8 resultados para Mean square analysis
em Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland
Resumo:
This Master’s Thesis is dedicated to the investigation and testing conventional and nonconventional Kramers-Kronig relations on simulated and experimentally measured spectra. It is done for both linear and nonlinear optical spectral data. Big part of attention is paid to the new method of obtaining complex refractive index from a transmittance spectrum without direct information of the sample thickness. The latter method is coupled with terahertz tome-domain spectroscopy and Kramers-Kronig analysis applied for testing the validity of complex refractive index. In this research precision of data inversion is evaluated by root-mean square error. Testing of methods is made over different spectral range and implementation of this methods in future is considered.
Resumo:
In this thesis different parameters influencing critical flux in protein ultrafiltration and membrane foul-ing were studied. Short reviews of proteins, cross-flow ultrafiltration, flux decline and criticalflux and the basic theory of Partial Least Square analysis (PLS) are given at the beginning. The experiments were mainly performed using dilute solutions of globular proteins, commercial polymeric membranes and laboratory scale apparatuses. Fouling was studied by flux, streaming potential and FTIR-ATR measurements. Critical flux was evaluated by different kinds of stepwise procedures and by both con-stant pressure and constant flux methods. The critical flux was affected by transmembrane pressure, flow velocity, protein concentration, mem-brane hydrophobicity and protein and membrane charges. Generally, the lowest critical fluxes were obtained at the isoelectric points of the protein and the highest in the presence of electrostatic repulsion between the membrane surface and the protein molecules. In the laminar flow regime the critical flux increased with flow velocity, but not any more above this region. An increase in concentration de-creased the critical flux. Hydrophobic membranes showed fouling in all charge conditionsand, furthermore, especially at the beginning of the experiment even at very low transmembrane pressures. Fouling of these membranes was thought to be due to protein adsorption by hydrophobic interactions. The hydrophilic membranes used suffered more from reversible fouling and concentration polarisation than from irreversible foul-ing. They became fouled at higher transmembrane pressures becauseof pore blocking. In this thesis some new aspects on critical flux are presented that are important for ultrafiltration and fractionation of proteins.
Resumo:
Tämän diplomityön tavoitteena oli tutkia kohinan poistoa spektrikuvista käyttäen pehmeitä morfologisia suodattimia. Työssä painotettiin impulssimaisen kohinan suodattamista. Suodattimien toimintaa arvioitiin numeerisesti keskimääräisen itseisarvovirheen, neliövirheen sekä signaali-kohinasuhteen avulla ja visuaalisesti tarkastelemalla suodatettuja kuvia sekä niiden yksittäisiä spektritasoja. Käytettyjä suodatusmenetelmiä olivat suodatus kuvapisteittäin spektrin suunnassa, suodatus koko spektrissä sekä kuutiomenetelmä ja komponenteittainen suodatus. Suodatettavat kuvat sisälsivät joko suola ja pippuri- tai bittivirhekohinaa. Parhaimmat suodatustulokset sekä numeeristen virhekriteerien että visuaalisen tarkastelun perusteella saatiin komponenteittaisella sekä kuvapisteittäisellä menetelmällä. Työssä käytetyt menetelmät on esitetty algoritmimuodossa. Suodatinalgoritmien toteutukset ja suodatuskokeet tehtiin Matlab-ohjelmistolla.
Resumo:
Stochastic differential equation (SDE) is a differential equation in which some of the terms and its solution are stochastic processes. SDEs play a central role in modeling physical systems like finance, Biology, Engineering, to mention some. In modeling process, the computation of the trajectories (sample paths) of solutions to SDEs is very important. However, the exact solution to a SDE is generally difficult to obtain due to non-differentiability character of realizations of the Brownian motion. There exist approximation methods of solutions of SDE. The solutions will be continuous stochastic processes that represent diffusive dynamics, a common modeling assumption for financial, Biology, physical, environmental systems. This Masters' thesis is an introduction and survey of numerical solution methods for stochastic differential equations. Standard numerical methods, local linearization methods and filtering methods are well described. We compute the root mean square errors for each method from which we propose a better numerical scheme. Stochastic differential equations can be formulated from a given ordinary differential equations. In this thesis, we describe two kind of formulations: parametric and non-parametric techniques. The formulation is based on epidemiological SEIR model. This methods have a tendency of increasing parameters in the constructed SDEs, hence, it requires more data. We compare the two techniques numerically.
Resumo:
In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.
Resumo:
ABSTRACT Maria Peltola Electrical status epilepticus during sleep – Continuous spikes and waves during sleep Department of Clinical Neurophysiology, University of Turku Department of Clinical Neurophysiology and Department of Pediatric Neurology, Children’s Hospital, Helsinki University Central Hospital Annales Universitatis Turkuensis, Medica-Odontologica, Turku, Finland, 2014 Background: Electrical status epilepticus during sleep (ESES) is an EEG phenomenon of frequent spikes and waves occurring in slow sleep. ESES relates to cognitive deterioration in heterogeneous childhood epilepsies. Validated methods to quantitate ESES are missing. The clinical syndrome, called epileptic encephalopathy with continuous spikes and waves during sleep (CSWS) is pharmacoresistant in half of the patients. Limited data exists on surgical treatment of CSWS. Aims and methods: The effects of surgical treatment were studied by investigating electroclinical outcomes in 13 operated patients (nine callosotomies, four resections) with pharmacoresistant CSWS and cognitive decline. Secondly, an objective paradigm was searched for assessing ESES by the semiautomatic quantification of spike index (SI) and measuring spike strength from EEG. Results: Postoperatively, cognitive deterioration was stopped in 12 (92%) patients. Three out of four patients became seizure-free after resective surgery. Callosotomy resulted in greater than 90% reduction of atypical absences in six out of eight patients. The preoperative propagation of ESES from one hemisphere to the other was associated with a good response. Semiautomatic quantification of SI was a robust method when the maximal interspike interval of three seconds was used to determine the “continuous” discharge in ten EEGs. SI of the first hour of sleep appeared representative of the whole night SI. Furthermore, the spikes’ root mean square was found to be a stable measure of spike strength when spatially integrated over multiple electrodes during steady NREM sleep. Conclusions: Patients with pharmacoresistant CSWS, based on structural etiology, may benefit from resective surgery or corpus callosotomy regarding both seizure outcome and cognitive prognosis. The semiautomated SI quantification, with proper userdefined settings and the new spatially integrated measure of spike strength, are robust and promising tools for quantifying ESES. Keywords: Electrical status epilepticus during sleep, ESES, continuous spikes and waves during sleep, CSWS, epilepsy surgery, spike index, spike strength, RMS TIIVISTELMÄ Maria Peltola Unenaikainen sähköinen status epilepticus Kliininen neurofysiologia, Turun yliopisto Kliininen neurofysiologia ja lastenneurologia, Lasten ja nuorten sairaala, Helsingin yliopistollinen keskussairaala Annales Universitatis Turkuensis, Medica-Odontologica, Turku, Suomi, 2014 Tausta: Sähköinen status epilepticus unessa (ESES) on aivosähkökäyrä (EEG)-ilmiö, jossa hidasaaltounen aikana esiintyy tiheä piikkihidasaaltopurkaus. ESES:n kvantifioimiseen ei ole olemassa validoituja menetelmiä. ESES on liitetty kognitiivisen tason laskuun ja tällöin puhutaan CSWS (continuous spikes and waves during sleep) - oireyhtymästä. CSWS ei vastaa lääkehoitoon puolella potilaista ja sen epilepsiakirurgisesta hoidosta on olemassa vain vähän tietoa. Tavoitteet ja menetelmät: Selvitimme retrospektiivisesti epilepsiakirurgian vaikusta elektrokliinisiin löydöksiin 13:lla lääkeresistenttiä CSWS-oireyhtymää sairastavalla lapsella, joilla oli rakenteellinen aivojen poikkeavuus. Toinen tavoite oli löytää objektiivinen puoliautomaattinen tapa mitata purkauksen määrää ja piikkien voimakkuutta EEG:stä. Tulokset: Kognitiivisen tason jatkuva heikentyminen loppui 12 (92 %) potilaalla leikkauksen jälkeen. Kolme neljästä resektiopotilaasta tuli kohtauksettomaksi. Kallosotomian jälkeen kuudella kahdeksasta potilaasta päivittäiset kohtaukset vähenivät yli 90 %:lla. Purkauksen leviäminen leikkausta edeltävästi vain yhdestä hemisfääristä toiseen liittyi hyvään leikkaushoitovasteeseen. Piikki-indeksi, jossa käytetään jatkuvan purkauksen määritelmänä maksimissaan kolmea sekuntia piikkien välillä, osoittautui luotettavaksi menetelmäksi ESES:n kvantifioimiseen. Useammasta elektrodista integroitu piikkien neliöllinen keskiarvo oli piikin voimakkuuden vakaa mitta häiriintymättömässä NREM-unessa. Päätelmät: Lääkehoidolle vastaamatonta CSWS:ää sairastavat potilaat, joilla on rakenteellinen aivopoikkeavuus ja yhdensuuntainen purkauksen leviämismalli, näyttävät kohtausten vähenemisen lisäksi hyötyvän epilepsiakirurgiasta kognitiivisesti. Puoliautomaattinen piikki-indeksin kvantifiointi sopivilla käyttäjäasetuksilla ja uusi spatiaalisesti integroitu piikin voimakkuuden mittari ovat stabiileja ja lupaavia ESES:n kvantitatiivisia mittareita. Avainsanat: Unenaikainen sähköinen status epilepticus, ESES, CSWS, epilepsiakirurgia, piikki-indeksi, piikin voimakkuus, neliöllinen keskiarvo
Resumo:
The main objective of this master’s thesis was to quantitatively study the reliability of market and sales forecasts of a certain company by measuring bias, precision and accuracy of these forecasts by comparing forecasts against actual values. Secondly, the differences of bias, precision and accuracy between markets were explained by various macroeconomic variables and market characteristics. Accuracy and precision of the forecasts seems to vary significantly depending on the market that is being forecasted, the variable that is being forecasted, the estimation period, the length of the estimated period, the forecast horizon and the granularity of the data. High inflation, low income level and high year-on-year market volatility seems to be related with higher annual market forecast uncertainty and high year-on-year sales volatility with higher sales forecast uncertainty. When quarterly market size is forecasted, correlation between macroeconomic variables and forecast errors reduces. Uncertainty of the sales forecasts cannot be explained with macroeconomic variables. Longer forecasts are more uncertain, shorter estimated period leads to higher uncertainty, and usually more recent market forecasts are less uncertain. Sales forecasts seem to be more uncertain than market forecasts, because they incorporate both market size and market share risks. When lead time is more than one year, forecast risk seems to grow as a function of root forecast horizon. When lead time is less than year, sequential error terms are typically correlated, and therefore forecast errors are trending or mean-reverting. The bias of forecasts seems to change in cycles, and therefore the future forecasts cannot be systematically adjusted with it. The MASE cannot be used to measure whether the forecast can anticipate year-on-year volatility. Instead, we constructed a new relative accuracy measure to cope with this particular situation.