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11th IEEE International Conference on Healthcare Informatics, ICHI 2023

作者： Guo, Jiaqi Aqeel, Marah M. Lin, Luotao Gelfand, Saul B. Eicher-Miller, Heather A. Bhadra, Anindya Hennessy, Erin Richards, Elizabeth A. Delp, Edward J. Purdue University School of Electrical and Computer Engineering West LafayetteIN United States Purdue University Department of Nutrition Science West LafayetteIN United States Purdue University Department of Statistics West LafayetteIN United States Tufts University Friedman School of Nutrition Science and Policy BostonMA United States Purdue University School of Nursing West LafayetteIN United States

ISBN: (纸本)9798350302639

Physical activity (PA) is known to be a risk factor for obesity and chronic diseases such as diabetes and metabolic syndrome. Few attempts have been made to pattern the time of physical activity while incorporating intensity and duration in order to determine the relationship of this multi-faceted behavior with health. In this paper, we explore a distance-based approach to cluster the daily physical activity time series from the National Health and Nutrition Examination Survey 2003-2006 (NHANES). The clusters are used to find temporal physical activity patterns among US adults. A number of distance measures and distance-based clustering methods are investigated and compared using various metrics. These metrics include the Silhouette and the Dunn Index (internal criteria), and the associations of the clusters with health status indicators (external criteria). Our experiments indicate that using a distance-based cluster analysis approach to estimate temporal physical activity patterns through the day, has the potential to describe the complexity of behavior rather than characterizing physical activity patterns solely by sums or labels of maximum activity levels. © 2023 IEEE.

关键词： Nutrition

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Valuing DER Location based on the Impact on Transmission Network: A Planning Tool

Valuing DER Location based on the Impact on Transmission Net...

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IEEE Green Technologies Conference (IEEE-Green)

作者： Sarangan Rajendran Visvakumar Aravinthan Al Tamimi Charles R. Yokley Electrical and Computer Engineering Department Wichita State University Wichita KS USA Transmission Planning and Policy Sunflower Electric Power Corporation Wichita KS USA

Distributed energy resources (DERs) are increasingly being considered as an attractive solution to the non-wire alternative problem, given their various benefits to the system. However, high levels of DER penetration can result in unanticipated electric grid operating conditions, as they are traditionally not considered in planning processes by the transmission utilities. Consequently, there arises a need to value DERs based on the impacts they have on the overall system as viewed from a transmission level. This paper presents three metrics to quantify the impacts caused by the addition of DERs to an existing system. The proposed approach employs a statistical approach to measure the shift of critical system parameters from normal operating conditions due to the addition of DERs. A test system developed based on the western Kansas region is used to analyze the impact of DERs and demonstrate the usefulness of the proposed method. The results provide additional insights on how value of DERs can be studied based on the candidate location of installation.

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Performance Comparison of Deep Learning Algorithms in Twitter Bot Detection

Performance Comparison of Deep Learning Algorithms in Twitte...

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Recent Trends in Information Technology (CSCTIT), College of Science International Conference of

作者： Samer Emad Neama Al-ibadi Mesut Cevik Department of Electrical and Computer Engineering Altinbas University Istanbul Turkey Ministry of Communications Information & Telecom Public Company Baghdad Iraq Electrical and Computer Engineering Department Altinbas University Istanbul Turkey Istanbul Turkey

Difficulty in detecting social media bots - software-controlled accounts that seem as human - has serious consequences. Bots have been used to affect the financial market, political elections, and spreading conspiracy theories. Most approaches detect bots at the account level by studying social media posts and using network structure, system dynamics, content analytics, etc. In this study, we compare the performance of the recurrent neural network (RNN), convolutional neural network (CNN), and feed forward neural network (FNN) in detecting Twitter bots by evaluating the textual content of 10,416,240 tweets from humans and bots. Deep learning models utilize sample data to gauge performance. The CNN model identified recent social bot samples most accurately (97.67 %). In comparison, the RNN model achieved great accuracy (97.04 %) with a longer training time. RNN and CNN algorithms perform well when compared to other deep learning classifiers.

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Incentivizing or Disincentivizing Participation in Demand Response Programs? Estimation of Customer Willingness to Accept

SSRN

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SSRN 2025年

作者： Wang, Yu Howard, Gregory Zhang, Wendong Brown, Marilyn Morton, Abigail Cetin, Kristen McCalley, James Department of Political Science Iowa State University United States Department of Economics East Carolina University United States The Charles H. Dyson School of Applied Economics and Management Cornell University United States School of Public Policy Georgia Institute of Technology United States Federal Energy Regulatory Commission United States Department of Civil and Environmental Engineering Michigan State University United States Department of Electrical and Computer Engineering Iowa State University United States

Demand response has great potential to provide value flexibility services to the grid, which can be harnessed through direct load control (DLC) programs. Many utilities have provided direct financial incentives to encourage residential customers to enroll in DLC programs. However, financial incentives do not always result in high participation rate. This study aims to assess residential customers’ willingness to enroll in utility-run DLC programs and analyze how incentives affect participation intention. A survey using the contingent valuation method (CVM) collected a random sample of 591 residents in the U.S. Midwest from 2020-2022, which finds, overall, 56.7% of residents are willing to enroll in DLC. It is estimated that 61.5% of residents are willing to participate due to intrinsic motivations (i.e., without monetary incentive), while a small token ($15-24 sign-on bonus) only leads to about 50% of the customers agreeing to enroll. To reach the same level of participation from intrinsic motivations, utilities must offer a much higher incentive - $83-$90. Additionally, we find several factors affect acceptance of DLC programs, including knowledge learning, concerns of comfort loss, and personal beliefs on energy savings, reliability, and environmental benefit. Findings from this study have significant policy implications and help improve the design of utility DLC programs. When developing strategies to recruit participants, no mention of monetary incentives could increase the participation of resident customers. We also recommend that utilities consider educational and demonstration programs to engage stakeholders, and information campaigns to emphasize the potential benefits without compromising thermal comfort. © 2025, The Authors. All rights reserved.

关键词： Sales

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Strengthening Capacity in Industrial Revolution 4.0 (IR 4.0): A Case Study on Training Product-service Systems Between the US and Vietnam

Strengthening Capacity in Industrial Revolution 4.0 (IR 4.0)...

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Portland International Conference on Management of engineering and Technology (PICMET)

作者： Man Hang Yip Ana Sofia Castellanos Santarnaria Julia Babcock Centre for Technology Management Department of Engineering University of Cambridge United Kingdom National Policy Consensus Center College of Urban and Public Affairs Portland State University Portland USA Maseeh College of Engineering and Computer Science Portland State University Portland USA

ISBN: (数字)9781890843458

ISBN: (纸本)9798350369137

This case study analyzes how value is created in the development process of a collaborative training product-service system (PSS) designed for provincial government officials in Vietnam. This initiative, part of the USAID Strengthening Provincial Capacity (USAID SPC) project, encapsulates a series of training solutions designed to empower officials with key skills in engineering and technology management, including the knowledge to improve global value chains, upgrade technology infrastructure and sustainable production models. A primary driver for the “four-day course delivery”, the PSS analyzed in this study, was to bolster economic competitiveness at the provincial level in the context of Industrial Revolution 4.0 (IR 4.0). This case study offers lessons on stakeholder-value mapping and recommendations for future approaches to training courses development that enhances capacity development. These trainings aligned with the demands of balancing organizational development and public value creation in the context of developing countries transitioning to a knowledge economy, such as Vietnam. The learnings from this case study may provide a blueprint for similar endeavors in other countries navigating the complexities of digital transformation of public services.

关键词： Training Knowledge engineering Technology management Navigation Government Collaboration Production

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Optimal Restoration of a Power Distribution System During Extreme Events Considering Load Criticality

Optimal Restoration of a Power Distribution System During Ex...

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Annual North-American Power Symposium

作者： Adithya Melagoda Arun-Kaarthick Manoharan Mohd Abrar Rahman Visvakumar Aravinthan Al Tamimi Department of Electrical & Computer Engineering Wichita State University Wichita KS USA Transmission Planning & Policy Sunflower Electric Power Corporation Wichita KS USA

Recent natural and man-made disasters leading to sustained power outage has urged the system planners and policy makers for improving grid resiliency. This requires innovation across diverse facets ranging from better anticipation of such events to post event response. This work focuses on the problem of supporting the critical loads when the system is undergoing such an event. As a preliminary step towards the big picture, this work aims to develop a load criticality-based model to operable microgrid formation. A new firefly search inspired metaheuristic algorithm is developed in this work for microgrid formation with the objective of maximizing the critical load restoration with minimal switching operation. Numerical analysis is conducted on a modified Iowa state 240-bus distribution system and the results show the scalability and critical load restoration capabilities of the proposed algorithm.

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Capturing Physiological Correlates of Stress-Induced Blood Pressure Elevation Using a Multimodal Wearable Sensing Patch in Patients with a History of Myocardial Infarction*

Capturing Physiological Correlates of Stress-Induced Blood P...

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

作者： Afra Nawar Jesus Antonio Sanchez-Perez Farhan N. Rahman Asim H. Gazi Michael Chan 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 School of Biomedical Engineering Georgia Institute of Technology Atlanta GA USA Department of Epidemiology Rollins School of Public Health Emory University Atlanta GA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Persons who have experienced a prior myocardial infarction (MI) are at a greater risk of experiencing a secondary event. External factors, such as stress, can cause transient hypertension, further increasing cardiovascular disease risk. However, challenges to monitoring stress-induced hypertension include obtrusiveness of monitoring devices, unknown validity in high-risk populations, and unsubstantiated sensitivity in stressful environments. In this pilot study, we investigated data from a validated, multimodal, wearable patch to examine physiological correlates of laboratory-based hypertensive stress responses. The device collected electrocardiogram, seismocardiogram, and photoplethysmogram signals from 35 participants (26 post-MI and 9 healthy participants) during a protocol involving a public speaking stressor. We calculated the change from rest to stress in 10 features extracted from these signals and assessed their correlation to stress-induced changes in mean arterial pressure (MAP) derived from a validated brachial pressure cuff. MAP changes correlated significantly with changes in heart rate (p<0.001, r=0.69), left ventricular ejection time (p<0.001, r=-0.63), pulse arrival time (p<0.001, r=-0.58), and pulse transit time (p<0.001, r=-0.56) captured by the *** relevance— We demonstrate the potential of a multimodal patch to capture multiple physiological correlates of stress-induced hypertension with moderate dose-response effect sizes. Future work with larger populations could combine these physiological correlates for the purpose of cardiovascular risk stratification using low-burden technologies.

关键词： Hypertension Heart rate Correlation Myocardium Feature extraction Sensors Biomedical monitoring Transient analysis Stress Monitoring

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Irregular warfare in strategic competition

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Defence Studies 2024年第1期24卷 148-158页

作者： Chinchilla, Alexandra Atwell, Kyle Bradstreet, Alexis Crombe, Catherine Leblanc, Luther Department of International Affairs Texas A&M University Bush School of Government and Public Service College Station TX United States Irregular Warfare Initiative and a Nonresident Atlantic Council’s Forward Defense Practice Scowcroft Center for Strategy and Security United States Department of Electrical Engineering and Computer Science United States Military at West Point Cyber Science West Point United States United States Military Academy Washington DC United States Department of Social Sciences United States Military Academy at West Point West Point United States

After two decades of counterinsurgency and counterterrorism in Afghanistan and Iraq, the US military has shifted to preparing for large-scale combat operations. However, it would be a mistake to discard hard-earned lessons from these conflicts. Despite contemporary advances in technology and important differences between current US competitors and the Soviet Union, irregular warfare will play a prominent role in the new era of strategic competition. It was a prominent form of US-Soviet competition during the Cold War, is already used extensively by the United States and its competitors and remains attractive given concerns about escalation between nuclear-armed powers. Given the continued relevance of irregular warfare, we focus on two main lessons from the US experience. First, since irregular warfare is about influencing populations and achieving political goals without large-scale combat operations, influencing and working alongside the partner is the primary mission. The second lesson follows directly from the first;if irregular warfare is ultimately about achieving policy goals with an economy of military force, IW is a team sport requiring joint and interagency collaboration to be effectively implemented. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： counterinsurgency great powers Irregular warfare security strategic competition

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High-Precision Positioning in GNSS-Challenged Environments: A LiDAR-Based Multi-Sensor Fusion Approach with 3D Digital Maps Registration

TechRxiv

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TechRxiv 2023年

作者： Mounier, Eslam Elhabiby, Mohamed Korenberg, Michael Noureldin, Aboelmagd The Department of Electrical and Computer Engineering Queen’s University KingstonONK7L 3N6 Canada The Department of Computer and Systems Engineering Ain Shams University Cairo11535 Egypt Micro Engineering Tech. Inc. Canada The Public Works Department Ain Shams University Cairo11535 Egypt The Department of Electrical and Computer Engineering Royal Military College of Canada KingstonONK7K 7B4 Canada

Autonomous vehicles (Avs) necessitate highly accurate and robust positioning capabilities. This calls for developing innovative algorithms that leverage the diversity of sensors and systems available in AVs to fulfill such demands across various environmental and operational conditions. This paper presents a multi-sensor positioning and navigation system that leverages cost-effective commercial-grade sensors for GNSS-challenging urban and indoor environments. The system fuses onboard motion sensor data with LiDAR point clouds registered to high-accuracy 3D digital maps to achieve sustained decimeter-level positioning. Key contributions include accurate LiDAR scans geo-referencing with motion compensation, efficient map-to-map registration, and an effective decentralized fusion. Real-world driving data from downtown Kingston, Ontario, Canada, and a high-accuracy 3D city geodatabase were used to examine the proposed methods’ performance and benefits. Results demonstrate the efficacy of the proposed technique, achieving accurate positioning with an average RMSE of 20cm horizontally and 13cm vertically, and a sustainable positioning sub-meter level of positioning accuracy 100% of the time. The proposed method was also able to sustain high precision positioning in such GNSS-denied environments with position errors of less than 50cm for 96.8% of the time and less than 30cm for 91% of the time. The performance achieved demonstrates that the proposed system is a practical option for the positioning and navigation of self-driving cars and has the potential for cooperative mapping and updating 3D city maps. © 2023, CC BY.

关键词： Autonomous vehicles

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Prediction New Cases of COVID-19 in Indonesia Using Vector Autoregression (VAR) and Long-Short Term Memory (LSTM) Methods

Prediction New Cases of COVID-19 in Indonesia Using Vector A...

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2021 International Conference on Artificial Intelligence and Big Data Analytics, ICAIBDA 2021

作者： Danistya, Daffa Rahmansyah Qaulifa, Fahira Ramadani, Yovi Adhi Nurma Yulita, Intan Ardisasmita, Mulya Nurmansyah Agustian, Dwi Research Center for Artificial Intelligence and Big Data Padjadjaran University Bandung Indonesia Department of Electrical Engineering Brawijaya University Malang Indonesia Department of Electrical Engineering Telkom University Bandung Indonesia Department of Informatics Engineering Semarang University Semarang Indonesia Department of Computer Science Padjadjaran University Bandung Indonesia Department of Public Health Padjadjaran University Bandun Indonesia

ISBN: (纸本)9781665408905

The addition of Covid-19 cases is still uncontrolled, especially in Indonesia. Often the addition of Covid-19 cases in Indonesia always experiences a significant upward trend after a slightly loose government policy. This is because the government does not think there will be a spike in cases after cases go down. This is where the importance of predicting new cases of Covid-19 in Indonesia to be a reference for the government in taking policy. With deep learning, the prediction results will be more accurate. The implementation of vector autoregression (VAR) and long-short term memory (LSTM) methods can reach an accretion rate of up to 98%. With this method, the prediction results can be used for the government in anticipating if there is a surge in new cases per day because it has been predicted from the beginning. In fact, this method can predict new cases for up to a year. © 2021 IEEE.

关键词： Long short-term memory

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