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14th International Conference on Information Optics and Photonics, CIOP 2023

作者： Guan, Gangyun Liu, Anqi Pi, Mingquan Zheng, Chuantao Zheng, Kaiyuan Zhu, Bingjie Wu, Xuyang Yang, Yue Zheng, Jie Wang, Yiding Tittel, Frank K. State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University 2699 Qianjin Street Changchun China Photonics Research Center The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen China Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States

ISBN: (纸本)9781510671744

Here, we present an innovative integrated near-infrared Kerr comb sensor chip designed for the simultaneous detection of multi-gas. This sensor is comprised of a microcavity featuring a feedback arm for backward coupling and a compact air-slotted sensing element. The microcavity used to form the optical comb has a symmetric dual-spiral structure with an ultra-high resolution of the absorbance spectrum and a roundtrip length of roughly 1.46 μm, which corresponds to a low repetition frequency of about 9.82 GHz. Through the application of horizontal single-slotted structured silicon nitride (Si3N4) waveguides, optimized dispersion engineering of the microcavity can be achieved, resulting in spectra with an octave-spanning bandwidth. The sensing element employs a large-length waveguide, instead of a conventional discrete gas absorption cell, which brings high compactness. By optimizing the degree of thermally tuned interference and pumping parameters of the microcavity structure, a coupling-free light source in the form of a multi-soliton comb with a pump-comb conversion efficiency of over 50% can be achieved. The sensor performance is accurately evaluated through theoretical simulations of noise-containing absorption spectra of multiple gases, confirming its applicability. © 2023 SPIE.

关键词： Light absorption

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CPS, Sustainability and Efficiency in an Aquaponic Greenhouse

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2022年 20卷 210-215页

作者： Galvão, J. Santos, P. Aires, L. Neves, F. Department of Electrical Engineering/ESTG Polytechnic of Leiria Leiria2411-901 Portugal LSRE-LCM-Laboratory of Separation and Reaction Engineering Laboratory of Catalysis and Materials Escola Superior de Tecnologia e Gestão Instituto Politécnico de Leiria Leiria2411-901 Portugal ALiCE-Associate Laboratory in Chemical Engineering Faculty of Engineering University of Porto Rua Dr. Roberto Frias Porto4200-465 Portugal R&D Unit Institute for Systems Engineering and Computers/INESC Coimbra Portugal Department of Computer Science Polytechnic of Leiria Leiria2411-901 Portugal

Nowadays the humanity needs to produce food in greater quantity and quality, in order to satisfy citizens and markets, supported by advanced technologies such as cyber-physical system (CPS) or intelligent systems where software and electronic devices and receivers of physical variables are mechanisms that control the management of multiple actions inherent to plant growth. Electronic devices and receivers of physical variables are mechanisms that involve a network of IoT components and control multiple actions inherent to plant growth in agricultural greenhouses, which in short, are intensive food production systems. In this context, this paper presents a case study that uses a monitoring and management system that considers environmental parameters measured inside a greenhouse, allowing the manager to assess the working status of an aquaponic systems, and detect critical conditions that require quick decisions in real time. It is intended to develop technology that can be used in greenhouses, close to consumer centres or cities, to mitigate the impact of transport, lowering CO2 emissions resulting from the distribution channels between the producers and consumers. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Energy efficiency

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Modeling Multi-Threaded Aggregated I/O for Asynchronous Checkpointing on HPC Systems

Modeling Multi-Threaded Aggregated I/O for Asynchronous Chec...

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International Symposium on Parallel and Distributed Computing

作者： Mikaila J. Gossman Bogdan Nicolae Jon C. Calhoun Holcombe Department of Electrical and Computing Engineering Clemson University Clemson SC Mathematics and Computer Science Division Argonne National Laboratory Lemont IL

HPC systems encompass more components with each new generation. As a result, the process of interacting with stable storage systems like parallel file systems (PFS) becomes increasingly difficult. Larger systems often result in more frequent failures, increasing the need and frequency to incorporate fault-tolerant mechanisms. One example is checkpoint-restart (C/R), where applications or systems save their data to non-volatile storage devices, such as a PFS. On failure, the system or application is restored to a saved state and computation continues. Today, asynchronous C/R is gaining traction for its ability to checkpoint data to permanent storage concurrently with the application. However, asynchronous C/R brings about many new challenges. For starters, asynchronous C/R introduces complex resource contention between the application and the C/R implementation. Additionally, some implementations adopt file-per-process writing strategies, which overwhelm PFS’ at high core counts. In this work, we explore how multi-threaded POSIX I/O impacts aggregated throughput. To this extent we characterize the influence of different I/O parameters, such as the number of writer threads and how they access storage devices, has on aggregated I/O. We use the information gathered in this study to identify best practices when performing aggregated I/O as a first step in designing an efficient I/O aggregation scheme for asynchronous C/R.

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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2023 China Automation Congress, CAC 2023

作者： Du, Sheng Ma, Xian Li, Xiang Wu, Min Cao, Weihua Pedrycz, Witold School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

ISBN: (纸本)9798350303759

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes. © 2023 IEEE.

关键词： Time series

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Reviewing Power System Stabilizer (PSS) Parameters Optimization Using Evolutionary Meta-Heuristic Algorithms for Power System Stability 21

Reviewing Power System Stabilizer (PSS) Parameters Optimizat...

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21st IEEE International Symposium on Intelligent Systems and Informatics, SISY 2023

作者： Honarvar, Mohammad Amin Shahgholian, Ghazanfar Mahmoodian, Hamid Yaghoubi, Somaye Mosavi, Amirhosein Fathollahi, Arman Najafabad Branch Islamic Azad University Department of Electrical Engineering Najafabad Iran Islamic Azad University Najafabad Branch Smart Microgrid Research Center Najafabad Iran Islamic Azad University Digital Processing and Machine Vision Research Center Najafabad Branch Najafabad Iran Islamic Azad University Aerospace and Energy Conversion Research Center Najafabad Branch Najafabad Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350343366

The dynamic nature of the power system often results in sudden changes in the load that can lead to power and voltage disturbances. Unfortunately, the interference generator is unable to respond quickly, thereby leading to a disruption in the stability of the energy system. Any form of disturbance or change in the input typically results in oscillations in real and reactive power, frequency, and voltage. One of the most effective devices used in damping low-frequency oscillations (LFO) in the power system is the power system stabilizer (PSS). However, the PSS parameters must be appropriately designed and coordinated with other stabilizers to not only dampen oscillations but also improve the stability of the power system. This paper presents a short review of the optimization of PSS parameters using meta-heuristic optimization algorithms that are based on evolution. The main optimization strategies investigated are harmony search optimization (HSO) and genetic algorithm (GA). The results have been expressed using a review of more than 40 different studies. © 2023 IEEE.

关键词： Soft computing

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Low-Complexity Markov Chain Monte Carlo Detector for Single-Carrier Underwater Acoustic Communications

Low-Complexity Markov Chain Monte Carlo Detector for Single-...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Zhang, Zhiheng Tao, Jun Yang, Le Huang, Weimin School of Information Science and Engineering Southeast University Nanjing210096 China Pengcheng Laboratory Shenzhen518000 China University of Canterbury Department of Ece Christchurch8020 New Zealand Memorial University of Newfoundland Department of Electrical and Computer Engineering St. John'sNL A1B 3X5 Canada

ISBN: (纸本)9798218142186

Markov chain Monte Carlo (MCMC) technique is an approximate Bayesian inference method and generally outperforms linear estimation. It has been adopted to realize channel estimation and symbol detection in underwater acoustic (UWA) communications. Despite its better performance, the high complexity limits its practical applications. To address this issue, we propose a low-complexity MCMC detector based on Gibbs sampling in this paper. The key idea is to utilize the reliability information in the Gibbs sampler. Specifically, when a bit is believed to be sufficiently reliable, it will be fixed, instead of being sampled and possibly modified in the remaining iterations. The proposed scheme is evaluated using both numerical simulations and experimental data collected in an at-sea UWA communication experiment conducted at Xiamen, China in 2019. The new method exhibits significant complexity reduction at the cost of slight performance degradation. © 2023 The Marine Technology Society (MTS).

关键词： Underwater acoustic communication

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GNS3 Communication Network Emulation for Substation GOOSE Based Protection Schemes

GNS3 Communication Network Emulation for Substation GOOSE Ba...

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International Conference on Environment and electrical engineering (EEEIC)

作者： Abdulmueen Alrashide Mahmoud S. Abdelrahman Ibtissam Kharchouf Osama A. Mohammed Department of Electrical and Computer Engineering Florida International University Energy Systems Research Laboratory Miami Fl USA Department of Electrical &#x0026 Computer Engineering Florida International University Miami FL USA

ISBN: (数字)9781665485371

ISBN: (纸本)9781665485388

This paper presents a communication network model used to study the impact of the communication layer on smart grid protection schemes. Communication failure due to hardware malfunction or cyber-attack could lead to major impacts on the smart grid. Some critical devices require real-time data or close to real-time to make the right decision at the right time and keep the system in a stable zone, such as a protection relay. Active man in the middle attack adds additional latency to the packet delivery. A false data injection attack could drive the system to an unstable point, eventually leading to a blackout. This paper designed and implemented communication emulation model for a local area network on the FIU smart grid testbed. Two cases were tested in the developed communication model. In the first case, two physical raspberry pi boards were used to represent two nodes in the smart grid and one traffic generated within the virtual network. In the second case, five ABB Protection Relays and COM600 substation automation devices were connected to GNS3. The two cases showed that GNS3 could be used to emulate the communications network and study the impact of the network performance on the real-world substation environment.

关键词： Performance evaluation LAN emulation Substation automation Emulation Europe Real-time systems Hardware

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Artificial Intelligence Assistive Non-Destructive Testing of Welding Joints: A Review

SSRN

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SSRN 2025年

作者： Mian Qaisar, Saeed Say, Dalila Krichen, Moez Zidi, Salah Hatem Bettaher Laboratory IResCoMath University of Gabes Gabes6029 Tunisia Electrical and Computer Engineering Department Effat University Jeddah21478 Saudi Arabia Faculty of Computer Science and Information Technology Al-Baha University ReDCAD Laboratory National School of Engineers of Sfax University of Sfax Al-Baha65528 Saudi Arabia

Non-Destructive Testing (NDT) is an appealing technique for confirming the welding quality and ensuring structural integrity without causing damage. It assists in regulatory compliance, risk mitigation, and process optimization for improved industrial reliability. This study reviews potential NDT approaches. Through a rigorous analysis of existing surveys, we aim to decipher the current landscape and highlight the significant advancements in the field. Because of the potential of artificial intelligence (AI) assistive X-ray imaging-based NDT, we particularly examine the integration of AI algorithms and X-ray imaging in the NDT of welds. This convergence represents a paradigm shift, redefining traditional methods and ushering in a new era of precision, automation, and efficiency. As we navigate through potential X-ray imaging datasets, delve into crucial image processing techniques, examine feature extraction methods, and explore AI algorithms, our survey reveals the intricate interplay of technologies that drive automated weld defect categorization. The review broadens its focus by including various practical applications, highlighting the adaptable utility of AI-assistive X-ray imaging for weld defect detection in potential industrial applications. Moreover, the promising opportunities and nuanced challenges associated with integrating X-ray imaging and AI in weld integrity assessment are highlighted. It provides a comprehensive perspective on this rapidly evolving field. © 2025, The Authors. All rights reserved.

关键词： Fracture mechanics

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Human–AI interactive optimized shared control

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Journal of Automation and Intelligence 2025年

作者： Tan, Junkai Xue, Shuangsi Cao, Hui Ge, Shuzhi Sam School of Electrical Engineering Xi'an Jiaotong University Xi'an 710049 China State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University Xi'an 710049 China The Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore

This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven digital system functions as the virtual agent. In this digital twin architecture, the real agent acquires an optimal control strategy through observed actions, while the AI virtual agent mirrors the real agent to establish a digital replica system and corresponding control policy. Both the real and virtual optimal controllers are approximated using reinforcement learning (RL) techniques. Specifically, critic neural networks (NNs) are employed to learn the virtual and real optimal value functions, while actor NNs are trained to derive their respective optimal controllers. A novel shared mechanism is introduced to integrate both virtual and real value functions into a unified learning framework, yielding an optimal shared controller. This controller adaptively adjusts the confidence ratio between virtual and real agents, enhancing the system's efficiency and flexibility in handling complex control tasks. The stability of the closed-loop system is rigorously analyzed using the Lyapunov method. The effectiveness of the proposed AI–human interactive system is validated through two numerical examples: a representative nonlinear system and an unmanned aerial vehicle (UAV) control system. © 2025 The Authors

关键词： Adaptive dynamic programming (ADP) Data-driven Digital-twin system Human–AI interaction Optimal shared control

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Human-Guided Artificial Intelligence (HGAI), a Framework for Overcoming AI's Blind Spots

Human-Guided Artificial Intelligence (HGAI), a Framework for...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Chen, Ziyu Kelil, Selam B. Naser, Mohammad Y. M. Metcalfe, Jason S. Bhattacharya, Sylvia Kennesaw State University Electrical & Computer Engineering Department Marietta United States Kennesaw State University Computer Science Department Marietta United States Us Devcom Army Research Laboratory Aberdeen Proving Ground Aberdeen United States Kennesaw State University Engineering Technology Department Marietta United States

ISBN: (纸本)9798331504847

Artificial Intelligence (AI) systems frequently exhibit systematic blind spots, often referred to as hallucinations in Large Language Models (LLMs), posing risks in high-stakes applications such as autonomous systems, security, and military operations. This paper explores how human intuitive responses, along with conscious reasoning processes, can be integrated to mitigate AI blind spots and enhance decision-making effectiveness within Human-AI teams. We introduce Human-Guided Artificial Intelligence (HGAI) as a framework for achieving this goal. Specifically, we examine the role of System-1 intuitive processing, as captured through physiological signals such as electroencephalography (EEG) and electrocardiography (ECG), System-2 reasoning-based decision-making, and Multi-Modal Fusion (MMF) mechanisms while assessing the knowledge required to develop more reliable, context-aware, and ethically aligned intelligent decision-making systems for highly complex environments. © 2025 IEEE.

关键词： Artificial Intelligence (AI) Human-AI Teams Multi-Modal Fusion (MMF) Physiological Signals

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