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arXiv 2023年

作者： Neil, Sean P.O. Langbein, Frank C. Jonckheere, Edmond Shermer, S. Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science and Engineering Physics Singleton Park SwanseaSA2 8PP United Kingdom

As shown in previous work, in some cases closed quantum systems exhibit a non-conventional absence of trade-off between performance and robustness in the sense that controllers with the highest fidelity can also provide the best robustness to parameter uncertainty. As the dephasing induced by the interaction of the system with the environment guides the evolution to a more classically mixed state, it is worth investigating what effect the introduction of dephasing has on the relationship between performance and robustness. In this paper we analyze the robustness of the fidelity error, as measured by the logarithmic sensitivity function, to dephasing processes. We show that introduction of dephasing as a perturbation to the nominal unitary dynamics requires a modification of the log-sensitivity formulation used to measure robustness about an uncertain parameter with non-zero nominal value used in previous work. We consider controllers optimized for a number of target objectives ranging from fidelity under coherent evolution to fidelity under dephasing dynamics to determine the extent to which optimizing for a specific regime has desirable effects in terms of robustness. Our analysis is based on two independent computations of the log-sensitivity: a statistical Monte Carlo approach and an analytic calculation. We show that despite the different log-sensitivity calculations employed in this study, both demonstrate that the log-sensitivity of the fidelity error to dephasing results in a conventional trade-off between performance and robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Robust control

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Size/Location Estimation for Loss of Generation Events in Power Systems with High Penetration of Renewables

SSRN

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SSRN 2022年

作者： Cortés, Jesús Sánchez Rezaei Jegarluei, Mohammad Aristidou, Petros Li, Kang Azizi, Sadegh School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol3026 Cyprus

The increasing penetration of renewable energy sources (RESs) is significantly impacting the performance of traditional control and protection schemes employed in power systems. The early detection and size estimation of loss of generation (LoG) events contribute to the timely provision of remedial actions required to preserve the system's frequency stability. This paper proposes an effective method for locating and estimating the size of LoG events in power systems with high penetration of RESs. A system of linear equations for every candidate LoG location is formulated based upon KVL and KCL equations. Suitable nodal current sources are employed to model RESs and the candidate tripped generator in the superimposed circuit. The nodal current injections of RESs are estimated based on their power setpoints and available measurements provided by PMUs. The solution of the systems of equations established provides the LoG location and size using the least-squares method. The proposed method is more accurate than existing LoG identification methods that resort to frequency measurements and the knowledge of system inertia. Centralized under-frequency load shedding is presented as a potential application for the proposed method. Extensive simulations conducted on the IEEE 39-bus test system verify the effectiveness and accuracy of the proposed method. © 2022, The Authors. All rights reserved.

关键词： Renewable energy resources

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Maintaining Fuse in the Presence of Distributed Generation Sources in the Distribution Network to Improve Protection System

Maintaining Fuse in the Presence of Distributed Generation S...

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Mahdi Taleb Bahador Fani Ghazanfar Shahgholian Amir Mosavi Arman Fathollahi Department of Electrical Engineering Najafabad Branch Islamic Azad University Najafabad Iran Smart Microgrid Research Center Najafabad Branch Islamic Azad University Najafabad Iran John von Neumann Faculty of Informatics Obuda University Budapest Hungary Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

The need to reduce greenhouse gas emissions and the high price of fossil fuels have made renewable resources attractive in energy-based economies around the world. Renewable energy sources will make up a significant part of the modern energy system in the future because they have promising potential. Many countries are already working to increase their capacity for renewable energy. The placement of renewable resources in power systems has gained significant attention due to their potential to provide power to distribution feeders or near consumers. However, the integration of these resources can have adverse effects on the distribution network, which necessitates their placement to be carefully considered. In this study, a novel method of coordinating protection devices based on the current control of distributed production sources using their current-voltage diagram during fault conditions is suggested. The proposed method aims to address the coordination and regulation problems encountered when integrating scattered resources into the network. To evaluate the effectiveness of our approach, we compare the impact of the presence of renewable sources at various points on the network during flooding. We conduct simulations using the ETAP software and present the obtained results. The proposed protection coordination method effectively mitigates the challenges of integrating renewable resources into the distribution network, providing a promising solution to support the transition towards a more sustainable energy future.

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Robustness of Energy Landscape Controllers for Spin Rings under Coherent Excitation Transport

arXiv

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arXiv 2023年

作者： O'Neil, Sean P. Langbein, Frank C. Jonckheere, Edmond Shermer, S. Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science and Engineering Physics Singleton Park SwanseaSA2 8PP United Kingdom

The design and analysis of controllers to regulate excitation transport in quantum spin rings presents challenges in the application of classical feedback control techniques to synthesize effective control, and generates results in contradiction to the expectations of classical control theory. This paper examines the robustness of controllers designed to optimize the fidelity of an excitation transfer to uncertainty in system and control parameters. We use the logarithmic sensitivity of the fidelity error as the robustness measure, drawing on the classical control analog of the sensitivity of the tracking error. Our analysis shows that quantum systems optimized for coherent transport demonstrate significantly different correlation between error and the log-sensitivity depending on whether the controller is optimized for readout at an exact time T or over a time-window T ± ∆/2. Copyright © 2023, The Authors. All rights reserved.

关键词： Controllers

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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Semi-supervised trajectory classification using convolutional auto-encoders 1

Semi-supervised trajectory classification using convolutiona...

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1st ACM SIGSPATIAL International Workshop on on Animal Movement Ecology and Human Mobility, HANIMOB 2021

作者： Makris, Antonios Kontopoulos, Ioannis Psomakelis, Evangelos Tserpes, Konstantinos Department of Informatics and Telematics Harokopio University of Athens Distributed Knowledge and Media Systems Group National Technical University of Athens Athens Greece Harokopio University of Athens Department of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (纸本)9781450391221

Massive volumes of high-frequency and high-volume data are constantly being generated by the vast amount of available tracking sensors of moving objects. This phenomenon can be strongly observed in the maritime domain since most of the vessels transmit their location periodically. Automated methodologies able to extract meaningful information from vessel data is of utmost importance since it can reveal abnormal or illegal vessel activities in due time. Supervised deep learning approaches such as convolutional neural networks (CNNs) require a large-scale annotated image dataset. Thus, semi-and unsupervised feature learning algorithms which learn image features from unlabelled data are able to mitigate this problem. To this end, in this work we propose a semi-supervised convolutional autoencoder (CAE) model for trajectory classification which is able to provide high-precision classification of mobility patterns. Experimental results demonstrated that the vessel activity classification performance can reach an F1-score of over 94%. © 2021 ACM.

关键词： Learning algorithms

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A survey of disaster management datasets

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Journal of Information and Telecommunication 2025年

作者： Abbas, Sohail Talib, Manar Abu Nasir, Qasim Belal, Omar Al-Haidary, Mohammed Emad Ahmed, Ibtihal Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Natural disasters are characterized as a combination of natural hazards and vulnerabilities that endanger communities and result in significant financial and human losses. The uncertain occurrence of these events and the limited resources in affected regions constitute their core attributes. Leveraging information technology to assess, forecast, and depict these occurrences can improve handling such disasters. Artificial Intelligence (AI) technology has recently been employed practically in various disciplines for various applications, and disaster management applications are among the most critical areas. The dataset is essential to any AI system and must be corrected to deliver reliable results. Disaster datasets are presented and described in literature. This study examines the datasets available for disaster evaluation and detection frameworks. A general framework consisting of five main dataset properties is designed for the presentation of the datasets. Some features include general information, data volume, recording environment, data nature, and evaluation. There are two or three sub-properties under each of the five main properties. Each dataset is examined in comparison to the listed attributes. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Artificial Intelligence datasets properties image dataset Natural disasters social media dataset

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INTERPRETABLE GRAPH CONVOLUTIONAL NETWORK OF MULTI-MODALITY BRAIN IMAGING FOR ALZHEIMER’S DISEASE DIAGNOSIS

arXiv

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arXiv 2022年

作者： Zhou, Houliang He, Lifang Zhang, Yu Shen, Li Chen, Brian Department of Computer Science and Engineering Lehigh University PA United States Department of Bioengineering Lehigh University PA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PA United States

Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer’s disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting the difference of features between regions in healthy control (HC), mild cognitive impairment (MCI), and AD groups. We conducted the experiments on the ADNI database with imaging data from three modalities, including VBM-MRI, FDG-PET, and AV45-PET, and showed that the ROI features learned by our method were effective for enhancing the performances of both clinical score prediction and disease status identification. It also successfully identified biomarkers associated with AD and *** Codes 68T07, 68T20, 68T45 © 2022, CC BY.

关键词： Brain mapping

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Pending Interest Table (PIT) Performance Analysis in Named Data Networking on Palapa Ring Topology

Pending Interest Table (PIT) Performance Analysis in Named D...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Adi Sucipto Jupriyadi Syaiful Ahdan Hasan Nur Arifin Eki Ahmad Zaki Hamidi Nana Rachmana Syambas School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Lampung Indonesia Department of Electrical Engineering UIN Sunan Gunung Djati Bandung Indonesia

The concept of the internet in the future will prioritize content, by reducing delays in data transmission. Named Data Networking (NDN) is a content-based future internet concept that changes the paradigm of using IP. Inside the NDN router, there are three data structures, namely Content Store (CS), Pending Interest Table (PIT), and Forwarding Information Base (FIB). Pending Interest Table (PIT) contains a list of unfulfilled interests. This condition occurs when the node has not received a response after the interest forwarding process. Measurable and fast PIT performance is a challenge in Named Data Networks. In this study, we will try to do a simulation to measure and analyze the performance of PIT in NDN in the Palapa Ring topology. The research was conducted using the NDNSim simulator, to see the performance in the PIT. The simulation and analysis of the results show that the granularity of a prefix has an effect on In Satisfied Interest in an NDN network. At the number of interests of 100, the result obtained from the simulation is that there is a decrease in the percentage of interest data served, amounting to more than 20%. At the amount of interest in 1000 about more than 30%. The length of the prefix and the number of interest sent by the consumer affect the performance of the PIT, seen from the number of In Satisfied Interests.

关键词： Analytical models Network topology Simulation Data models Topology Performance analysis Delays

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Multi-behavior Learning for Socially Compatible Autonomous Driving

Multi-behavior Learning for Socially Compatible Autonomous D...

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International Conference on Intelligent Transportation

作者： Sanjula Jayawardana Vindula Jayawardana Kaneeka Vidanage Cathy Wu University of Westminster UK Informatics Institute of Technology Colombo Sri Lanka Department of Electrical Engineering and Computer Science MIT Laboratory for Information & Decision Systems Cambridge MA USA Department of Civil and Environmental Engineering MIT Laboratory for Information Decision Systems Institute of Data Systems and Society Cambridge MA USA

Autonomous vehicles (AVs) have the potential to revolutionize transportation, but their effective integration into the real world requires addressing the challenge of interacting with human drivers. Real-world driving involves negotiating and cooperating with fellow drivers through social cues, necessitating AVs to also demonstrate such social compatibilities. However, despite the popularity, current learning-based control methods for AV policy synthesis often overlook this crucial aspect. In this work, we look at the problem of enabling socially compatible driving when AV control policies are learned. We leverage human driving data to learn a social preference model of human driving and then integrate it with reinforcement learning-based AV policy synthesis using Social Value Orientation theory. In particular, we propose to use multi-task reinforcement learning to learn diverse social compatibility levels in driving (ex: altruistic, prosocial, individualistic, and competitive), focusing on the requirement of having diverse behaviors in real-world driving. Using highway driving scenarios, we demonstrate through experiments that socially compatible AV driving not only enables naturalistic driving behaviors but also reduces collision rates from the baseline. Our findings reveal that without social compatibility, AV policies tend to adopt dangerously competitive driving behaviors, while the incorporation of social compatibility fosters smoother vehicle maneuvers.

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