“双减”背景下计算机化自适应多阶段测试的设计与算法改进

计算机化自适应多阶段测试是精准减负的一种有效手段,因为它会自动引导学生尽可能作答与其能力水平相适应的题目,从而节省出作答太难或太易题目所浪费的大量时间和精力。不过,我国目前的一些计算机测试系统缺乏现代测评技术的有力支撑,部分题库在知识内容和能力维度的标识与编码、题目参数的估计与等值,以及分数的算法与使用方面存在着较大缺陷。本文简要分析了自适应测试的基本模式、操作流程、使用条件和主要优点,具体讨论了计算机化自适应多阶段测试系统的设计,以及基于测验总分的单参数logistic模型和基于作答反应模式的双参数logistic模型的算分方法,为提升计算机化自适应测试的水平,进而促进教师因材施教、减轻学生作业负担和考试负担提供了考试科学视角下的新办法。

New rank learning algorithm

To overcome the limitation that complex data types with noun attributes cannot be processed by rank learning algorithms, a new rank learning algorithm is designed. In the learning algorithm based on the decision tree, the splitting rule of the decision tree is revised with a new definition of rank impurity. A new rank learning algorithm, which can be intuitively explained, is obtained and its theoretical basis is provided. The experimental results show that in the aspect of average rank loss, the ranking tree algorithm outperforms perception ranking and ordinal regression algorithms and it also has a faster convergence speed. The rank learning algorithm based on the decision tree is able to process categorical data and select relative features.

Optimal Boundary Control Method for Domain Decomposition Algorithm

To study the domain decomposition algorithms for the equations of elliptic type, the method of optimal boundary control was used to advance a new procedure for domain decomposition algorithms and regularization method to deal with the ill posedness of the control problem. The determination of the value of the solution of the partial differential equation on the interface——the key of the domain decomposition algorithms——was transformed into a boundary control problem and the ill posedness of the control problem was overcome by regularization. The convergence of the regularizing control solution was proven and the equations which characterize the optimal control were given therefore the value of the unknown solution on the interface of the domain would be obtained by solving a series of coupling equations. Using the boundary control method the domain decomposion algorithm can be carried out.

Analytical Algorithms for the Blend Ratios by Fibre-bundle Tensile Curves Part I: Typical Tensile Curve and the Strength and Work Methods

The blend ratio and tensile properties are vital important characteristics of blended fibre bundles. Fibre-bundle tensile behaviour has been measured by means of the Sirolan-Tensor in order to derive a typical specific stress — extension curve. According to the typical tensile curve, the relationships between the blend ratio and the tensile parameters are discussed and established. Two new algorithms, so-called the strength method and the work method, have been developed for the blend-ratio estimation of blended fibre bundles. The relevant blend-ratios and calculations of the two methods are defined in detail. Also, the characteristics have been explained for every calculation and compared to each others. The experimental results show that the blend ratios estimated from the theoretical methods are high coincidence with the measured results. Meanwhile, the specific stress — extension curve of blended fibre bundles depends directly on the blend ratios of the fibre bundles.

An Experimental Comparative Study on Three Classification Algorithms

Classification algorithm is one of the key techniques to affect text automatic classification system’s performance, play an important role in automatic classification research area. This paper comparatively analyzed k-NN. VSM and hybrid classification algorithm presented by our research group. Some 2000 pieces of Internet news provided by ChinaInfoBank are used in the experiment. The result shows that the hybrid algorithm’s performance presented by the groups is superior to the other two algorithms.

Slope stability analysis considering joined influences of nonlinearity and dilation

The soil masses of slopes were assumed to follow a nonlinear failure criterion and a nonassociated flow rule.The stability factors of slopes were calculated using vertical slice method based on limit analysis.The potential sliding mass was divided into a series of vertical slices as well as the traditional slice technique.Equating the external work rate to the internal energy dissipation,the optimum solutions to stability factors were determined by the nonlinear programming algorithm.From the numerical results,it is found that the present solutions agree well with previous results when the nonlinear criterion reduces to the linear criterion,and the nonassociated flow rule reduces to the associated flow rule.The stability factors decrease by 39.7%with nonlinear parameter varying from 1.0 to 3.0.Dilation and nonlinearity have significant effects on the slope stability factors.

Stereo garage parking space allocation model and simulation analysis

Based on grey neural network and particle swarm optimization algorithm,an automated stereo garage decision model is proposed to solve the problems of long waiting queue and low efficiency of automated parking garage.The gray neural network is used to forecast the stay time of the vehicle and particle swarm optimization algorithm is used to allocate the parking spaces in the stereo garage.The proposed stereo garage mathematical model is established on condition that vehicle arrival interval obeys Poisson distribution.The performance of stereo garage is evaluated by the average waiting time,average waiting queue length,average service time and average energy consumption of the customers.By comparing the efficiency indexes of the existing model based on near-distribution principle and the proposed model based on gray neural network and particle swarm algorithm,it is proved that the proposed model based on gray neural network and particle swarm algorithm is effective in improving the efficiency of garage operation and reducing the energy consumption of garage.

Research on coning compensation algorithms for SINS of angular rate input

To compensate the coning error of Strap-down Inertial Navigation Systems (SINS) under high dynamic angular motion, many rotation vector algorithms have been developed using angle increments information. However, most SINS use angular rate gyros. Aimed at this problem, 18 algorithms are derived based on analysis of the conventional algorithms, and corresponding coning error expressions are given. At last simulation is made which indicates that the new algorithms have much higher precision.

An Effective Concept Extraction Method for Improving Text Classification Performance

This paper presents anew way to extract concept that can beused to improve text classification per-formance (precision and recall). Thecomputational measure will be dividedinto two layers. The bottom layercalled document layer is concernedwith extracting the concepts of parti-cular document and the upper layercalled category layer is with findingthe description and subject concepts ofparticular category. The relevant im-plementation algorithm that dramatic-ally decreases the search space is dis-cussed in detail. The experiment basedon real-world data collected from Info-Bank shows that the approach is supe-rior to the traditional ones.

Relaxation labeling for non-rigid point matching under neighbor preserving

Non-rigid point matching has received more and more attention.Recently,many works have been developed to discover global relationships in the point set which is treated as an instance of a joint distribution.However,the local relationship among neighboring points is more effective under non-rigid transformations.Thus,a new algorithm taking advantage of shape context and relaxation labeling technique,called SC-RL,is proposed for non-rigid point matching.It is a strategy that joints estimation for correspondences as well as the transformation.In this work,correspondence assignment is treated as a soft-assign process in which the matching probability is updated by relaxation labeling technique with a newly defined compatibility coefficient.The compatibility coefficient is one or zero depending on whether neighboring points preserving their relative position in a local coordinate system.The comparative analysis has been performed against four state-of-the-art algorithms including SC,ICP,TPS-RPM and RPM-LNS,and the results denote that SC-RL performs better in the presence of deformations,outliers and noise.

Visualizing Data Tree-Table Based on the Database

The data tree table is a type of data structure consisting of data tree and table, which has a wide field of applications. The visual and dynamic growing algorithm of data tree table and its software method are presented based on the theory of the data structure and visual technology of software. The method of the expression and management of data tree table with relational mode are explored.

THE MACHINE LEARNING SYSTEM OF TELEPHONE NETWORKS MANAGEMENT

The problem of fault information process in telephone networks manage ment system in AT & T in the US has been solved with stepanwise learning approach.This method makes the information decrease step by step by means of merge and sort, classifies the information to several typical classes and establishes the knowledge base (KB) eventually. If new fault information is inputted, we will call the knowl edge in KB and predict the related faults which will happen.

THE ALGORITHMS OF AN INTEGER PARTITIONING WITH ITS APPLICATIONS

In the light of the ideals of Artificial Intelligence(AI), three algorithms of an integer partitioning have been given in this paper:generate and test algorithm,and two heuristic algorithms about forward partition and backward partition. PROLOG has been used to deseribe dsorithms,it is reasonable,direct and simple. In the sight of describing algorithms,it is a new and valid try. At last,some intresting appllcations of the algorithms mentioned in the paper have been presented.

Road network extraction in classified SAR images using genetic algorithm

Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road network from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervisedly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Finally, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordinates, the genetic operations are used to search global optimization roads. The experimental results show that the algorithm can effectively extract road network from high-resolution SAR images.

A New Regularizing Algorithm for Solving the First Kind of Fredholm Integral Equations

Singular value system is applied to construct a new class of improved regularizing methods for solving the first kind of Fredholm integral equations with noisy data. By a priori choosing regularizing parameters, optimal convergence order of the regularized solution is obtained. And with aids of MATLAB software, numerical results are presented which roughly coincide with the theoretical analysis.

PARALLEL STABILIZATION

A new algorithm for the stabilization or (possibly turbulent, chaotic) distributed systems,governed by linear or non linear systems of equations is presented.The SPA (Stabilization Parallel Algorithm) is based on a systematic parallel decompositionof the problem (related to arbitrarily overlapping decomposition of domains) and on a penaltyargument.SPA is presented here for the case of linear parabolic equations, with distributed or boundarycontrol. It extends to practically all linear and non linear evolution equations, as it will bepresented in several other publications.