Using data-fuzzification technology in small data set learning to improve FMS scheduling accuracy

2005 ◽  
Vol 27 (3-4) ◽  
pp. 321-328 ◽  
Author(s):  
Der-Chiang Li ◽  
Chihsen Wu ◽  
Fengming M. Chang
Keyword(s):  
Small Data ◽  
Data Set ◽  
Small Data Set ◽  
Fms Scheduling ◽  
Using Data

Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge

2006 ◽  
Vol 33 (6) ◽  
pp. 1857-1869 ◽  
Author(s):  
Der-Chiang Li ◽  
Chih-Sen Wu ◽  
Tung-I Tsai ◽  
Fengming M. Chang
Keyword(s):  
Small Data ◽  
Data Set ◽  
Trend Estimation ◽  
Small Data Set ◽  
Fms Scheduling

The Lyapunov Exponent Variance of an Electronic Manufacturing Enterprise’s Daily Qualified Rate Time Series by Improved Small Data Sets Approach

2012 ◽  
Vol 197 ◽  
pp. 271-277
Author(s):  
Zhu Ping Gong
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Chaotic Time Series ◽  
Quality System ◽  
Quality Level ◽  
Largest Lyapunov Exponent ◽  
Small Data ◽  
Data Set ◽  
Electronic Manufacturing ◽  
Small Data Set

Small data set approach is used for the estimation of Largest Lyapunov Exponent (LLE). Primarily, the mean period drawback of Small data set was corrected. On this base, the LLEs of daily qualified rate time series of HZ, an electronic manufacturing enterprise, were estimated and all positive LLEs were taken which indicate that this time series is a chaotic time series and the corresponding produce process is a chaotic process. The variance of the LLEs revealed the struggle between the divergence nature of quality system and quality control effort. LLEs showed sharp increase in getting worse quality level coincide with the company shutdown. HZ’s daily qualified rate, a chaotic time series, shows us the predictable nature of quality system in a short-run.

Automatic Speech Recognition for Portuguese with Small Data Set

2021 ◽  
pp. 1-13
Author(s):  
Yapeng Wang ◽  
Ruize Jia ◽  
Chan Tong Lam ◽  
Ka Cheng Choi ◽  
Koon Kei Ng ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Small Data ◽  
Data Set ◽  
Small Data Set

scholarly journals Ambulatory and Laboratory Stress Detection Based on Raw Electrocardiogram Signals Using a Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4408 ◽  
Author(s):  
Hyun-Myung Cho ◽  
Heesu Park ◽  
Suh-Yeon Dong ◽  
Inchan Youn
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Mental Arithmetic ◽  
Small Data ◽  
Data Sets ◽  
Learning Method ◽  
Data Set ◽  
Electrocardiogram Signals ◽  
Proposed Model ◽  
Small Data Set

The goals of this study are the suggestion of a better classification method for detecting stressed states based on raw electrocardiogram (ECG) data and a method for training a deep neural network (DNN) with a smaller data set. We suggest an end-to-end architecture to detect stress using raw ECGs. The architecture consists of successive stages that contain convolutional layers. In this study, two kinds of data sets are used to train and validate the model: A driving data set and a mental arithmetic data set, which smaller than the driving data set. We apply a transfer learning method to train a model with a small data set. The proposed model shows better performance, based on receiver operating curves, than conventional methods. Compared with other DNN methods using raw ECGs, the proposed model improves the accuracy from 87.39% to 90.19%. The transfer learning method improves accuracy by 12.01% and 10.06% when 10 s and 60 s of ECG signals, respectively, are used in the model. In conclusion, our model outperforms previous models using raw ECGs from a small data set and, so, we believe that our model can significantly contribute to mobile healthcare for stress management in daily life.

A latent information function to extend domain attributes to improve the accuracy of small-data-set forecasting

2014 ◽  
Vol 129 ◽  
pp. 343-349 ◽  
Author(s):  
Che-Jung Chang ◽  
Der-Chiang Li ◽  
Wen-Li Dai ◽  
Chien-Chih Chen
Keyword(s):  
Small Data ◽  
Information Function ◽  
Data Set ◽  
Small Data Set
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