Introduction to Measurement Units

2022 ◽  
pp. 29-54
Author(s):  
Marcelo de Carvalho Alves ◽  
Luciana Sanches
Keyword(s):  
Measurement Units

Estimação de Fontes Harmônicas em Sistemas de Potência

2020 ◽  
Vol 20 ◽  
pp. 11
Author(s):  
Arthur Mesquita ◽  
Marcelo Lima ◽  
Thales Wulfert ◽  
Igor Delgado ◽  
Leandro Manso ◽  
...  
Keyword(s):  
Phasor Measurement Units ◽  
Phasor Measurement ◽  
Measurement Units

Este artigo apresenta uma metodologia para a estimação de fontes de correntes harmônicas em sistemas elétricos de potência a partir do uso da Análise de Componentes Independentes (ICA). Através dos fasores de tensão harmônica complexa obtidos por PMUs (Phasor Measurement Units), os perfis harmônicos podem ser observados e considerados para a estimação das fontes. Um algoritmo baseado em um filtro de média móvel é responsável por dissociar as variações lentas e rápidas dos perfis harmônicos que são empregados à ICA para a estimação das fontes de corrente, rastreando-as ao longo do dia. Simulações computacionais no sistema IEEE 14 Barras são realizadas no intuito de validar a metodologia proposta.

Steady-state Security Assessment Based on K-Means Clustering Algorithm and Phasor Measurement Units

2020 ◽  
Vol 13 (4) ◽  
pp. 559-570
Author(s):  
Bassam A. Hemade ◽  
Hamed A. Ibrahim ◽  
Hossam E.A. Talaat
Keyword(s):  
Steady State ◽  
Clustering Algorithm ◽  
System Security ◽  
Phasor Measurement Units ◽  
Operating Conditions ◽  
Security Assessment ◽  
State Security ◽  
Phasor Measurement ◽  
Measurement Units ◽  
Silhouette Analysis

Background: The security assessment plays a crucial role in the operation of the modern interconnected power system network. Methods: Hence, this paper addresses the application of k-means clustering algorithm equipped with Principal Component Analysis (PCA) and silhouette analysis for the classification of system security states. The proposed technique works on three principal axes; the first stage involves contingency quantification based on developed insecurity indices, the second stage includes dataset preparation to enhance the overall performance of the proposed method using PCA and silhouette analysis, and finally the application of the clustering algorithm over data. Results: The proposed composite insecurity index uses available synchronized measurements from Phasor Measurement Units (PMUs) to assess the development of cascading outages. Considering different operational scenarios and multiple levels of contingencies (up to N-3), Fast Decoupled Power Flow (FDPF) have been used for contingency replications. The developed technique applied to IEEE 14-bus and 57-bus standard test system for steady-state security evaluation. Conclusion: The obtained results ensure the robustness and effectiveness of the established procedure in the assessment of the system security irrespective of the network size or operating conditions.

scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

scholarly journals Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 194
Author(s):  
Sarah Gonzalez ◽  
Paul Stegall ◽  
Harvey Edwards ◽  
Leia Stirling ◽  
Ho Chit Siu
Keyword(s):  
Activity Recognition ◽  
Principal Components ◽  
Classification Accuracy ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Measurement Units ◽  
The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

scholarly journals Validation method for body-mounted sensor attachments

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Katharina Schmidt ◽  
David Hochmann
Keyword(s):  
Evaluation Method ◽  
Tracking System ◽  
The Body ◽  
Exploratory Research ◽  
Body Segment ◽  
Validation Method ◽  
Sensor Position ◽  
Validation Parameters ◽  
Measurement Units ◽  
Soft Tissue Artifact

AbstractSmall sensor devices like inertial measurement units enable mobile movement and gait analysis, whereby existing systems differ in data acquisition, data processing, and gait parameter calculation. Concerning the validation, recent studies focus on the captured motion and the influence of sensor positioning with respect to the accuracy of the computed biomechanical parameters in comparison to a reference system. Although soft tissue artifact is a major source of error for skin-mounted sensors, there are no investigations regarding the relative movement between the body segment and sensor attachment itself. The aim of this study is to find an evaluation method and to determine parameters that allow the validation of various sensor attachment types and different sensor positionings. The analysis includes the comparison between an adhesive and strap attachment variant as well as the frontal and lateral sensor placement. To validate different attachments, an optical marker-based tracking system was used to measure the body segment and sensor position during movement. The distance between these two positions was calculated and analyzed to determine suitable validation parameters. Despite the exploratory research, the results suggest a feasible validation method to detect differences between the attachments, independent of the sensor type. To have representative and statistically validated results, further studies that involve more participants are necessary.

scholarly journals Development of a trunk motor paradigm for use in neuroimaging

2020 ◽  
Vol 11 (1) ◽  
pp. 193-200
Author(s):  
Elizabeth Saunders ◽  
Brian C. Clark ◽  
Leatha A. Clark ◽  
Dustin R. Grooms
Keyword(s):  
Motor Control ◽  
Maximum Voluntary Contraction ◽  
Voluntary Contraction ◽  
Erector Spinae ◽  
Head Motion ◽  
Low Back ◽  
Cyclic Contraction ◽  
Cyclic Contractions ◽  
Measurement Units ◽  
Healthy Participants

AbstractThe purpose of this study was to quantify head motion between isometric erector spinae (ES) contraction strategies, paradigms, and intensities in the development of a neuroimaging protocol for the study of neural activity associated with trunk motor control in individuals with low back pain. Ten healthy participants completed two contraction strategies; (1) a supine upper spine (US) press and (2) a supine lower extremity (LE) press. Each contraction strategy was performed at electromyographic (EMG) contraction intensities of 30, 40, 50, and 60% of an individually determined maximum voluntary contraction (MVC) (±10% range for each respective intensity) with real-time, EMG biofeedback. A cyclic contraction paradigm was performed at 30% of MVC with US and LE contraction strategies. Inertial measurement units (IMUs) quantified head motion to determine the viability of each paradigm for neuroimaging. US vs LE hold contractions induced no differences in head motion. Hold contractions elicited significantly less head motion relative to cyclic contractions. Contraction intensity increased head motion in a linear fashion with 30% MVC having the least head motion and 60% the highest. The LE hold contraction strategy, below 50% MVC, was found to be the most viable trunk motor control neuroimaging paradigm.

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