Qatar University

Serkan Kiranyaz

Turker Ince

Moncef Gabbouj

Osama Abdel Qader Abdel Jaber

Onur Avci

Levent Eren

Alexandros Iosifidis

Certain embodiments may generally relate to various techniques for machine learning. Feed-forward, fully-connected Artificial Neural Networks (ANNs), or the so-called Multi-Layer Perceptrons (MLPs) are well-known universal approximators. However, their learning performance may vary significantly depending on the function or the solution space that they attempt to approximate for learning. This is because they are based on a loose and crude model of the biological neurons promising only a linear transformation followed by a nonlinear activation function. Therefore, while they learn very well those problems with a monotonous, relatively simple and linearly separable solution space, they may entirely fail to do so when the solution space is highly nonlinear and complex. In order to address this drawback and also to accomplish a more generalized model of biological neurons and learning systems, Generalized Operational Perceptrons (GOPs) may be formed and they may encapsulate many linear and nonlinear operators.

Generalized operational perceptrons: new generation artificial neural networks

Certain embodiments may generally relate to structural damage detection. An embodiment may be directed to method for identifying a presence and a location of structural damage. Such method may include training a convolutional neural network (CNN) for a joint of a structure, sending instructions to a modal shaker to induce an input to the structure, receiving, as a result of the induced input, a raw acceleration signal at the joint, computing, based on the trained CNN and the raw acceleration signal, an index value of the joint, and identifying, according to the index value, a presence of a location of structural damage of the structure. In a further embodiment, the index value represents a likelihood of damage at the joint.

Real-time structural damage detection by convolutional neural networks

A method of detecting abnormal heartbeats includes providing a library of abnormal beat synthesis (ABS) filters, wherein each ABS filter corresponds to a specific cause of a cardiac problem. The method further includes obtaining an ECG of a normal heartbeat of a person and applying an ABS filter from the library of ABS filters to the ECG of the normal heartbeat of the person to generate a potential abnormal ECG. The method further includes monitoring a heartbeat of the person and classifying each heartbeat as either normal or abnormal.

Personalized ECG monitoring for early detection of cardiac abnormalities

A method and apparatus may include receiving a signal from a motor. The signal is received while the motor is operating. The method also includes performing a pre-processing of the signal. The method also includes inputting the signal to a 1D convolutional neural network. The method also includes detecting a fault of the motor based on the output of the neural network.

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Serkan Kiranyaz