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Stealth UAV through Coandă Effect

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Stealth UAV through Coandă Effect

IEEE International Conference on Robotic Computing (IRC)

作者： Dongyoon Shin Hyeji Kim Jihyuk Gong Uijeong Jeong Yeeun Jo Eric Matson Computer Science and Engineering Sogang University Seoul Republic of Korea Computer Science and Engineering Dongguk University Seoul Republic of Korea Computer Engineering Jeju National University Jeju Republic of Korea Computer Science and Engineering Ewha Womans University Seoul Republic of Korea Computer and Information Technology Purdue University West Lafayette United States of America

ISBN: (数字)9781728152370

ISBN: (纸本)9781728152387

This paper uses CoandăEffect to reduce motors, the source of noise, and finds low noise materials with sufficient lift force so that it can achieve acoustical stealth *** to NASA research [1], the noise of UAVs is better heard to people. But there must be some moments when we need to operate the drones quietly, so how can we reduce the noise? In previous research, there have also been steady attempts to produce UAVs using CoandăEffect, but have never tried to achieve Acoustic Stealth through Coanda UAVs. But Coanda Effect uses only one motor and is structurally quiet. So we tried to find quiet methods (materials, structures) while at the same time having sufficient stimulus through the CoandăEffect. Verification went through experiments. The control group used the most common type of Quadrone, and determine if the hypothesis is correct by testing various structures and materials under the same conditions, and measuring noise. UAVs using CoandăEffect are not of any shape or structure that is not changeable, and internal space is also empty. That's why the CoandăEffect UAV we present can be improved through follow-up research. That's why the CoandăEffect UAV could open up a new frontier for the Stealth UAVs.

关键词： Shape Noise reduction NASA Three-dimensional printing Noise measurement Robots Testing

Stealth UAV through Coandă Effect

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arXiv 2020年

作者： Shin, Dongyoon Gong, Jihyuk Jo, Yeeun Kim, Hyeji Jeong, Uijeong Matson, Eric Computer Science and Engineering Sogang University Seoul Korea Republic of Computer Engineering Jeju National University Jeju Korea Republic of Computer Science and Engineering Ewha Womans University Seoul Korea Republic of Computer Science and Engineering Dongguk University Seoul Korea Republic of Computer and Information Technology Purdue University West Lafayette United States

This paper uses Coandă Effect to reduce motors, the source of noise, and finds low noise materials with sufficient lift force so that it can achieve acoustical stealth *** to NASA research [1], the noise of UAVs is better heard to people. But there must be some moments when we need to operate the drones quietly, so how can we reduce the noise? In previous research, there have also been steady attempts to produce UAVs using Coandă Effect, but have never tried to achieve Acoustic Stealth through Coanda UAVs. But Coandă Effect uses only one motor and is structurally quiet. So we tried to find quiet methods (materials, structures) while at the same time having sufficient stimulus through the Coandă Effect. Verification went through experiments. The control group used the most common type of Quadrone, and determine if the hypothesis is correct by testing various structures and materials under the same conditions, and measuring noise. UAVs using Coandă Effect are not of any shape or structure that is not changeable, and internal space is also empty. That's why the Coandă Effect UAV we present can be improved through follow-up research. That's why the Coandă Effect UAV could open up a new frontier for the Stealth UAVs. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

Snowmass 2021 White Paper: The Windchime Project

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arXiv 2022年

作者： Attanasio, Alaina Bhave, Sunil A. Blanco, Carlos Carney, Daniel Demarteau, Marcel Elshimy, Bahaa Febbraro, Michael Feldman, Matthew A. Ghosh, Sohitri Hickin, Abby Hong, Seongjin Lang, Rafael F. Lawrie, Benjamin Li, Shengchao Liu, Zhen Maldonado, Juan P.A. Marvinney, Claire Oo, Hein Zay Yar Pai, Yun-Yi Pooser, Raphael Qin, Juehang Sparmann, Tobias J. Taylor, Jacob M. Tian, Hao Tunnell, Christopher Purdue University West LafayetteIN United States Princeton University PrincetonNJ United States Stockholm University Stockholm Sweden Lawrence Berkeley National Laboratory BerkeleyCA United States Oak Ridge National Laboratory Oak RidgeTN United States Joint Quantum Institute/Joint Center For Quantum Information And Computer Science University Of Maryland College Park National Institute Of Standards And Technology GaithersburgMD United States School Of Physics And Astronomy University Of Minnesota MinneapolisMN United States Universidad Nacional De Colombia Bogotá Colombia Rice University HoustonTX United States

The absence of clear signals from particle dark matter in direct detection experiments motivates new approaches in disparate regions of viable parameter space. In this Snowmass white paper, we outline the Windchime project, a program to build a large array of quantumenhanced mechanical sensors. The ultimate aim is to build a detector capable of searching for Planck mass-scale dark matter purely through its gravitational coupling to ordinary matter. In the shorter term, we aim to search for a number of other physics targets, especially some ultralight dark matter candidates. Here, we discuss the basic design, open R&D challenges and opportunities, current experimental efforts, and both short- and long-term physics targets of the Windchime project. © 2022, CC BY.

关键词： Gravitation

On the impacts of redundancy, diversity, and trust in resilient distributed state estimation

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arXiv 2020年

作者： Mitra, Aritra Ghawash, Faiq Sundaram, Shreyas Abbas, Waseem School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Department of Electrical Engineering Information Technology University Lahore Pakistan Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37212 United States

We address the problem of distributed state estimation of a linear dynamical process in an attack-prone environment. Recent attempts to solve this problem impose stringent redundancy requirements on the measurement and communication resources of the network. In this paper, we take a step towards alleviating such strict requirements by exploring two complementary directions: (i) making a small subset of the nodes immune to attacks, or "trusted", and (ii) incorporating diversity into the network. We define graph-theoretic constructs that formally capture the notions of redundancy, diversity, and trust. Based on these constructs, we develop a resilient estimation algorithm and demonstrate that even relatively sparse networks that either exhibit node-diversity, or contain a small subset of trusted nodes, can be just as resilient to adversarial attacks as more dense networks. Finally, given a finite budget for network design, we focus on characterizing the complexity of (i) selecting a set of trusted nodes, and (ii) allocating diversity, so as to achieve a desired level of robustness. We establish that, unfortunately, each of these problems is NP-complete. Copyright © 2020, The Authors. All rights reserved.

关键词： Redundancy

Time-of-use pricing for energy storage investment

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arXiv 2021年

作者： Zhao, Dongwei Wang, Hao Huang, Jianwei Lin, Xiaojun MIT Energy Initiative Massachusetts Institute of Technology CambridgeMA02139 United States Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University MelbourneVIC3800 Australia School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

Time-of-use (ToU) pricing is widely used by the electricity utility to shave peak load. Such a pricing scheme provides users with incentives to invest in behind-the-meter energy storage and to shift peak load towards low-price intervals. However, without considering the implication on energy storage investment, an improperly designed ToU pricing scheme may lead to significant welfare loss, especially when users over-invest the storage, which leads to new energy consumption peaks. In this paper, we will study how to design a social-optimum ToU pricing scheme by explicitly considering its impact on storage investment. We model the interactions between the utility and users as a two-stage optimization problem. To resolve the challenge of asymmetric information due to users’ private storage cost, we propose a ToU pricing scheme based on different storage types and the aggregate demand per type. Each user does not need to reveal his private cost information. We can further compute the optimal ToU pricing with only a linear complexity. Simulations based on real-world data show that the suboptimality gap of our proposed ToU pricing, compared with the social optimum achieved under complete information, is less than 5%. © 2021, CC BY.

关键词： Investments

Flexible principal component analysis for exponential family distributions

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arXiv 2021年

作者： Zhang, Tonglin Yang, Baijian Song, Qianqian Su, Jing Department of Statistics Purdue University 250 North University Street West LafayetteIN47907-2066 United States Department of Computer and Information Technology Purdue University 401 North Grant Street West LafayetteIN47907 United States Department of Cancer Biology Wake Forest School of Medicine One Medical Center Boulevard Winston-SalemNC27157-1082 United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN46202-3002 United States

Traditional principal component analysis (PCA) is well known in high-dimensional data analysis, but it requires to express data by a matrix with observations to be continuous. To overcome the limitations, a new method called exible PCA (FPCA) for exponential family distributions is proposed. The goal is to ensure that it can be implemented to arbitrary shaped region for either count or continuous observations. The methodology of FPCA is developed under the framework of generalized linear models. It provides statistical models for FPCA not limited to matrix expressions of the data. A maximum likelihood approach is proposed to derive the decomposition when the number of principal components (PCs) is known. This naturally induces a penalized likelihood approach to determine the number of PCs when it is unknown. By modifying it for missing data problems, the proposed method is compared with previous PCA methods for missing data. The simulation study shows that the performance of FPCA is always better than its competitors. The application uses the proposed method to reduce the dimensionality of arbitrary shaped sub-regions of images and the global spread patterns of COVID-19 under normal and Poisson distributions, *** Codes 62H25, 62H35, 62J05 © 2021, CC0.

关键词： Principal component analysis

SHREC’20 benchmark: Classification in cryo-electron tomograms

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computers and Graphics (Pergamon) 2020年 91卷 279-289页

作者： Gubins, Ilja Chaillet, Marten L. van der Schot, Gijs Veltkamp, Remco C. Förster, Friedrich Hao, Yu Wan, Xiaohua Cui, Xuefeng Zhang, Fa Moebel, Emmanuel Wang, Xiao Kihara, Daisuke Zeng, Xiangrui Xu, Min Nguyen, Nguyen P. White, Tommi Bunyak, Filiz Department of Information and Computing Sciences Utrecht University Netherlands Department of Chemistry Utrecht University Netherlands Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology Shandong University China Inria Rennes Bretagne Atlantique France Department of Computer Science Purdue University United States Department of Biological Sciences Purdue University United States Computational Biology Department Carnegie Mellon University United States Department of Electrical Engineering and Computer Science University of Missouri United States Department of Biochemistry University of Missouri United States

Cryo-electron tomography (cryo-ET) is an imaging technique that allows us to three-dimensionally visualize both the structural details of macro-molecular assemblies under near-native conditions and its cellular context. Electrons strongly interact with biological samples, limiting electron dose. The latter limits the signal-to-noise ratio and hence resolution of an individual tomogram to about 50 (5 nm). Biological molecules can be obtained by averaging volumes, each depicting copies of the molecule, allowing for resolutions beyond 4 (0.4 nm). To this end, the ability to localize and classify components is crucial, but challenging due to the low signal-to-noise ratio. Computational innovation is key to mine biological information from cryo-electron tomography. To promote such innovation, we provide a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in cryo-electron tomograms. Our publicly available dataset contains ten tomographic reconstructions of simulated cell-like volumes. Each volume contains twelve different types of complexes, varying in size, function and structure. In this paper, we have evaluated seven different methods of finding and classifying proteins. Six research groups present results obtained with learning-based methods and trained on the simulated dataset, as well as a baseline template matching, a traditional method widely used in cryo-ET research. We find that method performance correlates with particle size, especially noticeable for template matching which performance degrades rapidly as the size decreases. We learn that neural networks can achieve significantly better localization and classification performance, in particular convolutional networks with focus on high-resolution details such as those based on U-Net architecture. © 2020 Elsevier Ltd

关键词： computer vision

MOOC Videos in Project MANEUVER

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MOOC Videos in Project MANEUVER

Learning With MOOCS (LWMOOCS)

作者： Magesh Chandramouli Ge Jin Daniel Cubillos Department of Computer Information Technology and Graphics Purdue University Northwest Hammond USA

ISBN: (纸本)9781728125503

Project MANEUVER (Manufacturing Education Using Virtual Environment Resources) is developing an affordable virtual reality (VR) framework to address the imminent demand for well-trained digital manufacturing (DM) technicians. MANEUVER project has a set of MOOC (Massive Open Online Course) video contents that provides a process of setting up local VR server, navigating inside 3D environment, interacting with 3D objects, simulating CNC machines and different 3D printer models. The previously stated topics are the list of successfully created MOOC videos that have been deployed on the project MANEUVER website. MANEUVER is developing an innovative multi-modal VR framework for DM instruction. Using a "train-the-trainers" approach, a replicable faculty development model is being developed for secondary and post-secondary institutions. By addressing regional and national entry-level needs of the workforce, the project benefits society and contributes to national economic progress and prosperity.

关键词： Electronic learning computer aided instruction Videos Three-dimensional displays Tools

Blockchain-powered software defined network-enabled networking infrastructure for cloud management

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arXiv 2019年

作者： Fernando, Praveen Wei, Jin Department of Computer and Information Technology Purdue University West LafayetteIN United States

— Cloud architecture has become a valuable solution for different applications, such as big data analytics, due to its high-degree of availability, scalability and strategic value. However, there still remain challenges in managing cloud architecture, in areas such as cloud security. In this paper, we exploit software-defined networking (SDN) and blockchain technologies to secure cloud management platforms from a networking perspective. We develop a blockchain-powered SDN-enabled networking infrastructure in which the integration between blockchain-based security and autonomy management layer and multi-controller SDN networking layer is defined to enhance the integrity of the control and management messages. Furthermore, our proposed networking infrastructure also enables the autonomous bandwidth provisioning to enhance the availability of cloud architecture. In the simulation section, we evaluate the performance of our proposed blockchain-powered SDN-enabled networking infrastructure by considering different scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： Software defined networking