Uncovering the Cognitive Mechanisms of Risk Decision-Making among ICU Nurses in Complex Clinical Contexts.

Adaptive control via deep neural networks with an event-triggered mechanism

Fuzzy adaptive asymptotic tracking control for uncertain nonlinear systems with full-state constraints of arbitrary time

Dynamic adaptive model predictive control for prosumers-based energy communities

Adaptive nonlinear model predictive control of monoclonal antibody glycosylation in CHO cell culture

Adaptive droop control scheme with error functioned-based SoC balancing for distributed battery storage system in renewable energy-based DC shipboard microgrid

Prescribed performance adaptive neural network tracking control of electro-hydraulic servo system with input delay

Underwater thrusters are the primary actuators in marine vehicles. The thruster drive and control methods for these thrusters considerably influence their dynamic response, accuracy, and efficiency. This study reviewed the structure of field-oriented control (FOC) and six-step commutation (SSC)–driven thrusters, comparing their advantages and disadvantages in underwater applications. Considering the dynamic performance of open-loop SSC, the precision of closed-loop SSC, and the energy efficiency of FOC, an adaptive proportional–integral (API) speed controller was introduced for FOC-driven underwater thrusters, which demonstrated superior performance in underwater conditions. Simulations were conducted to reveal the performance differences between FOC- and SSC-driven underwater thrusters and the advantages of the designed API controller in FOC-driven systems. FOC with a conventional PI speed controller exhibited a chattering-free steady state and 22.9% higher energy efficiency than the closed-loop SSC, and the API-based FOC showed improved step signal response and a 25.9% increase in amplitude bandwidth compared with conventional PI and integral–separated PI control. Furthermore, the API controller avoided speed chattering at low speed and displayed lower steady-state error than the conventional PI controller. These results support the conclusions of the higher bandwidth and overshoot avoidance of API and the higher energy efficiency of the FOC-driven motor.

Adaptive air-fuel ratio control for hydrogen internal combustion engines

Adaptive neural network fault-tolerant control of tendon actuated continuum robots under actuator failures and external disturbances

Adaptive active fault-tolerant control for autonomous underwater vehicle with actuator failure

Hybrid adaptive fault-tolerant control of variable structure non-gaussian stochastic systems with feedback packet loss

Fixed-time adaptive fuzzy event-triggered fault-tolerant containment control for nonlinear multi-agent systems

Maintaining thermal comfort in large public waiting halls is challenging due to rapidly varying occupancy, spatial non-uniformity, and tight energy budgets. We propose an AI-enabled, occupant-centric control framework that passively estimates per-person features from synchronized RGB and thermal-infrared (TIR) streams. The framework first identifies occupancy-invariant, physically grounded features—metabolic rate, clothing insulation, action state (sit/walk) and intensity (walking speed)—and combines them with environmental drivers (air temperature, mean radiant temperature, air velocity) to inform thermal prediction. A lightweight vision pipeline (detection, tracking, optical-flow speed, and TIR torso temperature) extracts these features in real time without individual profiling. A sequence model then predicts short-horizon Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD), and a comfort-focused regulator maps these forecasts to native HVAC setpoints (temperature and air speed) under device constraints. In a simulated waiting-hall scenario (10-40 occupants), the calibrated predictor attains PMV-TSV MAE , the controller keeps – of votes within the ASHRAE comfort band , and typically achieves within – min after density changes. These results show that coupling edge-efficient vision with sequential comfort prediction and actionable HVAC control can improve crowd-level comfort in complex public spaces while preserving privacy. • Occupant-centric. • YOLO detection. • Optical-flow motion estimation. • Sequential Mamba estimation.

A lightweight adaptive torque control of PMSG-based offshore wind turbines using a finite-step incremental search strategy for region-III operation under variable air density conditions

Adaptive speed control strategy for the high-pressure pump in a ground-based hydraulic wind turbine-driven reverse osmosis seawater desalination system

Brain-controlled operator model-driven deep reinforcement learning for adaptive brain-machine collaborative control

Adaptive fault tolerance control for marine mining vehicles with actuator faults and uncertain disturbances

Coordinated frequency regulation of fixed-variable speed pumped storage hybrid systems: an adaptive control framework integrating dynamic prediction and rolling optimization

Tracing the learning path: Revealing skill acquisition phases in a virtual environment through behavioral tracking.