each other, which might be related to the different basal levels of several biomarkers. Furthermore, by phenomics and genomic-based clustering, the differences between these medaka fishes were further investigated. Here, the phenomic-based clustering was based on the behavior results, while the genomic-based clustering was based on the sequence of the nd2 gene. As we expected, both clusterings showed some resemblances to each other in terms of the interspecies relationship between medaka and zebrafish. However, this similarity was not displayed by both clusterings in the medaka interspecies comparisons. Therefore, these results suggest a re-interpretation of several prior studies in comparative biology. We hope that these results contribute to the growing database of medaka fish phenotypes and provide one of the foundations for future phenomics studies of medaka fish.     

Boron Nitride Nanosheets Strengthened PVA/Borax Hydrogels with Highly Efficient Self-Healing and Rapid pH-Driven Shape Memory Effect

Self-healing hydrogels often possess poor mechanical properties which largely limits their applications in many fields. In this work, boron nitride nanosheets are introduced into a network of the poly(vinyl alcohol)/borax (PVA/borax) hydrogels to enhance the mechanical properties of the hydrogel without compromising the self-healing abilities. The obtained hydrogels exhibit excellent mechanical properties with a tensile strength of 0.410 ± 0.007 MPa, an elongation at break of 1712%, a Young's Modulus of 0.860 ± 0.023 MPa, and a toughness of 3.860 ± 0.075 MJ m⁻³. In addition, the self-healing efficiency of the hydrogels is higher than 90% within 10 min at room temperature. Benefiting from the excellent self-healing properties, the shapeability of the hydrogel fragments is observed using different molds. In addition, the hydrogels display rapid pH-driven shape memory effects and can recover to their original shape within 260 s. Overall, this work provides a new approach to hydrogels with integrated excellent mechanical properties, self-healing abilities, and rapid pH-driven shape memory effects.