Development and Validation of an In-Situ Utility Pole Simulation Model for Virtual Modal Testing

Author(s):  
Plinio Ferreira Pinto ◽  
Geoff Rideout

Modal testing is being investigated as a non-destructive test (NDT) method for wood poles. Modal properties of the pole must be extracted from sensor data containing frequency content associated with the interaction of the pole with its conductors. A dynamic model of a utility pole with attached conductors has been developed and validated through experimentation. The model will allow controlled, repeatable simulations of modal hammer hits for preliminary verification of pole property identification methods. The cable is modeled as a series of point masses connected by translational springs. The pole is represented by a modal expansion based on separation of variables. To facilitate creating and connecting the pole and cable models, scaling the model to represent longer pole lines, and introducing modal hammer inputs; the bond graph formalism was employed. To validate the model, an instrumented reduced-scale pole and cable system was built and tested in the laboratory. Time series measurements of cable tension and transverse motion, along with frequency-domain accelerometer data, show that the simulation model has sufficient fidelity to predict the effect of conductors on a pole’s response spectrum over the frequency range of interest.

2021 ◽  
pp. 158-166
Author(s):  
Noah Balestra ◽  
Gaurav Sharma ◽  
Linda M. Riek ◽  
Ania Busza

<b><i>Background:</i></b> Prior studies suggest that participation in rehabilitation exercises improves motor function poststroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. <b><i>Objectives:</i></b> The objectives of this study were to assess the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigate which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. <b><i>Methods:</i></b> MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (<i>n</i> = 13) and individuals with upper extremity weakness due to recent stroke (<i>n</i> = 13) while the subjects performed 3 preselected arm exercises. Sensor data were then labeled by exercise type and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets. <b><i>Results:</i></b> We achieved a repetition counting accuracy of 95.6% overall, and 95.0% in patients with upper extremity weakness due to stroke when using both accelerometer and gyroscope data. Accuracy was decreased when using fewer sensors or using accelerometer data alone. <b><i>Conclusions:</i></b> Our exploratory study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in poststroke patients during clinical rehabilitation or clinical trials.


2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Melinda G. Conners ◽  
Théo Michelot ◽  
Eleanor I. Heywood ◽  
Rachael A. Orben ◽  
Richard A. Phillips ◽  
...  

AbstractBackgroundInertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective  classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors.MethodsWe deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: ‘flapping flight’, ‘soaring flight’, and ‘on-water’. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data.ResultsHMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for ‘flapping flight’, ‘soaring flight’ and ‘on-water’, respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale.ConclusionsThe use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Gloria Vergara-Diaz ◽  
Jean-Francois Daneault ◽  
Federico Parisi ◽  
Chen Admati ◽  
Christina Alfonso ◽  
...  

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.


2011 ◽  
Vol 54 (9-10) ◽  
pp. 1896-1906 ◽  
Author(s):  
Seiichi Yamaguchi ◽  
Daisuke Kato ◽  
Kiyoshi Saito ◽  
Sunao Kawai

Author(s):  
Alexey Likhvarev ◽  
Eduard Babkin

Responding to the rapidly growing market share for Information Systems (IS) based on Service-Oriented Architecture (SOA), the demand emerges for methods of measuring the value of SOA-based IS projects. The goal of the present research is to adapt available methods of project assessment to this expanding demand. This study describes a new method which takes into consideration a possibility to divide deployment and evolution of SOA-based IS into separate flows, one per service. Like that the process of value assessment could become more precise and exact compared to other known methods which use the single flow for the whole project. In addition the work proposes Real Options for calculating such components of the value as flexibility. The described method is validated using a specific simulation model. Value assessment of a real IS project is performed using the developed method and the simulation model.


2013 ◽  
Vol 37 (5) ◽  
pp. 601-607 ◽  
Author(s):  
Kwangseok Oh ◽  
Seungjae Yun ◽  
Hakgu Kim ◽  
Kyungeun Ko ◽  
Kyongsu Yi

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