Koordinaattimittauksen kehittäminen metsäkonetuotannossa

Tämän diplomityön tarkoituksena oli koordinaattimittauksen kehittäminen ja systematisointi metsäkoneen valmistuksessa. Työssä tutkittiin kannettavalla nivelvarsimittalaitteella tehtävää koordinaattimittausta tehtaan omille tuotteille, valmistuksen apuvälineille ja toimittajilta tuleville osille. Tavoitteena oli mittaustoiminnan kokonaisvaltainen kehittäminen ja vastauksia haettiin seuraaviin tutkimuskysymyksiin: Mitä kohteita tulee mitata? Missä tuotantovaiheessa mittausta tarvitaan? Kuinka mittaustuloksia tulee käsitellä ja hyödyntää? Kuinka paljon mittaustoiminta vaatii resursseja ja kuinka hyvin käytössä oleva koordinaattimittauslaite soveltuu yrityksen tuotantoon? Lisäksi pohdittiin koordinaattimittauksen merkitystä yritykselle ja etsittiin kehityskohteita mittaustoiminnasta? Tutkimusaineistona käytettiin vuosien 2007 – 2008 aikana saatuja koordinaattimittauskokemuksia ja mittaustuloksia. Lisäksi tutkittiin yrityksen sisäisiä poikkeamaraportteja ja tutustuttiin koordinaattimittauslaitteen ja mittausohjelman toimintaan sekä yrityksen tuotantoon. Tutkimuksessa tultiin siihen tulokseen, että koordinaattimittauksen ensisijainen tavoite tulee olla valmistusprosessin kehittäminen ja mittauslaitetta tulee käyttää tehokkaasti hyväksi uusien tuotteiden valmistuksen alkuvaiheessa.

Wooden packaging waste reuse and refining

The aim of the thesis was both to study wooden packaging waste reuse and refining generated in the forestry machine factory environment, and to find alternative wooden packaging waste utilization options in order to create a new operating model which would decrease the overall amount of waste produced. As environmental and waste legislation has become more rigid and companies' own environmental management systems’ requirements and control have increased, companies have had to consider their environmental aspects more carefully. Companies have to take into account alternative ways of reducing waste through an increase in reuse and recycling. A part of this waste is from different forms of packaging. In the metal industry the most heavily used packaging material is wooden packaging, as such material is heavy and the packaging has to be able to bear heavy stress. In the theoretical part of the thesis, the requirements of packaging and packaging waste legislation, as well as environmental management systems governing companies’ processing of their packaging waste, are studied. The theoretical part includes a process study of systems, which direct packaging waste and wooden packaging waste refining. In addition, methods related to the continuous improvement of these processes are introduced. This thesis concentrates on designing and creating a new operating model in relation to wooden packaging waste processing. The main target was to find an efficient model in order to decrease the total amount of wooden packaging waste and to increase refining. The empirical part introduces methods for approaches to wooden packaging waste re-utilization, as well as a description of a new operating model and its impact.

Nonlinear set-membership estimation: a support vector machine approach

In this paper a support vector machine (SVM) approach for characterizing the feasible parameter set (FPS) in non-linear set-membership estimation problems is presented. It iteratively solves a regression problem from which an approximation of the boundary of the FPS can be determined. To guarantee convergence to the boundary the procedure includes a no-derivative line search and for an appropriate coverage of points on the FPS boundary it is suggested to start with a sequential box pavement procedure. The SVM approach is illustrated on a simple sine and exponential model with two parameters and an agro-forestry simulation model.

Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

Classification of breast masses using a committee machine of artificial neural networks

This work proposes a new approach using a committee machine of artificial neural networks to classify masses found in mammograms as benign or malignant. Three shape factors, three edge-sharpness measures, and 14 texture measures are used for the classification of 20 regions of interest (ROIs) related to malignant tumors and 37 ROIs related to benign masses. A group of multilayer perceptrons (MLPs) is employed as a committee machine of neural network classifiers. The classification results are reached by combining the responses of the individual classifiers. Experiments involving changes in the learning algorithm of the committee machine are conducted. The classification accuracy is evaluated using the area A. under the receiver operating characteristics (ROC) curve. The A, result for the committee machine is compared with the A, results obtained using MLPs and single-layer perceptrons (SLPs), as well as a linear discriminant analysis (LDA) classifier Tests are carried out using the student's t-distribution. The committee machine classifier outperforms the MLP SLP, and LDA classifiers in the following cases: with the shape measure of spiculation index, the A, values of the four methods are, in order 0.93, 0.84, 0.75, and 0.76; and with the edge-sharpness measure of acutance, the values are 0.79, 0.70, 0.69, and 0.74. Although the features with which improvement is obtained with the committee machines are not the same as those that provided the maximal value of A(z) (A(z) = 0.99 with some shape features, with or without the committee machine), they correspond to features that are not critically dependent on the accuracy of the boundaries of the masses, which is an important result. (c) 2008 SPIE and IS&T.

Análise técnica e econômica de prestadores de serviços na colheita florestal

The objective of this Study was to describe the financial conditions of forestry contractors, concerning life quality aspects, condition of work and equipments, operational costs, and economic credit to invest in new technologies. Five companies had been analyzed, with an annual income between US$ 400,000.00 and US$ 1,720,000.00, with an average of US$ 950,000.00. The number of employees varied between 33 and 181, and the companies were classified in terms of size as: one small, two average, and two big. The main difficulties to invest in new machines were high financial taxes, more than 12% an year, and a lack of long term contracts to guarantee the payment capability. It was observed that the contractors did not consider the capital remuneration and a correct depreciation of machines, resulting in an average machine life higher than 10 years. The final conclusions were that the costs were above the paid values for the services, when computed the depreciation and capital remuneration, with negative results In the financial analyzes of three companies. Finally, the mechanization process increased the workers life quality, however, the annual income was around US$ 2,112.00 per worker, approximately 39% lower than the average Brazilian population.

Thermal error evaluation and modelling of a CNC cylindrical grinding machine

With the relentless quest for improved performance driving ever tighter tolerances for manufacturing, machine tools are sometimes unable to meet the desired requirements. One option to improve the tolerances of machine tools is to compensate for their errors. Among all possible sources of machine tool error, thermally induced errors are, in general for newer machines, the most important. The present work demonstrates the evaluation and modelling of the behaviour of the thermal errors of a CNC cylindrical grinding machine during its warm-up period.

Hygroexpansivity profiles on a commercial paper machine

Paper products show dimensional changes when subjected to moisture content modification. Hygroexpansivity was investigated in a commercial paper machine operating at 1256 m/min by a set of measurements on 75 g/m(2) reprographic bleached eucalyptus pulp paper samples. The present work shows hygroexpansivity development in different sections of the paper machine along the manufacturing direction. The measurement results demonstrate the effects of papermaking process operations on paper hygroexpansivity and lead to the confirmation of fiber orientation degree, drying restraint and shrinkage and paper tension as significant influencing factors. Structural, strength and elastic properties of paper were also measured as a function of machine direction position and presented for discussion purposes.

Scheduling in a two-machine flowshop for the minimization of the mean absolute deviation from a common due date

This paper addresses the minimization of the mean absolute deviation from a common due date in a two-machine flowshop scheduling problem. We present heuristics that use an algorithm, based on proposed properties, which obtains an optimal schedule fora given job sequence. A new set of benchmark problems is presented with the purpose of evaluating the heuristics. Computational experiments show that the developed heuristics outperform results found in the literature for problems up to 500 jobs. (C) 2007 Elsevier Ltd. All rights reserved.

Minimizing total tardiness in a stochastic single machine scheduling problem using approximate dynamic programming

This paper addresses the non-preemptive single machine scheduling problem to minimize total tardiness. We are interested in the online version of this problem, where orders arrive at the system at random times. Jobs have to be scheduled without knowledge of what jobs will come afterwards. The processing times and the due dates become known when the order is placed. The order release date occurs only at the beginning of periodic intervals. A customized approximate dynamic programming method is introduced for this problem. The authors also present numerical experiments that assess the reliability of the new approach and show that it performs better than a myopic policy.

The single machine earliness and tardiness scheduling problem: lower bounds and a branch-and-bound algorithm

This paper addresses the single machine scheduling problem with a common due date aiming to minimize earliness and tardiness penalties. Due to its complexity, most of the previous studies in the literature deal with this problem using heuristics and metaheuristics approaches. With the intention of contributing to the study of this problem, a branch-and-bound algorithm is proposed. Lower bounds and pruning rules that exploit properties of the problem are introduced. The proposed approach is examined through a computational comparative study with 280 problems involving different due date scenarios. In addition, the values of optimal solutions for small problems from a known benchmark are provided.

Evolutionary conservation of the membrane fusion machine

Recent structural studies of proteins mediating membrane fusion reveal intriguing similarities between diverse viral and mammalian systems. Particularly striking is the close similarity between the transmembrane envelope glycoproteins from the retrovirus HTLV-1 and the filovirus Ebola. These similarities suggest similar mechanisms of membrane fusion. The model that fits most currently available data suggests fusion activation in viral systems is driven by a symmetrical conformational change triggered by an activation event such as receptor binding or a pH change. The mammalian vesicle fusion mediated by the SNARE protein complex most likely occurs by a similar mechanism but without symmetry constraints.