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Human Factors in Future Vertical Lift, the U.S. Army’s Next Generation Rotorcraft

Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2021年第1期64卷 149 - 153页

作者： Katie Ernst Michael Wilson David Durbin Roth Roth Laura Militello Robert Copeland Applied Decision Science LLC USA United States Army Aeromedical Research Laboratory USA United States Army Data and Analysis Center Human Systems Integration Division USA Emilie Cognitive Engineering USA Applied Decision Science LLC USA Kevin Oden Lockheed Martin Rotary and Mission Systems USA United States Army Futures Command Aviation and Missile Center USA

The united States Army is developing and procuring their next generation of rotorcraft, called the Future Vertical Lift (FVL) program. FVL rotorcraft will fly faster and incorporate new work requirements such as command of unmanned systems and optionally piloted variants. Successful design and implementation of human automation teaming will be paramount to the mission success and safety of FVL systems, presenting both challenges and opportunities for human factors personnel supporting FVL development. The purpose of this panel is to highlight the range of human factors challenges within the FVL program. Panelists are drawn from both government and industry, and represent a diverse set of perspectives including user, researcher, and practitioner. The session will include time for panelists to introduce their work and challenges that they are addressing within the FVL program as well as interact with audience members.

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NFDI4Energy Task Area 4: FAIR data for Energy System Research

NFDI4Energy Task Area 4: FAIR Data for Energy System Researc...

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2023 Designing Futures: Zukunfte gestalten, INFORMATIK 2023 - 2023 Designing Futures: Shaping the Future, INFORMATIK 2023

作者： Wein, Amanda Reinkensmeier, Jan Weidlich, Anke Lilliestam, Johan Hagenmeyer, Veit Richter, Mascha Auer, Sören Nieße, Astrid Lehnhoff, Sebastian OFFIS E.V. RandD Division Energy Escherweg 2 Oldenburg26121 Germany Friedrichstr. 39 Freiburg79110 Germany Friedrich-Alexander-Universität Erlangen-Nörnberg Lehrstuhl for Sustainability Transition Policy Lange Gasse 20 Nörnberg90403 Germany Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Kaiserstr. 89 Karlsruhe76131 Germany Reiner Lemoine Institut GGmbH Research Unit Transformation of Energy Systems Rudower Chaussee 12 Berlin12489 Germany Technische Informationsbibliothek Data Science and Digital Libraries Research Group Welfengarten 1 B Hannover30167 Germany Carl von Ossietzky Universität Oldenburg Department for Informatik Ammerländer Heerstr. 114-118 Oldenburg26129 Germany

ISBN: (纸本)9783885797319

The NFDI4Energy consortium will create a research data infrastructure for energy system research, emphasizing the openness and FAIRness of data and models in this research domain. Within the consortium, Task Area 4 focuses on the development of resources and services that will provide a semantic layer for the overall platform built by NFDI4Energy. The team of this Task Area will produce artifacts including a domain ontology, metadata standards, a knowledge graph, a Persistent Identifier service, and integration infrastructure to join these artifacts to the NFDI4Energy platform. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Metadata

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Enhancing Social Vulnerability Assessment with Energy Resilience: A Comprehensive Study of the Netherlands

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SSRN 2023年

作者： Song, Wen Li, Yinshuai Jie, Cheng Chen, Ruishan Wu, Jun Jia, Nan Department of landscape architecture School of Design Shanghai Jiao Tong University Dongchuan Road 800 Shanghai200240 China East China Normal University Dongchuan Road 500 Shanghai200241 China Department of Data Science and Artificial Intelligence Breda University of Applied Science Monseigneur Hopmansstraat 2 Breda4817 JS Netherlands Center for Systems Integration and Sustainability part of College of Agriculture and Natural Resources Michigan State University East LansingMI48824 United States

The huge disaster risk and chain effect caused by multiple disaster shocks hinder the realization of the Sustainable Development Goals of the united Nations. Comprehensive vulnerability assessment is the scientific basis for balanced regional development. Taking the Netherlands as an evaluation object, a social vulnerability index system involving the economy, society, housing and energy is constructed. Results show: 1) The introduction of energy indicators complements the comprehensive assessment of social vulnerability. There are some spatial differences in energy use in the Netherlands, which is consistent with the pattern of urban economic development levels. 2) There are spatial differences in social vulnerability in the Netherlands, and the spatial agglomeration effect is high in the border areas, which may be related to the nature of the country and the city itself, and also exists in other developed countries and regions. 3) Urbanization intensifies the urban-rural divide, and rural and cross-border areas often face a series of economic structure, infrastructure and policy neglect problems. Sustainable social development requires cross-regional comprehensive governance. Spatial patterns that comprehensively assess social vulnerability provide opportunities for sustainable urban development, and cross-regional coordination helps to reduce social equity and thus achieve true sustainable development in the Netherlands. © 2023, The Authors. All rights reserved.

关键词： Disasters

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BLOCKCHAIN IN HEALTHCARE TODAY: 2022 PREDICTIONS

Blockchain in Healthcare Today

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Blockchain in Healthcare Today 2022年 5卷

作者： Conway, Daniel Venkataraman, Mohan Laverick, Daniel Pelin, Gabriela Hasselgren, Anton Blockchain Center of Excellence Clinical Professor of Information Systems University of Arkansas United States Chainyard A Business Unit of IT People Corp United States Vice President of Digital & Data Solutions MobileAL United States Chief Innovation Officer Avaneer Health United States Depar tment of Neuromedicine and Movement Science Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway

Each year, Blockchain and Healthcare Today reaches out to journal board members, annual ConV2X Symposium speakers, and ecosystem subject matter experts to share their near-term views and perspectives for blockchain technology advances in healthcare. This article presents insights into where authors anticipate market opportunities and where gaps exist that should be addressed for regional and global collaboration, governance, and efficiency for the year 2022. © 2022 Daniel Conway et al.

关键词： Supply chains

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FedMonitor: An Implementation Architecture for Monitoring of Resources and Events'Notifications on Federated Cloud Computing Infrastructures

FedMonitor: An Implementation Architecture for Monitoring of...

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International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)

作者： Emmanuel Adetiba Uche Nnaji John S. Wejin Festus Ojesanmi Matthew B. Akanle Priscilla O. Ajayi Faith O. Sweetwiliams Surendra C. Thakur Ezekiel F. Adebiyi Department of Electrical and Information Engineering and Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Computer and Information Science & CApIC-ACE Covenant University Ota Nigeria Department of Electrical and Information Engineering & CApIC-ACE Covenant University Jalingo Nigeria Department of Computer Science Tareba State University Jalingo Nigeria Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria Department of Electrical and Information Engineering Covenant University & CApIC-ACE Covenant University Ota Nigeria Center for Systems and Information Services & CApIC-ACE Covenant University Ota Nigeria Department of Information Technology and KZN e-Skills Co-Laboratory Durban University of Technology Durban South Africa Covenant University Bioinformatics Research (CUBRe) Covenant University Ota Ogun State Nigeria African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala Uganda

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

In the evolving landscape of cloud computing, federated cloud infrastructures present unique challenges and opportunities for resource and application monitoring. Monitoring the diverse array of cloud resources and applications within a federated cloud environment is crucial for delivering consistent Quality of Service (QoS) and continuous oversight across various cloud applications and infrastructure. Despite the various successes documented in the literature on multi-cloud monitoring, existing frameworks have not fully explored or provided enough insights into centralized monitoring of resources, applications, and network devices in a federated cloud environment. This paper introduces FedMonitor, an implementation architecture designed to enhance the monitoring and notification capabilities in federated cloud environments by building on the theoretical ideals in our earlier work (i.e. FEDARGOS-V1). FedMonitor leverages Prometheus and Grafana to crawl, manage and visualize metrics from diverse software services, hardware resources, and network devices within a federated cloud environment. The results demonstrate hat FedMonitor improves the effectiveness of resource and application monitoring in federated cloud environments.

关键词： Measurement Cloud computing Visualization Computer architecture Quality of service Production Software Real-time systems Stakeholders Monitoring

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A Tumor Inpainting Framework for MRI Using Automated Masks Based on Channel-Specificconditions Across the Volume

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SSRN 2024年

作者： Kim, Kyuri Cho, Hyungjoo Na, Yoonho Ye, Sung-Joon Lee, Jimin Ahn, Sung Soo Park, Ji Eun Kim, Hwiyoung Department of Applied Bioengineering Seoul National University Seoul08826 Korea Republic of Research Institute for Convergence Science Seoul National University Seoul08826 Korea Republic of Department of Nuclear Engineering Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of Department of Radiology the Research Institute of Radiological Science Yonsei University College of Medicine Seoul03722 Korea Republic of Department of Radiology Research Institute of Radiology University of Ulsan College of Medicine Seoul05505 Korea Republic of Department of Biomedical Systems Informatics Center for Clinical Imaging Data Science Yonsei University College of Medicine Seoul03722 Korea Republic of

Despite the significant enhancements that deep learning has brought to its application in medical screening, acquiring realisticannotation labels for specific disease group data remains a challenge. To address this issue, the proposed inpainting framework employsa range of realistic annotation masks that integrate channel-specific conditions across the image volume, thereby generatinghigh-quality paired datasets. Specifically, free-form annotation masks were obtained for 2D normal MR images using a variationalautoencoder adjusted by the z-axis index. Furthermore, a framework was proposed to inpaint localized areas using generativeadversarial networks (GANs), which contain a cascaded generator with mask-guided boundary attention and a discriminator operatingin a hyperspherical embedding space. This approach ensures the synthesis of high-coverage tumors and allows for seamlessintegration with adjacent tissues. Each component of our framework not only improved the reconstruction score across variousevaluation metrics, but also demonstrated its value as an augmentation in downstream segmentation tasks. Our trained model andinference code are available on ***/kyurikeem/Tumor-Inpainting. © 2024, The Authors. All rights reserved.

关键词： Tumors

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Developing a Segmentation Model for Microscopic Images of Microplastics Isolated from Clams 25th

Developing a Segmentation Model for Microscopic Images of Mi...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Baek, Ji Yeon de Guzman, Maria Krishna Park, Ho-min Park, Sanghyeon Shin, Boyeon Velickovic, Tanja Cirkovic Van Messem, Arnout De Neve, Wesley Center for Biotech Data Science Ghent University Global Campus Incheon Korea Republic of Center for Food Chemistry and Technology Ghent University Global Campus Incheon Korea Republic of Department of Food Technology Safety and Health Ghent University Ghent Belgium University of Belgrade - Faculty of Chemistry Belgrade Serbia Serbian Academy of Sciences and Arts Belgrade Serbia IDLab Department of Electronics and Information Systems Ghent University Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium

ISBN: (纸本)9783030687793

Microplastics (MP) have become a major concern, given the threat they pose to marine-derived food and human health. One way to investigate this threat is to quantify MP found in marine organisms, for instance making use of image analysis to identify ingested MP in fluorescent microscopic images. In this study, we propose a deep learning-based segmentation model to generate binarized images (masks) that make it possible to clearly separate MP from other background elements in the aforementioned type of images. Specifically, we created three variants of the U-Net model with a ResNet-101 encoder, training these variants with 99 high-resolution fluorescent images containing MP, each having a mask that was generated by experts using manual color threshold adjustments in ImageJ. To that end, we leveraged a sliding window and random selection to extract patches from the high-resolution images, making it possible to adhere to input constraints and to increase the number of labeled examples. When measuring effectiveness in terms of accuracy, recall, and F2 -score, all segmentation models exhibited low scores. However, compared to two ImageJ baseline methods, the effectiveness of our segmentation models was better in terms of precision, F0.5 -score, F1 -score, and mIoU: U-Net (1) obtained the highest mIoU of 0.559, U-Net (2) achieved the highest F1 -score of 0.682, and U-Net (3) had the highest precision and F0.5 -score of 0.594 and 0.626, respectively, with our segmentation models, in general, detecting less false positives in the predicted masks. In addition, U-Net (1), which used binary cross-entropy loss and stochastic gradient descent, and U-Net (2), which used dice loss and Adam, were most effective in discriminating MP from other background elements. Overall, our experimental results suggest that U-Net (1) and U-Net (2) allow for more effective MP identification and measurement than the macros currently available in ImageJ. © 2021, Springer Nature Switzerland AG.

关键词： Image segmentation

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Automatic liver-vessel examination from CT slice using Kapur’s thresholding and watershed algorithm

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Procedia Computer science 2024年 235卷 1824-1831页

作者： Seifedine Kadry Laith Abualigah Rubén González Crespo Elena Verdú Robertas Damasevicius Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

Computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur’s tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme.

关键词： liver CT liver-vessel Kapur’s thresholding hummingbird-optimizer watershed-algorithm

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CNN segmentation of skin melanoma in pre-processed dermoscopy images

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Procedia Computer science 2024年 235卷 2775-2782页

作者： Seifedine Kadry Elena Verdú Robertas Damasevicius Laith Abualigah Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu’s thresholding, (iii) Kapur’s thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database.

关键词： Watershed-algorithm skin melanoma segmentation Fuzzy-Tsallis entropy Moth-Flame algorithm evaluation

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions

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Procedia Computer science 2024年 235卷 3438-3446页

Computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays.

关键词： Covid19 X-ray Vision-transformer Patch-size Classification

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