PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning

Deep learning has emerged as an effective solution for addressing the challenges of short-term voltage stability assessment (STVSA) in power systems. However, existing deep learning-based STVSA approaches face limitations in adapting to topological changes, sample labeling, and handling small datasets. To overcome these challenges, this paper proposes a novel phasor measurement unit (PMU) measurements-based STVSA method by using deep transfer learning. The method leverages the real-time dynamic information captured by PMUs to create an initial dataset. It employs temporal ensembling for sample labeling and utilizes least squares generative adversarial networks (LSGAN) for data augmentation, enabling effective deep learning on small-scale datasets. Additionally, the method enhances adaptability to topological changes by exploring connections between different faults. Experimental results on the IEEE 39-bus test system demonstrate that the proposed method improves model evaluation accuracy by approximately 20% through transfer learning, exhibiting strong adaptability to topological changes. Leveraging the self-attention mechanism of the Transformer model, this approach offers significant advantages over shallow learning methods and other deep learning-based approaches.Comment: Accepted by IEEE Transactions on Instrumentation & Measuremen

Good People Don\u27t Need Medication: How Moral Character Beliefs Affect Medical Decision-Making

How do people make decisions? Prior research focuses on how people\u27s cost-benefit assessments affect which medical treatments they choose. We propose that people also worry about what these health decisions signal about who they are. Across four studies, we find that medication is thought to be the easy way out , signaling a lack of willpower and character. These moral beliefs lower the appeal of medications. Manipulating these beliefs--by framing medication as a signal of superior willpower or by highlighting the idea that treatment choice is just a preference--increases preferences for medication

Pre-commitment to Moral Values

When faced with reoccurring tradeoffs between moral values, people can address them by considering the specifics of each case or by setting policies that predetermine how they will address similar cases. Previous research on moral judgment has often focused on isolated tradeoffs, and therefore, it is unclear which decision strategies are preferred in contexts with reoccurring tradeoffs. Across our studies, participants judged people who precommitted to always prioritizing one value more positively than people who adjusted their priorities based on the specifics of each case. Our findings have important implications for understanding public perceptions of complex policies

The Social Consequences of Absolute Moral Proclamations

Across six studies (N = 3348), we find that people prefer targets who make absolute proclamations (i.e. It is never okay for people to lie ) over targets who make ambiguous proclamations ( It is sometimes okay for people to lie ), even when both targets tell equivalent lies. Preferences for absolutism stem from the belief that moral proclamations send a true signal about moral character--they are not cheap talk. Therefore, absolute proclamations signal moral character, despite also signaling hypocrisy. This research sheds light on the consequences of absolute proclamations and identifies circumstances in which hypocrisy is preferred over consistency

Photoplethysmography based atrial fibrillation detection: an updated review from July 2019

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field. This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 59 pertinent studies. Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis

A Deep CNN Framework for UAV Intrusion Detection in Intelligent Systems

Unmanned Ariel Vehicle (UAV) s are dealing with several safety and protection issues including internal hardware/software and potential attacks. In addition, detecting UAV anomalies will be a crucial responsibility to defend against hostile enemies and prevent accidents. In this research, we present a UAV and an Automatic Dependent (AD) system using surveillance and Machine Learning (ML) algorithms to analyze data from their detectors in real-time. Proposed Improved Region based Convolutional Neural Network (IRCNN) model used to generate and acquire the characteristics of untreated sensor information and characteristics to facilitate AD. The proposed model creating an Inertial Measurement Unit (IMU) & UAV sensors dataset using cyber security simulation system and Active Learning (AL) identifies aggressions based on the least probable interrogation method. This proposed model enables the identification to efficiently improve the occurrences of unexplained aggressions discovered of IRCNN at reduced labeling cost. A thorough trial showed that IRCNN-AL is effective at detecting unknown threats with frequency improvements of between 9% and 30% on comparison approaches. The AL methodology presented with as few as 1% of a labeled unexpected aggressions