Academic literature on the topic 'Mean Square Error (RMSE)'
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Journal articles on the topic "Mean Square Error (RMSE)"
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Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?" Geoscientific Model Development Discussions 7, no. 1 (2014): 1525–34. http://dx.doi.org/10.5194/gmdd-7-1525-2014.
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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error and thus the MAE would be a better metric for that purpose. Their paper has been widely cited and may have influenced many researchers in choosing MAE when presenting their model evaluation statistics. However, we contend that the proposed avoidance of RMSE and the use of MAE is not the solution to the problem. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric.
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Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature." Geoscientific Model Development 7, no. 3 (2014): 1247–50. http://dx.doi.org/10.5194/gmd-7-1247-2014.
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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.
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Hodson, Timothy O. "Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not." Geoscientific Model Development 15, no. 14 (2022): 5481–87. http://dx.doi.org/10.5194/gmd-15-5481-2022.
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Abstract. The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use, Willmott and Matsuura (2005) and Chai and Draxler (2014) give arguments for favoring one metric or the other. However, this comparison can present a false dichotomy. Neither metric is inherently better: RMSE is optimal for normal (Gaussian) errors, and MAE is optimal for Laplacian errors. When errors deviate from these distributions, other metrics are superior.
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Robeson, Scott M., and Cort J. Willmott. "Decomposition of the mean absolute error (MAE) into systematic and unsystematic components." PLOS ONE 18, no. 2 (2023): e0279774. http://dx.doi.org/10.1371/journal.pone.0279774.
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When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE). In terms of quantifying average error, however, absolute-value-based measures such as the mean absolute error (MAE) are more interpretable than MSE or RMSE. Part of that historical preference for sums-of-squares measures is that they are mathematically amenable to decomposition and one can then form ratios, such as those based on separating MSE into its systematic and unsystematic components. Here, we develop and illustrate a decomposition of MAE into three useful submeasures: (1) bias error, (2) proportionality error, and (3) unsystematic error. This three-part decomposition of MAE is preferable to comparable decompositions of MSE because it provides more straightforward information on the nature of the model-error distribution. We illustrate the properties of our new three-part decomposition using a long-term reconstruction of streamflow for the Upper Colorado River.
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Ren, Tao, Xiaoqing Kang, Wen Sun, and Hong Song. "Study of Dynamometer Cards Identification Based on Root-Mean-Square Error Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 02 (2017): 1850004. http://dx.doi.org/10.1142/s0218001418500040.
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The surface dynamometer cards are important working condition data of sucker-rod pumping system. It has a very important practical significance for the analysis of transmission system and the diagnosis of oil production condition of sucker-rod pumping system. The pump dynamometer cards are important reference for the diagnosis of oil production condition, and its key technology is the identification of pump dynamometer cards. A new similar pattern recognition algorithm based on root-mean-square error (RMSE) is proposed, a theoretical model of the similarity matching algorithm based on RMSE is established, and the algorithm is studied and analyzed. The three-dimensional vibration mathematical models for the surface dynamometer cards are created, by which the surface dynamometer cards can be transformed to the pump dynamometer cards. The accuracy, reliability and stability between the algorithm of RMSE similarity matching and the classical algorithms of similarity pattern matching are studied. The research shows that the resistance to the graphics deformation of RMSE algorithm is the highest among all algorithms. The application of RMSE algorithm and classic similarity matching algorithms to the identification of real pump dynamometer cards and the fault diagnosis of oil wells indicates that the RMSE algorithm has very high identification reliability and accuracy. The remarkable feature of the RMSE algorithm is that it has very high identification accuracy for small difference, while the classical similarity matching algorithms do not have this feature.
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Karno, Adhitio Satyo Bayangkari. "Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long ShortTerm Memory)." Journal of Informatic and Information Security 1, no. 1 (2020): 1–8. http://dx.doi.org/10.31599/jiforty.v1i1.133.
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Abstract
 
 This study aims to measure the accuracy in predicting time series data using the LSTM (Long Short-Term Memory) machine learning method, and determine the number of epochs needed to produce a small RMSE (Root Mean Square Error) value. The result of this research is a high level of variation in RMSE value to the number of epochs needed in the data processing. This variation is quite difficult to obtain the right epoch value. By doing an iteration of the LSTM process on the number of different epochs (visualized in the graph), then the number of epochs with a minimum RMSE value will be easier to obtain. From the research of BBRI's stock data prediction, a good RMSE value was obtained (RMSE = 227.470333244533).
 
 Keywords: long short-term memory, machine learning, epoch, root mean square error, mean square error.
 
 Abstrak
 
 Penelitian ini bertujuan untuk mengukur ketelitian dalam memprediksi data time series menggunakan metode mesin belajar LSTM (Long Short-Term Memory), serta menentukan banyaknya epoch yang diperlukan untuk menghasilkan nilai RMSE (Root Mean Square Error) yang kecil. Hasil dari penelitian ini adalah tingkat variasi yang tinggi nilai rmse terhdap jumlah epoch yang diperlukan dalam proses pengolahan data. Variasi ini cukup menyulitkan untuk memperoleh nilai epoch yang tepat. Dengan melakukan iterasi dari proses LSTM terhadap jumlah epoch yang berbeda (di visualisasikan dalam grafik), maka jumlah epoch dengan nilai RMSE minimal akan lebih mudah diperoleh. Dari penelitan prediksi data saham BBRI diperoleh nilai RMSE yang cukup baik yaitu 227,470333244533.
 Kata kunci: long short-term memory, machine learning, epoch, root mean square error, mean square error.
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Wang, Weijie, and Yanmin Lu. "Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model." IOP Conference Series: Materials Science and Engineering 324 (March 2018): 012049. http://dx.doi.org/10.1088/1757-899x/324/1/012049.
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Ivan, Eliansion, and Hindriyanto Dwi Purnomo. "FORECASTING PRICES OF FERTILIZER RAW MATERIALS USING LONG SHORT TERM MEMORY." Jurnal Teknik Informatika (Jutif) 3, no. 6 (2022): 1663–73. http://dx.doi.org/10.20884/1.jutif.2022.3.6.433.
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This study uses long short term memory (LSTM) modeling to predict time series data on the price of fertilizer raw materials, namely prilled urea, granular urea, ammonium sulphate((NH4)2SO4), ammonia (NH3), diammonium phosphate((NH4)2HPO4 ), phosphoric acid (H3PO4), phosphate rock (P2O5), NPK 16-16-16, potash, sulfur, and sulfuric acid (H2SO4). Predictions are made based on data that existed in the past using the long short term memory method, which is a derivative of the recurrent neural network. Carry out the evaluation process by looking at the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the model that has been created. The results obtained are quite good, as seen from the root mean square error (RMSE) and mean absolute percentage error (MAPE) which are close to 0 and not too high. Sulfur raw material got the smallest root mean square error (RMSE) with a score of 0.053 and diammonium phosphate raw material got the smallest mean absolute percentage error (MAPE) evaluation value with 2.3%, while the largest value was for the root mean square error (RMSE) of raw materials. Phosphoric acid fertilizer raw material with a value of 22,979 and the largest mean absolute percentage error (MAPE) comes from sulfuric acid fertilizer raw material with a value of 9.180%.
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Purva, Sharma, Saini Deepak, and Saxena Akash. "Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN." Bulletin of Electrical Engineering and Informatics 5, no. 3 (2016): 284–95. https://doi.org/10.11591/eei.v5i3.537.
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Recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN aretested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed that ANN architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network.
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Ganji, Homayoon, and Takamitsu Kajisa. "Error propagation approach for estimating root mean square error of the reference evapotranspiration when estimated with alternative data." Journal of Agricultural Engineering 50, no. 3 (2019): 120–26. http://dx.doi.org/10.4081/jae.2019.909.
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Estimation of reference evapotranspiration (ET0) with the Food and Agricultural Organisation (FAO) Penman-Monteith model requires temperature, relative humidity, solar radiation, and wind speed data. The lack of availability of the complete data set at some meteorological stations is a severe restriction for the application of this model. To overcome this problem, ET0 can be calculated using alternative data, which can be obtained via procedures proposed in FAO paper No.56. To confirm the validity of reference evapotranspiration calculated using alternative data (ET0(Alt)), the root mean square error (RMSE) needs to be estimated; lower values of RMSE indicate better validity. However, RMSE does not explain the mechanism of error formation in a model equation; explaining the mechanism of error formation is useful for future model improvement. Furthermore, for calculating RMSE, ET0 calculations based on both complete and alternative data are necessary. An error propagation approach was introduced in this study both for estimating RMSE and for explaining the mechanism of error formation by using data from a 30-year period from 48 different locations in Japan. From the results, RMSE was confirmed to be proportional to the value produced by the error propagation approach (ΔET0). Therefore, the error propagation approach is applicable to estimating the RMSE of ET0(Alt) in the range of 12%. Furthermore, the error of ET0(Alt) is not only related to the variables’ uncertainty but also to the combination of the variables in the equation.
Dissertations / Theses on the topic "Mean Square Error (RMSE)"
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Thomas, Robin Rajan. "Optimisation of adaptive localisation techniques for cognitive radio." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/27076.
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Spectrum, environment and location awareness are key characteristics of cognitive radio (CR). Knowledge of a user’s location as well as the surrounding environment type may enhance various CR tasks, such as spectrum sensing, dynamic channel allocation and interference management. This dissertation deals with the optimisation of adaptive localisation techniques for CR. The first part entails the development and evaluation of an efficient bandwidth determination (BD) model, which is a key component of the cognitive positioning system. This bandwidth efficiency is achieved using the Cramer-Rao lower bound derivations for a single-input-multiple-output (SIMO) antenna scheme. The performances of the single-input-single-output (SISO) and SIMO BD models are compared using three different generalised environmental models, viz. rural, urban and suburban areas. In the case of all three scenarios, the results reveal a marked improvement in the bandwidth efficiency for a SIMO antenna positioning scheme, especially for the 1×3 urban case, where a 62% root mean square error (RMSE) improvement over the SISO system is observed. The second part of the dissertation involves the presentation of a multiband time-of arrival (TOA) positioning technique for CR. The RMSE positional accuracy is evaluated using a fixed and dynamic bandwidth availability model. In the case of the fixed bandwidth availability model, the multiband TOA positioning model is initially evaluated using the two-step maximum-likelihood (TSML) location estimation algorithm for a scenario where line-of-sight represents the dominant signal path. Thereafter, a more realistic dynamic bandwidth availability model has been proposed, which is based on data obtained from an ultra-high frequency spectrum occupancy measurement campaign. The RMSE performance is then verified using the non-linear least squares, linear least squares and TSML location estimation techniques, using five different bandwidths. The proposed multiband positioning model performs well in poor signal-to-noise ratio conditions (-10 dB to 0 dB) when compared to a single band TOA system. These results indicate the advantage of opportunistic TOA location estimation in a CR environment.<br>Dissertation (MEng)--University of Pretoria, 2012.<br>Electrical, Electronic and Computer Engineering<br>unrestricted
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Degtyarena, Anna Semenovna. "The window least mean square error algorithm." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2385.
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In order to improve the performance of LMS (least mean square) algorithm by decreasing the amount of calculations this research proposes to make an update on each step only for those elements from the input data set, that fall within a small window W near the separating hyperplane surface. This work aims to describe in detail the results that can be achieved by using the proposed LMS with window learning algorithm in information systems that employ the methodology of neural network for the purposes of classification.
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Cui, Xiangchen. "Mean-Square Error Bounds and Perfect Sampling for Conditional Coding." DigitalCommons@USU, 2000. https://digitalcommons.usu.edu/etd/7107.
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In this dissertation, new theoretical results are obtained for bounding convergence and mean-square error in conditional coding. Further new statistical methods for the practical application of conditional coding are developed.
Criteria for the uniform convergence are first examined. Conditional coding Markov chains are aperiodic, π-irreducible, and Harris recurrent. By applying the general theories of uniform ergodicity of Markov chains on genera l state space, one can conclude that conditional coding Markov cha ins are uniformly ergodic and further, theoretical convergence rates based on Doeblin's condition can be found.
Conditional coding Markov chains can be also viewed as having finite state space. This allows use of techniques to get bounds on the second largest eigenvalue which lead to bounds on convergence rate and the mean-square error of sample averages. The results are applied in two examples showing that these bounds are useful in practice.
Next some algorithms for perfect sampling in conditional coding are studied. An application of exact sampling to the independence sampler is shown to be equivalent to standard rejection sampling. In case of single-site updating, traditional perfect sampling is not directly applicable when the state space has large cardinality and is not stochastically ordered, so a new procedure is developed that gives perfect samples at a predetermined confidence interval.
In last chapter procedures and possibilities of applying conditional coding to mixture models are explored. Conditional coding can be used for analysis of a finite mixture model. This methodology is general and easy to use.
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Fodor, Balázs [Verfasser]. "Contributions to Statistical Modeling for Minimum Mean Square Error Estimation in Speech Enhancement / Balázs Fodor." Aachen : Shaker, 2015. http://d-nb.info/1070151815/34.
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Xing, Chengwen, and 邢成文. "Linear minimum mean-square-error transceiver design for amplify-and-forward multiple antenna relaying systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44769738.
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Nicolson, Aaron M. "Deep Learning for Minimum Mean-Square Error and Missing Data Approaches to Robust Speech Processing." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/399974.
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Speech corrupted by background noise (or noisy speech) can cause misinterpretation and fatigue during phone and conference calls, and for hearing aid users. Noisy speech can also severely impact the performance of speech processing systems such as automatic speech recognition (ASR), automatic speaker verification (ASV), and automatic speaker identification (ASI) systems. Currently, deep learning approaches are employed in an end-to-end fashion to improve robustness. The target speech (or clean speech) is used as the training target or large noisy speech datasets are used to facilitate multi-condition training. In this dissertation, we propose competitive alternatives to the preceding approaches by updating two classic robust speech processing techniques using deep learning. The two techniques include minimum mean-square error (MMSE) and missing data approaches. An MMSE estimator aims to improve the perceived quality and intelligibility of noisy speech. This is accomplished by suppressing any background noise without distorting the speech. Prior to the introduction of deep learning, MMSE estimators were the standard speech enhancement approach. MMSE estimators require the accurate estimation of the a priori signal-to-noise ratio (SNR) to attain a high level of speech enhancement performance. However, current methods produce a priori SNR estimates with a large tracking delay and a considerable amount of bias. Hence, we propose a deep learning approach to a priori SNR estimation that is significantly more accurate than previous estimators, called Deep Xi. Through objective and subjective testing across multiple conditions, such as real-world non-stationary and coloured noise sources at multiple SNR levels, we show that Deep Xi allows MMSE estimators to produce the highest quality enhanced speech amongst all clean speech magnitude spectrum estimators. Missing data approaches improve robustness by performing inference only on noisy speech features that reliably represent clean speech. In particular, the marginalisation method was able to significantly increase the robustness of Gaussian mixture model (GMM)-based speech classification systems (e.g. GMM-based ASR, ASV, or ASI systems) in the early 2000s. However, deep neural networks (DNNs) used in current speech classification systems are non-probabilistic, a requirement for marginalisation. Hence, multi-condition training or noisy speech pre-processing is used to increase the robustness of DNN-based speech classification systems. Recently, sum-product networks (SPNs) were proposed, which are deep probabilistic graphical models that can perform the probabilistic queries required for missing data approaches. While available toolkits for SPNs are in their infancy, we show through an ASI task that SPNs using missing data approaches could be a strong alternative for robust speech processing in the future. This dissertation demonstrates that MMSE estimators and missing data approaches are still relevant approaches to robust speech processing when assisted by deep learning.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Eng & Built Env<br>Science, Environment, Engineering and Technology<br>Full Text
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Septarina, Septarina. "Micro-Simulation of the Roundabout at Idrottsparken Using Aimsun : A Case Study of Idrottsparken Roundabout in Norrköping, Sweden." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79964.
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Microscopic traffic simulation is useful tool in analysing traffic and estimating the capacity and level of service of road networks. In this thesis, the four legged Idrottsparken roundabout in the city of Norrkoping in Sweden is analysed by using the microscopic traffic simulation package AIMSUN. For this purpose, data regarding traffic flow counts, travel times and queue lengths were collected for three consecutive weekdays during both the morning and afternoon peak periods. The data were then used in model building for simulation of traffic of the roundabout. The Root Mean Square Error (RMSE) method is used to get the optimal parameter value between queue length and travel time data and validation of travel time data are carried out to obtain the basic model which represents the existing condition of the system. Afterward, the results of the new models were evaluated and compared to the results of a SUMO model for the same scenario model. Based on calibrated and validated model, three alternative scenarios were simulated and analysed to improve efficiency of traffic network in the roundabout. The three scenarios includes: (1) add one free right turn in the north and east sections; (2) add one free right turn in the east and south sections; and (3) addition of one lane in roundabout. The analysis of these scenarios shows that the first and second scenario are only able to reduce the queue length and travel time in two or three legs, while the third scenario is not able to improve the performance of the roundabout. In this research, it can be concluded that the first scenario is considered as the best scenario compared to the second scenario and the third scenario. The comparison between AIMSUN and SUMO for the same scenario shows that the results have no significance differences. In calibration process, to get the optimal parameter values between the model measurements and the field measurements, both of AIMSUN and SUMO uses two significantly influencing parametersfor queue and travel time. AIMSUN package uses parameter of driver reaction time and the maximum acceleration, while SUMO package uses parameter of driver imperfection and also the driver rection time.
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Nassr, Husam, and Kurt Kosbar. "PERFORMANCE EVALUATION FOR DECISION-FEEDBACK EQUALIZER WITH PARAMETER SELECTION ON UNDERWATER ACOUSTIC COMMUNICATION." International Foundation for Telemetering, 2017. http://hdl.handle.net/10150/626999.
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This paper investigates the effect of parameter selection for the decision feedback equalization (DFE) on communication performance through a dispersive underwater acoustic wireless channel (UAWC). A DFE based on minimum mean-square error (MMSE-DFE) criterion has been employed in the implementation for evaluation purposes. The output from the MMSE-DFE is input to the decoder to estimate the transmitted bit sequence. The main goal of this experimental simulation is to determine the best selection, such that the reduction in the computational overload is achieved without altering the performance of the system, where the computational complexity can be reduced by selecting an equalizer with a proper length. The system performance is tested for BPSK, QPSK, 8PSK and 16QAM modulation and a simulation for the system is carried out for Proakis channel A and real underwater wireless acoustic channel estimated during SPACE08 measurements to verify the selection.
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Ding, Minhua. "Multiple-input multiple-output wireless system designs with imperfect channel knowledge." Thesis, Kingston, Ont. : [s.n.], 2008. http://hdl.handle.net/1974/1335.
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Thompson, Grant. "Effects of DEM resolution on GIS-based solar radiation model output: A comparison with the National Solar Radiation Database." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1258663688.
Full textBooks on the topic "Mean Square Error (RMSE)"
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Clements, Michael P. On the limitations of comparing mean square forecast error. Oxford University, Institute of Economics and Statistics, 1992.
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G, Kalit, and Ames Research Center, eds. Mean-square error bounds for reduced-order linear state estimators. National Aeronautics and Space Administration, Ames Research Center, 1987.
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G, Kalit, and Ames Research Center, eds. Mean-square error bounds for reduced-order linear state estimators. National Aeronautics and Space Administration, Ames Research Center, 1987.
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Baram, Yoram. Mean-square error bounds for reduced-order linear state estimators. National Aeronautics and Space Administration, Ames Research Center, 1987.
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Hoque, Asraul. The exact multiperiod mean-square forecast error for the first-order autoregressive model. London School of Economics, 1986.
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Magnus, Jan R. The exact multiperiod mean-square forecast error for the first-order autoregressive modelwith an intercept. National Institute of Economic and Social Research, 1988.
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Magnus, Jan R. The exact multiperiod mean-square forecast error for the first-order autoregressive model with an intercept. International Centre for Economics and Related Disciplines, 1988.
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Cardot, Hervé, and Pascal Sarda. Functional Linear Regression. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.2.
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This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account a penalized least squares minimization problem. The article proceeds by discussing the main asymptotic properties separating results on mean square prediction error and results on L2 estimation error. It also describes some related models, including generalized functional linear models and FLR on quantiles, and concludes with a complementary bibliography and some open problems.
Book chapters on the topic "Mean Square Error (RMSE)"
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Poorahad Anzabi, Pooria, Mahmoud R. Shiravand, and Shima Mahboubi. "Machine Learning-Aided Prediction of Seismic Response of RC Bridge Piers Exposed to Chloride-Induced Corrosion." In Lecture Notes in Civil Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-69626-8_118.
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AbstractDifferent environmental issues such as carbonation and corrosion due to chloride threaten aging reinforced concrete (RC) bridges that are in service in areas highly prone to corrosion and earthquakes. Significant experimental and numerical efforts have been put into scrutinizing the effect of corrosion on nonlinear behavior of structural elements. With the rapid development of artificial intelligence, useful methods are now provided to allow for the assessment of such bridges without the drawbacks and limitations of the experimental and numerical methods. In this paper, four machine learning (ML) algorithms are employed; linear regression (LR), decision tree (DT), random forest (RF), and XGBoost for data fitting of the models, and Bayesian search is used for optimization of hyperparameters. Numerical models of RC piers with stochastic parameters defining geometry, loading, and materials are built, and the degradation due to corrosion is applied with a randomly determined level of corrosion. Then, the corroded models are nonlinearly analyzed with random ground motions scaled to design-based and maximum credible earthquake spectra, and maximum drift ratios are stored. Using the created database, different ML models are compared to find the most accurate one. R-squared, mean absolute error, mean squared error, and root mean squared error metrics are considered as the criteria for the selection of the most accurate model. LR model with R2 = 0.53, MAE = 0.0026, mean squared error (MSE) = 1.4 × 10−5, and root mean squared error (RMSE) = 0.0036 has the lowest accuracy while XGBoost with R2 = 0.8, MAE = 0.0015, MSE = 5 × 10−6, and RMSE = 0.0028 is the most accurate model. DT and RF models with R2 = 0.7 and R2 = 0.73, respectively, are in between.
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Li, Jingyi, and Hong Chen. "Optimization and Prediction of Design Variables Driven by Building Energy Performance—A Case Study of Office Building in Wuhan." In Proceedings of the 2020 DigitalFUTURES. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4400-6_22.
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AbstractThis research focuses on the energy performance of office building in Wuhan. The research explored and predicted the optimal solution of design variables by Multi-Island Genetic Algorithm (MIGA) and RBF Artificial neural networks (RBF-ANNs). Research analyzed the cluster centers of design variable by K-means cluster method. In the study, the RBF-ANNs model was established by 1,000 simulation cases. The RMSE (root mean square error) of the RBF-ANNs model in different energy aspects does not exceed 15%. Comparing to the reference case (the largest energy consumption case in the optimization), the 214 elite cases in RBF-ANNs model save at least 37.5% energy. By the cluster centers of the design variables in the elite cases, the study summarized the benchmark of 14 design variables and also suggested a building energy guidance for Wuhan office building design.
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Faiem, Nabid, Tunc Asuroglu, Koray Acici, Antti Kallonen, and Mark van Gils. "Assessment of Parkinson’s Disease Severity Using Gait Data: A Deep Learning-Based Multimodal Approach." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_3.
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AbstractThe ability to regularly assess Parkinson’s disease (PD) symptoms outside of complex laboratories supports remote monitoring and better treatment management. Multimodal sensors are beneficial for sensing different motor and non-motor symptoms, but simultaneous analysis is difficult due to complex dependencies between different modalities and their different format and data properties. Multimodal machine learning models can analyze such diverse modalities together, thereby enhancing holistic understanding of the data and overall patient state. The Unified Parkinson’s Disease Rating Scale (UPDRS) is commonly used for PD symptoms severity assessment. This study proposes a Perceiver-based multimodal machine learning framework to predict UPDRS scores.We selected a gait dataset of 93 PD patients and 73 control subjects from the PhysioNet repository. This dataset includes two-minute walks from each participant using 16 Ground Reaction Force (GRF) sensors, placing eight on each foot. This experiment used both raw gait timeseries signals and extracted features from these GRF sensors. The Perceiver architecture’s hyperparameters were selected manually and through Genetic Algorithms (GA). The performance of the framework was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and linear Correlation Coefficient (CC).Our multimodal approach achieved a MAE of 2.23 ± 1.31, a RMSE of 5.75 ± 4.16 and CC of 0.93 ± 0.08 in predicting UPDRS scores, outperforming previous studies in terms of MAE and CC.This multimodal framework effectively integrates different data modalities, in this case illustrating by predicting UPDRS scores using sensor data. It can be applied to diverse decision support applications of similar natures where multimodal analysis is needed.
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Yizhuo, Wang, Li Zhonlian, Li Long, Li Runhua, Cui Xinglei, and Fang Zhi. "Prediction and Evaluation Method of Modification Effect of Large-Scale DBD Insulation Materials Based on Distributed Current Measurement and Neural Network Model." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4856-6_8.
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Abstract Wide dielectric barrier discharge (DBD) has broad application prospects in the modification of insulating materials, but the aging of the electrode directly affects the modification effect in the application process. As the size of the DBD device increases, the real-time evaluation of its modification effect becomes more complicated. Therefore, this paper proposes a real-time prediction and evaluation method for the modification effect of wide DBD insulation materials based on distributed current measurement and neural network model. The operating condition parameters such as DBD excitation voltage amplitude, repetition frequency, discharge working gas flow rate and reaction medium flow rate were changed. The discharge current at different positions was measured by self-made current coil. The water contact angle and flashover voltage at the corresponding position were tested experimentally as the evaluation criteria of the modification effect. The feature extraction of distributed current is carried out by manual and image recognition methods, and the prediction and evaluation models are established by BP neural network and convolutional neural network (CNN) respectively. The accuracy and generalization ability of the two models are compared. The results show that the CNN model based on image recognition has higher accuracy and generalisation ability in predicting the water contact angle and flashover voltage on the material surface compared to the BP neural network model based on manual feature extraction. Compared with the BP neural network, the CNN model reduces the mean absolute error (MAE) by 41.3% and the root mean square error (RMSE) by 36.1% in predicting the water contact angle of the material surface, and the mean absolute error (MAE) reduces by 47.7% and the RMSE reduces by 40.2% in the flashover voltage. The experimental results with different processing distances are used to examine the generalisation ability of the two models, and the results show that the generalisation ability of the CNN model based on image recognition is better than that of the BP neural network. This study is of great reference significance for real-time online diagnosis and industrial application of DBD material modification.
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Akanmu, Abiola, Adedeji Afolabi, and Akinwale Okunola. "Predicting Mental Workload of Using Exoskeletons for Construction Work: A Deep Learning Approach." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.69.
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Exoskeletons are gaining attention as a potential solution for addressing low back injury in the construction industry. However, use of active back-support exoskeletons in construction can trigger unintended consequences which could increase mental workload of users while working with exoskeletons. Prolonged increase in mental workload could impact workers’ wellbeing and productivity. Prediction of mental workload during exoskeleton-use could inform strategies to mitigate the triggers. This study investigates a machine-learning framework for predicting mental workload of workers while using active back-support exoskeletons for construction work. Laboratory experiments were conducted wherein Electroencephalography (EEG) data were collected from participants wearing active back-support exoskeletons to perform flooring task. The EEG data underwent preprocessing, including band filtering, notch filtering, and independent component analysis, to remove artifacts and ensure data quality. A regression-based Long Short-Term Memory network was trained to forecast future time steps of the processed EEG data. The performance of the network was evaluated using root mean square error (RMSE) and r-squared (R2). A RMSE of 0.1527 and R2 of 0.9665 indicating good fit and strong correlation, respectively, were observed between the predicted and actual EEG data. Results of the comparison between the actual and predicted mental workload also show strong correction with an R2 of 0.8692. The findings motivate research directions into real-time monitoring of mental workload of workers during exoskeleton-use. The study has significant implications for stakeholders, enabling them to gain a deeper understanding of the impact of mental workload while using exoskeletons thereby providing opportunities for mitigation
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Akanmu, Abiola, Adedeji Afolabi, and Akinwale Okunola. "Predicting Mental Workload of Using Exoskeletons for Construction Work: A Deep Learning Approach." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.69.
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Exoskeletons are gaining attention as a potential solution for addressing low back injury in the construction industry. However, use of active back-support exoskeletons in construction can trigger unintended consequences which could increase mental workload of users while working with exoskeletons. Prolonged increase in mental workload could impact workers’ wellbeing and productivity. Prediction of mental workload during exoskeleton-use could inform strategies to mitigate the triggers. This study investigates a machine-learning framework for predicting mental workload of workers while using active back-support exoskeletons for construction work. Laboratory experiments were conducted wherein Electroencephalography (EEG) data were collected from participants wearing active back-support exoskeletons to perform flooring task. The EEG data underwent preprocessing, including band filtering, notch filtering, and independent component analysis, to remove artifacts and ensure data quality. A regression-based Long Short-Term Memory network was trained to forecast future time steps of the processed EEG data. The performance of the network was evaluated using root mean square error (RMSE) and r-squared (R2). A RMSE of 0.1527 and R2 of 0.9665 indicating good fit and strong correlation, respectively, were observed between the predicted and actual EEG data. Results of the comparison between the actual and predicted mental workload also show strong correction with an R2 of 0.8692. The findings motivate research directions into real-time monitoring of mental workload of workers during exoskeleton-use. The study has significant implications for stakeholders, enabling them to gain a deeper understanding of the impact of mental workload while using exoskeletons thereby providing opportunities for mitigation
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Chen, Guanhua, and Xinqi Gong. "The Application of Time Series Analysis in the Fiscal Budget Variance of China." In Financial Mathematics and Fintech. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2366-3_12.
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AbstractDuring the process of budget planning and execution, irregular behaviors will be reflected in the level of the difference between budgeted and actual figures (named budget variance). Considering that these two processes are both led by Government Of China (hereinafter called GOC), the budget variance is widely used to evaluate the fiscal system. This chapter collects State General Public Budget data from 2000 to 2018 and analyzes their influence on budget variance. Then the forecast for budget variance is completed by modeling the budget execution and budget variance rate separately. The descriptive analysis and AIC (Akaike Information Criterion) contributes to decide the candidate model, the RMSE (Root Mean Square Error) on test data is used to select the final optimal model. The forecast shows that the extent of budget variance will be further controlled in 2011 and 2012, this chapter explains the result with fiscal theories to enhance the credibility of it and thereby provides a couple of policy advice on Chinese budget reform.
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Levinson, Norman. "The Wiener RMS (Root Mean Square) Error Criterion in Filter Design and Prediction." In Selected Papers of Norman Levinson. Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-5335-8_16.
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Ariyarathna, Imaya, and Katsuo Sasahara. "Procedure of Data Processing for the Improvement of Failure Time Prediction of a Landslide Based on the Velocity and Acceleration of the Displacement." In Progress in Landslide Research and Technology. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44296-4_14.
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AbstractTime prediction methods based on monitoring surface displacement (SD) are effective for early warning against shallow landslides. However, failure time prediction by Fukuzono’s original inverse-velocity (INV) method is less accurate due to variation in the inverse-velocity (1/v) caused by noise in the measured SD, which amplifies the fluctuation in the resultant 1/v. Therefore, the present study incorporates pre-analysis to acquire better prediction by reducing the effect of noise on the measured SD. The data extraction (DE) and moving average (MA) methods are used to filter the measured SD for better smoothing of 1/v. The root mean square error (RMSE) and determining factor (f) values are used to select the optimum SD interval (Δx) in the DE method. The RMSE and f values are used to evaluate the reproducibility of the measured data and the scattering in the relationship between velocity and acceleration in an orderly. The data, treated by the DE and MA methods, are utilized to predict the failure time based on the INV method and the relationship between velocity and acceleration on a logarithmic scale (VAA) method. Accordingly, Δx gives the smallest sum of the normalized RMSE and normalized (1-f), which offers a better prediction. When the SD at failure changes, Δx is changed. The best prediction is obtained by DE preprocessing with the VAA method because it minimizes the effect of the individual 1/v by reducing the scatter in the relationship between velocity and acceleration. However, the time prediction using data processed by the MA method shows poor prediction due to some scattering of the inverse velocity. In some cases, the prediction by the VAA method using MA data provides better prediction than the results of the INV method.
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Adenuga, Olukorede Tijani, Khumbulani Mpofu, and Ragosebo Kgaugelo Modise. "Application of ARIMA-LSTM for Manufacturing Decarbonization Using 4IR Concepts." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_12.
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AbstractIncreasing climate change concerns call for the manufacturing sector to decarbonize its process by introducing a mitigation strategy. Energy efficiency concepts within the manufacturing process value chain are proportional to the emission reductions, prompting decision makers to require predictive tools to execute decarbonization solutions. Accurate forecasting requires techniques with a strong capability for predicting automotive component manufacturing energy consumption and carbon emission data. In this paper we introduce a hybrid autoregressive moving average (ARIMA)-long short-term memory network (LSTM) model for energy consumption forecasting and prediction of carbon emission within the manufacturing facility using the 4IR concept. The method could capture linear features (ARIMA) and LSTM captures the long dependencies in the data from the nonlinear time series data patterns, Root means square error (RMSE) is used for data analysis comparing the performance of ARIMA which is 448.89 as a single model with ARIMA-LSTM hybrid model as actual (trained) and predicted (test) 59.52 and 58.41 respectively. The results depicted RMSE values of ARIMA-LSTM being extremely smaller than ARIMA, which proves that hybrid ARIMA-LSTM is more suitable for prediction than ARIMA.
Conference papers on the topic "Mean Square Error (RMSE)"
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Jadoon, Usman Khan, Ismael D�az, and Manuel Rodr�guez. "Comparative Assessment of Aspen Plus Modeling Strategies for Biomass Steam Co-gasification." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.124830.
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The urgent need for sustainable energy drives the exploration of biomass and plastic waste co-gasification, a promising route for producing clean fuels and chemicals, reducing greenhouse gas emissions, and minimizing fossil fuel dependence. Modeling and simulation are vital for optimizing this process, particularly syngas yield, yet comparative studies on Aspen Plus modeling techniques for steam co-gasification are limited. This research addresses this gap by comparing three Aspen Plus strategies: thermodynamic equilibrium modeling (TEM), restricted thermodynamic modeling (RTM), and kinetic modeling (KM), for simulating the co-gasification of pine sawdust and polyethene (PE) with steam in bubbling fluidized bed gasifier (BFBG). The primary objective is to evaluate the effectiveness of each strategy in predicting the syngas composition under varying conditions. Three models were developed in Aspen Plus on the basis of each strategy, and their predicted syngas compositions were compared with published experimental data. A detailed sensitivity analysis was performed within the RTM framework to obtain optimized values of the approach temperatures to enhance predictive accuracy. The results showed that RTM provided the highest precision, with an average root mean square error (RMSE) of 0.0296, while TEM showed the lowest precision with RMSE of 0.1234. KM performed moderately with an RMSE of 0.0929. These findings demonstrate that RTM is superior for predicting the syngas composition for this co-gasification process, and its optimized solution presents a viable alternative when detailed kinetic data are lacking, thus reducing computational expense.
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Sinha, Tanaya, Mahmoud Hayajnh, and J. V. R. Prasad. "Development of Rotor Control Equivalent Gust Input (RCEGI) Models." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-292.
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This study investigates the application of neural network architectures to predict control inputs required to replicate rotorcraft responses under vertical gust disturbances. Two modeling approaches are developed: the Control Equivalent Gust Input (CEGI) model, using body-axis inputs and the Rotor Control Equivalent Gust Input (RCEGI) model using rotor-specific inputs. Initial models employed single-input single-output (SISO) LSTM networks, which demonstrated limitations in capturing transient behavior and exhibited delay in predicted control inputs. By incorporating multiple vehicle response features and increasing the number of hidden neurons, multiple-input single-output (MISO) architectures significantly improved accuracy and reduced Root Mean Square Error (RMSE). Further enhancement was achieved by implementing bidirectional LSTM (BiLSTM) layers, which reduced both delay and transient error. Comparisons with inverted linear time-invariant (LTI) approximations showed that neural networks provided superior performance, particularly in modeling nonlinear dynamics. The results highlight the potential of deep learning approaches to improve the accuracy of control input mapping and inform real-time control strategies in unsteady flight environments.
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Oliveira Junior, Adair da Silva, Marcio Carneiro Brito Pache, Fábio Prestes Cesar Rezende, et al. "An Investigation of Parameter Optimization in Fingerling Counting Problems." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wvc.2021.18881.
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The objective of this paper is to investigate which combination of parameters for the fingerling counting software results in the smallest Mean Absolute Error (MAE) and smallest Root Mean Squared Error (RMSE). For this, an image dataset called FISHCV155V was created and separated into training and test sets, where different combinations of parameters for the software were tested. From the obtained results were extracted individual performance metrics for each combination of parameters, such as MAE, Mean Square Error (MSE) and RMSE. Video frames were analysed comparing the parameter combination that obtained the best and worst results, in order to investigate the influence of such parameters in the performance of the software. From such results, it was concluded that the best combination reached 5.99 MAE and 9.96 RMSE.
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Narayan, Subrahmanya Keremane, Viren S. Ram, and Rajshekhar Gannavarpu. "Conditional generative modelling based fringe pattern normalization." In 3D Image Acquisition and Display: Technology, Perception and Applications. Optica Publishing Group, 2023. http://dx.doi.org/10.1364/3d.2023.jw2a.25.
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In this article, we propose a generative adversarial network based fringe pattern normalization method. We investigate the method's effectiveness under various noise levels by evaluating root mean square error (RMSE) and structural similarity index measure (SSIM).
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Gavrilenko, A. D. "DEVELOPMENT OF A MODEL FOR PREDICTING PROTEIN DENATURATION TEMPERATURE BY MACHINE LEARNING METHODS." In OpenBio-2023. ИПЦ НГУ, 2023. http://dx.doi.org/10.25205/978-5-4437-1526-1-12.
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he protein language neural network model ESM-2 and the neural network regression model TabNet. ESM-2 is used to calculate vector representations of amino acid sequences, based on which the melting temperature is predicted using TabNet. Comparison of the developed method with one of the best existing methods, ProTstab2, showed that PMTPred had a lower prediction error. The root mean square error (RMSE) between the observed and predicted Tm values using PMTPred was 5,76 °C, while for ProTstab2 the RMSE value was 9,09 °C.
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Effiong, Augustine James, Joseph Okon Etim, and Anietie Ndarake Okon. "Artificial Intelligence Model for Predicting Formation Damage in Oil and Gas Wells." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/207129-ms.
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Abstract An artificial neural network (ANN) was developed to predict skin, a formation damage parameter in oil and gas drilling, well completion and production operations. Four performance metrics: goodness of fit (R2), mean square error (MSE), root mean square error (RMSE), average absolute percentage relative error (AAPRE), was used to check the performance of the developed model. The results obtained indicate that the model had an overall MSE of 355.343, RMSE of 18.850, AAPRE of 4.090 and an R2 of 0.9978. All the predictions agreed with the measured result. The generalization capacity of the developed ANN model was assessed using 500 randomly generated datasets that were not part of the model training process. The results obtained indicate that the developed model predicted 97% of these new datasets with an MSE of 375.021, RMSE of 19.370, AAPRE of 6.090 and R2 of 0.9731, while Standing (1970) equation resulted in R2of −0.807, MSE of 9.34×1016, AAPRE of 3.10×106 and RMSE of 4.10×105. The relative importance analysis of the model input parameters showed that the flow rates (q), permeability (k), porosity (φ) and pressure drop (Δp) had a significant impact on the skin (S) values estimated from the downhole. Thus, the developed model if embedded in a downhole (sensing) tool that capture these basic or required reservoir parameters: pressure, flowrate, permeability, viscosity, and thickness, would eliminate the diagnostic approach of estimating skin factor in the petroleum industry.
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Sabancioglu, Mert, Mustafa Demirci, and Yunus Ziya Kaya. "Estimation of Beginning Points of Cross-Shore Sandbars Using Artificial Neural Network." In Air and Water – Components of the Environment 2025 Conference Proceedings. Casa Cărţii de Ştiinţă, 2025. https://doi.org/10.24193/awc2025_06.
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Sediment transport is critical for the design of coastal structures. In this paper, beginning points of cross-shore sandbars predicted using artificial neural network (ANN), multi-linear regression (MLR), and Quadratic-Multivariable Regression (Q-MR). The dataset was obtained as a result of a physical model . In experiments, 3 different bed slopes and 5 different grain sizes were used. Bed slope, grain size, wave period, and wave steepness were used as independent variables. The dependent variable was the beginning point of cross-shore sandbars (Xb). Mean Average Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) results, and correlation and regression results were checked to compare the created models. When the results were compared, it was concluded that the ANN model gave better results than traditional statistical methods.
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Šošić, Darko, Mileta Žarković, and Goran Dobrić. "THE FORECAST OF MEDIUM-VOLTAGE FEEDER LOAD USING NEURAL NETWORK AND CLUSTERING." In 14. Savetovanje o elektrodistributivnim mrežama Srbije, sa regionalnim učešćem. CIRED Liaison Committee of Serbia, 2024. http://dx.doi.org/10.46793/cired24.r-5.01ds.
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This paper presents a model for forecasting the load of medium-voltage distribution network feeders. The model conducts load forecasting with a 15-minute resolution, employing a neural network. Meteorological data relevant to the analyzed location and data on previous electrical energy demand at the observed feeders were utilized for training the model. To enhance forecasting precision, characteristic load diagrams for the observed feeders were established. The first group comprises load diagrams corresponding to typical working and non-working days for each month. The second group of diagrams used in neural network training includes those for working and nonworking days within each season, with the year divided into three seasons (winter, spring/autumn, and summer). Data division into training, validation, and testing groups occurred following clustering using the k-means method, with the number of clusters determined based on the Davis Bouldin index. Forecast accuracy was evaluated using standard statistical measures: MAPE (Mean Absolute Percentage Error), RMSE (Root Mean Square Deviation), CV-RMSE (Coefficient of Variation of RMSE). In this paper, one medium-voltage feeder supplying consumers with central heating will be considered.
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Adeleke, Oluwatobi, and Tien-Chien Jen. "Prediction of Electrical Energy Consumption in University Campus Residence Using FCM-Clustered Neuro-Fuzzy Model." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-96793.
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Abstract Developing a viable data-driven policy for the management of electrical-energy consumption in campus residences is contingent on the proper knowledge of the electricity usage pattern and its predictability. In this study, an adaptive neuro-fuzzy inference systems (ANFIS) was developed to model the electrical energy consumption of students’ residence using the University of Johannesburg, South Africa as a case study. The model was developed based on the environmental conditions vis-à-vis meteorological parameters namely temperature, wind speed, and humidity of the respective days as the input variables while electricity consumption (kWh) was used as the output variable. The fuzzy c-means (FCM) is a type of clustering technique that is preferred owing to its speed boost capacity. The best FCM-clustered ANFIS-model based on a range of 2–10 clusters was selected after evaluating their performance using relevant statistical metrics namely; mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute deviation (MAD). FCM-ANFIS with 7 clusters outperformed all other models with the least error and highest accuracy. The RMSE, MAPE, MAD, and R2-values of the best models are 0.043, 0.65, 1.051, and 0.9890 respectively. The developed model will assist in optimizing energy consumption and assist in designing and sizing alternative energy systems for campus residences.
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Satimehin, A. A., M. O. Oluwamukomi, V. N. Enujiugha, and M. Bello. "Drying characteristics and mathematical modelling of the drying kinetics of oyster mushroom (Pleurotus ostreatus)." In 21st International Drying Symposium. Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/ids2018.2018.7847.
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This study was conducted to determine the drying characteristics of oyster mushroom (Pleurotus ostreatus) at 50, 60 and 70 °C. Pleurotus ostreatus were cleaned and dried in a laboratory cabinet dryer. The drying data were fitted to six model equations namely Newton, Pabis and Henderson, Logarithmic, Two-term diffusion, Wang and Singh, as well as Modified Henderson and Pabis equations. The goodness of fit of the models were evaluated by means of the coefficient of determination (R2), root mean square error (RMSE) and reduced chi-square (χ2). The Logarithmic model best describes the drying data and could be used to predict its drying behaviour. Keywords: oyster mushroom; thin-layer drying; characteristics; modelling
Reports on the topic "Mean Square Error (RMSE)"
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Fisher, Andmorgan, Taylor Hodgdon, and Michael Lewis. Time-series forecasting methods : a review. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49450.
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Time-series forecasting techniques are of fundamental importance for predicting future values by analyzing past trends. The techniques assume that future trends will be similar to historical trends. Forecasting involves using models fit on historical data to predict future values. Time-series models have wide-ranging applications, from weather forecasting to sales forecasting, and are among the most effective methods of forecasting, especially when making decisions that involve uncertainty about the future. To evaluate forecast accuracy and to compare among models fitted to a time series, three performance measures were used in this study: mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE).
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สิริภัทราวรรณ, อุบลรัตน์, та สุวัสสา พงษ์อำไพ. การประเมินอายุการเก็บแบบรวดเร็วของผลิตภัณฑ์อาหารแปรรูปโดยใช้ NIR specytoscopy และ Chemometrics : รายงานการวิจัย. จุฬาลงกรณ์มหาวิทยาลัย, 2014. https://doi.org/10.58837/chula.res.2014.58.
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งานวิจัยนี้พัฒนาการประเมินคุณภาพและอายุการเก็บของผลิตภัณฑ์อาหารแปรรูปพร้อมบริโภค ด้วยวิธี แบบรวดเร็วโดยใช้ NIR spectroscopy ร่วมกับ chemometrics ทำโดยเตรียมผลิตภัณฑ์ไส้กรอกหมูบรรจุใน ถุงพลาสติกภายใต้ภาวะสุญญากาศและเก็บรักษาที่อุณหภูมิ 4 °C ติดตามการเปลี่ยนแปลงคุณภาพทางเคมี (ค่า pH) ทางกายภาพ (ค่าแรงตัดขาด และ ค่าสี) ทางจุลินทรีย์ (จำนวนแบคทีเรียทั้ง หมด และ แบคทีเรียแลกติก) และทาง ประสาทสัมผัส (odor, color, appearance และ overall acceptability) ของผลิตภัณฑ์ในระหว่างการเก็บรักษา รวมทั้งวิเคราะห์โดยใช้ near infrared spectroscopy จากผลการทดลองพบว่า ผลิตภัณฑ์มีค่า pH ค่าแรงตัดขาด ค่าสี (L* (ความเข้ม-สว่าง), a* (เขียว-แดง) และ b* (น้ำเงิน-เหลือง)) ลดลง เมื่อระยะเวลาการเก็บเพิ่มขึ้น จากการวัดการเปลี่ยนแปลงคุณภาพทางจุลินทรีย์ของผลิตภัณฑ์ พบว่า จำนวนแบคทีเรียทั้ง หมด เพิ่มขึ้น เมื่อ ระยะเวลาการเก็บรักษาเพิ่มขึ้น เมื่อเก็บรักษาผลิตภัณฑ์ไว้นาน 8 วัน จำนวนแบคทีเรียแลกติกจึงเพิ่มสูงขึ้น และเมื่อ เก็บรักษานาน 16 วัน พบว่าการเพิ่มของจำนวนจุลินทรีย์ส่งผลให้ ค่าแรงตัดขาด ค่าสี ลดลง นอกจากนี้ยังพบว่าค่า pH ที่ลดลงเป็นผลมาจากปริมาณ แบคทีเรียแลกติกที่เพิ่มขึ้น ซึ่งส่งผลต่อการยอมรับด้านประสาทสัมผัสของผู้บริโภค จากการประเมินคุณภาพทางประสาทสัมผัสของผลิตภัณฑ์ พบว่า เมื่อระยะเวลาการเก็บเพิ่มขึ้นตัวอย่างมีการเปลี่ยนแปลงของกลิ่น (off-odor) ลักษณะปรากฏ (slimy appearance) และสี (off-color) เพิ่มมากขึ้น ส่วนค่าการ ยอมรับโดยรวม (overall acceptability) มีคะแนนลดลง โดยการเปลี่ยนแปลงดังกล่าวเพิ่มมากขึ้น เมื่อระยะเวลาการ เก็บนานขึ้น ซึ่งเป็ นผลมาจากการเจริญของจุลินทรีย์ที่เพิ่มขึ้น ในระหว่างการเก็บรักษา และพบว่าผลคะแนนทาง ประสาทสัมผัสสอดคล้องกับค่าการเปลี่ยนแปลงทางเคมี และทางกายภาพของผลิตภัณฑ์ สำหรับการวัดการเปลี่ยนแปลงคุณภาพผลิตภัณฑ์ระหว่างการเก็บรักษาด้วย NIR spectroscopy โดยวัดค่า reflectance ในช่วงความยาวคลื่น 400-1000 nm ปรับแต่งสเปคตรัมด้วยวิธี Savitzky-Golay 2nd derivatives ซึ่ง เป็นวิธีที่เหมาะสม จากนั้น วิเคราะห์ข้อมูลด้วย chemometrics โดยใช้ principal component analysis (PCA) ใน การลดจำนวนข้อมูล และจำแนกตัวอย่างที่มีคุณภาพแตกต่างกัน พบว่าการใช้ NIR spectroscopy ร่วมกับ PCA สามารถจัดจำแนกตัวอย่างเป็นกลุ่ม (clusters) ที่มีคุณภาพแตกต่างกันตามระยะเวลาการเก็บรักษาได้ การทำนาย จำนวนโคโลนีของแบคทีเรียทั้งหมดที่เจริญบนผลิตภัณฑ์จากค่า reflectance ที่ได้จาก NIR spectroscopy ทำโดยใช้ partial least square regression (PLSR) พบว่าการใช้ PLSR ให้ค่า coefficient of determination (R2) เป็น 0.85 และ 0.81 สำหรับการ calibration และ validation ตามลำดับ และค่า root mean square error of calibration (RMSEC) และ root mean square error of validation (RMSEV) เป็น 1.88 และ 1.22 Log (CFU/g) ตามลำดับ จากผลการวิเคราะห์จะเห็นได้ว่า NIR spectroscopy ร่วมกับ PLSR สามารถใช้ในการ ทำนายการเจริญของแบคทีเรียทัง้ หมดที่เจริญบนผลิตภัณฑ์ และมีความถูกต้องสูง
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Konsam, Manis Kumar, Amanda Thounajam, Prasad Vaidya, Gopikrishna A, Uthej Dalavai, and Yashima Jain. Machine Learning-Enhanced Control System for Optimized Ceiling Fan and Air Conditioner Operation for Thermal Comfort. Indian Institute for Human Settlements, 2024. http://dx.doi.org/10.24943/mlcsocfacotc6.2023.
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This paper proposes and tests the implementation of a sustainable cooling approach that uses a machine learning model to predict operative temperatures, and an automated control sequence that prioritises ceiling fans over air conditioners. The robustness of the machine learning model (MLM) is tested by comparing its prediction with that of a straight-line model (SLM) using the metrics of Mean Bias Error (MBE) and Root Mean Squared Error (RMSE). This comparison is done across several rooms to see how each prediction method performs when the conditions are different from those of the original room where the model was trained. A control sequence has been developed where the MLM’s prediction of Operative Temperature (OT) is used to adjust the adaptive thermal comfort band for increased air speed delivered by the ceiling fans to maintain acceptable OT. This control sequence is tested over a two-week period in two different buildings by comparing it with a constant air temperature setpoint (24ºC).
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Brodie, Katherine, Brittany Bruder, Richard Slocum, and Nicholas Spore. Simultaneous mapping of coastal topography and bathymetry from a lightweight multicamera UAS. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41440.
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A low-cost multicamera Unmanned Aircraft System (UAS) is used to simultaneously estimate open-coast topography and bathymetry from a single longitudinal coastal flight. The UAS combines nadir and oblique imagery to create a wide field of view (FOV), which enables collection of mobile, long dwell timeseries of the littoral zone suitable for structure-from motion (SfM), and wave speed inversion algorithms. Resultant digital surface models (DSMs) compare well with terrestrial topographic lidar and bathymetric survey data at Duck, NC, USA, with root-mean-square error (RMSE)/bias of 0.26/–0.05 and 0.34/–0.05 m, respectively. Bathymetric data from another flight at Virginia Beach, VA, USA, demonstrates successful comparison (RMSE/bias of 0.17/0.06 m) in a secondary environment. UAS-derived engineering data products, total volume profiles and shoreline position, were congruent with those calculated from traditional topo-bathymetric surveys at Duck. Capturing both topography and bathymetry within a single flight, the presented multicamera system is more efficient than data acquisition with a single camera UAS; this advantage grows for longer stretches of coastline (10 km). Efficiency increases further with an on-board Global Navigation Satellite System–Inertial Navigation System (GNSS-INS) to eliminate ground control point (GCP) placement. The Appendix reprocesses the Virginia Beach flight with the GNSS–INS input and no GCPs.
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Pradhan, Nawa Raj. Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region, USA. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42128.
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A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km2 Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R2 value of 0.72 and average root mean square error (RMSE) of 0.042.
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Conery, Ian, Brittany Bruder, Connor Geis, Jessamin Straub, Nicholas Spore, and Katherine Brodie. Applicability of CoastSnap, a crowd-sourced coastal monitoring approach for US Army Corps of Engineers district use. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47568.
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This US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, technical report details the pilot deployment, accuracy evaluation, and best practices of the citizen-science, coastal-image monitoring program CoastSnap. Despite the need for regular observational data, many coastlines are monitored infrequently due to cost and personnel, and this cell phone-image-based approach represents a new potential data source to districts in addition to providing an outreach opportunity for the public. Requiring minimal hardware and signage, the system is simple to install but requires user-image processing. Analysis shows the CoastSnap-derived shorelines compare well to real-time kinematic and lidar-derived shorelines during low-to-moderate wave conditions (root mean square errors [RMSEs] <10 m). During high-wave conditions, errors are higher (RMSE up to 18 m) but are improved when incorporating wave run-up. Beyond shoreline quantification, images provide other qualitative information such as storm-impact characteristics and timing of the formation of beach scarps. Ultimately, the citizen-science tool is a viable low-cost option to districts for monitoring shorelines and tracking the evolution of coastal projects such as beach nourishments.
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Anderson, Dylan, Annika O'Dea, Jessamin Straub, et al. Evaluation of the Version 1 Advanced Tactical Awareness Kit–Expeditionary Radar (ATAK-ER) for accuracy and reliability in surf-zone characterization in a range of environmental conditions. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48760.
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This Coastal and Hydraulics Engineering Technical Note (CHETN) presents the evaluation of a rapidly deployable radar and associated software for characterizing surf-zone waves, currents, and bathymetries at the US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory (CHL), Field Research Facility (FRF), in Duck, North Carolina. This project was conducted at the request of the US Marine Corps (USMC) Warfighting Laboratory. The Version 1 Advanced Tactical Awareness Kit–Radar Expeditionary (ATAK-ER V1) system was deployed 15 times between July and August 2023 to observe a range of wave, water level, and wind conditions that could each affect radar processing. Products from the system were then compared to the FRF’s continuously operating in situ instruments and monthly bathymetric surveys to quantify the accuracy and reliability of the output. A number of issues with the unit are identified, including potential error sources contributing to inaccuracies, but the black-box nature of the commercial off-the-shelf (COTS) unit prevents a confident understanding of why wave heights are underpredicted (by 65% on average), why bathymetries consistently have root-mean-square errors (RMSE) over 1 m with progressively greater errors with distance offshore, or why some collections are unable to generate all of the advertised products. This Version 1 COTS unit is not recommended for operational use at this time.
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Pompeu, Gustavo, and José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004491.
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The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental theories from macroeconomics, such as monetary and Taylor rule models, and compare the results to those of a random walk model by using the root mean squared error (RMSE) and the Diebold-Mariano (DM) test. We show that it is possible to beat the random walk by these metrics.
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Sun, Winston Y. Linear adaptive noise-reduction filters for tomographic imaging: Optimizing for minimum mean square error. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10148667.
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Rincón-Torres, Andrey Duván, Andrés Felipe Salas-Ávila, and Juan Manuel Julio-Román. Inflation Expectations: Rationality, Disagreement and the Role of the Loss Function in Colombia. Banco de la República, 2023. http://dx.doi.org/10.32468/be.1262.
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We study the behaviour of three quantitative sample surveys and a non sample inflation expectation report for Colombia. We found that expectations in Colombia; (i) are not strongly, i.e. a la Muth, rational because they show cross-section disagreement, (ii) expectations, however, show some features of weak rationality, (iii) expectations disagreement is time varying and relate to inflation, inflation changes and the output gap, thus suggesting a staggered information flow to agents, (iv) the forecast error loss function employed by agents is not symmetric and increasingly penalizes higher expectations than finally observed inflation as the horizon grows, and (v) this fact also explains the stylised fact that observed expectation share with theoretical rational expectations that expectations look like lagged versions of inflation that dampen with the horizon. The latest finding also arises from a very general econometric set up we develop in this paper. These results imply that the effect of weakening the rational expectations assumption in Colombian monetary policy models should be assessed, especially when compared to sticky information and heterogeneous agents choosing non Mean Square forecast Error losses.