A Smart Recognition Method for Otological Drill Milling through a Bone Tissue Wall

2014 ◽  
Vol 571-572 ◽  
pp. 331-338
Author(s):  
Xi Sheng Li ◽  
Yong Ming Xie ◽  
Zhi Qiang Gao ◽  
Guo Dong Feng
Keyword(s):  
Bone Tissue ◽  
Information Fusion ◽  
Recognition Rate ◽  
Fusion Method ◽  
Recognition Method ◽  
Ear Surgery ◽  
Minimally Invasive Operation ◽  
Average Recognition Rate ◽  
Sensor Information ◽  
Medical Equipments

Surgeons are striving to achieve higher quality results in minimally invasive operation. Intelligent medical equipments are able to improve operation safety. Otological drill milling through a bone tissue wall is a common milling fault in ear surgery. In this paper a multi-sensor information fusion method for identifying milling faults is presented. Five surgeons experimented on calvarian bones using the intelligent otological drill. The average recognition rate of milling faults was 91%, and only 0.8% of normal millings were identified as milling faults.

Stressed Speech Recognition Method Based on Difference Subspace Combined with Dynamic Time Warping

2012 ◽  
Vol 241-244 ◽  
pp. 1640-1646
Author(s):  
Cheng Guo Lv ◽  
Ru Bo Zhang ◽  
Pei Hua Li
Keyword(s):  
Dynamic Time Warping ◽  
Recognition Rate ◽  
Decision Criterion ◽  
Time Warping ◽  
Training Set ◽  
Recognition Method ◽  
Average Recognition Rate ◽  
Normal Environment ◽  
Acceleration Of Gravity ◽  
Dynamic Time

Speech under G-force which produced when speaker was under different acceleration of gravity was analyzed and researched, considered as principal part and stressed part to research. An isolated word recognition approach was proposed which combined difference subspace means with dynamic time warping technique. The method recognized speech under G-force by constructing a difference subspace to remove the stressed part. Dynamic time warping technique was adopted to make all feature vectors of one word in the training set have equal length, and a corresponding decision criterion was suggested. For a small vocabulary including 15 words, the method obtained the average recognition rate of 98.3%, which almost equal to the rate in normal environment. The method not only worked well in normal conditions but also had good performance for speech under G-force.

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