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As the stability of surrounding rock of coal roadway is affected by many factors, which makes the classification result hard to be consistent with the field practice. To solve the above problems, this paper proposes a method for the classification of stability of rock which is present in roadway of coal using the artificial intelligence algorithm. In this paper, the influencing factors of stability of rock which is present in roadway are analyzed, and seven influential factors are selected as classification indexes. To solve the problem of slow convergence speed and easy to fall into the local minimum of the back propagation artificial neural network (BP-ANN), an improved BP-ANN algorithm based on additional momentum and Levenberg-Marquardt optimization is proposed based on the analysis of the existing improved methods, which improves the convergence speed and avoids the local minimum effectively. Based on the learning model available, classification system based on fuzzy rule have been implemented and yielded better behavior in the situation of uncertain data sets. Finally, the stability classification model of surrounding rocks of coal roadway using BP-ANN was established in MATLAB environment, and the model was applied to 13 data samples of coal roadway for testing, with the identification rate of 92.3%. The experimental results verify that the method proposed based on fuzzy rule classification system in this paper has a high accuracy of type identification and is applicable to the stability classification of surrounding rock in the coal roadway. [ABSTRACT FROM AUTHOR]

Most existing trackers based on RGB/grayscale frames may collapse due to the unreliability of conventional sensors in some challenging scenarios (e.g., motion blur and high dynamic range). Event-based cameras as bioinspired sensors encode brightness changes with high temporal resolution and high dynamic range, thereby providing considerable potential for tracking under degraded conditions. Nevertheless, events lack the fine-grained texture cues provided by RGB/grayscale frames. This complementarity encourages us to fuse visual cues from the frame and event domains for robust object tracking under various challenging conditions. In this paper, we propose a novel event feature extractor to capture spatiotemporal features with motion cues from event-based data by boosting interactions and distinguishing alterations between states at different moments. Furthermore, we develop an effective feature integrator to adaptively fuse the strengths of both domains by balancing their contributions. Our proposed module as the plug-in can be easily applied to off-the-shelf frame-based trackers. We extensively validate the effectiveness of eight trackers extended by our approach on three datasets: EED, VisEvent, and our collected frame-event-based dataset FE141. Experimental results also show that event-based data is a powerful cue for tracking. [ABSTRACT FROM AUTHOR]

Near infrared (NIR) phosphors have been booming as promising candidates for agriculture due to the adjustable spectrum and excellent match with the dyes of the plants. Here, Mn4+ activated double perovskite LaMg 2/3 Mn 1/3 O 3 NIR phosphor was prepared, which showed high match with the absorption spectra of phytochrome P FR. Concentration of Mg2+ was decreased to create octahedron vacancy into the lattice to improve the luminescence performance. Structure and luminescence variation of the phosphors LaMg 2/3- x Nb 1/3 O 3 :Mn4+ along with the increased vacancy concentration were carried out. Relative sensitivity S r of the optimized LaMg 0.57 Nb 0.33 O 3 :Mn4+ reaches 3.109 % and exhibits great application potential in temperature sensor. Both the NIR light emitting diode (LED) and white LED were fabricated by using this double perovskite NIR phosphor and applied to the cultivation of dragon fruit as light compensation. The dragon fruits matured faster than those without any light compensation. These results confirm the great application potential of this double perovskite NIR phosphor in temperature sensor and plant cultivation. [ABSTRACT FROM AUTHOR]

• Excited at 520 nm light, the designed phosphor can emit broad NIR emission peaking at 876 nm. • The luminescence properties of studied samples are improved by constructing Sc3+ vacancy. • Based on FWHM value, the maximum S a and S r values are 42.61 % and 0.16 % K−1, respectively. • The fabricated NIR LED using designed phosphors exhibits various applications. Near-infrared (NIR) emitting phosphors exhibit admirable photophysical performances which enable their promising application in various fields, whereas the development of a broad NIR emitting phosphors with high performance is still a challenge. Herein, series of Ca 2.94 Sc 2- x Si 3 O 12 :0.06Eu2+ (Ca 3 Sc 2- x Si 3 O 12 :Eu2+) garnet NIR emitting phosphors were designed and the defect engineering was used to regulate the luminescence properties. Upon 520 nm excitation, the resulting phosphors emit intense broad NIR emission peaking at 876 nm arising from Eu2+. Through decreasing Sc3+ content, the defect (i.e. Sc3+ vacancy) was formed and it can efficiently increase the luminescence characteristics of designed compounds, in which Ca 3 Sc 1.9 Si 3 O 12 :Eu2+ phosphor possess the strongest intensity. Moreover, based on density functional theory, theoretical calculation was performed to verify the existence of Sc3+ vacancy in studied samples. Furthermore, the fluorescence intensity and full-width at half maximum (FWHM) were all dependent on temperature. Through analyzing the temperature-dependent FWHM value, it was found that the maximum absolute and relative sensitivities were 42.61 % and 0.16 % K−1, respectively. Via utilizing the synthesized phosphors as NIR converters, a new NIR light-emitting diode (LED) was packaged and it presented potential applications in food detection, bioimaging, NIR solid-state lighting and night-vision. Our findings proposed an efficient strategy to manipulate the luminescence properties of NIR emitting phosphors via constructing defect so as to realize their vivid applications. [ABSTRACT FROM AUTHOR]

Jointly exploiting multiple different yet complementary domain information has been proven to be an effective way to perform robust object tracking. This paper focuses on effectively representing and utilizing complementary features from the frame domain and event domain for boosting object tracking performance in challenge scenarios. Specifically, we propose common features extractor to learn potential common representations from the RGB domain and event domain. For learning the unique features of the two domains, we utilize a unique extractor for event based on Spiking neural networks to extract edge cues in the event domain which may be missed in RGB in some challenging conditions, and a unique extractor for RGB based on deep convolutional neural networks to extract texture and semantic information in RGB domain. Extensive experiments on standard RGB benchmark and real event tracking dataset demonstrate the effectiveness of the proposed approach. We show our approach outperforms all compared state-of-the-art tracking algorithms and verify event-based data is a powerful cue for tracking in challenging scenes. [ABSTRACT FROM AUTHOR]

Near infrared (NIR) phosphor has continued booming in plant cultivation due to the excellent match with the phytochrome P FR and P R. Herein, the garnet NIR phosphor Gd 3 Ga 5 O 12 :Cr3+ is prepared and energy transfer engineering is created by co-doping with Bi3+. Enhanced luminescence improvement is observed in the Bi3+ co-doped samples and the energy transfer between Bi3+ and Cr3+ is confirmed. Optimized Gd 2.96 Bi 0.04 Ga 4.96 Cr 0.04 O 12 exhibits abnormal thermal quenching behavior and the integral emission intensity reaches 102% of that at room temperature. The relative sensitivity S r and absolute sensitivity S a were estimated to be 0.19% K−1 and 0.252 K−1, respectively. This garnet NIR phosphor is coated to the blue chip to fabricate the NIR light emitting diodes for dragon fruit tree cultivation as light compensation during the night. The spectrum of the device covers well of all the absorption spectra of chlorophyll B and phytochrome P FR , exhibiting huge application of this garnet NIR phosphor in plant cultivation. • Energy transfer induced luminescence enhancement of Cr3+. • Abnormal thermal quenching of this NIR phosphor. • NIR LED application in the cultivation of dragon fruit. [ABSTRACT FROM AUTHOR]

Cr3+ doped near infrared (NIR) phosphors have remained exploding in the area of bioimaging, food detection and plant cultivation. Herein, the spinel ZnGa 2 O 4 :Cr3+ NIR phosphor was prepared and the defect was introduced into the matrix via substituting the bivalent Zn2+ with the trivalent Ga3+. Structure variation and the microstructure of the phosphors were investigated and the state of the elements was confirmed. The existence of the defect in the lattice was verified by X-ray photoelectron spectroscopy and thermoluminescence. By substituting Zn2+ with Ga3+ both the emission intensity and the thermal stability were improved. Thermal stability improvement was explored as the result of the defect level in the lattice. The emission intensity of the optimized Zn 0.98 Ga 2.02 O 4+δ :Cr3+ kept 77.3% at 423 K than that at room temperature. This work provides a strategy to enhance the luminescence efficiency and thermal stability simultaneously of Cr3+ doped NIR phosphors. • Simple strategy to introduce defects • Defect engineering induced thermal stability improvement • Good thermal stability of optimized Zn 0.98 Ga 2.02 O 4 :Cr3+ [ABSTRACT FROM AUTHOR]