scholarly journals Sensor-fusion Location Tracking System using Hybrid Multimodal Deep Neural Network

2021 ◽  
Author(s):  
Xijia Wei ◽  
Zhiqiang Wei ◽  
Valentin Radu
Keyword(s):  
Neural Network ◽  
Good Accuracy ◽  
Indoor Localization ◽  
Tracking System ◽  
Data Driven ◽  
Location Tracking ◽  
Neural Architecture ◽  
Data Driven Approach ◽  
Complex Features ◽  
The Hard Problem

Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localization. However, specialising solutions for the edge cases remains challenging. Here we propose to build the solution with zero hand-engineered features, but having everything learned directly from data. We use a modality specific neural architecture for extracting preliminary features, which are then integrated with cross-modality neural network structures. We show that each modality-specific neural architecture branch is capable of estimating the location with good accuracy independently. But for better accuracy a cross-modality neural network fusing the features of those early modality-specific representations is a better proposition. Our multimodal neural network, MM-Loc, is effective because it allows the uniform flow of gradients during training across modalities. Because it is a data driven approach, complex features representations are learned rather than relying heavily on hand-engineered features.

scholarly journals Application of deep neural network reveals novel effects of maternal pre-conception exposure to nicotine on rat pup behavior

2020 ◽  
Author(s):  
Reza Torabi ◽  
Serena Jenkins ◽  
Allonna Harker ◽  
Ian Q. Whishaw ◽  
Robbin Gibb ◽  
...  
Keyword(s):  
Neural Network ◽  
Motor Development ◽  
Deep Neural Network ◽  
Data Driven ◽  
Nicotine Exposure ◽  
Movement Initiation ◽  
Warm Up ◽  
Rat Pups ◽  
The Neural Network ◽  
Data Driven Approach

We present a deep neural network for data-driven analyses of infant rat behavior in an open field task. The network was applied to study the effect of maternal nicotine exposure prior to conception on offspring motor development. The neural network outperformed human expert designed animal locomotion measures in distinguishing rat pups born to nicotine exposed dams versus control dams. Notably, the network discovered novel movement alterations in posture, movement initiation and a stereotypy in warm-up behavior (the initiation of movement along specific dimensions) that were predictive of nicotine exposure. The results suggest that maternal preconception nicotine exposure delays and alters offspring motor development. In summary, we demonstrated that a deep neural network can automatically assess animal behavior with high accuracy, and that it offers a data-driven approach to investigating pharmacological effects on brain development.

scholarly journals ‘Ring Breaker’: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space

2020 ◽  
Author(s):  
Amol Thakkar ◽  
Nidhal Selmi ◽  
Jean-Louis Reymond ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Network ◽  
Chemical Space ◽  
Ring System ◽  
Data Driven ◽  
Ring Systems ◽  
The Neural Network ◽  
Synthetic Accessibility ◽  
Synthesis Planning ◽  
Data Driven Approach ◽  
Synthetic Routes

<p></p><p>Ring systems in pharmaceuticals, agrochemicals and dyes are ubiquitous chemical motifs. Whilst the synthesis of common ring systems is well described, and novel ring systems can be readily computationally enumerated, the synthetic accessibility of unprecedented ring systems remains a challenge. ‘Ring Breaker’ uses a data-driven approach to enable the prediction of ring-forming reactions, for which we have demonstrated its utility on frequently found and unprecedented ring systems, in agreement with literature syntheses. We demonstrate the performance of the neural network on a range of ring fragments from the ZINC and DrugBank databases and highlight its potential for incorporation into computer aided synthesis planning tools. These approaches to ring formation and retrosynthetic disconnection offer opportunities for chemists to explore and select more efficient syntheses/synthetic routes. </p><br><p></p>

Bundling in Molecular Dynamics Simulations to Improve Generalization Performance in High-Dimensional Neural Network Potentials

2019 ◽  
Author(s):  
Yasuharu Okamoto
Keyword(s):  
Neural Network ◽  
Force Field ◽  
Data Driven ◽  
High Dimensional ◽  
Field Calculation ◽  
Force Field Calculations ◽  
Quantitative Accuracy ◽  
Force Field Calculation ◽  
Data Driven Approach ◽  
Dynamics Simulations

<p>High dimensional neural network potential (HDNNP) is interested as an alternative to classical force field calculations by data-driven approach. HDNNP has an advantage over classical force field calculation, such as being able to handle chemical reactions, but there are many points yet to be understood with respect to the chemical transferability in particular for non-organic compounds. In this paper, we focused on Au<sub>13</sub><sup>+</sup> and Au<sub>11</sub><sup>+</sup> clusters and showed that the energy of clusters of different sizes can be predicted by HDNNP with semi-quantitative accuracy.</p>

Investigating the GDP-CO2 relationship using a neural network approach

2020 ◽  
Author(s):  
Sebastian Jensen ◽  
Eric Hillebrand ◽  
Mikkel Bennedsen
Keyword(s):  
Neural Network ◽  
Co2 Emissions ◽  
Functional Relationship ◽  
National Level ◽  
Data Driven ◽  
Sample Analysis ◽  
Intergovernmental Panel ◽  
Neural Network Approach ◽  
Data Driven Approach ◽  
Complex Relationships

&lt;p&gt;Exploiting a national-level panel of per capita CO2 emissions and GDP data, we investigate the GDP-CO2 relationship, using a data-driven approach. We conduct an in-sample analysis in which we investigate the shape of the GDP-CO2 relationship. Utilizing the shape of the GDP-CO2 relationship learned, we project CO2 emissions through 2100, using the same set of GDP and population growth scenarios as used by the Intergovernmental Panel of Climate Change (IPCC) for their sixth assessment report due for release in 2021-22. Our analysis is carried out at two levels: at a global, and at the level of &amp;#64257;ve large regions of the world. We consider a semiparametric model speci&amp;#64257;cation which places no restrictions on the functional relationship between GDP and CO2, but which allows for country and time speci&amp;#64257;c &amp;#64257;xed e&amp;#64256;ects. The nonparametric component of our model is speci&amp;#64257;ed as a feedforward neural network, ensuring universal approximation capabilities, theoretically. In a simulation study, we show that our model is able to capture various complex relationships in &amp;#64257;nite samples of realistic sizes.&lt;/p&gt;

Data-Driven Simulation for Fast Prediction of Pull-Up Process in Bottom-Up Stereo-Lithography

2016 ◽  
Author(s):  
Jun Wang ◽  
Sonjoy Das ◽  
Chi Zhou ◽  
Rahul Rai
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Stress Distribution ◽  
Material Properties ◽  
Simulation Models ◽  
Data Driven ◽  
Bottom Up ◽  
Grid Mapping ◽  
Data Driven Approach ◽  
Fast Prediction

Developing cohesive finite element simulation models of the pull-up process in bottom-up stereo-lithography (SLA) system can significantly increase the reliability and through-put of the bottom-up SLA process. Pull-up process modeling investigates relation between motion profile and crack initialization and propagation during the separation process. However, finite element (FE) simulation of the pull-up process is computationally very expensive and time-consuming. This paper outlines a method to quickly predict the separation stress distribution based on 2D shape grid mapping and neural network. Sixteen cohesive FE models with various cross-section shapes form our database. Specific 2D shape grid mapping was utilized to describe each shape by generating a sorted binary vector. A backpropagation (BP) neural network was then trained using binary vectors, material properties, and FE simulated pull-up separation stress distribution. Given material properties, the trained model can then be used to predict the pull-up separation stress distribution of a new shape. The results demonstrate that the proposed data driven method can drastically reduce computing costs. The comparison between the predicted values by the data driven approach and simulated FE models verify the validity of the proposed method.

scholarly journals Contributions towards Data driven Deep Learning methods to predict Steady State Fluid Flow in mechanical Heart Valves

2021 ◽  
Vol 7 (2) ◽  
pp. 625-628
Author(s):  
Jan Oldenburg ◽  
Julian Renkewitz ◽  
Michael Stiehm ◽  
Klaus-Peter Schmitz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Steady State ◽  
Flow Field ◽  
Heart Valves ◽  
Data Driven ◽  
High Shear ◽  
Blood Flows ◽  
The Neural Network ◽  
Data Driven Approach

Abstract It is commonly accepted that hemodynamic situation is related with cardiovascular diseases as well as clinical post-procedural outcome. In particular, aortic valve stenosis and insufficiency are associated with high shear flow and increased pressure loss. Furthermore, regurgitation, high shear stress and regions of stagnant blood flow are presumed to have an impact on clinical result. Therefore, flow field assessment to characterize the hemodynamic situation is necessary for device evaluation and further design optimization. In-vitro as well as in-silico fluid mechanics methods can be used to investigate the flow through prostheses. In-silico solutions are based on mathematical equitation’s which need to be solved numerically (Computational Fluid Dynamics - CFD). Fundamentally, the flow is physically described by Navier-Stokes. CFD often requires high computational cost resulting in long computation time. Techniques based on deep-learning are under research to overcome this problem. In this study, we applied a deep-learning strategy to estimate fluid flows during peak systolic steady-state blood flows through mechanical aortic valves with varying opening angles in randomly generated aortic root geometries. We used a data driven approach by running 3,500 two dimensional simulations (CFD). The simulation data serves as training data in a supervised deep learning framework based on convolutional neural networks analogous to the U-net architecture. We were able to successfully train the neural network using the supervised data driven approach. The results showing that it is feasible to use a neural network to estimate physiological flow fields in the vicinity of prosthetic heart valves (Validation error below 0.06), by only giving geometry data (Image) into the Network. The neural network generates flow field prediction in real time, which is more than 2500 times faster compared to CFD simulation. Accordingly, there is tremendous potential in the use of AIbased approaches predicting blood flows through heart valves on the basis of geometry data, especially in applications where fast fluid mechanic predictions are desired.

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