Liguo Shuai

This study investigates the self-propulsion hydrodynamics of an underwater robot combining four oscillating-fin units with pulsed-jet actuation. A numerical framework couples three-dimensional unsteady computational fluid dynamics, dynamic meshing, and longitudinal self-propulsion dynamics for parallel (PS) and circular (CS) fin-motion arrangements. Fin-only propulsion provides the baseline for evaluating amplitude effects and jet assistance. Increasing the oscillation amplitude raises the stable swimming velocity U_stable in both arrangements, with CS maintaining a higher baseline speed. Under hybrid propulsion, the central jet entrains and stretches the fin wakes, reorganises vortical structures, and redistributes the pressure field. In CS mode, the mean swimming-speed enhancement was approximately 145.5%, 92.1%, and 65.3% at oscillation amplitudes of 20°, 25°, and 30°, respectively, demonstrating an amplitude-dependent benefit of pulsed-jet assistance. Averaged across the investigated amplitudes over the acceleration interval, positive streamwise impulse increases by approximately 116.9% and 117.6% in PS and CS modes, respectively, while propulsive impulse effectiveness increases by 2.9 and 1.6 percentage points. These impulse measures describe hydrodynamic loading rather than energetic efficiency. The results are consistent with direct jet momentum input accompanied by unsteady fin–jet interactions and provide a hydrodynamic basis for the configuration design and parameter selection of hybrid underwater propulsion systems.

Adaptive locomotion across continuous terrain transitions remains difficult for hexapod robots because contact timing, body attitude, support height, and load distribution change simultaneously along a route. This paper presents a unified proprioception-driven deep reinforcement learning and central pattern generator (DRL-CPG) framework for terrain-transition locomotion without visual terrain classification, explicit terrain labels, or terrain-specific controller switching. A high-level proximal policy optimization policy maps a 46-dimensional proprioceptive observation to a three-dimensional CPG modulation action comprising oscillation amplitude, swing-phase frequency, and turn modulation. A coupled six-node Hopf oscillator network then expands these modulated parameters into phase-coordinated rhythmic commands, which are mapped to the 18 joint targets of a JetHexa hexapod and executed by a low-level proportional-derivative controller. The observation space contains body linear velocity, body angular velocity, relative joint positions, relative joint velocities, the previous three-dimensional policy action, and inertial measurement unit (IMU)yaw/heading relative to the initial track direction. A continuous route consisting of flat ground, uphill stairs, irregular terrain, downhill stairs, and a recovery segment is defined to evaluate transition-aware locomotion using route completion, velocity-tracking error, lateral deviation, and roll/pitch fluctuation. Compared with the fixed-parameter CPG and end-to-end DRL baselines, the proposed method increased the full-distance success rate at 4.7 m from 9% and 20%, respectively, to 88%, while maintaining smoother velocity, lateral deviation, and roll/pitch responses. The framework preserves the rhythmic prior of CPG control while reducing the exploration burden of reinforcement learning, providing a compact formulation for adaptive hexapod locomotion across terrain transitions.

Multi-dimensional multiplexing technology is pivotal for enhancing on-chip communication capacity. In this paper, we proposed and fabricated an 8-channel hybrid (de)multiplexer chip based on a lithium niobate on insulator (LNOI) platform, which can simultaneously implement mode-division multiplexing (MDM) and wavelength-division multiplexing (WDM). The proposed (de)multiplexer comprises a two-channel mode (de)multiplexer utilizing an asymmetric directional coupler (ADC) and a four-channel WDM (de)multiplexer employing a set of second-order microring resonator (MRR) arrays. To further increase the number of channels, the two ports of the mode demultiplexer are interconnected, thereby doubling the communication capacity while maintaining the same number of MRRs. Experimental results demonstrate that the device achieves intermode crosstalk below -10 dB with a multiplexing bandwidth of exceeding 50 nm. The adjacent channel crosstalk and the non-adjacent channel crosstalk of the multiplexer's eight channels are below -13 dB and -27 dB, respectively, over a free spectral range (FSR) from 1556 to 1562 nm. This research paves new avenues in the field of multi-dimensional multiplexing techniques on the LNOI platform.

Developing a high-quality scenario library is crucial for evaluating more reliable autonomous driving systems. A fundamental prerequisite for constructing such a library is the generation of safety-critical scenarios. In this paper, we propose a nested game algorithm to assign trajectories and their time series to multiple background vehicles. This method aimed to generate safety-critical scenarios with varying degrees of interference for the tested vehicle. To ensure the realism of the generated scenarios, we extracted a series of natural trajectories from an existing dataset as the input for the algorithm. We then analyzed multiple types of scenarios generated by this method and evaluated their danger using generalized metrics, such as the Time-to-Collision (TTC) and Minimum Safe Distance Factor (MSDF), which demonstrated the effectiveness of our approach. The experimental results demonstrate that the nested game-based approach could efficiently construct safety-critical scenarios, contributing to the development of high-quality test scenario libraries.

In air conditioning systems, air curtains play a crucial role in reducing the exchange of hot and cold air between the interior and exterior environments. Nevertheless, the majority of current air curtains suffer from limited airtightness and real-time performance due to their complex jet trajectory, relying on traditional control methods. Thus, this paper introduces an angle control algorithm for air curtains based on a GA-optimized quadratic BP neural network. Initially, the BP neural network is trained using the Hayes dataset to develop the prediction model for temperature-jet angle. Subsequently, the optimization model for jet angles-windshield angle is constructed, and the optimal angles set meeting the fitness function is identified using GA global search. Later, the prediction model and the optimal angles set are once again trained using the BP neural network to generate prediction model for temperature-jet angles and windshield angle. Following CFD simulation, the airtightness indicator demonstrated a 26.5% improvement with the proposed control method compared to traditional ones, highlighting the superior airtightness. In comparison to other algorithms, the proposed algorithm demonstrates a remarkable 89% enhancement in real-time performance and stronger robustness. This study presents a novel approach for the intelligent control of air curtains, holding significant importance in advancing the intelligent development of air curtain technology and facilitating energy efficiency and emission reduction.

The effect of a high-entropy design on martensitic transformation and magnetic field-induced strain has been investigated in the present study for Ni-Mn-Ga-Co-Gd ferromagnetic shape-memory alloys. The purpose was to increase the martensitic transition temperature, as well as the magnetic field-induced strain, of these materials. The results show that there is a co-existence of β, γ, and martensite phases in the microstructure of the alloy samples. Additionally, the martensitic transformation temperature shows a markedly increasing trend for these high-entropy samples, with the largest value being approximately 500 °C. The morphology of the martensite exhibits typical twin characteristics of type L1₀. Moreover, the magnetic field-induced strain shows an increasing trend, which is caused by the driving force of the twin martensite re-arrangement strengthening.