The Perception of Visually Simulated Environments

1993 ◽  
pp. 971-977
Author(s):  
J. K. Caird
Keyword(s):  
Simulated Environments

Ion beam-treated space polymers: long-term stability in GEO-simulated environments

2021 ◽  
Author(s):  
Jacob Kleiman ◽  
Zelina Iskanderova ◽  
Leonid Krishtein ◽  
J. R. Dennison ◽  
Brian Wood ◽  
...  
Keyword(s):  
Ion Beam ◽  
Term Stability ◽  
Long Term Stability ◽  
Simulated Environments

Virtual Reality, Augmented Reality, and Virtual Environments: Demonstrations of Current Technologies and Future Directions

2020 ◽  
Vol 64 (1) ◽  
pp. 2119-2123
Author(s):  
Sarah Beadle ◽  
Randall Spain ◽  
Benjamin Goldberg ◽  
Mahdi Ebnali ◽  
Shannon Bailey ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Augmented Reality ◽  
Human Factors ◽  
Virtual Environments ◽  
Medical Training ◽  
Risk Environment ◽  
Future Directions ◽  
Simulated Environments ◽  
Interactive Session ◽  
Immersive Technologies

Virtual environments and immersive technologies are growing in popularity for human factors purposes. Whether it is training in a low-risk environment or using simulated environments for testing future automated vehicles, virtual environments show promise for the future of our field. The purpose of this session is to have current human factors practitioners and researchers demonstrate their immersive technologies. This is the eighth iteration of the “Me and My VE” interactive session. Presenters in this session will provide a brief introduction of their virtual reality, augmented reality, or virtual environment work before engaging with attendees in an interactive demonstration period. During this period, the presenters will each have a multimedia display of their immersive technology as well as discuss their work and development efforts. The selected demonstrations cover issues of designing immersive interfaces, military and medical training, and using simulation to better understand complex tasks. This includes a mix of government, industry, and academic-based work. Attendees will be virtually immersed in the technologies and research presented allowing for interaction with the work being done in this field.

scholarly journals Drone Deep Reinforcement Learning: A Review

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 999
Author(s):  
Ahmad Taher Azar ◽  
Anis Koubaa ◽  
Nada Ali Mohamed ◽  
Habiba A. Ibrahim ◽  
Zahra Fathy Ibrahim ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Real Life ◽  
Environment Monitoring ◽  
Simulated Environments ◽  
Infrastructure Inspection ◽  
Remote Sensing Mapping ◽  
And Control ◽  
The Military ◽  
Uav Navigation

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios.

A Survey on Data-driven Network Intrusion Detection

2022 ◽  
Vol 54 (9) ◽  
pp. 1-36
Author(s):  
Dylan Chou ◽  
Meng Jiang
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Real World ◽  
Data Driven ◽  
Network Intrusion Detection ◽  
Large Network ◽  
Learning Models ◽  
Simulated Environments ◽  
Network Intrusion ◽  
Machine Learning Models

Data-driven network intrusion detection (NID) has a tendency towards minority attack classes compared to normal traffic. Many datasets are collected in simulated environments rather than real-world networks. These challenges undermine the performance of intrusion detection machine learning models by fitting machine learning models to unrepresentative “sandbox” datasets. This survey presents a taxonomy with eight main challenges and explores common datasets from 1999 to 2020. Trends are analyzed on the challenges in the past decade and future directions are proposed on expanding NID into cloud-based environments, devising scalable models for large network data, and creating labeled datasets collected in real-world networks.

scholarly journals The origin of life: the first self-replicating molecules were nucleotides

2019 ◽  
Author(s):  
Ken Ohsaka
Keyword(s):  
Origin Of Life ◽  
Clay Minerals ◽  
The Self ◽  
Rna Molecules ◽  
Simulated Environments ◽  
The Origin Of Life ◽  
Prebiotic Earth ◽  
Self Replication

Difficulties to synthesize RNA nucleotides from their subunits in modern labs under simulated environments leads us to propose a possible process for the synthesis by cross complimentary self-replication with help of clay minerals, which might be operated on prebiotic Earth. Clay minerals are known to be good catalysts and certainly existed on prebiotic Earth. The self-replication of RNA nucleotides (monomers) may be considered as the origin of potential self-replication of some extant RNA polymers, and also the reason for homochirality of RNA molecules.

scholarly journals MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics

2020 ◽  
Author(s):  
Mohammadamin Barekatain ◽  
Ryo Yonetani ◽  
Masashi Hamaya
Keyword(s):  
Reinforcement Learning ◽  
Task Performance ◽  
Experimental Evaluation ◽  
Control Problems ◽  
Target Task ◽  
Learning Efficiency ◽  
Simulated Environments ◽  
Discrete Action ◽  
Key Techniques ◽  
Action Spaces

Transfer reinforcement learning (RL) aims at improving the learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks. However, it remains challenging to transfer knowledge between different environmental dynamics without having access to the source environments. In this work, we explore a new challenge in transfer RL, where only a set of source policies collected under diverse unknown dynamics is available for learning a target task efficiently. To address this problem, the proposed approach, MULTI-source POLicy AggRegation (MULTIPOLAR), comprises two key techniques. We learn to aggregate the actions provided by the source policies adaptively to maximize the target task performance. Meanwhile, we learn an auxiliary network that predicts residuals around the aggregated actions, which ensures the target policy's expressiveness even when some of the source policies perform poorly. We demonstrated the effectiveness of MULTIPOLAR through an extensive experimental evaluation across six simulated environments ranging from classic control problems to challenging robotics simulations, under both continuous and discrete action spaces. The demo videos and code are available on the project webpage: https://omron-sinicx.github.io/multipolar/.

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