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2021 International Conference on Unmanned Aircraft systems, ICUAS 2021

作者： Silano, Giuseppe Bednar, Jan Nascimento, Tiago Capitan, Jesus Saska, Martin Ollero, Anibal Czech Technical University Faculty of Electrical Engineering Prague Czech Republic Universidade Federal da Paraiba Department of Computer Systems Brazil Grvc Robotics Laboratory University of Seville Spain

ISBN: (纸本)9780738131153

This paper presents a multi-layer software architecture to perform cooperative missions with a fleet of quad-rotors providing support in electrical power line inspection operations. The proposed software framework guarantees the compliance with safety requirements between drones and human workers while ensuring that the mission is carried out successfully. Besides, cognitive capabilities are integrated in the multi-vehicle system in order to reply to unforeseen events and external disturbances. The feasibility and effectiveness of the proposed architecture are demonstrated by means of realistic simulations. © 2021 IEEE.

关键词： Inspection

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SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization

SubPipe: A Submarine Pipeline Inspection Dataset for Segment...

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OCEANS

作者： Olaya Álvarez-Tuñón Luiza Ribeiro Marnet Martin Aubard László Antal Maria Costa Yury Brodskiy Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR-Lab) Aarhus University Aarhus C Denmark EIVA a/s Skanderborg Denmark OceanScan Marine Systems & Technology Matosinhos Portugal RWTH Aachen University Aachen Germany

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. SubPipe has been recorded using a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms. To the authors' knowledge, this is the first anno-tated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly avail-able online at https://***/remaro-network/SubPipe-dataset.

关键词： Location awareness Image segmentation Simultaneous localization and mapping Oceans Pipelines Object detection Inertial navigation

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Autonomous control management of synchronous generators in microgrid system

Autonomous control management of synchronous generators in m...

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International Conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Hafiz Ghulam Murtza Qamar Adil Ashraf Fareed Ahmad Ayesha Riaz Muhammad Imran Muhammad Zain Yousaf Imran Khan School of Electrical Engineering Yanshan University Qinhuangdao 066004 People's Republic of China Department of Electrical Electronics and Computer Systems University of Sargodha People's Republic of China Center for Renewable Energy and Microgrids Huanjiang Laboratory Zhejiang University Zhuji Zhejiang 311816 People's Republic of China

ISBN: (数字)9781837243150

Decentralized excitation control based on local asynchronous clock feedback measurement leads to the problem of decentralized incompatibility of excitation controllers, which reduces the ability of excitation control design to improve the stability of power systems. Therefore, the problem of decentralized compatible excitation control is proposed, and the effect of excitation control on the dynamic characteristics of the system under compatible and incompatible modes is compared and analyzed, and the shortcomings of decentralized incompatible excitation control and the advantages of decentralized compatible excitation control are given.

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Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

arXiv

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arXiv 2024年

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Wanna, Selma Solovyev, Nicholas Rasmussen, Kim Alexandrov, Boian S. Nicholas, Charles Matuszek, Cynthia Theoretical Division Los Alamos National Laboratory Los Alamos United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore CountyMD United States

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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Robust Event-Triggered Control of Nonlinear systems Under a Global Sector-Bound Condition

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Guidance, Navigation and Control 2022年第2期2卷 59-79页

作者： Pengpeng Zhang Tengfei Liu Zhong-Ping Jiang Department of Control Science&Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

This paper studies an event-tniggered control problem for nonlinear systems subject to both external disturbancoes and dy namic *** is assumed that the system satisfies a global sector bound *** avold infnitely fast samplng,a novel eventriggred sampling mechanism is propoeed,which use8 not only the measuned system state but also an estimation of the inluence of the *** the propoeed design,the intersampling intervals an be lower bounded by a poeitive constant,and it is independent of botb external disturbances and dynamie ***,the doeedl loop event-tniggered system i proved to be input-torstate stable with repect to the extemal *** smalgain techmigues are;used for the stability analysis of the dloeeil-bop system.

关键词： Event-triggered control external disturbance dynamic uncertainty input-to-state stability

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Adrias: Interference-Aware Memory Orchestration for Disaggregated Cloud Infrastructures

Adrias: Interference-Aware Memory Orchestration for Disaggre...

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IEEE Symposium on High-Performance computer Architecture

作者： Dimosthenis Masouros Christian Pinto Michele Gazzetti Sotirios Xydis Dimitrios Soudris Microprocessors and Digital Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece IBM Research Europe Dublin Ireland Department of Informatics and Telematics Harokopio University of Athens Greece

Workload co-location has become the de-facto approach for hosting applications in Cloud environments, leading, however, to interference and fragmentation in shared resources of the system. To this end, hardware disaggregation is introduced as a novel paradigm, that allows fine-grained tailoring of cloud resources to the characteristics of the deployed applications. Towards the realization of hardware disaggregated clouds, novel orchestration frameworks must provide additional knobs to manage the increased scheduling *** present Adrias, a memory orchestration framework for disaggregated cloud systems. Adrias exploits information from low-level performance events and applies deep learning techniques to effectively predict the system state and performance of arriving workloads on memory disaggregated systems, thus, driving cognitive scheduling between local and remote memory allocation modes. We evaluate Adrias on a state-of-art disaggregated testbed and show that it achieves 0.99 and 0.942 R 2 score for system state and application’s performance prediction on average respectively. Moreover, Adrias manages to effectively utilize disaggregated memory, by offloading almost 1/3 of deployed applications with less than 15% performance overhead compared to a conventional local memory scheduling, while clearly outperforms naive scheduling approaches (random and round-robin), by providing up to ×2 better performance.

关键词： Deep learning Memory management Interference Hardware Resource management

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Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

arXiv

引用

arXiv 2024年

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Boureima, Ismael Matuszek, Cynthia Alexandrov, Boian S. Theoretical Division Los Alamos National Laboratory Los Alamos United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore CountyMD United States

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as non-negative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter k to define the number of clusters or components that yield an ideal separation of samples or clean clusters. This selection, while difficult, is crucial to avoid overfitting or underfitting the data. Several ML applications use scoring methods (e.g., Silhouette and Davies Boulding scores) to evaluate the cluster pattern stability for a specific k. The score is calculated for different trials over a range of k, and the ideal k is heuristically selected as the value before the model starts overfitting, indicated by a drop or increase in the score resembling an elbow curve plot. While the grid-search method can be used to accurately find a good k value, visiting a range of k can become time-consuming and computationally resource-intensive. In this paper, we introduce the Binary Bleed method based on binary search, which significantly reduces the k search space for these grid-search ML algorithms by truncating the target k values from the search space using a heuristic with thresholding over the scores. Binary Bleed is designed to work with single-node serial, single-node multi-processing, and distributed computing resources. In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal k and the accuracy of the Binary Bleed in identifying the correct k for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores. We make our implementation of Binary Bleed for the NMF algorithm available on GitHub. Copyright © 2024, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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Faster Convergence of Local SGD for Over-Parameterized Models

arXiv

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arXiv 2022年

作者： Qin, Tiancheng Rasoul Etesami, S. Uribe, Cesár A. Department of Industrial and Systems Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Rice University United States

Modern machine learning architectures are often highly expressive. They are usually overparameterized and can interpolate the data by driving the empirical loss close to zero. We analyze the convergence of Local SGD (or FedAvg) for such over-parameterized models in the heterogeneous data setting and improve upon the existing literature by establishing the following convergence rates. For general convex loss functions, we establish an error bound of O(1/T) under a mild data similarity assumption and an error bound of O(K/T) otherwise, where K is the number of local steps and T is the total number of iterations. For non-convex loss functions we prove an _error bound of O(K/T). These bounds improve upon the best previous bound of O(1/√nT) in both cases, where n is the number of nodes, when no assumption on the model being over-parameterized is made. We complete our results by providing problem instances in which our established convergence rates are tight to a constant factor with a reasonably small stepsize. Finally, we validate our theoretical results by performing large-scale numerical experiments that reveal the convergence behavior of Local SGD for practical over-parameterized deep learning models, in which the O(1/T) convergence rate of Local SGD is clearly shown. Copyright © 2022, The Authors. All rights reserved.

关键词： Parameterization

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A Wireless Implantable Microsystem for Real-Time Bladder Volume Monitoring

TechRxiv

引用

TechRxiv 2022年

作者： Mandal, Soumyajit Dehghanzadeh, Parisa Zamani, Hossein Shaik, M. Sameer The Department of Electrical Computer and Systems Engineering Case Western Reserve University ClevelandOH44106 United States The Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Brookhaven National Laboratory United States

This paper describes an implantable wireless microsystem for making embedded ultrasound distance measurements and its applications in real-time bladder volume (BV) estimation. A tuned inductive link at 125 kHz is used for both power transfer and bidirectional data telemetry with the implanted unit. Implant power consumption is minimized by using a single-bit correlation receiver to estimate ultrasound time-of-flight (ToF) from A-mode data acquired by an on-board piezoelectric transducer. In vitro tests of the device (external diameter = 30 mm including coil) in saline solution show a maximum operating depth of 3.2 cm from a 10 cm diameter external coil operated at ±18 V. Wireless BV measurements using both spherical and non-spherical bladder phantoms show estimation errors ≤ 15% for volumes in the 50-300 mL range. © 2022, CC BY.

关键词： Energy transfer

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