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Due to the self-affine property of the grinding surface, the sample images with different roughness captured by the micron-scale camera exhibit certain similarities. This similarity affects the prediction accuracy of the deep learning model. In this paper, we propose an illumination method that can mitigate the impact of self-affinity using the two-scale fractal theory as a foundation. This is followed by the establishment of a machine vision detection method that integrates a neural network and correlation function. Initially, a neural network is employed to categorize and forecast the microscopic image of the workpiece surface, thereby determining its roughness category. Subsequently, the corresponding correlation function is determined in accordance with the established roughness category. Finally, the surface roughness of the workpiece was calculated based on the correlation function. The experimental results demonstrate that images obtained using this lighting method exhibit significantly enhanced accuracy in neural network classification. In comparison to traditional lighting methods, the accuracy of this method on the micrometer scale has been found to have significantly increased from approximately 50% to over 95%. Concurrently, the mean squared error (MSE) of the surface roughness calculated by the proposed method does not exceed 0.003, and the mean relative error (MRE) does not exceed 5%. The two-scale fractal geometry offers a novel approach to image processing and machine learning, with significant potential for advancement.

To address the issues of exploration imbalance and slow convergence speed in the Q‐learning path planning algorithm, an adaptive improved Q‐learning path planning algorithm based on an obstacle learning matrix and artificial potential field (APF) is proposed. First, an obstacle learning matrix is established to store the positions of obstacles encountered in each learning iteration, avoiding redundant learning of the same obstacle. Second, an adaptive exploration enhancement strategy is introduced by incorporating the concept of success rate. This strategy divides the decay process of the exploration rate into an exploration‐dominant initial stage and an exploitation‐dominant later stage. Finally, an APF‐weighted action selection strategy is introduced, utilizing the guiding force generated by the APF to encourage the mobile robot to avoid obstacles more efficiently. Simulation results show that the proposed method can effectively reduce the number of iterations and time consumption in the optimization process, resulting in a smoother and more stable planned path. [ABSTRACT FROM AUTHOR]

A new type of dynamic bus lane not only ensures the strategy of bus priority but also significantly improves the spatiotemporal utilization of road resources and reduces the conflicting demands of buses and social vehicles on road resources. Firstly, the heterogeneity of bus arrival time is analysed according to the process of aggregation and dissipation of vehicle queues at intersections. Considering the correlation between the operating states of social vehicles and buses, a dynamic control strategy for bus lane based on the insertable interval of social vehicles is established. Secondly, combining the setting conditions of the intermittent bus lane control area with the correlation scenario of signal control at intersections and then according to the HCM 2010 vehicle delay equation and the BPR function, the associated optimized control model with the minimum total travel time consumption of the road section as the objective function is constructed. The global optimal control of the intermittent bus lane is realized through the computational experiment. Finally, the setting conditions and benefits of three lane organization schemes (including intersect (i.e., no bus priority), parallel, and intermittent bus lanes) are compared and analysed through case study. The results indicate that the intermittent priority bus lanes have opening hours, which can not only ensure bus priority but also expand the right way for social vehicles and make full use of road space resources, thus improving the overall traffic efficiency., Author(s): Xiaolan Xie [1,2]; Luxi Dong (corresponding author) [3,4]; Liangyuan Feng [5]; Lieping Zhang [3]; Hailing Li [4] 1. Introduction With the increasing shortage of urban road resources and the [...]

To improve traffic efficiency and utilization of road resources and alleviate traffic congestion caused by imbalance of bidirectional traffic flow, in view of the conversion conditions of reversible lane function, the operating characteristics of associated intersections under dynamic reversible lanes are analysed in terms of capacity, and a reversible lane control model is constructed based on short-term traffic flow prediction. On this basis, the reversible lane segment clearing time and upstream and downstream signal control strategies under different states are studied. The collaborative control model of reversible lane clearing time and signal timing of associated intersections is established to obtain the optimal time for reversible lane function switching. Finally, using Chaoyang Road, Beijing, as an example, the effectiveness of the proposed model is verified by the simulation indexes of average vehicle delay and reversible lane clearing time. The results show that the optimized clearing efficiency exceeds 15% and the optimized average vehicle delay is reduced by more than 10%. Combined with the future traffic state, the traffic capacity and saturation flow are greatly improved, and the intelligent reversible lane control is better achieved., Author(s): Xiaolan Xie [1,2]; Luxi Dong (corresponding author) [3,4]; Huinan Gu [5]; Hailing Li [3]; Lieping Zhang [4] 1. Introduction In the case of limited supply of urban road resources, [...]

By integrating deep neural networks with reinforcement learning, the Double Deep Q Network (DDQN) algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots. However, the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data. Targeting those problems, an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed. First, to enhance the precision of the target Q-value, the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q network. Next, a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory information. Additionally, a reward-prioritized experience selection method is introduced, which ranks experience samples according to reward values to ensure frequent utilization of high-quality data. Finally, simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random environments. The experimental results show that compared to the traditional DDQN algorithm, the proposed algorithm achieves shorter average running time, higher average return and fewer average steps. The performance of the proposed algorithm is improved by 11.43% in the fixed scenario and 8.33% in random environments. It not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning, making it suitable for widespread application in autonomous navigation and industrial automation. [ABSTRACT FROM AUTHOR]

Targeting some problems of the RRT_Connect path planning algorithm, such as average search and low efficiency, proposes an improved RRT_Connect algorithm that may optimize the searched nodes and parts of planned paths. Firstly, an improved RRT_Connect algorithm based on destination and searched node bias strategy is proposed. Secondly, an improved RRT_Connect algorithm is put forward for the optimization of the searched nodes and some planned paths to deal with the problem of low quality reflected in the improved RRT_Connect path planning algorithm, and the optimization for the cost of path planning by figuring out valid new nodes and parent nodes of adjacent nodes within a certain range. On this basis, the path planning algorithm is verified by simulation and actual experiments. It is shown by the experimental results that the improved RRT_Connect algorithm proposed in this paper can not only shorten the time and length of path planning but also decrease the number of search iterations and nodes. [ABSTRACT FROM AUTHOR]