Object recognition and real-time spoken word recognition using two-fold dynamic time warping for autonomous arm manipulator

2017 ◽  
Author(s):  
Irfan Al-Hussaini ◽  
Zubayer Islam ◽  
Antik Mallick ◽  
Md. Ekramul Hoque
Keyword(s):  
Object Recognition ◽  
Word Recognition ◽  
Real Time ◽  
Dynamic Time Warping ◽  
Spoken Word Recognition ◽  
Spoken Word ◽  
Time Warping ◽  
Dynamic Time

Real-time fault diagnosis and trend prediction of rolling bearings based on resampling dynamic time warping and time-domain indicator analysis

2022 ◽  
Vol 64 (1) ◽  
pp. 38-44
Author(s):  
Maosheng Gao ◽  
Zhiwu Shang ◽  
Wanxiang Li ◽  
Shiqi Qian ◽  
Yan Yu
Keyword(s):  
Real Time ◽  
Time Domain ◽  
Dynamic Time Warping ◽  
Prediction Method ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Time Warping ◽  
Trend Prediction ◽  
Residual Signal ◽  
Dynamic Time

A sudden fault in a rolling bearing (RB) results in a large amount of downtime, which increases the cost of operation and maintenance. In this paper, a real-time diagnosis and trend prediction method for RBs is proposed. In this method, a novel resampling dynamic time warping (RDTW) algorithm is presented and two new time-domain indicators (NTDIRs) called TALAP and TRCKT are defined, which can describe the wear degree and trend of an RB inner ring wear fault (IRWF). TALAP and TRCKT are proposed by comprehensively considering the stability and sensitivity of existing time-domain indicators (TDIRs). First, RDTW is used to align the healthy vibration signal with the fault vibration signal. Then, the residual signal that can be used to monitor the running condition is obtained. TALAP and TRCKT of the residual signal are calculated to judge the degree of wear. When the wear limit is reached, a fault alarm is sent out and the downtime needed for replacement can be accurately indicated. The experimental results show that the method can perform accurate diagnosis and trend prediction of inner ring wear faults of RBs.

scholarly journals Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm

2018 ◽  
Author(s):  
Sang Hyuk Kim ◽  
Hee Soo Lee ◽  
Hanjun Ko ◽  
Seung Hwan Jeong ◽  
Hyun Woo Byun ◽  
...  
Keyword(s):  
Financial Markets ◽  
Real Time ◽  
Pattern Matching ◽  
Dynamic Time Warping ◽  
Series Data ◽  
Time Warping ◽  
Trading System ◽  
Index Futures ◽  
Data Movement ◽  
Dynamic Time

The futures market plays a significant role in hedging and speculating by investors. Although various models and instruments are developed for real-time trading, it is difficult to realize profit by processing and trading a vast amount of real-time data. This study proposes a real-time index futures trading strategy that uses the pattern of KOSPI 200 index futures time series data. We construct a pattern matching trading system (PMTS) based on a dynamic time warping algorithm that recognizes patterns of market data movement in the morning and determines the afternoon's clearing strategy. We adopt 13 and 27 representative patterns and conduct simulations with various ranges of parameters to find optimal ones. Our experimental results show that the PMTS provides stable and effective trading strategies with relatively low trading frequencies. Investor communities that have sustained financial markets are able to make more efficient investments by using the PMTS. In this sense, the system developed in this paper is a sustainable investment technique and helps financial markets achieve efficient sustainability.

Considerations in dynamic time warping algorithms for discrete word recognition

1978 ◽  
Vol 63 (S1) ◽  
pp. S79-S79 ◽  
Author(s):  
Lawrence R. Rabiner ◽  
Aaron E. Rosenberg ◽  
Stephen E. Levinson
Keyword(s):  
Word Recognition ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Dynamic Time
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