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Design of Subsea storage tanks for Arctic conditions - heat treatment of materials

Materials Today: Proceedings 2023年

作者： Kishor Tirumanur Shanmugavelu, Arvind Muraliraja, R. Shanmugam, Ragavanantham Pal Singh Pawar, Mayur Vishwakarma, Rajoo Sarkar, Proshanta Department of Naval Architecture and Offshore Engineering Vels Institute of Science Technology & Advanced Studies Chennai 600117 Tamil Nadu India Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Chennai 600117 Tamil Nadu India Advanced Manufacturing Engineering Technology Navajo Technical University NM 87313 United States Department of Civil Engineering IES College of Technology Bhopal 462044 Madhya Pradesh India Department of Mechanical Engineering Suyash Institute of Information Technology Gorakhpur 273016 Uttar Pradesh India Department of Computer Science Kalinga University Naya Raipur 492101 Chhattisgarh India

The Arctic region contains a plethora of recoverable hydrocarbon wealth in the form of oil and gas in existing conventional wells and other undiscovered fields. The main challenges faced in the Arctic region are both environmental and operational. Some environmental challenges are low ambient temperature, winterization, severity and unpredictability of the weather. However, the issue of multi-phase flow in the current operation coupled with the decline of the reservoir pressure are the major concerns in this gas field. In the early stages of field life, the reservoir pressure is sufficient to allow the natural flow from reservoir formation to surface without additional supporting compression. Unfortunately, the pressure naturally declines throughout the entire life cycle of the field. To achieve a better production profile in the arctic region the idea is to implement a Subsea processing concept employing a Subsea Storage Tank (SST) in order to maximize production from additional infield wells, introducing a novel step in this field development phase that will change the entire oil and gas industry especially for subsea processing field architecture. The incorporation of SST in the subsea system will enhance the production rate as well as it avoids the transportation problems through pipe lines. By this work, the production rate is increased by 50% and decrease the oil spill accidents considerably. © 2023 Elsevier Ltd. All rights reserved.

关键词： Heat transfer

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NoSQL security: can my data-driven decision-making be affected from outside?

arXiv

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arXiv 2022年

作者： Nikiforova, Anastasija Daskevics, Artjoms Azeroual, Otmane University of Tartu Faculty of Science and Technology Institute of Computer Science Chair of Software Engineering Narva mnt 18 Tartu Estonia European Open Science Cloud Brussels1050 Belgium University of Latvia Raina Boulevard 19 RigaLV-1050 Latvia Schützenstraße 6A Berlin10117 Germany

Nowadays, there are billions interconnected devices forming Cyber-Physical Systems, Internet of Things (IoT) and Industrial Internet of Things (IIoT) ecosystems. With an increasing number of devices and systems in use, amount and the value of data, the risks of security breaches increase. One of these risks is posed by open data sources, by which are meant databases, which are not properly protected. These poorly protected databases are accessible to external actors, which poses a serious risk to the data holder and the results of data-related activities such as analysis, forecasting, monitoring, decision-making, policy development, and the whole contemporary society. This chapter aims at examining the state of the security of open data databases representing both relational databases and NoSQL, with a particular focus on a later category. © 2022, CC BY.

关键词： Internet of Everything

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OCT based scanning system to enable 3D viewing, sharing and printing of artworks

OCT based scanning system to enable 3D viewing, sharing and ...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2021 - Part of Imaging and Applied Optics Congress 2021

作者： Zhou, Xingyu In, Darlene Xiong, Xinchang Yang, Kunze Chen, Xing McCune Bruhn, Heather Liu, Xuan Yang, Yi Dept. of Electrical and Computer Engineering New Jersey Institute of Technology University Heights NewarkNJ07102 United States Division of Science and Engineering Penn State Abington AbingtonPA19001 United States The College of Arts and Architecture Penn State University ParkPA16802 United States

We acquired 3D volumetric data of artworks with an OCT-based hybrid scanning platform. The data is then exported into standard 3D data formats that can be 3D printed, shared, and viewed in VR and AR. © 2021 The A... 详细信息

ISBN: (纸本)9781557528209

关键词： Volumetric analysis

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DC-store: eliminating noisy neighbor containers using deterministic I/O performance and resource isolation 20

DC-store: eliminating noisy neighbor containers using determ...

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Proceedings of the 18th USENIX Conference on File and Storage Technologies

作者： Miryeong Kwon Donghyun Gouk Changrim Lee Byounggeun Kim Jooyoung Hwang Myoungsoo Jung Computer Architecture and Memory Systems Laboratory Korea Advanced Institute of Science and Technology Computer Architecture and Memory Systems Laboratory Samsung

ISBN: (纸本)9781939133120

We propose DC-store, a storage framework that offers deterministic I/O performance for a multi-container execution environment. DC-store's hardware-level design implements multiple NVM sets on a shared storage pool, each providing a deterministic SSD access time by removing internal resource conflicts. In parallel, software support of DC-Store is aware of the NVM sets and enlightens Linux kernel to isolate noisy neighbor containers, performing page frame reclaiming, from peers. We prototype both hardware and software counterparts of DC-Store and evaluate them in a real system. The evaluation results demonstrate that containerized data-intensive applications on DC-Store exhibit 31% shorter average execution time, on average, compared to those on a baseline system.

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Precautionary principle for students' data protection

Precautionary principle for students' data protection

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Virtual Campus (JICV), X International Conference on

作者： Daniel Amo-Filva Alicia García-Holgado David Fonseca Lucía García-Holgado Francisco José García-Peñalvo Department of Engineering La Salle Campus Barcelona Universitat Ramon Llull Barcelona Spain GRIAL Research Group Research Institute for Educational Sciences Universidad de Salamanca Salamanca Spain Departmetn of Architecture La Salle Campus Barcelona Universitat Ramon Llull Barcelona Spain Department of Computer Science Research Institute for Educational Sciences Universidad de Salamanca Salamanca Spain

ISBN: (数字)9798350343441

ISBN: (纸本)9798350343458

This paper underscores the critical importance of data protection strategies in educational institutions to prevent potential emotional, physical, and financial harm to students arising from data leaks due cybersecurity incidents. The data of different cybersecurity reports reveals that most incidents pertain to unauthorized disclosures of personal data, potentially leading to significant financial harm as this data can be traded on the black market, indicating an urgent need for enhanced data protection measures. Existing data regulations are deemed insufficient in mitigating these risks, highlighting a need for preventive (ethical) principles to augment legal frameworks. This manuscript introduces the precautionary principle as a potent ethical guideline to enhance student data protection treatment. By bolstering these data processes, it aims to prevent cybersecurity incidents or at least limit their adverse impacts. The paper offers an in-depth exploration of the problem and makes a compelling case for the precautionary principle as a social value for caring for students' futures.

关键词： Ethics Privacy Law Data protection Pressing Regulation computer security

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Rainbow Differential Privacy

Rainbow Differential Privacy

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IEEE International Symposium on Information Theory

作者： Ziqi Zhou Onur Gü nlü Rafael G. L. D’ Oliveira Muriel Mé dard Parastoo Sadeghi Rafael F. Schaefer Department of Electrical Engineering and Computer Science Technical University of Berlin Germany Chair of Communications Engineering and Security University of Siegen Germany SMSS Clemson University USA RLE Massachusetts Institute of Technology USA SEIT University of New South Wales Canberra Australia

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

We extend a previous framework for designing differentially private (DP) mechanisms via randomized graph colorings that was restricted to binary functions, corresponding to colorings in a graph, to multi-valued functions. As before, datasets are nodes in the graph and any two neighboring datasets are connected by an edge. In our setting, we assume that each dataset has a preferential ordering for the possible outputs of the mechanism, each of which we refer to as a rainbow. Different rainbows partition the graph of datasets into different regions. We show that if the DP mechanism is pre-specified at the boundary of such regions and behaves identically for all same-rainbow boundary datasets, at most one optimal such mechanism can exist and the problem can be solved by means of a morphism to a line graph. We then show closed form expressions for the line graph in the case of ternary functions. Treatment of ternary queries in this paper displays enough richness to be extended to higher-dimensional query spaces with preferential query ordering, but the optimality proof does not seem to follow directly from the ternary proof.

关键词： Differential privacy Closed-form solutions Information theory

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TransHD: Spatial Transformer Features Extraction for HDC Synergetic Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Hassan, Eman Bettayeb, Meriem Halawani, Yasmin Genssler, Paul R. Tesfai, Huruy Zweiri, Yahya Amrouch, Hussam Hadjileontiadis, Leontios J. Saleh, Hani Mohammad, Baker Khalifa University System on Chip Lab Department of Computer and Information Engineering Abu Dhabi 127788 United Arab Emirates Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates University of Dubai College of Engineering and IT Dubai United Arab Emirates Munich Institute of Robotics and Machine Intelligence Technical University of Munich TUM School of Computation Information and Technology Chair of AI Processor Design Munich Germany Khalifa University of Science and Technology Department of Aerospace Engineering Advanced Research and Innovation Center (ARIC) Abu Dhabi United Arab Emirates University of Stuttgart Semiconductor Test and Reliability (STAR) Stuttgart Germany Khalifa University of Science and Technology Department of Biomedical Engineering Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece

Artificial intelligence (AI) relies on pattern recognition and classification algorithms to achieve accurate and efficient decision-making. Convolutional neural networks (CNNs) are the state-of-the-art for processing 2D image data, but their high computational and data requirements limit their use in resource-constrained environments. Hyperdimensional computing (HDC) offers a lightweight alternative, excelling in 1D tasks, but struggles to achieve competitive accuracy for 2D image classification. Hybrid frameworks combining HDC with CNNs have been proposed to improve accuracy but inherit the computational demands of deep learning, making them unsuitable for edge and IoT devices. To address this, we propose TransHD, a framework integrating lightweight spatial transformer networks (STNs) with HDC to enhance image classification performance while maintaining efficiency. TransHD achieves up to 9% higher accuracy than base HDC models on MNIST and Fashion-MNIST using only 30% of the training data, and reduces computational complexity by 2.5x through optimized STN feature map usage. On resource-constrained platforms like the Raspberry Pi 4, TransHD accelerates inference times by 3.4x and improves energy efficiency by 3.2x, with an accuracy trade-off of approximately 3% compared to CNNs. This study demonstrates the potential of combining HDC with STNs to develop efficient AI solutions for IoT and edge computing, where low energy consumption and computational efficiency are critical. © 2020 IEEE.

关键词： Brain-inspired Computing Hyperdimensional Computing Image Classification Spatial Transformers

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DEMONSTRATING DATA-TO-KNOWLEDGE PIPELINES FOR CONNECTING PRODUCTION SITES IN THE WORLD WIDE LAB A PREPRINT

arXiv

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arXiv 2024年

作者： Gorisen, Leon Schneider, Jan-Niklas Behery, Mohamed Brauner, Philipp Lennartz, Moritz Kotter, David Kaster, Thomas Petrovic, Oliver Hinke, Christian Gries, Thomas Lakemeyer, Gerhard Ziefle, Martina Brecher, Christian Hafner, Constantin Chair for Laser Technology RWTH Aachen University Aachen52074 Germany Knowledge Based Systems Group RWTH Aachen University Aachen52074 Germany Communication Science RWTH Aachen University Aachen52074 Germany Chair of Textile Technology RWTH Aachen University Aachen52074 Germany Laboratory for Machine Tools and Production Engineering RWTH Aachen University Aachen52074 Germany Human Computer Interaction Center RWTH Aachen University Aachen52074 Germany Fraunhofer Institute for Laser Technology Aachen52074 Germany

The digital transformation of production requires new methods of data integration and storage, as well as decision making and support systems that work vertically and horizontally throughout the development, production, and use cycle. In this paper, we propose Data-to-Knowledge (and Knowledge-to-Data) pipelines for production as a universal concept building on a network of Digital Shadows (a concept augmenting Digital Twins). We show a proof of concept that builds on and bridges existing infrastructure to 1) capture and semantically annotates trajectory data from multiple similar but independent robots in different organisations and use cases in a data lakehouse and 2) an independent process that dynamically queries matching data for training an inverse dynamics foundation model for robotic control. The article discusses the challenges and benefits of this approach and how Data-to-Knowledge pipelines contribute efficiency gains and industrial scalability in a World Wide Lab as a research outlook. Copyright © 2024, The Authors. All rights reserved.

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Landslide Disaster Assessment with Integration of Seismic, Social Media and Remote Sensing Data

Landslide Disaster Assessment with Integration of Seismic, S...

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International Conference on Signal Processing and Information Security (ICSPIS)

作者： Deepak Rawat M. L. Sharma Roshan Kumar Nishant Saxena Atul Kumar Abdul Kadir Centre of Excellence in Disaster Mitigation and Management Indian Institute of Technology Roorkee Roorkee Uttarakhand India Department of Earthquake Engineering Roorkee Indian Institute of Technology Roorkee Roorkee Uttarakhand India Department of Electronics and Information Technology Miami College Henan University Henan Kaifeng China Department of Architecture and Planning Indian Institute of Technology Roorkee Roorkee Uttarakhand India Computer Science and Engineering Department Indian Institute of Technology Roorkee Roorkee Uttarakhand India

On February 7, 2021, a rockfall in the Rishiganga Valley killed about 200 people. For the integration of seismic, social media, and remote sensing, a case study on this tragedy has been conducted. This study explores the role of social media in managing Himalayan landslides in conjunction with remote sensing data analytics. We look at how these technologies can enhance damage management, quick mobilization, and early warning systems. A literature review and case studies of the successful use of these technologies in other disaster-prone areas will support these suggestions. The intention is to equip decision-makers and experts in disaster management with the practical knowledge they need to effectively prepare for and respond to Himalayan landslide events, saving lives and minimizing the impact of natural hazards.

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Improving the prediction accuracy of cyanobacterial blooms using a hybrid model integrating dimensionality reduction and deep learning

Water Biology and Security

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Water Biology and Security 2025年

作者： Chen, Jiwei Hou, Zhongbao Li, Yunxi Li, Jiexian Li, Lin School of Naval Architecture and Ocean Engineering Huazhong University of Science and Technology Wuhan 430074 China Department of Public Health Fu Jen Catholic University Taiwan 242062 China Khoury College of Computer Sciences Northeastern University Vancouver V6B1Z3 Canada Institute of Hydrobiology Chinese Academy of Sciences Wuhan 430072 China

Cyanobacteria Harmful Blooms have negatively impacted human socio-economic activities and the natural ecological environment. Predicting cyanobacterial blooms requires the application of advanced analytical models powered by artificial intelligence to accurately model bloom development. In this study, we developed a hybrid model that integrates the Temporal Convolution Network (TCN) with the Long Short-Term Memory Network (LSTM) to achieve highly accurate predictions of cyanobacterial bloom events. We used Principal Component Analysis (PCA) during the preprocessing phase to reduce data dimensionality of model input variables and enhance computational efficiency. We used a data set from the Darling River in Australia to conduct a comparative analysis with traditional deep learning models and comprehensively assess the proposed model performance. We also investigated the application of the correlation coefficient matrix method in variable selection. The results demonstrate that the proposed model outperforms conventional methods in predicting cyanobacterial blooms. When benchmarked against the Recurrent Neural Network (RNN) model, our model demonstrates a superior ability to capture the intricacies of time series data. Specifically, it achieves a 79.5 % reduction in Mean Square Error (MSE) and a 197 % increase in the Coefficient of Determination (R²). Furthermore, comparative experiments were carried out across several prediction time steps, indicating that the model achieves optimal performance in minimizing both the MSE and Mean Absolute Error (MAE) when predicting one day ahead. Notably, the error distribution is most condensed at this time step, outperforming models with prediction horizons ranging from two to seven days. We also undertook a thorough analysis of the significance of each principal component in model predictions. The results highlight that principal component 5 and principal component 2 exhibit the most substantial contribution to the p

关键词： Cyanobacteria bloom Deep learning Long short-term memory network Prediction Principal component analysis Temporal convolution network

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