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Data provided by
National Institute of Standards and Technology
Simantha is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems.
Tags: discrete-event simulation,manufacturing,production,maintenance,python,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
This repository contains results for experiments on generative modeling of synthetic Orthogonal-Frequency Division Multiplexing (OFDM) communication signals. (This record supersedes Software and Data for Modeling OFDM Communication Signals with Generative Adversarial Networks, formerly at
https://doi.org/10.18434/mds2-2428)
Tags: generative adversarial network,machine learning,wireless communications,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
This is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform extractive question answering. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 360 QA AI models using a small set of model architectures.
Tags: Trojan Detection,Artificial Intelligence,ai,machine learning,Adversarial Machine Learning,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
This is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 360 QA AI models using a small set of model architectures.
Tags: Trojan Detection,Artificial Intelligence,ai,machine learning,Adversarial Machine Learning,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
This is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.
Tags: Trojan Detection,Artificial Intelligence,ai,machine learning,Adversarial Machine Learning,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
A linear axis testbed at the Center for Intelligent Maintenance Systems (IMS Center) at the University of Cincinnati was run to failure (the detection of backlash) over one year with periodic data collected from an inertial measurement unit (IMU) on the carriage, two triaxial accelerometers on the ball nut, and the controller.
Tags: manufacturing,Industry 4.0,smart manufacturing,linear axis,machine tool,ball screw,backlash,sensor,accelerometer,inertial measurement unit,IMU,error motion,data analysis,monitoring,diagnostics,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
This project produces synthetic datasets of spectrum sharing simulation results (I/Q data, metadata, and KPIs).
Tags: wireless coexistence,Spectrum sharing,machine learning,wireless communications and networks,4G,5G,6G,Wi-Fi,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
This project produces synthetic datasets of spectrum sharing simulation results (I/Q data, metadata, and KPIs).
Tags: wireless coexistence,Spectrum sharing,machine learning,wireless communications and networks,4G,5G,6G,Wi-Fi,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
A code repository and accompanying data for incorporating imperfect theory into machine learning for improved prediction and explainability. Specifically, it focuses on the case study of the dimensions of a polymer chain in different solvent qualities. Jupyter Notebooks for quickly testing concepts and reproducing figures, as well as source code that computes the mean squared error as a function of dataset size for various machine learning models are included.For additional details on the data, please refer to the README.md associated with the data.
Tags: polymers,machine learning,transfer learning,theory,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
SolDet is an object-oriented package for solitonic feature detection in absorption images of Bose-Einstein condensate. with wider use for cold atom image analysis. Featured with classifier, object detector, and Mexican hat metric methods. Technical details are explained in
https://arxiv.org/abs/2111.04881.
Tags: soliton,machine learning,python package,
Modified: 2022-07-29
Views: 0
