Research on acoustical hoods used in industry has been widely discussed; however, the assessment of shape optimization on space-constrained close-fitting acoustic hoods by adjusting design parameters has been neglected. Moreover, the acoustical performance for a one-layer acoustic hood used in a high intensity environment seems to be insufficient. Therefore, an assessment of an optimally shaped acoustical hood with two layers will be proposed. In this paper, a numerical case for depressing the noise level of a piece of equipment by optimally designing a shaped two-layer close-fitting acoustic hood under a constrained space will be introduced. Furthermore, to optimally search for a better designed set for the multi-layer acoustical hood, an artificial immune method (AIM) has been adopted as well. Consequently, this paper provides a quick and effective method to reduce equipment noise by optimally designing a shaped multi-layer close-fitting acoustic hood via the AIM searching technique.

This study stacked a thin, dense BCuP-5 (Cu-Ag-P based filler metal) on a Cu-plate using the laser cladding (L.C) process to develop a method to manufacture Ag reducing multilayer clad electrical contact material with an Ag-M(O)/Ag/Cu/BCuP-5 structure. Then, the microstructure and macroscopic properties of the manufactured BCuP-5 coating layer were analyzed. The thickness of the manufactured coating layer was approximately 1.7 mm (maximum). Microstructural observation of the coating layer identified Cu, Ag and Cu-Ag-Cu3P ternary eutectic phases like those in the initial BcuP-5 powder. To evaluate the properties of the manufactured coating layer, hardness and adhesion strength tests were performed. The average hardness of the laser cladded coating layer was 183.2 Hv, which is 2.6 times greater than conventional brazed BcuP-5. The average pull-off strength measured using the stud pull test was 341.6 kg/cm2. Cross-sectional observation of the pulled-off material confirmed that the coating layer and substrate maintained a firm adhesion after pull-off. Thus, the actual adhesion strength of Cu/BcuP-5 was inferred to be greater than 341.6 kg/cm2. Based on the above findings, it was confirmed that it is possible to manufacture a sound Ag reducing multilayer clad electrical contact material using the laser cladding process.

In this paper, by using a semi-analytical solution based on multi-layered approach, the authors present the solutions of temperature, displacements, and transient thermal stresses in functionally graded circular hollow cylinders subjected to transient thermal boundary conditions. The cylinder has finite length and is subjected to axisymmetric thermal loads. It is assumed that the functionally graded circular hollow cylinder is composed of N fictitious layers and the properties of each layer are assumed to be homogeneous and isotropic. Time variations of the temperature, displacements, and stresses are obtained by employing series solving method for ordinary differential equation, Laplace transform techniques and a numerical Laplace inversion.

In the extra-thick coal seams and multi-layered hard roofs, the longwall hydraulic support yielding, coal face spalling, strong deformations of goaf-side entry, and severe ground pressure dynamic events typically occur at the longwall top coal caving longwall faces. Based on the Key strata theory an overburden caving model is proposed here to predict the multilayered hard strata behaviour. The proposed model together with the measured stress changes in coal seam and underground observations in Tongxin coal mine provides a new idea to analyse stress changes in coal and help to minimise rock bursts in the multi-layered hard rock ground. Using the proposed primary Key and the sub-Key strata units the model predicts the formation and instability of the overlying strata that leads to abrupt dynamic changes to the surrounding rock stress. The data obtained from the vertical stress monitoring in the 38 m wide coal pillar located adjacent to the longwall face indicates that the Key strata layers have a significant influence on ground behaviour. Sudden dynamically driven unloading of strata was caused by the first caving of the sub-Key strata while reloading of the vertical stress occurred when the goaf overhang of the sub-Key strata failed. Based on this findings several measures were recommended to minimise the undesirable dynamic occurrences including pre-split of the hard Key strata by blasting and using the energy consumption yielding reinforcement to support the damage prone gate road areas. Use of the numerical modelling simulations was suggested to improve the key theory accuracy.

Solar air heater (SAH) is an important device for solar energy utilization which is used for space heating, crop drying, timber seasoning etc. Its performance mainly depends on system parameters, operating parameters and meteorological parameters. Many researchers have been used these parameters to predict the performance of SAH by analytical or conventional approach and artificial neural network (ANN) technique, but performance prediction of SAH by using relevant input parameters has not been done so far. Therefore, relevant input parameters have been considered in this study. Total ten parameters were used such as mass flow rate, ambient temperature, wind speed, relative humidity, fluid inlet temperature, fluid mean temperature, plate temperature, wind direction, solar elevation and solar intensity to find out the relevant parameters for ANN prediction. Seven different neural models have been constructed using these parameters. In each model 10 to 20 neurons have been selected to find out the optimal model. The optimal neural models for ANN-I, ANN-II, ANN-III, ANN-IV, ANN-V, ANN-VI and ANN-VII were obtained as 10-17-1, 8-14-1, 6-16-1, 5- 14-1, 4-17-1, 3-16-1 and 2-14-1, respectively. It has been found that ANN-II model with 8-14-1 is the optimal model as compared to other neural models. Values of the sum of squared errors, mean relative error, and coefficient of determination were found to be 0.02138, 1.82% and 0.99387, respectively, which shows that the ANN-II developed with mass flow rate, ambient temperature, inlet and mean temperature of air, plate temperature, wind speed and direction, relative humidity, and relevant input parameters performed better. The above results show that these eight parameters are relevant for prediction.

In this paper, a new Multi-Layer Perceptron Neural Network (MLP NN) classifier is proposed for classifying sonar targets and non-targets from the acoustic backscattered signals. Besides the capabilities of MLP NNs, it uses Back Propagation (BP) and Gradient Descent (GD) for training; therefore, MLP NNs face with not only impertinent classification accuracy but also getting stuck in local minima as well as lowconvergence speed. To lift defections, this study uses Adaptive Best Mass Gravitational Search Algorithm (ABGSA) to train MLP NN. This algorithm develops marginal disadvantage of the GSA using the bestcollected masses within iterations and expediting exploitation phase. To test the proposed classifier, this algorithm along with the GSA, GD, GA, PSO and compound method (PSOGSA) via three datasets in various dimensions will be assessed. Assessed metrics include convergence speed, fail probability in local minimum and classification accuracy. Finally, as a practical application assumed network classifies sonar dataset. This dataset consists of the backscattered echoes from six different objects: four targets and two non-targets. Results indicate that the new classifier proposes better output in terms of aforementioned criteria than whole proposed benchmarks.

This article accounts for the development of a powerful artificial neural network (ANN) model, designed for the prediction of relative humidity levels, using other meteorological parameters such as the maximum temperature, minimum temperature, precipitation, wind speed, and intensity of solar radiation in the Rabat-Kenitra region (a coastal area where relative humidity is a real concern). The model was applied to a database containing a daily history of five meteorological parameters collected by nine stations covering this region from 1979 to mid-2014.
It has been demonstrated that the best performing three-layer (input, hidden, and output) ANN mathematical model for the prediction of relative humidity in this region is the multi-layer perceptron (MLP) model. This neural model using the Levenberg–Marquard algorithm, with an architecture of [5-11-1] and the transfer functions Tansig in the hidden layer and Purelin in the output layer, was able to estimate relative humidity values that were very close to those observed. This was affirmed by a low mean squared error ( MSE) and a high correlation coefficient ( R), compared to the statistical indicators relating to the other models developed as part of this study.

In this paper, we propose a novel priority-aware solution named bypass to handle high- and low-priority traffic in multi-layer networks. Our approach assumes diversification of elastic optical spectrum to ensure additional resources reserved for emergency situations. When congestion occurs, the solution dynamically provides new paths, allocating a hidden spectrum to offload traffic from the congested links in the IP layer. Resources for a bypass are selected based on traffic priority. High-priority traffic always gets the shortest bypasses in terms of physical distance, which minimizes delay. Bypasses for low-priority traffic can be established if the utilization of the spectrum along the path is below the assumed threshold. The software-defined networking controller ensures the global view of the network and cooperation between IP and elastic optical layers. Simulation results show that the solution successfully reduces the amount of rejected high-priority traffic when compared to regular bypasses and when no bypasses are used. Also, overall bandwidth blocking probability is lower when our priority-aware bypasses are used.

The paper describes a novel online identification algorithm for a two-mass drive system. The multi-layer extended Kalman Filter (MKF) is proposed in the paper. The proposed estimator has two layers. In the first one, three single extended Kalman filters (EKF) are placed. In the second layer, based on the incoming signals from the first layer, the final states and parameters of the two-mass system are calculated. In the considered drive system, the stiffness coefficient of the elastic shaft and the time constant of the load machine is estimated. To improve the quality of estimated states, an additional system based on II types of fuzzy sets is proposed. The application of fuzzy MKF allows for a shorter identification time, as well as improves the accuracy of estimated parameters. The identified parameters of the two-mass system are used to calculate the coefficients of the implemented control structure. Theoretical considerations are supported by simulations and experimental tests.

The paper has presented the results of theoretical studies and experimental tests of the plastic deformation of multi-layered Ti/Al/Mg specimens. Theoretical studies were carried out using the Forge2011® computer program. Physical modeling, on the other hand, was performed using the Gleeble3800 simulator. Cuboidal specimens were cut off from the plates obtained in the explosive welding method. Based on the obtained investigation results it has been found non uniform deformation of the particular layer as a result their different value of flow stress.

Pavements are layered systems from both the geometrical and physical points of view. Flexible pavements most often include a sequence of asphalt layers, typically composed of the wearing course, binder course and base course. So far, there is no definite analytical solution of such a layered system in relation to the temperature distribution that would consider different thermal properties of the respective layers and follow the physical laws of the thermal wave nature of heat propagation. This being so, we are unable to assess the effect of the thermal properties of the respective pavement courses on the overall temperature distribution in the asphalt portion. In multi-layer pavement systems also important are the phenomena taking place at the interfaces between the pavement courses which have a bearing on the service life of pavement. This article presents a newanalytical solution to the problem of heat conduction and refraction in a multi-layer pavement. The solution was used to investigate and determine the effect of wave mode of heat propagation on the vertical temperature distribution, this considering that the pavement system is a sequence of layers comprising the soil subgrade, the base course and the wearing course. Moreover, the classical heat conduction equation is compared with the wave mode equation for a multi-layer pavement system and the temperature distribution in a layered system is compared with the temperature distribution in its homogenized equivalent. The solution of the heat conduction problem in a layered system showed a considerable effect of the thermal compatibility coefficients introduced by the authors and of the thermal refraction of the respective layers on the temperature distribution throughout the multi-layer pavement system. The output of this research includes prediction of the vertical temperature distribution in the pavement and definition of guidelines for reducing the effect of changing climatic conditions on the operation of layered flexible road and airfield pavements. In addition, the research results expand the toolkit for assessing the thermal effect on the actual pavements.

Based on data collected during an UCG pilot-scale experiment that took place during 2014 at Wieczorek mine, an active mine located in Upper Silesia (Poland), this research focuses on developing a dynamic fire risk prevention strategy addressing underground coal gasification processes (UCG) within active mines, preventing economic and physical losses derived from fires.

To achieve this goal, the forecasting performance of two different kinds of artificial neural network models (generalized regression and multi-layer feedforward) are studied, in order to forecast the syngas temperature at the georeactor outlet with one hour of anticipation, thus giving enough time to UCG operators to adjust the amount and characteristics of the gasifying agents if necessary.

The same model could be used to avoid undesired drops in the syngas temperature, as low temperature increases precipitation of contaminants reducing the inner diameter of the return pipeline. As a consequence the whole process of UGC might be stopped. Moreover, it could allow maintaining a high temperature that will lead to an increased efficiency, as UCG is a very exothermic process.

Results of this research were compared with the ones obtained by means of Multivariate Adaptative Regression Splines (MARS), a non-parametric regression technique able to model non-linearities that cannot be adequately modelled using other regression methods.

Syngas temperature forecast with one hour of anticipation at the georeactor outlet was achieved successfully, and conclusions clearly state that generalized regression neural networks (GRNN) achieve better forecasts than multi-layer feedforward networks (MLFN) and MARS models.