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International Symposium on Digital Forensic and Security (ISDFS)

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

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

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.

关键词： Semantics Digital forensics Buildings Knowledge graphs Organizations Ontologies Data models

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Robotic fish:Opening a new era of underwater detection

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The Innovation 2025年第3期6卷 13-14页

作者： Dongfang Li Suet To Zhicheng Xu Guanghong Liu Rob Law Edmond Q.Wu Limin Zhu State Key Laboratory of Ultraprecision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China School of Electrical Engineering and Automation Fuzhou UniversityFujian 350108China China Electronics Technology Group Corporation Information Science Research InstituteBeijing 100086China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Asia-Pacific Academy of Economics and Management Faculty of Business AdministrationUniversity of MacaoMacao 999078China Department of Computer Science and Engineering Shanghai JiaoTong UniversityShanghai 200240China

Escalating competition within aquatic environments,particularly in oceans,underscores the imperative for innovative solutions in underwater *** solutions necessitate the development of intelligent underwater equipment,with robotic fish emerging as a promising contender.1 Leveraging advancements in robotics and artificial intelligence,robotic fish offer a suite of advantages that position them as transformative assets in underwater exploration and *** fish are autonomous robots designed based on biomimetics principles that mimic the appearance of fish and can autonomously swim and perform specific tasks in water.

关键词： robotic fish intelligent underwater equipmentwith autonomous robots underwater detection oceans artificial intelligencerobotic fish aquatic environments innovative solutions

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Class-specific image encryption using a diffractive optical processor

Class-specific image encryption using a diffractive optical ...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Bai, Bijie Wei, Heming Yang, Xilin Gan, Tianyi Mengu, Deniz Jarrahi, Mona Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los Angeles90095 United States Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China University of California Los AngelesCA United States

We present a framework for all-optical image encryption that is class-specific, performing a distinct image transformation function for each data class. Experimental validation of this method was conducted using both ... 详细信息

ISBN: (纸本)9781957171395

关键词： Optical depth

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

Binary Bleed: Fast Distributed and Parallel Method for Autom...

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IEEE Conference on High Performance Extreme Computing (HPEC)

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

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as nonnegative 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.

关键词： Instruction sets Computational modeling Heuristic algorithms Power system stability Stability analysis Elbow Distributed computing Optimization Overfitting Software development management

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Impact and Mitigation of Electromagnetic Interference Between HVTL and Pipelines

Impact and Mitigation of Electromagnetic Interference Betwee...

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IEEE Southeastcon

作者： Hossam Hussein Abd El-Latif S. Ahmed Sabry Mousa El-Sayed M. El-Refaie Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA Department of Electrical Power and Machines Engineering Faculty of Engineering Helwan University Cairo Egypt

High-voltage transmission lines transmit bulk power from one place to another and may pass through densely populated areas or forests, causing an economic challenge. While following the right-of-way (ROW) rules, high-voltage transmission lines are forced to share the same corridors with others, like metallic gas pipelines. In this regard, the electromagnetic fields from high-voltage transmission lines will affect neighboring pipelines using inductive, conductive, and capacitive effects. As a result, there will be an induced voltage across the metallic pipes, which will have negative repercussions. An example of this includes corrosion in the pipeline itself and those in direct contact with the pipeline via touch and step voltages. This paper studies the electromagnetic interference for the El-Fayoum-Dahshour gas pipeline, which shares the same route for 72 km with three overhead transmission lines, two of which are 500 kV and the third is 220 kV. Several study cases were done to show the impact of soil resistivity variation on the pipeline-induced voltage. The results conclude that the LG fault is the worst-case scenario. Moreover, mitigating the total induced voltage to the recommended safe limits was achieved by applying the polarization cell method.

关键词： Power transmission lines Pipelines Electromagnetic interference Voltage High-voltage techniques Forestry Soil

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Real-Time Implementation of a Spiking Neural Network-Based Control: An Application for the Ball and Plate System

Real-Time Implementation of a Spiking Neural Network-Based C...

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2024 American Control Conference, ACC 2024

作者： Arana, Diego Chavez Garcia A, Omar A. Scola, Ignacio Rubio Espinoza, Eduardo S. Garcia Carrillo, Luis Rodolfo Sornborger, Andrew Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM United States National Polytechnic Institute Center for Research and Advanced Studies Mexico City Mexico National Institute of Industrial Technology CONICET National University of Rosario Department of Industrial Products Engineering Argentina Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9798350382655

We present a neuromorphic computing control architecture for the problem of stabilizing a benchmark sub-actuated system: the ball and plate platform. The proposed architecture makes use of Spiking Neural Networks (SNNs) as an alternative to traditional von Neumann computing. The Neural engineering Framework (NEF) is adopted to encode the SNN-based controller to accomplish position and trajectory tasks. Simulation results and a real-time implementation of the proposed SNN controller are presented over a homemade ball and plate prototype. The effectiveness of the proposed neuromorphic controller is demonstrated, even in situations where the system is affected by external impulsive disturbances. © 2024 AACC.

关键词： Neural networks

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Design, Dynamic Modeling and Control of a 2-DOF Robotic Wrist Actuated by Twisted and Coiled Actuators

arXiv

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arXiv 2025年

作者： Zhang, Yunsong Zhou, Xinyu Zhang, Feitian Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States

Robotic wrists play a pivotal role in the functionality of industrial manipulators and humanoid robots, facilitating manipulation and grasping tasks. In recent years, there has been a growing interest in integrating artificial muscle-driven actuators for robotic wrists, driven by advancements in technology offering high energy density, lightweight construction, and compact designs. However, in the study of robotic wrists driven by artificial muscles, dynamic model-based controllers are often overlooked, despite their critical importance for motion analysis and dynamic control of robots. This paper presents a novel design of a two-degree-of-freedom (2-DOF) robotic wrist driven by twisted and coiled actuators (TCA) utilizing a parallel mechanism with a 3RRRR configuration. The proposed robotic wrist is expected to feature lightweight structures and superior motion performance while mitigating friction issues. The Lagrangian dynamic model of the wrist is established, along with a nonlinear model predictive controller (NMPC) designed for trajectory tracking tasks. A prototype of the robotic wrist is developed, and extensive experiments are conducted to validate its superior motion performance and the proposed dynamic model. Subsequently, extensive comparative experiments between NMPC and PID controller were conducted under various operating conditions. The experimental results demonstrate the effectiveness and robustness of the dynamic model-based controller in the motion control of TCA-driven robotic wrists. Copyright © 2025, The Authors. All rights reserved.

关键词： Industrial manipulators

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Hybrid Three-Layer Sub-Harmonic Synchronous Machine using Consequent Pole Permanent Magnets

Hybrid Three-Layer Sub-Harmonic Synchronous Machine using Co...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： S M Sajjad Hossain Rafin Qasim Ali Fei Zhao Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami FL USA Department of Electrical Engineering Sukkur IBA University Sukkur Pakistan School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

A novel three-layer sub-harmonic synchronous machine is proposed, equipped with consequent permanent magnets and windings for superior torque generation and better overall performance. The consequent pole PM rotor design reduces the PM quantity by half versus conventional configurations without compromising performance. Finite element analysis of an 8-pole, 48-slot machine verifies a 6% increase in torque generation and 4 % lower torque ripple compared to a fully PM -assisted design. The proposed machine also outperforms alternatives on metrics, including torque density and efficiency. These favorable characteristics demonstrate the promise of the topology as a cost-effective, high-performing electric propulsion candidate. This work contributes to an innovative machine advancing progress toward affordable PM -assisted synchronous motors.

关键词： Measurement Torque Windings Rotors Magnetic devices Production Propulsion

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AI/ML Based Detection and Categorization of Covert Communication in IPv6 Network

arXiv

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arXiv 2025年

作者： Rahman, Mohammad Wali Ur Lin, Yu-Zheng Weeks, Carter Ruddell, David Gabriellini, Jeff Hayes, Bill Hariri, Salim Ziegler, Edward V. Department of Electrical and Computer Engineering University of Arizona TucsonAZ85721 United States Department of Management Information Systems University of Arizona TucsonAZ85721 United States Department of Systems and Industrial Engineering University of Arizona TucsonAZ85721 United States Laboratory for Advanced Cybersecurity Research National Security Agency United States

The flexibility and complexity of IPv6 extension headers allow attackers to create covert channels or bypass security mechanisms, leading to potential data breaches or system compromises. The mature development of machine learning has become the primary detection technology option used to mitigate covert communication threats. However, the complexity of detecting covert communication, evolving injection techniques, and scarcity of data make building machine-learning models challenging. In previous related research, machine learning has shown good performance in detecting covert communications, but oversimplified attack scenario assumptions cannot represent the complexity of modern covert technologies and make it easier for machine learning models to detect covert communications. To bridge this gap, in this study, we analyzed the packet structure and network traffic behavior of IPv6, used encryption algorithms, and performed covert communication injection without changing network packet behavior to get closer to real attack scenarios. In addition to analyzing and injecting methods for covert communications, this study also uses comprehensive machine learning techniques to train the model proposed in this study to detect threats, including traditional decision trees such as random forests and gradient boosting, as well as complex neural network architectures such as CNNs and LSTMs, to achieve detection accuracy of over 90%. This study details the methods used for dataset augmentation and the comparative performance of the applied models, reinforcing insights into the adaptability and resilience of the machine learning application in IPv6 covert communication. In addition, we also proposed a Generative AI-assisted interpretation concept based on prompt engineering as a preliminary study of the role of Generative AI agents in covert communication. © 2025, CC BY-NC-ND.

关键词： Decision trees

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Enhanced ferromagnetism in an artificially stretched lattice in quasi-two-dimensional Cr2Ge2Te6

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Physical Review B 2025年第2期111卷 L020402-L020402页

作者： Hiroshi Idzuchi Andres E. Llacsahuanga Allcca Anh Khoa Augustin Lu Mitsuhiro Saito Subhadip Das John F. Ribeiro Michel Houssa Ruishen Meng Kazutoshi Inoue WPI Advanced Institute for Materials Research (AIMR) Tohoku University Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (CSIS) Tohoku University Sendai 980-8577 Japan Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Mathematics for Advanced Materials Open Innovation Laboratory National Institute of Advanced Industrial Science and Technology Sendai 980-8577 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Institute of Engineering Innovation The University of Tokyo Tokyo 113-8656 Japan Institute of Physics and Astronomy and Villum Centers for Dirac Materials and for Hybrid Quantum Materials and Devices Aarhus University 8000 Aarhus-C Denmark School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Department of Physics and Astronomy Katholieke Universiteit Leuven 3001 Leuven Belgium

Crystal lattice is key in understanding magnetic interactions between atoms in solids. As the effective control of the lattice via tensile stress is limited, there are only a few demonstrations of controlling magnetic properties by expanding the lattice. This study reveals a clear correlation between enhanced magnetism, where Curie temperature (TC) is doubled from ∼60 to ∼120 K, and lattice expansion in prototypical van der Waals magnet Cr2Ge2Te6 with sputtered dielectric overlayers NiO and MgO. We ascribe the mechanism of this TC increase to a change in exchange interactions induced by strain, studied by several experimental probes and computational approaches.

关键词： Van der Waals forces

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