scholarly journals Emotion Prediction with Weighted Appraisal Models – Validating a Psychological Theory of Affect

2020 ◽  
Author(s):  
Laura Israel ◽  
Felix D. Schönbrodt
Keyword(s):  
Process Model ◽  
Learning Algorithm ◽  
Psychological Theory ◽  
Appraisal Theory ◽  
Subjective Perception ◽  
Model Parameters ◽  
Baseline Model ◽  
Component Process ◽  
Emotion Label ◽  
Emotion Labels

Appraisal theories are a prominent approach for the explanation and prediction of emotions. According to these theories, the subjective perception of an emotion results from a series of specific event evaluations. To validate and extend one of the most known representatives of appraisal theory, the Component Process Model by Klaus Scherer, we implemented four computational appraisal models that predicted emotion labels based on prototype similarity calculations. Different weighting algorithms, mapping the models' input to a distinct emotion label, were integrated in the models. We evaluated the plausibility of the models' structure by assessing their predictive power and comparing their performance to a baseline model and a highly predictive machine learning algorithm. Model parameters were estimated from empirical data and validated out-of-sample. All models were notably better than the baseline model and able to explain part of the variance in the emotion labels. The preferred model, yielding a relatively high performance and stable parameter estimations, was able to predict a correct emotion label with an accuracy of 40.2% and a correct emotion family with an accuracy of 76.9%. The weighting algorithm of this favored model corresponds to the weighting complexity implied by the Component Process Model, but uses differing weighting parameters.

scholarly journals Generalisable FPCA-based Models for Predicting Peak Power in Vertical Jumping using Accelerometer Data

2021 ◽  
Author(s):  
◽  
Mark G. E. White
Keyword(s):  
Peak Power ◽  
Process Model ◽  
Learning Algorithm ◽  
Inertial Sensor ◽  
Functional Principal Component Analysis ◽  
Sensor Data ◽  
Model Parameters ◽  
Anatomical Location ◽  
Accelerometer Data ◽  
Gold Standard Method

Peak power in the countermovement jump is correlated with various measures of sports performance and can be used to monitor athlete training. The gold standard method for determining peak power uses force platforms, but they are unsuitable for field-based testing favoured by practitioners. Alternatives include predicting peak power from jump flight times, or using Newtonian methods based on body-worn inertial sensor data, but so far neither has yielded sufficiently accurate estimates. This thesis aims to develop a generalisable model for predicting peak power based on Functional Principal Component Analysis applied to body-worn accelerometer data. Data was collected from 69 male and female adults, engaged in sports at recreational, club or national levels. They performed up to 16 countermovement jumps each, with and without arm swing, 696 jumps in total. Peak power criterion measures were obtained from force platforms, and characteristic features from accelerometer data were extracted from four sensors attached to the lower back, upper back and both shanks. The best machine learning algorithm, jump type and sensor anatomical location were determined in this context. The investigation considered signal representation (resultant, triaxial or a suitable transform), preprocessing (smoothing, time window and curve registration), feature selection and data augmentation (signal rotations and SMOTER). A novel procedure optimised the model parameters based on Particle Swarm applied to a surrogate Gaussian Process model. Model selection and evaluation were based on nested cross validation (Monte Carlo design). The final optimal model had an RMSE of 2.5 W·kg-1, which compares favourably to earlier research (4.9 ± 1.7 W·kg-1 for flight-time formulae and 10.7 ± 6.3 W·kg-1 for Newtonian sensor-based methods). Whilst this is not yet sufficiently accurate for applied practice, this thesis has developed and comprehensively evaluated new techniques, which will be valuable to future biomechanical applications.

Advocating a Componential Appraisal Model to Guide Emotion Recognition

2012 ◽  
Vol 3 (1) ◽  
pp. 18-32 ◽  
Author(s):  
Marcello Mortillaro ◽  
Ben Meuleman ◽  
Klaus R. Scherer
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Intermediate Layer ◽  
Process Model ◽  
Black Box ◽  
Emotion Classification ◽  
Body Movements ◽  
Component Process ◽  
Theoretical Predictions ◽  
Emotion Label

Most models of automatic emotion recognition use a discrete perspective and a black-box approach, i.e., they output an emotion label chosen from a limited pool of candidate terms, on the basis of purely statistical methods. Although these models are successful in emotion classification, a number of practical and theoretical drawbacks limit the range of possible applications. In this paper, the authors suggest the adoption of an appraisal perspective in modeling emotion recognition. The authors propose to use appraisals as an intermediate layer between expressive features (input) and emotion labeling (output). The model would then be made of two parts: first, expressive features would be used to estimate appraisals; second, resulting appraisals would be used to predict an emotion label. While the second part of the model has already been the object of several studies, the first is unexplored. The authors argue that this model should be built on the basis of both theoretical predictions and empirical results about the link between specific appraisals and expressive features. For this purpose, the authors suggest to use the component process model of emotion, which includes detailed predictions of efferent effects of appraisals on facial expression, voice, and body movements.

scholarly journals Characterization of Moisture Diffusion in Cured Concrete Slabs at Early Ages

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xiao Zhang ◽  
Hongduo Zhao
Keyword(s):  
Process Model ◽  
Model Simulation ◽  
Moisture Diffusion ◽  
Experimental Results ◽  
Concrete Slabs ◽  
Model Parameters ◽  
Early Age ◽  
Water Curing ◽  
Early Age Concrete

The objective of this paper is to investigate the characterization of moisture diffusion inside early-age concrete slabs subjected to curing. Time-dependent relative humidity (RH) distributions of three mixture proportions subjected to three different curing methods (i.e., air curing, water curing, and membrane-forming compounds curing) and sealed condition were measured for 28 days. A one-dimensional nonlinear moisture diffusion partial differential equation (PDE) based on Fick’s second law, which incorporates the effect of curing in the Dirichlet boundary condition using a concept of curing factor, is developed to simulate the diffusion process. Model parameters are calibrated by a genetic algorithm (GA). Experimental results show that the RH reducing rate inside concrete under air curing is greater than the rates under membrane-forming compound curing and water curing. It is shown that the effect of water-to-cement (w/c) ratio on self-desiccation is significant. Lower w/c ratio tends to result in larger RH reduction. RH reduction considering both effect of diffusion and self-desiccation in early-age concrete is not sensitive to w/c ratio, but to curing method. Comparison between model simulation and experimental results indicates that the improved model is able to reflect the effect of curing on moisture diffusion in early-age concrete slabs.

scholarly journals Determination of Combustion Process Model Parameters in Diesel Engine with Variable Compression Ratio

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Saša Milojević ◽  
Radivoje Pešić
Keyword(s):  
Diesel Engine ◽  
Compression Ratio ◽  
Process Model ◽  
Internal Combustion Engines ◽  
Combustion Process ◽  
Exhaust Emission ◽  
Model Parameters ◽  
Connecting Rod ◽  
Variable Compression Ratio ◽  
Variable Compression

Compression ratio has very important influence on fuel economy, emission, and other performances of internal combustion engines. Application of variable compression ratio in diesel engines has a number of benefits, such as limiting maximal in cylinder pressure and extended field of the optimal operating regime to the prime requirements: consumption, power, emission, noise, and multifuel capability. The manuscript presents also the patented mechanism for automatic change engine compression ratio with two-piece connecting rod. Beside experimental research, modeling of combustion process of diesel engine with direct injection has been performed. The basic problem, selection of the parameters in double Vibe function used for modeling the diesel engine combustion process, also performed for different compression ratio values. The optimal compression ratio value was defined regarding minimal fuel consumption and exhaust emission. For this purpose the test bench in the Laboratory for Engines of the Faculty of Engineering, University of Kragujevac, is brought into operation.

Model Calibration for the High-Purity Oxygen Activated Sludge Process – Algorithm Development and Evaluation

1993 ◽  
Vol 28 (11-12) ◽  
pp. 163-171 ◽  
Author(s):  
Weibo (Weber) Yuan ◽  
David Okrent ◽  
Michael K. Stenstrom
Keyword(s):  
Activated Sludge ◽  
High Purity ◽  
Model Calibration ◽  
Process Model ◽  
Model Parameters ◽  
Complex Method ◽  
Activated Sludge Process ◽  
Activated Sludge Model ◽  
Kinetic Coefficients ◽  
Calibration Algorithm

A model calibration algorithm is developed for the high-purity oxygen activated sludge process (HPO-ASP). The algorithm is evaluated under different conditions to determine the effect of the following factors on the performance of the algorithm: data quality, number of observations, and number of parameters to be estimated. The process model used in this investigation is the first HPO-ASP model based upon the IAWQ (formerly IAWPRC) Activated Sludge Model No. 1. The objective function is formulated as a relative least-squares function and the non-linear, constrained minimization problem is solved by the Complex method. The stoichiometric and kinetic coefficients of the IAWQ activated sludge model are the parameters focused on in this investigation. Observations used are generated numerically but are made close to the observations from a full-scale high-purity oxygen treatment plant. The calibration algorithm is capable of correctly estimating model parameters even if the observations are severely noise-corrupted. The accuracy of estimation deteriorates gradually with the increase of observation errors. The accuracy of calibration improves when the number of observations (n) increases, but the improvement becomes insignificant when n>96. It is also found that there exists an optimal number of parameters that can be rigorously estimated from a given set of information/data. A sensitivity analysis is conducted to determine what parameters to estimate and to evaluate the potential benefits resulted from collecting additional measurements.

Numerical and Experimental Analysis of Self Piercing Riveting Process with Carbon Fiber-Reinforced Plastic and Aluminium Sheets

2013 ◽  
Vol 554-557 ◽  
pp. 1045-1054 ◽  
Author(s):  
Welf Guntram Drossel ◽  
Reinhard Mauermann ◽  
Raik Grützner ◽  
Danilo Mattheß
Keyword(s):  
Finite Element ◽  
Carbon Fiber ◽  
Finite Element Model ◽  
Simulation Model ◽  
Process Parameters ◽  
Process Model ◽  
Element Model ◽  
Model Parameters ◽  
Fiber Reinforced ◽  
Carbon Fiber Reinforced

In this study a numerical simulation model was designed for representing the joining process of carbon fiber-reinforced plastics (CFRP) and aluminum alloy with semi-tubular self-piercing rivet. The first step towards this goal is to analyze the piercing process of CFRP numerical and experimental. Thereby the essential process parameters, tool geometries and material characteristics are determined and in finite element model represented. Subsequently the finite element model will be verified and calibrated by experimental studies. The next step is the integration of the calibrated model parameters from the piercing process in the extensive simulation model of self-piercing rivet process. The comparison between the measured and computed values, e.g. process parameters and the geometrical connection characteristics, shows the reached quality of the process model. The presented method provides an experimental reliable characterization of the damage of the composite material and an evaluation of the connection performances, regarding the anisotropic property of CFRP.

scholarly journals Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2467 ◽  
Author(s):  
Hery Mwenegoha ◽  
Terry Moore ◽  
James Pinchin ◽  
Mark Jabbal
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Navigation System ◽  
Process Model ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Moment Of Inertia ◽  
Model Parameters ◽  
Main Process ◽  
Model Based ◽  
Aerial Vehicles

The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapidly. Therefore, this paper details a model-based integration approach for fixed wing UAVs, using the Vehicle Dynamics Model (VDM) as the main process model aided by low-cost Micro-Electro-Mechanical Systems (MEMS) inertial sensors and GNSS measurements with moment of inertia calibration using an Unscented Kalman Filter (UKF). Results show that the position error does not exceed 14.5 m in all directions after 140 s of GNSS outage. Roll and pitch errors are bounded to 0.06 degrees and the error in yaw grows slowly to 0.65 degrees after 140 s of GNSS outage. The filter is able to estimate model parameters and even the moment of inertia terms even with significant coupling between them. Pitch and yaw moment coefficient terms present significant cross coupling while roll moment terms seem to be decorrelated from all of the other terms, whilst more dynamic manoeuvres could help to improve the overall observability of the parameters.

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