Deep Convolutional Neural Networks Based on Semi-Discrete Frames

Deep convolutional neural networks have led to breakthrough results in practical feature extraction applications. The mathematical analysis of these networks was pioneered by Mallat, 2012. Specifically, Mallat considered so-called scattering networks based on identical semi-discrete wavelet frames in each network layer, and proved translation-invariance as well as deformation stability of the resulting feature extractor. The purpose of this paper is to develop Mallat's theory further by allowing for different and, most importantly, general semi-discrete frames (such as, e.g., Gabor frames, wavelets, curvelets, shearlets, ridgelets) in distinct network layers. This allows to extract wider classes of features than point singularities resolved by the wavelet transform. Our generalized feature extractor is proven to be translation-invariant, and we develop deformation stability results for a larger class of deformations than those considered by Mallat. For Mallat's wavelet-based feature extractor, we get rid of a number of technical conditions. The mathematical engine behind our results is continuous frame theory, which allows us to completely detach the invariance and deformation stability proofs from the particular algebraic structure of the underlying frames.Comment: Proc. of IEEE International Symposium on Information Theory (ISIT), Hong Kong, China, June 2015, to appea

Deep Structured Features for Semantic Segmentation

We propose a highly structured neural network architecture for semantic segmentation with an extremely small model size, suitable for low-power embedded and mobile platforms. Specifically, our architecture combines i) a Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random layer realizing a radial basis function kernel approximation, and iii) a linear classifier. While stages i) and ii) are completely pre-specified, only the linear classifier is learned from data. We apply the proposed architecture to outdoor scene and aerial image semantic segmentation and show that the accuracy of our architecture is competitive with conventional pixel classification CNNs. Furthermore, we demonstrate that the proposed architecture is data efficient in the sense of matching the accuracy of pixel classification CNNs when trained on a much smaller data set.Comment: EUSIPCO 2017, 5 pages, 2 figure

Large-scale experimental investigations to evaluate the feasibility of producing methane rich gas (SNG) through underground coal gasification process. Effect of coal rank and gasification pressure

An experimental campaign on the methane-oriented underground coal gasification (UCG) process was carried out in a large-scale laboratory installation. Two different types of coal were used for the oxygen/steam blown experiments, i.e., “Six Feet” semi-anthracite (Wales) and “Wesoła” hard coal (Poland). Four multi-day gasification tests (96 h continuous processes) were conducted in artificially created coal seams under two distinct pressure regimes-20 and 40 bar. The experiments demonstrated that the methane yields are significantly dependent on both the properties of coal (coal rank) and the pressure regime. The average CH4 concentration for “Six Feet” semi-anthracite was 15.8%vol. at 20 bar and 19.1%vol. at 40 bar. During the gasification of “Wesoła” coal, the methane concentrations were 10.9%vol. and 14.8%vol. at 20 and 40 bar, respectively. The “Six Feet” coal gasificationwascharacterizedbymuchhigherenergyefficiencythangasificationofthe“Wesoła”coal and for both tested coals, the efficiency increased with gasification pressure. The maximum energy efficiency of 71.6% was obtained for “Six Feet” coal at 40 bar. A positive effect of the increase in gasification pressure on the stabilization of the quantitative parameters of UCG gas was demonstrate

Large-scale ex situ tests for CO 2 storage in coal beds

This publication discusses the experiments and findings of project ROCCS (Establishing a Research Observatory to Unlock European Coal Seams for Carbon Dioxide Storage), which aimed to investigate the potential for carbon dioxide storage in coal seams. The project involved large-scale ex situ laboratory tests, where CO2 was injected into an experimental coal seam using a high-pressure reactor at the Central Mining Institute in Poland. The reactor simulated underground conditions, and the experimental coal seam measured 3.05 m in length with a cross-section of 0.4 × 0.4 m. Parameters such as gas flow, temperatures, and pressures were monitored during the experiments. In the study conducted, the sorption capacity of coal from the Polish mine “Piast-Ziemowit” for CO2, at a sorption pressure of 30 bar, was determined to be 4.8% by weight relative to the raw coal mass. The data collected from these ex situ tests can support the design of a potential commercial-scale CO2 storage installation

CO 2 injection via a horizontal well into the coal seam at the Experimental Mine Barbara in Poland

This study, conducted as part of the ROCCS project, investigates the potential of coal seams for CO2 sequestration through in situ tests. The in situ tests, performed at Experimental Mine Barbara in Mikołów, Poland, involved injecting CO2 through a horizontal well into a coal seam, with variable well lengths and injection parameters. The experiments included monitoring for CO2 leakage and migration within the coal seam. The objective was to examine the correlation between the CO2 injection rate and the coal–CO2 contact area, monitoring for any potential leakage. The total mass of CO2 injected was about 7700 kg. Significant leakage, probably due to the formation of preferential pathways, prevented pressure buildup in the injection well. The results provide insights into challenges regarding CO2 injection into coal seams, with implications for the design of commercial-scale CO2 storage installations

Experimental simulations of methane-oriented underground coal gasification using hydrogen - The effect of coal rank and gasification pressure on the hydrogasification process

This paper presents a series of surface experimental simulations of methane-oriented underground coal gasification using hydrogen as gasification medium. The main aim of the experiments conducted was to evaluate the feasibility of methane-rich gas production through the in situ coal hydrogasification process. Two multi-day trials were carried out using large scale gasification facilities designed for ex situ experimental simulations of the underground coal gasification (UCG) process. Two different coals were investigated: the “Six Feet” semi-anthracite (Wales) and the “Wesoła" hard coal (Poland). The coal samples were extracted directly from the respective coal seams in the form of large blocks. The gasification tests were conducted in the artificial coal seams (0.41 × 0.41 × 3.05 m) under two distinct pressure regimes - 20 and 40 bar. The series of experiments conducted demonstrated that the physicochemical properties of coal (coal rank) considerably affect the hydrogasification process. For both gasification pressures applied, gas from “Six Feet” semi-anthracite was characterized by a higher content of methane. The average CH4 concentration for “Six Feet” experiment during the H2 stage was 24.12% at 20 bar and 27.03% at 40 bar. During the hydrogasification of “Wesoła" coal, CH4 concentration was 19.28% and 21.71% at 20 and 40 bar, respectively. The process was characterized by high stability and reproducibility of conditions favorable for methane formation in the whole sequence of gasification cycles. Although the feasibility of methane-rich gas production by underground hydrogasification was initially demonstrated, further techno-economic studies are necessary to assess the economic feasibility of methane production using this process