In order to overcome disturbances such as the instability of internal parameters or the actuator fault, the time-varying proportional-integral sliding-mode surface is defined for coordinated control of the excitation generator and the steam valve of waste heat power generation units, and a controller based on sliding-mode function is designed which makes the system stable for a limited time and gives it good performance. Based on this, a corresponding fault estimation law is designed for specific faults of systems, and a sliding-mode fault-tolerant controller is constructed based on the fixed-time control theory so that the systems can still operate stably when an actuator fault occurs and have acceptable performance. The simulation results show that the tracking error asymptotically tends to be zero, and the fixed-time sliding-mode fault-tolerant controller can obviously improve the dynamic performance of the system.
Coal slime flotation is crucial to improve the utilization rate of coal resources and reduce environmental pollution. However, the existing flotation foam image processing systems based on wired communication brings insufficient computational resources, poor scalability and high capital expenditure. In this paper, we propose a two-stage coal flotation foam image processing system based on 5G Industrial Internet of Things (IIoT) and edge computing. In the first stage, i.e., task offloading stage, we exploit Lyapunov optimization to decompose the coupling between long-term constrains and shortterm decisions. A matching theory-based task offloading algorithm is proposed to maximize the average network throughput. In the second stage, i.e., image processing stage, we employ gray level histogram and gray level co-occurrence matrix (GLCM) to extract the texture feature parameters of the coal slime flotation foam image. On that basis, we present an ash value prediction algorithm based on multiple linear regression. Finally, extensive simulations are carried out to verify the reliability, feasibility and efficiency of the proposed task offloading algorithm and ash value prediction algorithm. INDEX TERMS Coal slime flotation foam image processing system, 5G Industrial Internet of Things (IIoT), edge computing, task offloading, Lyapunov optimization, matching theory, multiple linear regression
Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.
Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.
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