ANALYSIS OF SURIMI GEL PROPERTIES BY COMPRESSION AND PENETRATION TESTS

Gel properties of surimi of different quality were analyzed using compression and penetration tests. Parameters used to vary the surimi quality were species, freshness, freeze-abuse and added ingredients. Compressive force at failure was more discriminative than penetration force. The result of a failure response analysis suggests that the compression test is useful in assessing the cohesive property, while the penetration test is better for assessing density and compactness. Good correlations between compression and penetration force values were seen in surimi gels prepared from the same species with and without ingredients, while poor correlations were observed in the surimi which either underwent freeze-abuse or was prepared from different species.     

Chemical and Microbial Quality of Irradiated Ground Ginger

Fresh ground ginger was irradiated with a 10-kGy ionizing radiation dose from a Cobalt-60 source. Gamma radiation decreased most of the extractable volatile flavor components. Those known to contribute to typical ginger flavor, e.g., zingiberene, β-sesquiphellandrene, β-bisabol-ene, famesene isomers, arcurcumene, and α-cubebene, decreased 27–59% following irradiation. Most odors recognized through gas chromatography/olfactometry produced similar patterns from irradiated and non-irradiated samples. The radiation treatment reduced the aerobic microbial population from 10⁸ to 10¹ CFU/g. Sensory qualities of flavor, color and odor of ginger powder were similar for treated and untreated ginger.     

一种支持向量机的快速分类算法

鉴于传统支持向量机分类过程的计算量和支持向量的个数成正比,为了提高分类决策的速度,提出一种约简支持向量的快速分类算法,该算法对原始的支持向量进行特定比例的模糊均值聚类操作,按照分类误差最小的原则构建最小线性二乘回归模型,求解新的支持向量系数和决策函数的偏置.人造数据集和标准数据集上的实验表明,约简50%支持向量后,可以在保持分类精度在无统计意义的明显损失的前提下,使得分类速度提高50%.     

An adaptive control scheme for robot manipulators

An adaptive control scheme is developed for a robot manipulator to track a desired trajectory as closely as possible in spite of a wide range of manipulator motions and parameter uncertainties of links and payload.

The presented control scheme has two components: a nominal control and a variational control. The nominal control, generated from direct calculation of the manipulator dynamics along a desired trajectory, drives the manipulator to a neighbourhood of the trajectory. Then a new adaptive regulation scheme is devised based on the Lyapunov direct method, which generates the variational control that regulates the perturbation in the vicinity of the desired trajectory.     

协作式整体和局部的分类机

提出了一种协作式整体局部分类算法,即C2M (Collaborative classification machine with local and global information),该算法利用两类样本各自的协方差作为整体方向信息, 获得两个带整体和局部信息的分类面,并通过组合分类器的平均规则将两个分类面组合, 得到最终的最优判决平面.该算法可用两次QP (Quadratic programming)求解,时间复杂度为O(2N3), 大大小于M4 (Maxi-min margin machine)的O(N4), 线性核时的分类精度高于只利用了局部信息的支持向量机 (Support vector machine, SVM).理论上证明了在交遇区较多时, C2M 可以比M4 更有效地利用全局信息,并提出了判断整体信息对分类是否有贡献的4个判别指标. 模拟数据和标准数据集上与M4 和SVM的对比实验证明了该算法的有效性.     

基于VEGA Grid的消防指挥系统研究

根据新时期新形势下灭火救援工作及消防指挥系统建设实际的需要，提出VEGA Grid消防指挥系统概念和具体设计思路、方法，具体分析VEGA Grid消防指挥系统所需要实现的目标，介绍了VEGA Grid消防指挥系统的设计构想以及各部分组成。     

THE EFFECT OF BORON ON THE NUCLEATION AND GROWTH OF PEARLITE

从实验结果证明,硼对珠光体的成核及成长是有影响的;在亚共析钢中硼促使珠光体的开始形成时间推迟,增长了珠光体成核的孕育期,但对成核率与时间的关系,在近于A_1温度(700℃)时,略为加速,而在珠光体转变速度最大的温度(650℃)时却没有影响。在共析钢中硼同样的使珠光体形成开始时间延迟,但对成核率与时间的关系没有改变,因此很可能硼促使奥氏体晶界能降低,珠光体得不到足够的能量成核,因而增加了奥氏作的稳定度。硼有加速珠光体成长率的作用,无论在亚共析或共析钢中皆有同样的现象产生。因此我们认为在珠光体形成温度范围内,硼虽使奥氏体转变开始时间推迟,但不影响转变终了时间的原因,是由于珠光体成长率加速所致。     

Formation of FeMo2B2 phase in boron containing 9Cr-1.5Mo ferritic steels

The segregation and diffusion of boron during heat treatments were studied.The influence of boron contents, aging time and applied stress on FeMo2B2 formation was also studied.Finally, the effects of boron contents and FeMo2B2 formation on the high temperature strength were studied.Boron atoms were segregated to prior austenite grain boundary during normalizing treatment.And these boron atoms were slowly diffused into the grain interior during tempering and aging at 700 ℃.The FeMo2B2 phase was only formed after 1,000 h aging at 700 ℃ in alloy containing 196 ppm boron.The formation of FeMo2B2 phase is accelerated by the applied stress.It was expected that the formation of FeMo2B2 is closely related to the redistribution of boron atoms.The tensile strengths at 700 ℃ are increased with the increase of boron contents.However, the formation of FeMo2B2 phase results in lower tensile strength.     

A kernel learning framework for domain adaptation learning

Domain adaptation learning(DAL) methods have shown promising results by utilizing labeled samples from the source(or auxiliary) domain(s) to learn a robust classifier for the target domain which has a few or even no labeled samples.However,there exist several key issues which need to be addressed in the state-of-theart DAL methods such as sufficient and effective distribution discrepancy metric learning,effective kernel space learning,and multiple source domains transfer learning,etc.Aiming at the mentioned-above issues,in this paper,we propose a unified kernel learning framework for domain adaptation learning and its effective extension based on multiple kernel learning(MKL) schema,regularized by the proposed new minimum distribution distance metric criterion which minimizes both the distribution mean discrepancy and the distribution scatter discrepancy between source and target domains,into which many existing kernel methods(like support vector machine(SVM),v-SVM,and least-square SVM) can be readily incorporated.Our framework,referred to as kernel learning for domain adaptation learning(KLDAL),simultaneously learns an optimal kernel space and a robust classifier by minimizing both the structural risk functional and the distribution discrepancy between different domains.Moreover,we extend the framework KLDAL to multiple kernel learning framework referred to as MKLDAL.Under the KLDAL or MKLDAL framework,we also propose three effective formulations called KLDAL-SVM or MKLDAL-SVM with respect to SVM and its variant μ-KLDALSVM or μ-MKLDALSVM with respect to v-SVM,and KLDAL-LSSVM or MKLDAL-LSSVM with respect to the least-square SVM,respectively.Comprehensive experiments on real-world data sets verify the outperformed or comparable effectiveness of the proposed frameworks.