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Power, Energy and Electrical engineering (CPEEE), International Conference on

作者： Gyu Yeon Kim Danyao Peng Sangyub Lee Seon Han Choi Graduate Program in Smart Factory Ewha Womans University Seoul South Korea Department of Electronic and Electrical Engineering Ewha Womans University Seoul South Korea Energy IT Convergence Research Center Korea Electronics Technology Institute Seongnam South Korea

ISBN: (数字)9798331520090

ISBN: (纸本)9798331520106

The high penetration of renewable energy sources poses a challenge to stability of power systems, particularly microgrids, which are more vulnerable due to their small capacity and low inertia. To address instability, system components and their interactions need to be represented to more efficiently understand and optimize complex systems. Agent-based modeling enables autonomous operation and collaborative interaction, making the system adaptable to changes efficiently, making it ideal for microgrid management. In this paper, an agent-based microgrid simulation model is proposed. By independently representing microgrid components such as generators, energy storage systems (ESS), loads, environments and controllers, models can dynamically simulate the behavior of power systems, respond to system changes in real time. To validate the proposed model, experiments are conducted based on IEEE 13 bus system, and the results show that with the use of ESS, the frequency peak is reduced from over 62 Hz to around 60 Hz. In the case of sudden demand fluctuations, the frequency remains relatively stable, staying close to 60 Hz. The model demonstrates flexibility and robustness in managing renewable energy fluctuations and sudden load changes.

关键词： Renewable energy sources Adaptation models Fluctuations Microgrids Power system stability Stability analysis Real-time systems Smart grids Thermal stability Load modeling

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Reducing Errors and Powers in Lpddr for Dnn Inference: A Compression and Iecc-Based Approach

SSRN

引用

SSRN 2025年

作者： Hong, Jae-Youn Jang, Je-Woo Cho, Sung-Hyuk Kong, Youngbae Kim, Sungkyu Kang, Youngjung Ko, Jaehyung Chung, Jaeyong Yang, Joon-Sung Department of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Memory Division Samsung Electronics Hwaseong Korea Republic of Department of System Semiconductor Engineering Yonsei University Seoul Korea Republic of BK21 Graduate Program in Intelligent Semiconductor Technology Yonsei University Seoul Korea Republic of

In modern edge systems, the demand for data processing for complex DNN tasks, is rapidly increasing. To address this, various compression schemes have been proposed to enable on-device AI while meeting the strictpower and storage constraints. However, despite these advancements, the compatibility of the compression methods with edge device memory(LPDDR), has not been thoroughly investigated. LPDDR operates at low voltage and faces reliability challenges, which is particularly concerning for applications where accuracy is critical, such as ADAS or medical devices. To address these concerns, ECC engine, known as IECC, is employed within each LPDDR bank. While IECC improves reliability, it also incurs performance penalties due to Read-Modify-Write(RMW) operations and parity storage overheads. This paper introduces RELIA, a DNN weight compression scheme with three-stage protection, aimed at enabling power-efficient and reliable DNN operations in mobile environments. RELIA reduces the operation granularity of the IECC engine from SEC(136,128) to SEC(72,64), eliminating RMW overhead. Additionally, it proposes SEC-FOEC(72,64) scheme that can correct 99.97% of LPDDR errors. To mitigate the added storage overhead, compression scheme based on DNN weight characteristics is introduced. Experimental results show RELIA reduces power, cycle, energy, and storage by 17.35%, 13.2%, 32.7%, and 23.6%, respectively, while offering superior reliability. © 2025, The Authors. All rights reserved.

关键词： Biomedical equipment

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BAQET: BRAM-aware Quantization for Efficient Transformer Inference via Stream-based Architecture on an FPGA 25

BAQET: BRAM-aware Quantization for Efficient Transformer Inf...

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Proceedings of the 2025 ACM/SIGDA International Symposium on Field programmable Gate Arrays

作者： Ling-Chi Yang Chi-Jui Chen Trung Le Bo-Cheng Lai Scott Hauck Shih-Chieh Hsu Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Degree Program of College of Electrical and Computer Engineering in National Yang Ming Chiao Tung University Hsinchu Taiwan Electrical and Computer Engineering in University of Washington Seattle USA Electrical and Computer Engineering University of Washington Seattle USA Physics University of Washington Seattle USA

ISBN: (纸本)9798400713965

FPGAs are a compelling substrate for supporting machine learning inference. Tools such as High-Level Synthesis and hls4ml can shorten the development cycle for deploying ML algorithms on FPGAs, but can struggle to handle the large on-chip storage needed for many of these models. In particular the high BRAM usage found in many of these flows can cause Place & Route failures during synthesis. In this paper we propose using a Simulated-Annealing based flow to perform BRAM-aware quantization. This approach trades off inference accuracy with BRAM usage, to provide a high-quality inference engine that still meets on-chip resource constraints. We demonstrate this flow for Transformer-based machine learning algorithms, which include Flash Attention in a Stream-based Dataflow architecture. Our system imposes minimal accuracy drops, yet can reduce BRAM usage by 20%-50%, and improve power efficiency by 264%-812% compared to existing Transformer-based accelerators on FPGAs

关键词： efficient transformer

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Reachability Analysis of Nonlinear Discrete-Time Systems Using Polyhedral Relaxations and Constrained Zonotopes

arXiv

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arXiv 2025年

作者： Rego, Brenner S. Raffo, Guilherme V. Terra, Marco H. Scott, Joseph K. Department of Chemical and Biomolecular Engineering Georgia Institute of Technology 311 Ferst Dr. AtlantaGA30318 United States Department of Electrical and Computer Engineering University of São Paulo SP São Carlos13566-590 Brazil Department of Electronics Engineering the Graduate Program in Electrical Engineering Federal University of Minas Gerais MG Belo Horizonte31270-901 Brazil

This paper presents a novel algorithm for reachability analysis of nonlinear discrete-time systems. The proposed method combines constrained zonotopes (CZs) with polyhedral relaxations of factorable representations of nonlinear functions to propagate CZs through nonlinear functions, which is normally done using conservative linearization techniques. The new propagation method provides better approximations than those resulting from linearization procedures, leading to significant improvements in the computation of reachable sets in comparison to other CZ methods from the literature. Numerical examples highlight the advantages of the proposed algorithm. © 2025, CC BY-NC-ND.

关键词： Numerical methods

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ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicular CAN Bus Networks

ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicula...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Jonathan Rendel William Balte Harrison Kurunathan Hazem Ismail Ali Alexandre Dos Santos Roque Wagner Ourique De Morais Mahdi Fazeli Department for Computing and Electronics for Real-time and Embedded Systems (CERES) School of Information Technology (ITE) Halmstad University Halmstad Sweden CISTER/ISEP Polytechnic Institute of Porto Portugal Graduate Program of Electrical Engineering Federal University of Rio Grande do Sul - UFRGS Porto Alegre Brazil

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Zero-day attacks present a significant security threat to vehicular networks, exploiting vulnerabilities at both software and hardware levels within such systems that remain undiscovered. Mitigating these threats is essential to ensuring the safety and security of vehicular systems. Support Vector Machine (SVM) is a good candidate for anomaly detection of zero-day attacks within vehicular networks because it can handle highdimensional data and effectively distinguish between normal and abnormal patterns in complex and dynamic environments. A trained SVM on the normal operation data of in-vehicular network can identify flag deviations, thus making it effective in the detection of any previously unknown attack patterns, which is a common behaviour of zero-day attacks. In this paper, we introduce an anomaly detection method called “ZeroCAN” which models the behaviour of every single electronic control unit on the network with a separate SVM and a set of high-level features that capture the timing and data payload aspects of CANbus traffic. This approach achieves an anomaly detection rate of over $\mathbf{9 9 \%}$ and a false positive rate below $\mathbf{0. 0 1 \%}$ during normal operation in most cases.

关键词： Support vector machines Telecommunication traffic Feature extraction Data models Timing Security Vehicle dynamics Anomaly detection Standards Payloads

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Extremum Seeking Boundary Control for Euler-Bernoulli Beam PDEs

arXiv

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arXiv 2025年

作者： Biazetto, Paulo H.F. de Andrade, Gustavo A. Oliveira, Tiago Roux Krstic, Miroslav Post-graduate Program in Automation and Systems Engineering Federal University of Santa Catarina Florianópolis88040-370 Brazil Department of Automation and Systems Engineering Federal University of Santa Catarina Florianópolis88040-370 Brazil Department of Electronics and Telecommunication Engineering State University of Rio de Janeiro Rio de Janeiro20550-900 Brazil Department of Mechanical and Aerospace Engineering University of California San DiegoCA92093-0411 United States

This paper presents the design and analysis of an extremum seeking (ES) controller for scalar static maps in the context of infinite-dimensional dynamics governed by the 1D Euler-Bernoulli (EB) beam Partial Differential Equation (PDE). The beam is actuated at one end (using position and moment actuators). The map’s input is the displacement at the beam’s uncontrolled end, which is subject to a sliding boundary condition. Notably, ES for this class of PDEs remains unexplored in the existing literature. To compensate for PDE actuation dynamics, we employ a boundary control law via a backstepping transformation and averaging-based estimates for the gradient and Hessian of the static map to be optimized. This compensation controller leverages a Schrödinger equation representation of the EB beam and adapts existing backstepping designs to stabilize the beam. Using the semigroup and averaging theory in infinite dimensions, we prove local exponential convergence to a small neighborhood of the unknown optimal point. Finally, simulations illustrate the effectiveness of the design in optimizing the unknown static map. Copyright © 2025, The Authors. All rights reserved.

关键词： Schrodinger equation

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A simple yet effective approach for predicting disease spread using mathematically-inspired diffusion-informed neural networks

引用

Scientific Reports 2025年第1期15卷 1-14页

作者： Jeong, ByeongChang Lee, Yeon Ju Han, Cheol E. Department of Electronics and Information Engineering Korea University Sejong South Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University 2511 Sejong-ro Sejong 30019 South Korea Department of Applied Mathematics Korea University Sejong South Korea

The COVID-19 outbreak has highlighted the importance of mathematical epidemic models like the Susceptible-Infected-Recovered (SIR) model, for understanding disease spread dynamics. However, enhancing their predictive accuracy complicates parameter estimation. To address this, we proposed a novel model that integrates traditional mathematical modeling with deep learning which has shown improved predicted power across diverse fields. The proposed model includes a simple artificial neural network (ANN) for regional disease incidences, and a graph convolutional neural network (GCN) to capture spread to adjacent regions. GCNs are a recent deep learning algorithm designed to learn spatial relationship from graph-structured data. We applied the model to COVID-19 incidences in Spain to evaluate its performance. It achieved a 0.9679 correlation with the test data, outperforming previous models with fewer parameters. By leveraging the efficient training methods of deep learning, the model simplifies parameter estimation while maintaining alignment with the mathematical framework to ensure interpretability. The proposed model may allow the more robust and insightful analyses by leveraging the generalization power of deep learning and theoretical foundations of the mathematical models. © The Author(s) 2025.

关键词： Artificial neural networks COVID-19 Deep learning Graph convolutional neural network

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Air-liquid microfluidics-integrated surface-enhanced Raman spectroscopy for selective molecular adsorption and detection to achieve bacterial discrimination

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Biosensors and Bioelectronics 2025年 285卷 117576页

作者： Ku, Chi-Yao Chiang, Yu-Wei Hsu, Huai-Yuan Cheng, Ho-Wen Chen, Ko-Lun Han, Yin-Yi Wang, Juen-Kai Wang, Yuh-Lin Huang, Nien-Tsu Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Atomic and Molecular Sciences Academia Sinica Taipei Taiwan Molecular Science and Technology Taiwan International Graduate Program Academia Sinica Taipei Taiwan International Graduate Program of Molecular Science and Technology National Taiwan University Taipei Taiwan Department of Anesthesiology National Taiwan University Hospital Taipei100229 Taiwan Department of Trauma National Taiwan University Hospital Taipei100229 Taiwan Center for Condensed Matter Sciences National Taiwan University Taipei Taiwan Center for Atomic Initiative for New Materials National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

Bacterial discrimination is crucial for accurate microbiological diagnosis and timely antibiotic treatment. Surface-enhanced Raman spectroscopy (SERS) is an ideal technique due to its non-invasive, label-free molecular sensing capabilities. By analyzing bacterial supernatants, containing various purine derivatives, SERS can differentiate bacterial species based on their unique spectral distributions. However, the same bacterial species with different antibiotic resistance may secrete similar purine derivatives, differing only slightly in composition. Furthermore, each purine derivative may have a different molecular affinity to the SERS substrate, making it difficult to distinguish the exact molecular ratio. To improve SERS-based bacterial discrimination, we propose an air-liquid microfluidics-integrated SERS system (ALM-SERS) that selectively adsorbs and detects bacterial secretions. By taking features of precise microdroplet manipulation and contact area from microfluidics and features of selective molecular adsorption and fingerprints characterization from the SERS technique, we successfully perform a "sequential molecular adsorption" strategy to address the signal interference of complicated molecular mixtures in existing SERS methods. As a proof of concept, we first evaluate the molecular affinity of purine derivatives and then demonstrate the competitive analyte adsorption using adenine/cytosine and hypoxanthine/uracil sample mixtures. Finally, we tested six bacterial supernatants, including two Gram types and four strains with identical taxonomy but differing antibiotic resistance. Another six clinically isolated bacterial samples with different antibiotic resistance were also applied. Our results showed that a successful bacterial discrimination between similar species was not possible using a single SERS spectrum. In summary, the ALM-SERS system offers a powerful approach for bacterial discrimination, even when spectral differences are subtle. Beyond microb

关键词： Raman spectroscopy

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Extremum Seeking Control for Multivariable Maps under Actuator Saturation

arXiv

引用

arXiv 2025年

作者： Silva, Enzo Ferreira Tomaz Coutinho, Pedro Henrique Silva Oliveira, Tiago Roux Krstić, Miroslav Graduate Program in Electronics Engineering State University of Rio de Janeiro Brazil Department of Electronics and Telecommunication Engineering State University of Rio de Janeiro Brazil Department of Mechanical and Aerospace Engineering University of California - San Diego United States

This paper deals with the gradient-based extremum seeking control for multivariable maps under actuator saturation. By exploiting a polytopic embedding of the unknown Hessian, we derive a LMI-based synthesis condition to ensure that the origin of the average closed-loop error system is exponentially stable. Then, the convergence of the extremum seeking control system under actuator saturation to the unknown optimal point is proved by employing Lyapunov stability and averaging theories. Numerical simulations illustrate the efficacy of the proposed approach. © 2025, CC BY.

关键词： Lyapunov methods

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Pani/MoS₂ composite for an efficient catalyst for photocatalytic reduction and adsorption of chromium Cr (VI)

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Inorganic Chemistry Communications 2025年 178卷

作者： Sharma, Krishna Prasad Shin, Miyeon Awasthi, Ganesh Prasad Gwak, Nayeong Kim, Sung Hoon Yu, Changho Division of Convergence Technology Engineering Jeonbuk National University Jeonbuk State Jeonju 54896 South Korea Department of Energy Storage/Conversion Engineering (BK21 FOUR) Jeonbuk National University Jeonbuk State Jeonju 54896 South Korea Department of Electronics and Information Engineering Korea University Sejong 30019 South Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart City Convergence Technology Korea University Sejong 30019 South Korea

The development and use of a cost-effective, eco-friendly catalyst for the efficient removal of Cr (VI) from wastewater are critical. In response, we have created a Pani/MoS2 composite for adsorption, utilizing a bina... 详细信息

The development and use of a cost-effective, eco-friendly catalyst for the efficient removal of Cr (VI) from wastewater are critical. In response, we have created a Pani/MoS₂ composite for adsorption, utilizing a binary organic–inorganic material produced through a combination of in-situ oxidative polymerization and hydrothermal reactions. This study highlights the significance of using a bifunctional organic–inorganic composite for Cr (VI) removal. The hydrothermally prepared Pani/MoS₂ material serves as a bifunctional catalyst, facilitating both adsorption and photocatalytic reduction. Comprehensive characterization, photocatalysis, and adsorption analyses of the Pani/MoS₂ composite provide valuable insights into its adsorption and reduction mechanisms. The results showed a reduction efficiency of 67.03% after 80 min and a removal efficiency of 73.60% at pH 3 after 24 h. Additionally, Pani/MoS₂ exhibited an adsorption capacity of 29.44 mg/g after 24 h. The hybrid organic–inorganic composite contains multiple adsorption sites, resulting in enhanced adsorption capacity. The adsorption process involves a variety of reactions, including electrostatic interactions, complexation reactions, physical and chemical adsorption, reduction reactions, and photocatalytic reduction, which incorporates simultaneous oxidation and reduction through electron-hole pairs. A study on the stability and the effect of radical scavengers demonstrated the comparable stability of Pani/MoS₂ and the role of radical interference during Cr (VI) adsorption. © 2025 Elsevier B.V.

关键词： Adsorption Composite Molybdenum disulfide Photocatalytic reduction Polyaniline

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