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This article proposes an integrated scheme (T&TCUSUM chart) which combines a Shewhart T chart and a TCUSUM chart (a CUSUM-type T chart) to monitor the time interval T between the occurrences of an event or the time between events. The performance studies show that the T&TCUSUM chart can effectively improve the overall performance over the entire T shift range. On average, it is more effective than the T chart by 26.66% and the TCUSUM chart by 14.12%. Moreover, the T&TCUSUM chart performs more consistently than other charts for the detection of either small or large T shifts, because it has the strength of both the T chart (more sensitive to large shifts) and the TCUSUM chart (more sensitive to small shifts). The implementation of the new chart is almost as easy as the operation of a TCUSUM chart.

Purpose Needle steering can improve targeting accuracy and guide the tip to areas that are currently not accessible. This paper proposes and validates a steering model to be capable of predicting the system dynamics during needle-tissue contact procedure. Methods The spring-beam-damper needle steering model we proposed has been extended with depthvarying mean parameters considering the tissue inhomogeneity. Local polynomial approximations in finite depth segments were adopted to estimate the unknown depth-varying mean parameters. Based on this approach, an online parameter estimator has been designed using modified least square method with forgetting factor to estimate the parameters using the online measured dataset.Results Extensive experiments have been carried out in various phantoms to validate the improved needle steering model based on online experiment data. Results have shown that the model can track the needle tip trajectory with satisfactory accuracy even in the presence of large disturbances and noises. The convergence rate and estimation accuracy can be greatly improved when a needle is suitably supported. Conclusion The model can give satisfactory prediction if the input data are properly collected to avoid the sensor noises.