Calibrating an EMG-Driven Muscle Model and a Regression Model to Estimate Moments Generated Actively by Back Muscles for Controlling an Actuated Exoskeleton with Limited Data

2021 ◽  
pp. 401-405
Author(s):  
Ali Tabasi ◽  
Maria Lazzaroni ◽  
Niels P. Brouwer ◽  
Idsart Kingma ◽  
Wietse van Dijk ◽  
...  
Keyword(s):  
Regression Model ◽  
Muscle Model ◽  
Limited Data ◽  
Back Muscles

scholarly journals The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jin Xu ◽  
Chao Yi
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Effective Theory ◽  
Standard Errors ◽  
Limited Data ◽  
Analysis Model ◽  
Multiple Objects ◽  
Model Categories ◽  
Model Expression

Cluster regression analysis model is an effective theory for a reasonable and fair player scoring game. It can roughly predict and evaluate the performance of athletes after the game with limited data and provide scientific predictions for the performance of athletes. The purpose of this research is to achieve the player’s postmatch scoring through the cluster regression model. Through the research and analysis of past ball games, the comparison and experiment of multiple objects based on different regression analysis theories, the following conclusions are drawn. Different regression models have different standard errors, but if the data in other model categories are put into the centroid model expression, the standard error and the error of the original model are within 0.3, which can replace other models for calculation. In the player’s postmatch scoring, although the expert’s prediction of the result is very accurate, within the error range of 1 copy, the player’s postmatch scoring mechanism based on the cluster regression analysis model is more accurate, and the error formula is in the 0.5 range. It is best to switch the data of the regression model twice to compare the scoring mechanism using different regression experiments.

scholarly journals New method to calculate the dynamic factor–flow velocity in Geomorphologic instantaneous unit hydrograph

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yingbing Chen ◽  
Peng Shi ◽  
Xiaomin Ji ◽  
Simin Qu ◽  
Lanlan Zhao ◽  
...  
Keyword(s):  
Regression Model ◽  
Flow Velocity ◽  
Dynamic Factor ◽  
Limited Data ◽  
Unit Hydrograph ◽  
Runoff Simulation ◽  
Simulation Based ◽  
Instantaneous Unit Hydrograph ◽  
Geomorphological Parameters

Abstract The determination of characteristic flow velocity is a hydrodynamic problem needs to be solved in the application of geomorphologic instantaneous unit hydrograph (GIUH) for runoff simulation in areas with no or limited data. In this study, 120 watersheds are collected to construct a regression model; 85 of these basins are used for regression analysis, and the 35 remaining basins are utilized to verify the feasibility of the constructed model. Random forest algorithm is applied to screen out important geomorphologic factors from the 16 extracted factors that may affect flow velocity. Multivariate regression is used to establish the numerical relationship between velocity and the selected factors. Sensitivity analysis of each adopted factor in the constructed model is conducted using the LH-OAT method. The rationality and feasibility of the regression model are validated by comparing the flow velocity calculation with a previous approach, which is also calculated based on geomorphological parameters. Subsequently, the runoff simulation based on the GIUH model is evaluated using the proposed technique. Results demonstrate that the proposed formula possesses high fitting accuracy and can be easily used to calculate flow velocity and generate GIUH.

scholarly journals Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic Recession

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoping Fang ◽  
Jake Ansell ◽  
Weiya Chen
Keyword(s):  
Regression Model ◽  
Goodness Of Fit ◽  
Time Lag ◽  
Economic Recession ◽  
Ratio Method ◽  
Limited Data ◽  
Growth Trend ◽  
Macroeconomic Factors ◽  
Start Up ◽  
Simple Regression Model

This paper presents a modeling method for analyzing a small transportation company’s start-up and growth during a global economic crisis which had an impact on China which is designed to help the owners make better investment and operating decisions with limited data. Since there is limited data, simple regression model and binary regression model failed to generate satisfactory results, so an additive periodic time series model was built to forecast business orders and income. Since the transportation market is segmented by business type and transportation distance, a polynomial model and logistic curve model were constructed to forecast the growth trend of each segmented transportation market, and the seasonal influence function was fitted by seasonal ratio method. Although both of the models produced satisfactory results and showed very nearly the same of goodness-of-fit in the sample, the logistic model presented better forecasting performance out of the sample therefore closer to the reality. Additionally, by checking the development trajectory of the case company’s business and the financial crisis in 2008, the modeling and analysis suggest that the sample company is affected by national macroeconomic factors such as GDP and import & export, and this effect comes with a time lag of one to two years.

Analysis of power law assumptions for short-period comets and Kuiper bodies

1999 ◽  
Vol 173 ◽  
pp. 289-293 ◽  
Author(s):  
J.R. Donnison ◽  
L.I. Pettit
Keyword(s):  
Power Law ◽  
Pareto Distribution ◽  
Limited Data ◽  
Short Period ◽  
Probability Plots ◽  
Exponential Probability

AbstractA Pareto distribution was used to model the magnitude data for short-period comets up to 1988. It was found using exponential probability plots that the brightness did not vary with period and that the cut-off point previously adopted can be supported statistically. Examination of the diameters of Trans-Neptunian bodies showed that a power law does not adequately fit the limited data available.

Predictive value of the ankle brachial index in patients with acute ischemic stroke

VASA ◽  
2014 ◽  
Vol 43 (1) ◽  
pp. 55-61 ◽  
Author(s):  
Konstantinos Tziomalos ◽  
Vasilios Giampatzis ◽  
Stella Bouziana ◽  
Athinodoros Pavlidis ◽  
Marianna Spanou ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Hospital Mortality ◽  
Acute Ischemic Stroke ◽  
Ankle Brachial Index ◽  
Arterial Disease ◽  
National Institutes Of Health ◽  
Limited Data ◽  
Severe Stroke ◽  
Hospital Outcome ◽  
Peripheral Arterial

Background: Peripheral arterial disease (PAD) is frequently present in patients with acute ischemic stroke. However, there are limited data regarding the association between ankle brachial index (ABI) ≤ 0.90 (which is diagnostic of PAD) or > 1.40 (suggesting calcified arteries) and the severity of stroke and in-hospital outcome in this population. We aimed to evaluate these associations in patients with acute ischemic stroke. Patients and methods: We prospectively studied 342 consecutive patients admitted for acute ischemic stroke (37.4 % males, mean age 78.8 ± 6.4 years). The severity of stroke was assessed with the National Institutes of Health Stroke Scale (NIHSS)and the modified Rankin scale (mRS) at admission. The outcome was assessed with the mRS and dependency (mRS 2 - 5) at discharge and in-hospital mortality. Results: An ABI ≤ 0.90 was present in 24.6 % of the patients whereas 68.1 % had ABI 0.91 - 1.40 and 7.3 % had ABI > 1.40. At admission, the NIHSS score did not differ between the 3 groups (10.4 ± 10.6, 8.3 ± 9.3 and 9.3 ± 9.4, respectively). The mRS score was also comparable in the 3 groups (3.6 ± 1.7, 3.1 ± 1.8 and 3.5 ± 2.3, respectively). At discharge, the mRS score did not differ between the 3 groups (2.9 ± 2.2, 2.3 ± 2.1 and 2.7 ± 2.5, respectively) and dependency rates were also comparable (59.5, 47.6 and 53.3 %, respectively). In-hospital mortality was almost two-times higher in patients with ABI ≤ 0.90 than in patients with ABI 0.91 - 1.40 or > 1.40 but this difference was not significant (10.9, 6.6 and 6.3 %, respectively). Conclusions: An ABI ≤ 0.90 or > 1.40 does not appear to be associated with more severe stroke or worse in-hospital outcome in patients with acute ischemic stroke.

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