Low-fidelity Systems Research Articles

Reducing trainee mistakes. Better performance with changing to a high-fidelity simulation system?

Postpartum hemorrhage is a significant cause of both maternal morbidity and mortality worldwide and is increasing in incidence. This study aimed to assess improvement and identify shortcomings in trainee performance in different simulation systems in the management of postpartum hemorrhage. To perform a pilot study evaluating and comparing high- and low-fidelity simulation models, assessing improvement in repeated performance with high-fidelity mode and identifying mistakes made assessed using Objective Structured Assessment of Technical Skills and thereby exploring what aspects of emergency management of postpartum hemorrhage should be prioritized in teaching settings and assessing what simulation setup is most effective in achieving competence. This was a prospective randomized, single-blinded, single-institution trial in a population of 17 junior obstetrical trainees at the Charité University Hospital Obstetric Simulation Center in Berlin. Trainees were randomized into 2 groups, with either initial low-fidelity simulation or high-fidelity simulation, followed by repeated assessment of performance, using the high-fidelity model simulation system. Individual simulation sessions were video-recorded and transcribed, and the timing of interventions was documented. Strandardized Objective Structured Assessment of Technical Skills forms were used as a checklist for performance. There was a statistically significant general improvement in performance (P=.02; 24.7-27.2 of 31.0 points; average of 8.7%) in the second cycle of simulation assessment and a statistically significant training effect (P=.043; 24.4-28.4 of 31.0 points; average of 12.9%) in the group that underwent repeat simulation assessment from the initial low-fidelity system to the high-fidelity system compared with the group using the same high-fidelity setup (P=.276; 25.0-25.8 of 31.0; average of 2.4%). There was an improvement in the performance when trainees underwent a repeated cycle of simulation assessment changing from a low-fidelity system to a high-fidelity system. Simulation assessment can identify mistakes and learning gaps that are important for obstetrical trainees. This study found that trainees make the same mistakes, regardless of which simulation model was initially used.

A Two-Step Machine Learning Approach for Crop Disease Detection Using GAN and UAV Technology

Automated plant diagnosis is a technology that promises large increases in cost-efficiency for agriculture. However, multiple problems reduce the effectiveness of drones, including the inverse relationship between resolution and speed and the lack of adequate labeled training data. This paper presents a two-step machine learning approach that analyzes low-fidelity and high-fidelity images in sequence, preserving efficiency as well as accuracy. Two data-generators are also used to minimize class imbalance in the high-fidelity dataset and to produce low-fidelity data that are representative of UAV images. The analysis of applications and methods is conducted on a database of high-fidelity apple tree images which are corrupted with class imbalance. The application begins by generating high-fidelity data using generative networks and then uses these novel data alongside the original high-fidelity data to produce low-fidelity images. A machine learning identifier identifies plants and labels them as potentially diseased or not. A machine learning classifier is then given the potentially diseased plant images and returns actual diagnoses for these plants. The results show an accuracy of 96.3% for the high-fidelity system and a 75.5% confidence level for our low-fidelity system. Our drone technology shows promising results in accuracy when compared to labor-based methods of diagnosis.

An output-only ARX model-based sensor fusion framework on structural dynamic measurements using distributed optical fiber sensors and fiber Bragg grating sensors

The advent of distributed optical fiber sensing technologies has made continuous and dense measurement possible, and has a very broad application prospect in the field of structural health monitoring. However, not all of them are able to guarantee measurement accuracy in a good condition compared to traditional fiber sensors, such as FBGs (Fiber Bragg Grating sensor), strain gauges, etc. In our previous study (Cheng et al., 2019) of the calibration on static measurements for distributed sensors, it turns out that the signal to noise ratio from each single sensing point has proven to be much higher for FBGs than for distributed sensors.This paper presents a novel sensor fusion approach aiming to enhance the measurement quality of distributed fiber optical sensors in dynamic strain measurement. First of all, an output-only common-structured ARX (Auto-Regressive with exogenous input) model for dynamic MDOF (Multiple Degree of Freedom) systems with n degrees of freedom is studied where one of the measurement outputs is utilized as the input rather than actual external force, thus avoiding the inaccessibility of input signals in real structural engineering campaigns. After applying this model into a measurement system using a limited number of sensing points but with high fidelity, the outputs at unobserved points can then be roughly predicted using various curve fitting techniques on the ARX model coefficients against structural positions. However, the quality of the predicted performance cannot be guaranteed. Benefiting from a dense measurement but with low fidelity, the variation trend of the built ARX model coefficients against structural locations can be evaluated. Combined with EKR interpolation technique, the built ARX model from a high-fidelity system is then coupled to the evaluated trend of ARX model coefficients from a low-fidelity system, which leads to a further enhancement of its measurement quality at the unobserved points.To verify the feasibility and effectiveness of the presented sensor fusion approach, numerical studies on a 10-DOF MDOF system are conducted, indicating a satisfactory forecast with very small values of mean square errors for the unobserved DOFs. To further investigate the benefits of the proposed sensor fusion approach, experimental activities on a steel-beam are performed, aiming to enhance/calibrate the measurement performance on distributed fiber sensor (low fidelity) by incorporating four FBG sensors (high fidelity) as a calibration reference. The experimental results show that the intervention of the proposed approach yields significant improvement in the measurement quality for distributed fiber optic sensors.