AI News, Network Structure and Transfer Behaviors Embedding via Deep ... artificial intelligence
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The Augmented Reality assisted assembly training (ARAAT) is an effective and low-cost way in motor and electronic industry.
In ARAAT, assembly operations are the processes which mainly use the AR device to recognize the gestures and match the virtual workpieces to the hand based on the consistency of time and space.
According to the 2D and 3D features of the action unit, a scorer trained by the samples of specific actions gives the optimal label of each frame to recognize the action.
Nvidia developer blog
NVIDIA announced the Jetson Nano Developer Kit at the 2019 NVIDIA GPU Technology Conference (GTC), a $99 computer available now for embedded designers, researchers, and DIY makers, delivering the power of modern AI in a compact, easy-to-use platform with full software programmability.
The newly released JetPack 4.2 SDK provides a complete desktop Linux environment for Jetson Nano based on Ubuntu 18.04 with accelerated graphics, support for NVIDIA CUDA Toolkit 10.0, and libraries such as cuDNN 7.3 and TensorRT 5.The SDK also includes the ability to natively install popular open source Machine Learning (ML) frameworks such as TensorFlow, PyTorch, Caffe, Keras, and MXNet, along with frameworks for computer vision and robotics development like OpenCV and ROS.
The Jetson Nano Developer Kit fits in a footprint of just 80x100mm and features four high-speed USB 3.0 ports, MIPI CSI-2 camera connector, HDMI 2.0 and DisplayPort 1.3, Gigabit Ethernet, M.2 Key-E module, MicroSD card slot, and 40-pin GPIO header.
The Jetson Nano compute module is 45x70mm and will be shipping starting in June 2019 for $129 (in 1000-unit volume) for embedded designers to integrate into production systems. The production compute module will include 16GB eMMC onboard storage and enhanced I/O with PCIe Gen2 x4/x2/x1, MIPI DSI, additional GPIO, and 12 lanes of MIPI CSI-2 for connecting up to three x4 cameras or up to four cameras in x4/x2 configurations.
These networks can be used to build autonomous machines and complex AI systems by implementing robust capabilities such as image recognition, object detection and localization, pose estimation, semantic segmentation, video enhancement, and intelligent analytics.
Table 2 provides full results, including the performance of other platforms like the Raspberry Pi 3, Intel Neural Compute Stick 2, and Google Edge TPU Coral Dev Board: DNR (did not run) results occurred frequently due to limited memory capacity, unsupported network layers, or hardware/software limitations.
Fixed-function neural network accelerators often support a relatively narrow set of use-cases, with dedicated layer operations supported in hardware, with network weights and activations required to fit in limited on-chip caches to avoid significant data transfer penalties.
Jetson Nano’s flexible software and full framework support, memory capacity, and unified memory subsystem, make it able to run a myriad of different networks up to full HD resolution, including variable batch sizes on multiple sensor streams concurrently.
The video below shows Jetson Nano performing object detection on eight 1080p30 streams simultaneously with a ResNet-based model running at full resolution and a throughput of 500 megapixels per second (MP/s).
The project provides you with easy to learn examples through Jupyter notebooks on how to write Python code to control the motors, train JetBot to detect obstacles, follow objects like people and household objects, and train JetBot to follow paths around the floor.
Developers who want to try training their own models can follow the full “Two Days to a Demo” tutorial, which covers the re-training and customization of image classification, object detection, and semantic segmentation models with transfer learning.
Table 3 highlights some initial results of transfer learning from the Two Days to a Demo tutorial with PyTorch using Jetson Nano for training Alexnet and ResNet-18 on a 200,000 image, 22.5GB subset of ImageNet: The time per epoch is how long it takes to make a full pass through the training dataset of 200K images.
International PhD program
SL-DRF-19-0067 - Multiple scales analysis of vegetation fluorescence to assess photosynthesis of terrestrial ecosystems by present and future space missions.
SL-DRF-19-0428 - Estimating methane sources and sinks in the Arctic using atmospheric data assimilation.
SL-DRF-19-0431 - Automated, scalable and uncertainty-qualified inversion system for the monitoring of greenhouse gas fluxes.
SL-DRF-19-0447 - Relationship between interannual to multidecadal variability and climate long term evolution over the Holocene.
Numerical schemes for modelling the interplay between edge and core turbulent transport in tokamak plasmas.
High-performance Simulation for the design of compressed sensing trajectories in high resolution functional neuroimaging at 7 and 11.7 Tesla.
SL-DRT-19-0288 - modeling biomass torrefaction at pilot scale with data measured in laboratory at small scale.
SL-DRT-19-0546 - Methodology development for lifetime prediction of batteries for transportation to improve the durability simulation.
SL-DRT-19-0617 - Formalisation et simulation des mécanismes d'équilibrage du réseau électrique français.
SL-DRT-19-0657 - Machine learning based simulation of realistic signals for an enhanced automatic diagnostic in non-destructive testing applications.
SL-DRT-19-0677 - 4D ultrasonic imaging with fast reconstruction algorithms in the Fourier domain and data compression.
SL-DEN-19-0066 - Experimental study and numerical validation of vortex with gas entrainment creation criteria for free surface flows.
SL-DEN-19-0074 - Numerical simulation study of mass transfer in liquid-liquid extraction processes :
SL-DEN-19-0116 - Consideration of intra-granular localized slip bands in the numerical simulation of polycrystalline aggregates.
SL-DEN-19-0171 - Influence de la géométrie sur l'écoulement et la formation de l'émulsion dans un système d'agitation miniaturisé :
SL-DEN-19-0176 - Propagation of epistemic uncertainties of a mechanical structure submitted to seismic excitation on its estimated fragility curve via machine learning.
SL-DEN-19-0204 - First-principles electronic structure calculations applied to CALPHAD modelling of metastable or unstable phases.
SL-DEN-19-0215 - Multi-scale simulation of flow with high transverse velocity developing in fuel assembly.
SL-DEN-19-0226 - Thick Level Set method for the anisotropic damage to cohesive crack transition in quasi-brittle materials.
SL-DEN-19-0642 - Vers une approche Big Data orientée processus pour la détection, la prévention et la gestion des cyberattaques.
Expériences laser-plasma magnétisées sur les instabilités cinétiques, les chocs non-collisionnels et l’accélération de particules
- On 6. maj 2021
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