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arXiv 2022年

作者： Tan, Zhiwen Shen, Chang Subbarao, Padmaja Lou, Wendy Lu, Zihang Department of Public Health Sciences Queen's University KingstonON Canada Department of Electrical and Computer Engineering Queen's University KingstonON Canada Division of Respiratory Medicine Department of Pediatrics Hospital for Sick Children University of Toronto TorontoON Canada Dalla Lana School of Public Health University of Toronto TorontoON Canada

In epidemiological and clinical studies, identifying patients' phenotypes based on longitudinal profiles is critical to understanding the disease's developmental patterns. The current study was motivated by data from a Canadian birth cohort study, the CHILD Cohort Study. Our goal was to use multiple longitudinal respiratory traits to cluster the participants into subgroups with similar longitudinal respiratory profiles in order to identify clinically relevant disease phenotypes. To appropriately account for distinct structures and types of these longitudinal markers, we proposed a novel joint model for clustering mixed-type (continuous, discrete and categorical) multivariate longitudinal data. We also developed a Markov Chain Monte Carlo algorithm to estimate the posterior distribution of model parameters. Analysis of the CHILD Cohort data and simulated data were presented and discussed. Our study demonstrated that the proposed model serves as a useful analytical tool for clustering multivariate mixed-type longitudinal data. We developed an R package BCClong to implement the proposed model efficiently. © 2022, CC BY.

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The potential for electrifying industrial utility systems in existing chemical plants

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Applied Energy 2025年 392卷

作者： Bielefeld, Svenja Cvetković, Miloš Ramírez, Andrea Engineering Systems and Services Department Faculty of Technology Policy and Management Delft University of Technology Delft Netherlands Electrical Sustainable Energy Department Faculty of Electrical Engineering Mathematics & Computer Science Delft University of Technology Delft Netherlands Department of Chemical Engineering Faculty of Applied Science Delft University of Technology Delft Netherlands

The electrification of utility systems in energy-intensive plants is a promising measure for decarbonising the chemical industry in the short term. However, with the increasing deployment of renewable energy sources, the variability of electricity prices will become a challenge for plants with continuous and constant energy demand. It is thus uncertain whether electrification can become financially viable. This work models the electrification of utility systems in combination with storage technologies for five chemical plants with existing fossil fuel-based utility generation and uses historical data as energy price scenarios. The results show that partial electrification is cost-effective when using electricity is cheaper than natural gas for more than 600 h. Regarding the portfolio of technologies, electric boilers are installed first, followed by thermal energy storage and batteries. Hydrogen is not cost-effective in any of the scenarios explored. This is independent of the type of plant, the available grid connection capacity, and the minimal load of existing fossil fuel-based utility generation. This work thus highlights the potential for electrifying industrial utility systems and the role that electric boilers and energy storage units can play in electrification. © 2025 The Author(s)

关键词： Chemical plants

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Design and Implementation of an Instrumented walking cane for Detection of Freezing of Gait

Design and Implementation of an Instrumented walking cane fo...

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IEEE International Multidisciplinary Conference on engineering Technology (IMCET)

作者： Joelle Haidar Ahmad Alain El-Asmar Roy Abi Zeid Daou Ali Hayek Josef Boercsoek Faculty of Public Health Jounieh Lebanon MART Learning Education and Research Center Chananir Lebanon Faculty of Electrical Engineering/Computer Science University of Kassel Germany

ISBN: (纸本)9781665405881

Independent mobility is an important feature of an individual's normal life and with the elderly population continually growing, assistive devices such as walkers and canes provide help and guidance to users. Freezing of Gait (FoG) is an episodic phenomenon that is a common symptom of an age-related disease known as Parkinson Disease (PD). This paper is headed toward implementing a detection, diagnosis and correction system for FoG episodes. The proposed cane will generate visual, auditory and tactile cues to help patients overcome this problem, therefore reducing fall risks. In addition, a vibrating calf cuff will be attached to the calf for muscle stimulation. A compatible android phone application is designed to display heart rate measurements of the patient as well as his exact location in case of fall detection. The system is considered to be an electrically safe, portable, comfortable and a low-cost device. The performance of this system is analyzed thoroughly and the accuracy of all methodologies is considered carefully and evaluated. The results have shown no faulty errors and good accuracy and detection percentages.

关键词： Performance evaluation Legged locomotion Visualization Instruments Sociology Muscles Statistics

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Diffusion-based Dynamic Contract for Federated AI Agent Construction in Mobile Metaverses

arXiv

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arXiv 2025年

作者： Wen, Jinbo Kang, Jiawen Zhang, Yang Zhong, Yue Niyato, Dusit Xu, Jie Tang, Jianhang Yuen, Chau College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Automation Guangdong University of Technology China College of Computing and Data Science Nanyang Technological University Singapore Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen China State Key Laboratory of Public Big Data Guizhou University China School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Mobile metaverses have attracted significant attention from both academia and industry, which are envisioned as the next-generation Internet, providing users with immersive and ubiquitous metaverse services through mobile devices. Driven by Large Language Models (LLMs) and Vision-Language Models (VLMs), Artificial Intelligence (AI) agents hold the potential to empower the creation, maintenance, and evolution of mobile metaverses. Currently, AI agents are primarily constructed using cloud-based LLMs and VLMs. However, several challenges hinder their effective implementation, including high service latency and potential sensitive data leakage during perception and processing. In this paper, we develop an edge-cloud collaboration-based federated AI agent construction framework in mobile metaverses. Specifically, Edge Servers (ESs), acting as agent infrastructures, collaboratively create agent modules in a distributed manner. The cloud server then integrates these modules into AI agents and deploys them at the edge, thereby enabling low-latency AI agent services for users. Considering that ESs may exhibit dynamic levels of willingness to participate in federated AI agent construction, we design a two-period dynamic contract model to continuously motivate ESs to participate in agent module creation, effectively addressing the dynamic information asymmetry between the cloud server and the ESs. Furthermore, we propose an Enhanced Diffusion Model-based Soft Actor-Critic (EDMSAC) algorithm to efficiently generate optimal dynamic contracts, in which dynamic structured pruning is applied to DM-based actor networks to enhance denoising efficiency and policy learning performance. Extensive simulations demonstrate the effectiveness and superiority of the EDMSAC algorithm and the proposed contract model. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks

arXiv

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arXiv 2022年

作者： Zhu, Rui-Jie Zhang, Malu Zhao, Qihang Deng, Haoyu Duan, Yule Deng, Liang-Jian Department of Electrical Computer Engineering University of California Santa CruzCA United States School of Public Affairs and Administration University of Electronic Science and Technology of China Chengdu China The University of Electronic Science and Technology of China China The Kuaishou Technology Beijing China

Spiking Neural Networks (SNNs) are attracting widespread interest due to their biological plausibility, energy efficiency, and powerful spatio-temporal information representation ability. Given the critical role of attention mechanisms in enhancing neural network performance, the integration of SNNs and attention mechanisms exhibits tremendous potential to deliver energy-efficient and high-performance computing paradigms. In this paper, we present a novel Temporal-Channel Joint Attention mechanism for SNNs, referred to as TCJA-SNN. The proposed TCJA-SNN framework can effectively assess the significance of spike sequence from both spatial and temporal dimensions. More specifically, our essential technical contribution lies on: 1) We employ the squeeze operation to compress the spike stream into an average matrix. Then, we leverage two local attention mechanisms based on efficient 1-D convolutions to facilitate comprehensive feature extraction at the temporal and channel levels independently. 2) We introduce the Cross Convolutional Fusion (CCF) layer as a novel approach to model the inter-dependencies between the temporal and channel scopes. This layer effectively breaks the independence of these two dimensions and enables the interaction between features. Experimental results demonstrate that the proposed TCJA-SNN outperforms the state-of-the-art on all standard static and neuromorphic datasets, including Fashion-MNIST, CIFAR10, CIFAR100, CIFAR10-DVS, N-Caltech 101, and DVS128 Gesture. Furthermore, we effectively apply the TCJA-SNN framework to image generation tasks by leveraging a variation autoencoder. To the best of our knowledge, this study is the first instance where the SNN-attention mechanism has been employed for high-level classification and low-level generation tasks. Our implementation codes are available at https://***/ridgerchu/TCJA. © 2022, CC BY-NC-SA.

关键词： Neural networks

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False Data Injection Attacks against Low Voltage Distribution Systems

False Data Injection Attacks against Low Voltage Distributio...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Panagiotis Radoglou-Grammatikis Christos Dalamagkas Thomas Lagkas Magda Zafeiropoulou Maria Atanasova Pencho Zlatev Alexandros-Apostolos A. Boulogeorgos Vasileios Argyriou Evangelos K. Markakis Ioannis Moscholios Panagiotis Sarigiannidis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Innovation Hub of Public Power Corporation S.A. Kantza Attica Greece Department of Computer Science International Hellenic University Kavala Campus Greece Innovative Energy and Information Technologies LTD (IEIT) BIC-IZOT Sofia Bulgaria Department of Networks and Digital Media Kingston University London Kingston upon Thames Surrey UK Department of Electrical and Computer Engineering Hellenic Mediterranean University Crete Greece Department of Informatics & Telecommunications University of Peloponnese Tripolis Greece

ISBN: (纸本)9781665435413

The transformation of the conventional electrical grid into a digital ecosystem brings significant benefits, such as two-way communication between energy consumers and utilities, self-monitoring and pervasive controls. However, the advent of the smart electrical grid raises severe cybersecurity and privacy concerns, given the presence of legacy systems and communications protocols. This paper focuses on False Data Injection (FDI) cyberattacks against a low-voltage distribution system, taking full advantage of Man In The Middle (MITM) actions. The first cyberattack targets the communication between a smart meter and an Active Distribution Management System (ADMS), while the second FDI cyberattack targets the communication between a smart inverter and ADMS. In both cases, the cyberattacks affect the operation of the distribution transformer, thus resulting in devastating consequences. Moreover, this paper provides an Artificial Intelligence (AI)-based Intrusion Detection System (IDS), detecting and mitigating the above cyberattacks in a timely manner. The evaluation results demonstrate the efficiency of the proposed IDS.

关键词： Low voltage Privacy Protocols Ecosystems Intrusion detection Transformers Smart meters

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Understanding the dynamics between vaping and cannabis legalization using Twitter opinions

arXiv

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arXiv 2021年

作者： Adhikari, Shishir Uppal, Akshay Mermelstein, Robin Berger-Wolf, Tanya Zheleva, Elena Computer Science University of Illinois at Chicago United States Psychology Institute for Health Research and Policy University of Illinois at Chicago United States Computer Science and Engineering Electrical and Computer EngineeringEvolution Ecology and Organismal Biology Translational Data Analytics Institute Ohio State University United States

Cannabis legalization has been welcomed by many U.S. states but its role in escalation from tobacco e-cigarette use to cannabis vaping is unclear. Meanwhile, cannabis vaping has been associated with new lung diseases and rising adolescent use. To understand the impact of cannabis legalization on escalation, we design an observational study to estimate the causal effect of recreational cannabis legalization on the development of pro-cannabis attitude for e-cigarette users. We collect and analyze Twitter data which contains opinions about cannabis and JUUL, a very popular e-cigarette brand. We use weakly supervised learning for personal tweet filtering and classification for stance detection. We discover that recreational cannabis legalization policy has an effect on increased development of pro-cannabis attitudes for users already in favor of e-cigarettes. © 2021, CC BY-SA.

关键词： Tobacco

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The Bankable Economic Potential of Utility-Scale Solar Pv: A Novel Gis-Based Framework with Application to Indonesia

SSRN

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SSRN 2023年

作者： Langer, Jannis Ashqar, Ziad Zhou, Yilong Isabella, Olindo Praktiknjo, Aaron Quist, Jaco Kwee, Zenlin Blok, Kornelis Delft University of Technology Faculty of Technology Policy and Management Department of Engineering Systems and Services Jaffalaan 5 Delft2628 BX Netherlands Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Department of Electrical Sustainable Energy Photovoltaic Materials and Devices group Mekelweg 4 Delft2628 CD Netherlands RWTH Aachen University Energy System Economics Institute for Future Energy Consumer Needs and Behavior E.ON Energy Research Center Mathieustraße 10 Aachen52074 Germany Delft University of Technology Faculty of Technology Policy and Management Department of Values Technology and Innovation Jaffalaan 5 Delft2628 BX Netherlands

Geospatial analysis is useful to calculate and map solar photovoltaic’s (PV) economic potential. However, recent studies do not address PV’s bankable potential for practical implementation and show limitations in (1) potential and input reporting, (2) studied metrics, (3) uncertainty analysis, and (4) policy analysis. To tackle these limitations, we propose a novel framework for utility-scale PV’s bankable potential, which combines geospatial analysis and project finance. The framework involves 20 years of hourly solar electricity production, a debt sizing and cash flow analysis model, and Monte Carlo simulation. We report a conservative technical potential of 9.3 TWpeak and 12.1 PWh/year in Indonesia. Considering expected 2030 demand, PV’s bankable potential is up to 40.8 TWh/year mostly situated in East Indonesia where population and demand are low. PV’s bankability is currently limited by strict loan maturities and low electricity tariffs, and could be improved to up to 232 TWh/year (52% of 2030 demand) via a feed-in tariff of 11.5 US¢(2021)/kWh and a temporary lift of local content for solar panels. Despite their effectiveness, policymakers should be aware of possible side effects of PV policies, e.g. for local coal-based industries and rural communities with limited electricity access. © 2023, The Authors. All rights reserved.

关键词： Economic analysis

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Super-gain-boosted miller op-amp based on nested regulated cascode techniques with FoMAOLDC =24,614kV/***/µWatt 52

Super-gain-boosted miller op-amp based on nested regulated c...

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52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020

作者： Paul, Anindita Ramirez-Angulo, Jaime López-Martin, A.J. Carvajal, R.G. Sánchez, Alejandro Díaz Electrical and Computer Eng. NMSU Las Cruces United States Electrical and Computer Eng. INAOE Pueblo Mexico Electrical and Electronic Eng. Public Univ of Navarra Pamplona City Spain Electronic Engineering University of Seville Sevilla Spain Dept. of Electronics INAOE Pueblo Mexico

ISBN: (纸本)9781728133201

A simple technique to greatly enhance the DC open-loop gain of a Miller op-amp is introduced here. It is based on the utilization of nested regulated cascode amplifiers. It uses conventional Miller compensation and does not increase the supply voltage. The proposed scheme has a DC open-loop gain Figure of Merit FoMAOLDC=24,614kV/***/µWatt. It is especially appropriate for utilization in modern deep sub-micrometer CMOS technologies with low intrinsic gain. © 2020 IEEE

关键词： Differential amplifiers

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Using Arduino Iot Modules As A Low Cost Environmental Research Monitoring System

Using Arduino Iot Modules As A Low Cost Environmental Resear...

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computer Science and Emerging Technologies (CSET), International Conference on

作者： Murad Magableh Ziyad Marie Rajina R. Mohamed Mohd Hafizuddin Bin Ibrahim Julaily Aida Jusoh Rohit Kumar MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Public administration Department Faculty of Administrative sciences and Informatics Al-Istiqlal University Palestine College of Computing dan Informatics Universiti Tenaga Nasional Malaysia Department of Electrical Engineering Politeknik Kuala Terenngganu Kuala Terengganu Malaysia Faculty of Informatics and Computing UniSZA Malaysia School of Computer Science and Engineering Jain Deemed To Be University Bangalore India

Environmental monitoring plays a crucial role in understanding the impact of human activities on our ecosystems. However, the cost and complexity of traditional monitoring systems often hinder widespread deployment, especially in resource-constrained regions. This research paper explores the feasibility and effectiveness of employing Arduino Internet of Things (IoT) modules as a low-cost alternative for environmental research monitoring. The study focuses on designing and implementing an Arduino-based environmental monitoring system capable of gathering real-time data on key environmental parameters, such as temperature, humidity, air quality, and soil moisture. The hardware setup comprises Arduino microcontrollers interfaced with a range of environmental sensors and connectivity modules for data transmission. To assess the system's reliability and accuracy, a series of field experiments were conducted in diverse environmental settings, including urban areas, agricultural zones, and natural habitats. The collected data was compared against measurements from conventional monitoring equipment to validate the Arduino-based system's performance. The research demonstrates that the Arduino IoT modules can effectively monitor and transmit environmental data with a reasonable level of accuracy and stability, making them suitable for various environmental research applications. The low-cost nature of Arduino components and the open-source ecosystem facilitate customization and scalability, enabling researchers and organizations to develop tailored monitoring solutions for specific environmental contexts. Additionally, the paper discusses the challenges and limitations encountered during the deployment of Arduino-based monitoring systems, including power constraints, sensor calibration, and data processing techniques. Furthermore, it explores potential avenues for improvement and optimization to enhance the system's capabilities and data accuracy. In conclusion, this research

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