The study of mining-induced behaviors of faults and strata in underground coalmines is significant to know the mechanism and prediction of some accidents (i.e., water inrush, gas flowing and outburst). Equivalent materials are applied herein in an underground project to simulate a progressive mining operation with a normal fault occurrence. The failure–movement evolution of the overlying strata and the stress–displacement evolution of the fault are studied through a physical simulation test. The formation of a mining-induced fracture and the mechanism of accidents caused by the mining-induced fracture are analyzed. The results show that the footwall strata underwent a more notable movement compared to the hanging wall strata. Hence, the mining-induced fracture height of the footwall is higher than that of the hanging wall. The effect of the fault can be observed on the mining-induced fracture evolution of the footwall, hanging wall, and fault plane. The developed patterns of the fracture channel successively present an evolution in the shape of a “saddle”, a “trapezium”, and an “M”. The causes of accidents induced by the mining fracture are also discussed.
Distribution of the goods from a producer to a customer is one of the most important tasks of transportation. This paper focuses on the usage of genetic algorithms (GA) for optimizing problems in transportation, namely vehicle routing problem (VRP). VRP falls in the field of NP-hard problems, which cannot be solved in polynomial time. The problem was solved using genetic algorithm with two types of crossover, both including and leaving-out elitism, setting variable parameters of crossover and mutation probability, as well as prevention of creating invalid individuals. The algorithm was programmed in Matlab, tested on real world problem of spare parts distribution for garages, while the results were compared with another heuristic method (Clarke-Wright method). Genetic algorithm provided a better solution than the heuristic Clarke-Wright method.
A method for identification of parameters of a non-linear dynamic system, such as an induction motor with saturation effect taken into account, is presented in this paper. Adaptive identifier with structure similar to model of the system performs identification. This identifier can be regarded as a special neural network, therefore its adaptation is based on the gradient descent method and Back-Propagation well known in the neural networks theory. Parameters of electromagnetic subsystems were derived from the values of synaptic weights of the estimator after its adaptation. Testing was performed with simulations taking into account noise in measured quantities. Deviations of identified parameters in case of electrical parameters of the system were up to 1% of real values. Parameters of non-linear magnetizing curve were identified with deviations up to 6% of real values. Identifier was able to follow sudden changes of rotor resistance, load torque and moment of inertia.
This paper evaluates the feasibility of using an Artificial Neural Network (ANN) model for estimating the nominal shear capacity of Reinforced Concrete (RC) beams against diagonal shear failure subjected to shear and flexure. A feedforward back-propagation ANN model was developed utilizing 622 experimental data points of RC beams, which include 111 deep beams data and 20 beams tested for low longitudinal steel ratios. The ANN model was trained on 70% of the data and then it was validated using the remaining 30% data (new data were not used for training). The trained ANN model was compared with three existing approaches, including the American Concrete Institute (ACI) code. The ANN model predictions when compared to the experimental data were very favorable, regarding also the other approaches. The prediction of ANN model was also checked for size effect and deep beams separately. The ANN model was found to be very robust in all situations. The safe form of ANN model was also derived and compared with the design equations of the three methods.
The optimization problem of two or more special-purpose functions of the energy system is subjected to an analysis. Based on experience of our research and general knowledge of partial solutions of energy system optimization at the level of control of production and power energy supply by energy companies in the Czech Republic, a special-purpose (cost) function has been defined. By analysing the special-purpose function, penalty and limitations have been defined. Using the fuzzy logic, a set of suitable solutions for the special-purpose function is accepted. An optimum of the special-purpose function is looked for using the simulated annealing method. The history of electricity consumption is sorted by day and by hour, representing the multidimensional data. When using the cluster analysis, type daytime diagrams of consumption are defined. Type daytime diagrams form prototypes of identified clusters. The so-called self-organizing neural network with Kohonen map attached is used to perform the cluster analysis. The result of our research is presented by an experiment.
A new method to detect damages on crates of beverages is investigated. It is based on a pattern-recognition-system by an artificial neural network (ANN) with a feedforward multilayer-perceptron topology. The sorting criterion is obtained by mechanical vibration analysis which provides characteristic frequency spectra for all possible damage cases and crate models. To support the network training, a large number of numerical data-sets is calculated by the finite-elementmethod (FEM). The combination of artificial neural networks with methods of numerical simulation is a powerful instrument to cover the broad range of possible damages. First results are discussed with respect to the influence of modelling inaccuracies of the finite-element-model and the support of the ANN by training-data obtained from numerical simulation. Also the feasibility of neuro-numerical ANN training will be dwelled on.
The rock units of the NW Himalayan region are fragile, heavily fractured and highly deformed due to active tectonics and complex geological setup. Fast urbanization, road constructions along hill slopes and other infrastructural development activities also increased the slopes instability problems. The present study emphasizes the application of rock mass classification to estimate the rock mass properties along the Yadgar section Muzaffarabad, NW Himalayas, Pakistan. For this purpose, Rock Mass Rating (RMR) and Geological Strength Index (GSI) were used to characterize and classify the rock masses. In the present study, twenty-five sites have been investigated to evaluate rock properties along the Muzaffarabad-Neelum road, Sub-Himalayas, Pakistan. Result of the study shows that the Abbottabad Formation of Cambrian age is vulnerable in the Yadgar section with extremely poor RQD (Rock Quality Designation), lowest UCS (Unconfined Compression Strength) values and closely spaced discontinuities. and RMR values of the Abbottabad Formation ranges from 40-54 and classified as Poor to Fair having low GSI (20±3-35±3), blocky, disintegrated structure. The Paleocene Hangu Formation has lowest GSI (28±3-29±3; Blocky, Disturbed/ Seamy in nature) having RMR (40-45) and Eocene Kuldana Formation has GSI (30±3-45±3; Blocky) having RMR (34-67), are categorized as heavily broken, disintegrated and poorly interlocked rock masses. RMR values of rock units of the Paleocene Lockhart Formation (52-60), the Miocene Murree Formation (38-63), and the Eocene Margala Hill Limestone (38-61) are relatively higher values having GSI values ranges from (35±3-45±3; 35±3-50±3; 30±3-40±3) res)ectively. RMR and GSI values in Yadgar section, ranges between 34-67 and 20±3-50±3 respectively. Analysis shows positive correlation between GSI and RMR values. This approach to evaluate the rock mass classification through RMR and GSI will give the better estimation of rock mass properties along Muzaffarabad-Neelum road to identify the vulnerable slopes and design effective geotechnical measures.
Secondary deformations are ground movements occurring in areas of ceased underground mining. These are associated with delayed readjustment of rock mass resulting in subsidence, discontinuous deformations (sinks, cracks, etc.) due to destruction of underground, usually shallow, workings, and elevation of ground surface in response of rock mass to rising groundwater levels following the end of mine water drainage. Comparative analysis of secondary deformations in two former mining areas in the first period after cessation of underground hard coal mining is the subject of this study. We used ERS-1/2 and Envisat satellite radar interferometry data processed with PSInSAR technique and GIS to map vertical (in satellite’s line of sight, LOS) movements of the surface and analyse them in relation to location of coal fields and underground water table rise. In the study, two areas have been compared, the Ostrava city in the Czech part of the Upper Silesian Basin and the Wałbrzych Coal Basin in Poland. The results of analyses based on the results of PSInSAR processing between 1995 and 2000 for the Wałbrzych site indicate uplift (up to +12 mm/year) in closed parts of coal fields and subsidence (up to -8 mm/year) in areas of declining mining. Results of PSInSAR analysis over the Ostrava site indicate decaying subsidence after mine closures in the rate of up to -6 mm/year during 1995-2000. Residual subsidence and gentle uplift have been partly identified at surroundings of closed mines in Ostrava from 2003-2010 Envisat data. In Wałbrzych gentle elevation has been determined from 2002 to 2009 in areas previously subsiding. and Blachowski Jan, Jiránková Eva, Lazecký Milan, Kadlečík Pavel, Milczarek Wojciech.
In this work we report on the implementation of methods for data processing signals from microelectrode arrays (MEA) and the application of these methods for signals originated from two types of MEAs to detect putative neurons and sort them into subpopulations. We recorded electrical signals from firing neurons using titanium nitride (TiN) and boron doped diamond (BDD) MEAs. In previous research, we have shown that these methods have the capacity to detect neurons using commerciallyavailable TiN-MEAs. We have managed to cultivate and record hippocampal neurons for the first time using a newly developed custom-made multichannel BDD-MEA with 20 recording sites. We have analysed the signals with the algorithms developed and employed them to inspect firing bursts and enable spike sorting. We did not observe any significant difference between BDD- and TiN-MEAs over the parameters, which estimated spike shape variability per each detected neuron. This result supports the hypothesis that we have detected real neurons, rather than noise, in the BDD-MEA signal. BDD materials with suitable mechanical, electrical and biocompatibility properties have a large potential in novel therapies for treatments of neural pathologies, such as deep brain stimulation in Parkinson’s disease.
This work presents new application of the random field theory in medical imaging. Results from both integral geometry and random field theory can be used to detect locations with significantly increased radiotracer uptake in images from positron emission tomography (PET). The assumptions needed to use these results are verified on a set of real and simulated phantom images. The proposed method of detecting activation (locations with increased radiotracer concentration) is used to quantify the quality of simulated PET images. Dependence of the quality on the injection dose (amount of applied radiotracer) and patient's body parameters is estimated. It is used to derive curves of constant quality determining the injection dose needed to achieve desired quality of the resulting images. The curves are compared with the formula currently used in medical practice.