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Adaptive Power Flow Approximations With Second-Order Sensitivity Insights

IEEE Transactions on Power Systems 2025年第3期40卷 2648-2660页

作者： Buason, Paprapee Misra, Sidhant Watson, Jean-Paul Molzahn, Daniel K. Los Alamos National Laboratory Los AlamosNM87545 United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Lawrence Livermore National Laboratory LivermoreCA94550 United States

The power flow equations are fundamental to power system planning, analysis, and control. However, the inherent non-linearity and non-convexity of these equations present formidable obstacles in problem-solving processes. To mitigate these challenges, recent research has proposed adaptive power flow linearizations that aim to achieve accuracy over wide operating ranges. The accuracy of these approximations inherently depends on the curvature of the power flow equations within these ranges, which necessitates considering second-order sensitivities. In this paper, we leverage second-order sensitivities to both analyze and improve power flow approximations. We evaluate the curvature across broad operational ranges and subsequently utilize this information to inform the computation of various sample-based power flow approximation techniques. Additionally, we leverage second-order sensitivities to guide the development of rational approximations that yield linear constraints in optimization problems. This approach is extended to enhance accuracy beyond the limitations of linear functions across varied operational scenarios. © 1969-2012 IEEE.

关键词： Load flow optimization

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Generalized Critical Observability of Labeled Petri Nets Under Nondeterministic Observations

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14633-14645页

作者： Zhang, Yuling Hadjicostis, Christoforos N. Li, Zhiwu Xidian University School of Electro-Mechanical Engineering Xi’an710071 China University of Cyprus Department of Electrical and Computer Engineering Nicosia1678 Cyprus Macau University of Science and Technology Institute of Systems Engineering China

This paper addresses the property of critical observability in labeled Petri nets. A system is deemed critically observable if its state estimate (formed by observing the system output) can be determined to belong to a set of either critical markings or non-critical markings. More generally, for a critically observable system, there may be multiple types of critical markings and the operator may desire to know the type to which the current state estimate belongs. We propose a generalized version of critical observability, namely generalized critical observability, and provide two necessary and sufficient conditions to check it based on basis markings and integer linear programs, effectively circumventing the need for an exhaustive enumeration of all reachable markings of the net. In addition, we investigate (generalized) critical observability of Petri nets under nondeterministic observations, which may arise due to the loss of transmission signals or due to interference with sensor measurement outputs. To handle this, an augmented system is constructed from the original Petri net by adding new transitions that capture the effect of transitions associated with nondeterministic labels. Thus, we transform the verification of (generalized) critical observability of a Petri net under nondeterministic observations to that of the augmented model. Two real-world examples illustrate the applicability of the presented notion and methodology. © 2004-2012 IEEE.

关键词： Petri nets

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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U-Net-Based Optical Camera Communication in Intensity-Constrained Scenarios

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Journal of Lightwave technology 2025年第10期43卷 4712-4721页

作者： Ali, Md Osman Hasan, Moh Khalid Wang, Yitong Shehata, Mohamed Fu, Jing Luong, Stanley Wang, Ke RMIT University School of Engineering MelbourneVIC3000 Australia Noakhali Science and Technology University Department of Electrical and Electronic Engineering Noakhali3814 Bangladesh Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States RMIT University School of Engineering Ho Chi Minh City700000 Viet Nam

Optical Camera Communication (OCC), which utilizes the unlicensed visible light spectrum, is regarded as a key enabler for future sixth-generation wireless technologies. In OCC, the communication performance, specifically the maximum achievable distance and bit-error rate (BER), relies on the pixel values captured by the image sensor. However, spatial and temporal intensity variations across pixels in rolling shutter-based OCC systems present a significant challenge for data decoding. This study investigates a low-illumination indoor environment where overlapping dark and bright intensity histograms are heavily impacted by noise, further complicating the data extraction process. To address this issue, this paper proposes UOCC, a U-Net-based OCC architecture for image segmentation, designed to extract data under extremely low-light conditions in intensity-constrained scenarios by framing data pattern detection as pixel classification tasks. We further incorporate bit-plane slicing-based pre-processing and test-time augmentation-based post-processing to enhance the reliability of the model. Experimental results show that the proposed UOCC method achieves an excellent BER of 5 × 10-4 and a 97.20% intersection-over-union score after 50 epochs, significantly outperforming Otsu thresholding (10-1) and both second- and third-order polynomial fitting methods (3 × 10-2) in this context. This performance is achieved by reducing noise through the exclusion of the lowest-order bit-plane of test images, which significantly enhances system reliability. Furthermore, the proposed method presents substantial potential for future applications, including non-line-of-sight and underwater communication environments. © 1983-2012 IEEE.

关键词： Image segmentation

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Reviewing metal supported solid oxide fuel cells for efficient electricity generation with biofuels for mobility

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Journal of Energy Chemistry 2025年第04期103卷 106-153页

作者： Fábio C.Antunes Jo?o P.J.de Oliveira Ricardo S.de Abreu Thiago Dias Bruno B.N.S.Brand?o Josué M.Gon?alves Josimar Ribeiro Julian Hunt Hudson Zanin Gustavo Doubek School of Electrical and Computer Engineering University of Campinas School of Food Engineering University of Campinas School of Chemical Engineering University of Campinas Mackenzie Institute for Research in Graphene and Nanotechnologies (Mack Graphe) Mackenzie Presbyterian Institute Electrochemistry Research and Development Laboratory (LPDE) Department of Chemistry Center of Exact Sciences Federal University of Espírito Santo Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology

Metal-Supported Solid Oxide Fuel Cells（MS-SOFCs） hold significant potential for driving the energy *** electrochemical devices represent the most advanced generation of Solid Oxide Fuel Cell（SOFCs） and can pave the way for mass production and wider adoption than Proton Exchange Membrane Fuel Cells（PEMFCs） due to their fuel flexibility,higher power density and the absence of noble metals in the fabrication *** review examines the state-of-the-art of SOFCs and MS-SOFCs,presenting perspectives and research directions for these key technological devices,highlighting novel materials,techniques,architectures,devices,and degradation mechanisms to address current challenges and future *** such as infiltration/impregnation,ex-solution catalyst synthesis,and the use of a pre-catalytic reformer layer are discussed as their impact on efficiency and prolonged *** concepts are also described and connected with well-dispersed nano particles,hindrance of coarsening,and an increased number of Triple Phase Boundaries（TPBs）.This review also describes the synergistic use of reformers with MS-SOFCs to compose solutions in energy generation from readily available ***,the End-of-Life（EoL）,recycling,and life-cycle assessments（LCAs） of the Fuel Cell Hybrid Electric Vehicles（FCHEVs） were *** comparing Fuel Cell Electric Vehicles（FCEVs）equipped with（PEMFCs） and FCHEVs equipped with MS-SOFCs,both powered with hydrogen（H2） generated by different routes were *** review aims to provide valuable insights into these key technological devices,emphasizing the importance of robust research and development to enhance performance and lifespan while reducing costs and environmental impact.

关键词： Biofuel Fuel cell Hybrid electric vehicle Metal-supported solid oxide fuel cell Powertrain systems Reform

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Construction of an Array of Biosensors Using Density Evolution for MicroRNA Monitoring

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IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2025年

作者： Sethuraman, Muralikrishnna G. McSweeney, Megan A. Styczynski, Mark P. Fekri, Faramarz Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta United States Georgia Institute of Technology School of Chemical and Biomolecular Engineering Atlanta United States

Monitoring the levels of biomarkers for diagnostic applications has significant potential for impacts on patient care, but the measurement of all relevant biomarkers for a given set of conditions is often too expensive or unwieldy to be feasible at scale. Here, we propose a novel computational method for detecting changes in the levels of multiple target molecules from a complex sample via a small, cost-effective group of biosensors. We use the framework of density evolution (DE), a technique commonly used in the design of linear error-correcting codes for transmission over noisy channels, to develop an approach for localizing changes to a small subset of input signals based on a few simple output signals. As a biologically relevant testbed, we sought to detect the changes in the levels of multiple different microRNAs (miRNAs), which are nucleic acid molecules that are being increasingly studied and used as biomarkers. We accomplished this via the use of a class of molecules called »toehold switches» to create biosensors each capable of detecting multiple different miRNA sequences via a single output, with an overlap in sensitivity patterns between the different biosensors. A small number of these sensors were then used for inference of miRNA profiles. We demonstrate the potential utility of our approach with real data. Experimental results indicate the promising outcomes regarding the effectiveness of our method in detecting changes in miRNA concentrations. © 2025 IEEE.

关键词： Biosensors

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LLMaaS: Serving Large-Language Models on Trusted Serverless Computing Platforms

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 405-415页

作者： Cai, Zinuo Ma, Rongbo Fu, Yicheng Zhang, Weishan Ma, Ruhui Guan, Haibing Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Qingdao Institute of Software College of Computer Science and Technology Qingdao266555 China

In recent years, the emergence of large-language models (LLMs) has profoundly transformed our production and lifestyle. These models have shown tremendous potential in fields, such as natural language processing, speech recognition, and recommendation systems, and are increasingly playing crucial roles in applications such as human-computer interaction and intelligent customer service. Efficient inference solutions for LLMs in data centers have been extensively researched, with a focus on meeting users' quality of service requirements. In this article, we focus on two additional requirements that responsible LLM inference should meet under QoS conditions: security throughout the model execution process and low maintenance requirements for the inference system. Therefore, we propose LLMaaS, a trusted model inference platform based on a serverless computing platform aimed at providing inference as a service for LLMs. First, we design a trusted serverless computing platform based on software guard extension (SGX), which includes distributed identity verification and SGX device plugins to ensure the security and trustworthiness of the inference process. Additionally, to reduce the maintenance requirements of the system, we enhance the SGX-based deep learning computing framework, including replacing PyTorch and using a greedy algorithm for graph partitioning. We conduct tests on four typical large models, and the experimental results demonstrate that, with minimal overhead and user code modifications, we can ensure the security of model execution. © 2020 IEEE.

关键词： Quality of service

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Machine Learning-Assisted Modeling of AC Stress-Induced Bias Temperature Instability in Oxide Channel Transistors for 2T Gain Cell eDRAM

Machine Learning-Assisted Modeling of AC Stress-Induced Bias...

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Annual International Symposium on Reliability Physics

作者： Jungyoun Kwak Chengyang Zhang Gyujun Jeong Omkar Phadke Sharadindu G. Kirtania Junmo Lee Suman Datta Shimeng Yu School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia USA

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

Physics-based compact modeling requires substantial work to formulate equations and fine-tune coefficients. In this work, we propose a machine learning (ML)-assisted compact model to capture alternating current (AC)-induced positive bias temperature instability (PBTI) in top-gate indium tungsten oxide (IWO) transistors, demonstrating high accuracy (R2 = 0.94, _ 0.98) across various operating conditions. Aging simulations of 2T gain cell (GC) embedded DRAM (eDRAM) reveal that the write transistor is more vulnerable to AC stress, leading to data hold failures as the number of AC cycles increases. This study highlights the advantages of ML-assisted compact modeling in improving reliability predictions for emerging semiconductor devices.

关键词： Semiconductor device modeling Solid modeling Random access memory Machine learning Predictive models Mathematical models Data models Transistors Integrated circuit reliability Stress

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A Novel Contingency Analysis-Driven Deep Learning Framework for Evaluation of a Primary Power System Security Metric

A Novel Contingency Analysis-Driven Deep Learning Framework ...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Adam King Santiago Grijalva School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Continuous operation of power systems is critical to modern society, even during unexpected or emergency situations. One method to evaluate the security of power systems is contingency analysis, a computationally-intensive process which considers how a set of possible scenarios would affect the electrical grid. This paper presents a novel security evaluation method which enables a topology- and parameter-free estimation of a system-level security metric, the system aggregate megawatt contingency overload (SysAMWCO), by leveraging a novel deep learning-based approach. Using the results of conventional contingency analysis for a simulated power system, a deep neural network (DNN) is trained to predict the SysAMWCO value using only bus loads as input. Experimentation across different neural network parameters reveals a complex relationship between these parameters and the accuracy of the predicted SysAMWCO value. While each DNN is trained on data generated using conventional contingency analysis methods, fully-trained models achieve R-squared scores in excess of 0.98 without needing electrical grid topology or power device parameter information as input. Given these results, this framework shows promise for potential real-world applications.

关键词： Measurement Network topology Contingency management Power system control Estimation Artificial neural networks Power systems Topology Power system security Load modeling

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Optimally Managing the Impacts of Convergence Tolerance for Distributed Optimal Power Flow

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IEEE Transactions on Power Systems 2025年

作者： Harris, Rachel Alkhraijah, Mohannad Molzahn, Daniel K. Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States

The future power grid may rely on distributed optimization to determine the set-points for huge numbers of distributed energy resources. There has been significant work on applying distributed algorithms to optimal power flow (OPF) problems, which require separate controllers to agree on shared boundary variable values. Although controllers find feasible solutions to subproblems which optimize power flow within their region of the network, they must reach consensus on shared variables at tie-lines between regions to find a system-wide feasible solution. While running the distributed algorithm, controllers iteratively compute local subproblem solutions and communicate shared variable information until the mismatches between shared variables fall below a user-selected primal convergence tolerance. Looser tolerances for the mismatches in these shared variables generally yield faster convergence at the expense of exacerbating constraint violations, but there is little quantitative understanding of how the convergence tolerance affects solution quality. To address this gap, we first quantify how convergence tolerance impacts constraint violations when the distributed OPF solution is applied to the power system. Using insights from this analysis, we then develop a bound tightening algorithm which guarantees that operating points from distributed OPF algorithms will not result in violations despite the possibility of shared variable mismatches within the convergence tolerance. We also explore how bounding the cumulative shared variable mismatches can prevent unnecessary conservativeness in the bound tightening. While ensuring feasibility, the proposed approach enables control of the trade-off between computational speed, which improves with looser convergence tolerance, and distributed OPF solution cost, which degrades with looser convergence tolerance due to tightened constraints. © 1969-2012 IEEE.

关键词： Load flow optimization

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