面向基因数据分类的AGA-ELM算法研究

对基因表达数据进行分类时,超限学习机(ELM)算法具有学习效率高、泛化能力强、分类精度高的优点.为了解决超限学习机算法受输入权值矩阵和隐含层偏差随机初始化的影响,本文利用自适应遗传算法(AGA)具有良好的全局搜索效果对超限学习机的输入权值矩阵和隐含层偏差进行优化,提出了基于自适应遗传算法优化超限学习机(AGA-ELM)的分类算法.通过实验表明,该算法与已有的ELM、GA-ELM以及SVM算法相比,分类精度更高,可用于基因数据分类.     

代价敏感不确定决策树的不确定单批测试算法研究

不确定数据(uncertain data)广泛存在于现实世界的各个领域.研究界开始逐渐重视不确定数据的研究.目前,针对不确定数据的代价敏感决策树(CSDTU)的研究中只能进行简单测试,其过多的测试造成很大的代价浪费.本文扩展确定数据上代价敏感决策树的单批测试算法,将其应用到代价敏感不确定决策树(CSDTU).通过不确定数据模型中属性值的概率势(ProbabilityCardinality)计算树属性选择过程中的代价,实现了不确定单批测试算法.在UCI数据集上的实验表明,不确定单批测试优于简单测试,可以有效的降低测试的总代价,提升分类器效果,具有很好的合理性和实用性.     

一种基于ADS-B的雷达跟踪精度计算方法

分析目前雷达精度测量方法的弊端,介绍广播式自动相关监视技术(ADS-B)的基本原理和数据格式转换方法,并提出一种基于ADS-B数据的雷达跟踪精度计算方法。在利用ADS-B系统进行多目标跟踪的过程中,运用改进的数据关联算法(MPDA)和基于"当前"统计模型的自适应卡尔曼滤波算法,计算出雷达的跟踪精度。仿真结果表明:运用该方法 ADS-B系统能够高精度地跟踪目标,可以满足测量雷达跟踪精度的要求。     

一种提高低温流体比体积计算精度的方法

采用了一个既能提高立方型状态方程比体积计算精度，又不影响气液平衡条件的简单摩尔修正项来改进PR方程比体积的计算。本文对19种低温流体给出其修正项的值，计算饱和蒸气和液体比体积，并与未修正的PR方程的计算值比较，结果表明这种修正能显著提高低温流体（包括量子流体氢、氦和强极性的氨气）饱和液体比体积的计算精度，并对蒸气比体积的计算也略有改进。     

确定测量仪器精度的一种方法

为保证测量仪器的精确度,必须经常对测量仪器进行检测和校准。本文提出一种确定测量仪器精度的步骤和计算方法,而且它有助于评价单次测量值的置信度。     

一种结合网络编码的路径代价衡量方法

针对无线路由协议中的路径代价衡量问题,结合网络编码改善无线节点信息互换的思想,提出了一种结合网络编码的路径代价衡量方法--RMNC,其核心思想是利用流量参数反映信息流的网络编码"搭乘"程度和逐节点计算路径的代价.通过将传输流流量参数和路径中节点左右链路信息流流量参数进行运算,获得路径上的各个节点的传输代价;网络中某一条路径的代价等于组成这条路径的节点传输代价之和,通过比较不同路径的逐节点计算代价值,获得最短路径.分析和模拟测试结果表明,RMNC可以有效地获得结合网络编码的最短路径,达到提高传输性能的目的.尽管传输延时有所增加,但可以接受,方法可行.     

提高同步采样精度的一种改进算法

由于同步采样在实际情况中很难实现严格同步，当采样信号与待测信号不严格同步时，必将产生频谱泄漏误差和截断误差。为了克服同步采样法的这一缺点，本文给出一种改进算法，其基本思想是修改采样序列使之接近理想采样序列。仿真研究表明，改进算法提高了测量精度，计算量较少，实时性也有所提高。     

一种保证电影放映机装配精度的有效方法

本文分析了电影放映机因装配精度不够而造成放映时画面不清晰的原因,介绍了为保证装配精度而使用的一套装配辅助工具,及在装配放映机时辅具的使用方法;同时提请放映机用户注意,在没有专用辅具的情况下不能自行拆卸转塔镜头架机构.     

一种简便的地面激光扫描仪精度检测方法

一、引言 地面激光扫描仪（Terrestrial Laser Scanner,TLS）是一种不同于传统单点测量方式的新型光电测量仪器,它采用非接触式高速激光测量方式,可在复杂的现场和空间对被测物体进行快速高密度扫描测量,以便快速、高效、精确、全面地获取被测目标表面的空间点位及亮度信息——点云数据。点云数据经过计算机处理后,     

基于核超限学习机的中文文本情感分类

针对传统情感分类算法存在的参数学习困难及分类性能较低等问题,提出了一种基于核超限学习机的中文文本情感分类方法.首先通过信息增益对训练数据进行特征选择以降低输入维数,然后通过构建基于小波核超限学习机的分类器实现对中文文本的情感分类.实验结果表明,新方法参数学习容易,且其文本情感分类性能通常优于支持向量机和朴素贝叶斯.     

基于像元分解的亚像元级遥感数据分类精度评估方法

为减小混合像元现象给遥感数据分类精度评估过程带来的影响,将像元分解技术引入到分类精度评估中,提出了基于像元分解的亚像元级分类精度评估方法,并给出了相应的处理流程及精度评估所需误差矩阵的计算方法.实验结果表明,亚像元级分类精度评估方法能够体现混合像元在分类精度评估过程中的影响,能够从一定程度上细化各分类算法的优劣,降低由于空间分辨率所引起的分类精度评估结果的不确定性.     

Learning automata algorithms for pattern classification

This paper considers the problem of learning optimal discriminant functions for pattern classification. The criterion of optimality is minimising the probability of misclassification. No knowledge of the statistics of the pattern classes is assumed and the given classified sample may be noisy. We present a comprehensive review of algorithms based on the model of cooperating systems of learning automata for this problem. Both finite action set automata and continuous action set automata models are considered. All algorithms presented have rigorous convergence proofs. We also present algorithms that converge to global optimum. Simulation results are presented to illustrate the effectiveness of these techniques based on learning automata. This work is supported in part by an Indo-US project under ONR grant number N-00014-J-1324.     

A cost comparison of two static condensation-stress recovery algorithms

We consider finite elements that have internal degrees-of-freedom. These d.o.f. are condensed before elements are assembled into the structure, and must be recovered later for use in element stress computation. References 1 and 2 describe two different ways to do the calculations. This paper compares the computational costs of the two methods. Test runs were made to assess costs. Relative costs depend on several factors. The factors are discussed, and suggestions are made as to the better method in various circumstances.     

A calculation method for tangential spray dryers

A method is presented for calculating the design parameters of a cylindrical spray dryer with tangential heat-carrier input; the relationships are analyzed.Translated from Inzhenerno-Fizicheskii Zhurnal, Vol. 30, No. 5, pp. 891–898, May, 1976.     

A multi-objective variable-fidelity optimization method for genetic algorithms

A novel variable-fidelity optimization (VFO) scheme is presented for multi-objective genetic algorithms. The technique uses a low- and high-fidelity version of the objective function with a Kriging scaling model to interpolate between them. The Kriging model is constructed online through a fixed updating schedule. Results for three standard genetic algorithm test cases and a two-objective stiffened panel optimization problem are presented. For the stiffened panel problem, statistical analysis of four performance metrics are used to compare the Pareto fronts between the VFO method, full high-fidelity optimizer runs, and Pareto fronts developed by enumeration. The fixed updating approach is shown to reduce the number of high-fidelity calls significantly while approximating the Pareto front in an efficient manner.     

A novel classification method for paper-reviewer recommendation

Reviewer recommendation problem in the research field usually refers to invite experts to comment on the quality of papers, proposals, etc. How to effectively and accurately recommend reviewers for the submitted papers and proposals is a meaningful and still tough task. At present, many unsupervised recommendation methods have been researched to solve this task. In this paper, a novel classification method named Word Mover’s Distance–Constructive Covering Algorithm (WMD–CCA, for short) is proposed to solve the reviewer recommendation problem as a classification issue. A submission or a reviewer is described by some tags, such as keywords, research interests, and so on. First, the submission or the reviewer is represented as some vectors by a word embedding method. That is to say, each tag describing a submission or a reviewer is represented as a vector. Second, the Word Mover’s Distance (WMD, for short) method is used to measure the minimum distances between submissions and reviewers. Actually, the papers usually have research field information, and utilizing them well might improve the reviewer recommendation accuracy. So finally, the reviewer recommendation task is transformed into a classification problem which is solved by a supervised learning method- Constructive Covering Algorithm (CCA, for short). Comparative experiments are conducted with 4 public datasets and a synthetic dataset from Baidu Scholar, which show that the proposed method WMD–CCA effectively solves the reviewer recommendation task as a classification issue and improves the recommendation accuracy.     

A method for raising the accuracy of frequency measurement

A method is considered for making high-precision frequency measurements with simple, inexpensive, and inexact frequency meters.Translated from Izmeritel'naya Tekhnika, No. 10, pp. 53–54, October, 1993.     

A new method for finite element calculation of micromagneticproblems

A novel method has been developed to compute static 2-D and 3-D micromagnetic structures using finite elements. The method can be applied to domain studies, i.e. bubble configuration, Bloch line memories, or magnetooptical materials. All energy terms are minimized without approximation, especially the long-range demagnetizing energy, which causes generally long running time; boundary finite elements are used in this program. The algorithm for minimizing the total energy is gradient or Newton type