3D point cloud—a new signal representation of volumetric objects—is a discrete collection of triples marking exterior object surface locations in 3D space. Conventional imperfect acquisition processes of 3D point cloud—e.g., stereo-matching from multiple viewpoint images or depth data acquired directly from active light sensors—imply non-negligible noise in the data. In this paper, we extend a previously proposed low-dimensional manifold model for the image patches to surface patches in the point cloud, and seek self-similar patches to denoise them simultaneously using the patch manifold prior. Due to discrete observations of the patches on the manifold, we approximate the manifold dimension computation defined in the continuous domain with a patch-based graph Laplacian regularizer, and propose a new discrete patch distance measure to quantify the similarity between two same-sized surface patches for graph construction that is robust to noise. We show that our graph Laplacian regularizer leads to speedy implementation and has desirable numerical stability properties given its natural graph spectral interpretation. Extensive simulation results show that our proposed denoising scheme outperforms state-of-the-art methods in objective metrics and better preserves visually salient structural features like edges.

Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the “Dual-Carbon” target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations.

Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.

Offshore Wind Power Systems (OWPS) offer great energy and environmental advantages, but also pose significant Operation and Maintenance (O&M) challenges. In this survey, we analyze these challenges and propose some optimization strategies and technologies for OWPS comprehensively. The existing literature review mainly focuses on a certain field of offshore wind power O&M, but lacks a comprehensive introduction to offshore wind power. We consider the energy efficiency, reliability, safety, and economy of OWPS from various aspects, such as offshore wind and wave energy utilization, offshore wind turbine components, and wind power operation parameters, and compare them with onshore wind power systems. We suggest that OWPS can benefit from advanced design optimization, digital twin, monitoring and forecasting, fault diagnosis, and other technologies to enhance their O&M performance. This paper aims to provide theoretical guidance and practical reference for the technological innovation and sustainable development of OWPS.

Given a convex and differentiable objective $Q({\mathbf M})$ for a real symmetric matrix ${\mathbf M}$ in the positive definite (PD) cone—used to compute Mahalanobis distances—we propose a fast general metric learning framework that is entirely projection-free. We first assume that ${\mathbf M}$ resides in a space ${\mathcal S}$ of generalized graph Laplacian matrices corresponding to balanced signed graphs. ${\mathbf M}\in {\mathcal S}$ that is also PD is called a graph metric matrix. Unlike low-rank metric matrices common in the literature, ${\mathcal S}$ includes the important diagonal-only matrices as a special case. The key theorem to circumvent full eigen-decomposition and enable fast metric matrix optimization is Gershgorin disc perfect alignment (GDPA): given ${\mathbf M}\in {\mathcal S}$ and diagonal matrix ${\mathbf S}$ , where $S_{ii} = 1/v_i$ and ${\mathbf v}$ is the first eigenvector of ${\mathbf M}$ , we prove that Gershgorin disc left-ends of similarity transform ${\mathbf B}= {\mathbf S}{\mathbf M}{\mathbf S}^{-1}$ are perfectly aligned at the smallest eigenvalue $\lambda _{\min }$ . Using this theorem, we replace the PD cone constraint in the metric learning problem with tightest possible linear constraints per iteration, so that the alternating optimization of the diagonal / off-diagonal terms in ${\mathbf M}$ can be solved efficiently as linear programs via the Frank-Wolfe method. We update ${\mathbf v}$ using Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) with warm start as entries in ${\mathbf M}$ are optimized successively. Experiments show that our graph metric optimization is significantly faster than cone-projection schemes, and produces competitive binary classification performance.

Quality sleep is a basic human need for well-being, yet sleep deprivation has been a long-term global problem. A common type of sleep deprivation is obstrucive sleep apnea, where people repeatedly stop breathing during sleep with subsequent abnormal vital signs, namely, respiration rate and heart rate. While tremendous effort has been made for vital signs monitoring systems during sleep, existing works still lack portability for bulky and intrusive systems and reliability for consumer-level, nonintrusive systems. To bridge the gap between practicability and accuracy and facilitate Internet of Things for smart healthcare, in this article, we propose a vital signs estimation system during sleep via a thermal camera. The system first captures thermal image sequences of a sleeping subject and then processes the facial regions within the thermal images for vital signs signal extraction. Specifically, leveraging on the inherent graph structure among subregions of the facial area, we propose a graph-based, spatial–temporal signal denoising scheme. Experimental results show that the graph-based denoising scheme in our system effectively reduces the noise level introduced by cameras and subjects, and our proposed system outperforms state-of-the-art nonintrusive vital signs monitoring systems. Since the algorithm components in our system have relatively low time complexity and no model training is required, our system can be deployed efficiently at the edge devices in a smart home setting. The extracted vital signs can then be used for sleep abnormality detection and disease screening.