Baseline drift spectra are used for quantitative and qualitative analysis, which can easily lead to inaccurate or even wrong results. Although there are several baseline correction methods based on penalized least squares, they all have one or more parameters that must be optimized by users. For this purpose, an automatic baseline correction method based on penalized least squares is proposed in this paper. The algorithm first linearly expands the ends of the spectrum signal, and a Gaussian peak is added to the expanded range. Then, the whole spectrum is corrected by the adaptive smoothness parameter penalized least squares (asPLS) method, that is, by turning the smoothing parameter λ of asPLS to obtain a different root-mean-square error (RMSE) in the extended range, the optimal λ is selected with minimal RMSE. Finally, the baseline of the original signal is well estimated by asPLS with the optimal λ. The paper concludes with the experimental results on the simulated spectra and measured infrared spectra, demonstrating that the proposed method can automatically deal with different types of baseline drift.

The spectrum scanned by a Fourier transform spectrometer (FTIR) often has a baseline drift. However, baseline distortion rarely occurs in a laboratory owing to the insignificant effects of environmental vibrations and electromagnetic factors. Even if it occurs, the distorted spectrum can be manually eliminated. However, in a complex environment, especially after the long-term operation of a spectrometer, the scanned spectrum may be distorted to different degrees. Herein, the origins of spectral baseline drifts and distortions are analyzed and simulated using MATLAB; furthermore, a baseline correction method based on the baseline-type model is proposed. The results of experiments performed on the methane spectrum confirm that the proposed method outperformed the improved modified multi-polynomial fitting and iterative averaging methods.

Accurate and reliable detection of coal mine gases is the key to ensuring the safe service of coal mine production. Fourier Transform Infrared (FTIR) spectroscopy, due to its high sensitivity, non-destructive nature, and potential for online monitoring, has emerged as a key technique in gas detection. However, the complex underground environment often causes baseline drift in IR spectra. Furthermore, the variety of gas species and uneven distribution of concentrations make it difficult to achieve precise and reliable online analysis using existing quantitative methods. This paper aims to perform a quantitative analysis of coal mine gases by FTIR. It utilized the adaptive smoothness parameter penalized least squares method to correct the drifted spectra. Subsequently, based on the infrared spectral distribution characteristics of coal mine gases, they could be classified into gases with mutually distinct absorption peaks and gases with overlapping absorption peaks. For gases with distinct absorption peaks, three spectral lines, including the absorption peak and its adjacent troughs, were selected for quantitative analysis. Spline fitting, polynomial fitting, and other curve fitting methods are used to establish a functional relationship between characteristic parameters and gas concentration. For gases with overlapping absorption peaks, a wavelength selection method bassed on the impact values of variables and population analysis was applied to select variables from the spectral data. The selected variables were then used as input features for building a model with a backpropagation (BP) neural network. Finally, the proposed method was validated using standard gases. Experimental results show detection limits of 0.5 ppm for CH₄, 1 ppm for C₂H₆, 0.5 ppm for C₃H₈, 0.5 ppm for n-C₄H₁₀, 0.5 ppm for i-C₄H₁₀, 0.5 ppm for C₂H₄, 0.2 ppm for C₂H₂, 0.5 ppm for C₃H₆, 1 ppm for CO, 0.5 ppm for CO₂, and 0.1 ppm for SF₆, with quantification limits below 10 ppm for all gases. Experimental results show that the absolute error is less than 0.3% of the full scale (F.S.) and the relative error is within 10%. These results demonstrate that the proposed infrared spectral quantitative analysis method can effectively analyze mine gases and achieve good predictive performance.

Unmanned aerial manipulators (UAMs), composed of unmanned aerial vehicles (UAVs) and manipulators, have great application potential in aerial manipulation like precision inspection, disaster rescue, etc. However, strong dynamic coupling exists between UAVs and manipulators. In addition, UAMs meet external disturbances such as gusts of wind during movements. Also, the control performance metrics, such as tracking accuracy and control stability, are seriously affected. Therefore, a cooperative control method is developed for a UAM system with a UAV and a 2-degree-of-freedom manipulator. First, the Euler–Lagrange formulation is employed to study the UAM dynamics like inertial forces and coupling effects. Then, an integral sliding mode control (ISMC) method with an integral term is developed to enhance robustness and eliminate steady-state errors. Finally, the proposed ISMC method is validated through numerical simulations in Matlab R2024a, introducing comparative analyses with the Proportional–Integral–Derivative (PID) and SMC controllers. The simulation results and the comparative analyses validate the effectiveness of ISMC, showing its superiority over the PID and SMC controllers in handling dynamic coupling and external disturbances, where the overshoot of ISMC is reduced by an average of more than 90%. The ISMC method provides a high-performance control strategy to promote the practical application of UAMs in various aerial manipulation tasks and lays the foundation for further optimizing control methods for more complex UAM systems.

This article presents a technique to carry out fault classification using an equal-angle integer-period array convolutional neural network (EAIP-CNN) to process the electrostatic signal of working roller bearings. Firstly, electrostatic signals were collected using uniform angle sampling to ensure the angle intervals between two adjacent data points stayed the same and the signal length was fixed to a pre-determined number of rotation cycles. Then, this one-dimensional signal was transformed into a two-dimensional matrix, where the component of each row was the signal in one period, and the ordinate value of each row represented the corresponding rotation period. Therefore, the row and column indexes of the matrix had a specific meaning instead of simply splitting and stacking the data. Finally, the matrixes were utilized to train the CNN network and test the classification performance. The results show that the classification rate using this technique reaches 95.6%, which is higher than that of 2D CNNs without equal-angle integer-period arrays.

Accurate and reliable detection of coal mine gases is the key to ensuring the safe service of coal mine production. However, the baseline of infrared absorption spectrum is easily disturbed by the complex underground environment, while the variety of gas species and uneven distribution of concentrations make it difficult to achieve precise and reliable online analysis using existing quantitative methods. This paper aims at the poor reliability and accuracy of infrared spectroscopy in the detection of coal mine gases. It utilizes the adaptive smoothness parameter penalized least squares method to correct the drifted spectra. Subsequently, based on the infrared spectral distribution characteristics of mine gases, they can be classified into gases with mutually distinct absorption peaks and gases with overlapping absorption peaks. For gases with distinct absorption peaks, three spectral lines including the absorption peak and its adjacent troughs are selected for quantitative analysis. Spline fitting, polynomial fitting, and other curve fitting methods are used to establish a functional relationship between characteristic parameters and gas concentration. For gases with overlapping absorption peaks, a wavelength selection method based on the impact value of variables and population analysis was applied to select variables from the spectral data. The selected variables are then used as input features for building a model with a BP neural network. Finally, the proposed method was validated using standard gases. The experimental results show that the reference error for 10 coal mine gases is less than 3‰F.S., and the relative error is less than 10%. These results demonstrate that the proposed infrared spectral quantitative analysis method can effectively analyze mine gases and achieve good predictive performance.