Abstract
Use error is one of the leading causes of medical device incidents. It is crucial for all stakeholders to have a unified means to better understand, classify, communicate, and prevent/avoid medical device use errors. In this paper, we present our ongoing work on developing a new use error taxonomy for medical devices that has the potential to enable fine-grained analysis of use errors and their root causes in system design. Our ultimate goal is to create a generic framework that can be used by medical device designers to better identify effective design solutions to mitigating use errors.

Abstract
This work describes an analysis of the impact of distributed generation, focusing on the generation from renewable sources, in the technical losses of electrical energy distribution systems. To do so, the OPENDSS software was used to simulate the operation of two distribution feeders, considering: (a) different generation penetration levels; (b) generation with different power factors; (c) variations in the location of distributed sources in the distribution system. The results show that these factors can both interfere positively (decrease) or negatively (increase) in the distribution network technical losses levels, and therefore, there may be distortions in the recognized electrical losses by regulatory agencies, mainly due to the generation unpredictability of renewable sources.

Abstract
We consider the problem of planning the aggregate energy consumption for a set of thermostatically controlled loads for demand response, accounting price forecast trajectory, and thermal comfort constraints. We address this as a continuous-time optimal control problem, and analytically characterize the structure of its solution in the general case. In the special case when the price forecast is monotone and the loads have equal dynamics, we show that it is possible to determine the solution in an explicit form. Taking this fact into account, we handle the non-monotone price case by considering several subproblems, each corresponding to a time subinterval where the price function is monotone, and then allocating to each subinterval a fraction of the total energy budget. This way, for each time subinterval, the problem reduces to a simple convex optimization problem with a scalar decision variable, for which a descent direction is also known. The price forecasts for the day-ahead energy market typically have no more than four monotone segments, so the resulting optimization problem can be solved efficiently with modest computational resources. © 2017 IEEE.

Abstract

Abstract

Abstract
An online data-based methodology for early damage detection and localisation under the effects of environmental and operational variations (EOVs) is proposed. The methodology is described in detail and implemented in a large prestressed concrete cable-stayed bridge of which 3.5 years of data are available. The effects of EOVs are suppressed by the combined application of two well-established multivariate data analysis methods: multiple linear regression and principal component analysis. Criteria for the systematic choice of the predictor variables and the number of principal components to retain are proposed. Because the bridge is new and sound, the experimental time series are corrupted with numerically simulated damage scenarios in order to evaluate the damage detection ability. It is demonstrated that the sensitivity to damage is increased when daily, 2-day, or 3-day averaged data are used instead of hourly data. The effectiveness of the proposed methodology is also demonstrated with the detection of a real, small, and temporary sensor anomaly. The implemented methodology has revealed to be robust and efficient, presenting a contribution to the transition of structural health monitoring from academia to industry.