Damage Identification by Discriminant Analysis Using Mahalanobis Distance.
نویسندگان
چکیده
منابع مشابه
Max-Mahalanobis Linear Discriminant Analysis Networks
A deep neural network (DNN) consists of a nonlinear transformation from an input to a feature representation, followed by a common softmax linear classifier. Though many efforts have been devoted to designing a proper architecture for nonlinear transformation, little investigation has been done on the classifier part. In this paper, we show that a properly designed classifier can improve robust...
متن کاملMahalanobis Distance Learning for Person Re-identification
Recently, Mahalanobis metric learning has gained a considerable interest for single-shot person re-identification. The main idea is to build on an existing image representation and to learn a metric that reflects the visual camera-to-camera transitions, allowing for a more powerful classification. The goal of this chapter is twofold. We first review the main ideas of Mahalanobis metric learning...
متن کاملAnalysis of motor fan radiated sound and vibration waveform by automatic pattern recognition technique using “Mahalanobis distance”
In recent years, as the weight of IT equipment has been reduced, the demand for motor fans for cooling the interior of electronic equipment is on the rise. Sensory test technique by inspectors is the mainstream for quality inspection of motor fans in the field. This sensory test requires a lot of experience to accurately diagnose differences in subtle sounds (sound pressures) of the fans, and t...
متن کاملUnsupervised Phoneme Segmentation Using Mahalanobis Distance
Abstract One of the fundamental problems in speech engineering is phoneme segmentation. Approaches to phoneme segmentation can be divided into two categories: supervised and unsupervised segmentation. The approach of this paper belongs to the 2nd category, which tries to perform phonetic segmentation without using any prior knowledge on linguistic contents and acoustic models. In an earlier wor...
متن کاملAnomaly detection for IGBTs using Mahalanobis distance
In this study, a Mahalanobis distance (MD)-based anomaly detection approach has been evaluated for non-punch through (NPT) and trench field stop (FS) insulated gate bipolar transistors (IGBTs). The IGBTs were subjected to electrical–thermal stress under a resistive load until their failure. Monitored on-state collector–emitter voltage and collector–emitter currents were used as input parameters...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series A
سال: 2001
ISSN: 0387-5008,1884-8338
DOI: 10.1299/kikaia.67.1242