Real-time Large-scale Planar Object Recognition Based on Spatial Locality

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

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A Model-Based Real-Time Intrusion Detection System for Large Scale Heterogeneous Networks

10.21236/ada420824 ◽

2003 ◽

Cited By ~ 1

Author(s):

Richard A. Kemmer ◽

Giovanni Vigna

Keyword(s):

Intrusion Detection ◽

Real Time ◽

Heterogeneous Networks ◽

Intrusion Detection System ◽

Large Scale ◽

Detection System ◽

Model Based

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BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i10.7197 ◽

2020 ◽

Vol 34 (10) ◽

pp. 13849-13850

Author(s):

Donghyeon Lee ◽

Man-Je Kim ◽

Chang Wook Ahn

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Real Time ◽

Large Scale ◽

Outcome Predictor ◽

Short Term ◽

Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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Augmented Reality Maintenance Assistant Using YOLOv5

Applied Sciences ◽

10.3390/app11114758 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4758

Author(s):

Ana Malta ◽

Mateus Mendes ◽

Torres Farinha

Keyword(s):

Neural Network ◽

Deep Learning ◽

Object Recognition ◽

Augmented Reality ◽

Real Time ◽

Recognition System ◽

High Accuracy ◽

Video Streams ◽

The Neural Network ◽

Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

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Impacts of real-time information levels in public transport: A large-scale case study using an adaptive passenger path choice model

Transportation Research Part A Policy and Practice ◽

10.1016/j.tra.2021.03.011 ◽

2021 ◽

Vol 148 ◽

pp. 155-182

Author(s):

Mads Paulsen ◽

Thomas Kjær Rasmussen ◽

Otto Anker Nielsen

Keyword(s):

Real Time ◽

Public Transport ◽

Large Scale ◽

Choice Model ◽

Real Time Information ◽

Time Information

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Modeling Fused Filament Fabrication using Artificial Neural Networks

Production Engineering ◽

10.1007/s11740-021-01020-y ◽

2021 ◽

Author(s):

Paul Oehlmann ◽

Paul Osswald ◽

Juan Camilo Blanco ◽

Martin Friedrich ◽

Dominik Rietzel ◽

...

Keyword(s):

Real Time ◽

Large Scale ◽

Cost Effective ◽

Print Quality ◽

Ann Model ◽

Production Environment ◽

Fused Filament Fabrication ◽

Artificial Neural ◽

Using Data ◽

Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

Fusion Science & Technology ◽

10.1080/15361055.2020.1851073 ◽

2021 ◽

Vol 77 (2) ◽

pp. 98-108

Author(s):

R. M. Churchill ◽

C. S. Chang ◽

J. Choi ◽

J. Wong ◽

S. Klasky ◽

...

Keyword(s):

Real Time ◽

International Collaboration ◽

Large Scale ◽

Data Transfer ◽

Time Analysis ◽

Real Time Analysis ◽

Large Scale Data ◽

Scale Data

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Hierarchical organizations for real-time large-scale task and team environments

Proceedings of the first international joint conference on Autonomous agents and multiagent systems part 2 - AAMAS '02 ◽

10.1145/545056.545089 ◽

2002 ◽

Cited By ~ 1

Author(s):

Osher Yadgar ◽

Sarit Kraus ◽

Charles L. Ortiz

Keyword(s):

Real Time ◽

Large Scale ◽

Hierarchical Organizations

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Prevention of Mountain Disasters and Maintenance of Residential Area through Real-Time Terrain Rendering

Sustainability ◽

10.3390/su13052950 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2950

Author(s):

Su-Kyung Sung ◽

Eun-Seok Lee ◽

Byeong-Seok Shin

Keyword(s):

Real Time ◽

Graphics Processing Units ◽

High Speed ◽

Large Scale ◽

Civil Engineering ◽

Three Dimensional ◽

Sampled Data ◽

Residential Areas ◽

Terrain Rendering ◽

Mountain Disasters

Climate change increases the frequency of localized heavy rains and typhoons. As a result, mountain disasters, such as landslides and earthworks, continue to occur, causing damage to roads and residential areas downstream. Moreover, large-scale civil engineering works, including dam construction, cause rapid changes in the terrain, which harm the stability of residential areas. Disasters, such as landslides and earthenware, occur extensively, and there are limitations in the field of investigation; thus, there are many studies being conducted to model terrain geometrically and to observe changes in terrain according to external factors. However, conventional topography methods are expressed in a way that can only be interpreted by people with specialized knowledge. Therefore, there is a lack of consideration for three-dimensional visualization that helps non-experts understand. We need a way to express changes in terrain in real time and to make it intuitive for non-experts to understand. In conventional height-based terrain modeling and simulation, there is a problem in which some of the sampled data are irregularly distorted and do not show the exact terrain shape. The proposed method utilizes a hierarchical vertex cohesion map to correct inaccurately modeled terrain caused by uniform height sampling, and to compensate for geometric errors using Hausdorff distances, while not considering only the elevation difference of the terrain. The mesh reconstruction, which triangulates the three-vertex placed at each location and makes it the smallest unit of 3D model data, can be done at high speed on graphics processing units (GPUs). Our experiments confirm that it is possible to express changes in terrain accurately and quickly compared with existing methods. These functions can improve the sustainability of residential spaces by predicting the damage caused by mountainous disasters or civil engineering works around the city and make it easy for non-experts to understand.

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