Wyniki wyszukiwania - BazTech

1

Soft computing-based technique as a predictive tool to estimate blast-induced ground vibration

Arthur Clement Kweku, Temeng Victor Amoako, Ziggah Yao Yevenyo

Journal of Sustainable Mining

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2019

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Vol. 18, iss. 4

287--296

EN

The safety of workers, the environment and the communities surrounding a mine are primary concerns for the mining industry. Therefore, implementing a blast-induced ground vibration monitoring system to monitor the vibrations emitted due to blasting operations is a logical approach that addresses these concerns. Empirical and soft computing models have been proposed to estimate blast-induced ground vibrations. This paper tests the efficiency of the Wavelet Neural Network (WNN). The motive is to ascertain whether the WNN can be used as an alternative to other widely used techniques. For the purpose of comparison, four empirical techniques (the Indian Standard, the United State Bureau of Mines, Ambrasey-Hendron, and Langefors and Kilhstrom) and four standard artificial neural networks of backpropagation (BPNN), radial basis (RBFNN), generalised regression (GRNN) and the group method of data handling (GMDH) were employed. According to the results obtained from the testing dataset, the WNN with a single hidden layer and three wavelons produced highly satisfactory and comparable results to the benchmark methods of BPNN and RBFNN. This was revealed in the statistical results where the tested WNN had minor deviations of approximately 0.0024 mm/s, 0.0035 mm/s, 0.0043 mm/s, 0.0099 and 0.0168 from the best performing model of BPNN when statistical indicators of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Correlation Coefficient (R) and Coefficient of determination (R2) were considered.

2

Reservoir water level forecasting using group method of data handling

Zaji A. H., Bonakdari H., Gharabaghi B.

Acta Geophysica

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2018

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Vol. 66, no. 4

717--730

EN

Accurately forecasted reservoir water level is among the most vital data for efficient reservoir structure design and management. In this study, the group method of data handling is combined with the minimum description length method to develop a very practical and functional model for predicting reservoir water levels. The models’ performance is evaluated using two groups of input combinations based on recent days and recent weeks. Four different input combinations are considered in total. The data collected from Chahnimeh#1 Reservoir in eastern Iran are used for model training and validation. To assess the models’ applicability in practical situations, the models are made to predict a non-observed dataset for the nearby Chahnimeh#4 Reservoir. According to the results, input combinations (L, L-1) and (L, L-1, L-12) for recent days with root-mean-squared error (RMSE) of 0.3478 and 0.3767, respectively, outperform input combinations (L, L-7) and (L, L-7, L-14) for recent weeks with RMSE of 0.3866 and 0.4378, respectively, with the dataset from https://www. typingclub.com/st. Accordingly, (L, L-1) is selected as the best input combination for making 7-day ahead predictions of reservoir water levels.

3

Noise Elimination of Reciprocating Compressors Using FEM, Neural Networks Method, and the GA Method

Chang Y.-C., Chiu M.-C., Xie J.-L.

Archives of Acoustics

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2017

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Vol. 42, No. 2

189--197

EN

Industry often utilizes acoustical hoods to block noise emitted from reciprocating compressors. However, the hoods are large and bulky. Therefore, to diminish the size of the compressor, a compact discharge muffler linked to the compressor outlet is considered. Because the geometry of a reciprocating compressor is irregular, COMSOL, a finite element analysis software, is adopted. In order to explore the acoustical performance, a mathematical model is established using a finite element method via the COMSOL commercialized package. Additionally, to facilitate the shape optimization of the muffler, a polynomial neural network model is adopted to serve as an objective function; also, a Genetic Algorithm (GA) is linked to the OBJ function. During the optimization, various noise abatement strategies such as a reverse expansion chamber at the outlet of the discharge muffler and an inner extended tube inside the discharge muffler, will be assessed by using the artificial neural network in conjunction with the GA optimizer. Consequently, the discharge muffler that is optimally shaped will decrease the noise of the reciprocating compressor.

4

Shape Optimisation of Multi-Chamber Acoustical Plenums Using BEM, Neural Networks, and GA Method

Chang Y.-C., Cheng H.-C., Chiu M.-C., Chien Y.-H.

Archives of Acoustics

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2016

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Vol. 41, No. 1

43--53

EN

Research on plenums partitioned with multiple baffles in the industrial field has been exhaustive. Most researchers have explored noise reduction effects based on the transfer matrix method and the boundary element method. However, maximum noise reduction of a plenum within a constrained space, which frequently occurs in engineering problems, has been neglected. Therefore, the optimum design of multi-chamber plenums becomes essential. In this paper, two kinds of multi-chamber plenums (Case I: a two-chamber plenum that is partitioned with a centre-opening baffle; Case II: a three-chamber plenum that is partitioned with two centre-opening baffles) within a fixed space are assessed. In order to speed up the assessment of optimal plenums hybridized with multiple partitioned baffles, a simplified objective function (OBJ) is established by linking the boundary element model (BEM, developed using SYSNOISE) with a polynomial neural network fit with a series of real data – input design data (baffle dimensions) and output data approximated by BEM data in advance. To assess optimal plenums, a genetic algorithm (GA) is applied. The results reveal that the maximum value of the transmission loss (TL) can be improved at the desired frequencies. Consequently, the algorithm proposed in this study can provide an efficient way to develop optimal multi-chamber plenums for industry.

5

GMDH jako metoda prognozowania efektów ekonomicznych wdrożenia innowacji w przedsiębiorstwach sektora MSP

Patalas J.

Zarządzanie Przedsiębiorstwem

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2007

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Vol. 10, nr 1

29-36

EN

In this paper is the new concept of measures of innovations in small and medium-sized enterprises (SME) proposed. Innovation is in two area discussed: (1) ERP systems and (2) intellectual capital. The paper presents the issue of the evaluation of the effectiveness of implementing ERP systems in small and medium enterprises. A polynomial decision model for evaluating the effectiveness of implementing ERP systems in small and medium enterprises is deigned, and it is shown that it gives the possibility of objectivising the process of searching for an appropriate ERP system for small and medium enterprises, taking into account the assumed costs and the existing resource limitations. The elaborated advisory system of computer forecasting of the effectiveness of implementing ERP systems in small and medium enterprises allows obtaining a forecast of the value of given small and medium enterprise performance indicators following ERP system implementation. Consequently the concept of measures of intellectual capital in small and medium-sized enterprises (SME) using a GMDH approach is discussed.

6

The ERP class system objective assessment method

Patalas J., Krupa T.

Applied Computer Science

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2005

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Vol.1, no 1

115--132

EN

This paper presents ERP class system objective assessment method when using the neural systems GMDH basing on the Ivachnienko algorithm. An approach to ERPs evaluation aimed at their successful implementation into a class of the small and medium-sized enterprises (SMEs) is considered. The set of performance indices supporting an ERP evaluation in the context of its implementation into a given SME is proposed. Consequently the decision model binding the selected indicators of effectiveness of SME implementation with the parameters of a given ERP system and the parameters of the company as such, which introduced this system is discussed.

7

Parameter estimation of dynamic GMDH neural networks with the bounded-error technique

Mrugalski M., Witczak M.

Journal of Applied Computer Science

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2002

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Vol. 10, nr 1

77-90

EN

This paper presents a new identification method based on ANNs (Artificial Neural Networks). In particular, a GMDH (Group Method of Data Handling) type neural network whose neurons have hyperbolic tangent activation junctions is considered. For such a network type. a new approach based on a bounded-error set estimation technique is employed to estimate the parameters of the ANN. The final part of this work contains an illustrative example regarding modeling the juice temperature at the outlet of an evaporator at the Lublin Sugar Factory S.A.