20,728 research outputs found
Rock falls impacting railway tracks. Detection analysis through an artificial intelligence camera prototype
During the last few years, several approaches have been proposed to improve early warning systems for managing geological risk
due to landslides, where important infrastructures (such as railways, highways, pipelines, and aqueducts) are exposed elements.
In this regard, an Artificial intelligence Camera Prototype (AiCP) for real-time monitoring has been integrated in a multisensor
monitoring system devoted to rock fall detection. An abandoned limestone quarry was chosen at Acuto (central Italy) as test-site
for verifying the reliability of the integratedmonitoring system. A portion of jointed rockmass, with dimensions suitable for optical
monitoring, was instrumented by extensometers. One meter of railway track was used as a target for fallen blocks and a weather
station was installed nearby. Main goals of the test were (i) evaluating the reliability of the AiCP and (ii) detecting rock blocks that
reach the railway track by the AiCP. At this aim, several experiments were carried out by throwing rock blocks over the railway
track. During these experiments, the AiCP detected the blocks and automatically transmitted an alarm signal
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases
The impact of the maximally possible batch size (for the better runtime) on
performance of graphic processing units (GPU) and tensor processing units (TPU)
during training and inference phases is investigated. The numerous runs of the
selected deep neural network (DNN) were performed on the standard MNIST and
Fashion-MNIST datasets. The significant speedup was obtained even for extremely
low-scale usage of Google TPUv2 units (8 cores only) in comparison to the quite
powerful GPU NVIDIA Tesla K80 card with the speedup up to 10x for training
stage (without taking into account the overheads) and speedup up to 2x for
prediction stage (with and without taking into account overheads). The precise
speedup values depend on the utilization level of TPUv2 units and increase with
the increase of the data volume under processing, but for the datasets used in
this work (MNIST and Fashion-MNIST with images of sizes 28x28) the speedup was
observed for batch sizes >512 images for training phase and >40 000 images for
prediction phase. It should be noted that these results were obtained without
detriment to the prediction accuracy and loss that were equal for both GPU and
TPU runs up to the 3rd significant digit for MNIST dataset, and up to the 2nd
significant digit for Fashion-MNIST dataset.Comment: 10 pages, 7 figures, 2 table
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