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A learning Dempster-Shafer model for automated building detection

Khoshelham, K. (author), Nardinocchi, C. (author)

This paper presents a learning Dempster-Shafer model for the detection of buildings in aerial image and range data. The process of evidence assignment in the Dempster-Shafer method is implemented through membership functions in an adaptive network-based fuzzy inference system, where a back propagation learning rule is employed to tune the...

conference paper 2009

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A NAO robot playing tic-tac-toe: Comparing alternative methods for Inverse Kinematics

Poddighe, R. (author), Roos, N. (author)

In this paper, two alternative methods to the Inverse Kinematics problem are compared to traditional methods regarding computation time, accuracy, and convergence rate. The test domain is the arm of the NAO humanoid robot. The results show that FABRIK, a heuristic iterative approximation algorithm outperforms the two traditional methods, which...

conference paper 2013

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Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

Kayastha, N. (author), Solomatine, D.P. (author), Lal Shrestha, D. (author)

In the MLUE method (reported in Shrestha et al. [1, 2]) we run a hydrological model M for multiple realizations of parameters vectors (Monte Carlo simulations), and use this data to build a machine learning model V to predict uncertainty (quantiles) of the model M output. In this paper, for model V, we employ three machine learning techniques,...

conference paper 2014

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Large-Scale Flight Phase Identification from ADS-B Data Using Machine Learning Methods

Sun, Junzi (author), Ellerbroek, Joost (author), Hoekstra, J.M. (author)

With the increasing availability of ADS-B transponders on commercial aircraft, as well as the rapidly growing deployment of ground stations that provide public access to their data, accessing open aircraft flight data is becoming easier for researchers. Given the large number of operational aircraft, significant amounts of flight data can be...

conference paper 2016

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Efficient Learning of Communication Profiles from IP Flow Records

Hammerschmidt, C.A. (author), Marchal, Samuel (author), State, Radu (author), Pellegrino, G. (author), Verwer, S.E. (author)

The task of network traffic monitoring has evolved drastically with the ever-increasing amount of data flowing in large scale networks. The automated analysis of this tremendous source of information often comes with using simpler models on aggregated data (e.g. IP flow records) due to time and space constraints. A step towards utilizing IP flow...

conference paper 2016

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Estimating building age with 3D GIS

Biljecki, F. (author), Sindram, M. (author)

Building datasets (e.g. footprints in OpenStreetMap and 3D city models) are becoming increasingly available worldwide. However, the thematic (attribute) aspect is not always given attention, as many of such datasets are lacking in completeness of attributes. A prominent attribute of buildings is the year of construction, which is useful for some...

conference paper 2017

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CaptureBias: Supporting Media Scholars with Ambiguity-Aware Bias Representation for News Videos

de Jong, M. (author), Mavridis, P. (author), Aroyo, Lora (author), Bozzon, A. (author), Vos, Jesse de (author), Oomen, Johan (author), Dimitrova, Antoaneta (author), Badenoch, Alec (author)

In this project we explore the presence of ambiguity in textual and visual media and its influence on accurately understanding and<br/>capturing bias in news. We study this topic in the context of supporting<br/>media scholars and social scientists in their media analysis. Our focus<br/>lies on racial and gender bias as well as framing and the...

conference paper 2018

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A Human in the Loop Approach to Capture Bias and Support Media Scientists in News Video Analysis

Mavridis, P. (author), de Jong, M. (author), Aroyo, Lora (author), Bozzon, A. (author), Vos, Jesse de (author), Oomen, Johan (author), Dimitrova, Antoaneta (author), Badenoch, Alec (author)

Bias is inevitable and inherent in any form of communication. News often appear biased to citizens with dierent political orientations, and understood dierently by news media scholars and the broader public. In this paper we advocate the need for accurate methods for bias identication in video news item, to enable rich analytics capabilities in...

conference paper 2018

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Multi-Level Driver Workload Prediction Using Machine Learning and Off-The-Shelf Sensors

van Gent, P. (author), Melman, T. (author), Farah, H. (author), Nes, Nicole Van (author), van Arem, B. (author)

The present study aims to add to the literature on driver workload prediction using machine learning methods. The main aim is to develop workload prediction on a multi-class basis, rather than a binary high/low distinction as often found in litearature. The presented approach relies on measures that can be obtained unobtrusively in the driving...

conference paper 2018

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Derivative Target Line (DTL) for Continuous Human Activity Detection and Recognition

Guendel, Ronny (author), Fioranelli, F. (author), Yarovoy, Alexander (author)

In this paper, we investigate the classification of Activities of Daily Living (ADL) by using a pulsed ultra-wideband radar. Specifically, we focus on contiguous activities that can be inseparable in time and share a common transition, such as walking and falling. The range-time data domain is deliberately exploited to determine transitions from...

conference paper 2020

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Distributed Radar Information Fusion for Gait Recognition and Fall Detection

Li, Haobo (author), Le Kernec, Julien (author), Mehul, Ajay (author), Fioranelli, F. (author)

This paper discusses a fusion framework with data from multiple, distributed radar sensors based on conventional classifiers, and transfer learning with pre-trained deep networks. The application considered is the classification of gait styles and the detection of critical accidents such as falls. The data were collected from a network comprised...

conference paper 2020

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Detection of False Data Injection Attacks Using the Autoencoder Approach

Wang, C. (author), Tindemans, Simon H. (author), Pan, K. (author), Palensky, P. (author)

State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids....

conference paper 2020

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A Machine learning approach for rapid disaster response based on multi- modal data: The case of housing & shelter needs

Saldaña Ochoa, Karla (author), Comes, M. (author)

Along with climate change, more frequent extreme events, such as flooding and tropical cyclones, threaten the livelihoods and wellbeing of poor and vulnerable populations. One of the most immediate needs of people affected by a disaster is finding shelter. While the proliferation of data on disasters is already helping to save lives, identifying...

conference paper 2021

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Fast and robust scheme for uncertainty quantification in naturally fractured reservoirs

de Hoop, S. (author), Voskov, D.V. (author)

The main objective of this study is to perform Uncertainty Quantification (UQ) using a detailed representation of fractured reservoirs. This is achieved by creating a simplified representation of the fracture network while preserving the main characteristics of the high-fidelity model. We include information at different scales in the UQ...

conference paper 2021

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Considering Airport Planners’ Preferences and Imbalanced Datasets when Predicting Flight Delays and Cancellations

Hendrickx, Rik (author), Zoutendijk, M. (author), Mitici, M.A. (author), Schäfer, Jeffrey (author)

A key part of efficient airport operational planning is to have insight into potential flight delays and cancellations. For airport planners, it is important to obtain flight delay or cancellation predictions with a high degree of certainty, i.e. a high precision. This allows planners to make sound decisions based on these predictions. To obtain...

conference paper 2021

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Designing Depression Screening Chatbots

Giunti, Guido (author), Isomursu, M. (author), Gabarron, E. (author), Solad, Y. (author)

Advances in voice recognition, natural language processing, and artificial intelligence have led to the increasing availability and use of conversational agents (chatbots) in different settings. Chatbots are systems that mimic human dialogue interaction through text or voice. This paper describes a series of design considerations for...

conference paper 2021

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Radar sensing for human healthcare: Challenges and results

Fioranelli, F. (author), Le Kernec, Julien (author)

In this paper, radar sensing in the domain of human healthcare is discussed, specifically looking at the typical applications of human activity classification (including fall detection), gait analysis and gait parameters extraction, and vital signs monitoring such as respiration and heartbeat. A brief overview of open research challenges and...

conference paper 2021

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Using machine learning to predict concrete’s strength: Learning from small datasets

OUYANG, Boya (author), LI, Yuhai (author), SONG, Yu (author), WU, Feishu (author), YU, Huizi (author), WANG, Yongzhe (author), BAUCHY, Mathieu (author), SANT, Gaurav (author)

Despite previous efforts to relate concrete proportioning and strength, a robust knowledgebased model for accurate concrete strength predictions is still lacking. As an alternative to physical or chemical-based models, machine learning (ML) methods offer a new solution to this problem. Although ML can handle the complex, non-linear, non-additive...

conference paper 2021

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Health monitoring: a machine learning approach for anomaly detection in multi-sensor networks

Hajee, Bram (author), Wisse, Kees (author), Mohajerin Esfahani, P. (author)

Multi-sensor networks are becoming more and more popular in order to assess the post-occupancy performance of smart buildings, since they enable continuous monitoring with a high spatial resolution of the occupancy, thermal comfort and indoor air quality. An urgent, but poorly attended topic in this field is the automated detection of sensor...

conference paper 2022

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An Approach for Sleep Apnea Detection based on Radar Spectrogram Envelopes

Han, Y. (author), Yarovoy, Alexander (author), Fioranelli, F. (author)

This research aims to develop a contactless, radar-based sleep apnea detection method. A novel identification approach for this is proposed, based on the envelope of UWB radar spectrograms and machine learning. The envelope of the spectrogram is extracted by an image-based method, followed by signal smoothing via variational mode decomposition ...

conference paper 2022