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48 results found

A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-nets and Generative Adversarial Networks

Data provided by  National Institute of Standards and Technology

Digital light processing (DLP) vat photopolymerization (VP) additive manufacturing (AM) uses patterned UV light to selectively cure a liquid photopolymer into a solid layer. Subsequent layers are printed on to preceding layers to eventually form a desired 3 dimensional (3D) part. This data set characterizes the 3D geometry of a single layer of voxels (volume pixels) printed with photomasks assigned random intensity levels at every pixel. The masks are computer generated, then printed onto a glass cover slide.

Tags: 3D printing,additive manufacturing,machine learning,generative adversarial network,Photopolymer,

Modified: 2024-02-22

Views: 0

Towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) MIg analyZeR (mizr) Package

Data provided by  National Institute of Standards and Technology

Our work towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) aims to provide plots, tools, methods, and strategies to extract insights out of various machine learning (ML) and Artificial Intelligence (AI) data.Included in this software is the MIg analyZeR (mizr) R software package that produces various plots.

Tags: analysis software,Artificial Intelligence,machine learning,design of experiments,

Modified: 2024-02-22

Views: 0

Microplastic and nanoplastic chemical characterization by thermal desorption and pyrolysis mass spectrometry with unsupervised machine learning

Data provided by  National Institute of Standards and Technology

This data publication contains the mass spectrometry chemical characterization of microplastic and nanoplastic chemical analysis. The data from this study includes mass spectra of pure, mixed, and weathered microplastics and nanoplastics at high and low fragmentation, extracted ion chronograms, Kendrick mass defect plots, code, and the derived and processed data. The data analysis code (MATLAB 2022a*) used for unsupervised learning of cluster and compositional relationships is also included.

Tags: Microplastic,Nanoplastics,environment,mass spectrometry,GC-MS,Chemical Characterization,machine learning,

Modified: 2024-02-22

Views: 0

ns-3 ORAN Module

Data provided by  National Institute of Standards and Technology

This module for ns-3 implements the classes required to model a network architecture based on the O-RAN Alliance's specifications. These models include a Radio Access Network (RAN) Intelligent Controller (RIC) that is functionally equivalent to O-RAN's Near-Real Time (Near-RT) RIC, and reporting modules that attach to simulation nodes and serve as communication endpoints with the RIC in a similar fashion as the E2 Terminators in O-RAN.

Tags: Artificial Intelligence,machine learning,Open RAN,RAN Intelligent Controller,

Modified: 2024-02-22

Views: 0

Noise Datasets for Evaluating Deep Generative Models

Data provided by  National Institute of Standards and Technology

Synthetic training and test datasets for experiments on deep generative modeling of noise time series. Consists of data for the following noise types: 1) band-limited thermal noise, i.e., bandpass filtered white Gaussian noise, 2) power law noise, including fractional Gaussian noise (FGN), fractional Brownian motion (FBM), and fractionally differenced white noise (FDWN), 3) generalized shot noise, 4) impulsive noise, including Bernoulli-Gaussian (BG) and symmetric alpha stable (SAS) distributions.

Tags: generative adversarial network,machine learning,time series,band-limited noise,power law noise,shot noise,impulsive noise,colored noise,fractional Gaussian noise,fractional Brownian motion,

Modified: 2024-02-22

Views: 0

Data for Modeling OFDM Communication Signals with Generative Adversarial Networks

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: 2024-02-22

Views: 0

Trojan Detection Software Challenge - nlp-question-answering-sep2021-test

Data provided by  National Institute of Standards and Technology

Round 8 Test DatasetThis 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: 2024-02-22

Views: 0

Trojan Detection Software Challenge - nlp-question-answering-sep2021-holdout

Data provided by  National Institute of Standards and Technology

Round 8 Holdout DatasetThis 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.

Tags: Trojan Detection,Artificial Intelligence,ai,machine learning,Adversarial Machine Learning,

Modified: 2024-02-22

Views: 0

Trojan Detection Software Challenge - nlp-summary-jan2022-train

Data provided by  National Institute of Standards and Technology

Round 9 Train DatasetThis 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: 2024-02-22

Views: 0

Dataset for paper Y. Ma, S. Mosleh and J. Coder, "Analyzing 5G NR-U and WiGig Coexistence with Multiple-Beam Directional LBT," 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), 2022, pp. 272-275, doi: 10.1109/CCNC49033.2022.9700690

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: 2024-02-22

Views: 0