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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
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 tool calculates metrics for investment analysis documented in NIST Advanced manufacturing Series 200-5. It calculates net present value, internal rate of return, and payback period along with executing sensitivity analysis using Monte Carlo techniques. The tool helps to identify the most economical projects/investments. For instance, it could be used to identify the most economical heating and cooling system or it might be used to rank a set of potential investments.
Tags: net present value,internal rate of return,payback,investment,monte carlo,manufacturing,cost effective,economics,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
The data being generated and disseminated is the test data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). 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
The data being generated and disseminated is the holdout data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). 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
The data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. 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 1104 trained, human level, image classification AI models using a variety 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
The data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. 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
The data being generated and disseminated is the test data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). 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
The data being generated and disseminated is the holdout data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). 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
The data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. 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
