Dimensions of data sparseness and their effect on supply chain visibility

Supply chain visibility concerns the ability to track parts, components, or products in transit from supplier to customer. The data that organizations can obtain to establish or improve supply chain visibility is often sparse. This paper presents a classification of the dimensions of data sparseness and quantitatively explores the impact of...

Classifying Human Manual Control Behavior Using LSTM Recurrent Neural Networks

This article discusses a long short-term memory (LSTM) recurrent neural network that uses raw time-domain data obtained in compensatory tracking tasks as input features for classifying (the adaptation of) human manual control with single- and double-integrator controlled element dynamics. Data from two different experiments were used to train...

Global identification and characterization of drivers of shoreline evolution: A novel method using Satellite Derived Shorelines and spatiotemporal characteristics

Since early history, humans have been attracted to coastal areas. This can be related to the economic benefits of these areas due to access of ocean navigation, coastal fisheries, tourism and recreation (Seas and Plans, 2011). Around 40% of the world’s population lives within 100 km of the coast (Seas and Plans, 2011). People are drawn to sandy...

Vegetation monitoring in coastal areas using readily available Remote Sensing data

Monitoring of the status of vegetation is required for nature conservation. However, doing this for large areas is a time intensive task. Now the use of aerial data from airplanes has made this task easier. However this data source is dependent on the weather conditions and permission to fly in the busy airspace above the Netherlands. An...

The impact of spatial resolution and satellite data type characteristics on Automated Damage Assessment using a Convolutional Neural Network

Satellite data, such as optical and Synthetic Aperture Radar imagery, can provide information about the location and level of destruction caused by natural hazards. This information is essential to optimise the rescue mission logistics by humanitarian aid organisations and save people in need. Currently, many Automatic Damage Assessment (ADA)...

Probabilistic vegetation transitions in dunes by combining spectral and lidar data

Monitoring the status of the vegetation is required for nature conservation. This monitoring task is time consuming as kilometers of area have to be investigated and classified. To make this task more manageable, remote sensing is used. The acquisition of airplane remote sensing data is dependent on weather conditions and permission to fly in...

Automatic detection of waterbeds in shallow muddy water bodies in the Netherlands using green LiDAR

Bathymetric Airborne LiDAR technology is used to map the depth of water bodies. It uses a green light sensor which is able to penetrate the water surface and reach the bottom part of the interesting water areas. However, water conditions affect the capability of the green laser penetration. Factors such as the water clarity, the water turbidity ...

Classification in football: Activity classification using sensor data in football

The goal of this report is to present and describe the effort surrounding the completion of the Master Thesis Project of classification in football. Classification is a procedure which belongs in the field of Statistics. The objective is to capture, detect and distinguish certain actions relative to the environment of analysis. In our case we are...

A New Hybrid Predictive Model to Predict the Early Mortality Risk in Intensive Care Units on a Highly Imbalanced Dataset

Due to the development of biomedical equipment and healthcare level, especially in the Intensive Care Unit (ICU), a considerable amount of data has been collected for analysis. Mortality prediction in the ICUs is considered as one of the most important topics in the healthcare data analysis section. A precise prediction of the mortality risk...

Interpretable Machine Learning for Biomarker Discovery in Imaging Mass Spectrometry Data

Imaging mass spectrometry (IMS) is a multiplexed chemical imaging technique that enables the spatially targeted molecular mapping of biological samples at cellular resolutions. Within a single experiment, IMS can measure the spatial distribution and relative concentration of thousands of distinct molecular species across the surface of a tissue...

The Use of Data Science for Sports Analysis Purposes

This thesis is dedicated to the application of data science to sports data. The research for this thesis is part of a bigger project on injury prevention and sport performance called Citius Altius Sanius (CAS). Two data sets from two different projects within CAS are analysed, with two different goals; one focusses on sports injury prevention in...

Exploiting synthetic images for real-world image recognition

Creating big datasets is often difficult or expensive which causes people to augment their dataset with rendered images. This often fails to significantly improve accuracy due to a difference in distribution between real and rendered datasets. This paper shows that the gap between synthetic and real-world image distributions can be closed by...

Voxel-Based Extraction and Classification of 3-D Pole-Like Objects From Mobile LiDAR Point Cloud Data

The digital mapping of road environment is an important task for road infrastructure inventory and urban planning. Automatic extraction and classification of pole-like objects can remarkably reduce mapping cost and enhance work efficiency. Therefore, this paper proposes a voxel-based method that automatically extracts and classifies three...

The Iqmulus urban showcase: Automatic tree classification and identification in huge mobile mapping point clouds

Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This...

Localization and Classification using an Acoustic Sensor Network - experimental data processing for urban acoustic surveillance

The Acoustic Sensor Network (ASN) has emerged as an important research area, because acoustic sensors can significantly increase situational awareness in many situations. Although little is currently known about acoustic surveillance, Thales Nederland is interested in the potential offered by an ASN in urban environments and specifically for...