Finnish embryo transfer breeding program "ASMO" : description of the goals and a summary of the results of initial selection

Selostus: Alkionsiirtojalostusohjelma "ASMO", sen tavoitteet ja yhteenveto alkuvalinnan tuloksista

Description of three new arctic bramble cultivars and proposal for cultivar identification

Selostus: Kolmen uuden mesimarjalajikkeen kuvaukset ja lajikekuvausohjeet mesimarjalle ja jalomaaraimelle

Description and implementation of cut calculation-method in a sawmill

Työn tavoitteena oli kuvata ja ottaa käyttöön sahauseräkohtaisen kannattavuuden laskentamenetelmä sahalle, sekä tehdä laskentamalli menetelmän tueksi. Sahauksen peruskäsitteiden jälkeen työssä on esitelty sahan tuotantoprosessi. Tuotantoprosessi on kuvattu kirjallisuuden ja asiantuntijoiden haastattelujen perusteella. Seuraavaksi kartoitettiin hyötyjä ja vaikutuksia, mitä laskentamenetelmältä odotetaan.. Kustannuslaskennan teoriaa selvitettiin kirjallisuuslähteitä käyttäen silmälläpitäen juuri tätä kehitettävää laskentamenetelmää. Lisäksi esiteltiin Uimaharjun sahalla käytettävät ja laskentaan liittyvät laskenta- ja tietojärjestelmät.Nykyisin sahalla ei ole minkäänlaista menetelmää sahauseräkohtaisen tuloksen laskemiseksi. Pienillä muutoksilla sahan tietojärjestelmään ja prosessikoneisiin voidaan sahauserä kuljettaa prosessin läpi niin, että jokaisessa prosessin vaiheessa sille saadaan kohdistettua tuotantotietoa. Eri vaiheista saatua tietoa käyttämällä saadaan tarkasti määritettyä tuotteet, joita sahauserä tuotti ja paljonko tuotantoresursseja tuottamiseen kului. Laskentamalliin syötetään tuotantotietoja ja kustannustietoa ja saadaan vastaukseksi sahauserän taloudellinen tulos.Toimenpide ehdotuksena esitetään lisätutkimusta tuotantotietojen automaattisesta keräämisestä manuaalisen työn ja virheiden poistamiseksi. Suhteellisen pienillä panoksilla voidaan jokaiselle sahauserälle kerätä tuotantotiedot täysin automaattisesti. Lisäksi kehittämäni laskentamallin tilalle tulisi hankkia sovellus, joka käyttäisi paremmin hyväksi nykyisiä tietojärjestelmiä ja poistaisi manuaalisen työvaiheen laskennassa.

Classification and inventory management of abnormal demand items

The main objective of the study is to form a framework that provides tools to recognise and classify items whose demand is not smooth but varies highly on size and/or frequency. The framework will then be combined with two other classification methods in order to form a three-dimensional classification model. Forecasting and inventory control of these abnormal demand items is difficult. Therefore another object of this study is to find out which statistical forecasting method is most suitable for forecasting of abnormal demand items. The accuracy of different methods is measured by comparing the forecast to the actual demand. Moreover, the study also aims at finding proper alternatives to the inventory control of abnormal demand items. The study is quantitative and the methodology is a case study. The research methods consist of theory, numerical data, current state analysis and testing of the framework in case company. The results of the study show that the framework makes it possible to recognise and classify the abnormal demand items. It is also noticed that the inventory performance of abnormal demand items differs significantly from the performance of smoothly demanded items. This makes the recognition of abnormal demand items very important.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.