A Just-in-Time Learning Based Monitoring and Classification Method for Hyper/Hypocalcemia Diagnosis

Transfer Learning-Motivated Intelligent Fault Diagnosis Designs: A Survey, Insights, and Perspectives

Over the last decade, transfer learning has attracted a great deal of attention as a new learning paradigm, based on which fault diagnosis (FD) approaches have been intensively developed to improve the safety and reliability of modern automation systems. Because of inevitable factors such as the varying work environment, performance degradation of components, and heterogeneity among similar automation systems, the FD method having long-term applicabilities becomes attractive. Motivated by these facts, transfer learning has been an indispensable tool that endows the FD methods with self-learning and adaptive abilities. On the presentation of basic knowledge in this field, a comprehensive review of transfer learning-motivated FD methods, whose two subclasses are developed based on knowledge calibration and knowledge compromise, is carried out in this survey article. Finally, some open problems, potential research directions, and conclusions are highlighted. Different from the existing reviews of transfer learning, this survey focuses on how to utilize previous knowledge specifically for the FD tasks, based on which three principles and a new classification strategy of transfer learning-motivated FD techniques are also presented. We hope that this work will constitute a timely contribution to transfer learning-motivated techniques regarding the FD topic.

Sparse Reconstruction of Glucose Fluxes Using Continuous Glucose Monitors

A new technique for estimating postprandial glucose flux profiles without the use of glucose tracers is proposed. A sparse vector space representation is first found for the space of plausible glucose flux profiles using sparse encoding. A Lasso formulation is then used to estimate the glucose fluxes that combines (1) known patient model parameters; (2) the vector space of plausible glucose flux profiles; (3) continuous glucose monitor measurements taken during the meal; (4) amount of insulin injected; (5) amount of meal carbohydrates; and (6) an estimate of the initial conditions. Three glucose fluxes are then estimated, namely; glucose rate of appearance from the intestine; endogenous glucose production from the liver; insulin dependent glucose utilization; and other important state variables. The simulation results show that the technique is capable of estimating the glucose fluxes with high accuracy, even for complex meal scenarios. The experimental results indicate that the technique is capable of reproducing the triple tracer measurements for three T1DM undergoing the triple tracer protocol while estimating the missing measurements for a certain model parameter selection.