From Operational Scenarios to Synthetic Data: Simulation-Based Data Generation for AI-Based Airborne Systems

2022 ◽  
Author(s):  
Siddhartha Gupta ◽  
Umut Durak ◽  
Oliver Ellis ◽  
Christoph Torens
Keyword(s):  
Synthetic Data ◽  
Data Generation ◽  
Data Simulation ◽  
Simulation Based

Intrusion detection of railway clearance from infrared images using generative adversarial networks

2020 ◽  
pp. 1-13
Author(s):  
Yundong Li ◽  
Yi Liu ◽  
Han Dong ◽  
Wei Hu ◽  
Chen Lin
Keyword(s):  
Intrusion Detection ◽  
Synthetic Data ◽  
Generative Adversarial Networks ◽  
Generation Model ◽  
Single Shot ◽  
Data Generation ◽  
Infrared Images ◽  
Adversarial Networks ◽  
Training Samples ◽  
Rgb Images

The intrusion detection of railway clearance is crucial for avoiding railway accidents caused by the invasion of abnormal objects, such as pedestrians, falling rocks, and animals. However, detecting intrusions using deep learning methods from infrared images captured at night remains a challenging task because of the lack of sufficient training samples. To address this issue, a transfer strategy that migrates daytime RGB images to the nighttime style of infrared images is proposed in this study. The proposed method consists of two stages. In the first stage, a data generation model is trained on the basis of generative adversarial networks using RGB images and a small number of infrared images, and then, synthetic samples are generated using a well-trained model. In the second stage, a single shot multibox detector (SSD) model is trained using synthetic data and utilized to detect abnormal objects from infrared images at nighttime. To validate the effectiveness of the proposed method, two groups of experiments, namely, railway and non-railway scenes, are conducted. Experimental results demonstrate the effectiveness of the proposed method, and an improvement of 17.8% is achieved for object detection at nighttime.

Synthetic data generation of high-resolution hyperspectral data using DIRSIG

2007 ◽  
Author(s):  
Marek K. Jakubowski ◽  
David Pogorzala ◽  
Timothy J. Hattenberger ◽  
Scott D. Brown ◽  
John R. Schott
Keyword(s):  
High Resolution ◽  
Synthetic Data ◽  
Hyperspectral Data ◽  
Data Generation ◽  
Synthetic Data Generation

Synthetic Data Generation to Support Irregular Sampling in Sensor Networks

2004 ◽  
pp. 211-234 ◽  
Author(s):  
Lewis Girod ◽  
Ramesh Govindan ◽  
Deepak Ganesan ◽  
Deborah Estrin ◽  
Yan Yu
Keyword(s):  
Sensor Networks ◽  
Synthetic Data ◽  
Irregular Sampling ◽  
Data Generation ◽  
Synthetic Data Generation

scholarly journals Instance Segmentation in CARLA: Methodology and Analysis for Pedestrian-oriented Synthetic Data Generation in Crowded Scenes

2021 ◽  
Author(s):  
Maria Lyssenko ◽  
Christoph Gladisch ◽  
Christian Heinzemann ◽  
Matthias Woehrle ◽  
Rudolph Triebel
Keyword(s):  
Synthetic Data ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Crowded Scenes ◽  
Instance Segmentation

scholarly journals Inferring viral occurrence patterns through a synthetic data simulation

2021 ◽  
Author(s):  
Ville N Pimenoff ◽  
Ramon Cleries
Keyword(s):  
Linear Models ◽  
Population Sample ◽  
Synthetic Data ◽  
Interaction Patterns ◽  
Viral Strain ◽  
Data Simulation ◽  
Synthetic Datasets ◽  
Pathogen Occurrence ◽  
Log Linear ◽  
Occurrence Patterns

Viruses infecting humans are manifold and several of them provoke significant morbidity and mortality. Simulations creating large synthetic datasets from observed multiple viral strain infections in a limited population sample can be a powerful tool to infer significant pathogen occurrence and interaction patterns, particularly if limited number of observed data units is available. Here, to demonstrate diverse human papillomavirus (HPV) strain occurrence patterns, we used log-linear models combined with Bayesian framework for graphical independence network (GIN) analysis. That is, to simulate datasets based on modeling the probabilistic associations between observed viral data points, i.e different viral strain infections in a set of population samples. Our GIN analysis outperformed in precision all oversampling methods tested for simulating large synthetic viral strain-level prevalence dataset from observed set of HPVs data. Altogether, we demonstrate that network modeling is a potent tool for creating synthetic viral datasets for comprehensive pathogen occurrence and interaction pattern estimations.

Synthetic Data Generation Capabilties for Testing Data Mining Tools

MILCOM 2006 ◽  
2006 ◽  
Author(s):  
Daniel Jeske ◽  
Pengyue Lin ◽  
Carlos Rendon ◽  
Rui Xiao ◽  
Behrokh Samadi
Keyword(s):  
Data Mining ◽  
Synthetic Data ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Testing Data ◽  
Mining Tools

Simulation und Optimierung kombiniert/A simulation-based framework for operational optimization of industrial energy systems

2020 ◽  
Vol 110 (01-02) ◽  
pp. 12-17
Author(s):  
Niklas Panten ◽  
Heiko Ranzau ◽  
Thomas Kohne ◽  
Daniel Moog ◽  
Eberhard Abele ◽  
...  
Keyword(s):  
Synthetic Data ◽  
Cost Savings ◽  
Energy Systems ◽  
Decisive Role ◽  
Optimization Methods ◽  
Energy System ◽  
Significant Cost ◽  
Operational Optimization ◽  
Simulation Based ◽  
Industrial Energy

Die optimierte Betriebsweise von industriellen Energiesystemen ist eine Schlüsseltechnologie, um signifikante Kosteneinsparpotenziale durch Steigerung der Energieeffizienz und -flexibilität zu heben. Weil dabei eine Vielzahl dynamischer und stochastischer Einflüsse berücksichtigt werden müssen, spielt die Simulation des Energiesystems eine entscheidende Rolle. Zur Evaluierung unterschiedlicher Betriebsoptimierungsverfahren wird ein simulationsgestütztes Framework vorgestellt, welches bei KI (Künstliche Intelligenz)-Algorithmen unter anderem für das Anlernen mit synthetischen Daten verwendet werden kann.   The optimized operation of industrial energy systems is a key technology to unlock significant cost savings by increasing energy efficiency and flexibility. Since a variety of dynamic and stochastic influences must be considered, the simulation of the energy system plays a decisive role. A simulation-based framework is presented for evaluating various operational optimization methods, which can also be used for learning based on synthetic data with AI (artificial intelligence) algorithms.

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