Sudden process changes occurring dining automobile body assembly processes will influence the downstream assembly process and the functionality and final appearance of the vehicle. Furthermore, these faults could resu...
详细信息
Sudden process changes occurring dining automobile body assembly processes will influence the downstream assembly process and the functionality and final appearance of the vehicle. Furthermore, these faults could result in a decreased production rate and an increase in the cost if sudden process changes are so serious that the production line has to be stopped for investigation. Thus, sudden process changes should be detected and eliminated as soon as possible to prevent defective products from being produced and to reduce the cost of repairs/reworks. A monitoring algorithm is developed to detect, classify, and group process changes by analyzing the dimensional data of car bodies. The results of this monitoring algorithm can help diagnose the root causes of variation according to the locations of measurement points, body structure, assembly sequence, and tooling layout. Measurement data obtained from an optical coordinate measuring machine (OCMM) are used to demonstrate the monitoring technique.
This study has focused on one aspect of arrhythmia monitoring which poses a difficult problem for automatic analysis. The results show that neutral network are capable of recognising patterns in the spectrum of the EC...
详细信息
This study has focused on one aspect of arrhythmia monitoring which poses a difficult problem for automatic analysis. The results show that neutral network are capable of recognising patterns in the spectrum of the ECG during VF and can distinguish genuine VF from VF-like artifact. The best performance was obtained a network which required some preprocessing of the signal spectrum as well as the additional training. This neural network had a better sensitivity for VF detection than conventional techniques assessed previously using the same data set.
Four ventricular fibrillation (VF) detection techniques were assessed using recordings of VF to evaluate sensitivity and VF-like recordings to evaluate specificity. The recordings were obtained from Coronary Care Unit...
详细信息
Four ventricular fibrillation (VF) detection techniques were assessed using recordings of VF to evaluate sensitivity and VF-like recordings to evaluate specificity. The recordings were obtained from Coronary Care Unit patients. The techniques were: threshold crossing intervals (TCI);peaks in the autocorrelation function (ACF);signal content outside the mean frequency (VF-filter);and signal spectrum shape (spectrum). Using 70 extracts, each 4 s long, from VF recordings, the VF filter achieved a sensitivity of 77 per cent, the ACF, TCI and spectrum algorithms had sensitivities of 67, 53 and 46 per cent, respectively. Susceptibility to false alarms was assessed using 40 extracts from VF-like recordings. The TCI algorithm was the most specific (93 per cent), while the spectrum, VF filter and ACF algorithms had specificities of 72, 55 and 38 per cent, respectively. The TCI algorithm achieved overall sensitivity of 93 per cent and specificity of 60 per cent. The spectrum, VF filter and ACF algorithms had overall sensitivities of 80, 93 and 87 per cent, and overall specificities of 60, 20 and 0 per cent, respectively.
暂无评论