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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Jiawei Yang Hongxu Huang Yiwen Zhang Jiahong Dai H B Gooi School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Electrical Engineering China University of Mining and Technology China Lee Kuan Yew School of Public Policy National University of Singapore

ISBN: (纸本)9798350399677

With the rapid development of renewable energy generation, governments have released various policies to reduce the portion of the energy generated from the burning of fossil fuel, of which the carbon emission tax is one of the most effective methods. Blockchain is considered as the optimal ecosystem to protect and trace the energy transactions with carbon taxes. This paper proposes a distributed Proof-of-Stake (DPoS) public blockchain with a smart contract function to support a double auction based pricing scheme and execute a carbon tax compensation mechanism, which reduce the trading cost of prosumers and improve the social welfare. A 13-prosumer energy trading model is performed in the case study to experiment the blockchain performance. Numerical results prove the effectiveness and feasibility of the proposed method.

关键词： Renewable energy sources Costs Smart contracts Ecosystems Asia Finance Pricing

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Pain-Evoked Functional Activation Combining Transcranial Direct Current Stimulation with Brain Imaging in Older Adults with Alzheimer’s Disease and Related Dementias

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Brain Stimulation 2025年第1期18卷 393-394页

作者： Park, Juyoung Montero-Hernandez, Samuel Huff, Allison Lee, Chiyoung Pollonini, Luca Park, Lindsey Lin, Lifeng Galvin, James Ahn, Hyochol The University of Arizona College of Nursing USA University of Birmingham UK The University of Arizona College of Medicine Tucson USA University of Houston Department of Electrical and Computer Engineering USA University of Houston Department of Biomedical Engineering USA The University of Arizona Mel and Enid Zuckerman College of Public Health USA University of Miami Miller School of Medicine USA

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FixCyprus: Crowdsourcing Smartphone Imagery Data For Managing Road Safety Hazards

FixCyprus: Crowdsourcing Smartphone Imagery Data For Managin...

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International Conference on Mobile Data Management

作者： Georgios Christou Andreas Georgiou Christos Laoudias Aristotelis Savva Christos G. Panayiotou KIOS Center of Excellence University of Cyprus Cyprus Public Works Department Ministry of Transport Communications and Works Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus

In this demonstration paper, we present FixCyprus, which is a cost-effective crowdsourcing service for road transportation authorities in Cyprus to gather information and manage defects and incidents on the road network and surrounding infrastructure (e.g., pavements, lighting, drinking water pipes, etc.). The production service, which includes a lightweight and user-friendly mobile application for sharing image-annotated incident reports, is already operating nationwide for six months providing significant budget savings by reducing field inspections and the use of expensive equipment. We will demonstrate FixCyprus using two modes: i) Interactive mode, where attendees will be able to create and submit their own dummy incident reports and see the end-to-end processing flow in a test environment and ii) Trace-driven mode, where attendees will be able to visualize a large number of synthetic reports, see the workload for manually managing them, and explore enhancements that are underway for automating some of the underlying tasks using machine learning.

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Towards Quantifying Stress in Patients with a History of Myocardial Infarction: Validating ECG-Derived Patch Features

Towards Quantifying Stress in Patients with a History of Myo...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Afra Nawar Asim H. Gazi Michael Chan Jesus Antonio Sanchez-Perez Farhan N. Rahman Carrie Ziegler Obada Daaboul George Haddad Omar A. Al-Abboud Hashir Ahmed Nancy Murrah Viola Vaccarino Amit J. Shah Omer T. Inan School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Department of Epidemiology Rollins School of Public Health Emory University Atlanta GA USA

Patients with prior myocardial infarction (MI) have an increased risk of experiencing a secondary event which is exacerbated by mental stress. Our team has developed a miniaturized patch with the capability to capture electrocardiogram (ECG), seismocardiogram (SCG) and photoplethysmogram (PPG) signals which may provide multimodal information to characterize stress responses within the post-MI population in ambulatory settings. As ECG-derived features have been shown to be informative in assessing the risk of MI, a critical first step is to ensure that the patch ECG features agree with gold-standard devices, such as the Biopac. However, this is yet to be done in this population. We, thus, performed a comparative analysis between ECG-derived features (heart rate (HR) and heart rate variability (HRV)) of the patch and Biopac in the context of stress. Our dataset contained post-MI and healthy control subjects who participated in a public speaking challenge. Regression analyses for patch and Biopac HR and HRV features (RMSSD, pNN50, SD1/SD2, and LF/HF) were all significant (p<0.001) and had strong positive correlations (r>0.9). Additionally, Bland-Altman analyses for most features showed tight limits of agreement: 0.999 bpm (HR), 11.341 ms (RMSSD), 0.07% (pNN50), 0.146 ratio difference (SD1/SD2), 0.750 ratio difference (LF/HF).Clinical relevance— This work demonstrates that ECG-derived features obtained from the patch and Biopac are in agreement, suggesting the clinical utility of the patch in deriving quantitative metrics of physiology during stress in post-MI patients. This has the potential to improve post-MI patients' outcomes, but needs to be further evaluated.

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Earlier identification of hypertensive events in a telemonitoring system

Earlier identification of hypertensive events in a telemonit...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Edmund Do Suhrit Lavu Hye-Chung Kum Bobak J. Mortazavi Computer Science & Engineering Texas A&M University College Station TX Health Policy & Management School of Public Health Texas A&M University College Station TX

Hypertension is a prevalent risk factor for cardiovascular disease and premature mortality. Telemonitoring can be used to provide a communication pipeline between patients and clinicians for diagnosing hypertension and staging early intervention. However, it takes healthcare resources to monitor patients and identify patients at risk of experiencing a hypertensive event. To reduce the burden on the health care system, we present an automated early warning system to predict patients at risk of a hypertensive event. We first construct a fusion model that utilizes a dual stage attention mechanism to determine whether a hypertensive event occurs in the next seven days and compare its performance to XGBoost and logistic regression. Then, we measure its performance in an early warning system to determine whether it can detect the onset of the first hypertensive event for each patient. With the best threshold, the early warning system using this model has an F1 score of 0.61.

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The Neural Networks Based Needle Detection for Medical Retinal Surgery

arXiv

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arXiv 2023年

作者： Xu, Jidong Yu, Jinglun Yao, Jianing Zhang, Rendong Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore United States

In recent years, deep learning technology has developed rapidly, and the application of deep neural networks in the medical image processing field has become the focus of the spotlight. This paper aims to achieve needle position detection in medical retinal surgery by adopting the target detection algorithm based on YOLOv5 as the basic deep neural network model. The state-of-the-art needle detection approaches for medical surgery mainly focus on needle structure segmentation. Instead of the needle segmentation, the proposed method in this paper contains the angle examination during the needle detection process. This approach also adopts a novel classification method based on the different positions of the needle to improve the model. The experiments demonstrate that the proposed network can accurately detect the needle position and measure the needle angle. The performance test of the proposed method achieves 4.80 for the average Euclidean distance between the detected tip position and the actual tip position. It also obtains an average error of 0.85 degrees for the tip angle across all test sets. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Anomaly Detection of Patient Data in public Hospital used Internet of Things Sensors

Anomaly Detection of Patient Data in Public Hospital used In...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Evizal Abdul Kadir Lenni Dianna Putri Sri Listia Rosa Apri Siswanto Farhan Assidiqi Muhammad Fikri Evizal Department of Informatics Engineering Universitas Islam Riau Pekanbaru Indonesia Department of Public Health Universitas Prima Indonesia Medan Indonesia Department of Electrical Engineering Telkom University Bandung Indonesia Computer Science and Information Eng National Dong Hwa University Hualien County Taiwan

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Increasing number of patients doing treatment in a public hospital become as issue for the management. Limited number of equipment to detect abnormal patient and support from medical staff. This research aims to detect on the anomaly detection of hospital patient data through the utilization of Internet of Things (IoT) sensors. With the increasing integration of IoT devices in healthcare settings, such as sensors monitoring vital signs like temperature, blood pressure, and heart rate, the need for robust anomaly detection mechanisms becomes imperative. The research explores advanced algorithms and machine learning techniques to identify irregular patterns or outliers in patient data, aiming to enhance the early detection of potential health issues or abnormalities. Leveraging the vast amount of data generated by IoT sensors in hospital environments, the research aims to contribute to the development of more efficient and accurate anomaly detection systems, ultimately improving the quality of patient care and facilitating timely intervention by healthcare professionals. The findings of this research have significant implications for the evolving landscape of healthcare technologies, emphasizing the importance of ensuring data integrity and patient safety in the era of IoT-driven healthcare solutions. Patient data shows in graph to see how the anomaly indication for the patient. Multi sensor with parallel system will develop in future research plan.

关键词： Temperature sensors Hospitals Data integrity Sensor systems Sensors Safety Internet of Things

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Distributed Ledger Technology in Energy Services: The InEExS Project Objectives and Approach

Distributed Ledger Technology in Energy Services: The InEExS...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Aikaterini Papapostolou Ioanna Andreoulaki Sokratis Divolis Filippos Anagnostopoulos Vangelis Marinakis Decision Support Systems Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Institute for European Energy and Climate Policy (IEECP) Amsterdam The Netherlands

The digitalization trend is prominent in a wide variety of sectors, and the energy sector is no exception. The incorporation of blockchain, a Distributed Ledger Technology (DLT), in energy services has been examined in literature and quite a few endeavors of DLT adoption in energy applications have been implemented by companies, universities and other organizations. The European LIFE project “InEExS” pursues to apply DLT through specific business cases, so as to offer improved digital energy services and encourage energy efficiency. In this paper, a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis has been conducted as a first stage of DLT potential usages evaluation in the contexts of the InEExS, with the purpose of demonstrating the reasons why blockchain could support the digitalization of energy applications and acceleration of energy transition, while pointing out the most important barriers that should be addressed to ensure that DLT integration would be truly beneficial. The decentralized nature of blockchain, combined with the transparency and safety it provides, make it a very promising technology for energy management and trading implementations, among others. However, technical constraints, such as the scalability problem, security threats, as well as sociopolitical and regulatory barriers should not be neglected. The findings of our SWOT analysis are combined with an assessment of prospective blockchain usages in the business cases deployed by the InEExS, so that the best practices to optimally exploit DLT in various energy applications, within and beyond the project, are identified.

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As an AI Language Model, "Yes I Would Recommend Calling the Police": Norm Inconsistency in LLM Decision-Making

arXiv

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arXiv 2024年

作者： Jain, Shomik Calacci, D. Wilson, Ashia Institute for Data Systems and Society Massachusetts Institute of Technology United States Center for Information Technology Policy Princeton University United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We investigate the phenomenon of norm inconsistency: where LLMs apply different norms in similar situations. Specifically, we focus on the high-risk application of deciding whether to call the police in Amazon Ring home surveillance videos. We evaluate the decisions of three state-of-the-art LLMs – GPT-4, Gemini 1.0, and Claude 3 Sonnet – in relation to the activities portrayed in the videos, the subjects’ skin-tone and gender, and the characteristics of the neighborhoods where the videos were recorded. Our analysis reveals significant norm inconsistencies: (1) a discordance between the recommendation to call the police and the actual presence of criminal activity, and (2) biases influenced by the racial demographics of the neighborhoods. These results highlight the arbitrariness of model decisions in the surveillance context and the limitations of current bias detection and mitigation strategies in normative decision-making. © 2024, CC BY.

关键词： Decision making

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Object Detection for Vehicles with Yolo

Object Detection for Vehicles with Yolo

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Pouria Maleki Abbas Ramazani Hassan Khotanlou Sina Ojaghi Milad Mousavi Alexey Kalinin Amir Mosavi Department of Electrical Engineering Bu-Ali Sina University Hamedan Iran Department of Computer Engineering Bu-Ali Sina University Hamedan Iran School of Computer and Electrical Engineering University of Tehran Tehran Iran K. N. Toosi University of Technology Tehran Iran Abylkas Saginov Karaganda Technical University Kazakistan John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

The rapid urban population growth has intensified the challenges associated with urban and suburban traffic, necessitating effective traffic control and management. The efficient movement of emergency vehicles, particularly ambulances and fire trucks, has emerged as a critical concern. This article presents the Vehicle Dataset, a comprehensive benchmark for object detection, encompassing seven vehicle classes, including cars, motorcycles, buses, trucks, vans, ambulances, and fire trucks. The dataset, includes 29,759 meticulously labeled images obtained from freely available online sources, enables the identification of traffic patterns through deep neural networks. Notably, the dataset emphasizes the facilitation of emergency vehicle movement. The Vehicle Dataset in this study is divided into three subsets, with 25,369 images assigned for training, 2,896 for validation, and 1,494 for testing. Through the utilization of the dataset, object detection algorithms based on YOLO versions 5, 6, and 7 have been trained. Remarkably, YOLO version 7 has yielded outstanding results, achieving a final precision of 85% and a mAP of 85% at an IoU threshold of 0.5. Moreover, at IoU thresholds ranging from 0.5 to 0.9, a mAP of 64% has been attained. The Vehicle Dataset represents significant resource for researchers and practitioners in the transportation and traffic management field. Its inclusion of emergency vehicles such as ambulances and fire trucks contribute to its unique value. This article presents a detailed exploration of the dataset, underscoring its significance in advancing object detection methodologies.

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