Single input fuzzy logic controller for unmanned underwater vehicle

This paper describes a control scheme that provides an efficient way to design a Fuzzy Logic Controller (FLC) for the unmanned underwater vehicle (UUV). The proposed method, known as the Single Input Fuzzy Logic Controller (SIFLC), reduces the conventional two-input FLC (CFLC) to a single input single output (SISO) controller. The SIFLC offers significant reduction in rule inferences and simplify the tuning of control parameters. Practically it can be easily implemented by a look-up table using a low cost microprocessor due its piecewise linear control surface. To verify its effectiveness, the control algorithm is simulated using the Marine Systems Simulator (MSS) on the Matlab/Simulink® platform. The result indicates that both the SIFLC and CFLC give identical response to the same input sets. However SIFLC requires very minimum tuning effort and its execution time is in the orders of two magnitudes less than CFLC

Robot-Assisted Radical Cystectomy Versus Open Radical Cystectomy

Radical cystectomy is suited to a minimally invasive approach, and robotic surgery holds the potential for improving perioperative morbidity compared with open surgery, without a compromise of oncological efficacy. Recent meta-analyses have shown that minimally invasive cystectomy is associated with lower morbidity, shorter length of stay, reduced blood loss and transfusion rates, less post-operative ileus and a reduced need for analgesics. The short and medium term oncological efficacy of robotic cystectomy has been shown to be equivalent to open surgery. However, larger studies with longer follow-up are needed in order to obtain higher levels of evidence

A functional inference for multivariate current status data with mismeasured covariate

[[abstract]]Covariate measurement error problems have been recently studied for current status failure time data but not yet for multivariate current status data. Motivated by the three-hypers dataset from a health survey study, where the failure times for three-hypers (hyperglycemia, hypertension, hyperlipidemia) are subject to current status censoring and the covariate self-reported body mass index may be subject to measurement error, we propose a functional inference method under the proportional odds model for multivariate current status data with mismeasured covariates. The new proposal utilizes the working independence strategy to handle correlated current status observations from the same subject, as well as the conditional score approach to handle mismeasured covariate without specifying the covariate distribution. The asymptotic theory, together with a stable computation procedure combining the Newton–Raphson and self-consistency algorithms, is established for the proposed estimation method. We evaluate the method through simulation studies and illustrate it with three-hypers data.[[notice]]補正完