Vector quantization and signal compression
Vector quantization and signal compression
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Classification of Fatigue Bill Based on Support Vector Machine by Using Acoustic Signal
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Transient-based identification of wireless sensor nodes
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
Attacks on physical-layer identification
Proceedings of the third ACM conference on Wireless network security
Physical-layer identification of RFID devices
SSYM'09 Proceedings of the 18th conference on USENIX security symposium
Physical-layer identification of UHF RFID tags
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Research on detection and material identification of particles in the aerospace power
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
On physical-layer identification of wireless devices
ACM Computing Surveys (CSUR)
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In this paper, a system is described that uses the wavelet transform to automatically identify the particular failure mode of a known defective transmission device. The problem of identifying a particular failure mode within a costly failed assembly is of benefit in practical applications. In this system, external acoustic sensors, instead of intrusive vibrometers, are used to record the acoustic data of the operating transmission device. A skilled factory worker, who is unfamiliar with statistical classification, helps to determine the feature vector of the particular failure mode in the feature extraction process. In the automatic identification part, an improved learning vector quantization (LVQ) method with normalizing the inputting feature vectors is proposed to compensate for variations in practical data. Some acoustic data, which are collected from the manufacturing site, are utilized to test the effectiveness of the described identification system. The experimental results show that this system can identify the particular failure mode of a defective transmission device and find out the causes of failure successfully.