Due to the relatively high costs of phasor measurement units (PMUs), the optimal PMU placement problem, which focuses on minimizing the number of PMUs for full system observability, has long been a popular research topic. However, PMUs alone will not make a system observable. An appropriate communication network that transmits all PMU data to the phasor data concentrator plays a key role. This paper proposes the optimal PMU-communication link placement (OPLP) problem that investigates the placement of PMU and communication links (CLs) for full observability in a power system. Besides the location of PMUs and CLs, to ensure the reliable and timely transmission of PMU data, the communication capacity needed on every CL is also captured by the OPLP problem. We carry out numerical studies on the IEEE 30-bus, 57-bus, 118-bus and 300-bus systems for the proposed models. The results show that, compared with the traditional optimal PMU placement model, OPLP can reduce the total installation cost significantly.

Comprehensive visualization and accurate extraction of tumor vasculature are essential to study the nature of glioma. Nowadays, tissue clearing technology enables 3D visualization of human glioma vasculature at micron resolution, but current vessel extraction schemes cannot well cope with the extraction of complex tumor vessels with high disruption and irregularity under realistic conditions. Here, we developed a framework, FineVess, based on deep learning to automatically extract glioma vessels in confocal microscope images of cleared human tumor tissues. In the framework, a customized deep learning network, named 3D ResCBAM nnU-Net, was designed to segment the vessels, and a novel pipeline based on preprocessing and post-processing was developed to refine the segmentation results automatically. On the basis of its application to a practical dataset, we showed that the FineVess enabled extraction of variable and incomplete vessels with high accuracy in challenging 3D images, better than other traditional and state-of-the-art schemes. For the extracted vessels, we calculated vascular morphological features including fractal dimension and vascular wall integrity of different tumor grades, and verified the vascular heterogeneity through quantitative analysis.

In the microgird (MG) system, cooperation of microgrids is a very essential issue. Hence, multi-microgrid (MMG) system has been proposed to allocate the energy and information exchange for each MG. However, the uncertainties of generations and loads are very difficult to deal with when considering the utilization of renewable energy (RE) resources. To tackle this issue, we study the optimal energy management for the cooperation of multiple MGs in this work. We propose an adaptive energy scheduling algorithm for MMG systems in order to reduce the long-term time-averaged operation cost under the constraints of renewable energy resource utilization. To deal with the unused renewable energy, virtual queues are constructed and Lyapunov framework are adopted to improvement of reliability, reduction of end-user power loss, and cultivation of renewable resource penetration. The simulation results show that the proposed algorithm is effective in coordinating multiple MGs within an MMG system so as to reduce the operation cost and improve the utilization of RE resources dramatically.

The Electric Bus Dynamic Wireless Charging (EB-DWC) system is a bus charging system that enables electric buses to receive power wirelessly from ground-based electromagnetic induction devices. In this system, how to optimally configure the charging infrastructures while considering the unpredictable nature of bus movement is a great challenge. This paper presents an optimization problem for an EB-DWC system in urban settings, addressing stochastic elements inherent in the vehicle speed, initial charging state, and dwell time at bus stops. We formulate a stochastic planning problem for the EB-DWC system by integrating these uncertainties and apply Monte Carlo sampling techniques to effectively solve this problem. The proposed method can improve the system’s robustness effectively.

Non-orthogonal multiple access (NOMA) techniques are widely used to increase quality-of-experience and network performance requirements in vehicle-to-everything (V2X) communication. However, dynamic vehicular communication conditions lead to the base station (BS) with limited knowledge about perfect channel state information (CSI), which incurs a challenging problem on spectrum resource allocation for complex communication systems with multiple transmission links. In particular, unstable communication of high-mobility vehicles degrades the performance of NOMA-V2X networks. To address these difficulties, this paper proposes a resource allocation scheme for vehicular communication to comprehensively consider user scheduling and power allocation, whereas it considers the channel fading in time-varying networks. By satisfying the constraints on the transmission power and rate of each user, the formulated problem aims to minimize the total system delay. To effectively solve the formulated problem, a deep deterministic policy gradient (DDPG) is deployed to find the solutions of the proposed scheme. Simulation results show that the proposed algorithm significantly outperforms the baseline in terms of both delay and convergence stability while satisfying realistic V2X constraints.