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In order to improve the detection accuracy of coal mine gas and coal dust explosion, recognition method of coal mine gas and coal dust explosion based on the sound spectrogram and SVM was proposed: installing mining pickups in key monitoring areas of underground coal mines, monitoring of working sounds of underground coal mine equipment and environmental sound in real-time. The collected sound was extracted from the sound spectrogram composed of MFCC, and the energy, entropy, contrast, and correlation of 0°, 45°, 90°, and 135° were obtained by calculating the gray co-generation matrix of the sound spectrogram. Calculated their mean and standard deviation as the texture features of the sound spectrogram image, which constituted the feature quantity of the sound, which brought to SVM to establish the sound recognition model of coal mine gas and coal dust explosion. For the sound to be tested, the texture features were also extracted and input to the trained recognition model for sound recognition and classification, which has passed the test verification. Firstly, the MFCC feature values of different sounds, such as mining equipment operation, gas and coal dust explosion were extracted, and the distribution of MFCC feature values of different sounds was analyzed; the sound spectrograms of different sounds were extracted, and the mean and standard deviation of the sound spectrograms of different sounds were analyzed by analyzing the feature parameters of energy, entropy, contrast, and correlation, and the grayscale co-generation matrix feature parameters of the sound spectrograms constitute the feature quantities that can effectively characterize the sound signals. Secondly, the sound to be measured was input into the recognition model established to complete the recognition and classification. The results show that the recognition rate of the proposed method reaches 95%, and the overall recognition performance is higher than others models. Finally, the experimental results of Bayesian parameter optimization show that the recall rate and recognition rate of the optimized SVM recognition model increased by 10% and 3% respectively. It was better than the recognition model before optimization, which can meet the needs of coal mine gas and coal dust explosion sensing and alarming.

At present, it is difficult to meet the emergency rescue needs of gas and coal dust explosion accidents due to the rate of missing and false alarms in coal mine gas and coal dust explosion monitoring. In order to solve the above problems, a coal mine gas and coal dust explosion sound recognition method based on wavelet packet energy is proposed. This method installs mine-used pickups in the key monitoring areas of the coal mine to collect the working sound and environmental sound of the coal mine equipment in real-time. The wavelet packet energy ratio of sound is extracted through wavelet packet decomposition, and the feature vector characterizing the sound signal is formed. The feature vector is input into the BP neural network to obtain the sound recognition model of coal mine gas and coal dust explosion. The wavelet packet energy ratio of the sound signal to be measured is extracted and input into the model as the feature vector to recognize the type of sound signal to be measured. According to the requirements of feature vectors and output results, a BP neural network with 8, 8 and 1 nodes in the input layer, hidden layer and output layer is established to train the recognition model. By analyzing the results of wavelet packet decomposition of underground acoustic signals in coal mines, it is confirmed that the Haar wavelet function is used and the number of wavelet packet decomposition layers is chosen to be 3. The experimental results show that the energy proportion of gas and coal dust explosion sound decomposed by wavelet packet is obviously different from other sounds. The wavelet packet energy proportion distribution of the same sound signal with different time is stable. Therefore, the wavelet packet energy proportion can effectively represent the features of the sound signal and has strong robustness. BP neural network training speed is fast, and only a small number of training iterations can achieve the expected error. The recognition accuracy is up to 95% in the presence of many disturbing sound signals in the coal mine. BP neural network has the best recognition effect compared with the extreme learning machine model and support vector machine model.

The characteristics of coal mine gas and coal dust explosion are analyzed. The gas concentration changes suddenly. The ambient temperature rises rapidly. The air pressure rises suddenly. It produces fireballs and smoke. It produces strong infrared and ultraviolet radiation. It generates explosion shock waves and flame waves. It produces explosive sound. The coal mine gas and coal dust explosion perception based on explosion sound has the following advantages. ① Explosion shock waves and flame waves attenuate quickly and travel close distances. Sound waves attenuate slowly and travel over long distances. The mine sound pickup equipment far away from the explosion source can be used for the perception of coal mine gas and coal dust explosion. ② Compared with the coal mine gas and coal dust explosion perception method based on gas concentration and temperature sensors, the proposed method has the advantage of fast response. ③ Compared with the coal mine gas and coal dust explosion perception method based on video images, the proposed method has the advantages of not being affected by dust, light, shelter, etc. ④ Mine sound pickup equipment has low cost and is easy to install. ⑤ The sound travels over a long distance and it is less affected by roadways and branches. ⑥ The sound processing speed is fast. The gas and coal dust explosion sound can be quickly recognized from various sound signals in a short time. A perception method of coal mine gas and coal dust explosion based on explosion sound recognition is proposed. The sound signals in the monitoring area are collected using microphone array pickups. After preprocessing such as normalization, framing, and adding category labels, the sound signal features are extracted. The features are input into a statistical classifier for training. The sound recognition model for coal mine gas and coal dust explosion is established. The sound signal of the monitoring area is collected in real-time. The extracted sound signal features are input into the trained coal mine gas and coal dust explosion sound recognition model. Whether it is the coal mine gas and coal dust explosion sound can be determined. If so, an alarm will be given.

In this paper, the problem of periodic solutions is studied for second order differential equations with indefinite singularities

Heating through a solar assisted ground-source heat pump system is an efficient method, and optimizing its parameters can significantly improve its energy efficiency. However, the research on the impact of the start-stop temperature difference of the heat collection cycle on the system is currently limited. To address this issue, this study aims to develop a SAGSHP system model located in Tianjin using TRNSYS software, investigate the effect of parameters such as the start-stop temperature difference on the system performance, and guide the optimization process. The optimization parameters include the collector's area, the water tank's volume, and temperature difference during the heat collection cycle. The NSGA-II algorithm is applied to perform multi-objective optimization to enhance the system's energy efficiency and economy. Furthermore, a sensitivity analysis is conducted to study each variable's impact on the optimization results. The variation in temperature between the start and stop of the collection cycle has a significant impact on the system's performance, as revealed by the study. The optimal operation scheme lies within the boundary range of parameter settings. The optimization leads to a decrease of 8.63% in the system's annual operating cost and a reduction of 1555 kWh in the annual power consumption. The relationship between the area of the collector and the temperature difference during heat collection startup is expressed as 0.034°C/m2, and the ratio to the tank volume is 1.33°C/m3. The degree of influence of optimization parameters on optimization results is ranked as Vi>Ai>Ts>Te. [ABSTRACT FROM AUTHOR]

In this paper, we use the Leray–Schauder degree theory to study the following singular periodic problems: x ″ + f (x ′) + g (t , x) = s , x (0) − x (T) = 0 = x ′ (0) − x ′ (T) , where f : ℝ → ℝ is a continuous function with f (0) = 0 , function g : ℝ / T ℤ × ℝ + → ℝ is continuous with an attractive singularity at the origin, and s is a constant. We consider the case where the friction term f satisfies a local superlinear growth condition but not necessarily of the Nagumo type, and function g does not need to satisfy coercivity conditions. An Ambrosetti–Prodi type result is obtained. [ABSTRACT FROM AUTHOR]

The existence and multiplicity of T-periodic solutions to a class of differential equations with attractive singularities at the origin are investigated in the paper. The approach is based on a new method of construction of strict upper and lower functions. The multiplicity results of Ambrosetti–Prodi type are established using a priori estimates and certain properties of topological degree. [ABSTRACT FROM AUTHOR]