Zuojin Li

Tracked agricultural robots operating on soft ground may experience asymmetric left–right track slip, while two-dimensional global navigation satellite system (GNSS) observations may contain a dominant anomaly in one coordinate. This simulation study evaluates a robust multi-sensor localization method for this coupled degradation. Independent left–right slip ratios are introduced into a six-state extended Kalman filter (EKF) to improve motion prediction. A coordinate-adaptive Huber extended Kalman filter (CAH-EKF) is then developed for GNSS updating. It combines component-wise normalized innovations and innovation jumps for coordinate-level anomaly detection, maps Huber weights to effective measurement covariance, and uses finite-state switching to confirm, maintain, and recover robust processing while retaining information from the relatively reliable coordinate. Simulation experiments on S-shaped and multi-U paths separate prediction-stage and update-stage effects. The independent-slip model improves over the common-slip model by 16.99–34.34% in position root mean square error (RMSE) and 29.11–36.27% in heading RMSE. Under sustained single-coordinate GNSS anomalies, CAH-EKF outperforms IS-EKF and remains competitive with Huber-IEKF. Under the main joint degradation experiments, the combined method yields 42.78–77.43% position and 27.05–76.33% heading improvements. Additional simulation checks covering slip excitation, path and motion changes, GNSS-degradation variants, and runtime further characterize the operating range of the method. These results support robust low-cost localization using encoders, a yaw-rate gyroscope, and a two-dimensional GNSS within the tested simulation conditions.

This paper proposes a novel neuroadaptive asymptotic tracking control method for a class of uncertain multi-input multi-output (MIMO) robotic manipulators. Firstly, an intrinsic plasticity (IP)-driven cycle echo state network (ESN) is constructed. The intrinsic plasticity mechanism can adaptively adjust neuronal excitability, enhancing the network’s ability to capture complex time-varying dynamics. Meanwhile, the cycle reservoir structure significantly reduces the number of neural connections, thus improving computational efficiency. Secondly, the proposed IP-driven cycle ESN is integrated with the robust integral of the sign of the error (RISE) framework to form a neuroadaptive controller. The IP-driven cycle ESN serves to dynamically approximate the unknown nonlinearities inherent in the robotic system, whereas the RISE term compensates for approximation errors and external disturbances to ensure satisfactory robust performance. Then, a rigorous stability analysis is given to demonstrate that the proposed controller can achieve asymptotic convergence of the tracking error. Finally, simulation experiments are conducted on two typical two-joint manipulators to evaluate the performance of the proposed method. Comparative results demonstrate that, in contrast to the Radial Basis Function Neural Network (RBFNN)-based PI control method, the proposed method achieves faster error convergence rate, higher tracking precision, and smoother control inputs. The results highlight the effectiveness of combining the approximation capability of the IP-driven cycle ESN with the robust compensation capability of the RISE framework for high-precision control of uncertain nonlinear robotic systems.

Considering the significant role of surfactant-flooding in Enhanced Oil Recovery(EOR) for low-permeability and ultra-low permeability reservoirs, it is necessary to get insight into the dynamic mechanism of surfactant micelles behavior at gas-liquid interface. In this study, oblique-incidence reflectivity difference (OIRD) analysis is demonstrated to be a powerful tool for the in-situ analysis of surfactant micelles behavior. Based on the detection of cationic surfactant solutions, we develop a detailed model of molecular dynamic micellization process that it can be used to predict actual micellization and physical features. The results demonstrate a significant upward trend followed by a sharp decline in the OIRD signal as the CTAB concentration increases, which exactly reveals surfactant micelles mechanism including molecular migration and adsorption, pre-micellar generation, and micelle formation. More detailed characteristics of the surfactant micelles also can be inferred, which include not only the critical micelle concentration (CMC) but also the critical pre-micelle concentration(CPC). This work aid in further understanding the dynamic mechanisms underlying the adsorption and micellization processes of surfactant molecules at the gas-liquid interface on a molecular scale, which is helpful to the utilization of surfactant-flooding in enhanced oil recovery (EOR).

Driver fatigue recognition is a highly challenging issue because of the complexity of road conditions, the dynamics of traffic flow, and the differences between drivers. This article proposes a biologically inspired long short-term memory (LSTM) model with neural plasticity (NP-LSTM) to improve the learning and memory ability of the traditional driver fatigue recognition method, thus improving effectiveness and robustness of monitoring and early-warning systems. First, the approximate entropy (ApEn) of the time series of drivers’ operation behaviors and vehicle status is investigated to explore the features of potential irregularity in fatigue-driving behaviors; then, inspired by the plastic learning mechanism of biological neurons, the intrinsic plasticity and synaptic plasticity are embedded into the LSTM neural network to realize the classified storage of complex road patterns, the dynamics of traffic flow, and the memory of drivers’ individual differences; finally, the dropout technology is introduced to further build a “sparse” neural network, which avoids the repeated training of an unchanged neural network under different conditions and enhances the adaptability and generalization of the whole monitoring and early-warning system. Experimental study on real roads is conducted to demonstrate the effectiveness of the proposed method. The results show that the average recognition accuracy is 88.73%, demonstrating a better recognition performance of the proposed method.

Mountainous roads are severely affected by environmental factors such as insufficient lighting and shadows from tree branches, which complicates the detection of drivers’ facial features and the determination of fatigue states. An improved method for recognizing driver fatigue states on mountainous roads using the YOLOv5 neural network is proposed. Initially, modules from Deformable Convolutional Networks (DCNs) are integrated into the feature extraction stage of the YOLOv5 framework to improve the model’s flexibility in recognizing facial characteristics and handling postural changes. Subsequently, a Triplet Attention (TA) mechanism is embedded within the YOLOv5 network to bolster image noise suppression and improve the network’s robustness in recognition. Finally, the Wing loss function is introduced into the YOLOv5 model to heighten the sensitivity to micro-features and enhance the network’s capability to capture details. Experimental results demonstrate that the modified YOLOv5 neural network achieves an average accuracy rate of 85% in recognizing driver fatigue states.

Fatigue driving has emerged as the predominant causative factor for road traffic safety accidents. The fatigue driving detection method, derived from laboratory simulation data, faces challenges related to imbalanced data distribution and limited recognition accuracy in practical scenarios. In this study, we introduce a novel approach utilizing a gated recurrent neural network method, employing whale optimization algorithm for fatigue driving identification. Additionally, we incorporate an attention mechanism to enhance identification accuracy. Initially, this study focuses on the driver's operational behavior under authentic vehicular conditions. Subsequently, it employs wavelet energy entropy, scale entropy, and singular entropy analysis to extract the fatigue-related features from the driver's operational behavior. Subsequently, this study adopts the cross-validation recursive feature elimination method to derive the optimal fatigue feature index about operational behavior. To effectively capture long-range dependence relationships, this study employs the gated recurrent unit neural network method. Lastly, an attention mechanism is incorporated in this study to concentrate on pivotal features within the data sequence of driving behavior. It assigns greater weight to crucial information, mitigating information loss caused by the extended temporal sequence. Experimental results obtained from real vehicle data demonstrate that the proposed method achieves an accuracy of 89.84% in third-level fatigue driving detection, with an omission rate of 10.99%. These findings affirm the feasibility of the approach presented in this study.

Prosthetic attack is a problem that must be prevented in current finger vein recognition applications. To solve this problem, a finger vein liveness detection system was established in this study. The system begins by capturing short-term static finger vein videos using uniform near-infrared lighting. Subsequently, it employs Gabor filters without a direct-current (DC) component for vein area segmentation. The vein area is then divided into blocks to compute a multi-scale spatial–temporal map (MSTmap), which facilitates the extraction of coarse liveness features. Finally, these features are trained for refinement and used to predict liveness detection results with the proposed Light Vision Transformer (Light-ViT) model, which is equipped with an enhanced Light-ViT backbone, meticulously designed by interleaving multiple MN blocks and Light-ViT blocks, ensuring improved performance in the task. This architecture effectively balances the learning of local image features, controls network parameter complexity, and substantially improves the accuracy of liveness detection. The accuracy of the Light-ViT model was verified to be 99.63% on a self-made living/prosthetic finger vein video dataset. This proposed system can also be directly applied to the finger vein recognition terminal after the model is made lightweight.

Prosthetic attack is a problem that must be prevented in the current finger vein recognition application. To solve this problem, a finger vein living detection system was established in this article. The system first captures short-term static finger vein video by uniform near-infrared lighting, segments the veins by Gabor filters with current removing, calculates the multi-Scale spatial-Temporal maps(MSTmap) from the selected vein blocks, and trains the MSTmaps in the proposed Light-ViT network for the liveness detection. The MS maps are used to extract the coarse feature and Light-ViT is used to refine the liveness feature and predict the liveness result. Light-ViT, featuring an enhanced L-ViT backbone as its core, is constructed by interleaving multiple MN blocks and L-ViT blocks. This architecture effectively balances the learning of local image features, controls network parameter complexity, and substantially improves the accuracy of liveness detection. The accuracy of the Light-ViT network is verified to be 99.63% on the self-made living / prosthetic finger vein video dataset. This proposed system can also be directly applied to the finger vein recognition terminal after the model lightweighting.

Vein segmentation and projection correction constitute the core algorithms of an auxiliary venipuncture device, responding to accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting veins and a coaxial correction for image alignment in the self-built vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by means of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combines coaxial correction and a homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, we used a hot mirror to adjust the light paths of both the projector and the camera to be coaxial, and then aligned the projected image with the dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection-over-union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in 0.09 s, and the largest distance error of vein projection onto the dorsal hand is 0.53 mm. The experiments show that the device has reached practical accuracy and has values of research and application.

Vein segmentation and projection correction constitute the core algorithms of auxiliary venipuncture device, responding to the accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting vein and a coaxial correction for image alignment in the self-build vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by the mean of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combining coaxial correction and homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, use a hot mirror to adjust the light paths of both projector and camera to be coaxial, and then align the projected image with dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection over union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in a 0.09 second, and the largest distance error of vein projection onto dorsal hand is 0.53mm. Experiments show that the device has reached practical accuracy and has the value of research and application.