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检索条件"主题词=software algorithms"

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High Performance Uncertainty Quantification with Parallelized Multilevel Markov Chain Monte Carlo

High Performance Uncertainty Quantification with Parallelize...

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Supercomputing Conference

作者： Linus Seelinger Anne Reinarz Leonhard Rannabauer Michael Bader Peter Bastian Robert Scheichl Institute for Scientific Computing Heidelberg University Heidelberg Germany Department of Computer Science Durham University Durham United Kingdom Department of Informatics Technical University of Munich Garching Germany Institute for Applied Mathematics Heidelberg University Heidelberg Germany

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

Numerical models of complex real-world phenomena often necessitate High Performance Computing (HPC). Uncertainties increase problem dimensionality further and pose even greater challenges. We present a parallelization strategy for multilevel Markov chain Monte Carlo, a state-of-the-art, algorithmically scalable Uncertainty Quantification (UQ) algorithm for Bayesian inverse problems, and a new software framework allowing for large-scale parallelism across forward model evaluations and the UQ algorithms themselves. The main scalability challenge presents itself in the form of strong data dependencies introduced by the MLMCMC method, prohibiting trivial parallelization. Our software is released as part of the modular and open-source MIT Uncertainty Quantification Library (MUQ), and can easily be coupled with arbitrary user codes. We demonstrate it using the Distributed and Unified Numerics Environment (DUNE) and the ExaHyPE Engine. The latter provides a realistic, large-scale tsunami model in which we identify the source of a tsunami from buoy-elevation data.

关键词： Uncertainty Monte Carlo methods Inverse problems Computational modeling High performance computing software algorithms Markov processes

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Recurrent Neural Networks algorithms and applications

Recurrent Neural Networks algorithms and applications

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Big Data & Artificial Intelligence & software Engineering (ICBASE), International Conference on

作者： Yuexing Chen Jiarun Li Rutgers Business School The State University of New Jersey New Brunswick New Jersey United Stated School of Beijing Institute of Petrochemical Technology Beijing China

ISBN: (纸本)9781665427104

Since recurrent neural networks (RNNs) were firstly proposed, it is widely used, and many extended RNNs algorithms have been developed, which achieve good results in many application fields. To report the latest research results of RNNs and help researchers quickly understand the latest progress of RNNs algorithm. In this research, we briefly introduce the basic principle of RNN, review more than 20 papers about RNNs and summarize the previous work. Then, we make qualitative and quantitative analyses on four representative algorithms (CNN-RNN, ResNet-110, ResNet-164, and ResNet-1001). Among them, the model of CNN-RNN obtains the best performance on the CIFAR-100 dataset with standard data augmentation. Finally, we discuss the future development trend of RNNs.

关键词： Recurrent neural networks Statistical analysis software algorithms Diversity reception Prediction algorithms Market research Data models

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Research on intelligent manufacturing equipment visual inspection control based on machine vision

Research on intelligent manufacturing equipment visual inspe...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Chunyan Huo Tianshu Li Harbin Huade University Harbin Heilongjiang China

ISBN: (纸本)9781665428408

Enterprise mass production and zero waste control require on-line automatic detection in production and processing. Machine vision plays a key role in automatic detection. In recent years, with the development of economy and the progress of science and technology, the level of production and manufacturing technology has made remarkable achievements, and industrial robots are more and more widely used in the field of production and manufacturing. With the wide application of industrial robots, the technical level of industrial robots has also made significant progress. At present, industrial robots have higher functionality and stability, and industrial robots have also introduced visual monitoring technology, which makes the manufacturing field more intelligent. In order to meet the huge demand of the intelligent manufacturing equipment industry for machine vision technology, this paper combines the technical characteristics of equipment and special application environment to propose a general machine vision inspection and control technology system, which makes up for the current research deficiencies.

关键词： Manufacturing industries Visualization Service robots Machine vision software algorithms Production Manufacturing

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Development of Algorithm for Treatment of Extreme Outliers in Numerical Data, Conditional on Joint Distribution Relationship

Development of Algorithm for Treatment of Extreme Outliers i...

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International Scientific-Practical Conference Problems of Infocommunications Science and Technology

作者： Nikola Iliychev Iliev Marin Marinov Sergey Radukanov Raiffeisenbank Bulgaria Sofia Bulgaria D.A. Tsenov Academy of Economics Svishtov Bulgaria St. Cyril and St. Methodius University of Veliko Tarnovo Veliko Tarnovo Bulgaria

ISBN: (纸本)9781665406833

The paper describes the development of a model for treating extreme outliers in numerical data variables, using an alternative point of view - relationship conditional on data variables' joint distribution. The mentioned algorithm finds its roots in existing practices like Winsorization (or truncation) of extreme outliers independently for each variable. After establishing an overview of historical outlier treating, the authors turn their attention towards common erroneous assumptions, that, while often, being made, bring research analysis and conclusions towards big caveats. A common example is a joint distribution relationship shape's shift from that of a circular towards a rectangular shape, unexplainable or indefensible by statistics. Next the paper makes some propositions how to fix this prominent problem, by looking at existing joint distribution relationship's shape and how it could give insight on outlier treatment. Following that, the authors try to circumscribe a methodology for application of the proposed fix by going through the algorithm's mathematical specifics and assumptions. Lastly the paper studies the algorithm's application results, including possibilities for future studies, including interdisciplinary application within programing environment, for the possible purpose of creating an actual software solution.

关键词： Shape Statistical analysis software algorithms Data processing Data models software Mathematical models

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Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehicles

Pylot: A Modular Platform for Exploring Latency-Accuracy Tra...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Ionel Gog Sukrit Kalra Peter Schafhalter Matthew A. Wright Joseph E. Gonzalez Ion Stoica UC Berkeley

We present Pylot, a platform for autonomous vehicle (AV) research and development, built with the goal to allow researchers to study the effects of the latency and accuracy of their models and algorithms on the end-to-end driving behavior of an AV. This is achieved through a modular structure enabled by our high-performance dataflow system that represents AV software pipeline components (object detectors, motion planners, etc.) as a dataflow graph of operators which communicate on data streams using timestamped messages. Pylot readily interfaces with popular AV simulators like CARLA, and is easily deployable to real-world vehicles with minimal code *** reduce the burden of developing an entire pipeline for evaluating a single component, Pylot provides several state-of-the-art reference implementations for the various components of an AV pipeline. Using these reference implementations, a Pylot-based AV pipeline is able to drive a real vehicle, and attains a high score on the CARLA Autonomous Driving Challenge. We also present several case studies enabled by Pylot, including evidence of a need for context-dependent components, and per-component time allocation. Pylot is open source, with the code available at https://***/erdos-project/pylot.

关键词： Codes Runtime Pipelines software algorithms Reinforcement learning Detectors Resource management

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Mining for Graph-Based Library Usage Patterns in COBOL Systems

Mining for Graph-Based Library Usage Patterns in COBOL Syste...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Ruben Opdebeeck Johan Fabry Tim Molderez Jonas De Bleser Coen De Roover Vrije Universiteit Brussel Belgium Raincode Labs Belgium

When migrating legacy systems to more contemporary platforms, it is not always feasible to migrate their external library dependencies as well. A workaround can be to partially reimplement the library dependencies on the new platform, limiting oneself to the features that are used by the migrated system. For contemporary programming languages, several approaches to mining for library usage patterns have been proposed. For legacy programming languages, in contrast, such tools are lacking. This encumbers establishing what parts of a library need to be rewritten during a migration project, especially when considering large-scale *** this industry track paper, we present an approach to mining library usage patterns in COBOL code. We describe a library usage extractor for COBOL, which produces graphs that capture the control and data flow involved in library calls. The extractor supports legacy control flow features, such as GO TO. We use these graphs as input to two state-of-the-art frequent subgraph mining algorithms, and report on the scalability of their use for mining common library usage patterns in two industrial COBOL systems. The mined library usage patterns can help assess and subsequently steer the migration effort.

关键词： Industries Computer languages Limiting Scalability software algorithms Tools Feature extraction

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Continuous learning with random memory for object detection in robotic applications

Continuous learning with random memory for object detection ...

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Nonlinearity, Information and Robotics, (NIR) International Conference

作者： Ivan Nenakhov Ruslan Mazhitov Kirill Artemov SeyedHassan Zabihifar Aleksandr Semochkin Sergey Kolyubin ITMO University St. Petersburg Russia PJSC Sberbank Moscow Russia Innopolis University Innopolis Russia

ISBN: (纸本)9781665424080

If the robot is to interact with environment designed for humans it has to be able to cope with new objects in it’s surrounding, and not only to classify but also effectively localize objects in its reach based on visual sensing. We cannot predict all objects the robot will face. So it needs to learn them while operating, without an external update of software. One of the promising approaches to handle this task is using detectors based on convolution neural networks (CNN) with continuous learning setup. However, there is a well-known problem of catastrophic forgetting (CF) that negatively affects accuracy of neural networks, the main algorithms in object recognition nowadays. Moreover, we need to reach a trade off between training time (because it is unacceptable if the robot trains for hours) and good acccuracy of detection. In this paper we review some of the latest approaches in the field and adapt one of SOtA methods in classification task to detection task. Particularly, we adapt YOLOv5 to work in scenario with continuous learning by adding random memory mechanism from [1] and applying layer freezing in order to increase training speed. We benchmark the proposed method using iCubWorld Transformations dataset and report results with significantly improved metrics and inference speed.

关键词： Training Visualization Neural networks software algorithms Object detection Robot sensing systems software

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Fibonacci Path Planning Algorithm for Robotic Waiters

Fibonacci Path Planning Algorithm for Robotic Waiters

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IEEE International Conference on Control, Measurement and Instrumentation (CMI)

作者： Chervu Sowmya Akhil. S. Menon Garapati Sri Maanvitha Sharan Vijayraghavan M. Jayakumar Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9781728193427

ISBN: (纸本)9781728193434

This paper portrays the idea and implementation of Robotic Waiters. This project implements a path planning algorithm for hotel management. Usually, we find humans serving food at the restaurants, sometimes there might be errors while delivering the food at the defined table or serving wrong order at a wrong table. So, to overcome these errors, we have tried to implement a prototype of Robotic Waiter to serve the food. With Raspberry pi as the main controller, we have integrated L298N motor driver and IR sensor array. The Robotic Waiter works on a path planning algorithm, “Fibonacci algorithm”, to count the table number through which it can check if the entered table exists or not. This method aims in reducing the work of going to each table and check for its existence. Further the line following algorithm makes the robot to follow the laid track properly.

关键词： software algorithms Prototypes Robot sensing systems Turning Path planning software Task analysis

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Stopping Criteria for Satellite Imaging based on Improved Differential Evolution Algorithm

Stopping Criteria for Satellite Imaging based on Improved Di...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Chong Chen Dongcheng Li Hui Li Jie Zhang Zhiming Wu China University of Geosciences Wuhan China University of Texas at Dallas Richardson USA

ISBN: (纸本)9781665478373

The differential evolution algorithm is practical in solving multisatellite and multitask problems faced by satellite earth observation. The adaptive control scale factor and mutation strategy were improved in this study to balance the convergence rate and diversity of the differential evolution algorithm and ensure a globally optimal solution. Specifically, the consistency of the optimal population and the population diversity are adopted to measure whether the algorithm is converged to the globally optimal solution as the stopping criterion. The results of comparative experiments indicate that the control factors adopting adaptive variation are more favorable to the convergence of the differential evolution algorithm. Moreover, the number of iterations for the algorithm reaching the global optimal solution is reduced after the stop criterion.

关键词： Earth Satellites Heuristic algorithms Sociology software algorithms software quality software reliability

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Islanding Detection of PV-based Microgrid based on Empirical Mode Decomposition and Decision Tree

Islanding Detection of PV-based Microgrid based on Empirical...

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Innovations in Power and Advanced Computing Technologies (i-PACT)

作者： Anasuya Roy Choudhury Ranjan Kumat Mallick Ramachandra Agrawal Sairam Mishra Pravati Nayak Ashis Ranjan Behera Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India

ISBN: (纸本)9781665426923

Islanding detection in microgrid has become an integral part of protection system. To enhance reliability, stability and power quality it is required to detect islanding condition accurately and quickly as per IEEE standard. This article proposes a new islanding detection strategy for PV-based microgrid using an efficient signal processing tool Empirical mode decomposition (EMD) and Decision Tree (DT) machine learning classifier. The efficacy of proposed technique is validated by comparing with competitive published techniques

关键词： Technological innovation Empirical mode decomposition software algorithms Signal processing algorithms Microgrids Switches Voltage

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