Advanced drilling detection and multi-information identification of water-conducting channel of coal floor

The coal floor develops various water-conducting channels, which seriously threaten the safe production of mines. In order to establish a more scientific multi-information identification technology system for the water-conducting channels in the coal floor, and prevent floor water inrush. Firstly, the principles of reasonable trajectory, reasonable target layer and exploration area maximization are proposed based on the characteristics of the water-conducting channels and the advantages of the ground directional drilling area exploration technology. Diversified types, unclear locations, and significant water inrush hazards are the characteristics of water-conducting channels. Secondly, the summary analysis is conducted on the exploration process of water diversion channels in areas such as HuaiBei Mining Area, Huainan Mining Area, Xingtai Mining Area, and Huanghebei Coal Field. When revealing the water-conducting channels, there are significant differences in visual indicators such as rock debris, drilling time, drilling fluid leakage, and confirmatory indicators such as permeability and grouting parameters compared to revealing normal formations. Therefore, the identification indicators of multi-information during the drilling process are divided into two types: qualitative and quantitative. Based on the variation amplitude of two qualitative indicators, rock debris and drilling time, when encountering water-conducting channels, it gives corresponding standard curves, and determines the classification system for water-conducting channel types. Subsequently, a comprehensive analysis is conducted on the changes in the two quantitative indicators of drilling fluid leakage and permeability when encountering water-conducting channels. 30 m3/h of drilling fluid leakage and 10 Lu of permeability are proposed as the classification criteria for conductivity. Based on this, a dual factor comprehensive classification system for water-conducting channels conductivity is established, and the conductivity is divided into four levels. Finally, taking a typical mine in the North China coalfield as a case study, the principle of exploration is adopted to explore the water-conducting channels. The multi-information identification technology is applied to successfully identify four faults, two karst fracture zones and a collapse in the detection area, and determines conductivity levels of water-conducting channels. The research results have guiding effect and important significance for improving the identification and control of water-conducting channels

A Metamaterial-Based Cross-Polarization Converter Characterized by Wideband and High Efficiency

Metamaterial-based polarization converters, which have all kinds of polarizations realizable via adjusting metamaterial parameters, have been springing up at an increasing rate. However, the reported metamaterial-based polarization converters suffer from either limited bandwidth or low polarization conversion ratios. In this study, a metamaterial-based polarization converter consisting of multilayer copper split-ring resonators and a copper ground separated by dielectrics was demonstrated and was characterized by the cross-polarization with wideband and high-efficiency. For normal incidence, the simulated results illustrated that the expanded bandwidth benefited from the superposition of cross-polarization electromagnetic resonances around 2.78, 3.09, 3.68, and 4.54 GHz, and the polarization conversion ratio was higher than 99% in the frequency range of 2.73 and 4.63 GHz. For oblique incidence, the design can provide larger angle tolerance in the investigated band, except for a very narrow stopband. Moreover, the experimental results agreed well with the simulations, which verified the reliability of the performance

Broadband Perfect Absorber in the Visible Range Based on Metasurface Composite Structures

The broadband perfect absorption of visible light is of great significance for solar cells and photodetectors. The realization of a two-dimensional broadband perfect absorber in the visible range poses a formidable challenge with regard to improving the integration of optical systems. In this paper, we numerically demonstrate a broadband perfect absorber in the visible range from 400 nm to 700 nm based on metasurface composite structures. Simulation results show that the average absorptance is ~95.7% due to the combination of the intrinsic absorption of the lossy metallic material (Au) and the coupling resonances of the multi-sized resonators. The proposed perfect absorber may find potential applications in photovoltaics and photodetection

Area Dependence of Effective Electromechanical Coupling Coefficient Induced by On-Chip Inductance in LiNbO₃-Based BAW Resonators

To solve the problem of filter bandwidth in 5G communication, it is urgent to develop an acoustic resonator with a large effective electromechanical coupling coefficient (Keff2). In this paper, the dependence between the resonance area and the performance of the bulk acoustic wave (BAW) resonator is studied. The solidly mounted resonators (SMRs) based on 43° Y cut lithium niobate (LN) were fabricated by the wafer transfer technique. The on-chip inductor was integrated with the BAW resonator through a pad electrode. Resonators with different resonant areas were fabricated and tested. Finite element modeling (FEM) simulation of acoustic resonators and electromagnetic (EM) simulation of layout were carried out, respectively. The Modified Butterworth Van Dyke (MBVD) model was used to analyze the results, and simulation of the Mason model was adopted. The results show that the dependency relationship between the resonant area and the effective electromechanical coupling coefficient can be induced by on-chip inductance. In the resonant area range of 20 × 20 μm2~160 × 160 μm2, the Keff2 increases from 11.97% to 43.28%

Learn to Make Decision with Small Data for Autonomous Driving: Deep Gaussian Process and Feedback Control

Autonomous driving is a popular and promising field in artificial intelligence. Rapid decision of the next action according to the latest few actions and status, such as acceleration, brake, and steering angle, is a major concern for autonomous driving. There are some learning methods, such as reinforcement learning which automatically learns the decision. However, it usually requires large volume of samples. In this paper, to reduce the sample size, we exploit the deep Gaussian process, where a regression model is trained on small sample datasets and captures the most significant features correctly. Besides, to realize the real-time and close-loop control, we combine the feedback control into the process. Experimental results on the Torcs simulation engine illustrate smooth driving on virtual road which can be achieved. Compared with the amount of training data in deep reinforcement learning, our method uses only 0.34% of its size and obtains similar simulation results. It may be useful for real road tests in the future

Learn to Make Decision with Small Data for Autonomous Driving : Deep Gaussian Process and Feedback Control

Autonomous driving is a popular and promising field in artificial intelligence. Rapid decision of the next action according to the latest few actions and status, such as acceleration, brake, and steering angle, is a major concern for autonomous driving. There are some learning methods, such as reinforcement learning which automatically learns the decision. However, it usually requires large volume of samples. In this paper, to reduce the sample size, we exploit the deep Gaussian process, where a regression model is trained on small sample datasets and captures the most significant features correctly. Besides, to realize the real-time and close-loop control, we combine the feedback control into the process. Experimental results on the Torcs simulation engine illustrate smooth driving on virtual road which can be achieved. Compared with the amount of training data in deep reinforcement learning, our method uses only 0.34\% of its size and obtains similar simulation results. It may be useful for real road tests in the future

Protective Effects of <i>Lactobacillus gasseri</i> against High-Cholesterol Diet-Induced Fatty Liver and Regulation of Host Gene Expression Profiles

Fatty liver is one of the most pervasive liver diseases worldwide. Probiotics play an important role in the progression of liver disease, but their effects on host regulation are poorly understood. This study investigated the protective effects of lactobacillus gasseri (L. gasseri) against high-cholesterol diet (HCD)-induced fatty liver injury using a zebrafish larvae model. Liver pathology, lipid accumulation, oxidative stress and hepatic inflammation were evaluated to demonstrate the changes in a spectrum of hepatic injury. Moreover, multiple indexes on host gene expression profiles were comprehensively characterized by RNA screening. The results showed that treatment with L. gasseri ameliorated HCD-induced morphological and histological alterations, lipid regulations, oxidative stress and macrophage aggregation in the liver of zebrafish larvae. Furthermore, the enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway revealed that the core pathways of L. gasseri regulation were interleukin-17 (IL-17) signaling, phosphoinositide 3-kinase (PI3K)-AKT signaling pathway, the regulation of lipolysis and adipocytes and fatty acid elongation and estrogen signaling. The genes at key junction nodes, hsp90aa1.1, kyat3, hsd17b7, irs2a, myl9b, ptgs2b, cdk21 and papss2a were significantly regulated by L. gasseri administration. To conclude, the current research extends our understanding of the protective effects of L. gasseri against fatty liver and provides potential therapeutic options for fatty liver treatment