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2021 North American Power Symposium, NAPS 2021

作者： Chen, Yang Amasyali, Kadir Park, Byungkwon Olama, Mohammed M. Telsang, Bhagyashri Djouadi, Seddik M. Oak Ridge National Laboratory Environmental Sciences Division Oak Ridge United States Oak Ridge National Laboratory Computational Sciences and Engineering Division Oak Ridge United States University of Tennessee Department of Electrical Engineering and Computer Science Knoxville United States

ISBN: (纸本)9781665420815

In recent years, demand response (DR) has been explored as a fundamental strategy for demand-side management due to its advantages in mediating intermittency of renewable energy generation, load shifting, etc. To engage customers in DR programs, several deterministic price-based DR strategies have been developed and implemented. However, the stochastic weather conditions and occupants' consumption behaviors often make the deterministic solution less robust to uncertainties. In this paper, with the consideration of the uncertainties, a stochastic Stackelberg game is proposed to model the price-demand negotiation between a distributed system operator and load aggregators, where the virtual battery constraints are extracted from the building thermostatically controlled loads (TCLs)' characteristics to guarantee comfortable TCLs' levels. Following the negotiation, a priority-based control method is used to allocate the optimal aggregated power DR profile at the building level and track the power signal. Several groups of experiments have demonstrated the effectiveness and robustness of the stochastic solutions. © 2021 IEEE.

关键词： Stochastic systems

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Secure analysis over generalized-K channels

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Science China(Information Sciences) 2020年第4期63卷 242-244页

作者： Luyao ZHANG Hui ZHAO Gaofeng PAN Liang YANG Jiawei CHEN School of Mathematics and Statistics Southwest University Computer Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology School of Information and Electronics Beijing Institute of Technology College of Computer Science and Electronic Engineering Hunan University

Dear editor,The secrecy outage probability (SOP) is used to estimate the secrecy outage performance when the transmitter has no state information about potential wiretap channels [1], i.e., silent eavesdropping. Most previous work, such as [2, 3], has focused on the SOP defined in [1], which we call the conventional SOP in this study, where both un-

关键词： SOP Secure analysis over generalized-K channels

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Real-time Accident Anticipation for Autonomous Driving Through Monocular Depth-Enhanced 3D Modeling

arXiv

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arXiv 2024年

作者： Liao, Haicheng Li, Yongkang Wang, Chengyue Lai, Songning Li, Zhenning Bian, Zilin Lee, Jaeyoung Cui, Zhiyong Zhang, Guohui Xu, Chengzhong State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China Department of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China State Key Laboratory of Internet of Things for Smart City University of Macau China State Key Laboratory of Internet of Things for Smart City Departments of Civil and Environmental Engineering and Computer and Information Science University of Macau China Guangzhou China Transportation Planning and Engineering The Department of Civil and Urban Engineering New York University New York United States School of Traffic and Transportation Engineering Central South University Changsha China School of Transportation Science and Engineering Beihang University Beijing China Department of Civil and Environmental Engineering University of Hawaii HonoluluHI United States

The primary goal of traffic accident anticipation is to foresee potential accidents in real time using dashcam videos, a task that is pivotal for enhancing the safety and reliability of autonomous driving technologies. In this study, we introduce an innovative framework, AccNet, which significantly advances the prediction capabilities beyond the current state-of-the-art 2D-based methods by incorporating monocular depth cues for sophisticated 3D scene modeling. Addressing the prevalent challenge of skewed data distribution in traffic accident datasets, we propose the Binary Adaptive Loss for Early Anticipation (BA-LEA). This novel loss function, together with a multi-task learning strategy, shifts the focus of the predictive model towards the critical moments preceding an accident. We rigorously evaluate the performance of our framework on three benchmark datasets—Dashcam Accident Dataset (DAD), Car Crash Dataset (CCD), and AnAn Accident Detection (A3D), and DADA-2000 Dataset—demonstrating its superior predictive accuracy through key metrics such as Average Precision (AP) and mean Time-To-Accident (mTTA). Copyright © 2024, The Authors. All rights reserved.

关键词： Multi-task learning

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Framework for Assessing Collection-Based Reactive Air Treatment Systems

SSRN

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SSRN 2023年

作者： Anandan, Sudharshan Fix, Andrew J. Freeman, Andrew Miller, Lance Scheg, Devon Morgan, Xavier Park, Jae Hong Horton, William Travis Blatchley, Ernest R. Warsinger, David Martin School of Mechanical Engineering Purdue University United States Elmore Family School of Electrical and Computer Engineering Purdue University United States Davidson School of Chemical Engineering Purdue University United States School of Health Sciences Purdue University United States Lyles School of Civil Engineering Purdue University United States Division of Environmental & Ecological Engineering Purdue University United States Electrical and Computer Engineering University of Illinois Urbana-Champaign United States

The COVID-19 pandemic demonstrated the need for reactive air treatment systems, but a lack of understanding on the impact of different reactor geometries has stymied adoption of these technologies. This study uses a non-dimensional framework to examine the applicability of photocatalytic oxidation (PCO) to reduce bioaerosol concentration in indoor settings, particularly in heating, ventilation, and air conditioning (HVAC) systems. Three non-dimensional parameters: aspect ratio, collection efficiency, and dimensionless photon flux are used to evaluate the performance of two PCO reactor geometries: 1) honeycomb (HC) and 2) parallel plate (PP). Collection efficiencies of the HC for bioaerosols (0.01-10 µm) were modeled by varying aspect ratios from 25-100, air velocities from 0.5-2 m/s, and charge numbers from 10-10000. The collection efficiencies and the photon fluxes of HC and PP were compared using prior literature studies. The modeling results show that larger bioaerosols (5-10 µm) have a higher collection efficiency than other aerosols for low velocities and longer channels. Additionally, the air velocity impacted the collection efficiency of only larger bioaerosols. Longer aspect ratios are needed than those found in most commercial HC products, since the effects of gravitational settling and diffusion are inadequate for bioaerosol collection. Collection efficiency increased 2 to 5 times for 0.01-1 µm aerosols for charge numbers on the order of 1000;however, this increase is insufficient to enhance the performance of PCO systems. PP requires longer channel lengths than HC to achieve the same collection efficiencies. However, the PP configuration has superior radiation distribution characteristics than HC. © 2023, The Authors. All rights reserved.

关键词： Aspect ratio

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Determination Of Bitumen Concentration by Analyzing Laser-Induced Breakdown Spectra (LIBS) Using Partial Least Squared Regression (PLSR) Method

Determination Of Bitumen Concentration by Analyzing Laser-In...

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IEEE International Conference on Plasma Science (ICOPS)

作者： S. Mohajan F. Mehravaran Y. Huang F. Keserwan L. Droog N. F. Beier A. Bais R. Fedosejevs M. G. El-Din A. E Hussein Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada Department of Civil and Environmental Engineering University of Alberta Edmonton Alberta Canada Electronic Systems Engineering University of Regina Regina Saskatchewan Canada

ISBN: (纸本)9781665479264

The determination of bitumen content in oil sands is essential for the characterization of tailings and enhancement of oil extraction efficiency. However, traditional bitumen content measurement methods, such as Dean-Stark Extraction, necessitate extensive lab-based sample preparation, increasing processing time. In-field bitumen content evaluation would substantially assist decision-making in order to run more effective mining and recovery operations. Here, we demonstrate the rapid determination of bitumen concentration using laser-induced breakdown spectra (LIBS) and four different normalization approaches in partial least squares regression (PLSR) analysis of wet and dry tailing sands consisting of 0.5-12% bitumen. The results reveal that wet bitumen samples require fewer PLSR components to explain 90% of the variance in LIBS spectra than dry bitumen samples, demonstrating the suitability of LIBS for analyzing samples with little preparation. A good correlation (R2 > 0.96) between LIBS spectra and bitumen content for both wet and dry samples was obtained, however the prediction standard deviation (SD < 4%) was improved for wet samples. The chemical composition of bitumen was also determined by computing the percentage abundance of each atomic element from the PLSR coefficient of the LIBS spectra.

关键词： Asphalt Correlation Electric breakdown Oils Conferences Decision making Hydrocarbons

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Bacteria cells estimation in wastewater treatment plants using data-driven models ⁎

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IFAC-PapersOnLine 2024年第14期58卷 718-723页

作者： Fahad Aljehani Ibrahima N’Doye Pei-Ying Hong Mohammad K. Monjed Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Biology Faculty of Science Umm Al-Qura University Makkah 21961 Saudi Arabia National Institute for Research in Digital Science and Technology (INRIA) Saclay France

Estimating or predicting the concentrations of bacteria cells is crucial for achieving better control of the operation of wastewater treatment plants. However, measuring the bacterial concentration along the influent wastewater stage to the treated effluent process is challenging as it involves lab access and trained personnel. Additionally, wastewater plants are generally nonlinear systems involving time-varying physical and biological characteristics, increasing the difficulty in estimating the bacterial concentration from a model-based approach. This paper proposes data-driven models based on four machine-learning models to estimate the bacterial cell density with a limited dataset in a wastewater treatment plant. The performance results demonstrate that the machine-learning models (i.e., K-Nearest Neighbour (kNN), Random Forest (RF), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGB)) have the potential to estimate accurately the bacterial concentration. RF displays better bacteria estimation in the influent by 10.7% compared to GBR and 7.4% compared to XGB and kNN. Whereas for the effluent, XGB improved the estimation by 12.8%, 2.4%, 14.6% compared to GBR, RF, and kNN, respectively. Also, results show that conductivity as a single feature is the most significant parameter affecting the bacterial cell estimation in the influent stage for the four machine learning algorithms. Similarly, the chemical oxygen demand (COD) and turbidity have pronounced effects in the effluent stage. These results reveal potential signs of designing a universal data-driven model-based approach applicable for bacteria estimation at influent and effluent based on the minimum feature combinations (conductivity, COD, and turbidity).

关键词： Wastewater treatment plant data-driven models bacterial estimation models machine learning algorithms

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Pathfinders in the Sky: Formal Decision-Making Models for Collaborative Air Traffic Control in Convective Weather

arXiv

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arXiv 2025年

作者： Choi, Jimin Anand, Kartikeya Idris, Husni R. Tran, Huy T. Li, Max Z. Department of Aerospace Engineering University of Michigan Ann ArborMI48109 United States Department of Electrical and Computer Engineering University of Michigan Ann ArborMI48109 United States Aviation Systems Division NASA Ames Research Center Moffett FieldCA94035 United States Grainger College of Engineering Department of Aerospace Engineering University of Illinois Urbana-Champaign ChampaignIL61820 United States Departments of Aerospace Engineering Civil and Environmental Engineering Industrial and Operations Engineering University of Michigan Ann ArborMI48109 United States

Air traffic can be significantly disrupted by weather. Pathfinder operations involve assigning a designated aircraft to assess whether airspace that was previously impacted by weather can be safely traversed through. Despite relatively routine use in air traffic control, there is little research on the underlying multi-agent decision-making problem. We seek to address this gap herein by formulating decision models to capture the operational dynamics and implications of pathfinders. Specifically, we construct a Markov chain to represent the stochastic transitions between key operational states (e.g., pathfinder selection). We then analyze its steady-state behavior to understand long-term system dynamics. We also propose models to characterize flight-specific acceptance behaviors (based on utility trade-offs) and pathfinder selection strategies (based on sequential offer allocations). We then conduct a worst-case scenario analysis that highlights risks from collective rejection and explores how selfless behavior and uncertainty affect system resilience. Empirical analysis of data from the US Federal Aviation Administration demonstrates the real-world significance of pathfinder operations and informs future model calibration. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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High-Rate Structural Health Monitoring and Prognostics: An Overview 39th

High-Rate Structural Health Monitoring and Prognostics: An O...

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39th IMAC, A Conference and Exposition on Structural Dynamics, 2021

作者： Dodson, Jacob Downey, Austin Laflamme, Simon Todd, Michael D. Moura, Adriane G. Wang, Yang Mao, Zhu Avitabile, Peter Blasch, Erik Air Force Research Laboratory Eglin AFB ValparaísoFL United States Department of Mechanical Engineering University of South Carolina ColumbiaSC United States Department of Civil and Environmental Engineering University of South Carolina ColumbiaSC United States Department of Civil Construction and Environmental Engineering Iowa State University AmesIA United States Department of Structural Engineering University of California San Diego La JollaCA United States Emerald Coast Division Applied Research Associates NicevilleFL United States School of Civil and Environmental Engineering School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Structural Dynamics and Acoustic Systems Laboratory University of Massachusetts Lowell LowellMA United States Air Force Office of Scientific Research ArlingtonVA United States

ISBN: (纸本)9783030760038

Structural health monitoring (SHM) includes both static and highly dynamic engineering systems. With the advent of real-time sensing, edge-computing, and high-bandwidth computer memory, there is an ability to enable high-rate SHM (HR-SHM). The paper defines the technical area of high-rate structural health monitoring and prognostics and presents the HR-SHM technical grand challenges including multi-timescales of the problem, adequate sensor network and response, real-time assessment, and decision-making with quantified uncertainty and risk. Key issues to address in such challenges include the time duration of the event, timescales of the physics, multiple sources of uncertainty, as well as limited spatiotemporal constraints for hardware execution. The paper defines the high-rate timescale as 1 ms on the integrated paradigm including data acquisition, assessment execution, and decision-making. The spatial issues include the resolution of the area monitored, the communication distance, and the number of edge sensors. The temporal issue includes the sensor type (e.g., THz) as well as multiple sources of uncertainty. These constraints must be coupled to allow for high-rate implementation that is robust, adaptable, and beneficial to the missions of interest. To address the grand challenge, we propose physics-informed real-time fusion (PIRF) of high-speed dynamic data. Technologies such as machine learning and edge-computing can be further harnessed to enable structural and functional prognostics for high-rate dynamic systems. Quantification of uncertainty, both aleatory and epistemic, is necessary for real-time state estimation to be connected with the confidences to integrate risks into the decision-making. © 2022, The Society for Experimental Mechanics, Inc.

关键词： Structural health monitoring

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Multi-agent Federated Reinforcement Learning for Resource Allocation in UAV-enabled Internet of Medical Things Networks

TechRxiv

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TechRxiv 2023年

作者： Abegaz, Mohammed Seid Erbad, Aiman Nahom Abishu, Hayla Albaseer, Abdullatif Abdallah, Mohamed Guizani, Mohsen The Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Abu Dhabi United Arab Emirates

In the 5G/B5G network paradigms, intelligent medical devices known as the Internet of Medical Things (IoMT) have been used in the healthcare industry to monitor remote users’ health status, such as elderly monitoring, injuries, stress, and patients with chronic diseases. Since IoMT devices have limited resources, mobile edge computing (MEC) has been deployed in 5G networks to enable them to offload their tasks to the nearest computational servers for processing. However, when IoMTs are far from network coverage or the computational servers at the terrestrial MEC are overloaded/emergencies occur, these devices cannot access computing services, potentially risking the lives of patients. In this context, unmanned aerial vehicles (UAVs) are considered a prominent aerial connectivity solution for healthcare systems. In this paper, we propose a multiagent federated reinforcement learning (MAFRL)-based resource allocation framework for a multi-UAV-enabled healthcare system. We formulate the computation offloading and resource allocation problems as a Markov decision process game in federated learning with multiple participants. Then, we propose a MAFRL algorithm to solve the formulated problem, minimize latency and energy consumption, and ensure the quality of service. Finally, extensive simulation results on a real-world heartbeat dataset prove that the proposed MAFRL algorithm significantly minimizes the cost, preserves privacy, and improves accuracy compared to the baseline learning algorithms. © 2023, CC BY.

关键词： Reinforcement learning

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LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarios

arXiv

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arXiv 2025年

作者： Peng, Mingxing Xie, Yuting Guo, Xusen Yao, Ruoyu Yang, Hai Ma, Jun Guangzhou511453 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China Guangzhou511453 China Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong

Ensuring the safety and robustness of autonomous driving systems necessitates a comprehensive evaluation in safety-critical scenarios. However, these safety-critical scenarios are rare and difficult to collect from real-world driving data, posing significant challenges to effectively assessing the performance of autonomous vehicles. Typical existing methods often suffer from limited controllability and lack user-friendliness, as extensive expert knowledge is essentially required. To address these challenges, we propose LD-Scene, a novel framework that integrates Large Language Models (LLMs) with Latent Diffusion Models (LDMs) for user-controllable adversarial scenario generation through natural language. Our approach comprises an LDM that captures realistic driving trajectory distributions and an LLM-based guidance module that translates user queries into adversarial loss functions, facilitating the generation of scenarios aligned with user queries. The guidance module integrates an LLM-based Chain-of-Thought (CoT) code generator and an LLM-based code debugger, enhancing the controllability and robustness in generating guidance functions. Extensive experiments conducted on the nuScenes dataset demonstrate that LD-Scene achieves state-of-the-art performance in generating realistic, diverse, and effective adversarial scenarios. Furthermore, our framework provides fine-grained control over adversarial behaviors, thereby facilitating more effective testing tailored to specific driving scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Controllability

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