Map-building and map-based localization in an underground-mine by statistical pattern matching

2002 ◽  
Author(s):  
R. Madhavan ◽  
G. Dissanayake ◽  
H. Durrant-Whyte
Keyword(s):  
Pattern Matching ◽  
Underground Mine ◽  
Map Building ◽  
Statistical Pattern

scholarly journals Component-based face recognition using statistical pattern matching analysis

2020 ◽  
Author(s):  
Sushil Kumar Paul ◽  
Saida Bouakaz ◽  
Chowdhury Mofizur Rahman ◽  
Mohammad Shorif Uddin
Keyword(s):  
Face Recognition ◽  
Pattern Matching ◽  
Statistical Pattern

Statistical pattern matching facilitates the design of polyvalent inhibitors of anthrax and cholera toxins

2006 ◽  
Vol 24 (5) ◽  
pp. 582-586 ◽  
Author(s):  
Prakash Rai ◽  
Chakradhar Padala ◽  
Vincent Poon ◽  
Arundhati Saraph ◽  
Saleem Basha ◽  
...  
Keyword(s):  
Pattern Matching ◽  
Statistical Pattern ◽  
Polyvalent Inhibitors

Map Building and Terrain-Aided Localisation in an Underground Mine

1998 ◽  
pp. 40-45 ◽  
Author(s):  
Gamini Dissanayake ◽  
Martial Hebert ◽  
Anthony Stentz ◽  
Hugh Durrant-Whyte
Keyword(s):  
Underground Mine ◽  
Map Building

Statistical pattern matching

1989 ◽  
Vol 9 (3) ◽  
pp. 163-168 ◽  
Author(s):  
Thomas G. Ryall ◽  
John Sandor
Keyword(s):  
Pattern Matching ◽  
Statistical Pattern

scholarly journals Statistical Pattern-Based Assessment of Structural Health Monitoring Data

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mohammad S. Islam ◽  
Ashutosh Bagchi
Keyword(s):  
Structural Health Monitoring ◽  
Pattern Matching ◽  
Health Monitoring ◽  
Data Interpretation ◽  
Structural Condition ◽  
Sensor Data ◽  
Structural Health ◽  
Statistical Pattern ◽  
Potential Damage ◽  
Matching Techniques

In structural health monitoring (SHM), various sensors are installed at critical locations of a structure. The signals from sensors are either continuously or periodically analyzed to determine the state and performance of the structure. An objective comparison of the sensor data at different time ranges is essential for assessing the structural condition or excessive load experienced by the structure which leads to potential damage in the structure. The objectives of the current study are to establish a relationship between the data from various sensors to estimate the reliability of the data and potential damage using the statistical pattern matching techniques. In order to achieve these goals, new methodologies based on statistical pattern recognition techniques have been developed. The proposed methodologies have been developed and validated using sensor data obtained from an instrumented bridge and road test data from heavy vehicles. The application of statistical pattern matching techniques are relatively new in SHM data interpretation and current research demonstrates that it has high potential in assessing structural conditions, especially when the data are noisy and susceptible to environmental disturbances.

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