Forecasting of wind speeds and directions with artificial neural networks

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.

Spatial modeling techniques for mapping and characterization of acid sulfate soils

Acid sulfate (a.s.) soils constitute a major environmental issue. Severe ecological damage results from the considerable amounts of acidity and metals leached by these soils in the recipient watercourses. As even small hot spots may affect large areas of coastal waters, mapping represents a fundamental step in the management and mitigation of a.s. soil environmental risks (i.e. to target strategic areas). Traditional mapping in the field is time-consuming and therefore expensive. Additional more cost-effective techniques have, thus, to be developed in order to narrow down and define in detail the areas of interest. The primary aim of this thesis was to assess different spatial modeling techniques for a.s. soil mapping, and the characterization of soil properties relevant for a.s. soil environmental risk management, using all available data: soil and water samples, as well as datalayers (e.g. geological and geophysical). Different spatial modeling techniques were applied at catchment or regional scale. Two artificial neural networks were assessed on the Sirppujoki River catchment (c. 440 km2) located in southwestern Finland, while fuzzy logic was assessed on several areas along the Finnish coast. Quaternary geology, aerogeophysics and slope data (derived from a digital elevation model) were utilized as evidential datalayers. The methods also required the use of point datasets (i.e. soil profiles corresponding to known a.s. or non-a.s. soil occurrences) for training and/or validation within the modeling processes. Applying these methods, various maps were generated: probability maps for a.s. soil occurrence, as well as predictive maps for different soil properties (sulfur content, organic matter content and critical sulfide depth). The two assessed artificial neural networks (ANNs) demonstrated good classification abilities for a.s. soil probability mapping at catchment scale. Slightly better results were achieved using a Radial Basis Function (RBF) -based ANN than a Radial Basis Functional Link Net (RBFLN) method, narrowing down more accurately the most probable areas for a.s. soil occurrence and defining more properly the least probable areas. The RBF-based ANN also demonstrated promising results for the characterization of different soil properties in the most probable a.s. soil areas at catchment scale. Since a.s. soil areas constitute highly productive lands for agricultural purpose, the combination of a probability map with more specific soil property predictive maps offers a valuable toolset to more precisely target strategic areas for subsequent environmental risk management. Notably, the use of laser scanning (i.e. Light Detection And Ranging, LiDAR) data enabled a more precise definition of a.s. soil probability areas, as well as the soil property modeling classes for sulfur content and the critical sulfide depth. Given suitable training/validation points, ANNs can be trained to yield a more precise modeling of the occurrence of a.s. soils and their properties. By contrast, fuzzy logic represents a simple, fast and objective alternative to carry out preliminary surveys, at catchment or regional scale, in areas offering a limited amount of data. This method enables delimiting and prioritizing the most probable areas for a.s soil occurrence, which can be particularly useful in the field. Being easily transferable from area to area, fuzzy logic modeling can be carried out at regional scale. Mapping at this scale would be extremely time-consuming through manual assessment. The use of spatial modeling techniques enables the creation of valid and comparable maps, which represents an important development within the a.s. soil mapping process. The a.s. soil mapping was also assessed using water chemistry data for 24 different catchments along the Finnish coast (in all, covering c. 21,300 km2) which were mapped with different methods (i.e. conventional mapping, fuzzy logic and an artificial neural network). Two a.s. soil related indicators measured in the river water (sulfate content and sulfate/chloride ratio) were compared to the extent of the most probable areas for a.s. soils in the surveyed catchments. High sulfate contents and sulfate/chloride ratios measured in most of the rivers demonstrated the presence of a.s. soils in the corresponding catchments. The calculated extent of the most probable a.s. soil areas is supported by independent data on water chemistry, suggesting that the a.s. soil probability maps created with different methods are reliable and comparable.

A conspectus on estimating function theory and its applicability to recurrent modeling issues in forest biometry

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Modelling non-industrial private forest landowners' strategic decision making by using logistic regression and neural networks: case of predicting the choice of forest taxation basis

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Modeling Forest Growth with Management Data: A Matrix Approach for the Italian Alps

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Modeling carbon sequestration and timber production in a regional case study

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Software Process Modeling with Eclipse Process Framework

The software development industry is constantly evolving. The rise of the agile methodologies in the late 1990s, and new development tools and technologies require growing attention for everybody working within this industry. The organizations have, however, had a mixture of various processes and different process languages since a standard software development process language has not been available. A promising process meta-model called Software & Systems Process Engineering Meta- Model (SPEM) 2.0 has been released recently. This is applied by tools such as Eclipse Process Framework Composer, which is designed for implementing and maintaining processes and method content. Its aim is to support a broad variety of project types and development styles. This thesis presents the concepts of software processes, models, traditional and agile approaches, method engineering, and software process improvement. Some of the most well-known methodologies (RUP, OpenUP, OpenMethod, XP and Scrum) are also introduced with a comparison provided between them. The main focus is on the Eclipse Process Framework and SPEM 2.0, their capabilities, usage and modeling. As a proof of concept, I present a case study of modeling OpenMethod with EPF Composer and SPEM 2.0. The results show that the new meta-model and tool have made it possible to easily manage method content, publish versions with customized content, and connect project tools (such as MS Project) with the process content. The software process modeling also acts as a process improvement activity.

Modeling and Simulation ofReal-Time System as a Tool to Improve Capacity Management - Case Fenix

Tämän tutkimustyön kohteena on TietoEnator Oy:n kehittämän Fenix-tietojärjestelmän kapasiteettitarpeen ennustaminen. Työn tavoitteena on tutustua Fenix-järjestelmän eri osa-alueisiin, löytää tapa eritellä ja mallintaa eri osa-alueiden vaikutus järjestelmän kuormitukseen ja selvittää alustavasti mitkä parametrit vaikuttavat kyseisten osa-alueiden luomaan kuormitukseen. Osa tätä työtä on tutkia eri vaihtoehtoja simuloinnille ja selvittää eri vaihtoehtojen soveltuvuus monimutkaisten järjestelmien mallintamiseen. Kerätyn tiedon pohjaltaluodaan järjestelmäntietovaraston kuormitusta kuvaava simulaatiomalli. Hyödyntämällä mallista saatua tietoa ja tuotantojärjestelmästä mitattua tietoa mallia kehitetään vastaamaan yhä lähemmin todellisen järjestelmän toimintaa. Mallista tarkastellaan esimerkiksi simuloitua järjestelmäkuormaa ja jonojen käyttäytymistä. Tuotantojärjestelmästä mitataan eri kuormalähteiden käytösmuutoksia esimerkiksi käyttäjämäärän ja kellonajan suhteessa. Tämän työn tulosten on tarkoitus toimia pohjana myöhemmin tehtävälle jatkotutkimukselle, jossa osa-alueiden parametrisointia tarkennetaan lisää, mallin kykyä kuvata todellista järjestelmää tehostetaanja mallin laajuutta kasvatetaan.

Modeling competencies and capabilities in knowledge intensive business environment

Tämän diplomityön päämääränä oli tutkia Perloksen teknologiaosaamisia. Perloksen tavoitteena on tulevaisuudessa yhdistää ja soveltaa uusia teknologioita ja älykkäitä materiaaleja muovimekaniikkaan.Ideana oli mallintaa Perloksen osaamisia ja osaamisgapeja ottaen huomioon heidän tulevaisuuden visionsa. Projektituotteena osaamisten mallintamisessa oli Perlos Healthcaren asiakkaan analysoiva mittauslaite. Tutkimuksen arvo on huomattava sillä tunnistamalla osaamisensa ja kyvykkyytensä yritys pystyy luomaan paremman tarjooman vastatessaan koko ajan kasvaviin asiakasvaatimuksiin. Tutkimus on osa TEKESin rahoittamaa LIIMA -projektia. Työn ensimmäisessä osassa esitellään osaamiseen ja partneroitumiseen liittyviä teorioita. Osaamisten mallintaminen tehtiin Excel -pohjaisella työkalulla. Se sisältää projektituotteeseen liittyen osaamisriippuvuuksien mallintamisen ja gap -analyysin. Yhtenä tutkimusmetodina käytettiin haastattelututkimusta. Työ ja sen tulokset antavat operatiivista hyötyä teknologioiden ja markkinoiden välisessä kentässä.

Utilization of Knowledge Management in Business Process Modeling and Process Improvements

Tutkimuksen tavoitteena on tutkia telekommunikaatioalalla toimivan kohdeyrityksen ohjelmistojen toimitusprosessia° Tutkimus keskittyy mallintamaan toimitusprosessin, määrittelemään roolit ja vastuualueet, havaitsemaan ongelmakohdat ja ehdottamaan prosessille kehityskohteita. Näitä tavoitteita tarkastellaan teoreettisten prosessimallinnustekniikoiden ja tietojohtamisen SECI-prosessikehyksen läpi. Tärkein tiedonkeruun lähde oli haastatteluihin perustuva tutkimus, johon osallistuvat kaikki kohdeprosessiin kuuluvat yksiköt. Mallinnettu toimitusprosessi antoi kohdeyritykselle paremman käsityksen tarkasteltavasta prosessista ja siinä toimivien yksiköiden rooleistaja vastuualueista. Parannusehdotuksia olivat tiedonjaon kanavoinnin määritteleminen, luottamuksen ja sosiaalisten verkostojen parantaminen, ja tietojohtamisen laajamittainen implementointi.

Modeling of Pressurizer Using APROS and TRACE Thermal Hydraulic Codes

Diplomityön tavoitteena on paineistimen yksityiskohtainen mallintaminen APROS- ja TRACE- termohydrauliikkaohjelmistoja käyttäen. Rakennetut paineistinmallit testattiin vertaamalla laskentatuloksia paineistimen täyttymistä, tyhjentymistä ja ruiskutusta käsittelevistä erilliskokeista saatuun mittausdataan. Tutkimuksen päätavoitteena on APROSin paineistinmallin validoiminen käyttäen vertailuaineistona PACTEL ATWS-koesarjan sopivia paineistinkokeita sekä MIT Pressurizer- ja Neptunus- erilliskokeita. Lisäksi rakennettiin malli Loviisan ydinvoimalaitoksen paineistimesta, jota käytettiin turbiinitrippitransientin simulointiin tarkoituksena selvittää mahdolliset voimalaitoksen ja koelaitteistojen mittakaavaerosta johtuvat vaikutukset APROSin paineistinlaskentaan. Kokeiden simuloinnissa testattiin erilaisia noodituksia ja mallinnusvaihtoehtoja, kuten entalpian ensimmäisen ja toisen kertaluvun diskretisointia, ja APROSin sekä TRACEn antamia tuloksia vertailtiin kattavasti toisiinsa. APROSin paineistinmallin lämmönsiirtokorrelaatioissa havaittiin merkittävä puute ja laskentatuloksiin saatiin huomattava parannus ottamalla käyttöön uusi seinämälauhtumismalli. Työssä tehdyt TRACE-simulaatiot ovat osa United States Nuclear Regulatory Commissionin kansainvälistä CAMP-koodinkehitys-ja validointiohjelmaa.