Diagnostic Applications for Micro-Synchrophasor Measurements

This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems

Micro protocol engineering for unstructured carriers: On the embedding of steganographic control protocols into audio transmissions

Network steganography conceals the transfer of sensitive information within unobtrusive data in computer networks. So-called micro protocols are communication protocols placed within the payload of a network steganographic transfer. They enrich this transfer with features such as reliability, dynamic overlay routing, or performance optimization --- just to mention a few. We present different design approaches for the embedding of hidden channels with micro protocols in digitized audio signals under consideration of different requirements. On the basis of experimental results, our design approaches are compared, and introduced into a protocol engineering approach for micro protocols.Comment: 20 pages, 7 figures, 4 table

Using the literature to quantify the learning curve: a case study

Objective: To assess whether a literature review of a technology can allow a learning curve to be quantified. Methods: The literature for fibreoptic intubation was searched for studies reporting information relevant to the learning curve. The Cochrane Librar y, Medline, Embase and Science Citation index were searched. Studies that reported a procedure time were included. Data were abstracted on the three features of learning: initial level, rate of learning and asymptote level. Random effect meta-analysis was performed. Results: Only 21 studies gave explicit information concerning the previous experience of the operator(s). There were 32 different definitions of procedure time. From 4 studies of fibreoptic nasotracheal intubation, the mean starting level and time for the 10th procedure (95% confidence interval) was estimated to be 133s (113, 153) and 71s (62, 79) respectively. Conclusions: The review approach allowed learning to be quantified for our example technology. Poor and insufficient reporting constrained formal statistical estimation. Standardised reporting of non-drug techniques with adequate learning curve details is needed to inform trial design and costeffectiveness analysis

Predicting time to graduation at a large enrollment American university

The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto's Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).Comment: 28 pages, 11 figure

Detection of low-velocity impact-induced delaminations in composite laminates using Auto-Regressive models

In this paper, the detection of delaminations in carbon-fiber-reinforced-plastic (CFRP) laminate plates induced by low-velocity impacts (LVI) is investigated by means of Auto-Regressive (AR) models obtained from the time histories of the acquired responses of the composite specimens. A couple of piezoelectric patches for actuation and sensing purposes are employed. The proposed structural health monitoring (SHM) routine begins with the selection of the suitable locations of the piezoelectric transducers via the numerical analysis of the curvature mode shapes of the CFRP plates. The normalized data recorded for the undamaged plate configuration are then analyzed to obtain the most suitable AR model using five techniques based on the Akaike Information Criterion (AIC), the Akaike Final Prediction Error (FPE), the Partial Autocorrelation Function (PAF), the Root Mean Squared (RMS) of the AR residuals for different order p, and the Singular Value Decomposition (SVD). Linear Discriminant Analysis (LDA) is then applied on the AR model parameters to enhance the performance of the proposed delamination identification routine. Results show the effectiveness of the developed procedure when a reduced number of sensors is available