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2023 Resilience Week, RWS 2023

作者： Makrakis, Georgios Michail Roberson, Dakota Kolias, Constantinos University of Idaho Department of Computer Science Idaho FallsID83402 United States University of Idaho Department of Electrical & Computer Engineering Idaho FallsID83402 United States

ISBN: (纸本)9798350347470

Threats against critical infrastructure in various sectors (power, water, etc.) are constantly present as their cyber-physical control and communication systems introduce a combination of modern and legacy components. Various security standards facilitate the application of security controls in such environments, with systematic guidance about best practices, and priorities for requirements implementation. This article provides a model to assist the compliance of substation automation networks with the IEC 62443-3-3 standard by employing the Software-Defined Networking (SDN) paradigm. First, we explore how the requirements of the standard can be fulfilled by an asset owner by applying SDN solutions. Next, we present the applicability of such a model to a modern smart grid through the context of a distribution system substation. The model will be used to implement the necessary functionality for asset owners to meet the requirements of the industry standard and improve the grid's resilience, while at the same time automating a multitude of network security capabilities. © 2023 IEEE.

关键词： Software defined networking

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Automated Phase and Orientation Mapping of Multiphase, Polycrystalline Hafnia-Zirconia Thin Films Using 4D-STEM

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 300-302页

作者： Baucom, Garrett Hershkovitz, Eitan Sondhi, Kartik Nishida, Toshikazu Kim, Honggyu Department of Materials Science & Engineering University of Florida Gainesville FL United States Department of Electrical & Computer Engineering University of Florida Gainesville FL United States

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Lightweight Image Inpainting By Stripe Window Transformer With Joint Attention To CNN 33

Lightweight Image Inpainting By Stripe Window Transformer Wi...

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33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023

作者： Chen, Bo-Wei Liu, Tsung-Jung Liu, Kuan-Hsien National Chung Hsing University Department of Electrical Engineering Graduate Institute of Communication Engineering Taiwan National Taichung University of Science and Technology Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324112

Image inpainting is an important task in computer vision. As admirable methods are presented, the inpainted image is getting closer to reality. However, the result is still not good enough in the reconstructed texture and structure based on human vision. Although recent advances in computer hardware have enabled the development of larger and more complex models, there is still a need for lightweight models that can be used by individuals and small-sized institutions. Therefore, we propose a lightweight model that combines a specialized transformer with a traditional convolutional neural network (CNN). Furthermore, we have noticed most researchers only consider three primary colors (RGB) in inpainted images, but we think this is not enough. So we propose a new loss function to intensify color details. Extensive experiments on commonly seen datasets (Places2 and CelebA) validate the efficacy of our proposed model compared with other state-of-the-art methods. The source code and pretrained models are available at https://***/RzD11n. © 2023 IEEE.

关键词： computer hardware

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Colonic Pressure Signal Denoising Based on Improved Threshold Function and Threshold Optimization 24

Colonic Pressure Signal Denoising Based on Improved Threshol...

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3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Cui, Liu Du, Yupeng Zhao, Kai Shi, Yanjiao Yang, Fan Shanghai Institute of Technology Department of Computer Science and Information Engineering Shanghai201418 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai201418 China

ISBN: (纸本)9798400716416

Wavelet threshold noise reduction is widely used because of its simple algorithm and small computation. The properties of the threshold function are studied and a new two-parameter adjustable threshold function is designed to solve the problems of signal loss and local oscillations caused by soft and hard threshold function noise reduction. The threshold function has the properties of continuity, small constant deviation and high order differentiability. Meanwhile, an artificial hummingbird algorithm based on a mixture of Cubic chaotic mapping, global Cauchy variation and golden sine strategy is used for optimization of thresholds and adjustment factors. Applying this algorithm in noise reduction of colonic pressure signals, most of the noise can be filtered out and the details of colonic peristaltic signals are preserved. © 2024 ACM.

关键词： Noise abatement

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Mitigation of Sub-Synchronous Oscillations to Improve Grid Stability and Efficiency Using EMT-Based Analysis and Frequency Scanning Algorithms

Mitigation of Sub-Synchronous Oscillations to Improve Grid S...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Muhammad Umar Afzaal Yuqing Dong Yilu Liu Electrical Engineering and Computer Science University of Tennessee Knoxville USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Integrating inverter-based resources (IBRs) into the grid significantly alters the dynamics of modern power systems, and IBRs have been linked to sub-synchronous oscillation (SSO) events worldwide. Identifying the conditions favorable for the occurrence of SSO is essential to ensure the stability of the system and prevent adverse interactions that could damage the components of the power system or even lead to system failure. This paper investigates a specific 20 Hz oscillation event that took place at 5:30 AM on November 21, 2021, at the Kaua'i power system following the tripping of an oil power plant. Using an electromagnetic transient (EMT) model in PSCAD, we performed a sensitivity analysis to examine the conditions and scenarios that contribute to the creation of SSO's. Our findings reveal that specific Synchronous Reference Frame Phase-Locked Loop (SRF-PLL), when interacting with shunt compensation, plays a significant role in the generation of SSO's. To validate these findings, a frequency-scanning-based impedance analysis (FSBIA) is conducted. FSBIA provides a comprehensive assessment of the impedance characteristics of the system on a wide range of frequencies, which is used to identify the stability of the system. The study illustrates that precise tuning of SRF-PLL parameters can effectively mitigate SSO risks.

关键词： Sensitivity analysis Prevention and mitigation Power system dynamics Power system stability Stability analysis Power grids Impedance Phase locked loops Oscillators Tuning

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Ensemble Knowledge Distillation of Self-Supervised Speech Models

Ensemble Knowledge Distillation of Self-Supervised Speech Mo...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Huang, Kuan-Po Feng, Tzu-Hsun Fu, Yu-Kuan Hsu, Tsu-Yuan Yen, Po-Chieh Tseng, Wei-Cheng Chang, Kai-Wei Lee, Hung-Yi National Taiwan University College of Electrical Engineering and Computer Science Taiwan Asus Intelligent Cloud Services

ISBN: (纸本)9781728163277

Distilled self-supervised models have shown competitive performance and efficiency in recent years. However, there is a lack of experience in jointly distilling multiple self-supervised speech models. In our work, we performed Ensemble Knowledge Distillation (EKD) on various self-supervised speech models such as HuBERT, RobustHuBERT, and WavLM. We tried two different aggregation techniques, layerwise-average and layerwise-concatenation, to the representations of different teacher models and found that the former was more effective. On top of that, we proposed a multiple prediction head method for student models to predict different layer outputs of multiple teacher models simultaneously. The experimental results show that our method improves the performance of the distilled models on four downstream speech processing tasks, Phoneme Recognition, Speaker Identification, Emotion Recognition, and Automatic Speech Recognition in the hidden-set track of the SUPERB benchmark. © 2023 IEEE.

关键词： Distillation

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Prediction Analysis of Greeting Gestures based on Data Augmentation and LSTM 23

Prediction Analysis of Greeting Gestures based on Data Augme...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wibow, Angga Wahyu Sato-Shimokawara, Eri Kurnianingsih Graduate School of System Design Tokyo Metropolitan University Department of Computer Science Tokyo Japan Politeknik Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798331528041

The field of Human Activity Recognition (HAR) is growing significantly in several areas but little research focuses on cultural behavior. How machine learning can explain human activity as a promotional tool in understanding cultural differences in a region is very challenging. Studies using Recurrent Neural Network (RNN), especially Long Short-Term Memory (LSTM), require large data when used to research human activity recognition. Using small data is a challenge when using LSTM. This study aims to determine which data augmentation is most appropriate when used using LSTM to predict Japanese greeting gestures, namely eshaku, keirei, saikeirei, waving hands, and Indonesian greeting gestures, namely the movement of bringing both hands together in front of the chest with a slight bow of the head. This study proposes to compare several data augmentations, namely jittering, scaling, permutation, cropping, reverse, time shifting, frequency domain, and synthetic data generation. We evaluate model performance using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2) metrics. The experimental results show that Time Shifting has the best MSE, RMSE, and MAE values compared to others and Frequency Domain has the best R2 values compared to others. This study concludes that Time Shifting and Frequency Domain are the best data augmentation methods when using LSTM in predicting greeting gestures using small data. These findings provide valuable insights into the effectiveness of various data augmentation methods in motion prediction tasks with limited data sets, as well as the importance of selecting appropriate data augmentation methods in human motion analysis. Future work could use data from two or more people using model explanations generated by machine learning and developing a new method for cultural behavior. © 2024 IEEE.

关键词： Mean square error

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Literature Review of Quantum-dot Cellular Automata (QCA) Technology used in Cryptography 5

Literature Review of Quantum-dot Cellular Automata (QCA) Tec...

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5th College of science International Conference on Recent Trends in Information Technology, CSCTIT 2022

作者： Sayal, Maha A. Albermany, S.A. Alkaldy, Esam Al College of Computer and Mathematics Science University of Thi-Qar Computer Science Department Thi-Qar Iraq University of Kufa Faculty of Computer Science and Mathematics Computer Department Kufa Iraq Collage of Engineering University of Kufa Electrical Engineering Kufa Iraq

ISBN: (纸本)9798350334920

Cellular quantum dot (QCA) technology is a recent technology. It is used to get rid of the limitations of complementary metal semiconductor (CMOS). This is why high-speed QCA circuits, ultra-low consumption, and less space are designed. to encrypt securely, the purpose is the continuous development of the encryption and decryption process. Therefore, we use electronic circuits and gates such as the majority gate, inverter gate, and MUX circuit in this paper. The cipher used here is stream. © 2022 IEEE.

关键词： Cryptography

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Computational Fabrication and Assembly for In Situ Manufacturing

arXiv

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arXiv 2024年

作者： Nisser, Martin Eric William Department of Electrical Engineering and Computer Science United States

Fabrication today relies on disparate, large machines spread across industrial facilities. These are operated by domain experts to construct and assemble artefacts in sequential steps from large numbers of parts. This traditional, centralized mass manufacturing paradigm is characterized by large capital costs and inflexibility to changing needs, complex global supply chains hinged on economic and political stability, and waste and over-manufacturing of uniform artefacts that fail to meet the technical and personal needs of today’s diverse individuals and use cases. Today, these challenges are particularly severe at points of need, such as the space environment. The space environment is remote and unpredictable, and the ability to manufacture in situ offers unique opportunities to address new challenges as they arise. However, the challenges faced in space are often mirrored on Earth. In hospitals, disaster zones, low resource environments and laboratories, the ability to manufacture customized artefacts at points of need can significantly enhance our ability to respond rapidly to unforeseen events. In this thesis, I introduce digital fabrication platforms with co-developed hardware and software that draw on tools from robotics and human-computer interaction to automate manufacturing of customized artefacts at the point of need. Highlighting three research themes across fabrication machines, modular assembly, and programmable materials, the thesis will cover a digital fabrication platform for producing functional robots, a modular robotic platform for in-space assembly deployed in microgravity, and a method for programming magnetic material to selectively assemble. © 2024, CC BY-NC-ND.

关键词： Supply chains

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Identifying and Addressing Security Vulnerabilities in Hybrid Network Security Algorithms

Identifying and Addressing Security Vulnerabilities in Hybri...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Pipersenia, Arun Kumar Nidhya, M.S. Jain, Sandeep Kumar College of Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (纸本)9798350383348

Hybrid network protection algorithms represent a critical vicinity in facts safety techniques. Inside the computation of networking environments, such algorithms provide a cozy and dependable mechanism for site visitor management, records storage, entry to manipulation, and so on. The security of this mechanism is, but suffering from the presence of certain safety vulnerabilities within the gadget. To ensure the powerful operation of modern-day networks, these vulnerabilities need to be identified and addressed. This paper explores some of the procedures to be had for the identification and addressing of the safety vulnerabilities in hybrid community security algorithms: The security goals the ones want to be worked on, consisting of authentication, authorization, records integrity, and confidentiality, are mentioned. Several assault fashions, consisting of insider attacks, man-in-the-center assaults, and brute-pressure assaults, are also stated to detail the vulnerabilities in hybrid protection algorithms. Modern practices for figuring out and addressing safety vulnerabilities in hybrid community security algorithms are supplied. Those consist of techniques which include reading network site visitors, assessing the trustworthiness of customers, enforcing encryption techniques, and applying community entry to controls. Several protection models, in addition to protocols for reaching secure verbal exchange in hybrid networks, are examined. © 2024 IEEE.

关键词： Algorithms Critical Examined Network Protection Vicinity

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