Pesulaitteen horisontaalisen harjan nostomekanismin kehittäminen

Työssä on pyritty löytämään ratkaisuvaihtoehtoja pesulaitteen horisontaalisen harjan nostomekanismin parantamiseksi. Nostomekanismia pyrittiin kehittämään vähemmän huoltoa vaativaksi, toimintavarmemmaksi sekä kustannuksiltaan halvemmaksi. Uusia vaihtoehtoja mekanismille pyrittiin löytämään järjestelmällisen ideoinnin avulla, käyttäen hyödyksi alan kirjallisuutta, käytössä olevia pesulaitteita sekä jokapäiväisessä elämässä eteen tulleita havaintoja. Korroosiota aiheuttavat olosuhteet, horisontaalisen harjakokoonpanon pitkä liikerata sekä paino asettavat erityisvaatimuksia uudelle mekanismille. Lisäksi mekanismin täytyy liikuttaa harjaa vertikaalisessa suunnassa vakiona pysyvällä nopeudella. Valittuja ratkaisuvaihtoehtoja tarkasteltiin pistearvioinnin avulla ennen jatkokehittelyn aloittamista. Alustavassa komponenttien mitoituksessa pyrittiin mekanismin komponenteille löytämään oikea valmistusmateriaali sekä komponenttien geometriset mitat. Mekanismin valmistuskustannuksia eri valmistusmenetelmien kesken kartoitettiin yhteydenotoilla eri konepajojen kanssa. Alustavien kustannuslaskelmien perusteella jatkokehittelyyn otetun mekanismin kustannukset on 70 prosenttia pienemmät kuin nykyisen mekanismin kustannukset. Osien vähyys sekä mekanismin yksinkertaisuus lisää nostomekanismin luotettavuutta sekä vähentää tarvittavia huoltotoimenpiteitä.

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.

Mass loss of four commercially available heat-polymerized acrylic resins after toothbrushing with three different dentifrices

The association between a toothbrush and a dentifrice is the most used denture cleaning method. The purpose of this study was to evaluate the abrasiveness of specific and non-specific denture cleaning dentifrices on different heat-polymerized acrylic resins. Sixteen specimens (90x30x3mm) of each acrylic resin (QC-20, Lucitone 550, Clássico, Vipi-Cril) were prepared and randomly assigned to 4 groups: 1: control (distilled water), 2: Colgate, 3: Bonyplus and 4: Dentu-Creme. The specimens were subjected to simulated toothbrushing in an automatic brushing machine using 35,600 brush strokes for each specimen. Brushing abrasion run at a 200-g load with the specimens immersed in 2:1 dentifrice/water slurry. Specimens were reconditioned to constant mass and the mass loss (mg) was evaluated. Data were analyzed by 2-way ANOVA and Tukey's test (a=0.05). Analysis of dentifrices' abrasive particles was made by scanning electron microscopy. Colgate produced the greatest mass reduction (42.44 mg, p<0.05), followed by Dentu-Creme (33.60 mg). Bonyplus was the less abrasive (19.91 mg), similar to the control group (19.69 mg) (p>0.05). The mass loss values indicated that QC-20 (33.13 mg) and Lucitone 550 (33.05 mg) resins were less (p<0.05) resistant to abrasion than Clássico (26.04 mg) and Vipi-Cril (23.43 mg). In conclusion, Colgate produced the greatest abrasion. Specific dentifrices for dentures tend to cause less damage to acrylic resins.

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.

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.

Failure analysis of belt/roll tribological pair used for the production of eucalypt fiber panels

The premature failure of a large agglomeration machine used for the annual production of 360,000 m(3) of eucalypt fiber panels was investigated to identify the nucleation and growth mechanisms of cracking in PH stainless steel belts (126 m x 2.9 m x 3.0 mm). These belts are used to compress a cushion composed of eucalyptus fibers and glue, being the pressure transmitted from the pistons by the action of numerous case-hardening steel rolls. Examination of the belt working interfaces (belt/rolls and belt/eucalypt fibers) indicated that the main cracking was nucleated on the belt/roll interface and that there is a clear relationship between the crack nucleation and the presence of superficial irregularities, which were observed on the belt/roll working surface. Used rolls showed the presence of perimetric wear marks and 2 mu m silicon-rich encrusted particles (identified as silicon carbide). Lubricant residues contained the presence of helicoidal wires, which were originated by the release of the stainless steel cleaning brush bristles, and 15 mu m diameter metallic particles, which were generated by material detachment of the belt. The presence of foreign particles on the tribological interface contributed to an increase of the shear stresses at the surfaces and, consequently, the number of the contact fatigue crack nucleation sites in the belt/roll tribo-interface. The cracking was originated on the belt/roll interface of the stainless steel belt by a mixed rolling/slip contact fatigue mechanism, which promoted spalling and further nucleation and growth of conventional fatigue cracks. Finally, the system lubrication efficiency and the cleaning procedure should be optimised in order to increase the life expectancy of the belt. (c) 2006 Elsevier Ltd. All rights reserved.

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.

A study of the interaction between operator style and machine capability for a hydraulic mining excavator

This paper presents a case study that explores how operator digging style juxtaposes with mechanical capability for a class of hydraulic mining excavators. The relationships between actuator and digging forces are developed and these are used to identify the excavator's capability to apply forces in various directions. Two distinct modes of operation are examined to see how they relate to the mechanical capabilities of the linkage and to establish if one has merit over the other. It is found that one of these styles results in lower loading of the machine.

Improving Alzheimer`s Disease Diagnosis with Machine Learning Techniques

There is not a specific test to diagnose Alzheimer`s disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.