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Mitigating Voltage Sag in Power systems with Proportional Integral Control using Tangent Search Algorithm and Multi-Fidelity Bayesian Neural Network 8

Mitigating Voltage Sag in Power Systems with Proportional In...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Meena, Radhey Shyam Singh, Harsimran Vasudevan, K. Ekambaram, Saranya Katiyar, Atul Kaliappan, S. Indian Institute of Technology Industry Coordinator Odl Uttar Pradesh Kanpur208016 India Guru Kashi University Department of Electrical Engineering Punjab Bathinda151302 India Parul University Department of Electrical Engineering Gujarat Vadodara391760 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Mjp Rohilkhand University Bareilly Faculty of Engineering and Technology Department of Electrical Engineering Uttar Pradesh 243006 India Lovely Professional University Division of Research and Development Delhi Jalandhar144411 India

ISBN: (纸本)9798350376425

Mitigation strategies for voltage sag in power systems using dynamic voltage restorers (DVRs) involve compensating for voltage dips by injecting the required voltage to maintain stability. However, DVRs present problems such as high initial costs in installation and subsequent costs in maintenance and replacement;it only corrects voltage sag within its capability to handle severe or long periods of voltage sags. Moreover, DVRs may need complex control algorithms and most accurate tuning for optimal performance under various grid conditions. To overcome these drawbacks, this manuscript proposes a hybrid approach for voltage sag mitigation in power systems with Proportional Integral (PI) controller. The proposed method is the combined execution of both Tangent Search Algorithm (TSA) and Multi-fidelity Bayesian Neural Network (MFBNN). Hence, it is named as hybrid TSA-MFBNN approach. The major aim is to minimize the impact of voltage sags on sensitive equipment and maintain consistent power quality across the network and reduce the integral squared error (ISE) and Total Harmonic Distortion (THD) of the system. The PI controller's gain parameters are optimized by the proposed TSA, and its optimum values are predicted by the MFBNN. By then, the proposed technique is executed in MATLAB and compared with different existing approaches. The proposed method outperforms all existing approaches such as Artificial Neural Network (ANN), Salp Swarm Algorithm (SSA), and Adaptive-Network Fuzzy Inference System (ANFIS). The proposed technique demonstrates a THD of 2.38% and an ISE of 0.00410, indicating superior performance in reducing harmonic distortions and minimizing error. © 2024 IEEE.

关键词： Harmonic distortion

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A Unified Framework for Robots that Influence Humans over Long-Term Interaction

arXiv

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arXiv 2025年

作者： Sagheb, Shahabedin Parekh, Sagar Pandya, Ravi Mun, Ye-Ji Driggs-Campbell, Katherine Bajcsy, Andrea Losey, Dylan P. Virginia Tech Department of Mechanical Engineering United States Carnegie Melon University Robotics Institute United States University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering United States

Robot actions influence the decisions of nearby humans. Here influence refers to intentional change: robots influence humans when they shift the human’s behavior in a way that helps the robot complete its task. Imagine an autonomous car trying to merge;by proactively nudging into the human’s lane, the robot causes human drivers to yield and provide space. Influence is often necessary for seamless interaction. However, if influence is left unregulated and uncontrolled, robots will negatively impact the humans around them (e.g., autonomous cars that repeatedly merge in front of humans may cause human drivers to become more aggressive). Prior works have begun to address this problem by creating a variety of control algorithms that seek to influence humans. Although these methods are effective in the short-term, they fail to maintain influence over time as the human adapts to the robot’s behaviors. In this paper we therefore present an optimization framework that enables robots to purposely regulate their influence over humans across both short-term and long-term interactions. Here the robot maintains its influence by reasoning over a dynamic human model which captures how the robot’s current choices will impact the human’s future behavior. Our resulting framework serves to unify current approaches: we demonstrate that state-of-the-art methods are simplifications of our underlying formalism. Our framework also provides a principled way to generate influential policies: in the best case the robot exactly solves our framework to find optimal, influential behavior. But when solving this optimization problem becomes impractical, designers can introduce their own simplifications to reach tractable approximations. We experimentally compare our unified framework to state-of-the-art baselines and ablations, and demonstrate across simulations and user studies that this framework is able to successfully influence humans over repeated interactions. See videos of our experiments he

关键词： Human robot interaction

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Updating Top-k Dominate Individuals with Incomplete Data Addition

Updating Top-k Dominate Individuals with Incomplete Data Add...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Wu, Jimmy Ming-Tai Wang, Ke Yan, Huizhen Chen, Chao-Chun Chen, Pei-Wei Lin, Jerry Chun-Wei Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China National Cheng Kung University Institute of Manufacturing Information and Systems Tainan70101 Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9781665475549

Top-k dominance (TKD) query is an extended query method of skyline query and top-k query, which reveals the top-k dominant individuals in an incomplete dataset by analyzing the dominance relationships between individuals and is a common decision tool in intelligent recommendation applications. This research proposes two parallel query algorithms based on Spark computing engine to address the shortcomings of the parallel top-k-dominated query algorithms for dynamic incomplete datasets. The designed model achieves good performance in terms of runtime performance compared to previous studies. © 2023 IEEE.

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From Vague Instructions to Task Plans: A Feedback-Driven HRC Task Planning Framework based on LLMs

arXiv

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arXiv 2025年

作者： Shervedani, Afagh Mehri Walter, Matthew R. Žefran, Miloš Robotics Laboratory Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL60607 United States Robotics Laboratory Toyota Technological Institute at Chicago ChicagoIL60637 United States

Recent advances in large language models (LLMs) have demonstrated their potential as planners in human-robot collaboration (HRC) scenarios, offering a promising alternative to traditional planning methods. LLMs, which can generate structured plans by reasoning over natural language inputs, have the ability to generalize across diverse tasks and adapt to human instructions. This paper investigates the potential of LLMs to facilitate planning in the context of human-robot collaborative tasks, with a focus on their ability to reason from high-level, vague human inputs, and fine-tune plans based on real-time feedback. We propose a novel hybrid framework that combines LLMs with human feedback to create dynamic, context-aware task plans. Our work also highlights how a single, concise prompt can be used for a wide range of tasks and environments, overcoming the limitations of long, detailed structured prompts typically used in prior studies. By integrating user preferences into the planning loop, we ensure that the generated plans are not only effective but aligned with human intentions. Copyright © 2025, The Authors. All rights reserved.

关键词： Robot programming

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Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent systems under STL Specifications

arXiv

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arXiv 2024年

作者： Engelaar, Maico H.W. Zhang, Zengjie Vlahakis, Eleftherios E. Dimarogonas, Dimos V. Lazar, Mircea Haesaert, Sofie Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with real-time allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into sub-specifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing. © 2024, CC BY-NC-ND.

关键词： Stochastic systems

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User Acceptance of New HCI Technologies 6

User Acceptance of New HCI Technologies

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6th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2023

作者： Katona, Jozsef Kovari, Attila Institute of Electronics and Communication Systems Obuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Digital Technology Eszterházy Károly Catholic University Faculty of Computer Science Eger Hungary Institute of Engineering University of Dunaujvaros Department of Natural Sciences Dunaujvaros Budapest Hungary

ISBN: (纸本)9798350328752

Human-computer interaction (HCI) is an evolving field of research that focuses on understanding and improving the communication and interaction between humans and computers. Over the past decades, we have seen many significant advances in this field, which have contributed to the widespread adoption and integration of technology into our daily lives. HCI research and development aims to design information technology systems to meet the needs and preferences of users. Usability, efficiency, accessibility and user satisfaction are important considerations in HCI design. This paper presents the results of a pilot study on user acceptance of HCIs. The results show that users are positive about and willing to use HCIs. © 2023 IEEE.

关键词： Human computer interaction

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Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent systems under STL Specifications

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Ag...

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IEEE Conference on Decision and Control

作者： M. H. W. Engelaar Z. Zhang E. E. Vlahakis D.V. Dimarogonas M. Lazar S. Haesaert Department of Electrical Engineering (Control Systems Group) Eindhoven University of Technology The Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into subspecifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing.

关键词： Vehicle routing Stochastic processes Probabilistic logic Prediction algorithms Real-time systems Robustness Resource management Vehicle dynamics Multi-agent systems Predictive control

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Power Factor Estimation of Distributed Energy Resources Using Voltage Magnitude Measurements

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Journal of Modern Power systems and Clean Energy 2021年第4期9卷 859-869页

作者： Samuel Talkington Santiago Grijalva Matthew J.Reno School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30318USA Electric Power Systems Research Department Sandia National LaboratoriesAlbuquerqueNM 87123USA

This paper presents a new method for the estimation of the injection state and power factor of distributed energy resources (DERs) using voltage magnitude measurements only. A physics-based linear model is used to develop estimation heuristics for net injections of real and reactive power at a set of buses under study, allowing a distribution engineer to form a robust estimate for the operating state and the power factor of the DER at those buses. The method demonstrates and exploits a mathematical distinction between the voltage sensitivity signatures of real and reactive power injections for a fixed power system model. Case studies on various test feeders for a model of the distribution circuit and statistical analyses are presented to demonstrate the validity of the estimation method. The results of this paper can be used to improve the limited information about inverter parameters and operating state during renewable planning, which helps mitigate the uncertainty inherent in their integration.

关键词： Decentralized power system control detection and estimation power factor sparse approximation

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Self-supervised Contrastive Learning for 6G UM-MIMO THz Communications: Improving Robustness Under Imperfect CSI

Self-supervised Contrastive Learning for 6G UM-MIMO THz Comm...

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IEEE International Conference on Communications Workshops, ICC

作者： Rafid Umayer Murshed Md Saheed Ullah Mohammad Saquib Moe Z. Win Department of Electrical and Computer Engineering The University of Texas Dallas Texas USA Department of Electrical and Computer Engineering University of Delaware Delaware USA Laboratory for Information and Decision Systems Massachusetts Institute of Technology Massachusetts USA

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

This paper investigates the potential of contrastive learning in 6G ultra-massive multiple-input multiple-output (UM-MIMO) communication systems, specifically focusing on hybrid beamforming under imperfect channel state information (CSI) conditions at THz. UM-MIMO systems are promising for future 6G wireless communication networks due to their high spectral efficiency and capacity. The accuracy of CSI significantly influences the performance of UM-MIMO systems. However, acquiring perfect CSI is challenging due to various practical constraints such as channel estimation errors, feedback delays, and hardware imperfections. To address this issue, we propose a novel self-supervised contrastive learning-based approach for hybrid beamforming, which is robust against imperfect CSI. We demonstrate the power of contrastive learning to tackle the challenges posed by imperfect CSI and show that our proposed method results in improved system performance in terms of achievable rate compared to traditional methods.

关键词： 6G mobile communication Wireless communication Array signal processing Heuristic algorithms Conferences System performance Contrastive learning

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WildGaussians: 3D Gaussian splatting in the wild 24

WildGaussians: 3D Gaussian splatting in the wild

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Jonas Kulhanek Songyou Peng Zuzana Kukelova Marc Pollefeys Torsten Sattler Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague and Faculty of Electrical Engineering Czech Technical University in Prague and Department of Computer Science ETH Zurich Department of Computer Science ETH Zurich Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague

ISBN: (纸本)9798331314385

While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods primarily excel with well-controlled 3D scenes, while in-the-wild data - characterized by occlusions, dynamic objects, and varying illumination - remains challenging. NeRFs can adapt to such conditions easily through per-image embedding vectors, but 3DGS struggles due to its explicit representation and lack of shared parameters. To address this, we introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. By leveraging robust DINO features and integrating an appearance modeling module within 3DGS, our method achieves state-of-the-art results. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data, all within a simple architectural framework.

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