With the high proportion of distributed energy resources with randomness and intermittency penetrating the distribution network, traditional centralized optimization methods face problems such as communication packet loss, frequent failures, and low reliability and are difficult to apply to large-scale DC microgrids with wide-area dispersion effectively. Therefore, distributed optimization methods have attracted widespread attention due to their superior scalability and robustness. This paper investigates a convex relaxation-based distributed control strategy for DC microgrids with constant power loads (CPLs) and MPPT-controlled distributed generations (MPPT-DGs) to achieve global optimization. First, an optimal power flow (OPF) problem model for large-scale DC microgrids under a distributed framework is established, and a convex relaxation method taking exactness into account is proposed to transform the non-convex original problem into a new convex problem with the same optimal solution. Then, the Karush–Kuhn–Tucker (KKT) condition and its equivalent consistency-based condition are derived based on convex relaxation, and a distributed global optimization control method is proposed to achieve global optimal system operation, avoiding the solution of large-scale non-linear optimization problems. Finally, simulations and numerical experiments are presented to verify the correctness of the proposed strategy.

Photoplethysmography (PPG) is commonly used to gather health-related information but is highly affected by motion artifacts from daily activities. Inspired by the strong denoising capabilities and generalization of diffusion probabilistic models, this paper proposes a novel PPG denoising method using a diffusion probabilistic model to reduce the impact of these artifacts. While typical diffusion models handle Gaussian noises, motion artifacts often involve non-Gaussian noise. To address this, the proposed method incorporates noisy PPG signals into both the diffusion and reverse processes, allowing the model to adapt better to complex and non-Gaussian noises. A dataset with clean and noisy PPG signals from 15 subjects performing various motion tasks was collected for evaluation. The results show the proposed model significantly improves PPG signal quality, reducing the Peak-Rejection-Rate (PRR) from 0.24 to 0.03. It also enhances the accuracy of heart rate (HR) estimation and various heart rate variability (HRV) measures, showing robustness and good generalization across different tasks and subjects.

Fatigue is one of the main factors that contribute to operator performance and maritime safety, making it important to develop fatigue recognition algorithms that can predict operator fatigue. In this paper. we propose a subject independent algorithm for fatigue recognition from heart rate using machine learning techniques for human factors evaluation. The final model with the Random Forest Classifier produced a mean classification accuracy of $67.2 \%$ for recognizing 2-levels of stress for unseen data. With a 1-minute data window for fatigue recognition updated every second, the proposed method could be applied for human factors evaluation including vessel traffic operators’ fatigue monitoring.

Over time, repeated mouse-dragging manipulation may cause discomfort in the upper extremities. This study compared biomechanical parameters, mouse movement data, and the discomfort perception index between 2-min mouse dragging and moving manipulation tasks with higher or normal target objectives. We recruited 20 non-symptomatic graphic design students who frequently engage in intensive mouse-dragging manipulation. We assessed the impact of continued dragging versus non-dragging manipulation using electromyographic data, mouse fingertip pressure data, and mouse trajectory velocity data. We also performed a temporal correlation analysis between EMG, mouse velocity, and fingertip pressure to investigate the relationship between physiological data and mouse movement performance. The study revealed significant differences between the dragging and moving tasks regarding muscle activity, mouse velocity, and fingertip pressure. In particular, the percentage of muscle activation on the right side of the extensor carpi radialis longus (ECR) and the lateral head of the triceps brachii (TB) differed significantly between the two tasks, with higher muscle activation levels during the dragging task. Moreover, the average muscle activation of ECR and TB was significantly higher at high target operation levels. In addition, the study revealed that the horizontal, vertical, and mean mouse velocities were significantly higher for the dragging manipulation in the high target task than for the mouse moving manipulation. In the temporal correlation analysis, the correlation coefficients of muscle activation, fingertip pressure, and mouse mean velocity differed between the two mouse manipulation behaviors, with a higher correlation between muscle activation levels and pressure values during the dragging manipulation.