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We herein describe the synthesis of a new class of axially chiral aza/boracyclophanes (BDN1, BXN1, BDB1 and BXB1) using binaphthyls as chiral building blocks and the main-group (B/N) chemistry with tunable electronic effects. All macrocycles substituted with triarylamine donors or triarylborane acceptors are strongly luminescent. These macrocycles showed two distinct meta and para π-conjugation pathways, leading to the formation of quasi figure-of-eight and square-shaped conformations. Interestingly, comparison of such structural models revealed that the former type of macrocycles BXN1 and BXB1 gave higher racemization barriers relative to the other ones. The results reported here may provide a new approach to engineer the optical stability of π-conjugated chiral macrocycles by controlling π-substitution patterns. The ring constraints induced by macrocyclization were also demonstrated to contribute to the configurational persistence as compared with the open-chain analogues p-BTT and m-BTT.

The dual-arm cutting robot addresses the low efficiency of traditional single-arm roadheaders when cutting large cross-sections. However, its dynamic interaction with coal-rock affects control performance. In current studies, both arms of the dual-arm cutting robot interact with the same object, forming a closed kinematic chain, which fails to meet the control requirements for independent arm movement and the output force of each cutting head. To solve this issue, a force-position hybrid control system based on the robot’s relative dynamics model was designed. The kinematic and dynamics models of the dual-arm cutting robot were established, with the relative dynamics model derived using the robot’s relative Jacobian matrix and principles of virtual displacement and virtual work. This model used a single variable to describe the motion states of both arms, integrating their independent dynamics models into a unified one. Based on this relative dynamics model, a force-position hybrid control system was developed for the robot’s dual arms, with system stability and feasibility verified via the Lyapunov function. Simulation results indicated that the dual-arm cutting process had a larger workspace compared to single-arm cutting, allowing for efficient large cross-section cutting. The force-position hybrid control system enabled synchronized tracking of expected relative position and force, with the absolute error in tracking the target cutter position kept within 0.3132 m and a root mean square error of 0.1447 m.

Abstract When the coal gangue sorting robot sorts coal gangue, the position of the target coal gangue will change due to belt slippage, deviation, and speed fluctuations of the belt conveyor. This will cause the robotic to fail in grasping or miss grasping. We have developed a solution to this problem: the IMSSP-Net two-stage network gangue image fast matching method. This method will reacquire the target gangue position information and improve the robot’s grasping precision and efficiency. In the first stage, we use SuperPoint to guarantee the scene adaptability and credibility of feature point extraction. We have enhanced Superpoint’s ability to detect feature points further by using the improved Multi-scale Retinex with Color Restoration enhancement algorithm. In the second stage, we introduce SuperGlue for feature matching to improve the robustness of the matching network. We eliminated erroneous feature matching point pairs and improved the accuracy of image matching by adopting the PROSAC algorithm. We conducted image matching comparison experiments under different object distances, scales, rotation angles, and complex conditions. The experimental platform adopts the double-manipulator truss-type coal gangue sorting robot independently developed by the team. The matching precision, recall, and matching time of the method are 98.2%, 98.3%, and 84.6ms, respectively. The method can meet the requirements of efficient and accurate matching between coal gangue recognition images and sorting images.

The shield type temporary support robot is an important component of the intelligent excavation robot system for large section roadways that adapts to the coexistence of dirt and debris. Its main function is to provide a safe and reliable workspace for achieving 'parallel excavation and support' operations. In order to enhance the safe and stable support of the shield type temporary support robot for surrounding rock during its pushing and driving process, based on the structure, working environment, and operational requirements of the shield type temporary support robot, a mathematical model of its pushing amount and support force during pressurized driving, as well as a dynamic model of pressurized driving, are established. A hydraulic control system for the shield type temporary support robot driving under pressure is designed. The system mainly consists of a support hydraulic system and a driving hydraulic system. During static support, the support hydraulic system needs to constantly output a support force greater than the weight of the upper shield body itself, and the driving hydraulic system is in standby mode. When driving under pressure, the support hydraulic system and the driving hydraulic system work simultaneously, ensuring that the temporary support robot 'reducing stress without leaving the roof' while steadily moving forward with the roof under pressure at all times. A precise control method for shield type temporary support robot driving under pressure based on fuzzy PID is proposed. The pressure and displacement signals of the temporary support robot are collected in real time by displacement sensors integrated on the displacement cylinder and pressure sensors in the hydraulic circuit. The signals are used to reflect the changes in support force and driving displacement during the temporary support robot's driving under pressure. Based on the error and error rate of the support force and displacement, the fuzzy PID algorithm is used to modify the control parameters of the support force and displacement, achieving reliable control of driving under pressure based on the fuzzy PID algorithm. Both simulation and experimental results show that the effect of fuzzy PID control is superior to traditional PID control. Under fuzzy PID control, the relative error of support force during the pushing and driving process of the shield type temporary support robot is less than 1%, and the driving displacement error is less than 2 mm. Moreover, the response speed of support force and pushing amount control is fast, ensuring the safe and stable support of the surrounding rock during the pushing and driving process.

In recent years, ultrasonic guided waves technology has been widely used in nondestructive pipeline detection. However, the weak and insignificant defect echoes caused by the different types of tiny defects such as cracks, void, and dent deformation makes it difficult to identify and classify different types of miero defects. In order to identify the types of different tiny defects, the sensitivity of Duffing system to weak periodie signals was exploited and a signal feature classification method based on the dynamic index fractal dimension of the Duffing system and the back propagation (BP) neural network was proposed. By extracting the fractal dimension、 wavelet cocfficient and time domain signal parameters of the Duffing oscillator after inputting the defect signal to be tested as the characteristic parameters of the echo signal, inputting the BP neural network to complete the construction of the BP neural network, realizing the learning of the weak ultrasonie guided wave signal, classification. The numerical simulation and experimental verification show that the recognition accuracy is significantly improved by taking the fractal dimension of chaos index of three Duffing oscillators into consideration. The accuracy of numerical simulation is increased from 86.35% to 91.85%、 and the accuracy of experimental verification is increased from 83.16% to 86.06%. The numerical simulation and experiment verify that the combination of fractal dimension and BP neural network can effectively improve the identification of pipeline features and defects. The innovative use of fractal as the feature input of BP neural network effectivel y improves the accuracy of classification, facilitating identification and accurate classification, particularly in cases of insufficient experimental data or difficult detection scenarios invol ving small defects in the pipeline. The novel classification method that has been proposed has important significance for the pipeline defects classification and accidents prevention.

The adaptive cutting technology of the shearer is a key technology for achieving intelligent mining in fully mechanized working faces. In order to solve the problem of low automatic cutting precision of the shearer in complex coal seams, a prediction method for the height adjustment of shearer drum based on improved gated recurrent neural network (GRU) is proposed. Considering the correlation between adjacent data in the longitudinal and transverse directions of the cutting trajectory, the fixed length sliding time window method is used to obtain the height data of the shearer drum. The input data is divided into continuous and adjustable subsequences, while processing the feature information in the transverse and longitudinal directions. To improve the prediction efficiency of the model and meet the real-time requirements of cyclic cutting, causal convolution gated recurrent unit(CC-GRU) is proposed to perform dual feature extraction and dual data filtering on input data. CC-GRU utilizes causal convolution to focus on the local temporal features in the longitudinal direction of the sequence in advance, in order to reduce computational costs and improve computational speed. CC-GRU uses gating mechanism to serialize and model the features obtained from convolution to capture long-term dependencies between elements. The experimental results show that using the CC-GRU model to predict the height adjustment of the shearer drum, the MAE is 43.80 mm, MAPE is 1.90%, RMSE is 50.35 mm, the determination coefficient is 0.65, and the prediction time is only 0.17 seconds. Compared to long short term memory (LSTM) neural networks, GRU, and temporal convolutional network (TCN), the CC-GRU model has a faster prediction speed and higher prediction precision. It can more accurately predict the height adjustment trajectory of the shearer in real time. This provides a basis for the establishment of coal seam models in the working face and the prediction of the height adjustment trajectory of the shearer.