One broad-leaved pioneer tree, Alnus formosana, two broad-leaved understory shrubs, Ardisia crenata and Ardisia cornudentata, and four ferns with different light adaptation capabilities (ranked from high to low, Pyrrosia lingus, Asplenium antiquum, Diplazium donianum, Archangiopteris somai) were used to elucidate the light responses of photosynthetic rate and electron transport rate (ETR). Pot-grown materials received up to 3 levels of light intensity, i.e., 100%, 50% and 10% sunlight. Both gas exchange and chlorophyll (Chl) fluorescence were measured simultaneously by an equipment under constant temperature and 7 levels (0-2,000 μmol m-2 s-1) of photosynthetic photon flux density (PPFD). Plants adapted to-or acclimated to high light always had higher
light-saturation point and maximal photosynthetic rate. Even materials had a broad range of photosynthetic capacity [maximal photosynthetic rate ranging from 2 to 23 μmol(CO2) m-2 s-1], the ratio of ETR to gross photosynthetic rate (PG) was close for A. formosana and the 4 fern species when measured under constant temperature, but the PPFD varied. In addition, P. lingus and A. formosana grown under 100% sunlight and measured at different seasonal temperatures (15, 20, 25, and 30°C) showed increased ETR/P G ratio with increasing temperature and could be fitted by first- and second-order equations, respectively. With this equation, estimated and measured PG were closely correlated (r2 = 0.916 and r2 = 0.964 for P. lingus and A. formosana, respectively, p<0.001). These equations contain only the 2 easily obtained dynamic indicators, ETR and leaf temperature. Therefore, for some species with near ETR/PG ratio in differential levels of PPFD, these equations could be used to simulate dynamic variation of leaf scale photosynthetic rate under different temperature and PPFD conditions., S.-L.. Wong ... [et al.]., and Obsahuje bibliografii
Relations between two Boolean attributes derived from data can be
quantified by truth functions defined on four-fold tables corresponding to pairs of the attributes. Several classes of such quantifiers (implicational, double implicational, equivalence ones) with truth values in the unit interval were investigated in the frame of the theory of data mining methods. In the fuzzy logic theory, there are well-defined classes of fuzzy operators, namely t-norms representing various types of evaluations of fuzzy conjunction (and t-conorms representing fuzzy disjunction), and operators of fuzzy implications.
In the contribution, several types of constructions of quantifiers using fuzzy operators are described. Definitions and theorems presented by the author in previous contributions to WUPES workshops are summarized and illustrated by examples of well-known quantifiers and operators.
There is growing interest to analyze electroencephalogram (EEG) signals with the objective of classifying schizophrenic patients from the control subjects. In this study, EEG signals of 15 schizophrenic patients and 19 age-matched control subjects are recorded using twenty surface electrodes. After the preprocessing phase, several features including autoregressive (AR) model coefficients, band power and fractal dimension were extracted from their recorded signals. Three classifiers including Linear Discriminant Analysis (LDA), Multi-LDA (MLDA) and Adaptive Boosting (Adaboost) were implemented to classify the EEG features of schizophrenic and normal subjects. Leave-one (participant)-out cross validation is performed in the training phase and finally in the test phase; the results of applying the LDA, MLDA and Adaboost respectively provided 78%, 81% and 82% classification accuracies between the two groups. For further improvement, Genetic Programming (GP) is employed to select more informative features and remove the redundant ones. After applying GP on the feature vectors, the results are remarkably improved so that the classification rate of the two groups with LDA, MLDA and Adaboost classifiers yielded 82%, 84% and 93% accuracies, respectively.
Several algorithms have been developed for time series forecasting. In this paper, we develop a type of algorithm that makes use of the numerical methods for optimizing on objective function that is the Kullbak-Leibler divergence between the joint probability density function of a time series xi, X2, Xn and the product of their marginal distributions. The Grani-charlier expansion is ušed for estimating these distributions.
Using the weights that have been obtained by the neural network, and adding to them the Kullback-Leibler divergence of these weights, we obtain new weights that are ušed for forecasting the new value of Xn+k.
The paper deals with application of MF-ARTMAP neural network on
financial fraud data. The focus was on classification of data into 5 types of fraud based on expert knowledge with the aim to achieve the tool with highest classification accuracy. The fraud was characterized by 22 features and the verbal features were encoded into numerical values to be able to use them in the classification proceduře. The results show that in the čase of sufficient data (fraud) representation neural networks could be used with success; in case there are rather small examples, expert generated rules are preferred.
Hospitals must index each case of inpatient medical care with codes from the International Classification of Diseases, 9th Revision (ICD-9), under regulations from the Bureau of National Health Insurance. This paper aims to investigate the analysis of free-textual clinical medical diagnosis documents with ICD-9 codes using state-of-the-art techniques from text and visual mining fields. In this paper, ViSOM and SOM approaches inspire several analyses of clinical diagnosis records with ICD-9 codes. ViSOM and SOM are also used to obtain interesting patterns that have not been discovered with traditional, nonvisual approaches. Furthermore, we addressed three principles that can be used to help clinical doctors analyze diagnosis records effectively using the ViSOM and SOM approaches. The experiments were conducted using real diagnosis records and show that ViSOM and SOM are helpful for organizational decision-making activities.
Credit risk assessment, credit scoring and loan applications approval are one of the typical tasks that can be performed using machine learning or data mining techniques. From this viewpoint, loan applications evaluation is a classification task, in which the final decision can be either a crisp yes/no decision about the loan or a numeric score expressing the financial standing of the applicant. The knowledge to be used is inferred from data about past decisions. These data usually consist off both socio-demographic and economic characteristics of the applicant (e.g., age, income, and deposit), the characteristics of the loan, and the loan approval decision. A number of machine learning algorithms can be used for this purpose. In this paper we show how this task can be performed using the LISp- Miner system, a tool that is under development at the University of Economics, Prague. LISp-Miner is primarily focused on mining for various types of association rules, but unlike "classical" association rules proposed by Agrawal, LISp-Miner in- troduces a greater variety of different types of relations between the left-hand and right-hand sides of a rule. Two other procedures that can be used for classification task are implemented in LISp-Miner as well. We describe the 4ft-Miner and KEX procedures and show how they can be used to analyze data related to loan applications. We also compare the results obtained using the presented algorithms with results from standard rule-learning methods.
Accurate measurement of shallow flows is important for hydraulics, hydrology and water resources management. The objective of this paper is to discuss a technique for shallow flow and overland flow velocity estimation that uses infrared thermography. Laboratory flumes and different bare, vegetated and paved field surfaces were used to test the technique. Results show that shallow flow surface velocities estimated using thermal tracers and infrared technology are similar to estimates obtained using the Acoustic Doppler Velocimeter; similar results were also obtained for overland flow velocity estimates using thermography, here comparing with the dye tracer technique. The thermographic approach revealed some potential as a flow visualization technique, and leaves space for future studies and research.
Trh s učebnými pomôckami poskytuje množstvo rôznych pomôcok, experimentálnych zostáv, ktoré je možné na vyučovaní bez problémov využiť. V mnohých prípadoch si však ich zakúpenie vyžaduje nemalé finančné prostriedky. V súčasnosti existuje veľa možností na vytvorenie prototypu experimentálnej súpravy, ktorá by mala potenciál stať sa užitočnou a najmä lacnou pomôckou vo vzdelávaní mladých ľudí, nie je to však triviálna úloha. Cieľom predkladaného príspevku je ukázať, aké problémy sme objavili (a museli vyriešiť) pri konštrukcii základného prvku nami navrhovanej experimentálnej zostavy - zdroja jednosmerného napätia pre viaceré moduly v experimentálnej súprave., The market for teaching aids provides many different tools and experimental sets suitable for seamless teaching. However, in many cases, their purchase requires considerable funds. At present, there are any ways to create a prototype of an experimental set, which has the potential to become a useful and cheap tool in the education of young people, but this is not a trivial task. In this article, we introduce several problems which were discovered and solved during the construction of a basic part of a DC power supply suitable for individual experimental modules., Martin Hruška., and Obsahuje bibliografické odkazy