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Convolutional neural networks (CNNs) offer significant advantages when used in various image classification tasks and computer vision applications. CNNs are increasingly deployed in environments from edge and Internet of Things (IoT) devices to high-end computational infrastructures, such as supercomputers, cloud computing, and data centers. The growing amount of data and the growth in their model size and computational complexity, however, introduce major computational challenges. Such challenges present entry barriers for IoT and edge devices as well as increase the operational expenses of large-scale computing systems. Thus, it has become essential to optimize CNN algorithms. In this paper, we introduce the S-VELCRO compression algorithm, which exploits value locality to trim filters in CNN models utilized for specialized tasks. S-VELCRO uses structured compression, which can save costs and reduce overhead compared with unstructured compression. The algorithm runs in two steps: a preprocessing step identifies the filters with a high degree of value locality, and a compression step trims the selected filters. As a result, S-VELCRO reduces the computational load of the channel activation function and avoids the convolution computation of the corresponding trimmed filters. Compared with typical CNN compression algorithms that run heavy back-propagation training computations, S-VELCRO has significantly fewer computational requirements. Our experimental analysis shows that S-VELCRO achieves a compression-saving ratio between 6% and 30%, with no degradation in accuracy for ResNet-18, MobileNet-V2, and GoogLeNet when used for specialized tasks. [ABSTRACT FROM AUTHOR]

Offshore wind energy is increasingly being realized at deeper ocean depths where jacket foundations are now the greatest choice for dealing with the hostile environment. The structural stability of these undersea constructions is critical. This paper states a methodology to detect and classify damage in a jacket-type support structure for offshore wind turbines. Because of the existence of unknown external disturbances (wind and waves), standard structural health monitoring technologies, such as guided waves, cannot be used directly in this application. Therefore, using vibration-response-only accelerometer measurements, a methodology based on two in-cascade Siamese convolutional neural networks is proposed. The first Siamese network detects the damage (discerns whether the structure is healthy or damaged). Then, in case damage is detected, a second Siamese network determines the damage diagnosis (classifies the type of damage). The main results and claims of the proposed methodology are the following ones: (i) It is solely dependent on accelerometer sensor output vibration data, (ii) it detects damage and classifies the type of damage, (iii) it operates in all wind turbine regions of operation, (iv) it requires less data to train since it is built on Siamese convolutional neural networks, which can learn from very little data compared to standard machine/deep learning algorithms, (v) it is validated in a scaled-down experimental laboratory setup, and (vi) its feasibility is demonstrated as all computed metrics (accuracy, precision, recall, and F1 score) for the obtained results remain above 96%. [ABSTRACT FROM AUTHOR]