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Conference on Electrical Insulation and Dielectric Phenomena, (CEIDP)

作者： Jierui Zhou Marina Dabaghian Anna Marie LaChance Jing Hao Wesley Zhong Luyi Sun Yang Cao Department of Electrical and Computer Engineering University of Connecticut Storrs CT USA Electrical Insulation Research Center Institute of Materials Science University of Connecticut Storrs CT USA Department of Chemical and Biomolecular Engineering University of Connecticut Storrs CT USA Polymer Program Institute of Materials Science University of Connecticut Storrs CT USA

Polymer-thin films are critical dielectric materials for capacitive energy storage in power and electronic systems. The rapid development of wide bandgap semiconductors urgently calls for polymers that can attain high energy density and high charge-discharge efficiency at elevated temperatures. However, high-temperature polymers usually suffer from compromised dielectric breakdown strength and soaring conduction loss due to the intrinsic constraint between their bandgaps and thermal stability. In this work, we propose a facile and high throughput interface engineering technique utilizing nanocoatings formed by montmorillonite (MMT) nanoclays, which can serve as an out-of-the-box solution to bypass this constraint. The highly ordered MMT-based nanocoatings with layered structure can significantly block the excessive charge injection at high electric fields and dissipate the charge carriers along the in-plane direction. The MMT-coated PI films exhibit improved breakdown strength, suppressed conduction loss, and boosted charging-discharging efficiency. This work holds the promise of improving the dielectric properties of polymers through surface engineering, enabling their application at concurrent electrical and thermal extremes.

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Spacetime meshing with adaptive refinement and coarsening

Spacetime meshing with adaptive refinement and coarsening

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Proceedings of the Twentieth Annual Symposium on Computational Geometry (SCG'04)

作者： Abedi, Reza Chung, Shuo-Heng Erickson, Jeff Fan, Yong Garland, Michael Guoy, Damrong Haber, Robert Sullivan, John M. Thite, Shripad Zhou, Yuan Ctr. for Proc. Simulation and Design University of Illinois Urbana-Champaign Department of Theoretical Mechanics Univ. Illinois at Urbana-Champaign Urbana IL 61801 Department of Computer Science UIUC Ctr. for Simulat. of Adv. Rockets Compl. Sci. and Engineering Program UIUC Department of Mathematics UIUC Department of Mathematics Technische Universität Berlin

We propose a new algorithm for constructing finite-element meshes suitable for spacetime discontinuous Galerkin solutions of linear hyperbolic PDEs. Given a triangular mesh of some planar domain Ω and a target time value T, our method constructs a tetrahedral mesh of the spacetime domain Ω × [0, T] in constant running time per tetrahedron in 3 using an advancing front method. Elements are added to the evolving mesh in small patches by moving a vertex of the front forward in time. Spacetime discontinuous Galerkin methods allow the numerical solution within each patch to be computed as soon as the patch is created. Our algorithm employs new mechanisms for adaptively coarsening and refining the front in response to a posteriori error estimates returned by the numerical code. A change in the front induces a corresponding refinement or coarsening of future elements in the spacetime mesh. Our algorithm adapts the duration of each element to the local quality, feature size, and degree of refinement of the underlying space mesh. We directly exploit the ability of discontinuous Galerkin methods to accommodate discontinuities in the solution fields across element boundaries.

关键词： computer graphics

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Implementation of Apriori Algorithm for Analysis of Consumer Purchase Patterns 1

Implementation of Apriori Algorithm for Analysis of Consumer...

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1st International Conference on computer science and Applied Mathematic, ICCSAM 2018

作者： Panjaitan, Suprianto Sulindawaty Amin, Muhammad Lindawati, Sri Watrianthos, Ronal Sihotang, Hengki Tamando Sinaga, Bosker Teknik Informatika STMIK Pelita Nusantara Medan Sumatera Utara Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Program Doctoral Vocational Technology Education Postgraduate Engineering Faculty Padang State University Padang West Sumatera Indonesia Manajemen Informatika AMIK Logika Sumatera Utara Indonesia Manajemen Informatika AMIK Labuhan Batu Sumatera Utara Indonesia

Consumer purchasing patterns are a form of purchases made by consumers, whether someone or a lot of people to get the desired item by making a purchase transaction. One characteristic of the purchase pattern is the existence of acquiring something through exchanging money. This study aims to create an application that is used in determining consumer purchasing patterns by applying a priori algorithms and using Visual Basic 2010 as a tool for determining consumer purchase patterns. This application uses a priori algorithm calculation method where the sample consumer purchase data will be sorted and calculated by providing the value of the minium support and configuration parameters and based on the results of confidence the largest number of conclusions such as: can be used as information for determining sales, the application of a priori algorithms can provide information pattern combination item set from consumer purchase data that is with support above 15% and confidence above 50% on item set. © Published under licence by IOP Publishing Ltd.

关键词： Condensed matter physics

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Development of Automated Detection Methods for Calculating Blood Vessel Width and Stenosis Rate in Cardiac Catheterization Imaging 24

Development of Automated Detection Methods for Calculating B...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Huang, Hsien-Cheng Chang, Hsiao-Huang Ke, Xiang-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Cardiovascular Surgery Department Taipei Veterans General Hospital Taipei City Taiwan Taipei Medical University Taipei City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Coronary artery disease is the most prevalent type of heart disease and has a considerable mortality rate. In diagnosing and assessing coronary artery disease, physicians must integrate a variety of clinical information and test results to evaluate the degree of coronary artery obstruction and continually learn and improve their skills in clinical practice to ensure the most accurate assessment of patients. Currently, the use of information technology to assist medical staff allows more effective and accurate analysis of symptoms. This approach not only serves as an auxiliary standard for physician diagnosis, thereby reducing the possibility of misdiagnosis, but also significantly improves patient treatment outcomes and medical quality while reducing the time burden on physicians for diagnosing patients. This study utilized image processing techniques and thinning algorithms to automate the detection of coronary artery width and, following the recommended methods of physicians in related fields, used specific vessel widths to calculate the degree of coronary artery narrowing. This provides a more accurate and efficient assessment method for evaluating the degree of vascular constriction in patients with coronary artery disease. © 2024 Copyright ACM.

关键词： Patient treatment

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Machine learning identification of organic compounds using visible light

arXiv

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arXiv 2022年

作者： Bikku, Thulasi Fritz, Rubén A. Colón, Yamil J. Herrera, Felipe Department of Physics Universidad de Santiago de Chile Av. Victor Jara 3493 Santiago Chile Computer Science and Engineering Vignan's Nirula Institute of Technology and Science for Women Andhra Pradesh Guntur India Department of Chemical and Biomolecular Engineering University of Notre Dame IN United States ANID - Millennium Science Initiative Program Millennium Institute for Research in Optics Concepción Chile

Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and vibrational information for remote chemical identification. This has been exploited using the fingerprint region of infrared absorption spectra, which involves a dense set of absorption peaks that are unique to individual molecules, thus facilitating chemical identification. However, optical identification using visible light has not been realized. Using decades of experimental refractive index data in the scientific literature of pure organic compounds and polymers over a broad range of frequencies from the ultraviolet to the far-infrared, we develop a machine learning classifier that can accurately identify organic species based on a single-wavelength dispersive measurement in the visible spectral region, away from absorption resonances. The optical classifier proposed here could be applied to autonomous material identification protocols or applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Refractive index

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Synthesis and properties of non-curing graphene thermal interface materials

arXiv

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arXiv 2019年

作者： Naghibi, Sahar Kargar, Fariborz Wright, Dylan Huang, Chun Yu Tammy Mohammadzadeh, Amirmahdi Barani, Zahra Salgado, Ruben Balandin, Alexander Center University of California RiversideCA92521 United States Materials Science and Engineering Program Bourns College of Engineering University of California Riverside92521 United States Department of Electrical and Computer Engineering Bourns College of Engineering University of California RiversideCA92521 United States

Development of the next generation thermal interface materials with high thermal conductivity is important for thermal management and packaging of electronic devices. We report on the synthesis and thermal conductivity measurements of non-curing thermal paste, i.e. grease, based on mineral oil with the mixture of graphene and few-layer graphene flakes as the fillers. It was found that graphene thermal paste exhibits a distinctive thermal percolation threshold with the thermal conductivity revealing a sublinear dependence on the filler loading. This behavior contrasts with the thermal conductivity of curing graphene thermal interface materials, based on epoxy, where super-linear dependence on the filler loading is observed. The performance of graphene thermal paste was benchmarked against top-of-the-line commercial thermal pastes. The obtained results show that non-curing graphene thermal interface materials outperforms the best commercial pastes in terms of thermal conductivity, at substantially lower filler concentration of fg = 27 vol%. The obtained results shed light on thermal percolation mechanism in non-curing polymeric matrices laden with quasi-two-dimensional fillers. Considering recent progress in graphene production via liquid phase exfoliation and oxide reduction, we argue that our results open a pathway for large-scale industrial application of graphene in thermal management of electronics. Copyright © 2019, The Authors. All rights reserved.

关键词： Percolation (fluids)

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On Performance Prediction of Big Data Transfer in High-performance Networks

On Performance Prediction of Big Data Transfer in High-perfo...

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IEEE International Conference on Communications (ICC)

作者： Wuji Liu Daqing Yun Chase Q. Wu Nageswara S.V. Rao Aiqin Hou Wei Shen Department of Computer Science New Jersey Institute of Technology Newark NJ USA Computer and Information Sciences Program Harrisburg University Harrisburg PA USA Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge TN USA School of Information Science and Technology Northwest University Xi’an Shaanxi China School of Informatics Science and Technology Zhejiang Sci-Tech University Hangzhou Zhejiang China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

关键词： Data transfer Big Data Throughput Bandwidth Measurement Predictive models Protocols

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROscience 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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Integration of Artificial Intelligence in Software Defined Networking Technology through Developing Machine Learning Algorithms

Integration of Artificial Intelligence in Software Defined N...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Lamhot Naibaho Pulkit Raj Saxena Kuldeep Sharma Badria Sulaiman Alfurhood L. Pallavi Bhasker Pant English Language Education Study Program Faculty of Letters and Languages Christian University of Indonesia College of Computing Science and Information Technology Teer Thanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Engineering Lovely Professional University Phagwara Jalandhar Punjab India Department of Computer Sciences College of Computer Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia CSE B V Raju Institute of Technology Narsapur Telangana India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

A future networking design called "software-defined networking" combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regulator, which acts as the cable network processor and divides the data plane into a control plane and a data aircraft. Recent breakthroughs in artificial intelligence (Al) have shown a stronger inclination for the science community to benefit from their ability to give learning capabilities and enhance Indicator. The author of this research provides a comprehensive analysis of initiatives underway to incorporate Al with SDN. The study concluded that the three primary Al thread where scientific research was centered were computer science, conceptual, and fuzzy reasoning systems. The authors of this paper explore the several fields in which these approaches may be used, their potential future applications, and the innovations made possible by the integration of AI-based methods into the SDN paradigm. By choosing the right SDN controller, any large organization may lower network complexity, implementation expenses, and maintenance costs. The focus of this article is software defined networking's benefits and downsides (SDN). Following is a basic explanation of artificial intelligence and a list of some of its most significant applications in SDN.

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