A Cognitive Diagnosis Method Based on Mahalanobis Distance

2018 ◽  
pp. 319-333
Author(s):  
Jianhua Xiong ◽  
Fen Luo ◽  
Shuliang Ding ◽  
Huiqiong Duan
Keyword(s):  
Mahalanobis Distance ◽  
Cognitive Diagnosis ◽  
Diagnosis Method

A Simple Cognitive Diagnosis Method Based on Q-Matrix Theory

2015 ◽  
Vol 47 (2) ◽  
pp. 264 ◽  
Author(s):  
Zhaosheng LUO ◽  
Yujun LI ◽  
Xiaofeng YU ◽  
Chunlei GAO ◽  
Yafeng PENG
Keyword(s):  
Matrix Theory ◽  
Cognitive Diagnosis ◽  
Diagnosis Method ◽  
Q Matrix

On Permissible Attribute Classes in Noncompensatory Cognitive Diagnosis Models

Methodology ◽  
2014 ◽  
Vol 10 (3) ◽  
pp. 100-107 ◽  
Author(s):  
Jürgen Groß ◽  
Ann Cathrice George
Keyword(s):  
Diagnostic Information ◽  
Cognitive Diagnosis ◽  
Latent Classes ◽  
Psychometric Test ◽  
Cognitive Diagnosis Models ◽  
Dina Model

When a psychometric test has been completed by a number of examinees, an afterward analysis of required skills or attributes may improve the extraction of diagnostic information. Relying upon the retrospectively specified item-by-attribute matrix, such an investigation may be carried out by classifying examinees into latent classes, consisting of subsets of required attributes. Specifically, various cognitive diagnosis models may be applied to serve this purpose. In this article it is shown that the permission of all possible attribute combinations as latent classes can have an undesired effect in the classification process, and it is demonstrated how an appropriate elimination of specific classes may improve the classification results. As an easy example, the popular deterministic input, noisy “and” gate (DINA) model is applied to Tatsuoka’s famous fraction subtraction data, and results are compared to current discussions in the literature.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

Sign in / Sign up

Export Citation Format

Share Document