Relevant bibliographies by topics / Root mean square error (RMSE)

Contents

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

Academic literature on the topic 'Root mean square error (RMSE)'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Root 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 t
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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 MA
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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
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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 unsystema
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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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 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 va
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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
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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
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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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Fortin, V., M. Abaza, F. Anctil, and R. Turcotte. "Why Should Ensemble Spread Match the RMSE of the Ensemble Mean?" Journal of Hydrometeorology 15, no. 4 (2014): 1708–13. http://dx.doi.org/10.1175/jhm-d-14-0008.1.

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Abstract When evaluating the reliability of an ensemble prediction system, it is common to compare the root-mean-square error of the ensemble mean to the average ensemble spread. While this is indeed good practice, two different and inconsistent methodologies have been used over the last few years in the meteorology and hydrology literature to compute the average ensemble spread. In some cases, the square root of average ensemble variance is used, and in other cases, the average of ensemble standard deviation is computed instead. The second option is incorrect. To avoid the perpetuation of pra
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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 squar
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Dissertations / Theses on the topic "Root 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 lo
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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 (RMS
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Leksono, Catur Yudo, та Tina Andriyana. "Roundabout Microsimulation using SUMO : A Case Study in Idrottsparken RoundaboutNorrkӧping, Sweden". Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79771.

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Idrottsparken roundabout in Norrkoping is located in the more dense part of the city.Congestion occurs in peak hours causing queue and extended travel time. This thesis aims to provide alternative model to reduce queue and travel time. Types ofobservation data are flow, length of queue, and travel time that are observed during peakhours in the morning and afternoon. Calibration process is done by minimising root meansquare error of queue, travel time, and combination both of them between observation andcalibrated model. SUMO version 0.14.0 is used to perform the microsimulation. There are two
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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.

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Šimoník, Petr. "Měřič odstupu signálu od šumu obrazových signálů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217681.

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The diplomma thesis is dealing with possibilities of Signal to noise ratio measurement by method, which is based on direct measurement. It is chosen the most suitable method – signal and noise separation to two different parallel signal branches, where is measured signal strength in one branch and root mean square value in the other. The thesis is consisted of a concept of detail block scheme of Signal to noise ratio meter, which was designed in terms of theoretical knowledge. Particular functional blocks were circuit-designed, the active and passive parts were chosen and their function were d
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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, i
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Molapo, Mojalefa Aubrey. "Employing Bayesian Vector Auto-Regression (BVAR) method as an altenative technique for forecsating tax revenue in South Africa." Diss., 2017. http://hdl.handle.net/10500/25083.

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Simões, Carolina Ramos. "Estimação de funções densidade neutras face ao risco: uma aplicação a opções S&P 500." Master's thesis, 2017. http://hdl.handle.net/10316/86794.

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Dissertação de Mestrado em Métodos Quantitativos em Finanças apresentada à Faculdade de Ciências e Tecnologia<br>Com esta dissertação pretende-se estudar e implementar vários métodos de estimação de funções densidade de probabilidade neutras face ao risco, identificando as perspectivas dos investidores quanto à evolução do mercado. Assim, este trabalho incide sobre preços teóricos de opções europeias, gerados pelo modelo de Black-Scholes, e sobre preços de mercado de opções do mesmo estilo sobre o índice S&P 500, transacionadas no Chicago Board Options Exchange (CBOE) e relativas ao dia 4 de j
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McCarthy, Christabel. "Investigating the use of dasymetric techniques for assessing employment containment in Melbourne, Australia." Master's thesis, 2012. http://hdl.handle.net/10362/8307.

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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.<br>This project studies employment containment in Melbourne, Australia. Employment containment is a measure of the proportion of people that work in a location close to their home. Recent urban planning policies in Melbourne have aimed to improve employment containment in the city’s suburbs. While there has been analysis of the rates at which people both live and work within broadly defined ‘local areas’, little work has been done to investigate employment cont
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PÁLENÍKOVÁ, Lenka. "Geodetické práce v investiční výstavbě." Master's thesis, 2009. http://www.nusl.cz/ntk/nusl-51158.

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The accuracy appraisal and its evaluation with what the results of geodetic work are being achieved will be shown in this graduation thesis. As well as the related legislation and economic point of view of the price setting in comparison to other geodetic companies. One of the major surveyed area is testing of the used instruments - confrontation of achieved values with the values stated by manufacturer, accuracy analysis of the used methods and allignment of independent measurements of equal magnitude.
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Book chapters on the topic "Root 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 le
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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 optimizat
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Del Dottore, Emanuela, Alessio Mondini, Davide Bray, and Barbara Mazzolai. "Miniature Soil Moisture Sensors for a Root-Inspired Burrowing Growing Robot." In Biomimetic and Biohybrid Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38857-6_15.

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AbstractThis paper shows the implementation of miniature sensors for soil moisture measurement and their integration in a root-inspired burrowing growing robot. Three kinds of sensors are combined to estimate the water content in soil: a resistivity sensor composed of two brass electrodes, a commercial air humidity sensor interfaced with the soil by a filter membrane of PTFE with polyester scrim, and an RGB sensor used for visible reflectance spectroscopy. We show their integration and embeddability in a burrowing growing robot based on additive manufacturing with a 4 cm probe diameter. The mu
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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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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 Scal
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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. T
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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 exci
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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 ac
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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 ac
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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-ter
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Conference papers on the topic "Root mean square error (RMSE)"

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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
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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 mo
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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
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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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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
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Bin Masood, Junaid, Sajid Hussain, Ali AlAlili, Sara Zaidan, and Ebrahim Al Hajri. "Detailed Dynamic Model of an Institutional Building in Hot and Humid Climate Conditions." In ASME 2017 11th International Conference on Energy Sustainability collocated with the ASME 2017 Power Conference Joint With ICOPE-17, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/es2017-3582.

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This paper is an ASHRAE Level 3 study of the energy audit process carried out in an institutional building, The Umm Shaif Building, of The Petroleum Institute, Abu Dhabi, UAE. It undertakes the study by collecting data and conditional surveys. The energy loss locations are highlighted through psychrometric and infrared camera analysis. The detailed dynamic model has been simulated using the EnergyPlus® simulation engine. The details of the building envelope, and fenestration, the occupancy schedules, the equipment energy consumption and HVAC details are presented. The detailed building model i
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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 th
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Vaish, Jayati, Sandhya Shrivastava, Amit Kumar, and Bindeshwar Singh. "Prediction of Wind Speed using Random Forest Learning Model considering multi-meteorological Variables." In International Conference on Energy Transition and Innovation in Green Technology. REGIONAL ENERGY RESOURCES INFORMATION CENTER (RERIC), 2025. https://doi.org/10.64289/rericproc.25.0105.9569161.

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Wind power and other renewable energy sources are crucial for minimizing emission of greenhouse gas and preventing weather variation. The prediction of wind speed accurately is essential for maximizing production of wind energy and successfully integrating it into the electrical grid. In this paper, the real time data of wind speed for 24 hours of Bengaluru city (Karnataka, India) from 2011-2021 i.e., 11 years is considered to train Ensemble Learning based Random Forest (RF) learning model to forecast wind speed. The meteorological variable quantities such as dry bulb temperature, relative hum
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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
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Reports on the topic "Root 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, thre
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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* (ความเข
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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 ro
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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 Reyno
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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
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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 re
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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
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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
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Bruder, Brittany L., Katherine L. Brodie, Tyler J. Hesser, Nicholas J. Spore, Matthew W. Farthing, and Alexander D. Renaud. guiBath y : A Graphical User Interface to Estimate Nearshore Bathymetry from Hovering Unmanned Aerial System Imagery. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/39700.

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This US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, technical report details guiBathy, a graphical user interface to estimate nearshore bathymetry from imagery collected via a hovering Unmanned Aerial System (UAS). guiBathy provides an end-to-end solution for non-subject-matter-experts to utilize commercia-off-the-shelf UAS to collect quantitative imagery of the nearshore by packaging robust photogrammetric and signal-processing algorithms into an easy-to-use software interface. This report begins by providing brief background on coastal imaging and the ph
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Bak, A. Spicer, Patrick Durkin, Brittany Bruder, Matthew Saenz, Michael Forte, and Katherine Brodie. Amphibious uncrewed ground vehicle for coastal surfzone survey. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48130.

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The capability of a commercial off-the-shelf amphibious bottom crawling robot is explored for surveying seamless topography and bathymetry across the beachface, surfzone, and very nearshore. A real-time-kinematic (RTK) antenna on a mast was added to the robotic platform, a Bayonet-350 (previously the C2i SeaOx). Data collected from the robot were compared with those collected by the Coastal Research Amphibious Buggy (CRAB) and the Lighter Amphibious Resupply Cargo (LARC), unique amphibious vessels capable of collecting seamless topography and bathymetry in use for decades at the US Army Engine
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