Due to technological progress in forestry, seedlings with covered root systems-especially those grown in container nurseries-have become increasingly important in forest nursery production. One the trees that is most commonly grown this way is the common oak (Quercus robur L.). For an acorn to be sown in a container, it is necessary to remove its upper part during mechanical scarification, and evaluate its sowing suitability. At present, this is mainly done manually and by visual assessment. The low effectiveness of this method of acorn preparation has encouraged a search for unconventional solutions. One of them is the use of an automated device that consists of a computer vision-based module. For economic reasons related to the cost of growing seedlings in container nurseries, it is beneficial to minimize the contribution of unhealthy seeds. The maximum accuracy, which is understood as the number of correct seed diagnoses relative to the total number of seeds being assessed, was adopted as a criterion for choosing a separation threshold. According to the method proposed, the intensity and red components of the images of scarified acorns facilitated the best results in terms of the materials examined during the experiment. On average, a 10% inaccuracy of separation was observed. A secondary outcome of the presented research is an evaluation of the ergonomic parameters of the user interface that is attached to the unit controlling the device when it is running in its autonomous operation mode.
One- and two-compartmental models describe the concentration of the urea, creatinine, and uric acid very effectively, in contrast with phosphorus, in which modeling results are not satisfactory. Although two-compartmental models are more effective, they are much more complicated than one-compartmental models, which justifies using the one-compartmental model for hemodialysis modeling. A two-compartmental model must be used in the case of rebound phenomenon modeling. The total body water values we have obtained are similar to the anthropometrically based values for urea and creatinine and to a lesser degree for uric acid. Distribution volumes for one- and two-compartmental models obtained from patient weight are the simplest coefficients for mathematical models and have sufficient precision as well. The global value of both compartments is slightly greater than the corresponding value for a one-compartmental model. The effectiveness of dialyzers is in practice lower than might be expected on the basis of the data provided by their manufacturers. Urea cellular clearance is two times greater than creatinine and uric acid cellular clearances. The clearance differences are more prominent for the cellular membrane than for artificial semipermeable membranes.
This work presents analysis of chromatographic signal used to identify substances in samples. First part consists of chromatography overview and description of three classification methods (neural network with backpropagation, probabilistic neural network with Parzen window and support vector machines).Designed algorithm consists of several stages: signal filtering, peak detection and its approximation with sum of two Gaussian functions.The parameters of that two curves are the features vectors describing the peak of the substance. The last step is classification, for which two types of supervised machine learning were compared, based on the whole signal and on features vectors. Both types were tested for different classificators and their parameters.Verification was based on 55 chromatography signals. The best results for both methods of learning were achieved for probabilistic neural networks. The correct classification rate was 82% for the whole signal and 93% for feature vectors.
In the paper an experiment is described, that was designed and conducted to verify hypothesis that artificial neuron with sigmoidal activation function can efficiently solve the task of logistic regression in the case when the explaining variable is one-dimensional, and the explained variable is binomial. Computations were performed with 12 sets of statistical parameters, assumed for the generation of 65356 sets of data in each case. Comparative analysis of the obtained results with use of the reference values for the regression coefficients indicated that the investigated neuron can satisfactory perform the task, with efficiency similar to that obtained with classical logistic regression algorithm, when the teaching sets of input data, corresponding with output values 0 and 1, do not allow for simple separation. Moreover, it has been discovered that the simple formulas estimating the statistical distributions parameters from the samples, offer statistically superior assessment of the regression coefficient parameters.
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