No abstract

Aiming at solving the problem of dual resource constrained flexible job shop scheduling problem (DRCFJSP) with differences in operating time between operators, an artificial intelligence (AI)-based DRCFJSP optimization model is developed in this paper. This model introduces the differences between the loading and unloading operation time of workers before and after the process. Subsequently, the quantum genetic algorithm (QGA) is used as the carrier; the process is coded through quantum coding; and the niche technology is used to initialize the population, adaptive rotation angle, and quantum mutation strategy to improve the efficiency of the QGA and avoid premature convergence. Lastly, through the Kacem standard calculation example and the reliability analysis of the factory workshop processing process example, performance evaluation is conducted to show that the improved QGA has good convergence and does not fall into premature ability, the improved QGA can solve the problem of reasonable deployment of machines and personnel in the workshop, and the proposed method is more effective for the DRCFJSP than some existing methods. The findings can provide a good theoretical basis for actual production and application.

An on-board vision system is recognized as a promising tool for vehicle early warning and monitoring. Timely accurate estimation of vehicle speed is critical in allowing the on-board vision system to calculate the vehicle location, plan a driving path, and apply emergency brakes to avoid accidents. However, the scene images captured by the vision system always suffer from global motion blur, which causes great difficulty in precisely estimating vehicle speed. While extensive efforts have been focused on blurred image restoration and real-time driving speed estimation in highway scenarios, very limited work has addressed urban scenarios in which the vehicle speed is often less than 40 km h−1. In order to bridge this research gap, this study proposes a new method for real-time vehicle speed estimation. Firstly, the spectrum characteristics of blurred images at low vehicle speeds were investigated to determine the relationship between the direction and spacing of the spectrogram and vehicle motion parameters. Then, the blur-direction and blur-scale of the vehicle motion were analyzed by double Radon transform to develop a speed estimation model. Experimental evaluation results demonstrate that the proposed method was able to estimate vehicle speed in urban scenarios without updating the hardware of existing on-board vision systems. The estimation error was below 7.13% and the calculation efficiency of a single frame was 30 ms, both of which meet the practical application requirements of intelligent vehicles.

The raising process has been widely used in manufacturing fabric productions. After raising the surface of the fabric, productions are covered with a fluff layer. The quality of the fabric surface is often valuated by the fluffing type. In order to objectively assess the fluff quality of the fabric surface, an optimal sensing method is proposed in this paper. The fluff contour image was firstly collected by the light-cut imaging device. Then, the fluff region was segmented by the adaptive image segmentation method, the contour coordinates of the fabric were extracted using the freeman chain code and constructed in the form of the binary image. Lastly, a back-propagation neural network (BPNN) was used to learn the relationship between the contour coordinates and the fluff quality. On this basis, a practical fabric fluff detection platform was developed based on the optimal sensing technique. Experimental tests were conducted to evaluate the performance of the proposed method in detecting the fluff quality with four different colours and different fluffing processes. Furthermore, the actual fabric inspection was carried out. The detection correct rate can reach 94.17 %, which can meet the practical production requirement.

The precision spool valve is the core component of the electro-hydraulic servo control system, and its performance has an important influence on the flight control of aviation and aerospace products. The non-uniform surface topography error causes a non-uniform mating gap field inside the spool valve, which causes oil leakage and leads to deterioration of the spool valve performance. However, the current oil leakage calculation method only considers the influence of size errors, which is not comprehensive. Thus, how to characterize the mating behavior of the spool valve and its effect on oil leakage with consideration of surface topography errors is the key to evaluating the performance of the spool valve. This paper proposes a new way of analyzing the mating performance of precision spool valves, which considers the surface topography errors based on digital twin technology. Firstly, a general framework for the analysis of mating performance of precision spool valve based on a digital twin is proposed. Then, key technologies of assembly interface geometry modeling, matching behavior modeling and performance analysis are studied. Finally, a quantitative correlation between the mating parameters and the oil leakage of the precision spool valve is revealed. The method is tested on a practical case. This proposed method can provide theoretical support for the accurate prediction and evaluation of the mating performance of the precision spool valve.