Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory

Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure.     

Construction of Binary Quantum Error-Correcting Codes from Orthogonal Array

By using difference schemes, orthogonal partitions and a replacement method, some new methods to construct pure quantum error-correcting codes are provided from orthogonal arrays. As an application of these methods, we construct several infinite series of quantum error-correcting codes including some optimal ones. Compared with the existing binary quantum codes, more new codes can be constructed, which have a lower number of terms (i.e., the number of computational basis states) for each of their basis states.     

Intelligent Fault Diagnosis of Industrial Robot Based on Multiclass Mahalanobis-Taguchi System for Imbalanced Data

One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach’s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms.     

Synthesis of Halopyrazole Matrine Derivatives and Their Insecticidal and Fungicidal Activities

Matrine is a traditional botanical pesticide with a broad-spectrum biological activity that is widely applied in agriculture. Halopyrazole groups are successfully introduced to the C13 of matrine to synthesize eight new derivatives with a yield of 78–87%. The insecticidal activity results show that the introduction of halopyrazole groups can significantly improve the insecticidal activity of matrine on Plutella xylostella, Mythimna separata and Spodoptera frugiperda with a corrected mortality rate of 100%, which is 25–65% higher than matrine. The fungicidal activity results indicate that derivatives have a high inhibitory effect on Ceratobasidium cornigerum, Cibberella sanbinetti, Gibberrlla zeae and Collectot tichum gloeosporioides. Thereinto, 4-Cl-Pyr-Mat has the best result, with an inhibition rate of 23–33% higher than that of matrine. Therefore, the introduction of halogenated pyrazole groups can improve the agricultural activity of matrine.     

动态应力下功率n-LDMOS器件热载流子退化恢复效应

针对功率n-LDMOS器件在动态应力条件下的热载流子退化恢复效应进行了实验和理论研究.电荷泵实验测试和器件专用计算机辅助软件的仿真分析结果表明,在施加不同的动态应力条件下,功率n-LDMOS器件存在2种主要的热载流子退化机理,即鸟嘴区域的热空穴注入和沟道区域的界面态产生,均有明显的退化恢复效应.对功率n-LDMOS器件施加2个连续3 600 s的不同应力,观察每个阶段结束时刻的CP电流曲线,发现该器件在应力变换期间确实发生了热空穴的退陷阱效应.然后,对功率n-LDMOS器件施加3个连续3 600 s的不同应力,观察每个阶段结束时刻的CP电流曲线,发现器件处于关断阶段时,已产生的界面态存在一定程度的复合.     

对数图算法中的校正线

以酸碱平衡的对数浓度图为例,讨论了图算法中校正线的作法,提出了作校正线的一般条件。     

由双向可控硅组成的三相交流自动调压器

电网的电压是恒定的，要得到可调的交流电压必须使用调压器。常用的用晶闸管组成的调压器需要专用的触发装置和同步电源，结构复杂．本文介绍一种由双向可控硅组成的、不需要专用触发器和同步电源的三相自动调压器．     

刚性多支点传动轴主共振分析

根据质心运动定理、Galerkin法和Heaviside函数,求得非惯性系下倾斜刚性多支点传动轴的弯曲运动方程.在此基础上,用多尺度法求得稳态下主共振的一次近似定常解.分析了主共振的振型、最大振幅、稳定性和振幅突变性.结果表明:传动轴的弯曲运动方程是Duffing方程;主共振时,各段轴相互影响;每段轴主共振的振型与两支点轴主共振的振型相同;主共振的最大振幅在最大跨距的轴段上;主共振时,轴的振幅有可能突变,振幅突变的频率区间长度很小,其频率略小于固有频率.     

完全分配格上的商拓扑

给出了具有逆序对合对应的完全分配格上商拓扑的定义,讨论了商拓扑的一些性质。通过引进正则等价关系的概念,证明了用等价关系和连续映射两种方法定义的商拓扑 fuzz 是同胚的。     

闽江口表层沉积物中碎屑矿物的研究

研究闽江口表层沉积物中碎屑矿物的组成，含量及其分布特征，探讨泥沙的来源及重矿物与沉积环境的关系，结果表明，碎屑矿物共有４６种，其中轻矿物１２种，重矿物３４种。矿物组份的种类示出闽江口的碎屑物质主要来源于闽江流域的基岩风化、侵蚀产物，而重矿物的含量分布则与其所处的水力学条件和地形地貌关系密切。