Wyniki wyszukiwania - Biblioteka Nauki

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Modelowanie emisji NOx w zawiesinowym procesie wytwarzania miedzi przy wykorzystaniu metody drzew regresyjnych

100%

Talar J. , Jarosz P. , Stanisławczyk A. , Kusiak J.

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tom R. 52, nr 3

129-135

PL

Przedstawiono problem przewidywania emisji NOx w zawiesinowym procesie wytwarzania miedzi. Algorytm drzew regresyjnych CART został wykorzystany do przewidywania poziomu NOx w gazach. W modelowaniu tego zjawiska wykorzystano przemysłowe dane pomiarowe. Opracowany model na bazie drzew decyzyjnych pozwolił na identyfikację zmiennych niezależnych, które mają decydujące znaczenie dla przewidywania poziomu stężenia NOx w gazach. Wyniki modelowania uzyskane przez algorytm CART porównano z wynikami sztucznych sieci neuronowych oraz metod regresji liniowej i nieliniowej. Opracowane modele oparte o sztuczne sieci neuronowe oraz drzewo regresyjne mogą być zastosowane w optymalizacji i sterowaniu procesu wytwarzania miedzi pod kątem redukcji szkodliwej emisji NOx.

EN

The problem of prediction of NOx emission in the copper flash smelting process is presented. The CART algorithm was applied to prediction of the NOx content in exhausts. The industrial data were used to modelling of this phenomenon. The model developed on the base of the decision trees allows to identify the independent variables, which are significant for prediction of NOx content in gases. The results of CART algorithm were compared with the artificial neural networks and the linear and non-linear regression models. The elaborated models based on the artificial neural networks and regression tree method can be applied in optimisation and control of the copper production process for reduction of harmful emission of NOx.

2

On the use of acoustic emission to assess the wear in a tribosystem

84%

Kekez M. , Desaniuk T. , Duszczyk J. , Ozimina D.

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2019

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tom no. 2

99--103

EN

The article focuses on the analysis of acoustic emission signals generated under dry sliding friction conditions. Two tests were conducted using a TRB3 tribometer with the disc made of 100Cr6 steel with a DLC coating, and pin made of corundum (Al2O3) and steel 100Cr6, respectively. Two tests with the disc without DLC coating were also carried out. The audio data written in the 16-bit linear pulse-code modulation (LPCM) format were analysed using the SpectraPLUS software. An A-weighting filter and 1/1 and 1/3-octave band filters were used for sound level measurements. The analysis of the equivalent sound level calculated for 10-second time intervals was carried out. The highest A-weighted sound level occurred during the first 2 hours of the test with the disc having a DLC coating and pin made of 100Cr6 steel. At the end of this test, the sound level dropped by about 40 dB compared to the maximum. The lowest A-weighted sound level was recorded during the last 2 hours of the test with disc having a DLC coating and pin made of corundum. The time-dependent variability of sound parameters was predicted using the regression tree and random forest models, which proved to be accurate and easy to follow.

PL

W pracy przedstawiono analizę dźwięku zarejestrowanego podczas tarcia technicznie suchego w ruchu ślizgowym. Dwa testy przeprowadzono na tribometrze TRB3 dla próbek wykonanych ze stali 100Cr6 z powłoką DLC i przeciwpróbek wykonanych odpowiednio z korundu (Al2O3) i stali 100Cr6. Przeprowadzono również dwa testy dla próbek bez powłoki DLC. Dźwięk został zarejestrowany w standardzie 16-bitowego liniowego PCM, a następnie poddany analizie w programie SpectraPlus. Dla kolejnych chwil czasu wyznaczono wartości poziomu dźwięku A, a także poziomy dźwięku w wybranych pasmach oktawowych i 1/3-oktawowych. Przeprowadzono analizę równoważnego poziomu dźwięku obliczonego dla 10-sekundowych odcinków czasu. Najwyższy poziom dźwięku A występował podczas pierwszych 2 godzin testu próbki z powłoką DLC i przeciwpróbki wykonanej ze stali 100Cr6. Pod koniec tego testu poziom dźwięku spadł o około 40 dB względem dotychczasowego maksimum. Najniższy poziom dźwięku A zanotowano podczas ostatnich 2 godzin testu, w którym próbka miała powłokę DLC, a przeciwpróbka była wykonana z korundu. Utworzono modele opisujące zmienność w czasie wybranych parametrów dźwięku, oddzielnie dla każdej próbki. Do utworzenia modeli zastosowano drzewa regresji oraz Random Forest. W pracy zamieszczono analizę dokładności i przejrzystości otrzymanych modeli.

3

Analyzing rear-end crash severity for a mountainous expressway in China via a classification and regression tree with random forest approach

67%

Wang Y. , Luo X.

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tom Vol. 67, nr 4

591--604

EN

To understand the contributory factors to rear-end accident severity on mountainous expressways, a total of 1039 rear-end accidents, occurring on G5 Jingkun Expressway from Hechizhai to Qipanguan in Shaanxi, China over the period of 2012 to 2017, were collected, and a non-parametric Classification and Regression Tree (CART) model was used to explore the relationship between severity outcomes and driver factors, vehicle characteristics, roadway geometry and environmental conditions. Then the random forest model was introduced to examine the accuracy of variable selection and rank their importance. The results show that driver’s risky driving behaviours, vehicle type, radius of curve, angle of deflection, type of vertical curve, time, season, and weather are significantly associated with rear-end accident severity. Speeding and driving while drunk and fatigued are more prone to result in severe consequences for such accidents and driving while fatigued is found to have the highest fatality probability, especially during the night period (18:00-24:00). The involvement of heavy trucks increases the injury probability significantly, but decreases the fatality probability. In addition, adverse weather and sharp curve with radius less than 1000 m are the most risk combination of factors. These findings can help agencies more effectively establish stricter regulations, adopt technical measures and strengthen safety education to ensure driver's driving safety on mountainous expressways for today and tomorrow.

4

A comparison of advanced computational models and experimental techniques in predicting blast induced ground vibration in open pit coal mine

67%

Nguyen H. , Bui X. N. , Moayedi H.

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tom Vol. 67, no. 4

1025--1037

EN

Green mining is an essential requirement for the development of the mining industry. Of the operations in mining technology, blasting is one of the operations that signifcantly affect the environment, especially ground vibration. In this paper, four artificial intelligence (AI) models including artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), and classification and regression tree (CART) were developed as the advanced computational models for estimating blast-induced ground vibration in a case study of Vietnam. Some empirical techniques were applied and developed to predict ground vibration and compared with the four AI models as well. For this research, 68 events of blasting were collected; 80% of the whole datasets were used to build the mentioned models, and the rest 20% were used for testing/checking the models’ performances. Mean absolute error (MAE), determination coefficient (R2 ), and root-mean-square error (RMSE) were used as the standards to evaluate the quality of the models in this study. The results indicated that the advanced computational models were much better than empirical techniques in estimating blast-induced ground vibration in the present study. The ANN model (2-6-8-6-1) was introduced as the most superior model for predicting ground vibration with an RMSE of 0.508, R2 of 0.981 and MAE of 0.405 on the testing dataset. The SVM, CART, and KNN models provided poorer performance with an RMSE of 1.192, 2.820, 1.878; R2 of 0.886, 0.618, 0.737; and MAE of 0.659, 1.631, 0.762, respectively.

5

Methods of development fuzzy logic driven decision-support models in copper alloys processing

67%

Kluska-Nawarecka S. , Górny Z. , Mrzygłód B. , Wilk-Kołodziejczyk D. , Regulski K.

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2010

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tom Vol. 10, iss. 1 spec.

23--28

EN

Development of a diagnostic decision support system using different then divalent logical formalism, in particular fuzzy logic, allows the inference from the facts presented not as explicit numbers, but described by linguistic variables such as the "high level", "low temperature", "too much content", etc. Thanks to this, process of inference resembles human manner in actual conditions of decision-making processes. Knowledge of experts allows him to discover the functions describing the relationship between the classification of a set of objects and their characteristics, on the basis of which it is possible to create a decision-making rules for classifying new objects of unknown classification so far. This process can be automated. Experimental studies conducted on copper alloys provide large amounts of data. Processing of these data can be greatly accelerated by the classification trees algorithms which provides classes that can be used in fuzzy inference model. Fuzzy logic also provides the flexibility of allocating to classes on the basis of membership functions (which is similar to events in real-world conditions). Decision-making in foundry operations often requires reliance on knowledge incomplete and ambiguous, hence that the conclusions from the data and facts may be "to some extent" true, and the technologist has to determine what level of confidence is acceptable, although the degree of accuracy for specific criteria is defined by membership function, which takes values from interval <0,1>. This paper describes the methodology and the process of developing fuzzy logic-based models of decision making based on preprocessed data with classification trees, where the needs of the diverse characteristics of copper alloys processing are the scope. Algorithms for automatic classification of the materials research work of copper alloys are clearly the nature of the innovative and promising hope for practical applications in this area.