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21	Från hantering till prediciering : en uppsats om ordinalskalevariabler i linjära regressionsmodeller / From handling to predicition : a thesis about variables on ordinal level in linear regression models Grans Norgren, Selma January 2021 (has links) Uppsatsen undersöker om valet av hanteringsmetod av ordinalskalevariabler kan kopplas till den linjära regressionsmodellens prediceringsförmåga. Antalet hanteringsmetoder som undersöks begränsas till: dummyvariabler, summerat index och viktat summerat index. Stöd för alla tre metoder finns i litteraturen även om det för indexeringarna finns viss förvirring i begreppsanvändning. Med summerat index menas, i denna uppsats, när flera indikatorer som mätts med likertskalor adderas till en ny variabel som antas mäta den latenta variabeln. Med viktat summerat index tas det hänsyn till att indikatorerna kan vara något överlappande eller ha olika stor betydelse för den latenta variabeln. Därför appliceras någon form av viktningsanalys på indikatorerna innan summering sker, denna uppsats använder principalkomponentsanalys. K-delad korsvalidering har nyttjats som främsta analysverktyg för att kunna jämföra de olika hanteringsmetoderna. Jämförandet sker på basis av fyra jämförande mått: R2 , Steins R2j , RMSE samt MAE. Resultaten indikerar att modeller med dummyvariabler har bäst prediceringsförmåga men det ska förstås utifrån att modellerna hade problem med att uppfylla den linjära regressionsmetodens antaganden. Alla tre hanteringsmetoder har sina för- och nackdelar och därför behöver valet av hanteringsmetod alltid ske med hänsyn till aktuell undersökning. Linjär regression ordinalskalevariabler dummyvariabler summerade index viktade summerade index PCA förklaringsgrad R2 Steins justerade R2 rotmedelkvadratfelet RMSE medelabsolutfel MAE Nationella trygghetsundersökningen NTU Social Sciences Samhällsvetenskap
22	Machine Learning model applied to Reactor Dynamics / Maskininlärningsmodel Tillämpad på Reaktor Dynamik Nikitopoulos, Dionysios Dimitrios January 2023 (has links) This project’s idea revolved around utilizing the most recent techniques in MachineLearning, Neural Networks, and Data processing to construct a model to be used asa tool to determine stability during core design work. This goal will be achieved bycollecting distribution profiles describing the core state from different steady statesin five burn-up cycles in a reactor to serve as the dataset for training the model. Anadditional cycle will be reserved as a blind testing dataset for the trained model topredict. The variables that will be the target for the predictions are the decay ratioand the frequency since they describe the core stability.The distribution profiles extracted from the core simulator POLCA7 were subjectedto many different Data processing techniques to isolate the most relevant variablesto stability. The processed input variables were merged with the decay ratio andfrequency for those cases, as calculated with POLCA-T. Two different MachineLearning models, one for each output parameter, were designed with Pytorch toanalyze those labeled datasets. The goal of the project was to predict the outputvariables with an error lower than 0.1 for decay ratio and 0.05 for frequency. Themodels were able to predict the testing data with an RMSE of 0.0767 for decay ratioand 0.0354 for frequency.Finally, the trained models were saved and tasked with predicting the outputparameters for a completely unknown cycle. The RMSE was even better forthe unknown cycle, with 0.0615 for decay ratio and 0.0257 for frequency,respectively. / Idén bakom detta projekt var att använda de senaste teknikerna inom maskininlärning, neurala nätverk och databehandling för att konstruera en modell att använda som ett verktyg för att avgöra stabilitet under härddesignsarbete. Detta mål kommer uppnås genom att samla distribueringsprofiler av härdens tillstånd från olika stabila lägen i fem förbränningscyklar (burn-up cycles) i en reaktor, som tjänar som en datamängd att träna modellen på.En sjätte förbränningscykel användes som en datamängd för ett blindprov som den tränade modellen ska förutse. Variablerna som kommer tjäna som mål för förutsägelserna är sönderfallsförhållandet (decay ratio) och frekvensen, då dessa beskriver härdens stabilitet. Distribueringsprofilerna som extraherats från härdsimulatorn POLCA7 utsattes för många olika databehandlingstekniker för att isolera de mest relevanta variablerna för stabilitet. De behandlade indatavariablerna blandades med sönderfallsförhållandet och frekvensen för dessa fall, som beräknats med POLCA-T. Två olika maskininlärningsmodeller, en för varje utdataparameter, designades med Pytorch för att analysera dessa märkta datamängder. Projektets mål var att förutse utdatavariablerna med ett fel under 0.1 för sönderfallsförhållandet och 0.05 för frekvensen. Modellerna lyckades förutse testdatan med en RMSE på 0.0767 för sönderfallsförhållande och 0.0354 för frekvensen.Slutligen sparades de tränade modellerna och gavs uppgiften att förutse utdataparametrarna för en komplett okänd cykel. För den okända cykeln var RMSE ännu lägre, med 0.0615 för sönderfallsförhållande och 0.0257 för frekvensen. Master Thesis Machine Learning stability Energy distribution profiles Prediction frequency decay ratio Data processing POLCA-T Pytorch testing data RMSE. ii Physical Sciences Fysik
23	Study of evaluation metrics while predicting the yield of lettuce plants in indoor farms using machine learning models Chedayan, Divya, Geo Fernandez, Harry January 2023 (has links) A key challenge for maximizing the world’s food supply is crop yield prediction. In this study, three machine models are used to predict the fresh weight (yield) of lettuce plants that are grown inside indoor farms hydroponically using the vertical farming infrastructure, namely, support vector regressor (SVR), random forest regressor (RFR), and deep neural network (DNN).The climate data, nutrient data, and plant growth data are passed as input to train the models to understand the growth pattern based on the available features. The study of evaluation metrics majorly covers Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared, and Adjusted R-squared values.The results of the project have shown that the Random Forest with all the features is the best model having the best results with the least cross-validated MAE score and good cross-validated Adjusted R-squared value considering that the error of the prediction is minimal. This is followed by the DNN model with minor differences in the resulting values. The Support Vector Regressor (SVR) model gave a very poor performance with a huge error value that cannot be afforded in this scenario. In this study, we have also compared various evaluating metrics mentioned above and considered the cross-validated MAE and cross-validated Adjusted R-squared metrics. According to our study, MAE had the lowest error value, which is less sensitive to the outliers and adjusted R-squared value helps to understand the variance of the target variable with the predictor variable and adjust the metric to prevent the issues of overfitting. machine learning lettuce yield prediction Regression SVR RF DNN MAE MSE RMSE R-squared Adjusted R-squared Other Computer and Information Science Annan data- och informationsvetenskap
24	A Monte Carlo Study of Missing Data Treatments for an Incomplete Level-2 Variable in Hierarchical Linear Models Kwon, Hyukje 20 July 2011 (has links) No description available. Educational Tests and Measurements missing data treatment listwise deletion mean substitution EM multiple imputation inclusive restrictive bias RMSE confidence interval HLM intercepts- and slopes- as-outcomes
25	En jämförelse av Deep Learning-modeller för Image Super-Resolution / A Comparison of Deep Learning Models for Image Super-Resolution Bechara, Rafael, Israelsson, Max January 2023 (has links) Image Super-Resolution (ISR) is a technology that aims to increase image resolution while preserving as much content and detail as possible. In this study, we evaluate four different Deep Learning models (EDSR, LapSRN, ESPCN, and FSRCNN) to determine their effectiveness in increasing the resolution of lowresolution images. The study builds on previous research in the field as well as the results of the comparison between the different deep learning models. The problem statement for this study is: “Which of the four Deep Learning-based models, EDSR, LapSRN, ESPCN, and FSRCNN, generates an upscaled image with the best quality from a low-resolution image on a dataset of Abyssinian cats, with a factor of four, based on quantitative results?” The study utilizes a dataset consisting of pictures of Abyssinian cats to evaluate the performance and results of these different models. Based on the quantitative results obtained from RMSE, PSNR, and Structural Similarity (SSIM) measurements, our study concludes that EDSR is the most effective Deep Learning-based model. / Bildsuperupplösning (ISR) är en teknik som syftar till att öka bildupplösningen samtidigt som så mycket innehåll och detaljer som möjligt bevaras. I denna studie utvärderar vi fyra olika Deep Learning modeller (EDSR, LapSRN, ESPCN och FSRCNN) för att bestämma deras effektivitet när det gäller att öka upplösningen på lågupplösta bilder. Studien bygger på tidigare forskning inom området samt resultatjämförelser mellan olika djupinlärningsmodeller. Problemet som studien tar upp är: “Vilken av de fyra Deep Learning-baserade modellerna, EDSR, LapSRN, ESPCN och FSRCNN generarar en uppskalad bild med bäst kvalité, från en lågupplöst bild på ett dataset med abessinierkatter, med skalningsfaktor fyra, baserat på kvantitativa resultat?” Studien använder en dataset av bilder på abyssinierkatter för att utvärdera prestandan och resultaten för dessa olika modeller. Baserat på de kvantitativa resultaten som erhölls från RMSE, PSNR och Structural Similarity (SSIM) mätningar, drar vår studie slutsatsen att EDSR är den mest effektiva djupinlärningsmodellen. Bachelor’s thesis Image Super-Resolution Deep Learning models EDSR LapSRN ESPCN FSRCNN resolution enhancement low-resolution images quantitative evaluation RMSE PSNR SSIM Abyssinian cats dataset image quality. Kandidatexamensarbete Image Super-Resolution EDSR LapSRN ESPCN FSRCNN Djupinlärningsmodeller upplösningsförbättring lågupplösta bilder kvantitativ utvärdering RMSE PSNR SSIM abyssinska katter datamängd bildkvalitet. Computer Sciences Datavetenskap (datalogi)
26	Optimisation of adaptive localisation techniques for cognitive radio Thomas, Robin Rajan 06 August 2012 (has links) Spectrum, environment and location awareness are key characteristics of cognitive radio (CR). Knowledge of a user’s location as well as the surrounding environment type may enhance various CR tasks, such as spectrum sensing, dynamic channel allocation and interference management. This dissertation deals with the optimisation of adaptive localisation techniques for CR. The first part entails the development and evaluation of an efficient bandwidth determination (BD) model, which is a key component of the cognitive positioning system. This bandwidth efficiency is achieved using the Cramer-Rao lower bound derivations for a single-input-multiple-output (SIMO) antenna scheme. The performances of the single-input-single-output (SISO) and SIMO BD models are compared using three different generalised environmental models, viz. rural, urban and suburban areas. In the case of all three scenarios, the results reveal a marked improvement in the bandwidth efficiency for a SIMO antenna positioning scheme, especially for the 1×3 urban case, where a 62% root mean square error (RMSE) improvement over the SISO system is observed. The second part of the dissertation involves the presentation of a multiband time-of arrival (TOA) positioning technique for CR. The RMSE positional accuracy is evaluated using a fixed and dynamic bandwidth availability model. In the case of the fixed bandwidth availability model, the multiband TOA positioning model is initially evaluated using the two-step maximum-likelihood (TSML) location estimation algorithm for a scenario where line-of-sight represents the dominant signal path. Thereafter, a more realistic dynamic bandwidth availability model has been proposed, which is based on data obtained from an ultra-high frequency spectrum occupancy measurement campaign. The RMSE performance is then verified using the non-linear least squares, linear least squares and TSML location estimation techniques, using five different bandwidths. The proposed multiband positioning model performs well in poor signal-to-noise ratio conditions (-10 dB to 0 dB) when compared to a single band TOA system. These results indicate the advantage of opportunistic TOA location estimation in a CR environment. / Dissertation (MEng)--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted Bandwidth efficiency Cognitive radio (CR) Root mean square error (RMSE) Maximum-likelihood estimation Multiband positioning Least squares estimation Location awareness Single-input-multiple-output (SIMO) UCTD Time-of arrival (TOA)
27	Proactive university library book recommender system Mekonnen, Tadesse Zewdu January 2021 (has links) M. Tech. (Department of Information Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Too many options on the internet are the reason for the information overload problem to obtain relevant information. A recommender system is a technique that filters information from large sets of data and recommends the most relevant ones based on people‟s preferences. Collaborative and content-based techniques are the core techniques used to implement a recommender system. A combined use of both collaborative and content-based techniques called hybrid techniques provide relatively good recommendations by avoiding common problems arising from each technique. In this research, a proactive University Library Book Recommender System has been proposed in which hybrid filtering is used for enhanced and more accurate recommendations. The prototype designed was able to recommend the highest ten books for each user. We evaluated the accuracy of the results using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). A measure value of 0.84904 MAE and 0.9579 RMSE found by our system shows that the combined use of both techniques gives an improved prediction accuracy for the University Library Book Recommender System. Hybrid techniques Hybrid filtering Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) Dissertations, Academic -- South Africa Electronic data processing Self-organizing systems
28	Employing Bayesian Vector Auto-Regression (BVAR) method as an altenative technique for forecsating tax revenue in South Africa Molapo, Mojalefa Aubrey 11 1900 (has links) Statistics / M. Sc. (Statistics) Corporate income tax Personal income tax Total tax revenue Forecasting Bayesian Vector Autoregressive (BVAR) Error Trend Seasonal (ETS) Autoregressive moving Average (ARMA) Root mean squared error (RMSE) Autocorrelation 519.542 Bayesian statistical decision theory Vector automobile Tax revenue estimating Corporations -- Taxation Income tax Standard deviations
29	台指選擇權之市場指標實證分析吳建民, Wu,Jian-Min Unknown Date (has links) 本研究有系統地收集了2003年8月12日到2005年9月30日止共495個交易日的台指期貨、選擇權市場裡P/C量、P/C倉、隱含波動率(AIV)、不同天數的歷史波動率等收盤資料，進行這些因素與行情走勢間的關係，以及因素彼此的互動性。結果證實分析台指選擇權指標是需要區分金融重大衝擊前後期間，以及區分漲勢、跌勢、盤整的各期間，各期間的選擇權指標均會有不同意涵。本論文證實使用結構轉換的Chow-ARMA(2,1)模型可能比較符合模擬指數實況，且GARCH(1,1) 模型也很適合描述台期指貨波動度預測力。在選擇權指標方面：P/C量與AIV與台指期貨呈現負相關，P/C倉與台指期貨正相關。其中以P/C倉對指數漲跌的影響程度最大、P/C量的影響程度次之、AIV影響程度最小。若把隱含波動率區分成買權與賣權之各個波動率更有效地預測行情走勢，在大跌期間的買賣權隱含波動率更能表現出優越的預測能力，其中前兩期的賣權隱含波動率(PIV)更是效率性指標，實證結果使用20天的歷史波動率比較能貼近選擇權市場的變化，跟過去教科書慣用的90天不同。若比較歷史波動率與隱含波動率間的關係，結論是當「大跌期」歷史波動率大於買權隱含波動率(CIV)時，買權是會被低估的，其他的各種假設條件均不成立。理由有二：一是市場效率性決定了是否可使用隱含波動率與歷史波動率之間的高低關係。二是「大跌時期」相對於「大漲時期」的市場資訊被反應的更敏銳，而在「大跌時期」的賣權價格反應比買權價格反應更快速敏銳。本研究推論的Chow-ARMA(2,1) 台指期貨模型、GARCH(1,1) 波動率模型、P/C量-P/C倉-AIV的多變數模型、FMA20/XIV模型等等在研判指數變化上具有參考價值，進一步均可以做為選擇權操作策略參考依據之一。選擇權歷史波動率隱含波動率 Chow結構轉變選擇權風險係數移動平均線自我相關單根檢定時間序列金融衝擊未平倉量隱含波動率指數 Option Historical Volatility Implied Volatility ARMA(p,q) GARCH Option Greeks Moving Average Stationary ADF(Augmented Dickey-Fuller Test) RMSE P/C Ratio Time Series
30	Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms Vestin, Albin, Strandberg, Gustav January 2019 (has links) Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms. evaluation target tracking multiple sensors non-causal smoother smoothing tracking vehicle tracking camera lidar estimate estimation prediction vehicle dynamics sensor fusion real-time tracking extended kalman filter filter validation validation position estimation velocity estimation dynamic model model complexity multi object tracking multiple object tracking single object tracking data association tracking fundamentals iterated kalman filter track management gnn global nearest neighbour mahalanobis mahalanobis distance performance evaluation differential gps dgps roi ego several sensors sensors rmse root mean square error invertible motion anti-causal motion anti-causal tracking constant velocity gnn imu tfs two filter smoother ekf rts radar inertial measurement unit nonlinear nonlinear systems mono camera monocular camera noise model tracking performance fixed interval smoothing m/n logic centralized fusion non-causal object tracker car tracking car dynamics automotive active safety object tracking automotive industry thesis master reverse dynamics reverse tracking reverse sequence sequence tracking data propagation ground truth estimating ground truth additional sensors mounted sensors true estimates environment comparison algorithm independent targets overlapping measurements occluded track switch improve lower uncertainty more certain state process noise covariance sampling image sprt adas cnn cv pdf track target ego tracker tentative track observatiom online tracking offline tracking online offline recorded sequences robust self driving self-driving car traffic trajectory true state scenario scenarios future accurate output advanced driver assistance systems non-linear complex noise pedestrian truck bus maneuvering vehicles processed measurement frame state correction probability density function tuning likelihood transition measurement motion model recursion gaussian approximation distribution linear jacobian multiplicative noise ratio ad hoc ad hoc state space approach backward auction euclidean distance statistical threshold gating association margin normalize covariance matrix fusion confirmed rejected tentative history absolute error modular ego motion parameters variables logg hardware specification fused causal factorization independent uncorrelated transform moving rotation translation oncoming overtaking Control Engineering Reglerteknik

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