Time-Dependent Temperature Measurements in Post-Detonation Combustion: Current State-of-the-Art Methods and Emerging Technologies

2016 ◽  
Author(s):  
William K. Lewis ◽  
Nick G. Glumac ◽  
Eduardo G. Yukihara
Keyword(s):  
Emerging Technologies ◽  
State Of The Art ◽  
Temperature Measurements ◽  
Time Dependent ◽  
Current State ◽  
Detonation Combustion ◽  
Art Methods

scholarly journals Precision Agriculture Technology: A Literature Review

2020 ◽  
pp. 30-34
Author(s):  
C. A. Danbaki ◽  
N. C. Onyemachi ◽  
D. S. M. Gado ◽  
G. S. Mohammed ◽  
D. Agbenu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Precision Agriculture ◽  
State Of The Art ◽  
Irrigation Management ◽  
Conventional Farming ◽  
Livestock Farming ◽  
Current State ◽  
Art Methods ◽  
Increase In Productivity

This study is a survey on state-of-the-art methods based on artificial intelligence and image processing for precision agriculture on Crop Management, Pest and Disease Management, Soil and Irrigation Management, Livestock Farming and the challenges it presents. Precision agriculture (PA) described as applying current technologies into conventional farming methods. These methods have proved to be highly efficient, sustainable and profitable to the farmer hence boosting the economy. This study is a survey on the current state of the art methods applied to precision agriculture. The application of precision agriculture is expected to yield an increase in productivity which ultimately ends in profit to the farmer, to the society increase sustainability and also improve the economy.

scholarly journals The Physics of Convection in Massive Stars

2014 ◽  
Vol 9 (S307) ◽  
pp. 20-24
Author(s):  
Casey A. Meakin
Keyword(s):  
State Of The Art ◽  
Massive Stars ◽  
Difficult Problem ◽  
Turbulent Convection ◽  
Current State ◽  
Powerful Approach ◽  
Art Methods ◽  
Stellar Interiors ◽  
Stellar Models

AbstractI summarize current state-of-the-art methods for treating the difficult problem of turbulent convection in stellar interiors and I discuss a powerful approach for analysis that allows one to leverage the most from 3D stellar models.

scholarly journals Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model

2021 ◽  
Author(s):  
Christian Bahne Thygesen ◽  
Ahmad Salim Al-Sibahi ◽  
Lys Sanz Moreta ◽  
Christian Skjødt Steenmans ◽  
Anders Bundgård Sørensen ◽  
...  
Keyword(s):  
Protein Structure ◽  
Markov Model ◽  
State Of The Art ◽  
Epistemic Uncertainty ◽  
Protein Structures ◽  
Conformational Search ◽  
Current State ◽  
Fragment Library ◽  
Art Methods ◽  
Fragment Libraries

Fragment libraries are often used in protein structure prediction, simulation and design as a means to significantly reduce the vast conformational search space. Current state-of-the-art methods for fragment library generation do not properly account for aleatory and epistemic uncertainty, respectively due to the dynamic nature of proteins and experimental errors in protein structures. Additionally, they typically rely on information that is not generally or readily available, such as homologous sequences, related protein structures and other complementary information. To address these issues, we developed BIFROST, a novel take on the fragment library problem based on a Deep Markov Model architecture combined with directional statistics for angular degrees of freedom, implemented in the deep probabilistic programming language Pyro. BIFROST is a probabilistic, generative model of the protein backbone dihedral angles conditioned solely on the amino acid sequence. BIFROST generates fragment libraries with a quality on par with current state-of-the-art methods at a fraction of the run-time, while requiring considerably less information and allowing efficient evaluation of probabilities.

scholarly journals HIGH QUALITY FACADE SEGMENTATION BASED ON STRUCTURED RANDOM FOREST, REGION PROPOSAL NETWORK AND RECTANGULAR FITTING

2018 ◽  
Vol IV-2 ◽  
pp. 223-230 ◽  
Author(s):  
K. Rahmani ◽  
H. Mayer
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
High Quality ◽  
Current State ◽  
Forest Region ◽  
Building Facades ◽  
Art Methods

In this paper we present a pipeline for high quality semantic segmentation of building facades using Structured Random Forest (SRF), Region Proposal Network (RPN) based on a Convolutional Neural Network (CNN) as well as rectangular fitting optimization. Our main contribution is that we employ features created by the RPN as channels in the SRF.We empirically show that this is very effective especially for doors and windows. Our pipeline is evaluated on two datasets where we outperform current state-of-the-art methods. Additionally, we quantify the contribution of the RPN and the rectangular fitting optimization on the accuracy of the result.

Review: Interventions for Cartilage Disease: Current State-of-the-Art and Emerging Technologies

2017 ◽  
Vol 69 (7) ◽  
pp. 1363-1373 ◽  
Author(s):  
Luiz Felipe Ambra ◽  
Laura de Girolamo ◽  
Brian Mosier ◽  
Andreas H. Gomoll
Keyword(s):  
Emerging Technologies ◽  
State Of The Art ◽  
Current State ◽  
Cartilage Disease

scholarly journals An Appearance-and-Structure Fusion Network for Object Viewpoint Estimation

2018 ◽  
Author(s):  
Yueying Kao ◽  
Weiming Li ◽  
Zairan Wang ◽  
Dongqing Zou ◽  
Ran He ◽  
...  
Keyword(s):  
State Of The Art ◽  
Machine Intelligence ◽  
Intelligence Community ◽  
Challenging Problem ◽  
Single Image ◽  
Structure Information ◽  
Current State ◽  
Art Methods ◽  
Viewpoint Estimation ◽  
Visual Ambiguity

Automatic object viewpoint estimation from a single image is an important but challenging problem in machine intelligence community. Although impressive performance has been achieved, current state-of-the-art methods still have difficulty to deal with the visual ambiguity and structure ambiguity in real world images. To tackle these problems, a novel Appearance-and-Structure Fusion network, which we call it ASFnet that estimates viewpoint by fusing both appearance and structure information, is proposed in this paper. The structure information is encoded by precise semantic keypoints and can help address the visual ambiguity. Meanwhile, distinguishable appearance features contribute to overcoming the structure ambiguity. Our ASFnet integrates an appearance path and a structure path to an end-to-end network and allows deep features effectively share supervision from both the two complementary aspects. A convolutional layer is learned to fuse the two path results adaptively. To balance the influence from the two supervision sources, a piecewise loss weight strategy is employed during training. Experimentally, our proposed network outperforms state-of-the-art methods on a public PASCAL 3D+ dataset, which verifies the effectiveness of our method and further corroborates the above proposition.

Bariatric endoscopy: current state of the art, emerging technologies, and challenges

2017 ◽  
Vol 14 (2) ◽  
pp. 149-159 ◽  
Author(s):  
Sanja Klobucar Majanovic ◽  
Boris Brozovic ◽  
Davor Stimac
Keyword(s):  
Emerging Technologies ◽  
State Of The Art ◽  
Bariatric Endoscopy ◽  
Current State

scholarly journals SAIF: A Correction-Detection Deep-Learning Architecture for Personal Assistants

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5577
Author(s):  
Amos Azaria ◽  
Keren Nivasch
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Intelligent Agents ◽  
State Of The Art ◽  
Intelligent Agent ◽  
Current State ◽  
The Future ◽  
Art Methods ◽  
Personal Assistants ◽  
Unique Dataset

Intelligent agents that can interact with users using natural language are becoming increasingly common. Sometimes an intelligent agent may not correctly understand a user command or may not perform it properly. In such cases, the user might try a second time by giving the agent another, slightly different command. Giving an agent the ability to detect such user corrections might help it fix its own mistakes and avoid making them in the future. In this work, we consider the problem of automatically detecting user corrections using deep learning. We develop a multimodal architecture called SAIF, which detects such user corrections, taking as inputs the user’s voice commands as well as their transcripts. Voice inputs allow SAIF to take advantage of sound cues, such as tone, speed, and word emphasis. In addition to sound cues, our model uses transcripts to determine whether a command is a correction to the previous command. Our model also obtains internal input from the agent, indicating whether the previous command was executed successfully or not. Finally, we release a unique dataset in which users interacted with an intelligent agent assistant, by giving it commands. This dataset includes labels on pairs of consecutive commands, which indicate whether the latter command is in fact a correction of the former command. We show that SAIF outperforms current state-of-the-art methods on this dataset.

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