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The work proposed a method for recognising the green grapes in the orchard based on multi-source image fusion. First, the acquired multi-source images were denoised based on median filtering and wavelet transform. After extracting the feature points by the improved SURF (speeded up robust features) method, the registration was completed based on the consistency of feature offset and the affine relationship between images. The registered multi-source images were fused based on the CS (compressed sensing) and NSCT-DWT (non-down sampled contourlet transform-discrete wavelet transform). Then the MI-OPT (mutual-information optimal threshold) and the minimum circumscribed rectangle were used to segment the fused images and recognise fruits. The experimental results showed that the information of the target fruits in the fused images was complete. Therefore, compared with the K-means method using colour components of the visible light image and the OTSU (proposed by Nobuyuki Otsu and named after him) method based on near-infrared image, the fruit region obtained by the algorithm in the work was complete. On this basis, the average recognition rate of green grapes reached 92.1%. [ABSTRACT FROM AUTHOR]

• A method for apple growth morphology based on Mask RCNN + PointRend has been proposed. • SPPF structure, CA attention mechanism and PANet are introduced as improvements. • CWD algorithm is used for model distillation to improve the detection speed. Identifying apples with different growth forms is an urgent task to be solved by harvesting robots on the path to intelligence. A method based on Mask RCNN + PointRend was proposed for identifying apples with different growth forms in the orchard in the work. The SPPF structure and CA attention mechanism were introduced to the backbone network of the Mask RCNN + PointRend algorithm to improve network performance because of the complexity of the apple orchard. Meanwhile, a bottom-up pathway was introduced into the feature extraction network to enhance feature dissemination, reuse, and fitting. Finally, the number of model parameters was reduced to improve the detection speed, and the CWD algorithm was used for the knowledge distillation of the model. The improved algorithms were called Mask RCNN-Point and Mask RCNN-PointKD. The recognition accuracy of the two enhanced algorithms on the test set was 85.65 and 74.13%, respectively. Besides, average detection time per image was 93.67 and 62.1 ms, respectively. The work provides a reference for identifying apples with different growth forms. [ABSTRACT FROM AUTHOR]

• A visual recognition method of apple growth morphology is proposed. • An improved YOLOv5 deep learning network (called YOLOv5-B) was proposed. • On the basis of YOLOv5, BiFPN-S and ACON-C are introduced as improvements. The work aimed at the visual identification of the growth forms of fruits to facilitate the subsequent use of different harvesting mechanisms for different growth forms of fruits by robots. The improved YOLOv5 deep learning algorithm was used to propose a visual identification method for the growth forms of apples in the orchard. Specifically, the feature extraction module of the YOLOv5 algorithm imitated the BiFPN model to propose the BiFPN-S structure. The spread of features and feature reuse were enhanced to better fit features. The improved algorithm was called YOLOv5-B. The network SiLU activation function was replaced with the ACON-C activation function to improve its network performance. The COCO data set was used to pre-train the network, and then the data set of the work was trained by the transfer learning method. After the training, the generated optimal model was applied for the visual-identification test of the growth of apple fruits. The results showed that the improved algorithm model considered high accuracy and real-time performance, with the map reaching 98.4% and the F1 value of 0.928. The average accuracy of identifying the growth forms of apples for the test set was 98.45%, and the processing speed was 71 FPS. [ABSTRACT FROM AUTHOR]

In complex driving scenarios, automated vehicles should behave reasonably and respond adaptively with high computational efficiency. In this paper, a computational efficient motion planning method is proposed, which considers traffic interaction and accelerates calculation. Firstly, the behavior is decided by connecting the points on the unequally divided road segments and lane centerlines, which simplifies the decision-making process in both space and time span. Secondly, as the dynamic vehicle model with changeable longitudinal velocity is considered in the trajectory generation module, the C/GMRES algorithm is used to accelerate the calculation of trajectory generation and realize on-line solving in nonlinear model predictive control. Meanwhile, the motion of other traffic participants is more accurately predicted based on the driver's intention and kinematics vehicle model, which enables the host vehicle to obtain a more reasonable behavior and trajectory. The simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Presently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising approach for early diagnosis of autism spectrum disorder (ASD). However, the prevalent approach involves exclusively full-brain functional connectivity data for disease classification using GCN, while overlooking the prior information related to the functional connectivity of brain subnetworks associated with ASD. Therefore, in this study, the multiple functional connectivity–based graph convolutional network (MFC-GCN) framework is proposed, using not only full brain functional connectivity data but also the established functional connectivity data from networks of key brain subnetworks associated with ASD, and the GCN is adopted to acquire complementary feature information for the final classification task. Given the heterogeneity within the Autism Brain Imaging Data Exchange (ABIDE) dataset, a novel External Attention Network Readout (EANReadout) is introduced. This design enables the exploration of potential subject associations, effectively addressing the dataset's heterogeneity. Experiments were conducted on the ABIDE dataset using the proposed framework, involving 714 subjects, and the average accuracy of the framework was 70.31%. The experimental results show that the proposed EANReadout outperforms the best traditional readout layer and improves the average accuracy of the framework by 4.32%. [ABSTRACT FROM AUTHOR]

Twin delayed deep deterministic (TD3) policy gradient has several limitations when applied in planning a path in environment with a number of dilemmas according to our experiment, due to the complexity of the robot path planning task, the rate of convergence of TD3 algorithm is slow and the rate of collision is high. To address this problem, deep dense dueling twin delayed deep deterministic (D3-TD3) architecture is proposed, a method that preserves important information from cross-layer inputs through dense connections and divides the network into a value function and a dominance function, thus, allowing for faster convergence when solving complex tasks. Finally, a spatial model based on three-dimension (3D) point cloud is built, and simulation experimental results show that in static environment, the algorithm proposed in the paper has 40.6% fewer collisions compared to TD3, 30% fewer collisions compared to TD3-BC, 19.2% fewer collisions compared to Dueling TD3 and 17.4% fewer collisions compared to deep dense TD3. In dynamic and static environment, the algorithm proposed in the paper has 34.4% fewer collisions compared to TD3, 24% fewer collisions compared to TD3-BC, 6% fewer collisions compared to Dueling TD3 and 25% fewer collisions compared to deep dense TD3. [ABSTRACT FROM AUTHOR]

• Designed a agricultural residual film pollution monitoring system based on UAV. • Implemented autonomous random sampling on agricultural field surfaces using UAV. • The R2 of UAV evaluated film pollution and manual evaluation was 0.966. • The average monitoring and evaluation time of the system is 422.6 s. To evaluate residual film recovery quality and rapidly monitor residual film pollution in agricultural fields, a residual film pollution monitoring system based on UAV, ground station, and cloud server was proposed in this research. It integrated a random sampling point generation (RSPG) algorithm and a key-lock pairing (KLP) algorithm for fast planning of flight paths, and used the U-Net model to rapidly segment residual films from images and calculate residual film coverage. The results showed that the average deviation between the actual distance and the ideal distance of the randomly generated sampling points using the RSPG algorithm was 0.0733 m, and the average deviation of the azimuth angles was 0.2°. The KLP algorithm significantly reduced path planning time, while maintaining a high shortest path rate and reducing memory consumption. The U-Net model achieved an optimal mean intersection over union (MIOU) of 85.63 % on the test set. The simulation and field verification of the system found that automatic UAV path planning and rapid residual film image segmentation were achieved. The coefficient of determination (R2) between the predicted values and the manually labelled values was 0.966, and the root-mean-square error (RMSE) was 0.1173 %. The average relative error between the predicted residual film coverage and the manually labelled coverage was 11.55 %, and the average detection time of the monitoring system was 422.6 s. Therefore, the UAV-based residual film pollution monitoring system can realize integrated automatic sampling and rapid residual film pollution assessment. This study provides technical support for accurate assessment and control of residual film pollution. [ABSTRACT FROM AUTHOR]

As a hot topic in railways, virtual coupling technology connects multiple trains (units) as a virtually coupled train set (VCTS) and aims to improve the performance of train operation by keeping a small following distance between successive units. For the desired performance, robust constraint satisfaction and stability are crucial properties of a VCTS. However, they are hard to be simultaneously guaranteed with uncertain train dynamics. To solve this problem, we propose a dual-mode robust model predictive control (DRMPC) approach. First, by considering uncertain actuator gains and resistance parameters, the VCTS operation is modeled with safety and input constraints. Then, we put forward a dual-mode scheme to adopt a controller from two candidates. Among this, we formulate a feedback controller through the analysis of local and string stability and adopt it in an admissible region to efficiently guarantee the crucial properties. To handle the rest cases with the risk of constraint dissatisfaction, an optimal controller is yielded by solving a constrained optimization framework employing the homothetic-tube technique. Specifically, local tubes are recursively constructed online by the set-membership prediction, while considering the tubes from the preceding unit. Next, the robust constraint satisfaction and stability of the VCTS are proved within the closed-loop implementation of DRMPC. Finally, experimental results verify the performance and advantages of the proposed approach on a VCTS. • A homothetic-tube-based robust MPC approach for VCTS. • Robust constraint satisfaction and stability with uncertain dynamics. • A dual-mode control scheme to save the computational consumption. [ABSTRACT FROM AUTHOR]

Patients with attention-deficit/hyperactivity disorder (ADHD) have shown abnormal functional connectivity and network disruptions at the whole-brain static level. However, the changes in brain networks in ADHD patients from dynamic functional connectivity (DFC) perspective have not been fully understood. Accordingly, we executed DFC analysis on resting-state fMRI data of 25 ADHD patients and 27 typically developing (TD) children. A sliding window and Pearson correlation were used to construct the dynamic brain network of all subjects. The k -means+ + clustering method was used to recognize three recurring DFC states, and finally, the mean dwell time, the fraction of time spent for each state, and graph theory metrics were quantified for further analysis. Our results showed that ADHD patients had abnormally increased mean dwell time and the fraction of time spent in state 2, which reached a significant level (p < 0.05). In addition, a weak correlation between the default mode network was associated in three states, and the positive correlations between visual network and attention network were smaller than TD in three states. Finally, the integration of each network node of ADHD in state 2 is more potent than that of TD, and the degree of node segregation is smaller than that of TD. These findings provide new evidence for the DFC study of ADHD; dynamic changes may better explain the developmental delay of ADHD and have particular significance for studying neurological mechanisms and adjuvant therapy of ADHD. • Multiple recurring DFC patterns were first identified from resting state fMRI data of ADHD. • ADHD and TD had significant differences in mean dwell time and the fraction of time spent using DFC. • The degree of network integration and separation is related to different DFC states. [ABSTRACT FROM AUTHOR]

• A novel framework for the RDBM is proposed based on the predicted operation trajectory of the preceding train. • A cooperative collision-avoidance control methodology is proposed to ensure the safety and enhance the operation efficiency. • The DQN algorithm is introduced to learn the safe and efficient control strategy. • The effectiveness of the proposed approach is verified by experimental simulations To further improve the line transport capacity, virtual coupling has become a frontier hot topic in the field of rail transit. Specially, the safe and efficient following control strategy based on relative distance braking mode (RDBM) is one of the core technologies. This paper innovatively proposes a cooperative collision-avoidance control methodology, which can enhance the operation efficiency on the premise of ensuring the safety. Firstly, a novel framework for the RDBM based on the predicted trajectory of the preceding train is proposed for the train collision-avoidance control. To reduce the train following distance, a cooperative control model is further proposed and is formulated as a Markov decision process. Then, the Deep-Q-Network (DQN) algorithm is introduced to solve the efficient control problem by learning the safe and efficient control strategy for the following train where the critical elements of the reinforcement learning framework are designed. Finally, experimental simulations are conducted based on the simulated environment to illustrate the effectiveness of the proposed approach. Compared with the absolute distance braking mode (ADBM), the minimum following distance between the adjacent trains can be reduced by 70.23% on average via the proposed approach while the safety can be guaranteed. [ABSTRACT FROM AUTHOR]