Retinopathy of Prematurity (ROP) mainly occurs in premature infants with low birth weight, and it is the leading cause of childhood blindness. Early and accurate ROP diagnosis is imperative for appropriate treatment. However, less research concentrates on early-stage ROP diagnosis based on limited-labeled images in an imbalanced dataset. To address the dilemma, this study proposed a novel self-supervised network, MOCO-MIM, for early ROP grading. The proposed classification network was evaluated on a total of 553 labeled fundus images from 89 preterm infants. The trained network achieved a test accuracy of 98.29% and an AUC score of 97.6% for three stages of grading. The adopted method is verified that the proposed method can be detected early stages of ROP more efficiently and grade the severity more accurately based on limited-labeled fundus images, which is superior to the existing state-of-the-art methods.

This paper presents a decentralized prediction-based algorithm designed to address out-of-step (OOS) conditions in power systems. The algorithm utilizes generator data obtained from phasor measurement units. The transient stability of a multi-machine power system is evaluated using the equal-area criterion (EAC). The proposed algorithm calculates the characteristics of the P-δ curves within the EAC framework after a large disturbance. The critical P-δ trace is determined by analyzing the cumulative energy in the acceleration area following fault clearance. The stability margin of the rotor angle is then computed based on the actual active power and its relationship with the critical curve. The algorithm predicts the occurrence of OOS by comparing the measured active power with the corresponding value on the critical curve. The effectiveness of the proposed algorithm is validated through simulations conducted on the 73-bus IEEE test power system.

Focusing solely on enhancing the resilience of power systems at a systemic level would lead to a significant underestimation of the actual impact of extreme disasters. Equally vital is the assurance of livelihood security amidst such extreme conditions, which is crucial for the development of a truly resilient power system. Hence, this paper attempts to incorporate quantifiable metrics assessing public safety impacts into resilience enhancement works, thereby guiding precise allocation of funds. Considering that the residents’ intuitive feelings are the most direct reflection of the severity of the disaster, this paper employs the modified prospect theory to formulate functions representing residents’ psychological risk perception and risk-taking willingness to tolerate risks during disruptions in power, gas, and water supplies. Meanwhile, in order to accurately calculate the energy loss duration for each residential customer, a resilience enhancement method for post-disaster collaborative dispatch of electricity-gas-water systems (EGWSs) is proposed. With the objective of minimizing the public safety and economic impact of disasters, the optimal multi-source collaborative emergency restoration strategy is developed. The significant necessity and efficiency of the proposed strategy are verified with exhaustive case studies. Numerical results evince the resilience enhancement by considering the livelihood security in the post-disaster restoration stage.

Feature extraction from the normalized transformation domain is a key technique in target detection. Traditional normalization approaches assume that matrix elements follow a normal distribution, but any deviations from this assumption can lead to significant systematic errors. This article presents a novel method that modifies the normalization process in the fractional Fourier transform (FRFT) domain by incorporating a threshold mechanism to counteract the effects of non-normal distributions. Three modified FRFT features are then extracted from this modified FRFT domain. Furthermore, we propose a target detection method that utilizes these three adjusted features. Experimental results based on measured data indicate that the modified FRFT features exhibit superior classification capabilities for sea clutter and targets compared to the original ones. Additionally, the experiments also demonstrate that under the same conditions, the proposed detection method outperforms traditional FRFT feature detector and the tri-feature based detector.

Recently, distributed generators (DGs) have been widely integrated into distribution network, so that the distribution network is gradually transforming into an active distribution network (ADN). Due to the influence of meteorological conditions, the output of DGs has high uncertainty. At the same time, considering the increasing variety of loads in ADNs, the uncertainty of load demand of user side is also increasing. In order to fully consider the uncertainty of measurement and quantitatively evaluate the operational status, this paper proposes a steady-state analysis method for ADNs under uncertain conditions. Firstly, this paper proposes a steady-state analysis method including power flow analysis model and evaluation indicators for the operation status from the perspectives of node and network. Secondly, the uncertainty factors are elaborated from three aspects: sources, impact on evaluation index and impact on scheduling. The evaluation indicators considering uncertain conditions, the impact on system security and scheduling of network are further discussed. Finally, through the simulation analysis of the modified IEEE 33-node test system, The effectiveness of the proposed method are verified.

In a drone system with dual data links, this paper presents a redundant data processing algorithm that can minimize flight control instability without increasing the weight and price of the aircraft by software processing of duplicate received messages.

This article presents the new hybrid switched-capacitor-based transformerless DC-DC converters with a high step-up capability and common ground features. The proposed converter provides low voltage stress across semiconductor devices to utilize MOSFETs with lower Rdson and low voltage ratings. In addition, the diodes in switched-capacitor cells with low voltage stress can reduce the reverse recovery power loss of the converter. Thus, the overall efficiency of the proposed converter can be increased. The converter circuit, operating principle, and design guideline of the proposed converter are discussed. To confirm the performance of the proposed converter, a detailed comparison study with the conventional hybrid switched-capacitor converter is also presented. Finally, a laboratory prototype with 200 W, 25 V to 200 V is set up to correct the effectiveness of the proposed converter

This paper presents a nonlinear higher-order sliding mode control (HO-SMC) designed for a droop control-based grid-forming converter (GFM). In weak grid scenarios, where the rate of change of frequency (ROCOF) is notably high, achieving a rapid frequency response becomes imperative. The stable operation of a grid-forming converter using droop control, coupled with classical vector control employing cascaded voltage and current loops (multiloop), faces limitations when higher droop coefficients are applied. This constraint on the application of classical vector control (CVC) in weak grid conditions necessitates alternative solutions. Operating as a grid-forming converter, the grid-connected converter with an LCL filter represents a second-order system. HO-SMC mitigates the switching challenges associated with conventional SMC by integrating robust feedback linearized control. A graphical method is proposed for designing the switching gain using Lyapunov’s direct method to counteract the impact of a matched disturbance. The study demonstrates that the implementation of HO-SMC in the grid-forming converter enhances fast frequency response by increasing the gain margin of the power frequency (P-f) loop. Finally, it is illustrated that the proposed control method also improves the transient response of the converter.

This article introduces a Wasserstein distance-based distributionally robust optimization model to address the transmission expansion planning considering wind turbine-powered microgrids (MGs) under the impact of uncertainties. The primary objective of the presented methodology is to devise a robust expansion strategy that accounts for both short-term variability and long-term uncertainty over the planning horizon from the perspective of a central planner. In this framework, the central planner fosters the construction of appropriate transmission lines and the deployment of optimal MG-based generating units among profit-driven private investors. Short-term uncertainties, stemming from variations in load demands and production levels of stochastic units, are modeled through operating conditions. The Wasserstein distance uncertainty set is used to characterize the long-term uncertainty about the future load demand. To ensure the tractability of the proposed planning model, the authors introduce a decomposition framework embedded with a modified application of Bender’s method. To validate the efficiency and highlight the potential benefits of the proposed expansion planning methodology, two case studies based on simplified IEEE 6-bus and IEEE 118-bus systems are included. These case studies assess the effectiveness of the presented approach, its ability to navigate uncertainties, and its capacity to effectively optimize expansion decisions.

In modern power systems, the randomness and uncertainty of the generation and loads greatly increase the balance discrepancy between the power supply and demand, which introduces major potential security hazards to the stable operation of power systems. In particular, out-of-bounds frequencies emerge. To control the system frequencies, a frequency control method based on the state space model for power systems under small, uncertain disturbances is proposed in this paper. Considering the general model of the power system frequency response with the aim of improving the input‒output finite-time stability of the system, the frequency deviation of the power system is controlled in a closed loop. The Monte Carlo simulation of different systems shows that the proposed method can effectively control the frequency deviation of the power system. When the closed-loop system is disturbed by uncertain power fluctuations, its frequency will always remain within the allowable range to ensure the secure and stable operation of the power system.

This paper proposes a unilateral Adaptive update strategy based Interval Prediction (AIP) model for short-term load prediction, which is developed based on lower and upper bound estimation (LUBE) architecture. In traditional LUBE interval prediction model, the model training is usually trained by heuristic algorithms. In this paper, the model training is formulated as a bi-level optimization problem with the help of proposed unilateral adaptive update strategy and cost function. In lower-level problem, a simplified biased convex cost function is developed to supervise the learning direction of basic prediction engines. The basic prediction engine utilizes Gated Recurrent Unit (GRU) to extract features and Full Connected Neural Network (FNN) to generate interval boundary. In upper-level problem, a unilateral adaptive update strategy with unilateral coverage rate is put forward. It iteratively tunes hyper-parameters of cost function during training process. Comprehensive experiments based on residential load data are implemented and the proposed interval prediction model outperforms the tested state-of-the-art algorithms.

In this paper, we investigate joint dictionary representation for Massive MIMO satellite channel and discuss the representation performance. What kind of the dictionary model is satisfied for channel representation is still an unknown field. This paper mainly focuses on the analysis of the joint dictionary for channel representation including uplink and downlink. The main contributions are as follows. Firstly, the conditional constraints for satellite channel representation have been established with joint dictionary, including both uplink constraints and downlink constraints. Secondly, the maximum boundary that dictionary learning can represent channel characteristics is determined, that is, the optimal approximation of channel dictionary was achieved. Finally, channel sparse representation method for joint SVD decomposition at dictionary boundary conditions is proposed.

Entropically Secure Encryption (ESE) offers unconditional security with shorter keys compared to the One-Time Pad. In this paper, we present the first implementation of ESE for bulk encryption. The main computational bottleneck for bulk ESE is a multiplication in a very large finite field. This involves multiplication of polynomials followed by modular reduction. We have implemented polynomial multiplication based on the gf2x library, with some modifications that avoid inputs of vastly different length, thus improving speed. Additionally, we have implemented a recently proposed efficient reduction algorithm that works for any polynomial degree. We investigate two use cases: X-ray images of patients and human genome data. We conduct entropy estimation using compression methods whose results determine the key lengths required for ESE. We report running times for all steps of the encryption. We discuss the potential of ESE to be used in conjunction with Quantum Key Distribution (QKD), in order to achieve full information-theoretic security of QKD-protected links for these use cases.

Causal analysis paves the way for more interpretable assessment of complex systems and phenomena, such as human-in-the-loop components of energy systems. This paper will pursue novel approaches for causal analysis of prosumers' behavior. The knowledge of this causality is core for multiple smart grid applications including but not limited to the design of demand side management programs, retail electricity market design, development of effective distributed energy resources aggregation strategies, and net load forecasting. The complex nature of human interactions with energy relies on many factors and understanding behavior causality is a core, unsolved challenge. This paper presents a probabilistic algorithm for discovering causal relationships between the end users' consumption flexibility and its influencing factors. The obtained causal knowledge is then utilized to boost the precision of demand flexibility prediction. Two causal-oriented approaches are proposed to enhance the performance and interpretability of predictive models, incorporating causal information through causal regularization and data preprocessing. Simulation results demonstrate that the algorithm can effectively identify causal probabilities among different factors and unveil key characteristics of the prosumers' behavior. Additionally, these proposed causal-oriented approaches outperform the non-causal oriented predictive models in terms of both performance and interpretability, highlighting the advantages of incorporating causal information.

In this letter, we proposed and experimentally demonstrated a simple and efficient design method for high-voltage Floating Field Ring (FFR). Three floating FFRs is firstly used to determine the influence of rings’ distance (d) on the voltage drops of the highest voltage to second ring (Vn+1) and the second to the first ring (Vn). Then, two voltage curves (curve for Vn+1 and Vn) based on d are drawn. Then, for a target breakdown voltage, by iteration between the curves for Vn+1 and Vn, number of FFRs and corresponding distances can be found. Based on this method, an 8.0 kV PN diode with 110 FFRs is designed and fabricated. The measured breakdown voltage is 7840 V, which reaches 93% of the designed value. This method provides a simple and efficient design for high-voltage SiC devices, especially for ultra-high voltage applications where the number of rings exceeds dozens.

Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as Artificial Intelligence (AI), machine learning (ML), and Deep Learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large-scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, we provide insights into the transformative potential of ML in shaping the future of power system asset management.

The introduction of YOLOv9, the latest version of the You Only Look Once (YOLO) series, has led to its widespread adoption across various scenarios. This paper is the first to apply the YOLOv9 algorithm model to the fracture detection task as computer-assisted diagnosis (CAD) to help radiologists and surgeons to interpret X-ray images. Specifically, this paper trained the model on the GRAZPEDWRI-DX dataset and extended the training set using data augmentation techniques to improve the model performance. Experimental results demonstrate that compared to the mAP 50-95 of the current state-of-the-art (SOTA) model, the YOLOv9 model increased the value from 42.16% to 43.73%, with an improvement of 3.7%. The implementation code is publicly available at https://github.com/RuiyangJu/YOLOv9-Fracture-Detection.

Introductions of quantum communications, enabled by advancements in quantum computing, is expected to play a significant role in the field of communications. Inherent properties of quantum objects, such as superposition and entanglement have the potential to provide novel solutions to overcome the challenges encountered by classical communication systems in bandwidth-intensive applications such as media transmission. This research explores the performance of a quantum communication system in image transmission using quantum superposition and investigates its performance using a simple quantum channel model. With increase of channel noise, there are significant gains in the rate distortion performance of images transmitted over the quantum channel, compared to an ideal classical channel. This novel attempt in constructing a quantum communication-based image transmission system indicates the potential of the approach to be applied to satisfy the ever-increasing demands of high-quality media transmission applications.