Latest News
Level I MT-PT Training course scheduled for October 2010 in State College, PA.
Level II UT Training course scheduled for September 2010 in State College, PA.
Level I UT Training course scheduled for August 2010 in State College, PA.
Level III Acoustic Emission Training course completed (July 30) in State College, PA.
Level III UT Training course completed (July 23) in State College, PA.
Level II Acoustic Emission Testing course completed (July 16) in State College, PA
UT weld inspection course completed (July 8) in State College, PA
Level II Ultrasonic Testing course completed (July 2) in State College, PA
Level III UT Training course completed in State College, PA, June 2010
WINS personnel delivered invited speech on H-Pile inspection technology at "Life cycle performance of bridges and structures" conference at Changsha, China in June 2010
View a presentation on the Principles and applications of long range ultrasound, from ASNT Greater Phila chapter meeting, April 2010
Level III UT Training course completed in State College, PA, March 2010
WINS presents talk on ultrasonic guided wave potential towards helicopter maintenance to Indian Air force, February 2010
WINS funded by Transportation Research Board to develop Bridge Cable Inspection Technology, February 2010
Watch video of Wireless Acoustic Emission Sensor Network for Bridge Structural Health Monitoring
Pattern Recognition in Non Destructive Testing
Course Description
This course introduces the practical aspects of pattern recognition. Using WINS NDT's Super ICEPak as a framework, the course covers the theory and applications of statistical pattern recognition and neural networks. This course is suitable for:
Course material covers sufficient theoretical background and necessary terminlogy for trainees with no background in pattern recognition as well.
Course Dates
March 22-23, 2010
September 9-10, 2010
December 20-21, 2010
This course is also offered by appointment. Please contact us about your dates of convenience.
Course Location
This course is offered at our facility as well as client sites.
Course Duration
Two days (16 hours)
Course Price
$800 per trainee for group sessions of five or more trainees and $3,000 for individualized company training for up to three persons.
Course Outline
1. Overview of Artificial Intelligence
2. Terminology
3. Knowledge-Based Systems
4. When and When Not to use AI
5. Supervised Learning
6. Unsupervised Learning
7. Pattern Recognition Methods
8. Statistical Pattern Classifiers
9. Neural Networks
10. Waveform Representation
11. Waveform Transformations
12. Feature Extraction
13. Feature Set Optimization
14. Image Representation
15. Classifier Design
16. On-line Classification
17. Multi-Channel Data Acquisition
18. System Hardware and Software Design
19. Studies Case
This course introduces the practical aspects of pattern recognition. Using WINS NDT's Super ICEPak as a framework, the course covers the theory and applications of statistical pattern recognition and neural networks. This course is suitable for:
- Scientists and Engineers involved in the design and application of signal and image interpretation systems and control systems
- Basic Researchers in the engineering, physical, medical, education, environmental and military sciences who wish to apply advanced signal and image analysis and interpretation methods
- Test Engineers and Technicians looking for faster and more effective ways to develop automated testing and control systems.
Course material covers sufficient theoretical background and necessary terminlogy for trainees with no background in pattern recognition as well.
Course Dates
March 22-23, 2010
September 9-10, 2010
December 20-21, 2010
This course is also offered by appointment. Please contact us about your dates of convenience.
Course Location
This course is offered at our facility as well as client sites.
Course Duration
Two days (16 hours)
Course Price
$800 per trainee for group sessions of five or more trainees and $3,000 for individualized company training for up to three persons.
Course Outline
1. Overview of Artificial Intelligence
2. Terminology
3. Knowledge-Based Systems
4. When and When Not to use AI
5. Supervised Learning
6. Unsupervised Learning
7. Pattern Recognition Methods
8. Statistical Pattern Classifiers
9. Neural Networks
10. Waveform Representation
11. Waveform Transformations
12. Feature Extraction
13. Feature Set Optimization
14. Image Representation
15. Classifier Design
16. On-line Classification
17. Multi-Channel Data Acquisition
18. System Hardware and Software Design
19. Studies Case