Hybrid aerial underwater vehicles (HAUVs) hold great promise but face challenges like air-water integration and high underwater resistance. This paper presents Nezha-Morphing, a bio-inspired HAUV that emulates seabirds’ adaptive wing extension and retraction mechanisms for efficient movement in both aerial and aquatic domains. It has a servo-driven foldable-arm mechanism and is made of high-strength, low-mass materials with a double-system architecture for aerial and underwater control. This paper details the mechanical and electronic system design, dynamic analysis, and experimental results of Nezha-Morphing. The experimental findings are highly impressive: underwater, the folded-arm configuration effectively reduces resistance, enabling a maximum speed of 0.620 m/s and achieving a high average acceleration, significantly enhancing motion efficiency and flexible maneuverability in confined spaces. In the air, with its arms unfolded, the vehicle exhibits exceptional stability and strong wind resistance, maintaining steady flight even under level-4 wind conditions. Moreover, it completes the water-to-air cross-domain transition in just 1.5 seconds. Nezha-Morphing successfully integrates flight stability, cross-domain adaptability, and hydrodynamic efficiency, showcasing substantial potential for diverse applications.

Hybrid aerial-underwater vehicles (HAUVs) are attracting significant interest for their unique capability to operate across both air and water. However, achieving a lightweight design coupled with efficient cross-domain performance remains a formidable challenge. This paper introduces Nezha-T, a novel, ultra-lightweight HAUV featuring a dual-stable floating state capability. This is achieved through an innovative center-of-gravity (CoG) arrangement method, which enables seamless transitions between upright and horizontal floating postures. This ability to stably floating in either orientation is crucial for stable water entry and exit, mitigating impact forces on the vehicle and its payload. Furthermore, to counteract the residual buoyancy inherent in this design, a zero-lift pitch angle is incorporated into the control system, improving depth-keeping and pitch control performance. The proposed design were rigorously validated through computational fluid dynamics (CFD) simulations, pool experiments, and open-water field tests. The results confirm the feasibility of the dual-stable floating states, demonstrate stable cross-domain traversal, verify the effectiveness of the depth-keeping control system, and validate the vehicle’s fixed-wing flight capability.

This paper addresses the fault-tolerant trajectory tracking control problem of Euler-Lagrange (EL) systems subject to actuator faults and component faults (including system uncertainties). A novel finite-time Second-Order Sliding Mode Observer with Nonlinear feedback (SOSMON) incorporating adaptive fault estimators is proposed. Unlike linear feedback or complex sliding mode designs, the proposed SOSMON’s nonlinear feedback terms effectively handle the unknown high-order nonlinear terms in EL systems. To reconstruct the two faults, an adaptively weighted Radial Basis Function Neural Network (RBF NN) technique is introduced to estimate component faults without prior knowledge of fault characteristics, and an Adaptive actuator Fault Estimator (AFE) is designed. Then, accurately estimated faults allow for the simple design of a Sliding Mode Fault Tolerance Controller (SMFTC). Finite-time convergence of the observer and closed-loop stability are proved via Lyapunov theory. Finally, comparative simulations on an Unmanned Surface Vessel (USV) demonstrate the superiority of the proposed method over conventional observers and the Back Propagation (BP) NN-based estimator regarding estimation accuracy and convergence speed.

Consistency learning (CL) has proven to be a valuable technique for improving the robustness of models in conditional sentence generation (CSG) tasks by ensuring stable predictions across various input data forms.However, models augmented with CL often face challenges in optimizing consistency features, which can detract from their efficiency and effectiveness.To address these challenges, we introduce Curriculum Consistency Learning (CCL), a novel strategy that guides models to learn consistency in alignment with their current capacity to differentiate between features.CCL is designed around the inherent aspects of CL-related losses, promoting task independence and simplifying implementation.Implemented across four representative CSG tasks, including instruction tuning (IT) for large language models and machine translation (MT) in three modalities (text, speech, and vision), CCL demonstrates marked improvements.Specifically, it delivers +2.0 average accuracy point improvement compared with vanilla IT and an average increase of +0.7 in COMET scores over traditional CL methods in MT tasks.Our comprehensive analysis further indicates that models utilizing CCL are particularly adept at managing complex instances, showcasing the effectiveness and efficiency of CCL in improving CSG models.Code and scripts are available at https://github.com/xinxinxing/ Curriculum-Consistency-Learning.

With instruction tuning, Large Language Models (LLMs) can enhance their ability to adhere to commands.Diverging from most works focusing on data mixing, our study concentrates on enhancing the model's capabilities from the perspective of data sampling during training.Drawing inspiration from the human learning process, where it is generally easier to master solutions to similar topics through focused practice on a single type of topic, we introduce a novel instruction tuning strategy termed Com-monIT: Commonality-aware Instruction Tuning.Specifically, we cluster instruction datasets into distinct groups with three proposed metrics (TASK, EMBEDDING and LENGTH).We ensure each training mini-batch, or "partition", consists solely of data from a single group, which brings about both data randomness across mini-batches and intra-batch data similarity.Rigorous testing on LLaMa models demonstrates CommonIT's effectiveness in enhancing the instruction-following capabilities of LLMs through IT datasets (FLAN, CoT, and Alpaca) and models (LLaMa2-7B, Qwen2-7B, LLaMa 13B, and BLOOM 7B).CommonIT consistently boosts an average improvement of 2.1% on the general domain (i.e., the average score of Knowledge, Reasoning, Multilinguality and Coding) with the LENGTH metric, and 5.2% on the special domain (i.e., GSM, Openfunctions and Code) with the TASK metric, and 3.8% on the specific tasks (i.e., MMLU) with the EMBEDDING metric.Code is available at https://github.com/raojay7/CommonIT.

Summary Metaheuristic algorithms have special effects in solving optimization problems in real life and have become the focus of researchers. The sparrow search algorithm (SSA) is a newly proposed swarm‐based metaheuristic algorithm that has shown excellent optimization performance. Although compared with other algorithms, SSA shows good performance, the original SSA algorithm still has problems such as weak optimization ability, leading to falling into the local optimum, and being unable to balance exploration and exploitation well. Therefore, this paper proposes an improved SSA using chaotic opposition‐based learning and hybrid updating rules (CHSSA). First, chaotic opposition‐based learning is proposed to improve the diversity of the population. Second, two strategies, including adaptive weights and spiral search, are adopted to update the position. Finally, to evaluate the performance of the proposed CHSSA, this paper uses 23 benchmark functions, IEEE CEC 2017 functions and 4 practical engineering optimization problems to evaluate the algorithm performance. The experimental results show that compared with other advanced optimization algorithms, CHSSA has the characteristics of fast convergence speed, high search accuracy, and strong robustness.

The agile and safe transdomain in waves is a promising feature but the primary bottleneck of the hybrid aerial underwater vehicle (HAUV). In this article, the surfing algorithm is proposed for Nezha-mini, our predeveloped HAUV prototype, to search for the dynamic window facilitating takeoff in waves and avoiding hazardous waves. For the first time, the cross-domain window, i.e., the vehicle is at the wave crest and heading downstream, is characterized and defined through the vehicle-wave coupled dynamic model. The novel surfing algorithm consists of the gradient perceptron, time-limited momentum gradient search, heading server, and initial conditions. Nezha-mini senses, searches, and tracks the dynamic window in real-time, until the takeoff decisions are triggered. Numerical simulations and experiments in regular and irregular waves reveal the effectiveness of the algorithm. The vehicle maintains a healthy initial attitude and inaccessible wave disturbance during takeoff, thus alleviating the thrust distraction from stability recovery and uncertainty. The average transition time and energy cost are reduced by 59.2% and 26.1% compared with random takeoff cases, and the locomotion is smooth, graceful, and low-risk. The computation and cost are low as the algorithm only requires the basic flight controller and the data from the inertial measurement unit instead of the prior parameters of the HAUV and waves. In comparison with the adaptive robust controller, which resists wave disturbance directly, this article provides an enlightening strategy from the perspective of harnessing waves.

Underwater thermocline, common in the lakes and ocean, plays a vital role in meteorological forecasting in the ocean and lakes dynamics research. This letter proposes a method for rapid and multipoint observation of thermocline variations with time and space using an airdropped micro-profiler array, named the DRAGONFLY system. It comprises specially designed disposable low-cost micro-profilers, a general unmanned aerial carrier platform, and a ground control system. This system can conduct periodic profile observations at a single point or quickly survey a large area. A series of experiments to characterize the micro-profiler and the DRAGONFLY system were conducted in Qiandao Lake, China. We demonstrate the developed system with data from field experiments, which show very high flexibility, and feasibility to observe the lake thermocline, implying potential applications in ocean transient phenomena observation.

A new solution to improving the poor endurance of the existing hybrid aerial underwater vehicle (HAUV) is proposed in this paper. The proposed multimodal hybrid aerial underwater vehicle (MHAUV) merges the design concept of the fixed-wing unmanned aerial vehicle (UAV), the multirotor, and the underwater glider (UG) and has a novel lightweight pneumatic buoyancy adjustment system. MHAUV is well suited for moving in distinct medium and can achieve extended endurance for long distance travel in both air and water. The mathematical model is given based on Newton-Euler formalism. Necessary design principles of the vehicle's physical parameters are obtained through different gliding equilibrium points. Then, a control scheme composed of two separate proportional-integral-derivative (PID) is employed for the vehicle's motion control in multi-domain simulation. The simulation results are presented to verify the multi-domain mobility and the mode switch ability of the proposed vehicle intuitively. Finally, a prototype, NEZHA, is introduced to be the experimental platform. The success of the flight test, the hovering test, the underwater glide test, and the medium transition test all contribute to prove the feasibility of the proposed concept of the novel MHAUV.