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Design decision support system for the engineering/re-engineering of complex naval systems

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NAVAL ENGINEERS JOURNAL 1997年第4期109卷 23-33页

作者： Graham, JH Ragade, RK Shea, J James H. Graham:is the Henry Vogt Professor of computer science and engineering at the University of Louisville. Previous professional employment includes positions at Rensselaer Polytechnic Institute and General Motors Corporation. He received his master's and doctoral degrees at Purdue University and his bachelor's degree at Rose-Hulman Institute of Ethnology. His research interests include artificial intelligence robotics and paraliel computing. Rammohan K. Ragade:is professor of engineering mathematics and computer science at the University of Louisville. He received his Ph.D. from the Indian Institute of Technology at Kampur in 1968. He has been a faculty member at the University of Waterloo and held an engineering position with Bell Northern Research. His research interests include human-computer interaction decision support systems object-oriented simulation and modeling distributed database systems and fuzzy logic. John G. Shea Jr.:served until 1995 as a project manager in the Phalanx Engineering Section of Naval Surface Warfare Center Crane Division Louisville Station. He received his bachelor's and master's degrees from the Speed Scientific School at the University of Louisville. He was a member of the American Society of Naval Engineers.

Hybrid simulation includes both functional computer software models and actual hardware subsystems operating as an integrated whole. A ''hot test bed'' or a ''hybrid simulator-emulator'' enables design engineers to evaluate conceptual models in a more realistic real-lime context, but places increased cognitive demands on the designer. This paper describes an architecture, and results from a prototype implementation, for a design decision support system to allow a designer to make more productive and cost effective use of a test bed system.

关键词： Decision support systems

Dual band Antennas using Metamaterial Transmission Lines

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Dual band Antennas using Metamaterial Transmission Lines

2008 IEEE Antennas and Propagation Society International Symposium (APSURSI 2008), vol.8

作者： Jae-Hyun Park Young-Ho Ryu Seung-Wook Lee Jeong-Hae Lee Eun-Seok Park Young-Eil Kim Department of Electronic Information and Communication Engineering Hongik University Seoul South Korea School of Electrical Engineering and Computer Science Kyungpook National University Daegu South Korea Embedded Systems Solution Laboratory Samsung Advanced Institute of Technology Yongin si Gyeonggi South Korea

ISBN: (纸本)9781424420414

The planar metamaterial transmission (MTL) line is a consequence of extraordinary permittivity and permeability. As it is known well, the composite right/left-handed (CRLH) transmission line (TL) has unique property of an infinite-wavelength wave at specific non-zero frequency because of zero permittivity and permeability [1-2]. Recently, the infinite wavelength resonator antenna based on the fundamental infinite wavelength property of artificial epsilon-negative (ENG) and mu-negative (MNG) TLs was also presented [3-4]. In this paper, dual band antennas using epsilon zero resonance (EZR) and mu zero resonance (MZR) are presented. Since an infinite wavelength of artificial ENG and MNG TLs occurs when their permittivity and permeability are zero, the resonance frequency does not depend on its electrical length. To design dual-band antennas, two ENG and MNG resonators with different resonance frequency which supports the infinite wavelength are analyzed. The resulting characteristics of dual band antennas using the metamaterial TLs will be presented.

关键词： dual band antennas resonant frequencies left-handed materials Electronystagmography Dielectric constant Transmission Lines Infinite wavelengths Zero Permeability

Unfolding the Mutual Relation Between Timeliness and Scalability in Cloud Monitoring

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Unfolding the Mutual Relation Between Timeliness and Scalabi...

IEEE Symposium on computers and Communications

作者： Guilherme da Cunha Rodrigues Rodrigo N. Calheiros Glederson Lessa dos Santos Vinicius Tavares Guimaraes Lisandro Zambenedetti Granville Liane Tarouco Rajkumar Buyya Science and Technology Sul Rio-Grandense Federal Institute of Education Charqueadas Brazil School of Computing Engineering and Mathematics Western Sydney University Sydney Australia Computer Networks Group Institute of Informatics Federal University of Rio Grande do Sul Porto Alegre Brazil Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia

Cloud computing is a suitable solution for professionals, companies, and institutions that need to have access to computational resources on demand. Clouds rely on proper management to provide such computational resources with adequate quality of service, which is established by Service Level Agreements (SLAs), to customers. In this context, cloud monitoring is a critical function to achieve such proper management. Cloud monitoring systems have to accomplish requirements to perform its functions properly, and currently, there are plenty of requirements which includes: timeliness, adaptability, comprehensiveness, and scalability. However, such requirements usually have mutual influence, which is positive or negative, among them-selves, and it has prevented the development of complete cloud monitoring solutions. This paper presents a mathematical model to predict the mutual influence between timeliness and scalability, which is a step forward in cloud monitoring because it paves the way for the development of complete monitoring solutions. It complements our previous work that identified the monitoring parameters (e.g., frequency sampling, amount of monitoring data) that influence timeliness and scalability. Evaluations present the effectiveness of the mathematical model based on a comparison of the results provided by the mathematical model and the results obtained via simulation.

关键词： Monitoring Mathematical model Topology Scalability Cloud computing Network topology Time factors

Impact of intra-grain spin wave reflections on nano-contact spin torque oscillators

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arXiv 2020年

作者： Eklund, Anders J. Dvornik, Mykola Qejvanaj, Fatjon Jiang, Sheng Chung, Sunjae Åkerman, Johan Gunnar Malm, B. Department of Physics University of Oslo Box 1048 Blindern Oslo0316 Norway Department of Physics University of Gothenburg Gothenburg412 96 Sweden NanOsc AB Electrum 205 Kista164 40 Sweden Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology Electrum 229 Kista164 40 Sweden Department of Physics Education Korea National University of Education Cheongju28173 Korea Republic of Division of Electronics and Embedded Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Electrum 229 Kista164 40 Sweden

We investigate the origin of the experimentally observed varying current-frequency nonlinearity of the propagating spin wave mode in nano-contact spin torque oscillators. Nominally identical devices with 100 nm diameter are characterized by electrical microwave measurements and show large variation in the generated frequency as a function of drive current. This quantitative and qualitative device-to-device variation is described in terms of continuous and discontinuous nonlinear transitions between linear current intervals. The thin film grain microstructure in our samples is determined using atomic force and scanning electron microscopy to be on the scale of 30 nm. Micromagnetic simulations show that the reflection of spin waves against the grain boundaries results in standing wave resonance configurations. For a simulated device with a single artificial grain, the frequency increases linearly with the drive current until the decreased wavelength eventually forces another spin wave anti-node to be formed. This transition results in a discontinuous step in the frequency versus current relation. Simulations of complete, randomly generated grain microstructures additionally shows continuous nonlinearity and a resulting device-to-device variation in frequency that is similar to the experimental levels. The impact of temperature from 4 K to 300 K on the resonance mode-transition nonlinearity and frequency noise is investigated using simulations and it is found that the peak levels of the spectral linewidth as a function of drive current agrees quantitatively with typical levels found in experiments at room temperature. Copyright © 2020, The Authors. All rights reserved.

关键词： Scanning electron microscopy

Recognition of patient groups with sleep related disorders using bio-signal processing and deep learning

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arXiv 2021年

作者： Jarchi, Delaram Andreu-Perez, Javier Kiani, Mehrin Vysata, Oldrich Kuchynka, Jiri Prochazka, Ales Sanei, Saeid Smart Health Technologies Group School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Embedded and Intelligent Systems Laboratory School of Computer Science and Electronics University of Essex ColchesterCO4 3SQ United Kingdom Department of Computing and Control Engineering University of Chemistry and Technology in Prague Prague 6166 28 Czech Republic Department of Neurology Faculty of Medicine in Hradec Králové Charles University Hradec Králové500 05 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6160 00 Czech Republic School of Science and Technology Nottingham Trent University NottinghamNG11 8NS United Kingdom

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem. © 2021, CC BY-NC-ND.

关键词： Wavelet transforms

VisGraphNet: A complex network interpretation of convolutional neural features

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arXiv 2021年

作者： Florindo, Joao B. Lee, Young-Sup Jun, Kyungkoo Jeon, Gwanggil Albertini, Marcelo K. Institute of Mathematics Statistics and Scientific Computing University of Campinas Rua Sérgio Buarque de Holanda 651 Cidade Universitária"Zeferino Vaz" - Distr. Barão Geraldo SP CampinasCEP 13083-859 Brazil Department of Embedded Systems Engineering Incheon National University 119 Academy-ro Yeonsu-gu Incheon22012 Korea Republic of Department of Computer Science Federal University of Uberlandia Av. Joao Naves de Avila 2121 room Minas Gerais Uberlandia1B150 Brazil

Here we propose and investigate the use of visibility graphs to model the feature map of a neural network. The model, initially devised for studies on complex networks, is employed here for the classification of texture images. The work is motivated by an alternative viewpoint provided by these graphs over the original data. The performance of the proposed method is verified in the classification of four benchmark databases, namely, KTHTIPS-2b, FMD, UIUC, and UMD and in a practical problem, which is the identification of plant species using scanned images of their leaves. Our method was competitive with other state-of-the-art approaches, confirming the potential of techniques used for data analysis in different contexts to give more meaningful interpretation to the use of neural networks in texture classification. © 2021, CC BY.

关键词： Complex networks

Traffic control system for vehicles on Indian roads using raspberry Pi

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IOP Conference Series: Materials science and Engineering 2020年第3期981卷

作者： Bura Vijay Kumar Seena Naik Korra N Swathi D. Kothandaraman Nagender Yamsani Yerrolla Chanti Member Center for Embedded Systems & Internet of Things Department of Computer Science and Engineering S R University Warangal Telangana Department of Computer Science and Engineering S R University Warangal Telangana Sumathi Reddy Institute of Technology for Women Warangal Telangana India Member Center for AI & Deep Learning Department of Computer Science and Engineering S R Engineering College Warangal Telangana India Department of Computer Science and Engineering S R Engineering College Warangal Telangana India

In India at the roads the vehicles were growing because of the increase of population and controlling of traffic is one of the tough duties for the individuals who control the traffic. The everyday traffic congestion at crucial junctions becomes more issues for the emergency automobiles and ought to wait for until inexperienced signal. This results the growth in pollution ranges and wastage of time, pollutants stages may boom to a huge scale. Previously the traffic control strategies used like magnetic loop detectors, induction loop detectors are buried on the street aspect provide the constrained visitors information and necessitate separate tracking structures for site traffic counting and for traffic surveillance. Right here the undertaking proposes to enforce a synthetic density traffic manipulate gadget using photo processing and Raspberry pi.

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On the Transcriptomic Signature and General Stress State Associated with Aneuploidy

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arXiv 2020年

作者： Tsai, Hung-Ji Nelliat, Anjali R. Kucharavy, Andrei Choudhury, Mohammad Ikbal Sun, Sean X. Schatz, Michael C. Li, Rong Institute of Microbiology and Infections School of Biosciences University of Birmingham BirminghamB15 2TT United Kingdom Department of Systems Biology Harvard Medical School BostonMA02115 United States Distributed Computing Laboratory School of Computerand communication Sciences Ecole Polytechnique Federale de Lausanne Lausanne Switzerland Department of Mechanical Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Center for Cell Dynamics Department of Cell Biology Johns Hopkins University School of Medicine BaltimoreMD21205 United States Mechanobiology Institute Department of Biological Science National University of Singapore Singapore117411 Singapore

Continual Learning in the Presence of Repetition

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arXiv 2024年

作者： Hemati, Hamed Pellegrini, Lorenzo Duan, Xiaotian Zhao, Zixuan Xia, Fangfang Masana, Marc Tscheschner, Benedikt Veas, Eduardo Zheng, Yuxiang Zhao, Shiji Li, Shao-Yuan Huang, Sheng-Jun Lomonaco, Vincenzo van de Ven, Gido M. Institute for Computer Science University of St. Gallen Rosenbergstrasse 30 St. Gallen9000 Switzerland Department of Computer Science University of Bologna Via dell’Università 50 Cesena47521 Italy The University of Chicago 5801 S Ellis Ave Chicago60637 United States Argonne National Laboratory 9700 S Cass Ave Lemont60439 United States Graz University of Technology Rechbauerstraße 12 Graz8010 Austria TU Graz - SAL Dependable Embedded Systems Lab Silicon Austria Labs Graz8010 Austria Know-Center GmbH Sandgasse 36/4 Graz8010 Austria MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science University of Pisa Piano Secondo Largo Bruno Pontecorvo 3 Pisa56127 Italy Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 Leuven3001 Belgium

Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often considered in standard benchmarks for CL. Unlike with the rehearsal mechanism in buffer-based strategies, where sample repetition is controlled by the strategy, repetition in the data stream naturally stems from the environment. This report provides a summary of the CLVision challenge at CVPR 2023, which focused on the topic of repetition in class-incremental learning. The report initially outlines the challenge objective and then describes three solutions proposed by finalist teams that aim to effectively exploit the repetition in the stream to learn continually. The experimental results from the challenge highlight the effectiveness of ensemble-based solutions that employ multiple versions of similar modules, each trained on different but overlapping subsets of classes. This report underscores the transformative potential of taking a different perspective in CL by employing repetition in the data stream to foster innovative strategy design. © 2024, CC BY.

关键词： Contrastive Learning