“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

System Identification I

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

1D convolutional neural networks and applications: A survey

During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application-specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and motor-fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publically shared in a dedicated website.

1D Convolutional Neural Networks and Applications: A Survey

Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications

Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering

A cost effective solution for pavement crack inspection using cameras and deep neural networks

Densely connected deep neural network considering connectivity of pixels for automatic crack detection

Indirect health monitoring of bridges using Mel-frequency cepstral coefficients and principal component analysis

Mustafa Gül

Papers

Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications

Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering

A cost effective solution for pavement crack inspection using cameras and deep neural networks

Densely connected deep neural network considering connectivity of pixels for automatic crack detection

Indirect health monitoring of bridges using Mel-frequency cepstral coefficients and principal component analysis