Дисертації з теми "Predictive Spectral Analysis"
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Chhatwal, Harprit Singh. "Spectral modelling techniques for speech signals based on linear predictive analysis." Thesis, Imperial College London, 1988. http://hdl.handle.net/10044/1/46996.
Повний текст джерелаLosik, Len. "Adapting Fourier Analysis for Predicting Earth, Mars and Lunar Orbiting Satellite's Telemetry Behavior." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595773.
Повний текст джерелаPrognostic technology uses a series of algorithms, combined forms a prognostic-based inference engine (PBIE) for the identification of deterministic behavior embedded in completely normal appearing telemetry from fully functional equipment. The algorithms used to define normal behavior in the PBIE from which deterministic behavior is identified can be adapted to quantify normal spacecraft telemetry behavior while in orbit about a moon or planet or during interplanetary travel. Time-series analog engineering data (telemetry) from orbiting satellites and interplanetary spacecraft are defined by harmonic and non-harmonic influences, which shape it behavior. Spectrum analysis can be used to understand and quantify the fundamental behavior of spacecraft analog telemetry and relate the behavior's frequency and phase to its time-series behavior through Fourier analysis.
Losik, Len. "Adapting Fourier Analysis for Predicting Earth, Mars and Lunar Orbiting Satellite's Telemetry Behavior." International Foundation for Telemetering, 2010. http://hdl.handle.net/10150/604279.
Повний текст джерелаPrognostic technology uses a series of algorithms, combined forms a prognostic-based inference engine (PBIE) for the identification of deterministic behavior embedded in completely normal appearing telemetry from fully functional equipment. The algorithms used to define normal behavior in the PBIE from which deterministic behavior is identified can be adapted to quantify normal spacecraft telemetry behavior while in orbit about a moon or planet or during interplanetary travel. Time-series analog engineering data (telemetry) from orbiting satellites and interplanetary spacecraft are defined by harmonic and non-harmonic influences, which shape it behavior. Spectrum analysis can be used to understand and quantify the fundamental behavior of spacecraft analog telemetry and relate the behavior's frequency and phase to its time-series behavior through Fourier analysis.
Losik, Len. "Using Telemetry Science, An Adaptation of Prognostic Algorithms for Predicting Normal Space Vehicle Telemetry Behavior from Space for Earth and Lunar Satellites and Interplanetary Spacecraft." International Foundation for Telemetering, 2009. http://hdl.handle.net/10150/606150.
Повний текст джерелаPrognostic technology uses a series of algorithms, combined forms a prognostic-based inference engine (PBIE) for the identification of deterministic behavior embedded in completely normal appearing telemetry from fully functional equipment. The algorithms used to define normal behavior in the PBIE from which deterministic behavior is identified can be adapted to quantify normal spacecraft telemetry behavior while in orbit about a moon or planet or during interplanetary travel. Time-series analog engineering data (telemetry) from orbiting satellites and interplanetary spacecraft are defined by harmonic and non-harmonic influences which shape it behavior. Spectrum analysis can be used to understand and quantify the fundamental behavior of spacecraft analog telemetry and relate the behavior's frequency and phase to its time-series behavior through Fourier analysis.
Guldemir, Hanifi. "Prediction of induction motor line current spectra from design data." Thesis, University of Nottingham, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287180.
Повний текст джерелаKwag, Jae-Hwan. "A comparative study of LP methods in MR spectral analysis /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9962536.
Повний текст джерелаWang, Nancy. "Spectral Portfolio Optimisation with LSTM Stock Price Prediction." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273611.
Повний текст джерелаDen nobelprisvinnande moderna portföjlteorin (MPT) är utan tvekan en av de mest framgångsrika investeringsmodellerna inom finansvärlden och investeringsstrategier. MPT antar att investerarna är mindre benägna till risktagande och approximerar riskexponering med variansen av tillgångarnasränteavkastningar. Nyckeln till en lyckad portföljförvaltning är därmed goda riskestimat och goda förutsägelser av tillgångspris. Riskestimering görs vanligtvis genom traditionella prissättningsmodellerna som tillåter risken att variera i tiden, dock inte i frekvensrummet. Denna begränsning utgör bland annat ett större fel i riskestimering. För att tackla med detta har intresset för tillämpningar av spektraanalys på finansiella tidsserier ökat de senast åren. Bland annat är ett nytt tillvägagångssätt för att behandla detta den nyintroducerade spektralportföljteorin och spektralfak- tormodellen som påvisade ökad portföljenprestanda genom spektralriskskattning [1][11]. Samtidigt har prediktering av aktierpriser länge varit en stor utmaning på grund av dess icke-linjära och icke-stationära egenskaper medan maskininlärning har kunnat använts för att lösa annars omöjliga uppgifter. Färska studier har påvisat signifikant resultat i aktieprisprediktering med hjälp av artificiella LSTM neurala nätverk [6][34]. Detta arbete undersöker kombinerade effekten av dessa två framsteg i ett portföljoptimeringsproblem genom att optimera en spektral portfölj med framtida avkastningar predikterade av ett LSTM neuralt nätverk. Arbetet börjar med matematisk härledningar och teoretisk introduktion och sedan studera portföljprestation som genereras av spektra risk, LSTM aktieprispredikteringen samt en kombination av dessa två. Resultaten visar på att LSTM-predikteringen ensam presterade bättre än kombinationen, vilket i sin tur presterade bättre än enbart spektralriskskattningen.
Bahrampouri, Mahdi. "Ground Motion Prediction Equations for Non-Spectral Parameters using the KiK-net Database." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/87704.
Повний текст джерелаWinn, Olivia, and Sivaram Kiran Thekkemadathil. "Near-Infrared Spectral Measurements and Multivariate Analysis for Predicting Glass Contamination of Boiler Fuel." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-36058.
Повний текст джерелаBadenhorst, Dirk Jakobus Pretorius. "Improving the accuracy of prediction using singular spectrum analysis by incorporating internet activity." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/80056.
Повний текст джерелаENGLISH ABSTRACT: Researchers and investors have been attempting to predict stock market activity for years. The possible financial gain that accurate predictions would offer lit a flame of greed and drive that would inspire all kinds of researchers. However, after many of these researchers have failed, they started to hypothesize that a goal such as this is not only improbable, but impossible. Previous predictions were based on historical data of the stock market activity itself and would often incorporate different types of auxiliary data. This auxiliary data ranged as far as imagination allowed in an attempt to find some correlation and some insight into the future, that could in turn lead to the figurative pot of gold. More often than not, the auxiliary data would not prove helpful. However, with the birth of the internet, endless amounts of new sources of auxiliary data presented itself. In this thesis I propose that the near in finite amount of data available on the internet could provide us with information that would improve stock market predictions. With this goal in mind, the different sources of information available on the internet are considered. Previous studies on similar topics presented possible ways in which we can measure internet activity, which might relate to stock market activity. These studies also gave some insights on the advantages and disadvantages of using some of these sources. These considerations are investigated in this thesis. Since a lot of this work is therefore based on the prediction of a time series, it was necessary to choose a prediction algorithm. Previously used linear methods seemed too simple for prediction of stock market activity and a new non-linear method, called Singular Spectrum Analysis, is therefore considered. A detailed study of this algorithm is done to ensure that it is an appropriate prediction methodology to use. Furthermore, since we will be including auxiliary information, multivariate extensions of this algorithm are considered as well. Some of the inaccuracies and inadequacies of these current multivariate extensions are studied and an alternative multivariate technique is proposed and tested. This alternative approach addresses the inadequacies of existing methods. With the appropriate methodology chosen and the appropriate sources of auxiliary information chosen, a concluding chapter is done on whether predictions that includes auxiliary information (obtained from the internet) improve on baseline predictions that are simply based on historical stock market data.
AFRIKAANSE OPSOMMING: Navorsers en beleggers is vir jare al opsoek na maniere om aandeelpryse meer akkuraat te voorspel. Die moontlike finansiële implikasies wat akkurate vooruitskattings kan inhou het 'n vlam van geldgierigheid en dryf wakker gemaak binne navorsers regoor die wêreld. Nadat baie van hierdie navorsers onsuksesvol was, het hulle begin vermoed dat so 'n doel nie net onwaarskynlik is nie, maar onmoontlik. Vorige vooruitskattings was bloot gebaseer op historiese aandeelprys data en sou soms verskillende tipes bykomende data inkorporeer. Die tipes data wat gebruik was het gestrek so ver soos wat die verbeelding toegelaat het, in 'n poging om korrelasie en inligting oor die toekoms te kry wat na die guurlike pot goud sou lei. Navorsers het gereeld gevind dat hierdie verskillende tipes bykomende inligting nie van veel hulp was nie, maar met die geboorte van die internet het 'n oneindige hoeveelheid nuwe bronne van bykomende inligting bekombaar geraak. In hierdie tesis stel ek dus voor dat die data beskikbaar op die internet dalk vir ons kan inligting gee wat verwant is aan toekomstige aandeelpryse. Met hierdie doel in die oog, is die verskillende bronne van inligting op die internet gebestudeer. Vorige studies op verwante werk het sekere spesifieke maniere voorgestel waarop ons internet aktiwiteit kan meet. Hierdie studies het ook insig gegee oor die voordele en die nadele wat sommige bronne inhou. Hierdie oorwegings word ook in hierdie tesis bespreek. Aangesien 'n groot gedeelte van hierdie tesis dus gebasseer word op die vooruitskatting van 'n tydreeks, is dit nodig om 'n toepaslike vooruitskattings algoritme te kies. Baie navorsers het verkies om eenvoudige lineêre metodes te gebruik. Hierdie metodes het egter te eenvoudig voorgekom en 'n relatiewe nuwe nie-lineêre metode (met die naam "Singular Spectrum Analysis") is oorweeg. 'n Deeglike studie van hierdie algoritme is gedoen om te verseker dat die metode van toepassing is op aandeelprys data. Verder, aangesien ons gebruik wou maak van bykomende inligting, is daar ook 'n studie gedoen op huidige multivariaat uitbreidings van hierdie algoritme en die probleme wat dit inhou. 'n Alternatiewe multivariaat metode is toe voorgestel en getoets wat hierdie probleme aanspreek. Met 'n gekose vooruitskattingsmetode en gekose bronne van bykomende data is 'n gevolgtrekkende hoofstuk geskryf oor of vooruitskattings, wat die bykomende internet data inkorporeer, werklik in staat is om te verbeter op die eenvoudige vooruitskattings, wat slegs gebaseer is op die historiese aandeelprys data.
Goetz, Ryan P. Rosenblad Brent L. "Study of the horizontal-to-vertical spectral ratio (HVSR) method for characterization of deep soils in the Mississippi Embayment." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/5334.
Повний текст джерелаHernandez, Villapol Jorge Luis. "Spectrum Analysis and Prediction Using Long Short Term Memory Neural Networks and Cognitive Radios." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc1062877/.
Повний текст джерелаBoozer, Benjamin Bryan Permaloff Anne. "An analysis of economic efficiency in predicting legislative voting beyond a traditional liberal-conservative spectrum." Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Political_Science/Dissertation/Boozer_Benjamin_34.pdf.
Повний текст джерелаLoubaton, Philippe. "Prediction et representation markovienne des processus stationnaires vectoriels sur z::(2) : utilisation de techniques d'estimation spectrale 2-d en traitement d'antenne." Paris, ENST, 1988. http://www.theses.fr/1988ENST0012.
Повний текст джерелаDotto, André Carnieletto. "Espectroscopia do solo no Vis-IR: potencial predictivo e desenvolvimento de uma interface gráfica de usuário em R." Universidade Federal de Santa Maria, 2017. http://repositorio.ufsm.br/handle/1/11343.
Повний текст джерелаThis thesis presents a study of Visible Near-infrared spectroscopy technique applied to predict soil properties. The purpose was to develop quantitative soil information due to the demand of digital soil mapping, environmental monitoring, agricultural production and for increasing spatial information on soil. Soil spectroscopy emerge as an alternative to revolutionize soil monitoring, allowing rapid, low-cost, non-destructive samples sampling, environmental-friendly, reproducible, and repeatable analysis. To improve the efficiency of soil prediction using spectral data, several spectral preprocessing techniques and multivariate models were exploited. A graphical user interface (GUI) in R, named Alrad Spectra, was developed to perform preprocessing, multivariate modeling and prediction using spectral data. Hereby, the objectives were: The objectives were: i) to predict soil properties to improve soil information using spectral data, ii) to compare the performance of spectral preprocessing and multivariate calibration methods in the prediction of soil organic carbon, iii) to obtain reliable soil organic carbon prediction, and iv) to develop a graphical user interface that performs spectral preprocessing and prediction of the soil property using spectroscopic data. A total of 595 soil samples were collected in central region of Santa Catarina State, Brazil. Soil spectral reflectance was obtained using a FieldSpec 3 spectroradiometer with a spectral range of 350–2500 nm with 1 nm of spectral resolution. The outcomes of the thesis have demonstrated the great performance of predicting soil properties using Vis-NIR spectroscopy. Apparently, soil properties that are directly related to the chromophores such as organic carbon presented superior prediction statistics than particle size. Spectral preprocessing applied in the soil spectra contribute to the development of high-level prediction model. Comparing different spectral preprocessing techniques for soil organic carbon (SOC) prediction revealed that the scatter–corrective preprocessing techniques presented superior prediction results compared to spectral derivatives. In scatter–correction technique, continuum removal is the most suitable preprocessing to be used for SOC prediction. In the calibration modeling, excepting for random forest, all of methods presented robust prediction, with emphasis on the support vector machine method. The systematic methodology applied in this study can improve the reliability of SOC estimation by examining how techniques of spectral preprocessing and multivariate methods affect the prediction performance using spectral analysis. The development of easy-to-use graphical user interface may benefit a large number of users, who will take advantage of this useful chemometrics analysis. Alrad Spectra is the first GUI of its kind and the expectation is that this tool can expand the application of the spectroscopy technique.
Esta tese apresenta um estudo da técnica de espectroscopia do visível ao infravermelho próximo aplicado à predição de propriedades do solo. O proposito foi de desenvolver informações quantitativas sobre o solo, devido à demanda do mapeamento digital de solos, monitoramento ambiental, produção agrícola e aumento das informações espaciais do solo. A espectroscopia surge como uma alternativa para revolucionar a monitorização do solo, permitindo uma amostragem rápida, de baixo custo, não destrutiva, ambientalmente amigável, reprodutível e repetitiva. Para melhorar a eficiência da predição do solo usando dados espectrais, várias técnicas de pré-processamento espectral e modelos multivariados foram explorados. Uma interface gráfica de usuário (GUI) no R, denominada Alrad Spectra, foi desenvolvida para realizar pré-processamento, modelagem multivariada e predição usando dados espectrais. Os objetivos foram: i) predizer as propriedades do solo para melhorar a informação do solo usando dados espectrais, ii) comparar os desempenhos dos pré-processamentos espectrais e métodos de calibração multivariada na predição do carbono orgânico do solo, iii) obter predições confiáveis do carbono orgânico do solo, e iv) desenvolver uma interface gráfica de usuário que realize o pré-processamento espectral e a predição do atributo solo usando dados espectroscópicos. Um total de 595 amostras de solo foram coletadas na região central do estado de Santa Catarina, Brasil. A reflectância espectral do solo foi obtida utilizando um espectrorradiômetro FieldSpec 3 com uma alcance espectral de 350-2500 nm com 1 nm de resolução espectral. Os resultados da tese demonstraram o grande desempenho da predição de propriedades do solo usando espectroscopia do vísivel ao infravermelho próximo. As propriedades do solo que estão diretamente relacionadas aos cromóforos, como o carbono orgânico, apresentaram predições superiores comparados com o tamanho de partículas. O pré-processamento espectral aplicado nos espectros do solo contribui para o desenvolvimento de um modelo de predição de alto nível. Comparando diferentes técnicas de pré-processamento espectral para a predição de carbono orgânico revelou que as técnicas de pré-processamento de correção de dispersão apresentaram resultados de predição superiores em comparação com as técnicas de derivação espectrais. Na técnica de correção de dispersão, a remoção do contínuo é o pré-processamento mais adequado a ser usado para a predição de carbono. Na modelagem de calibração, com exceção da floresta aleatória, todos os métodos apresentaram uma elevada predição, sendo destaque o método máquina de vetores de suporte. A metodologia sistemática aplicada neste estudo pode melhorar a confiabilidade da estimativa do carbono orgânico ao examinar como as técnicas de pré-processamento espectral e métodos multivariados afetam a performance da predição usando a análise espectral. O desenvolvimento da GUI de fácil utilização pode beneficiar um grande número de usuários, os quais podem tirar proveito desta análise quimiométrica. Alrad Spectra é a primeira GUI desse tipo e a expectativa é que esta ferramenta possa expandir a aplicação da técnica de espectroscopia.
Li, Qi. "Intermittency of Global Solar Radiation over Reunion island : Daily Mapping Prediction Model and Multifractal Parameters." Thesis, La Réunion, 2018. http://www.theses.fr/2018LARE0016/document.
Повний текст джерелаDue to the heterogeneous and rapidly-changing cloudiness, tropical islands, such as Reunion Island in the South-west Indian Ocean (SWIO), have significant solar resource that is highly variable from day-to-day. In this study, we propose a new approach for deterministic prediction of daily surface solar radiation (SSR) maps based on four linear regression models: multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLSR), and stepwise regression (SR), that we have applied on the SARAH-E@5km satellite data (CM SAF) for the period during 2007-2016. To improve the accuracy of prediction, the multifractal parameters (H,C_1 and α) are proposed to include as new predictors in the predictive model. These parameters are obtained from the analysis of SSR intermittency based on arbitrary order Hilbert spectral analysis. This analysis is the extension of Hilbert Huang Transform (HHT) and it is used to estimate the generalized scaling exponent ξ(q). It is the combination of the Empirical Mode Decomposition and Hilbert spectral analysis (EMD+HSA). In a first step, the multifractal analysis is applied onto one-second SSR measurements form a SPN1 pyranometer in Moufia in 2016. The mean sub-daily, daily and seasonal daily multifractal patterns are derived, and the scaling exponent ξ(q) is analyzed. In a second step, the intermittency study is conducted on one-minute SSR measurements from a SPN1 network with 11 stations in 2014. The spatial patterns for all the stations with the multifractal parameters H,C_1 and α are shown. The variability of singularity spectrum width is considered to study the spatial intermittency at the daily and seasonal scale. Based on this intermittency analysis from measurements at several stations, the universal multifractal parameters (H,C_1 and α) could be taken as new predictors for indicating the multifractal properties of SSR
Manero, Font Jaume. "Deep learning architectures applied to wind time series multi-step forecasting." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/669283.
Повний текст джерелаLa predicció de vent és clau per a la integració de l'energia eòlica en els sistemes elèctrics. Els models meteorològics es fan servir per predicció, però tenen unes graelles geogràfiques massa grans per a reproduir totes les característiques locals que influencien la formació de vent, fent necessària la predicció d'acord amb les sèries temporals de mesures passades d'una localització concreta. L'objectiu d'aquest treball d'investigació és l'aplicació de xarxes neuronals profundes a la predicció \textit{multi-step} utilitzant com a entrada series temporals de múltiples variables meteorològiques, per a fer prediccions de vent d'ací a 12 hores. Les sèries temporals de vent són seqüències d'observacions meteorològiques tals com, velocitat del vent, temperatura, humitat, pressió baromètrica o direcció. Les sèries temporals de vent tenen dues propietats estadístiques rellevants, que són la no linearitat i la no estacionalitat, que fan que la modelització amb eines estadístiques sigui poc precisa. En aquesta tesi es validen i proven models de deep learning per la predicció de vent, aquests models d'arquitectures d'autoaprenentatge s'apliquen al conjunt de dades de vent més gran del món, que ha produït el National Renewable Laboratory dels Estats Units (NREL) i que té 126,692 ubicacions físiques de vent distribuïdes per total la geografia de nord Amèrica. L'heterogeneïtat d'aquestes sèries de dades permet establir conclusions fermes en la precisió de cada mètode aplicat a sèries temporals generades en llocs geogràficament molt diversos. Proposem xarxes neuronals profundes de tipus multi-capa, convolucionals i recurrents com a blocs bàsics sobre els quals es fan combinacions en arquitectures heterogènies amb variants, que s'entrenen amb estratègies d'optimització com drops, connexions skip, estratègies de parada, filtres i kernels de diferents mides entre altres. Les arquitectures s'optimitzen amb algorismes de selecció de paràmetres que permeten obtenir el model amb el millor rendiment, en totes les dades. Les capacitats d'aprenentatge de les arquitectures aplicades a ubicacions heterogènies permet establir relacions entre les característiques d'un lloc (complexitat del terreny, variabilitat del vent, ubicació geogràfica) i la precisió dels models, establint mesures de predictibilitat que relacionen la capacitat dels models amb les mesures definides a partir d'anàlisi espectral o d'estacionalitat de les sèries temporals. Els mètodes desenvolupats ofereixen noves i superiors alternatives als algorismes estadístics i mètodes tradicionals.
Arquitecturas de aprendizaje profundo aplicadas a la predición en múltiple escalón de series temporales de viento. La predicción de viento es clave para la integración de esta energía eólica en los sistemas eléctricos. Los modelos meteorológicos tienen una resolución geográfica demasiado amplia que no reproduce todas las características locales que influencian en la formación del viento, haciendo necesaria la predicción en base a series temporales de cada ubicación concreta. El objetivo de este trabajo de investigación es la aplicación de redes neuronales profundas a la predicción multi-step usando como entrada series temporales de múltiples variables meteorológicas, para realizar predicciones de viento a 12 horas. Las series temporales de viento son secuencias de observaciones meteorológicas tales como, velocidad de viento, temperatura, humedad, presión barométrica o dirección. Las series temporales de viento tienen dos propiedades estadísticas relevantes, que son la no linealidad y la no estacionalidad, lo que implica que su modelización con herramientas estadísticas sea poco precisa. En esta tesis se validan y verifican modelos de aprendizaje profundo para la predicción de viento, estos modelos de arquitecturas de aprendizaje automático se aplican al conjunto de datos de viento más grande del mundo, que ha sido generado por el National Renewable Laboratory de los Estados Unidos (NREL) y que tiene 126,682 ubicaciones físicas de viento distribuidas por toda la geografía de Estados Unidos. La heterogeneidad de estas series de datos permite establecer conclusiones válidas sobre la validez de cada método al ser aplicado en series temporales generadas en ubicaciones físicas muy diversas. Proponemos redes neuronales profundas de tipo multi capa, convolucionales y recurrentes como tipos básicos, sobre los que se han construido combinaciones en arquitecturas heterogéneas con variantes de entrenamiento como drops, conexiones skip, estrategias de parada, filtros y kernels de distintas medidas, entre otros. Las arquitecturas se optimizan con algoritmos de selección de parámetros que permiten obtener el mejor modelo buscando el mejor rendimiento, incluyendo todos los datos. Las capacidades de aprendizaje de las arquitecturas aplicadas a localizaciones físicas muy variadas permiten establecer relaciones entre las características de una ubicación (complejidad del terreno, variabilidad de viento, ubicación geográfica) y la precisión de los modelos, estableciendo medidas de predictibilidad que relacionan la capacidad de los algoritmos con índices que se definen a partir del análisis espectral o de estacionalidad de las series temporales. Los métodos desarrollados ofrecen nuevas alternativas a los algoritmos estadísticos tradicionales.
Koch, Tim Verfasser], Michael [Akademischer Betreuer] Wicke, Kay [Akademischer Betreuer] Raum, and Claus-Peter [Akademischer Betreuer] [Czerny. "Predicting the intramuscular fat content in porcine M. longissimus via ultrasound spectral analysis with consideration of structural and compositional traits / Tim Koch. Gutachter: Michael Wicke ; Kay Raum ; Claus-Peter Czerny. Betreuer: Michael Wicke." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2011. http://d-nb.info/1043719369/34.
Повний текст джерелаBelovič, Boris. "Řešení složitých problémů s využitím evolučních algoritmů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218189.
Повний текст джерелаJanstad, Tobias. "Case study of a contract system : considering pulp prices from 1996-2006." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-1684.
Повний текст джерелаSödra Cell sells 1 900 000 ton pulp every year. Of this 490 000 tonne is sold with a contract system based on a pricing index called PIX NBSK. This index was started in 1996 and reflects the price of pulp from conferious forest. We study the NBSK PIX value of softwood from October 1996 to December 2006.
People working in this branch known that there is strong periodicity in the prices. We use predictive analysis to see if clients can benefit from the periodicity and use the options in the contract system Södra offers today. We conclude that a drawback for the current contract system is that there are too many contracts in proportion to the duration time that is one year for all contracts. Using a time series model called ARMA we make successfull predictions the price difference between two contracts. Based on this prediction we change between these contracts, reducing the price with 0.81% in mean during 1997-2006. Due to the total turnover, if all clients would used such predictions during 1997-2006 Södra's income would have been reduced with 2.77 million USD a year in mean.
The prices used before PIX are called list prices. The list prices seem to behave like the PIX index. Supposing that the same contract system we see in PIX today was used 1975-2006 with the list price as the base index I made a prediction of the list prices from 1986-2006. Thanks to my predictions, if I had been a client during this period and under mentioned considerations I would have been buying pulp to a price reduced with 0.57%.
If clients had known the PIX between 1996-2006 in say 1995 Södra's contract system based on PIX would give them a price reduction that were 1.5% in mean during 1996-2006. Price reduction is not possible all years, but when it occurs it can be as big as 3% of the price. Suppose the clients always choose the contract with the lowest price and thereby get a reduced price over time. Then with 95% probability over a long period the price reduction is somewhere in between 0.4-2.7%.
To strangle this price reduction possibility for the clients there are two ways to go: either reduce the number of contracts or extend the duration time of the contracts.
To find a suitable duration time, we do spectral density estimation to get indications of which periods that are most important. From this we see that PIX index has a period of five years, wavelet approximated PIX index has 3.4 years and the list prices has a period of 5.6 years. This indicates that current duration time one year is too short. Therefore if it wouldn't effect Södra's clients, an extension of the duration time from one to five years would be good.
If Södra don't extend the duration time of the contracts my recommendation is to have fewer contracts. The possibility to change between the contracts ''average last three months'' and ''average current month'' every other year is the weakest point of today's system. Therefore I recommend stop selling pulp to the contract ''average PIX last three months''.
We can't prove any longterm difference between the contracts. If Södra chooses to have just one contract from this point of view it does not matter which one they choose. However, it seems like a good idea to follow the global market and therefore I recommend to choose ''average PIX current month'' rather than ''average PIX last three months'' which lags behind the market front. Since the price ''average current month'' is available at FOEX web page I think Södra should choose this contract if they decide to have only one contract.
Södra Cell säljer årligen 1 900 000 ton pappersmassa. Av denna mängd säljs 490 000 ton enligt ett kontraktsystem baserat på ett prisindex som heter PIX NBSK. Detta index introducerades 1996 och reflekterar priset på pappersmassa gjord av barrträd. Jag studerar priset på indexet från Oktober 1996 till December 2006.
Dagens kontraktsystem är baserat på kontrakt med löptiden ett år. Jag undersöker om man kan prediktera prisskilllnaden mellan kontrakten, dra nytta att dagens löptid som bara är ett år och välja det kontrakt som ger det billigaste priset så ofta att priset över lång tid reduceras. När man predikterar gör man en uppskattningen av framtiden utifrån en modell av hur framtid beror på dåtid och nutid. Den modell jag har använt kallas ARMA. Denna tillsammans med priserna på pappersmassa från 1975 och framåt gav mig ett fruktbart sätt att förutsäga priserna. Resultatet blev ett pris reducerat med 0.81% i medel under perioden 1996-2006. Eftersom Södra ha så stor försäljningsvolym skulle de ha förlorat 2.27 miljoner dollar per ton i medel om alla kunder ha spekulerat utifrån den modellen jag använde.
Om dagens kontraktsystem hade börjat användas 1975 med listpriserna som bas hade en kund som använt min prediktionsmetod fått ett pris reducerat med 0.57% under perioden 1986-2006.
Om kunderna i förväg hade vetat priset under 1996-2001 gav det nuvarande systemet en reducerad medelintäkt med 1.5% av priset. Enskilda år reducerades intäkten med så mycket som 3%. Beräknar man konfidensintervall för prisreduktionerna så inser man att på lång sikt kommer dessa vara av storleksordningen 0.4-2.7% med sannolikheten 95%. Detta förutsatt att klienterna kan se in i framtiden. Siffran 2.7% alltså ett mått på hur stor risk man tar med dagens system. Jag tror inte att klienterna kommer reducera priset med 2.7% med nuvarande system, men det är en övre gräns.
De gynsamma prediktionerna har sitt ursprung i att det finns periodicitet i priserna. Jag undersöker denna periodicitet med spektralanalys. Periodiciteten för PIX indexet är starkast kring 5 år. En wavelet-approximation av PIX-indexet hade störst periodicitet kring 3.4 år. Listpriserna hade starkast periodicitet kring 5.6 år. Detta indikerar att den nuvarande löptiden, ett år, är för kort. En lämpligare löptid för kontrakten är 5 år.
Förmodligen är fem års löptid alltför lång tid att binda sig för många kunder. Därför föreslår jag att man reducerar antalet kontrakt istället. Den största svagheten i dagens system är den korta löptiden tillsammans med kontrakten ''average current month'' och ''average last three months''. Jag rekommenderar att man slutar erbjuda kontraktet ''average last three months''. Det allra säkraste är att endast erbjuda ett kontrakt. Vi har inte kunnat påvisa några skillnader över lång sikt mellan kontrakten såtillvida att något kontrakt skulle ge ett lägre medelpris än ett annat. Ur den aspekten är det godtyckligt vilket kontrakt man väljer, men det verkar vettigt att följa den globala marknaden. Därför är det eftersläpande kontraktet ''average PIX last three months'' inte att rekommendera, välj heller ''average PIX current month''. Ett annat argument för att välja ''average PIX current month'' är att dessa priser finns på FOEX hemsida och inga extra beräkningar behöver göras.
Hrušovský, Enrik. "Automatická klasifikace výslovnosti hlásky R." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-377664.
Повний текст джерелаKahaei, Mohammad Hossein. "Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36044/7/36044_Digitised_Thesis.pdf.
Повний текст джерелаHanzálek, Pavel. "Praktické ukázky zpracování signálů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400849.
Повний текст джерелаWaddle, C. Allen. "Fast spectral multiplication for real-time rendering." Thesis, 2018. https://dspace.library.uvic.ca//handle/1828/9332.
Повний текст джерелаGraduate
Chen, Chien-An, and 陳建安. "Streamflow Prediction Using Support Vector Regression and Higher Order Spectral Analysis." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/98221480497689654894.
Повний текст джерела國立臺灣大學
土木工程學研究所
103
In this study, high efficient support vector regression (SVR) and higher order spectral analysis (HOSA) for developing streamflow prediction models. Furthermore, runoff coefficient, stage-discharge rating curve and HEC-HMS are also utilized to simulate and adjust storm hydrograph. First of all, the principle of third-order cumulants is introduced. The largest order of the autoregressive moving average (ARMA) model can be rapidly and accurately solved using singular value decomposition and hypothesis testing. This method could overcome complex calculations and errors resulted by determining orders using autocorrelation function and partial autocorrelation function. Secondly, establish a lag time using the determined order and a streamflow prediction model using SVR. To avoid drawbacks of SVR estimation using trial-and-error method, simulated annealing (SA) is utilized to seek out the optimal parameters. As results indicated SA, coupled with SVR, predict streamflow effectively, and prove the advantages of calculating orders using HOSA. Lastly, the streamflow prediction model developed from this study has been successfully applied to three actual watersheds in Taiwan. Meanwhile to deal with underestimate and missing data, traditional stage-discharge rating curve method has been improved by adjusting storm discharge using runoff coefficient, so as to represent the actual hydrological state and examine the reliability of streamflow adjustment. The results are proven to be realistic and can be utilized as a reference for water resources policy, flood prevention and decision-making.
Morais, Mariana Francisca Lira de. "Conventional, spectral and non-linear analysis of external uterine contraction recordings in prediction of dystocia during labour." Master's thesis, 2015. https://repositorio-aberto.up.pt/handle/10216/89787.
Повний текст джерелаMorais, Mariana Francisca Lira de. "Conventional, spectral and non-linear analysis of external uterine contraction recordings in prediction of dystocia during labour." Dissertação, 2015. https://repositorio-aberto.up.pt/handle/10216/89787.
Повний текст джерелаKuo, Hsuan-Wei, and 郭炫偉. "The Spectrum Analysis and Prediction of Phosphor-converted White LED with Green and Red Phosphors." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/76794747574256952040.
Повний текст джерела明新科技大學
光電系統工程系碩士班
103
Generally, the phosphor-converting white Light-Emitting-Diode (LED) packages are typically fabricated by blue chip coated with green and red phosphors. However, for the phosphor-converting white LED with green and red phosphors, the re-absorption between green and red phosphors would introduce intensity deviations of the green and red emissions. The package engineers will pay a lot of effort and take excessive time to achieve the correct chromaticity of the white LEDs. In this study, we provide a method to predict the spectrum of the white LEDs through experimental analysis. With the spectrum model, the deviations of the CIE 1931 x and y between the prediction and real sample is less than 0.002; the deviation of the CCT between the prediction and real sample is less than 20K; the deviation of the CRI between the prediction and real sample is less than 3. Finally, we provide a good method to predict the spectrum of the phosphor-converting white LED with green and red phosphors with high accuracy.
Koch, Tim. "Predicting the intramuscular fat content in porcine M. longissimus via ultrasound spectral analysis with consideration of structural and compositional traits." Doctoral thesis, 2011. http://hdl.handle.net/11858/00-1735-0000-0006-AB2A-F.
Повний текст джерелаBandarabadi, Mojtaba. "Low-complexity measures for epileptic seizure prediction and early detection based on classification." Doctoral thesis, 2015. http://hdl.handle.net/10316/27608.
Повний текст джерелаThis thesis concerns the problems of epileptic seizure prediction and detection. We analyzed multichannel intracranial electroencephalogram (iEEG) and surface electroencephalogram (sEEG) recordings of patients suffering from refractory epilepsy, to access the brain state in real time by using relevant EEG features and computational intelligence techniques, and aiming for detection of pre-seizure state (in the case of prediction) or seizure onset times (in the case of detection). Our main original contribution is the development of a novel relative bivariate spectral power feature to track gradual transient changes prior to ictal events for real-time seizure prediction. Furthermore a novel robust and generalized measure for early seizure detection is developed, aimed to be used in closed-loop neurostimulation systems. The development of a general platform embeddable on a transportable low-power-budget device is of utmost importance, for real time warning to patients and their relatives about the impending seizure or beginning of an occurring seizure. The portable device can also be integrated to work in conjunction with a closed-loop neurostimulation or fast-acting drug injection mechanism to eventually disarm the impending seizure or to suppress the just-occurring seizure. Therefore, in this thesis we try to meet the dual-objective of developing algorithms for seizure prediction and early seizure detection that provide high sensitivity and low number of false alarms, fulfilling the requirements of clinical applications, while being low computational cost. To seek the first objective, a patient-specific seizure prediction was developed based on the extraction of novel relative bivariate spectral power features, which were then preprocessed, dimensionally reduced, and classified using a machine-learning algorithm. The introduced feature bears low complexity, and was discriminated using the powerful support vector machine (SVM) classifier. We analyzed the preictal EEG dynamics across different brain regions and throughout several frequency bands, using relative bivariate features to uncover the underlying mechanisms ending in epileptic seizures. The suggested prediction system was evaluated on long-term continuous sEEG and iEEG recordings of 24 patients, and produced statistically significant results with average sensitivity of 75.8% and false prediction rate of 0.1 per hour. Furthermore a novel statistical method was developed for proper selection of preictal period, and also for the evaluation of predictive capability of features, as well as for the predictability of seizures. The method uses amplitude distribution histograms (ADHs) of the features extracted from the preictal and interictal iEEG and sEEG recordings, and then calculates a criterion of discriminability among two classes. The method was evaluated on spectral power features extracted from monopolar and bipolar iEEG and sEEG recordings of 18 patients, in overall consisting of 94 epileptic seizures. To approach the objective of early seizure detection, we have formulated power spectral density (PSD) of bipolar EEG signal in the form of a measure of neuronal potential similarity (NPS) between two EEG signals. This measure encompasses the phase and amplitude similarities of two EEG channels in a simultaneous fashion. The NPS measure was then studied in several narrow frequency bands to find out the most relevant sub-bands involved in seizure initiations, and the best performing ratio of two NPS measures for seizure onset detection was determined. Evaluating on long-term continuous iEEG recordings of 11 patients with refractory partial epilepsy (overall of 1785 h and 183 seizures) the results showed high performance, while requiring a very low computational cost. On average, we could achieve a sensitivity of 86.3%, a low false detection rate (FDR) of 0.048/h, and a mean detection latency of 14.2s from electrographic seizure onsets, while in average preceding clinical onsets by 1.1s. Apart from the above mentioned primary objectives, we introduced two new and robust methods for offline or real-time labelling of epileptic seizures in long-term continuous EEG recordings for further studies. Methods include mean phase coherence estimated from bandpass filtered iEEG signals in specific frequency bands, and singular value decomposition (SVD) of bipolar iEEG signals. Both methods were evaluated on the same dataset employed in the previous study and demonstrated sensitivity of 84.2% and FDR of 0.09/h for sub-band mean phase coherence, and sensitivity of 84.1% and FDR of 0.05/h for bipolar SVD, on average. Most of this work was established in collaboration with the EPILEPSIAE project, aimed to predict of pharmacoresistant epileptic seizures. The developed methods in this thesis were evaluated by the accessibility of long-term continuous multichannel EEG recordings of more than 275 patients with refractory epilepsy, referred to as The European Epilepsy Database. This database was collected by the three clinical centers involved in EPILEPSIAE, and contains well-documented metadata. The results of this thesis are backing the hypothesis of the predictability of most of epileptic seizures using linear bivariate spectral-temporal brain dynamics. Moreover, the promising results of early seizure detection sustain the feasibility of integrating the proposed method with closed-loop neurostimulation systems. We hope the developed methods could be a step forward towards the clinical applications of seizure prediction and onset detection algorithms.
Esta tese versa os problemas de predição e de deteção de crises epiléticas. Analisa-se o eletroencefalograma multicanal intracraniano (iEEG) e de superfície (sEEG) de pacientes que sofrem de epilepsia refratária, para a estimação em tempo real do estado cerebral, usando características relevantes do EEG e técnicas de inteligência computacional, ambicionando a deteção do estado pré-ictal (no caso de previsão) ou dos instantes de início de uma crise (no caso de deteção). A principal contribuição original é o desenvolvimento de uma característica de potência espectral bivariada relativa para captar as mudanças transitórias graduais que levam a crises e que poderão ser usadas para previsão em tempo real. Além disso, é desenvolvida uma nova medida, robusta e generalizada para a deteção precoce, destinada a ser utilizada em sistemas de neuro estimulação em malha fechada. O desenvolvimento de uma plataforma geral possível de ser integrada num dispositivo transportável, energeticamente económico, é de grande relevância para o aviso em tempo real do doente e dos seus próximos sobre a eminência da ocorrência de uma crise. O dispositivo transportável também pode ser usado em malha fechada com um neuro estimulador ou com um dispositivo de injeção rápida de um fármaco que desarme eventualmente a crise em curso. Por isso nesta tese persegue-se o objectivo de desenvolver algoritmos para previsão mas também para deteção de crises. Em ambos os casos, pretende-se que os algoritmos tenham uma elevada sensibilidade e uma baixa taxa de falsos positivos, tornando viável a sua utilização clínica. Para o objectivo de previsão, desenvolveu-se um método de previsão personalizado baseado na extração de uma característica nova, denominada de potência relativa espectral bivariada, que foi submetida a pre-processamento, redução de dimensão e classificação com Máquinas de Vetores de Suporte (SVM). Esta nova característica, de baixa complexidade, é computacionalmente simples, mas permite a análise da dinâmica do EEG preictal em diferentes regiões do cérebro e ao longo de várias bandas de frequência, de modo a descobrir os mecanismos subjacentes às crises epiléticas. O sistema de previsão obtido foi avaliado em registos contínuos de sEEG e iEEG de 24 pacientes, e produziu resultados estatisticamente significativos com sensibilidade média de 75.8% e taxa de predição falsa de 0.1 por hora. Além disso, foi desenvolvido um novo método estatístico para a seleção apropriada do período preictal, e também para a avaliação da capacidade preditiva das características, assim como para a própria previsibilidade das crises. O método utiliza os histogramas de distribuição de amplitude (ADHS) das características extraídas nos períodos pré-ictal e ictal dos registos de iEEG e sEEG e, em seguida, calcula um critério de discriminabilidade entre as duas classes. O método foi avaliado nas características de potencia espectral extraídas de registos iEEG e sEEG, monopolares e bipolares de 18 pacientes, consistindo num número total de crises epilépticas de 94. O segundo objetivo, a deteção precoce de crises, foi abordado através da formulação da densidade de potência espectral (PSD) de canais de EEG bipolares na forma de uma medida da similaridade do potencial neuronal (NPS) entre dois sinais de EEG. Esta medida usa as similaridades entre as fases e as amplitudes de dois canais de EEG de um modo simultâneo. A medida NPS foi estudada em várias bandas estreitas de frequência de modo a descobrir-se quais as sub-bandas mais envolvidas na inicialização das crises; buscou-se assim a melhor razão entre duas NPS do ponto de vista da deteção precoce. Avaliadas em iEEG contínuos de longa duração de 11 doentes com epilepsia refratária parcial (num total de 1785 h e 183 crises), os resultados apresentam um desempenho com sensibilidade de 86.3% e taxa de deteção falsa (FDR) de 0.048/h, uma latência de 14.2s em relação ao início eletrográfico, sendo uma crise detetada em média 1.1s antes da sua manifestação clínica. Para além dos objetivos principais referidos acima, introduziram-se dois novos métodos, robustos, para etiquetagem em diferido e em tempo real das crises em registos contínuos de EEG de longa duração para estudos posteriores. Esses métodos incluem a coerência de fase média (mean phase coherence) estimada a partir de registos iEEG em bandas de frequência específicas (usando filtros passa-banda), e a decomposição em valores singulares (SVD) de sinais iEEG bipolares. Ambos os métodos foram avaliados no mesmo conjunto de dados do estudo anterior e apresentaram, em média, uma sensibilidade de 84.2% e um FDR de 0.09/h para a coerência de fase média calculada para as sub-bandas, e sensibilidade de 84.1% e FDR de 0.05/h para a metodologia que usa a decomposição SVD bipolar. Grande parte deste trabalho foi feito no âmbito do projeto EPILEPSIAE, visando a previsão de crises em doentes epiléticos fármaco-resistentes. Os métodos desenvolvidos nesta tese aproveitaram a acessibilidade aos dados bem documentados de mais de 275 pacientes que constituem a Base de Dados Europeia de Epilepsia (European Epilepsy Database), provenientes dos três centros hospitalares participantes no projeto. Os resultados desta tese apoiam a hipótese da previsibilidade da maioria das crises epiléticas usando dinâmicas cerebrais bivariadas lineares espetrais e temporais. Além disso os resultados são promissores relativamente à deteção precoce de crises e sustentam a fazibilidade da integração desses métodos com técnicas de neuroestimulação em malha fechada. Esperamos que os métodos desenvolvidos resultem num avanço no que respeita à aplicação clínica de algoritmos de previsão e deteção de crises.
FCT - SFRH/BD/71497/2010
Wu, Bing-Han, and 吳秉翰. "Analysis of the time series and multi-variate model for fault detection and prediction in the semiconductor plasma using wider range of spectrum as multi-sensor." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/24850349965438907845.
Повний текст джерела國立東華大學
電機工程學系
93
It is a great challenge in semiconductor manufacturing for the control of relatively complicated plasma system at the stage of nano- scale device development, especially in the process window stability issues due to the actions among species in plasma not belong to stoichiometry. Real-time fault detection can help to make nano-scale device not only for detecting the micro-change inside plasma, but also for the good critical dimension control in the next generation manufacturing technology. In this research, we use the principal component analysis (PCA) of multivariate statistics on the gas emission spectrum to correlate the variation of inside plasma parameters, then develop a fault prediction system dependent on spectrum trends and dynamic time by using time series model to provide an excellent plasma monitoring system. The detectable range of plasma spectrum is from 300 to 1000 nm in this experiment. By using principal component analysis, we successfully extract three dominated wavelengths, 751nm, 764nm and 812nm, and establish a fault diagnostic system individually. The accuracy of this system is 5% variation at the condition of RF power 50W and chamber pressure 117mtorr. We also establish a predication system by time series based on these wavelengths in this experiment. We made 9 faults and successfully detected them in this model described in my thesis. All experimental data are collected at the condition of radio frequency power 50W and chamber pressure 117mtorr.