scholarly journals Interpretability and variability of metamodel validation statistics in engineering system design optimization: a practical study

2014 ◽  
Vol 5 ◽  
pp. A05
Author(s):  
Husam Hamad ◽  
Awad Al-Zaben ◽  
Rami Owies
Keyword(s):  
Prediction Accuracy ◽  
Absolute Error ◽  
Engineering System ◽  
Average Absolute Error ◽  
Electronic Engineering ◽  
Deterministic Simulations ◽  
The Difference ◽  
System Design Optimization ◽  
Engineering System Design ◽  
Difference Mode

Prediction accuracy of a metamodel of an engineering system in comparison to the simulation model it approximates is one fundamental criterion that is used in metamodel validation. Many statistics are used to quantify prediction accuracy of metamodels in deterministic simulations. The most frequently used ones include the root-mean-square error (RMSE) and the R-square metric derived from it, and to a lesser degree the average absolute error (AAE) and its derivates such as the relative average absolute error (RAAE). In this paper, we compare two aspects of these statistics: interpretability of results returned by these statistics and their sample-to-sample variations, putting more emphasis on the latter. We use the difference-mode to common-mode ratio (DMCMR) as a measure of sample-to-sample variations for these statistics. Preliminary results are obtained and discussed via a number of analytic and electronic engineering examples.

Effect of Database Length and Weights on Transactions Evidence Timber Appraisal Models

1994 ◽  
Vol 9 (3) ◽  
pp. 71-76
Author(s):  
Michael J. Niccolucci ◽  
Ervin G. Schuster
Keyword(s):  
Prediction Accuracy ◽  
Visual Inspection ◽  
Model Building ◽  
Absolute Error ◽  
Statistical Prediction ◽  
Average Absolute Error ◽  
The West ◽  
Market Changes

Abstract Although transaction evidence appraisal (TEA) is used extensively for timber appraisal throughout the West, the effect of database length and weighing of data has been largely ignored. This paper investigates how 1-, 2-, and 3-yr model-building database lengths, with equal, biannual, quarterly, and monthly weighing schemes effect TEA prediction accuracy and responsiveness. Accuracy was measured by the "average absolute error" of predicted stumpage value from actual stumpage value. Visual inspection of the prediction trajectory served to evaluate responsiveness to market changes. Results indicate that quarterly and monthly weighing and 1-yr database length improved statistical prediction accuracy, and 1-yr database length and monthly weighing proved most responsive. West. J. Appl. For. 9(2): 71-76.

A Comparative Study of Formulations and Algorithms for Reliability-Based Co-Design Problems

2019 ◽  
Author(s):  
Tonghui Cui ◽  
James T. Allison ◽  
Pingfeng Wang
Keyword(s):  
Design Optimization ◽  
System Design ◽  
Engineering System ◽  
Design Strategies ◽  
Parameter Uncertainties ◽  
Horizontal Axis Wind Turbine ◽  
Reliability Based Design ◽  
System Design Optimization ◽  
Engineering System Design ◽  
And Control

Abstract Co-design, or integrated physical and control system design, has been demonstrated successfully for several engineering system design optimization applications, primarily in a deterministic manner. An opportunity exists to study non-deterministic co-design strategies, including incorporation of uncertainty-induced failures, into an integrated co-design framework. Reliability-based design optimization (RBDO) is one such method that can be used to increase the likelihood of having a feasible design that satisfies all reliability constraints. While significant recent advancements have been made in co-design and RBDO separately, limited work has been done where reliability-based dynamic system design and control design optimization are considered jointly. In this paper, the co-design problem is integrated with the RBDO framework to yield a system-optimal design and the corresponding control trajectory, which satisfy all reliability constraints in the presence of parameter variations. Different problem formulations and RBDO algorithms are compared through numerical examples. The design of a horizontal-axis wind turbine (HAWT) supported by a lattice tower (with parameter uncertainties) is presented to demonstrate the applicability of the proposed method.

scholarly journals Difference Equation Model-Based PM 2.5 Prediction considering the Spatiotemporal Propagation: A Case Study of Bohai Rim Region, China

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ceyu Lei ◽  
Xiaoling Han ◽  
Chenghua Gao
Keyword(s):  
Difference Equation ◽  
Prediction Accuracy ◽  
Clustering Algorithm ◽  
Absolute Error ◽  
Equation Model ◽  
Concentration Data ◽  
Comprehensive Management ◽  
Bohai Rim ◽  
Local Air Pollution ◽  
The Difference

Accurate reporting and prediction of PM 2.5 concentration are very important for improving public health. In this article, we use a spectral clustering algorithm to cluster 44 cities in the Bohai Rim Region. On this basis, we propose a special difference equation model, especially the use of nonlinear diffusion equations to characterize the temporal and spatial dynamic characteristics of PM 2.5 propagation between and within clusters for real-time prediction. For example, through the analysis of PM 2.5 concentration data for 92 consecutive days in the Bohai Rim Region, and according to different accuracy definitions, the average prediction accuracy of the difference equation model in all city clusters is 97% or 90%. The mean absolute error (MAE) of the forecast data for each urban agglomeration is within 7 units μg / m 3 . The experimental results show that the difference equation model can effectively reduce the prediction time, improve the prediction accuracy, and provide decision support for local air pollution early warning and urban comprehensive management.

scholarly journals Hidden Semi-Markov Models for Predictive Maintenance

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
Author(s):  
Francesco Cartella ◽  
Jan Lemeire ◽  
Luca Dimiccoli ◽  
Hichem Sahli
Keyword(s):  
Markov Models ◽  
Real Data ◽  
Information Criterion ◽  
Absolute Error ◽  
Predictive Maintenance ◽  
Average Absolute Error ◽  
Current State ◽  
Automatic Model Selection ◽  
State Duration ◽  
Useful Lifetime

Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs) with (i) no constraints on the state duration density function and (ii) being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL) of the machine is calculated.

scholarly journals Study of a Privacy Preserving Logistic Regression Algorithm (PPLRA) For Data Privacy in the Context of Big Data

2021 ◽  
Vol 2083 (3) ◽  
pp. 032059
Author(s):  
Qiang Chen ◽  
Meiling Deng
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Privacy Protection ◽  
Data Privacy ◽  
Absolute Error ◽  
Average Absolute Error ◽  
Regression Algorithms ◽  
Hadoop Platform ◽  
Logistic Regression Algorithm ◽  
Computing Speed

Abstract Regression algorithms are commonly used in machine learning. Based on encryption and privacy protection methods, the current key hot technology regression algorithm and the same encryption technology are studied. This paper proposes a PPLAR based algorithm. The correlation between data items is obtained by logistic regression formula. The algorithm is distributed and parallelized on Hadoop platform to improve the computing speed of the cluster while ensuring the average absolute error of the algorithm.

scholarly journals The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA

2022 ◽  
Vol 13 ◽  
Author(s):  
Niklas Wulms ◽  
Lea Redmann ◽  
Christine Herpertz ◽  
Nadine Bonberg ◽  
Klaus Berger ◽  
...  
Keyword(s):  
White Matter ◽  
Sample Size ◽  
Mean Absolute Error ◽  
External Validation ◽  
Similarity Index ◽  
Absolute Error ◽  
Training Sample ◽  
White Matter Hyperintensity ◽  
Training Sample Size ◽  
The Difference

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are an important magnetic resonance imaging marker of cerebral small vessel disease and are associated with cognitive decline, stroke, and mortality. Their relevance in healthy individuals, however, is less clear. This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.Methods: We evaluated the effect of varying training sample sizes on the accuracy and the robustness of the predicted white matter hyperintensity volume in a population (n = 201) with a low prevalence of confluent WMH and a substantial proportion of participants without WMH. BIANCA was trained with seven different sample sizes between 10 and 40 with increments of 5. For each sample size, 100 random samples of T1w and FLAIR images were drawn and trained with manually delineated masks. For validation, we defined an internal and external validation set and compared the mean absolute error, resulting from the difference between manually delineated and predicted WMH volumes for each set. For spatial overlap, we calculated the Dice similarity index (SI) for the external validation cohort.Results: The study population had a median WMH volume of 0.34 ml (IQR of 1.6 ml) and included n = 28 (18%) participants without any WMH. The mean absolute error of the difference between BIANCA prediction and manually delineated masks was minimized and became more robust with an increasing number of training participants. The lowest mean absolute error of 0.05 ml (SD of 0.24 ml) was identified in the external validation set with a training sample size of 35. Compared to the volumetric overlap, the spatial overlap was poor with an average Dice similarity index of 0.14 (SD 0.16) in the external cohort, driven by subjects with very low lesion volumes.Discussion: We found that the performance of BIANCA, particularly the robustness of predictions, could be optimized for use in populations with a low WMH load by enlargement of the training sample size. Further work is needed to evaluate and potentially improve the prediction accuracy for low lesion volumes. These findings are important for current and future population-based studies with the majority of participants being normal aging people.

scholarly journals Performance Evaluation of Ground AR Anchor with WebXR Device API

2021 ◽  
Vol 11 (17) ◽  
pp. 7877
Author(s):  
Daehyeon Lee ◽  
Woosung Shim ◽  
Munyong Lee ◽  
Seunghyun Lee ◽  
Kye-Dong Jung ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Mixed Reality ◽  
Ground Truth ◽  
Application Programming Interface ◽  
Absolute Error ◽  
Frame Rate ◽  
Difference Image ◽  
Match Rate ◽  
The Difference ◽  
The Web

Recently, the development of 3D graphics technology has led to various technologies being combined with reality, where a new reality is defined or studied; they are typically named by combining the name of the technology with “reality”. Representative “reality” includes Augmented Reality, Virtual Reality, Mixed Reality, and eXtended Reality (XR). In particular, research on XR in the web environment is actively being conducted. The Web eXtended Reality Device Application Programming Interface (WebXR Device API), released in 2018, allows instant deployment of XR services to any XR platform requiring only an active web browser. However, the currently released tentative version has poor stability. Therefore, in this study, the performance evaluation of WebXR Device API is performed using three experiments. A camera trajectory experiment is analyzed using ground truth, we checked the standard deviation between the ground truth and WebXR for the X, Y, and Z axes. The difference image experiment is conducted for the front, left, and right directions, which resulted in a visible difference image for each image of ground truth and WebXR, small mean absolute error, and high match rate. In the experiment for measuring the 3D rendering speed, a frame rate similar to that of real-time is obtained.

scholarly journals Short-Term Traffic Flow Forecasting Model Based on GA-TCN

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rongji Zhang ◽  
Feng Sun ◽  
Ziwen Song ◽  
Xiaolin Wang ◽  
Yingcui Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Traffic Flow ◽  
Absolute Error ◽  
Forecasting Model ◽  
Average Absolute Error ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Fitness Value

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme.

scholarly journals Analysis and Development of Seawater Density Measurement Algorithms Using Arduino Uno and YL-69 Sensor

2020 ◽  
Vol 4 (5) ◽  
pp. 951-956
Author(s):  
Miftahul Walid ◽  
Hozairi ◽  
Madukil Makruf
Keyword(s):  
Sea Water ◽  
Density Measurement ◽  
Absolute Error ◽  
Error Level ◽  
Value Range ◽  
Density Values ◽  
The Mean ◽  
Arduino Uno ◽  
The Difference ◽  
Predicted Values

In this research, an analysis was carried out to develop a measuring instrument for seawater density in salt production using a microcontroller (Arduino Uno) and YL-69 sensor, this sensor was commonly used to measure soil moisture. The experimental method was used in this research to produce initial data in the form of resistance and seawater density values, then calculations are carried out using statistical methods to find equations and produce a constant variable that connects the resistance and seawater density values. The equation was used to compile the algorithm into Arduino Uno. As for the results of this research,  From six experiments conducted, two experiments produced the same sea water density value between the actual and the predicted, namely the 2nd and 5th experiments, while for other experiments there was a difference between the actual and predicted values, however, it was not too significant, the difference occurs between the value range 0 ~ 1, to determine the level of error, use the Mean Square Error (MSE) with an error level of = 0.5 and Mean Absolute Error (MAE) with an error level of = 0.6. The contribution of this research is an algorithm that can predict the density value (baume) based on the resistance value obtained from the YL 69 sensor.

scholarly journals Intraocular Lens Power Calculation According to the Difference between Anterior and Total Keratometry Using Scheimpflug Imaging

2021 ◽  
Vol 62 (9) ◽  
pp. 1181-1188
Author(s):  
Joong Hee Kim ◽  
Kyong Jin Cho ◽  
Ho Seok Chung
Keyword(s):  
Regression Analysis ◽  
Absolute Error ◽  
Power Calculation ◽  
Intraocular Lens Power Calculation ◽  
Refractive Power ◽  
Simple Regression Analysis ◽  
The Absolute ◽  
The Difference ◽  
Lens Power ◽  
Simple Regression

Purpose: We investigated the change in the absolute error according to the difference between anterior and total keratometry, to determine the criterion for the difference in keratometry, and to determine the indication for using total keratometry. Methods: Sagittal and total refractive power were measured with 2-, 3-, and 4-mm Pentacam® rings, and the absolute error of each was calculated in patients who underwent cataract surgery in our hospital. The correlation between the difference value the sagittal minus the total refractive power and each absolute error was analyzed by simple regression analysis. The analysis was performed by dividing the patients into two groups based on 0.6, which is the average of the difference between the sagittal and total refractive power for the 3-mm ring. Results: Sagittal power was larger than total refractive power for all rings and the absolute error obtained by applying the total refractive power was larger than the sagittal power for the 2- and 4-mm rings (p < 0.001). The simple regression analysis revealed that the absolute error using sagittal power was positively correlated with the difference between sagittal power and total refractive power. In the group with less than 0.6, the absolute error using the total refractive power of all rings was larger than the sagittal power (p < 0.001). In the group exceeding 0.6, the absolute error using the total refractive power was less than using the sagittal power for the 3 mm ring (p = 0.028). Conclusions: The greater the difference between sagittal and total refractive power, the greater the absolute error using sagittal power. Accuracy was higher in the group exceeding 0.6 after applying total refractive power measured at the 3 mm ring compared to sagittal power.

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