Relevant bibliographies by topics / RMSHE

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

Academic literature on the topic 'RMSHE'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "RMSHE"

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S, Khamar Sabha, Usha H.N, Mithun B.N, and Prathibha G.C. "Contrast enhancement using SHE, BHE, RMSHE and NOSHE with Entropy and EME." IJIREEICE 3, no. 10 (October 15, 2015): 29–32. http://dx.doi.org/10.17148/ijireeice.2015.31007.

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Hashmi, Adeel, Abhinav Juneja, Naresh Kumar, Deepali Gupta, Hamza Turabieh, Grima Dhingra, Ravi Shankar Jha, and Zelalem Kiros Bitsue. "Contrast Enhancement in Mammograms Using Convolution Neural Networks for Edge Computing Systems." Scientific Programming 2022 (April 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/1882464.

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A good contrast is significant for analysis of medical images, and if the images have poor contrast, then some methods of contrast enhancement can be of much benefit. In this paper, a convolution neural network-based transfer learning approach is utilized for contrast enhancement of mammographic images. The experiments are conducted on ISP and MIAS datasets, where ISP dataset is used for training and MIAS dataset is used for testing (contrast enhancement). Experimental comparison of the proposed technique is done with the most popular direct and indirect contrast enhancement techniques such as CLAHE, BBHE, RMSHE, and contrast stretching. A qualitative comparison is done using mean square error (MSE), signal to noise ratio (SNR), and peak signal to noise ratio (PSNR). It is observed that the proposed technique outperforms the other techniques HE, RMSHE, CLAHE, BBHE, and contrast stretching.
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KUMI-BOATENG, Bernard, and Yao Yevenyo ZIGGAH. "Empirical study on the integration of total least squares and radial basis function neural network for coordinate transformation." Ghana Journal of Science, Technology and Development 7, no. 1 (August 8, 2020): 38–57. http://dx.doi.org/10.47881/220.967x.

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Total Least Squares (TLS) is noted to be a solution approach to solving several geodetic problems. The method has the ability to estimate unknown quantities that are useful for many geodetic applications. Hence, the main objective of this study was to improve the estimation performance of TLS via Radial Basis Function Neural Network (RBFNN) in coordinate transformation. This hybrid approach called TLS-RBFNN was applied to Ghana geodetic reference network, which has a coverage area of 79857 km2 representing 33.5% of the total land mass (238540 km2). A comparative performance analysis of TLS, RBFNN and TLS-RBFNN was carried out using Root Mean Square Horizontal Error (RMSHE) and Standard Deviation (SD). Based on the testing results, it was found that the TLS-RBFNN improved the transformation accuracy of RBFNN and TLS by 20.2% and 37.3% based on the RMSHE. In addition, it was observed that the TLS-RBFNN improved the transformation precision based on SD by 0.37% and 8.52%, respectively. Furthermore, the Bayesian Information Criterion (BIC) applied confirmed the superiority of the hybrid approach than using TLS and RBFNN as independent transformation methods. Consequently, the hybrid approach is recommended for enhanced coordinate transformation results in Ghana geodetic reference network.
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Kumi-Boateng, B., and Y. Y. Ziggah. "A 3D Procrustean Approach to Transform WGS84 Coordinates to Ghana War Office 1926 Reference Datum." Ghana Mining Journal 20, no. 1 (July 7, 2020): 1–10. http://dx.doi.org/10.4314/gm.v20i1.1.

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Abstract Satellite positioning technique such as Global Positioning System (GPS) is available to all countries for geospatial positioning. The availability of such positioning technique has revolutionised surveying in Ghana. The GPS operates on a global reference frame to fix control points for surveying and mapping purposes. There is therefore the need to transform coordinates from the satellite-based datum to the Ghana War Office 1926 datum. Several iterative methods have been proposed over the years for coordinate transformation and have been found to exhibit good transformation accuracy. However, these iterative methods always demand the linearisation of the transformation model equations and initial approximation values of the yet to be determined transformation parameters. These computational processes further enhance the computational complexity of the iterative methods and longer convergence time. As alternative solution, the Procrustes method has been proposed and applied to solve coordinate transformation problems in different geodetic reference networks. Review of previous studies indicates that the Procrustes method is direct, simple to use and produce satisfactory transformation accuracy. This method, however, is yet to be applied to ascertain its efficiency in the Ghana geodetic reference network. Therefore, this study utilised the 3D Procrustean approach to transform coordinates from World Geodetic System 1984 (WGS84) to Ghana War Office 1926 reference datum. The technique produced Root Mean Square Horizontal Error (RMSHE), Arithmetic Mean of the Horizontal Error (AMHE) and Standard Deviation (SD) values of 1.003 m, 0.901 m and 0.452 m, respectively. This study is serving as an extension to the ongoing research works to determine optimal transformation model for Ghana geodetic reference network. Keywords: Procrustean Approach, Coordinate Transformation, Conformal Model, Satellite Positioning
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Adekoya, Adebayo Felix, Isaac Kofi Nti, and Benjamin Asubam Weyori. "Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi." FinTech 1, no. 1 (December 9, 2021): 25–43. http://dx.doi.org/10.3390/fintech1010002.

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An accurate prediction of the Exchange Rate (ER) serves as the basis for effective financial management, monetary policies, and long-term strategic decision making worldwide. A stable and competitive ER enables economic diversification. Economists, researchers, and investors have conducted several studies to predict trends and facts that influence the ER’s rise or fall. This paper used the Long Short-Term Memory Networks (LSTM) framework to predict the weekly exchange rate of one Ghanaian Cedis (GH₵) to three different currencies (United States Dollar, British Pound, and Euro), using Google Trends and historical macroeconomic data. We fused past exchange rates, fundamental macroeconomic variables, commodity prices (cocoa, gold, and crude oil) and public search queries (Google Trends) as input parameters. An empirical analysis using publicly available ER data from the Bank of Ghana (BoG) from January 2004 to October 2019 showed satisfactory results. We observed that the proposed LSTM model outperformed the Support Vector Regressor (SVR) and Back-propagation Neural Network (BPNN) models in accuracy and closeness metrics. That is, our LSTM model obtained (MAE = 0.033, MSE = 0.0035, RMSE = 0.0551, R2 = 0.9983, RMSLE = 0.0129 and MAPE = 0.0121) compared with SVR (MAE = 0.05, MAE = 0.005, RMSE = 0.0683, R2 = 0.9973, RMSLE = 0.0191 and MAPE = 0.0241) and BPNN (MAE = 0.04, MAE = 0.0056, RMSE = 0.0688, R2 = 0.9974, RMSLE = 0.0172 and MAPE = 0.0168). Moreover, we observed a strong positive correction (0.98–0.99) between Google Trends on the currency of focus and its exchange rate to the Ghanaian cedis. The study results show the importance of incorporating public search queries from search engines to predict the ER accurately.
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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 (June 30, 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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Mentaschi, L., G. Besio, F. Cassola, and A. Mazzino. "Problems in RMSE-based wave model validations." Ocean Modelling 72 (December 2013): 53–58. http://dx.doi.org/10.1016/j.ocemod.2013.08.003.

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Hoe, Michael S., Christopher J. Dunn, and Hailemariam Temesgen. "Multitemporal LiDAR improves estimates of fire severity in forested landscapes." International Journal of Wildland Fire 27, no. 9 (2018): 581. http://dx.doi.org/10.1071/wf17141.

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Landsat-based fire severity maps have limited ecological resolution, which can hinder assessments of change to specific resources. Therefore, we evaluated the use of pre- and post-fire LiDAR, and combined LiDAR with Landsat-based relative differenced Normalized Burn Ratio (RdNBR) estimates, to increase the accuracy and resolution of basal area mortality estimation. We vertically segmented point clouds and performed model selection on spectral and spatial pre- and post-fire LiDAR metrics and their absolute differences. Our best multitemporal LiDAR model included change in mean intensity values 2–10 m above ground, the sum of proportion of canopy reflection above 10 m, and differences in maximum height. This model significantly reduced root-mean-squared error (RMSE), root-mean-squared prediction error (RMSPE), and bias when compared with models using only RdNBR. Our top combined model integrated RdNBR with LiDAR return proportions <2 m above ground, pre-fire 95% heights and pre-fire return proportions <2 m above ground. This model also significantly reduced RMSE, RMSPE, and bias relative to RdNBR. Our results confirm that three-dimensional spectral and spatial information from multitemporal LiDAR can isolate disturbance effects on specific ecological resources with higher accuracy and ecological resolution than Landsat-based estimates, offering a new frontier in landscape-scale estimates of fire effects.
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Lara-Cerecedo, Luis O., Jesús F. Hinojosa, Nun Pitalúa-Díaz, Yasuhiro Matsumoto, and Alvaro González-Angeles. "Prediction of the Electricity Generation of a 60-kW Photovoltaic System with Intelligent Models ANFIS and Optimized ANFIS-PSO." Energies 16, no. 16 (August 18, 2023): 6050. http://dx.doi.org/10.3390/en16166050.

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The development and constant improvement of accurate predictive models of electricity generation from photovoltaic systems provide valuable planning tools for designers, producers, and self-consumers. In this research, an adaptive neuro-fuzzy inference model (ANFIS) was developed, which is an intelligent hybrid model that integrates the ability to learn by itself provided by neural networks and the function of language expression, how fuzzy logic infers, and an ANFIS model optimized by the particle swarm algorithm, both with a predictive capacity of about eight months. The models were developed using the Matlab® software and trained with four input variables (solar radiation, module temperature, ambient temperature, and wind speed) and the electrical power generated from a photovoltaic (PV) system as the output variable. The models’ predictions were compared with the experimental data of the system and evaluated with rigorous statistical metrics, obtaining results of RMSE = 1.79 kW, RMSPE = 3.075, MAE = 0.864 kW, and MAPE = 1.47% for ANFIS, and RMSE = 0.754 kW, RMSPE = 1.29, MAE = 0.325 kW, and MAPE = 0.556% for ANFIS-PSO, respectively. The evaluations indicate that both models have good predictive capacity. However, the PSO integration into the hybrid model allows for improving the predictive capability of the behavior of the photovoltaic system, which provides a better planning tool.
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Liu, Julin, and Ken Chen. "Enterprise Financial Risk Prevention and Control and Data Analysis Method Based on Blockchain Technology." Mobile Information Systems 2022 (June 23, 2022): 1–7. http://dx.doi.org/10.1155/2022/9452342.

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With the development of engineering technology and computer networks, artificial neural networks, which mimic human brain neural networks, are being used in financial market forecasting to improve the accuracy of stock predictions and are making significant progress. Therefore, there is a great need to actively investigate the method of financial data analysis based on blockchain technology. The purpose of this paper is to investigate the neural network method of financial data analysis based on blockchain technology. Shanghai Index and Shenzhen 200 Index are chosen as experimental data, which are divided into two subsets: training and test samples. The BP model is constructed based on blockchain technology to analyze MARE, RMSRE, MSPEE, RMSPE, and MARE errors. The results show that the mean absolute error rate (MARE), RMSPE, and MSPEE of training samples of blockchain-based BP model are 0.0056, 0.0787, and 0.0085, respectively. Blockchain-based BP model plays an important role in solving financial data analysis problems.
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Dissertations / Theses on the topic "RMSHE"

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SAH, BIKASH KUMAR. "A NOVEL CONVOLUTIONAL NEURAL NETWORK FOR AIR POLLUTION FORECASTING." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18792.

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Air pollution was a global problem a few decades back. It is still a problem and will continue to be a problem if not solved appropriately.Various machine learning and deep learining approaches have been purposed for accurate prediction, estimation and analysis of the air polution. We have purposed a novel five layer one-dimensional convolution neural network architecture to forecast the PM2.5 concentration. It is a deep learning approach. We have used the five year air pollution dataset from 2010 to 2014 recorded by the US embassy in Beijing, China taken from the database from UCI machine learining repository [19]. The dataset we are considering is in the .csv format. The dataset consists of feature columns like “Number,” “year,” “month,” “day,” “PM2.5”, “PM10”, “S02”, “dew,” “temp,” “pressure,” “wind direction,” “wind direction,” “snow” and “rain.” The dataset consisted of a total of 43,324 rows and nine feature columns.The model yields the best results in predicting PM2.5 levels with an RMSE of 28.1309 and MAE of 14.9727. On statistical analysis we found that ur proposed prediction model outperformed the traditional forecasting models like DTR, SVR and ANN models for the air pollution forecasting.
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Chermiti, Amro. "Hur kan injicerad aktivitet individanpassas vid skelettscintigrafi? Effekten av patientspecifika parametrar." Thesis, Örebro universitet, Institutionen för hälsovetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-84602.

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Bakgrund: Skelettscintigrafi är en nuklearmedicinsk undersökning. Undersökningen är den mest använda nukleardiagnostiska metoden och den genomförs ofta som en helkroppsundersökning. För att undersökningen ska kunna erhålla sin diagnostiska kvalitet, samt följa strålsäkerhetsmyndighetens rekommendationer behövs det mer kännedom till hur optimeringen ska följa as low as reasonably achievable (ALARA). Studiens syfte var att optimera patientstråldos samt att undersöka hur injicerad aktivitet kan anpassas efter patientens specifika parametrar. Metod: Studiegruppen bestod av 85 patienter som genomgick skelettscintigrafier vid Central sjukhuset i Karlstad, från perioden februari-april 2020. Resultat: Visade att både ålder och vikt är patientspecifika variabler som borde tas till betraktning vid bestämning av injicerad strålningsdos. Konklusionen: För att optimera undersökningen för varje patient bör injicerad aktivitet anpassas efter både kroppsvikt och ålder. Fler studier där andra parametrar undersöks måste genomföras.
Background: Bone scintigraphy is a nuclear medicine procedure. It is the most used nuclear diagnostic method and provides the opportunity to perform a full-body examination. For the method to retain its diagnostic quality, and to follow the recommendations of the Radiation Safety Authority, more knowledge is required on how the optimization should follow as low as reasonably achievable (ALARA). The purpose of the study was to optimize patient radiation dose and to investigate how the injected activity can be adapted to patient-specific parameters. Method: The study group consisted of 85 patients who underwent bone scintigraphy at the Central Hospital in Karlstad, from the period February-April 2020. Result: Showed that age and weight are patient-specific variables that should be considered when determining injected radiation dose. Conclusion: To optimize the examination for each patient, injected activity should be adjusted according to the patient’s body weight and age. More studies in where other parameters are investigated must be carried out.
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Hast, Isak. "Quality Assessment of Spatial Data: Positional Uncertainties of the National Shoreline Data of Sweden." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-18743.

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This study investigates on the planimetric (x, y) positional accuracy of the National Shoreline (NSL) data, produced in collaboration between the Swedish mapping agency Lantmäteriet and the Swedish Maritime Administration (SMA). Due to the compound nature of shorelines, such data is afflicted by substantial positional uncertainties. In contrast, the positional accuracy requirements of NSL data are high. An apparent problem is that Lantmäteriet do not measure the positional accuracy of NSL in accordance to the NSL data product specification. In addition, currently, there is little understanding of the latent positional changes of shorelines affected by the component of time, in direct influence of the accuracy of NSL. Therefore, in accordance to the two specific aims of this study, first, an accuracy assessment technique is applied so that to measure the positional accuracy of NSL. Second, positional time changes of NSL are analysed. This study provides with an overview of potential problems and future prospects of NSL, which can be used by Lantmäteriet to improve the quality assurance of the data. Two line-based NSL data sets within the NSL classified regions of Sweden are selected. Positional uncertainties of the selected NSL areas are investigated using two distinctive methodologies. First, an accuracy assessment method is applied and accuracy metrics by the root-means-square error (RMSE) are derived. The accuracy metrics are checked toward specification and standard accuracy tolerances. Results of the assessment by the calculated RMSE metrics in comparison to tolerances indicate on an approved accuracy of tested data. Second, positional changes of NSL data are measured using a proposed space-time analysis technique. The results of the analysis reveal significant discrepancies between the two areas investigated, indicating that one of the test areas are influenced by much greater positional changes over time. The accuracy assessment method used in this study has a number of apparent constraints. One manifested restriction is the potential presence of bias in the derived accuracy metrics. In mind of current restrictions, the method to be preferred in assessment of the positional accuracy of NSL is a visual inspection towards aerial photographs. In regard of the result of the space-time analysis, one important conclusion can be made. Time-dependent positional discrepancies between the two areas investigated, indicate that Swedish coastlines are affected by divergent degrees of positional changes over time. Therefore, Lantmäteriet should consider updating NSL data at different time phases dependent on the prevailing regional changes so that to assure the currently specified positional accuracy of the entire data structure of NSL.
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Abdelhafeid, Faraj. "The Effect Upon Antenna Arrays of Variations of Element Orientation and Spacing in the Presence of Channel Noise, with an Application to Direction Finding." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1525866099535246.

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Cantarello, Luca. "Analisi delle previsioni meteorologiche mensili mediante il modello GLOBO (ISAC-CNR)." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7690/.

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In questo lavoro sono presentate le principali caratteristiche delle previsioni meteorologiche mensili, nonché il progresso scientifico e storico che le ha coinvolte e le tecniche adibite alla loro verifica. Alcune di queste tecniche sono state applicate al fine di valutare ed analizzare l'errore sistematico (o bias) e l'RMSE di temperatura a 850 hPa (T850), altezza geopotenziale a 500 hPa (Z500) e precipitazioni cumulate del modello GLOBO, utilizzato presso l'Istituto per le Scienze dell'Atmosfera e del Clima del Consiglio Nazionale delle Ricerche per formulare previsioni mensili. I risultati mostrano la progressione temporale dell'errore, che aumenta nelle prime due settimane di integrazione numerica fino a stabilizzarsi tra la terza e la quarta. Ciò mostra che il modello, persa l'influenza delle condizioni iniziali, raggiunge un suo stato che, per quanto fisiologicamente distante da quello osservato, tende a stabilizzarsi e a configurarsi quindi come sistematico (eventualmente facilitandone la rimozione in fase di calibrazione delle previsioni). Il bias di T850 e Z500 presenta anomalie negative prevalentemente lungo le zone equatoriali, e vaste anomalie positive sulle aree extra-tropicali; quello delle precipitazioni mostra importanti sovrastime nelle zone continentali tropicali. La distribuzione geografica dell'RMSE (valutato solo per T850 e Z500) riscontra una generale maggiore incertezza nelle zone extra-tropicali, specie dell'emisfero settentrionale e nei mesi freddi.
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Mansour, Tony, and Majdi Murtaja. "Applied estimation theory on power cable as transmission line." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-46583.

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This thesis presents how to estimate the length of a power cable using the MaximumLikelihood Estimate (MLE) technique by using Matlab. The model of the power cableis evaluated in the time domain with additive white Gaussian noise. The statistics havebeen used to evaluate the performance of the estimator, by repeating the experiment fora large number of samples where the random additive noise is generated for each sample.The estimated sample variance is compared to the theoretical Cramer Raw lower Bound(CRLB) for unbiased estimators. At the end of thesis, numerical results are presentedthat show when the resulting sample variance is close to the CRLB, and hence that theperformance of the estimator will be more accurate.
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Reigota, Nilvana dos Santos. "Comparação da transformada wavelet discreta e da transformada do cosseno, para compressão de imagens de impressão digital." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-27042007-101810/.

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Este trabalho tem por objetivo comparar os seguintes métodos de compressão de imagens de impressão digital: transformada discreta do cosseno (DCT), transformada de wavelets de Haar, transformada de wavelets de Daubechies e transformada de wavelets de quantização escalar (WSQ). O propósito da comparação é identificar o método que resulta numa menor perda de dados, para a maior taxa de compressão possível. São utilizadas as seguintes métricas para avaliação da qualidade da imagem para os métodos: erro quadrático médio (ERMS), a relação sinal e ruído (SNR) e a relação sinal ruído de pico (PSNR). Para as métricas utilizadas a DCT apresentou os melhores resultados, seguida pela WSQ. No entanto, o melhor tempo de compressão e a melhor qualidade das imagens recuperadas avaliadas pelo software GrFinger 4.2, foram obtidos com a técnica WSQ.
This research aims to compare the following fingerprint image compression methods: the discrete cosseno transform (DCT), Haar wavelet transform, Daubechies wavelets transform and wavelet scalar quantization (WSQ). The main interest is to find out the technique with the smallest distortion and higher compression ratio. Image quality is measured using peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square (ERMS). Image quality using these metrics showed best results for the DCT followed by WSQ, although the WSQ had the best compression time and presented the best quality when evaluated by the GrFinger 4.2 software.
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Khurram, Jassal Muhammad. "The Effect of Optimization of Error Metrics." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20471.

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It is important for a retail company to forecast its sale in correct and accurate way to be ableto plan and evaluate sales and commercial strategies. Various forecasting techniques areavailable for this purpose. Two popular modelling techniques are Predictive Modelling andEconometric Modelling. The models created by these techniques are used to minimize thedifference between the real and the predicted values. There are several different errormetrics that can be used to measure and describe the difference. Each metric focuses ondifferent properties in the forecasts and it is hence important which metrics that is used whena model is created. Most traditional techniques use the sum of squared error which havegood mathematical properties but is not always optimal for forecasting purposes. This thesisfocuses on optimization of three widely used error metrics MAPE, WMAPE and RMSE.Especially the metrics protection against overfitting, which occurs when a predictive modelcatches noise and irregularities in the data, that is not part of the sought relationship, isevaluated in this thesis.Genetic Programming, a general optimization technique based on Darwin’s theories ofevolution. In this study genetic programming is used to optimize predictive models based oneach metrics. The sales data of five products of ICA (a Swedish retail company) has beenused to observe the effects of the optimized error metrics when creating predictive models.This study shows that all three metrics are quite poorly protected against overfitting even ifWMAPE and MAPE are slightly better protected than MAPE. However WMAPE is the mostpromising metric to use for optimization of predictive models. When evaluated against allthree metrics, models optimized based on WMAPE have the best overall result. The results oftraining and test data shows that the results hold in spite of overfitted models.
Program: Magisterutbildning i informatik
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Laskauskas, Ramūnas. "Vaizdo kontūrų nustatymo būdų analizė." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2008. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2008~D_20080929_113638-76811.

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Vaizdo kontūrų nustatymo metodų tyrimui buvo pasirinktas 100 įvairaus turinio paveikslų su įvairiu elementų dydžiu ir skaičiumi. Tyrimui buvo pasirinkti 8 populiariausi vaizdo kontūrų nustatymo metodai: Canny, Sobel, Prewitt, Roberts, Zerocross, Laplacian, LoG, Marr-Hildreth. Atliekant tyrimus visiems paveikslams, naudojant visus 8 metodus, buvo subjektyviai parinkta optimaliausia slenkstinė reikšmė. Gavus visų 100 įvairių paveikslų geriausias slenkstines reikšmes su visais 8 metodais, buvo nustatytos slenkstinių reikšmių kitimo ribos kiekvienam kontūro išskyrimo metodui. Kiekvienam paveikslui buvo pritaikyta vidutiniškai 10 slenkstinių reikšmių ir kiekvienam paveikslui buvo suskaičiuotas vidutinis kvadratinis nuokrypis (RMSE, Root Mean Square Error) su geriausiu pasirinktu kontūru.
One hundred various pictures with different size and number of elements were chosen for the method research of image outline evaluation. All these pictures were converted into grayscale pictures. Most of edge detection methods (filters) required to be blurred to reduce noise. Eight the most popular methods were chosen to evaluate the image outline: Canny, Sobel, Prewitt, Roberts, Zerocross, Laplacian, LoG, Marr-Hildreth. A Root Mean Square Error (RMSE) was computed for each edge picture with the best-chosen outline.
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Morelli, Stefano. "Optimal pose selection for the calibration of an overconstrained Cable-Driven Parallel Robot." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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In this project an optimal pose selection method for the calibration of an overconstrained Cable-Driven Parallel robot is presented. This manipulator belongs to a subcategory of parallel robots, where the classic rigid "legs" are replaced by cables. Cables are flexible elements that bring advantages and disadvantages to the robot modeling. For this reason, there are many open research issues, and the calibration of geometric parameters is one of them. The identification of the geometry of a robot, in particular, is usually called Kinematic Calibration. Many methods have been proposed in the past years for the solution of the latter problem. Although these methods are based on calibration using different kinematic models, when the robot’s geometry becomes more complex, their robustness and reliability decrease. This fact makes the selection of the calibration poses more complicated. The position and the orientation of the endeffector in the workspace become important in terms of selection. Thus, in general, it is necessary to evaluate the robustness of the chosen calibration method, by means, for example, of a parameter such as the observability index. In fact, it is known from the theory, that the maximization of the above mentioned index identifies the best choice of calibration poses, and consequently, using this pose set may improve the calibration process. The objective of this thesis is to analyze optimization algorithms which aim to calculate an optimal choice of poses both in quantitative and qualitative terms. Quantitatively, because it is of fundamental importance to understand how many poses are needed. Not necessarily a greater number of poses leads to a better result. Qualitatively, because it is useful to understand if the selected combination of poses actually gives additional information in the process of the identification of the parameters.
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Book chapters on the topic "RMSHE"

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Mousania, Younes, and Salman Karimi. "A Novel Improved Method of RMSHE-Based Technique for Mammography Images Enhancement." In Lecture Notes in Electrical Engineering, 31–42. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8672-4_3.

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Bradai, Sonia, Carlo Trigona, Slim Naifar, Salvatore Baglio, and Olfa Kanoun. "RMSHI Solutions for Electromagnetic Transducers from Environmental Vibration." In Lecture Notes in Electrical Engineering, 599–607. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04324-7_71.

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Su, Yong, and Qingchuan Zhang. "Quality Assessment of Speckle Patterns by Estimating RMSE." In International Digital Imaging Correlation Society, 71–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51439-0_17.

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Li, Wang, Xiaoan Tang, and Junda Zhang. "SRG and RMSE-Based Automated Segmentation for Volume Data." In Lecture Notes in Computer Science, 194–203. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71598-8_18.

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Haut, Nathan, Wolfgang Banzhaf, and Bill Punch. "Correlation Versus RMSE Loss Functions in Symbolic Regression Tasks." In Genetic and Evolutionary Computation, 31–55. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8460-0_2.

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Ko’adan, Mohammed A., Mohammed A. Bamatraf, and Khalid Q. Shafal. "Clustering Analysis to Improve Web Search Ranking Using PCA and RMSE." In Advances on Smart and Soft Computing, 93–105. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6048-4_9.

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Kobialka, Hans-Ulrich. "Friction estimation – optimization of sensor configuration with respect to RMSE and costs." In Proceedings, 741–55. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-05978-1_52.

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Gong, Fengxun, and Ma Yanqiu. "Analysis of Positioning Performance of the Algorithm of Time Sum of Arrival with RMSE." In Communications in Computer and Information Science, 579–91. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7305-2_49.

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Han, Bo, Bo He, Mengmeng Ma, Tingting Sun, Tianhong Yan, and Amaury Lendasse. "RMSE-ELM: Recursive Model Based Selective Ensemble of Extreme Learning Machines for Robustness Improvement." In Proceedings of ELM-2014 Volume 1, 273–92. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14063-6_24.

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Chowdary, Nama Deepak, Tadepally Hrushikesh, Kusampudi Madhava Varma, and Shaik Ali Basha. "Time Series Analysis and Forecast Accuracy Comparison of Models Using RMSE–Artificial Neural Networks." In Advances in Intelligent Systems and Computing, 317–25. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0550-8_26.

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Conference papers on the topic "RMSHE"

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Kuan, Sao-I., Jongmin Kim, Oh-Heum Kwon, and Ha-Joo Song. "Canopy�K-means Combined Collaborative Filtering Using RMSE-minimization." In 2022 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2022. http://dx.doi.org/10.1109/bigcomp54360.2022.00016.

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Patni, Hrithik, Akash Jagtap, Vaishali Bhoyar, and Aditya Gupta. "Speech Emotion Recognition using MFCC, GFCC, Chromagram and RMSE features." In 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2021. http://dx.doi.org/10.1109/spin52536.2021.9566046.

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Fang, Qiang. "Is average RMSE appropriate for evaluating acoustic-to-articulatory inversion?" In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2019. http://dx.doi.org/10.1109/apsipaasc47483.2019.9023269.

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Pinto, Breno, Varin Khera, and Chun Che Fung. "Detecting security anomalies from internet traffic using the MA-RMSE algorithms." In 2009 7th IEEE International Conference on Industrial Informatics (INDIN). IEEE, 2009. http://dx.doi.org/10.1109/indin.2009.5195920.

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Bradai, Sonia, Slim Naifar, Carlo Trigona, Salvatore Baglio, and Olfa Kanoun. "Electromagnetic transducer with bistable-RMSHI for energy harvesting from very weak kinetic sources." In 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2018. http://dx.doi.org/10.1109/i2mtc.2018.8409784.

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Mongia, Hukam C. "N+3 and N+4 Generation Aeropropulsion Engine Combustors: Part 3 — Small Engines’ Emissions and Axial Staging Combustion Technology." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94572.

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Comprehensive assessment of the small rich-dome engines was conducted leading to the following emissions correlations: NOxEI LEC = 0.02991 × OPR1.9791 RMSE = 3.0% NOxEI TALON II = 0.01666 × OPR2.1403 RMSE = 2.0% NOxEI CFM TI = 0.06763 × OPR1.7458 RMSE = 2.1% NOxEI CF34 = 0.0541 × OPR1.7917R2 = 0.9794 RMSE = 2.4% NOxEI SM = 0.04782 × OPR1.8388 RMSE = 4.2% NOxEI All = 0.03856 × OPR1.9058 RMSE = 3.9% The best of the small engines’ gaseous emissions, albeit at lower takeoff pressure ratios, were shown to be very competitive with the best of medium and large size engines. Axially-staged combustion with partially premixed jets in crossflow was identified as a promising concept to pursue for the (N+3) technology mixers.
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Sridhar, Bolli, and Mohammed Zafar Ali Khan. "RMSE comparison of path loss models for UHF/VHF bands in India." In 2014 IEEE Region 10 Symposium. IEEE, 2014. http://dx.doi.org/10.1109/tenconspring.2014.6863052.

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Idiou, Nesrine, Fatah Benatia, and Brahim Brahimi. "Bias And RMSE of Archimedean Copula using Moment And L-Moments Methods." In 2020 2nd International Conference on Mathematics and Information Technology (ICMIT). IEEE, 2020. http://dx.doi.org/10.1109/icmit47780.2020.9046998.

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Lodetti, Paula Zenni, Edison A. C. Aranha Neto, Marcos A. Izumida Martins, Gabriel H. Dos S. Costa, and Mathias Arno Ludwig. "MAE and RMSE Analysis of K-means Predictive Algorithm for Photovoltaic Generation." In 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2022. http://dx.doi.org/10.1109/icecet55527.2022.9872976.

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Shafin, Rubayet, Lingjia Liu, and Jianzhong Zhang. "DoA Estimation and RMSE Characterization for 3D Massive-MIMO/FD-MIMO OFDM System." In GLOBECOM 2015 - 2015 IEEE Global Communications Conference. IEEE, 2014. http://dx.doi.org/10.1109/glocom.2014.7417413.

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Reports on the topic "RMSHE"

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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.), August 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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Ermolayev, Vadim, Frédéric Mallet, Vitaliy Yakovyna, Vyacheslav Kharchenko, Vitaliy Kobets, Artur Korniłowicz, Hennadiy Kravtsov, Mykola Nikitchenko, Сергій Олексійович Семеріков, and Aleksander Spivakovsky, eds. ICTERI 2019: ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer : Proceedings of the 15th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops. Kherson, Ukraine, June 12-15, 2019. CEUR Workshop Proceedings, June 2019. http://dx.doi.org/10.31812/123456789/3170.

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This volume represents the proceedings of the Workshops co-located with the 15th International Conference on ICT in Education, Research, and Industrial Applications, held in Kherson, Ukraine, in June 2019. It comprises 82 contributed papers that were carefully peer-reviewed and selected from 218 submissions for the five workshops: 3L-Person, CoSinE, ITER, RMSE, and TheRMIT. The volume is structured in five parts, each presenting the contributions for a particular workshop. The topical scope of the volume is aligned with the thematic tracks of ICTERI 2019: (I) Advances in ICT Research; (II) Information Systems: Technology and Applications; (III) ICT in Education; and (IV) ICT Cooperation in Academia and Industry.
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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, September 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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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.), September 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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Patwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.

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First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose to use residual connections to improve each UNet block and residual paths to solve the semantic gap between UNet encoder and decoder which improves the performance of the model. Adding spatial information as an extra channel helped increase the RMSE performance of the first break predictions. Other techniques like data augmentation, multitask loss, and normalization methods, were further explored to evaluate model improvement.
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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.), September 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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Letcher, Theodore, Julie Parno, Zoe Courville, Lauren Farnsworth, and Jason Olivier. A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics. Engineer Research and Development Center (U.S.), June 2023. http://dx.doi.org/10.21079/11681/47122.

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A majority of snow radiative transfer models (RTMs) treat snow as a collection of idealized grains rather than an organized ice–air matrix. Here we present a generalized multi-layer photon-tracking RTM that simulates light reflectance and transmittance of snow based on X-ray micro- tomography images, treating snow as a coherent 3D structure rather than a collection of grains. The model uses a blended approach to expand ray-tracing techniques applied to sub-1 cm3 snow samples to snowpacks of arbitrary depths. While this framework has many potential applications, this study’s effort is focused on simulating reflectance and transmittance in the visible and near infrared (NIR) through thin snow- packs as this is relevant for surface energy balance and remote sensing applications. We demonstrate that this framework fits well within the context of previous work and capably reproduces many known optical properties of a snow surface, including the dependence of spectral reflectance on the snow specific surface area and incident zenith angle as well as the surface bidirectional reflectance distribution function (BRDF). To evaluate the model, we compare it against reflectance data collected with a spectroradiometer at a field site in east-central Vermont. In this experiment, painted panels were inserted at various depths beneath the snow to emulate thin snow. The model compares remarkably well against the reflectance measured with a spectroradiometer, with an average RMSE of 0.03 in the 400–1600 nm range. Sensitivity simulations using this model indicate that snow transmittance is greatest in the visible wavelengths, limiting light penetration to the top 6 cm of the snowpack for fine-grain snow but increasing to 12 cm for coarse-grain snow. These results suggest that the 5% transmission depth in snow can vary by over 6 cm according to the snow type.
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