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Sim-PROCESD: Simulated-Production Resource for Operations and Conditions Evaluation to Support Decision-making
Data provided by National Institute of Standards and Technology
Sim-PROCESD 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. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Sim-PROCESD provides class definitions for manufacturing devices/components that can be configured by the user to model various real-world manufacturing systems.
Tags: discrete-event simulation,manufacturing,production,maintenance,python,
Modified: 2024-02-22
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
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
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
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
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
Thermal Drift Monitoring Experiment 01
Data provided by National Institute of Standards and Technology
An experiment was set up within a machine tool at the National Institute of Standards and Technology (NIST) to test vision-based thermal drift tracking methods. A wireless microscope within a tool holder in the spindle is used to capture videos of image targets attached to the worktable. For each target, one video is captured during spindle rotation orthogonal to the worktable and another video is captured during axis translation orthogonal to the worktable.
Tags: Thermal error,machine tool,monitoring,manufacturing,Microscope,
Modified: 2024-02-22
Segmentation of lipid nanoparticles from cryogenic electron microscopy images
Data provided by National Institute of Standards and Technology
Lipid nanoparticles (LNPs) were prepared as described (https://doi.org/10.1038/s42003-021-02441-2) using the lipids DLin-KC2-DMA, DSPC, cholesterol, and PEG-DMG2000 at mol ratios of 50:10:38.5:1.5. Four sample types were prepared: LNPs in the presence and absence of RNA, and with LNPs ejected into pH 4 and pH 7.4 buffer after microfluidic assembly. To prepare samples for imaging, 3 ?L of LNP formulation was applied to holey carbon grids (Quantifoil, R3.5/1, 200 mesh copper).
Tags: Lipid Nanoparticle,LNP,cryogenic electron microscopy,CryoEM,machine learning,ai,mRNA,
Modified: 2024-02-22
Software for Evaluating Convolutional Generative Adversarial Networks with Classical Random Process Noise Models
Data provided by National Institute of Standards and Technology
This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models.
Tags: time series,machine learning,band-limited noise,power law noise,shot noise,impulsive noise,colored noise,fractional Gaussian noise,fractional Brownian motion,
Modified: 2024-02-22
Cost Assessment Tool for Sustainable Manufacturing (CATS)
Data provided by National Institute of Standards and Technology
This tool uses techniques from ASTM E3200 for evaluating manufacturing investments from the perspective of environmentally sustainable manufacturing by pairing economic methods of investment analysis with environmental aspect of manufacturing. The economic techniques used include net present value, internal rate of return, payback period, and hurdle rate. These four techniques are deterministic, meaning that they deal with known values that are certain. The tool also conducts a sensitivity analysis using Monte Carlo techniques.
Tags: investment analysis,manufacturing,net present value,internal rate of return,environmental impact,sustainability,
Modified: 2024-02-22