Relevant bibliographies by topics / Mean absolute errors

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Mean absolute errors'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Mean absolute errors"

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Frías-Paredes, Laura, Fermin Mallor, Martín Gastón-Romeo, and Teresa León. "Dynamic mean absolute error as new measure for assessing forecasting errors." Energy Conversion and Management 162 (April 2018): 176–88. http://dx.doi.org/10.1016/j.enconman.2018.02.030.

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Bingham, Rory J., and Keith Haines. "Mean dynamic topography: intercomparisons and errors." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 364, no. 1841 (2006): 903–16. http://dx.doi.org/10.1098/rsta.2006.1745.

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Knowledge of the ocean dynamic topography, defined as the height of the sea surface above its rest-state (the geoid), would allow oceanographers to study the absolute circulation of the ocean and determine the associated geostrophic surface currents that help to regulate the Earth's climate. Here a novel approach to computing a mean dynamic topography (MDT), together with an error field, is presented for the northern North Atlantic. The method uses an ensemble of MDTs, each of which has been produced by the assimilation of hydrographic data into a numerical ocean model, to form a composite MDT, and uses the spread within the ensemble as a measure of the error on this MDT. The r.m.s. error for the composite MDT is 3.2 cm, and for the associated geostrophic currents the r.m.s. error is 2.5 cm s −1 . Taylor diagrams are used to compare the composite MDT with several MDTs produced by a variety of alternative methods. Of these, the composite MDT is found to agree remarkably well with an MDT based on the GRACE geoid GGM01C. It is shown how the composite MDT and its error field are useful validation products against which other MDTs and their error fields can be compared.
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Zhang, Jun, Bingqing Lin, and Zhenghui Feng. "Conditional absolute mean calibration for partial linear multiplicative distortion measurement errors models." Computational Statistics & Data Analysis 141 (January 2020): 77–93. http://dx.doi.org/10.1016/j.csda.2019.06.009.

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Tcheou, Michel Pompeu, Lisandro Lovisolo, Alexandre Ribeiro Freitas, and Sin Chan Chou. "Reducing Forecast Errors of a Regional Climate Model Using Adaptive Filters." Applied Sciences 11, no. 17 (2021): 8001. http://dx.doi.org/10.3390/app11178001.

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In this work, the use of adaptive filters for reducing forecast errors produced by a Regional Climate Model (RCM) is investigated. Seasonal forecasts are compared against the reanalysis data provided by the National Centers for Environmental Prediction. The reanalysis is used to train adaptive filters based on the Recursive Least Squares algorithm in order to reduce the forecast error. The K-means unsupervised learning algorithm is used to obtain the number of filters to employ from the climate variables. The proposed approach is applied to some climate variables such as the meridional wind, zonal wind, and the geopotential height. The forecast is produced by the Eta RCM at 40-km resolution in a domain covering most of Brazil. Results show that the proposed approach is capable of reducing the forecast errors, according to evaluation metrics such as normalized mean square error, maximum absolute error, and maximum normalized absolute error, thus improving the seasonal climate forecasts.
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HE, XIAO-GANG, and A. ZEE. "GEOMETRIC MEAN NEUTRINO MASS RELATION." Modern Physics Letters A 22, no. 25n28 (2007): 2107–12. http://dx.doi.org/10.1142/s0217732307025352.

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Present experimental data from neutrino oscillations have provided much information about the neutrino mixing angles. Since neutrino oscillations only determine the mass squared differences [Formula: see text], the absolute values for neutrino masses mi, can not be determined using data just from oscillations. In this work we study implications on neutrino masses from a geometric mean mass relation [Formula: see text] which enables one to determined the absolute masses of the neutrinos. We find that the central values of the three neutrino masses and their 2σ errors to be m1 = (1.58 ± 0.18) meV , m2 = (9.04 ± 0.42) meV , and m3 = (51.8 ± 3.5) meV . Implications for cosmological observation, beta decay and neutrinoless double beta decays are discussed.
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Alam, S. M. Mahfuz, and Mohd Hasan Ali. "Equation Based New Methods for Residential Load Forecasting." Energies 13, no. 23 (2020): 6378. http://dx.doi.org/10.3390/en13236378.

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This work proposes two non-linear and one linear equation-based system for residential load forecasting considering heating degree days, cooling degree days, occupancy, and day type, which are applicable to any residential building with small sets of smart meter data. The coefficients of the proposed nonlinear and linear equations are tuned by particle swarm optimization (PSO) and the multiple linear regression method, respectively. For the purpose of comparison, a subtractive clustering based adaptive neuro fuzzy inference system (ANFIS), random forests, gradient boosting trees, and long-term short memory neural network, conventional and modified support vector regression methods were considered. Simulations have been performed in MATLAB environment, and all the methods were tested with randomly chosen 30 days data of a residential building in Memphis City for energy consumption prediction. The absolute average error, root mean square error, and mean average percentage errors are tabulated and considered as performance indices. The efficacy of the proposed systems for residential load forecasting over the other systems have been validated by both simulation results and performance indices, which indicate that the proposed equation-based systems have the lowest absolute average errors, root mean square errors, and mean average percentage errors compared to the other methods. In addition, the proposed systems can be easily practically implemented.
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Reza, Imran, Nedal T. Ratrout, and Syed Masiur Rahman. "Calibration protocol for PARAMICS microscopic traffic simulation model: application of neuro-fuzzy approach." Canadian Journal of Civil Engineering 43, no. 4 (2016): 361–68. http://dx.doi.org/10.1139/cjce-2015-0435.

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This study investigated the challenges of calibration of the PARAMICS microscopic simulation model for the local traffic conditions in the Kingdom of Saudi Arabia. It proposed an adaptive neuro-fuzzy inference system (ANFIS) based calibration protocol for the PARAMICS model. The developed ANFIS model performs adequately in modeling the queue length as a function of two key calibration parameters, namely mean headway time and mean reaction time. The selected values of the calibration parameters obtained through the ANFIS modeling approach were used as the input parameters for the PARAMICS model. The error indices such as mean absolute errors and mean absolute percentage errors of the developed ANFIS model in predicting the queue lengths varied between 1.11 and 1.24, and between 3.44 and 4.06, respectively. The conformance of the PARAMICS output and the measured queue length indicates the validity of the proposed calibration protocol.
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Radziukynas, V., and A. Klementavičius. "Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools." Latvian Journal of Physics and Technical Sciences 53, no. 2 (2016): 3–13. http://dx.doi.org/10.1515/lpts-2016-0008.

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Abstract The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).
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Shankar, P. Sai, and M. Krishna Reddy. "Forecasting Gold Prices in India using Time series and Deep Learning Algorithms." International Journal of Engineering and Advanced Technology 10, no. 5 (2021): 21–27. http://dx.doi.org/10.35940/ijeat.d2537.0610521.

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The primary object of this paper is to compare the traditional time series models with deep learning algorithm.The ARIMA model is developed to forecast Indian Gold prices using daily data for the period 2016 to 2020 obtained from World Gold Council.We fitted the ARIMA (2,1,2) model which exhibited the least AIC values. In the meanwhile, MLP, CNN and LSTM models are also examined to forecast the gold prices in India. Mean absolute error, mean absolute percentage error and root mean squared errors used to evaluate the forecasting performance of the models. Hence, LSTM model superior than that of the other three models for forecasting the gold prices in India
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Jia, Yunhong, Xiaodong Zhang, Zhenchong Wang, and Wei Wang. "Intelligent Calibration of a Heavy-Duty Mechanical Arm in Coal Mine." Electronics 9, no. 8 (2020): 1186. http://dx.doi.org/10.3390/electronics9081186.

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Accurate positioning of an airborne heavy-duty mechanical arm in coal mine, such as a roof bolter, is important for the efficiency and safety of coal mining. Its positioning accuracy is affected not only by geometric errors but also by nongeometric errors such as link and joint compliance. In this paper, a novel calibration method based on error limited genetic algorithm (ELGA) and regularized extreme learning machine (RELM) is proposed to improve the positioning accuracy of a roof bolter. To achieve the improvement, the ELGA is firstly implemented to identify the geometric parameters of the roof bolter’s kinematics model. Then, the residual positioning errors caused by nongeometric facts are compensated with the regularized extreme learning machine (RELM) network. Experiments were carried out to validate the proposed calibration method. The experimental results show that the root mean square error (RMSE) and the mean absolute error (MAE) between the actual mast end position and the nominal mast end position are reduced by more than 78.23%. It also shows the maximum absolute error (MAXE) between the actual mast end position and the nominal mast end position is reduced by more than 58.72% in the three directions of Cartesian coordinate system.
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Dissertations / Theses on the topic "Mean absolute errors"

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Loce, Robert P. "Morphological filter mean-absolute-error representation theorems and their application to optimal morphological filter design /." Online version of thesis, 1993. http://hdl.handle.net/1850/11065.

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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.
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Damdinsuren, Batnyam. "The Role of Financial Market in Macro Economic Modeling: Case of Mongolia." Master's thesis, 2012. http://www.nusl.cz/ntk/nusl-304463.

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- 4 - Abstract In this research we explored role of financial variables in macro modeling and their performance in case of Mongolia. We employed two different models for assessing performance of financial variables in macro modeling, structural VAR model and small scale macro model (SSMM). In doing so, we performed different analysis such as impulse response for seeing how financial variables fit into system and forecasting performance for how accurate model performs after introducing financial variables. So our result suggested that financial variables have substantial role on macro modeling and inclusion of financial variable is performing very good result in terms of forecasting in both models. JEL Classification C01, C51, C53, E12, E52, G17 Keywords Financial markets, Small scale macro model, Structural VAR, Impulse response, Mean absolute errors. Author's e-mail batnyamd@gmail.com Supervisor's e-mail roman.horvath@gmail.com
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Book chapters on the topic "Mean absolute errors"

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Fürnkranz, Johannes, Philip K. Chan, Susan Craw, et al. "Mean Absolute Error." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_525.

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Tomaselli, Venera, and Giulio Giacomo Cantone. "Multipoint vs slider: a protocol for experiments." In Proceedings e report. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.19.

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Since the broad diffusion of Computer-Assisted survey tools (i.e. web surveys), a lively debate about innovative scales of measure arose among social scientists and practitioners. Implications are relevant for applied Statistics and evaluation research since while traditional scales collect ordinal observations, data from sliders can be interpreted as continuous. Literature, however, report excessive times of completion of the task from sliders in web surveys. This experimental protocol is aimed at testing hypotheses on the accuracy in prediction and dispersion of estimates from anonymous participants who are recruited online and randomly assigned into tasks in recognition of shades of colour. The treatment variable is two scales: a traditional multipoint 0-10 multipoint vs a slider 0-100. Shades have a unique parametrisation (true value) and participants have to guess the true value through the scale. These tasks are designed to recreate situations of uncertainty among participants while minimizing the subjective component of a perceptual assessment and maximizing information about scale-driven differences and biases. We propose to test statistical differences in the treatment variable: (i) mean absolute error from the true value (ii), time of completion of the task. To correct biases due to the variance in the number of completed tasks among participants, data about participants can be collected through both pre-tasks acceptance of web cookies and post-tasks explicit questions.
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Lahmiri, Salim. "An Exploration of Backpropagation Numerical Algorithms in Modeling US Exchange Rates." In Advances in Business Information Systems and Analytics. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7272-7.ch022.

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This chapter applies the Backpropagation Neural Network (BPNN) trained with different numerical algorithms and technical analysis indicators as inputs to forecast daily US/Canada, US/Euro, US/Japan, US/Korea, US/Swiss, and US/UK exchange rate future price. The training algorithms are the Fletcher-Reeves, Polak-Ribiére, Powell-Beale, quasi-Newton (Broyden-Fletcher-Goldfarb-Shanno, BFGS), and the Levenberg-Marquardt (LM). The standard Auto Regressive Moving Average (ARMA) process is adopted as a reference model for comparison. The performance of each BPNN and ARMA process is measured by computing the Mean Absolute Error (MAE), Mean Absolute Deviation (MAD), and Mean of Squared Errors (MSE). The simulation results reveal that the LM algorithm is the best performer and show strong evidence of the superiority of the BPNN over ARMA process. In sum, because of the simplicity and effectiveness of the approach, it could be implemented for real business application problems to predict US currency exchange rate future price.
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"Mean Absolute Error." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_953.

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Pradhan, Rudra P. "Forecasting Inflation in India." In Business, Technology, and Knowledge Management in Asia. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2652-2.ch003.

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This paper presents an application of Artificial Neural Network (ANN) to forecast inflation in India during the period 1994-2009. The study presents four different ANN models on the basis of inflation (WPI), economic growth (IIP), and money supply (MS). The first model is a univariate model based on past WPI only. The other three are multivariate models based on WPI and IIP, WPI and MS, WPI, and IIP and MS. In each case, the forecasting performance is measured by mean squared errors and mean absolute deviations. The paper finally concludes that multivariate models show better forecasting performance over the univariate model. In particular, the multivariate ANN model using WPI, IIP, and MS resulted in better performance than the rest of other models to forecast inflation in India.
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Vaferi, Behzad. "Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids." In Deterministic Artificial Intelligence. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.89101.

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Nanofluids have recently been considered as one of the most popular working fluid in heat transfer and fluid mechanics. Accurate estimation of thermophysical properties of nanofluids is required for the investigation of their heat transfer performance. Thermal conductivity coefficient, convective heat transfer coefficient, and viscosity are some the most important thermophysical properties that directly influence on the application of nanofluids. The aim of the present chapter is to develop and validate artificial neural networks (ANNs) to estimate these thermophysical properties with acceptable accuracy. Some simple and easy measurable parameters including type of nanoparticle and base fluid, temperature and pressure, size and concentration of nanoparticles, etc. are used as independent variables of the ANN approaches. The predictive performance of the developed ANN approaches is validated with both experimental data and available empirical correlations. Various statistical indices including mean square errors (MSE), root mean square errors (RMSE), average absolute relative deviation percent (AARD%), and regression coefficient (R2) are used for numerical evaluation of accuracy of the developed ANN models. Results confirm that the developed ANN models can be regarded as a practical tool for studying the behavior of those industrial applications, which have nanofluids as operating fluid.
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Loce, Robert P., and Edward R. Dougherty. "Optimal Mean-Absolute-Error Nonincreasing Binary Filters." In Enhancement and Restoration of Digital Documents: Statistical Design of Nonlinear Algorithms. SPIE, 1997. http://dx.doi.org/10.1117/pm29.ch4.

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Rapoo, Mogari I., Elias Munapo, Martin M. Chanza, and Olusegun Sunday Ewemooje. "Modelling and Forecasting Portfolio Inflows." In Handbook of Research on Smart Technology Models for Business and Industry. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3645-2.ch014.

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This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution.
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Rapoo, Mogari I., Elias Munapo, Martin M. Chanza, and Olusegun Sunday Ewemooje. "Modelling and Forecasting Portfolio Inflows." In Research Anthology on Artificial Neural Network Applications. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch069.

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This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution.
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Dhupia, Bhawna, and M. Usha Rani. "Assessment of Electric Consumption Forecast Using Machine Learning and Deep Learning Models for the Industrial Sector." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7685-4.ch016.

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Power demand forecasting is one of the fields which is gaining popularity for researchers. Although machine learning models are being used for prediction in various fields, they need to upgrade to increase accuracy and stability. With the rapid development of AI technology, deep learning (DL) is being recommended by many authors in their studies. The core objective of the chapter is to employ the smart meter's data for energy forecasting in the industrial sector. In this chapter, the author will be implementing popular power demand forecasting models from machine learning and compare the results of the best-fitted machine learning (ML) model with a deep learning model, long short-term memory based on RNN (LSTM-RNN). RNN model has vanishing gradient issue, which slows down the training in the early layers of the network. LSTM-RNN is the advanced model which take care of vanishing gradient problem. The performance evaluation metric to compare the superiority of the model will be R2, mean square error (MSE), root means square error (RMSE), and mean absolute error (MAE).
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Conference papers on the topic "Mean absolute errors"

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Bielecki, Mark F., Jason J. Kemper, and Thomas L. Acker. "A Methodology for Comprehensive Characterization of Errors in Wind Power Forecasting." In ASME 2010 4th International Conference on Energy Sustainability. ASMEDC, 2010. http://dx.doi.org/10.1115/es2010-90381.

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Wind power forecasting will play a more important role in electrical system planning with the greater wind penetrations of the coming decades. Wind will most likely comprise a larger percentage of the generation mix, and as a result forecasting errors may have more significant effects on balancing operations. The natural uncertainties associated with wind along with limitations in numerical weather prediction (NWP) models lead to these forecasting errors, which play a considerable role in the impacts and costs of utility-scale wind integration. The premise of this project was to examine errors between the actual and commercially forecasted power production data from a typical wind power plant in the Northwestern United States. An exhaustive statistical characterization of the forecast behavior and error trends was undertaken, which allowed the most important metrics for describing wind power forecast errors to be identified. This paper presents only the metrics considered by the authors to be most significant. While basic information about wind forecast accuracy such as the mean absolute error (MAE) is valuable, a more detailed description is useful for system operators or in wind integration studies. System planners have expressed major concern in the area of forecast performance during large wind ramping events. For such reasons, this methodology included the development of a comprehensive ramp identification algorithm to select significant ramp events from the data record, and particular attention was paid to the error analysis during these events. The algorithm allows user input to select ramps of any desired magnitude, and also performs correlation analysis between forecasted ramp events and actual ramp events that coincide within a desired timing window. From this procedure, an investigation of the magnitude and phase of forecast errors was conducted for various forecast horizons. The metrics found to be of most importance for error characterization were selected based on overall impacts, and were ranked in a rudimentary (and perhaps subjective) order of significance. These metrics included: mean absolute error, root mean square error, average magnitude of step changes, standard deviation of step changes, mean bias levels, correlation coefficient of power values, mean temporal bias of ramp events, and others. While these metrics were selected and the methodology was developed for a single dataset, the entire process can be applied generally to any wind power and forecast time series. The implications for such a process include use for generating a synthetic wind power forecast for wind integration studies that will reproduce the same error trends as those found in a real forecast.
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Artz, Edward J., Amy A. Blank, and Marcia K. O’Malley. "Proportional sEMG Based Robotic Assistance in an Isolated Wrist Movement." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9979.

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Numerous studies have demonstrated the efficacy of robots for motor therapy. Surface electromyography (sEMG) is appealing for user intent detection as the signal relates the individual’s desired muscle contractile force. A drawback to sEMG interfaces is subject- and session-dependent calibration. We sought to investigate the effect of a simple sEMG assistive controller on user performance in the MAHI Exo-II, therapeutic exoskeleton. Agonist-antagonist muscles were related after normalization based on sub-maximal isometric contraction. Six subjects performed a target tracking task with four levels of assistance in wrist flexion-extension. Performance metrics were mean absolute position error and estimated muscular activity. In low levels of assistance, subject performance was not significantly affected, while increasing the assistance resulted in higher position errors. In characterizing the performance assistance trade-off, we better understand the capabilities of this simple controller. This investigation validates the feasibility of using a proportional control scheme for a therapeutic wrist exoskeleton system and motivates further testing with impaired subjects to optimize the system for use in a clinical setting.
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Yang, Yusheng, Willemijn S. Elkhuizen, Tao Hou, Tessa T. W. Essers, and Yu Song. "Optimal Camera Configuration for 3D Scanning of Human Hand." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97784.

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Abstract Fast, accurate and low-cost 3D scans are the key in designing personalized products. In this paper, using close-range photogrammetry technique, we aim at finding the “just enough” number of cameras and their spatial configurations for a full 3D reconstruction of the human hand. Given an object, we establish a mathematical model to describe the 3D constructible ratio based on the field of the view and the depth of field of each camera, as well as the visibility of each part of the object in the view of each camera. Furthermore, we introduce spatial constrains to arrange cameras along two rings for: 1) solving the problem of the large number of parameters in the unconstrained optimization, and 2) the feasibility and flexibility in the construction. Based on the found number of cameras and the spatial configuration of each camera, a prototype scanner was built to verify the effectiveness of the proposed method. The mean absolute error between the 3D scan of a 3D printed hand and its original CAD model was found to be 0.38mm, which is smaller than that (0.52mm) of using the conventional setup. Besides, the distribution of errors is smaller as well, which implicates a better full 3D reconstruction of the scanned hand.
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Westin, Johan K., Jayanta S. Kapat, and Louis C. Chow. "Evaluating a Thermoregulatory Model for Cooling Garment Applications With Transient Metabolic Rates." In ASME 2008 Heat Transfer Summer Conference collocated with the Fluids Engineering, Energy Sustainability, and 3rd Energy Nanotechnology Conferences. ASMEDC, 2008. http://dx.doi.org/10.1115/ht2008-56319.

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Current state-of-the-art thermoregulatory models do not predict body temperatures with the accuracies that are required for the development of automatic cooling control in liquid cooling garment (LCG) systems. Automatic cooling control would be beneficial in a variety of space, aviation, military, and industrial environments for optimizing cooling efficiency, for making LCGs as portable and practical as possible, for alleviating the individual from manual cooling control, and for improving thermal comfort and cognitive performance. In this paper, we adopt the Fiala thermoregulatory model, which has previously demonstrated state-of-the-art predictive abilities in air environments, for use in LCG environments. We compare the model’s tissue temperature predictions with analytical solutions to the bioheat equation, and with experimental data for a 700 W rectangular type activity schedule. The thermoregulatory model predicts rectal temperature, mean skin temperature, and body heat storage (BHS) with mean absolute errors of 0.13°C, 0.95°C, and 11.9 W·hr, respectively. Even though these accuracies are within state-of-the-art variations, the model does not satisfy the target BHS accuracy of ±6.5 W·hr. We identify model deficiencies, which will be addressed in future studies in order to achieve the strict BHS accuracy that is needed for automatic cooling control development.
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Loce, Robert P., and Edward R. Dougherty. "Morphological filter mean-absolute-error theorem." In SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, edited by Edward R. Dougherty, Jaakko T. Astola, and Charles G. Boncelet, Jr. SPIE, 1992. http://dx.doi.org/10.1117/12.58363.

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Loce, Robert P., and Edward R. Dougherty. "Mean-absolute-error theorem for computational morphology." In SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, edited by Edward R. Dougherty, Paul D. Gader, and Jean C. Serra. SPIE, 1993. http://dx.doi.org/10.1117/12.146680.

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Yan, Chaoxing, Changqi Yan, Licheng Sun, and Yang Wang. "Experimental Study on Resistance Characteristics in a 3 × 3 Rod Bundle." In 2014 22nd International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/icone22-30295.

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Experimental study on resistance of air-water two-phase flow in a vertical 3 × 3 rod bundle was carried out under normal temperature and pressure. The rod diameter and pitch were 8 mm and 11 mm, respectively. The ranges of gas and liquid superficial velocity were 0.013∼3.763 m/s and 0.076∼1.792 m/s, respectively. The result indicated that the existing correlations for calculating frictional coefficient in the rod bundle and local resistance coefficient could not give favorable predictions on the single-phase experimental data. For the case of two-phase flow, eight correlations for calculating two-phase equivalent viscosity poorly predicted the frictional pressure drop, with the mean absolute errors around 60%. Meanwhile, the eight classical two-phase viscosity formulae were evaluated against the local pressure drop at spacer grid. It is shown that Dukler model predicted the experimental data well in the range of Rel<9000 while McAdams correlation was the best for Rel⩾9000. For all the experimental data, Dukler model provided the best prediction with MRE of 29.03%. Furthermore, approaches to calculate two-phase frictional pressure drop and local resistance were proposed by considering mass quality, two-phase Reynolds number and densities in homogenous flow model, resulting in a good agreement with the experimental data.
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Justesen, Kristian K., Søren J. Andreasen, and Hamid R. Shaker. "Dynamic Modeling of a Reformed Methanol Fuel Cell System Using Empirical Data and Adaptive Neuro-Fuzzy Inference System Models." In ASME 2013 11th International Conference on Fuel Cell Science, Engineering and Technology collocated with the ASME 2013 Heat Transfer Summer Conference and the ASME 2013 7th International Conference on Energy Sustainability. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/fuelcell2013-18110.

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In this work, a dynamic MATLAB Simulink model of a H3-350 Reformed Methanol Fuel Cell (RMFC) stand-alone battery charger produced by Serenergy® is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseous hydrogen, which is difficult and energy consuming to store and transport. The models include thermal equilibrium models of the individual components of the system. Models of the heating and cooling of the gas flows between components are also modeled and Adaptive Neuro-Fuzzy Inference System models of the reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other’s output. The models take this into account using an empirical approach. Fin efficiency models for the cooling effect of the air are also developed using empirical methods. A fuel cell model is also implemented based on a standard model which is adapted to fit the measured performance of the H3-350 module. All the individual parts of the model are verified and fine-tuned through a series of experiments and are found to have mean absolute errors between 0.4% and 6.4% but typically below 3%. After a comparison between the performance of the combined model and the experimental setup, the model is deemed to be valid for control design and optimization purposes.
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Goswami, Hemen, and Samuel P. Kozaitis. "Bit allocation considering mean absolute error for image compression." In AeroSense 2000, edited by Stephen K. Park and Zia-ur Rahman. SPIE, 2000. http://dx.doi.org/10.1117/12.390488.

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Lin, Jean H., Thomas M. Sellke, and Edward J. Coyle. "Adaptive stack filtering under the mean absolute error criterion." In Electronic Imaging '90, Santa Clara, 11-16 Feb'95, edited by Edward J. Delp. SPIE, 1990. http://dx.doi.org/10.1117/12.19608.

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Reports on the topic "Mean absolute errors"

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Ruosteenoja, Kimmo. Applicability of CMIP6 models for building climate projections for northern Europe. Finnish Meteorological Institute, 2021. http://dx.doi.org/10.35614/isbn.9789523361416.

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In this report, we have evaluated the performance of nearly 40 global climate models (GCMs) participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). The focus is on the northern European area, but the ability to simulate southern European and global climate is discussed as well. Model evaluation was started with a technical control; completely unrealistic values in the GCM output files were identified by seeking the absolute minimum and maximum values. In this stage, one GCM was rejected totally, and furthermore individual output files from two other GCMs. In evaluating the remaining GCMs, the primary tool was the Model Climate Performance Index (MCPI) that combines RMS errors calculated for the different climate variables into one index. The index takes into account both the seasonal and spatial variations in climatological means. Here, MCPI was calculated for the period 1981—2010 by comparing GCM output with the ERA-Interim reanalyses. Climate variables explored in the evaluation were the surface air temperature, precipitation, sea level air pressure and incoming solar radiation at the surface. Besides MCPI, we studied RMS errors in the seasonal course of the spatial means by examining each climate variable separately. Furthermore, the evaluation procedure considered model performance in simulating past trends in the global-mean temperature, the compatibility of future responses to different greenhouse-gas scenarios and the number of available scenario runs. Daily minimum and maximum temperatures were likewise explored in a qualitative sense, but owing to the non-existence of data from multiple GCMs, these variables were not incorporated in the quantitative validation. Four of the 37 GCMs that had passed the initial technical check were regarded as wholly unusable for scenario calculations: in two GCMs the responses to the different greenhouse gas scenarios were contradictory and in two other GCMs data were missing from one of the four key climate variables. Moreover, to reduce inter-GCM dependencies, no more than two variants of any individual GCM were included; this led to an abandonment of one GCM. The remaining 32 GCMs were divided into three quality classes according to the assessed performance. The users of model data can utilize this grading to select a subset of GCMs to be used in elaborating climate projections for Finland or adjacent areas. Annual-mean temperature and precipitation projections for Finland proved to be nearly identical regardless of whether they were derived from the entire ensemble or by ignoring models that had obtained the lowest scores. Solar radiation projections were somewhat more sensitive.
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