This paper addresses the adaptive neural network-based boundary control problem for a rotating flexible beam system (RFBS) subject to unknown distributed and boundary disturbances, input dead-zone nonlinearity, and parametric uncertainties. The system is composed of a flexible beam with one end fixed to the center of a rotating disk, the other end is attached to a tip mass. The system is governed by coupled partial and ordinary differential equations (PDEs–ODEs), presenting significant challenges due to its infinite-dimensional nature. To achieve simultaneous vibration suppression and strict enforcement of boundary output constraints, a novel adaptive boundary control scheme is developed. The proposed controller incorporates a barrier Lyapunov function (BLF) to handle output constraints, radial basis function (RBF) neural networks to compensate for unknown system dynamics and dead-zone effects, and a disturbance estimator to reject boundary perturbations. By constructing a composite Lyapunov function candidate, the uniform ultimate boundedness (UUB) of the closed-loop system is proved via Lyapunov stability analysis. Numerical simulations are conducted to illustrate the effectiveness of the proposed control approach.

Multimodal named entity recognition (MNER) has become a prominent research area for information extraction from social media content. Recent statistics indicate a growing proportion of social media posts containing multiple images. However, most existing methods focus primarily on single-image scenarios. To bridge this gap, we propose a multimodal large language model-assisted denoising multiscale visual fusion framework (LD-MVF) for multiimage MNER (MNER-MI). Our framework follows a coordinated pipeline of filtering, enhancement, and fusion to systematically address the unique challenges of multiimage scenarios. Specifically, to reduce noise from irrelevant images, we introduce an image–text similarity discriminator trained with human feedback, which provides dynamic weights for visual feature aggregation. To enrich the often sparse textual context, we leverage a multimodal large language model (MLLM) to directly generate grounded auxiliary knowledge from the most relevant images, avoiding information loss caused by textual descriptions. Furthermore, we design an adaptive hierarchical fusion module that extracts multiscale visual features from multiple images and injects them as visual prefixes to enhance textual representations through a dynamic gating mechanism. These three components work cooperatively: the discriminator filters out noise to provide cleaner visual input, the MLLM enhances textual semantics with visually-grounded knowledge, and the fusion module integrates multiscale features for fine-grained cross-modal alignment. Extensive experiments demonstrate that LD-MVF achieves state-of-the-art performance on both the MNER-MI and MNER-MI-Plus datasets.

With the expanding use of Unmanned Aerial Vehicles (UAVs) in a variety of applications, their safety has become a critical concern. UAVs are confronted with a variety of unforeseen circumstances during missions; in these instances, the UAVs need to autonomously locate a suitable landing site, plan flight routes, and avoid obstacles in unstructured environments. Due to the limitations of computing power and sensors, it is challenging to attain the goal. The aim of research study is to investigate the monocular emergency autonomous landing algorithm. This work concentrates specifically on extracting depth and vision information. A topology information extractor is designed to transform images into graphs and assess the connectivity of terrain. In addition, a depth information extractor is designed to compute the slope and roughness of the ground. A$3$D topology optimizer is designed to optimize the graph based on depth information and evaluate the landing suitability using a heuristic strategy. For action decision making, a 3D topology decision method based on Depth-Enhanced Graph Structure (DEGS) is proposed. In order to demonstrate the efficacy of DEGS, this study constructed a simulated scenario based on an actual scene. The results of the experiment indicate that DEGS outperforms its counterparts in terms of the accuracy of action prediction and landing success rate.Note to Practitioners—Using DEGS, this study proposes a novel method for emergency UAV landing on unknown fields. The proposed method is founded on the following fundamental concepts: First, the UAV first generates a DEGS of the unknown field. Second, the UAV then evaluates the landing risk and guides the UAVs in planning a safe landing trajectory. Third, the UAV implements the landing trajectory and lands safely on the unknown field using monocular aerial vision. Frame Sequential and Self-supervision Network (FSSN), a multi-scale vision-based Monocular Depth Estimation (MDE) network, is proposed to estimate the depth map for real-time UAV flight phases. This method has been evaluated using simulations and real-world dataset of simulation images of monocular continuous frames and has shown to be effective in landing UAVs on unknown fields. A human-in-the-loop learning approach is proposed for updating DEGS with dynamic terrain classification that made the procedure feasible for unknown fields. In terms of landing success rate and action prediction accuracy, the results demonstrate that the proposed method is capable of landing a UAV on unknown terrain in a safe and efficient manner, and it is particularly useful in emergency situations where the UAV does not have prior knowledge of the field.

Achieving visual localization with excellent interactive performance is challenging for mobile robots. Based on real-time photo-realistic view synthesis, 3D Gaussian splatting (3DGS) representation has demonstrated vast potential for robots engaging with the physical world. In this work, we propose a novel coarse-to-fine visual localization method named L3DGS based on the 3DGS radiance field representation. Particularly, during the coarse stage, we exploit novel views synthesized by the pretrained 3DGS map to create geometric feature correspondences to perform geometric alignment. Then, we integrate both geometric and photometric alignment to refine the camera pose. Unlike previous radiance field-based approaches, we leverage geometric feature correspondences and the innovative 3DGS map to improve the localization accuracy. In our experiments, we evaluate the proposed method across two real-world indoor and outdoor datasets. Consequently, compared to the baselines, the proposed method achieves competitive or superior experimental results.

This article mainly investigates the problem of vibration suppression and angle cooperative tracking control of a multiple flexible manipulators described by partial differential equations (PDEs) with input quantization, actuator failures, and unmodeled system dynamics. An intermediate control law is designed, and a smooth function with a positive integrable time-varying function is introduced. Besides, a new smooth function is constructed in the control law to handle the influence of quantization and actuator faults. Under the designed controller, the angles of all flexible manipulators can reach consensus through mutual communication, and the elastic deformation of each flexible manipulator can also be suppressed. Furthermore, the asymptotic stability of a closed-loop system is realized based on the Lyapunov function. Finally, numerical simulation validates the effectiveness of the method.

The widespread use of electronic devices has contributed to an increase in poor posture, particularly when it comes to the cervical spine, leading to various cervical vertebral pain disorders. In this article, we focus on accurately monitoring the motion status of the cervical spine using the accelerometers and gyroscope sensors embedded in earphones. Our aim is to gain a better understanding of cervical spine health. To address the individual differences among subjects, we introduce fuzzy rules to the Re-ISDA method, proposing a novel approach known as FuzRe-ISDA. Unlike traditional domain adaptation methods, the FuzRe-ISDA method offers flexibility in adjusting the contribution from different source domains. It takes into account the collective impact of multiple models on predicting new user behavior. Moreover, this method can quickly adapt to new users without requiring extensive datasets. Experimental results demonstrate that our FuzRe-ISDA approach outperforms popular domain adaptation methods in terms of accuracy when predicting cervical motion. This highlights the effectiveness of our approach in addressing individual differences and improving the reliability of cervical spine motion prediction.

Localization and pose estimation algorithms play an important role in intelligent transportation systems (ITSs), as ITS need to accurately sense and understand the traffic environment to support autonomous navigation, traffic flow management, and autonomous material handling. This article proposes a pose estimation method in the front end of lidar odometry with geometric constraints. The proposed method can accurately capture the geometric information in the environment and ensure the effectiveness of the point cloud participating in the registration to improve the accuracy of registration. In the back end, an enhanced pose estimation strategy combining rough registration and fine registration is adopted to further improve localization accuracy. Comprehensive experimental results show that the proposed method achieves higher localization accuracy against other baselines, which also demonstrates that the proposed method can cope with challenging scenes such as complex road conditions and dynamic objects.

The adaptive fuzzy logic position and force control scheme are proposed for multimanipulators with nonlinear uncertainties, external disturbances, and input dead-zone nonlinearities. Specifically, the adaptive fuzzy logic system is employed for approximating the nonlinear dynamics of the manipulator, and the approximation error is compensated online by the designed robust control terms. In addition, to address the nonlinear input dead-zone effect, a new theorem is presented to stabilize the system without using specific Nussbaum functions. Consequently, the proposed robust control algorithm exhibits strong applicability. Finally, the stability of the multimanipulator tracking system is verified using a Lyapunov function. Simulation results demonstrate the effectiveness of this strategy.

In[1], inaccuracies in several critical equations along with their accompanying descriptions appear in the article. Furthermore, some references are missing, and certain analyses of experiments are flawed.

This article proposes an adaptive inverse neural network (NN) control of an uncertain flexible single-link manipulator with input backlash and output constraint. First, an adaptive inverse function is applied to eliminate the input backlash of the actuator. Second, an NN is applied to approximate the system uncertainty. Third, a barrier Lyapunov function is used to guarantee that the system is maintained within the constraints. Subsequently, the system’s semi-globally uniformly ultimately bounded stability is proved by the Lyapunov direct method. Finally, the simulation and experimental results manifest the feasibility of the proposed controller.

The purpose is to optimize the Beetle Antenna Search (BAS) algorithm and apply it to the Intelligent Transportation System (ITS) to process traffic data in time and solve traffic congestion. This work studies the development status of ITS and the application status of the BAS algorithm. It optimizes BAS to converge to local optimization prematurely in high-dimensional space, affecting the prediction accuracy. Then, combined with the Least Squares Support Vector Machine Algorithm (LSSVM), the algorithm with quadratic interpolation optimization is proposed. The proposed algorithm is named the Quadratic Interpolation Beetle Antenna Search (QIBAS). On this basis, a traffic flow prediction model based on QIBAS-LSSVM is established. Finally, the improved QIBAS algorithm and Traffic Flow Prediction (TFP) model are verified. The results show that the test Mean Square Error (MSE) of the TFP model based on QIBAS-LSSVM increases by 4.28%, 7.38%, and 18.23%, respectively compared with the other three models. The test Mean Absolute Percentage Error (MAPE) increases by 0.09%, 0.06%, and 0.36% respectively. The proposed QIBAS algorithm has a good effect and high accuracy in short-term TFP. The research has important reference value for the digital transformation of transportation systems in modern smart cities.

This work aims to improve the transmission and sharing efficiency of intelligent transportation data and promote the further development of intelligent transportation and smart city. In this work, an EEMR (Energy Efficient Multi-hop Routing) is designed for the intelligent transportation wireless sensor network. In the EEMR algorithm, the base station runs the IAP clustering algorithm for network clustering after receiving the information of all surviving nodes. A method called ACRR (Adaptive Cluster-head Round Robin) is proposed for local dynamic election of cluster heads. In addition, the deep learning-based stochastic gradient descent algorithm and its evolution algorithm are sorted out, and its application in practical scenarios is analyzed. Due to the disadvantages of the adaptive algorithm in the current data processing process, the gradient optimization algorithm based on deep learning is adopted and the concept of adaptive friction coefficient is applied to the Adam algorithm to obtain a new adaptive algorithm (TAdam). The simulation experiment reveals that the proportion of network surviving nodes of the EEMR algorithm is still as high as 90% in 1,500 rounds of data collection. This shows that the EEMR algorithm achieves the energy balance of the network nodes as much as possible while minimizing the system energy consumption. In application scenario 1, when the network surviving nodes of the four data collection algorithms compares dropped below 40%, the number of surviving nodes in the EEMR algorithm is still as high as 97.6%. On the PTB (Penn Tree Bank) test data set, the TAdam algorithm shows the fastest convergence speed and the best generalization performance. The TAdam algorithm based on deep learning discussed in this work was of great significance for improving the transmission and sharing efficiency of intelligent transportation data.