An English Handwriting Quality Evaluation Algorithm Based on Machine Learning

2021 ◽  
Author(s):  
Jingsong Yang ◽  
Xinxin Huang ◽  
Guang Chen ◽  
Xiaohui Duan
Keyword(s):  
Machine Learning ◽  
Quality Evaluation ◽  
Evaluation Algorithm

Response to “Comment on ‘A planning quality evaluation tool for prostate adaptive IMRT based on machine learning’ ” [Med. Phys. 38, 719 (2011)]

2011 ◽  
Vol 38 (5) ◽  
pp. 2821-2821 ◽  
Author(s):  
Xiaofeng Zhu ◽  
Taoran Li ◽  
Fang-Fang Yin ◽  
Q Jackie Wu ◽  
Yaorong Ge
Keyword(s):  
Machine Learning ◽  
Quality Evaluation ◽  
Evaluation Tool

Fuzzy Evaluation of Synthetic Quality for Fabric Materials

2010 ◽  
Vol 108-111 ◽  
pp. 878-883
Author(s):  
Xiu Chen Wang
Keyword(s):  
Data Model ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Fuzzy Theory ◽  
Quality Standard ◽  
Fuzzy Evaluation ◽  
Evaluation Algorithm ◽  
Index Value ◽  
Fabric Materials ◽  
Quality Evaluation System

Aaccording to the cases that the synthetic quality evaluation system for fabric is not reasonable at present, a new synthetic quality evaluation system for fabric on fuzzy theory is proposed. This new evaluation system is made of foundation layer, evaluating layer and result layer. In foundation layer, the quality standard model and evaluating data model based on multi-indexes are proposed, and the calculation method of each index value in these models is given. In evaluating layer, according to the problem that the result of existing closeness algorithm has greatly repeated, a new evaluation algorithm is proposed. In result layer, two formulas which are continuous result and discrete result are given. Also the accuracy and rationality of this evaluation system is validated by some examples in this paper, and the effect of evaluation result by the primary-secondary indexes, the significance of closeness, the expansibility of model, the accuracy of system and the application method are made analysis. At last, the conclusion shows that this system can make the synthetic evaluation for fabric quality rationally and accurately.

TU-E-BRB-03: A Planning Quality Evaluation Tool for Adaptive IMRT Treatment Based on Machine Learning

2010 ◽  
Vol 37 (6Part27) ◽  
pp. 3400-3400 ◽  
Author(s):  
X Zhu ◽  
T Li ◽  
D Thongphiew ◽  
Y Ge ◽  
F Yin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Evaluation ◽  
Evaluation Tool

scholarly journals Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2981
Author(s):  
Haotai Sun ◽  
Xiaodong Zhu ◽  
Yuanning Liu ◽  
Wentao Liu
Keyword(s):  
Machine Learning ◽  
Radio Frequency ◽  
Indoor Navigation ◽  
Experimental Results ◽  
Machine Learning Method ◽  
Learning Method ◽  
Dual Frequency ◽  
Evaluation Algorithm ◽  
Precision And Accuracy ◽  
Navigation Service

Radio frequency communication technology has not only greatly improved public network service, but also developed a new technological route for indoor navigation service. However, there is a gap between the precision and accuracy of indoor navigation services provided by indoor navigation service and the expectation of the public. This study proposed a method for constructing a hybrid dual frequency received signal strength indicator (HDRF-RSSI) fingerprint library, which is different from the traditional RSSI fingerprint library constructing method in indoor space using 2.4G radio frequency (RF) under the same Wi-Fi infrastructure condition. The proposed method combined 2.4G RF and 5G RF on the same access point (AP) device to construct a HDRF-RSSI fingerprint library, thereby doubling the fingerprint dimension of each reference point (RP). Experimental results show that the feature discriminability of HDRF-RSSI fingerprinting is 18.1% higher than 2.4G RF RSSI fingerprinting. Moreover, the hybrid radio frequency fingerprinting model, training loss function, and location evaluation algorithm based on the machine learning method were designed, so as to avoid limitation that transmission point (TP) and AP must be visible in the positioning method. In order to verify the effect of the proposed HDRF-RSSI fingerprint library construction method and the location evaluation algorithm, dual RF RSSI fingerprint data was collected to construct a fingerprint library in the experimental scene, which was trained using the proposed method. Several comparative experiments were designed to compare the positioning performance indicators such as precision and accuracy. Experimental results demonstrate that compared with the existing machine learning method based on Wi-Fi 2.4G RF RSSI fingerprint, the machine learning method combining Wi-Fi 5G RF RSSI vector and the original 2.4G RF RSSI vector can effectively improve the precision and accuracy of indoor positioning of the smart phone.

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