Classification of composite damage from FBG load monitoring signals

This paper describes a new method for the classification and identification of two major types of defects in composites, namely delamination and matrix cracks, by classification of the spectral features of fibre Bragg grating (FBG) signals. In aeronautical applications of composites, after a damage is detected, it is very useful to know the...

Diffuse reflectance spectroscopy as a tool for real-time tissue assessment during colorectal cancer surgery

Colorectal surgery is the standard treatment for patients with colorectal cancer. To overcome two of the main challenges, the circumferential resection margin and postoperative complications, real-time tissue assessment could be of great benefit during surgery. In this ex vivo study, diffuse reflectance spectroscopy (DRS) was used to...

ETA prediction for containerships at the Port of Rotterdam using Machine Learning Techniques

The hinterland transportation of incoming containers at container terminals is a complex problem, due to the various actors involved and their often conflicting interests. A promising solution towards the problem for hinterland network operators is that of synchromodality, a concept that refers to on-line network planning for hinterland...

A 41 μW real-time adaptive neural spike classifier

Robust, power- and area-efficient spike classifier, capable of accurate identification of the neural spikes even for low SNR, is a prerequisite for the real-time, implantable, closed-loop brain-machine interface. In this paper, we propose an easily-scalable, 128-channel, programmable, neural spike classifier based on nonlinear energy operator...

An Automated Method for Semantic Classification of Regions in Coastal Images

Large, long-term coastal imagery datasets are nowadays a low-cost source of information for various coastal research disciplines. However, the applicability of many existing algorithms for coastal image analysis is limited for these large datasets due to a lack of automation and robustness. Therefore manual quality control and site- and time...

Real-time estimation of surgical procedure duration

Each surgical procedure varies in length. There’s a need for dynamic planning to have an optimal patient flow from their beds to the operating room. This paper proposes an adaptive system that predicts the end of a gallbladder removal surgery 25 minutes in advance with the use of Support Vector Machines based on the activations of the...

Real-time estimation of surgical procedure duration

Efficiency in the Operating Room (OR) is a topic of growing interest. Planning of care is a crucial element to ensure optimal use of the ORs. Currently, OR scheduling is considered as a complex task based on predictions of surgery duration. The latter are often based on average times, but turn out to be inaccurate in practice because of...

A novel adaptive kernel method with kernel centers determined by a support vector regression approach

The optimality of the kernel number and kernel centers plays a significant role in determining the approximation power of nearly all kernel methods. However, the process of choosing optimal kernels is always formulated as a global optimization task, which is hard to accomplish. Recently, an algorithm, namely improved recursive reduced least...

Enhanced Question Classification with Optimal Combination of Features

An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language question. Question classification is typically done using machine learning techniques. Different lexical, syntactical and semantic features can be extracted from a...

Exchange Rate Prediction using Support Vector Machines: A comparison with Artificial Neural Networks

Financial forecasting in general, and exchange rate prediction in particular, is an issue of much interest to both academic and economic communities. Being able to accurately forecast exchange rate movements provides considerable benefits to both firms and investors. This research aims to propose a decision support aid to these firms and...

X-Ray image denoising with directional support value transform

Under the support vector machine framework, the support value analysis-based image fusion has been studied, where the salient features of the original images are represented by their support values. The support value transform (SVT)-based image fusion approach have demonstrated some advantages over the existing methods in multisource image...

Knowledge Discovery and Pavement Performance: Intelligent Data Mining

The main goal of the study was to discover knowledge from data about asphalt road pavement problems to achieve a better understanding of the behavior of them and via this understanding improve pavement quality and enhance its lifespan. Four pavement problems were chosen to be investigated; raveling of Porous Asphalt Concrete (PAC), cracking of...