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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Venkata Yaramasu Kristiyan Milev Apparao Dekka Jose Rodriguez School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff USA Lakehead University Thunder Bay Canada Faculty of Engineering Universidad Andres Bello Santiago Chile

ISBN: (数字)9781728158266

ISBN: (纸本)9781728158273

In this paper, an efficient modulated model predictive current control of back-to-back connected neutral-point clamped (NPC) converter in direct-driven surface-mounted permanent magnet synchronous generator-based variable-speed wind energy conversion system is presented. The proposed control scheme fulfils the wind energy requirements such as maximum power point tracking, control of neutral-point voltage and grid reactive power regulation through the control of generator and grid currents with fast transient response, smooth steady-state and fixed switching frequency operation simultaneously. The three voltage vectors corresponding to the optimal cost functions of NPC rectifier and inverter are synthesized by the space vector modulation with active control of dwell times to balance the DC capacitors voltage. To evaluate the proposed control scheme, dynamic simulation results are presented for a 3 MW and 3000 V wind turbine under varying wind speed conditions.

关键词： Voltage control Switches Generators Cost function Inverters Switching frequency

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PREDICTING THE FUTURE: DIVERSE PERSPECTIVES ON THE TRANSFORMATION OF HEALTHCARE DELIVERY OVER THE NEXT 15 YEARS 64th

PREDICTING THE FUTURE: DIVERSE PERSPECTIVES ON THE TRANSFORM...

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64th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2020

作者： Patterson, Emily S. Anders, Shilo Militello, Laura G. Saleem, Jason J. Perry, Shawna Rothwell, Clayton Weiler, Dustin School of Health and Rehabilitation Sciences The Ohio State University ColumbusOH United States Departments of Anesthesiology and Biomedical Informatics Vanderbilt University School of Medicine NashvilleTN United States Center for Research and Innovation in Systems Safety Vanderbilt University Medical Center NashvilleTN United States Applied Decision Science LLC DaytonOH United States Department of Industrial Engineering J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Department of Emergency Medicine University of Florida College of Medicine JacksonvilleFL United States Human Autonomy Systems Department Infoscitex Corporation DaytonOH United States Department of Industrial and Systems Engineering University of Wisconsin-Madison MadisonWI United States

Over the last 15 years, healthcare delivery has experienced enormous changes across every aspect of clinical care, both for participants in it and affiliated stakeholders. The plethora and breadth of these changes and ongoing struggles with their incorporation into the work of healthcare begs the question: In 2035, what emerging innovations and considerations will we wish we had prepared for with regard to human factors engineering research, technology development, and system redesign efforts? Panelists and co-chairs have deep experience with steering, and being impacted by, innovations in information technology in healthcare. The panelists represent diverse academic, clinical, government contract research and development, and small entrepreneurial business. Perhaps the biggest benefit of this session will be the explicit inclusion of the audience in brainstorming and discussion activities, which will be facilitated digitally and verbally during the session and shared afterwards electronically with participants. © 2020 by Human Factors and Ergonomics Society. All rights reserved.

关键词： Health care

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Tactile facial action units toward enriching social interactions for individuals who are blind 1st

Tactile facial action units toward enriching social interact...

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1st International Conference on Smart Multimedia, ICSM 2018

作者： McDaniel, Troy Devkota, Samjhana Tadayon, Ramin Duarte, Bryan Fakhri, Bijan Panchanathan, Sethuraman Center for Cognitive Ubiquitous Computing School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

ISBN: (纸本)9783030043742

Social interactions mediate our communication with others, enable development and maintenance of personal and professional relationships, and contribute greatly to our health. While both verbal cues (i.e., speech) and non-verbal cues (e.g., facial expressions, hand gestures, and body language) are exchanged during social interactions, the latter encompasses more information (~65%). Given their inherent visual nature, non-verbal cues are largely inaccessible to individuals who are blind, putting this population at a social disadvantage compared to their sighted peers. For individuals who are blind, embarrassing social situations are not uncommon due to miscommunication, which can lead to social avoidance and isolation. In this paper, we propose a mapping between visual facial expressions, represented as facial action units, which may be extracted using computer vision algorithms, to haptic (vibrotactile) representations, toward discreet and real-time perception of facial expressions during social interactions by individuals who are blind. © 2018, Springer Nature Switzerland AG.

关键词： Mapping

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k-core structure of real multiplex networks

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Physical Review Research 2020年第2期2卷 023176-023176页

作者： Saeed Osat Filippo Radicchi Fragkiskos Papadopoulos Deep Quantum Labs Skolkovo Institute of Science and Technology Moscow 143026 Russia Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 33 Saripolou Street 3036 Limassol Cyprus

Multiplex networks are convenient mathematical representations for many real-world—biological, social, and technological—systems of interacting elements, where pairwise interactions among elements have different flavors. Previous studies pointed out that real-world multiplex networks display significant interlayer correlations—degree-degree correlation, edge overlap, node similarities—able to make them robust against random and targeted failures of their individual components. Here, we show that interlayer correlations are important also in the characterization of their k-core structure, namely, the organization in shells of nodes with an increasingly high degree. Understanding of k-core structures is important in the study of spreading processes taking place on networks, as for example in the identification of influential spreaders and the emergence of localization phenomena. We find that, if the degree distribution of the network is heterogeneous, then a strong k-core structure is well predicted by significantly positive degree-degree correlations. However, if the network degree distribution is homogeneous, then strong k-core structure is due to positive correlations at the level of node similarities. We reach our conclusions by analyzing different real-world multiplex networks, introducing novel techniques for controlling interlayer correlations of networks without changing their structure, and taking advantage of synthetic network models with tunable levels of interlayer correlations.

关键词： Clustering Degree distributions Network phase transitions Network resilience Network structure Robustness

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Evaluating Automatically Generated YARA Rules and Enhancing Their Effectiveness

Evaluating Automatically Generated YARA Rules and Enhancing ...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Nitin Naik Paul Jenkins Roger Cooke Jonathan Gillett Yaochu Jin School of Informatics and Digital Engineering Aston University United Kingdom School of Computing University of Portsmouth United Kingdom Defence School of Communications and Information Systems Ministry of Defence United Kingdom University of Surrey United Kingdom

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

Emerging as a widely accepted technique for malware analysis, YARA rules due to its flexible and customisable nature, allows malware analysts to develop rules according to the requirements of a specific security domain. YARA rules can be automatically generated using tools, however, they may require post-processing for their optimisation, and may not be effective for the specific security domain. This compels the requirement to enhance automatically generated YARA rules and increase their effectiveness for malware analysis without increasing computational overheads. Reflecting on the above requirement, this paper initially evaluates automatically generated YARA rules using three YARA tools: yarGen, yaraGenerator and yabin. These tools are Python-based open-source tools used to generate YARA rules automatically utilising different underlying techniques. Subsequently, it proposes a method to enhance automatically generated YARA rules using a fuzzy hashing method. This proposed enhancement method can improve the effectiveness of YARA rules irrespective of the chosen YARA tool used to generate YARA rules, which is demonstrated through several experiments on samples of collected malware and goodware.

关键词： Tools Ransomware Operating systems Databases Cryptography Whitelists Libraries

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Predicting future cognitive decline with hyperbolic stochastic coding

arXiv

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arXiv 2021年

作者： Zhang, Jie Dong, Qunxi Shi, Jie Li, Qingyang Stonnington, Cynthia M. Gutman, Boris A. Chen, Kewei Reiman, Eric M. Caselli, Richard J. Thompson, Paul M. Ye, Jieping Wang, Yalin School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Department of Psychiatry and Psychology Mayo Clinic Arizona ScottsdaleAZ United States Armour College of Engineering Illinois Institute of Technology ChicagoIL United States Banner Alzheimer’s Institute PhoenixAZ United States Department of Neurology Mayo Clinic Arizona ScottsdaleAZ United States Imaging Genetics Center Institute for Neuroimaging and Informatics University of Southern California Los AngelesCA United States Department of Computational Medicine and Bioinformatics Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States

Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However, such approaches, similar to other surface-based brain morphology analysis methods, usually generate high dimensional features. It limits their statistical power in cognitive decline prediction research, especially in datasets with limited subject numbers. To address the above limitation, we propose a novel framework termed as hyperbolic stochastic coding (HSC). We first compute diffeomorphic maps between general topological surfaces by mapping them to a canonical hyperbolic parameter space with consistent boundary conditions and extracts critical shape features. Secondly, in the hyperbolic parameter space, we introduce a farthest point sampling with breadth-first search method to obtain ring-shaped patches. Thirdly, stochastic coordinate coding and max-pooling algorithms are adopted for feature dimension reduction. We further validate the proposed system by comparing its classification accuracy with some other methods on two brain imaging datasets for Alzheimer’s disease (AD) progression studies. Our preliminary experimental results show that our algorithm achieves superior results on various classification tasks. Our work may enrich surface-based brain imaging research tools and potentially result in a diagnostic and prognostic indicator to be useful in individualized treatment strategies. © 2021, CC BY-NC-SA.

关键词： Classification (of information)

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Coherent manipulation of graph states composed of finite-energy Gottesman-Kitaev-Preskill-encoded qubits

arXiv

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arXiv 2021年

作者： Seshadreesan, Kaushik P. Dhara, Prajit Patil, Ashlesha Jiang, Liang Guha, Saikat Wyant College of Optical Sciences The University of Arizona TucsonAZ85721 United States Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh PittsburghPA15260 United States Birla Institute of Technology & Science Pilani Rajasthan Pilani333031 India Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60637 United States

Graph states are a central resource in measurement-based quantum information processing. In the photonic qubit architecture based on Gottesman-Kitaev-Preskill (GKP) encoding, the generation of high-fidelity graph states composed of realistic, finite-energy approximate GKP-encoded qubits thus constitutes a key task. We consider the finite-energy approximation of GKP qubit states given by a coherent superposition of shifted finite-squeezed vacuum states, where the displacements are Gaussian distributed. We present an exact description of graph states composed of such approximate GKP qubits as a coherent superposition of a Gaussian ensemble of randomly displaced ideal GKP-qubit graph states. Using standard Gaussian dynamics, we track the transformation of the covariance matrix and the mean displacement vector elements of the Gaussian distribution of the ensemble under tools such as GKP-Steane error correction and fusion operations that can be used to grow large, high-fidelity GKP-qubit graph states. The covariance matrix elements capture the noise in the graph state due to the finite-energy approximation of GKP qubits, while the mean displacements relate to the possible absolute shift errors on the individual qubits arising conditionally from the homodyne measurements that are a part of these tools. Our work thus pins down an exact coherent error model for graph states generated from truly finite-energy GKP qubits, which can shed light on their error correction properties. © 2021, CC BY.

关键词： Covariance matrix

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A Univariate Persistent Brain Network Feature Based on the Aggregated Cost of Cycles from the Nested Filtration Networks

A Univariate Persistent Brain Network Feature Based on the A...

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IEEE International Symposium on Biomedical Imaging

作者： Mohammad Farazi Liang Zhan Natasha Lepore Paul M. Thompson Yalin Wang School of Computing Informatics and Decision Systems Eng. Arizona State Univ. Tempe AZ Electrical and Computer Engineering Univ. of Pittsburgh Pittsburgh PA CIBORG Lab Children's Hospital Los Angeles Los Angeles CA Imaging Genetics Center Inst. for Neuroimaging and Informatics Univ. of Southern California Los Angeles CA

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

A threshold-free feature in brain network analysis can help circumvent the curse of arbitrary network thresholding for binary network conversions. Here, Persistent Homology is the inspiration for defining a new aggregation cost based on the number of cycles, or for tracking the first Betti number in a nested filtration network within the graph. Our theoretical analysis shows that the proposed aggregated cost of cycles (ACC) is monotonically increasing and thus we define a univariate persistent feature based on the shape of ACC. The proposed statistic has advantages compared to the First Betti Number Plot (BNP1), which only tracks the total number of cycles at each filtration. We show that our method is sensitive to both the topology of modular networks and the difference in the number of cycles in a network. Our method outperforms its counterparts in a synthetic dataset, while in a realworld one it achieves results comparable with the BNP1. Our proposed framework enriches univariate measures for discovering brain network dissimilarities for better categorization of distinct stages in Alzheimer's Disease (AD).

关键词： Topology Indexes Network topology Diffusion tensor imaging Shape Biomedical imaging

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Predicting driver takeover time in conditionally automated driving

arXiv

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arXiv 2021年

作者： Ayoub, Jackie Du, Na Yang, X. Jessie Zhou, Feng Department of Industrial and Manufacturing Systems Engineering University of Michigan Dearborn 4901 Evergreen Rd. DearbornMI48128 United States Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh 135 N Bellefield Ave PittsburghPA15213 United States Industrial and Operations Engineering University of Michigan Ann Arbor 500 S State St Ann ArborMI48109 United States

It is extremely important to ensure a safe takeover transition in conditionally automated driving. One of the critical factors that quantifies the safe takeover transition is takeover time. Previous studies identified the effects of many factors on takeover time, such as takeover lead time, non-driving tasks, modalities of the takeover requests (TORs), and scenario urgency. However, there is a lack of research to predict takeover time by considering these factors all at the same time. Toward this end, we used eXtreme Gradient Boosting (XGBoost) to predict the takeover time using a dataset from a meta-analysis study [1]. In addition, we used SHAP (SHapley Additive exPlanation) to analyze and explain the effects of the predictors on takeover time. We identified seven most critical predictors that resulted in the best prediction performance. Their main effects and interaction effects on takeover time were examined. The results showed that the proposed approach provided both good performance and explainability. Our findings have implications on the design of in-vehicle monitoring and alert systems to facilitate the interaction between the drivers and the automated vehicle. © 2021, CC BY.

关键词： Forecasting

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A dynamic reconfigurable wearable device to acquire high quality PPG signal and robust heart rate estimate based on deep learning algorithm for smart healthcare system

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Biosensors and Bioelectronics: X 2022年 12卷

作者： Ngoc-Thang, Bui Tien Nguyen, Thi My Truong, Trong Toai Nguyen, Bang Le-Huy Nguyen, Tuy Tan Institute of Engineering HUTECH University 475A Dien Bien Phu Street Ward 25 Binh Thanh District Ho Chi Minh City Viet Nam Department D of Pediatrics at the Hospital for Tropical Diseases Ho Chi Minh City Viet Nam 3C Manufacturing Machinery Ltd. Ho Chi Minh Viet Nam National Renewable Energy Laboratory GoldenCO United States School of Informatics Computing and Northern Arizona University Cyber Systems FlagstaffAZ86011 United States

Photoplethysmography (PPG) is a noninvasively technique used to detect vital signs such as heart rate (HR), saturation of peripheral oxygen (SpO2) and blood pressure (BP). Recently, wearable finger-type PPG devices are increasingly developed toward convenience of person under monitoring (PUM). Two most critical features of wearable PPG devices are high accuracy and long operation time. To enhance these functions, this paper proposes a new architecture to select, process and transfer only high quality PPG signals. Hence, data quality is significantly improved and power consumption on wireless module is minimized. In the proposed architecture, parameters of PPG sensor are reconfigured in real-time being suitable with skin characteristics of PUM. Moreover, the adaptive LED current control algorithm is proposed to dynamically change the LED current to remove various motion artifacts (MA) and get high amplitude PPG signal. We also develop a heart rate (HR) estimation framework utilizing deep learning (DL) model based on convolutional neural network (CNN) and long short-term memory (LSTM) network. The combination of CNN-LSTM can extract features from both spatial and temporal correlation. A light-weight model with two CNN-LSTM layers is built to estimate HR with in only 5s. For validation, we conduct experiments with volunteers doing various physical exercises. The results show PPG signals with high amplitude and signal-to-noise ratio (SNR). HR estimation is more accurate even during irregular and muscle strength exercises. The proposed adaptive architecture and DL-based HR estimation can overcome MA and minimize the power consumption on wireless transfer module of wearable PPG devices. © 2022 The Author(s)

关键词： Heart

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