Selection of an Appropriate Mechanized Mining Technical Process for Thin Coal Seam Mining

Mechanized mining technical process (MMTP) related to the control method of the shearer is a vital process in thin coal seam mining operations. An appropriate MMTP is closely related to safety, productivity, labour intensity, and efficiency. Hence, the evaluation of alternative MMTP is an important part of the mining design. Several parameters should be considered in MMTP evaluation, so the evaluation is complex and must be compliant with a set of criteria. In this paper, two multiple criteria decision-making (MCDM) methods, Analytic Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), were adopted for this evaluation. Then, the most appropriate MMTP for a thin coal seam working face was selected in China

Modulation recognition of low-SNR UAV radar signals based on bispectral slices and GA-BP neural network

In this paper, we address the challenge of low recognition rates in existing methods for radar signals from unmanned aerial vehicles (UAV) with low signal-to-noise ratios (SNRs). To overcome this challenge, we propose the utilization of the bispectral slice approach for accurate recognition of complex UAV radar signals. Our approach involves extracting the bispectral diagonal slice and the maximum bispectral amplitude horizontal slice from the bispectrum amplitude spectrum of the received UAV radar signal. These slices serve as the basis for subsequent identification by calculating characteristic parameters such as convexity, box dimension, and sparseness. To accomplish the recognition task, we employ a GA-BP neural network. The significant variations observed in the bispectral slices of different signals, along with their robustness against Gaussian noise, contribute to the high separability and stability of the extracted bispectral convexity, bispectral box dimension, and bispectral sparseness. Through simulations involving five radar signals, our proposed method demonstrates superior performance. Remarkably, even under challenging conditions with an SNR as low as −3 dB, the recognition accuracy for the five different radar signals exceeds 90%. Our research aims to enhance the understanding and application of modulation recognition techniques for UAV radar signals, particularly in scenarios with low SNRs