document

A LSTM-based Generative Adversarial Network for End-use Water Modelling

Xie, yukun (author)

Research pertaining to end-use water analysis plays a pivotal role in enabling local communities to enhance their management of pipelines, water resources, and associated policies. Nowadays, various end-use models have been developed based on diverse databases and measurements. Nonetheless, a predominant drawback prevalent in most of these...

master thesis 2023

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Effects of Partial Observability Solver Methods on Training and Final Policies in Autonomous Driver RL: How do different methods for dealing with partial observability in the environment influence training and the robustness of final policies under various testing conditions?

Çil, Ata (author)

Autonomous driving is a complex problem that can potentially be solved using artificial intelligence. The complexity stems from the system's need to understand the surroundings and make appropriate decisions. However, there are various challenges in constructing such a sophisticated system. One of the main challenges is to make the agent learn...

bachelor thesis 2023

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Effects of weather data on traffic flow predictions using an LSTM deep learning model

Nachev, Nikola (author)

Accurate traffic forecasts are a key element in improving the traffic flow of urban cities. An efficient approach to this problem is to use a deep learning Long Short Term Memory (LSTM) model. Including weather data in the model can improve prediction accuracy because traffic volumes are sensitive to weather changes. The aim of this study is to...

bachelor thesis 2023

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Real-time pipeline leak detection and localization using an attention-based LSTM approach

Zhang, Xinqi (author), Shi, Jihao (author), Yang, M. (author), Huang, Xinyan (author), Usmani, Asif Sohail (author), Chen, Guoming (author), Fu, Jianmin (author), Huang, Jiawei (author), Li, Junjie (author)

Long short-term memory (LSTM) has been widely applied to real-time automated natural gas leak detection and localization. However, LSTM approach could not provide the interpretation that this leak position is localized instead of other positions. This study proposes a leakage detection and localization approach by integrating the attention...

journal article 2023

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Attack Graph Model for Cyber-Physical Power Systems Using Hybrid Deep Learning

Presekal, A. (author), Stefanov, Alexandru (author), Subramaniam Rajkumar, Vetrivel (author), Palensky, P. (author)

Electrical power grids are vulnerable to cyber attacks, as seen in Ukraine in 2015 and 2016. However, existing attack detection methods are limited. Most of them are based on power system measurement anomalies that occur when an attack is successfully executed at the later stages of the cyber kill chain. In contrast, the attacks on the Ukrainian...

journal article 2023

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An Area-Efficient Ultra-Low-Power Time-Domain Feature Extractor for Edge Keyword Spotting

Chen, Qinyu (author), Chang, Yaoxing (author), Kim, Kwantae (author), Gao, C. (author), Liu, Shih Chii (author)

Keyword spotting (KWS) is an important task on edge low-power audio devices. A typical edge KWS system consists of a front-end feature extractor which outputs mel-scale frequency cepstral coefficients (MFCC) features followed by a back-end neural network classifier. KWS edge designs aim for the best power-performance-area metrics. This work...

conference paper 2023

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From Data to Prediction: Vision-Based UAV Fault Detection and Diagnosis

de Alvear Cardenas, Jose Ignacio (author)

Despite the camera being ubiquitous to unmanned aerial vehicles (UAVs), it has not been used for fault detection and diagnosis (FDD) due to the nonexistence of UAV multi-sensor datasets that include actuator failure scenarios. This thesis proposes a knowledge-based FDD framework based on a lightweight Long-Short Term Memory network that fuses...

master thesis 2022

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Short-term Earthquake Prediction with Deep Neural Networks: Finding the optimal time prior to earthquake strikes to use in predictions

van den Belt, Glenn (author)

Earthquakes can have tremendous effects. They can result in casualties, massive damage, and hurt the economy. Therefore, one would like to predict earthquakes as early as possible and with the highest accuracy possible. This paper contains the proposal for the optimal prediction-time, which is the time between the execution of a prediction and...

bachelor thesis 2022

document

Impact of Focal Depth on Short-Term Earthquake Prediction using Deep Learning

Krisiukėnas, Pijus (author)

Due to the devastating consequences of earthquakes, predicting their occurrence before the first strike has been a long standing research topic. Deep learning models have been used to facilitate prediction, using seismograph data to attempt to classify an earthquake right before it happens. However, this is a difficult task and research needs to...

bachelor thesis 2022

document

Predicting Micro-Earthquakes with Deep Neural Networks

Zhu, Kevin (author)

Earthquake prediction is the field of seismology concerned with predicting the time, location, and magnitude of earthquakes within a small time frame, usually defined in terms of minutes or seconds before an event. Such predictions can have a large impact on minimizing the damage caused by these seismic events, by providing early warnings to the...

bachelor thesis 2022

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Impact of seismic wave length to detect high-magnitude earthquakes via deep learning

Georgiev, Gancho (author)

Earthquakes are one of the most destructive natural phenomena, both in terms of human lives, and property damage. Although they are treated as a random phenomenon, the ability to predict them, even few seconds before they occur, could be of great benefit to society. Lots of research has been done on this topic but without any significant results...

bachelor thesis 2022

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WMR prediction using recurrent neural networks on FX limit order book data

Kortekaas, Steven (author)

This thesis investigates the application of machine learning models on foreign exchange data around the WM/R 4pm Closing Spot Rate (colloquially known as the WMR Fix). Due to the nature of the market dynamics around the WMR Fix, inefficiencies can occur and therefore some predictability might be expected. We aim to find these inefficiencies....

master thesis 2022

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Short-Term Forecast of Demand for Train Station-Based Round-Trip Bikesharing: A Case Study of OV-fiets in The Netherlands

Wilkesmann, Florian (author)

Around the world, authorities try to increase the attractiveness of multimodal public transport (PT)-related trips to reduce car usage. To achieve this, a seamless combination between the different modes is necessary. The Dutch train station operator NS tries to enhance the combination of the bike and train by providing a train station-based...

master thesis 2022

document

Short-term Earthquake Prediction via Recurrent Neural Network Models: Comparison among vanilla RNN, LSTM and Bi-LSTM

Du, XIANGYU (author)

Earthquake prediction has raised many concerns nowadays, due to the massive loss caused by earthquakes, as well as the significance of accurate forecasting. Lots of trials have been investigated and experimented but few achieved satisfying results on short-term prediction (i.e., usually those earthquakes that will happen in three months). It is...

bachelor thesis 2022

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How does a CNN mixed with LSTM methods compare with the individual one in predicting earthquakes?

Hashmi, Irtaza (author)

Earthquakes are one of the most dangerous natural disasters that occur worldwide. Predicting them is one of the unsolved problems in the field of science. In the past decade, there has been an increase in seismic monitoring stations worldwide, which has allowed us to design and implement data-driven and deep learning solutions. In this paper, we...

bachelor thesis 2022

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Predictive Maintenance Decisions for a Multi-Component Aircraft based on Prognostics

Posthuma, Luuk (author)

Aircraft maintenance is critical to an airline's operations to ensure the reliability, availability, and safety of their assets. Recently, the approach of using component prognostics in aircraft maintenance has received increasing attention in academic- and industrial research. Predictive maintenance has demonstrated promising results in using...

master thesis 2022

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Recurrent Convolutional Neural Network-Based Assessment of Power System Transient Stability and Short-Term Voltage Stability

Tapia, Estefania Alexandra (author), Colomé, Delia Graciela (author), Rueda, José L. (author)

Transient stability (TS) and short-term voltage stability (STVS) assessment are of fundamental importance for the operation security of power systems. Both phenomena can be mutually influenced in weak power systems due to the proliferation of power electronic interface devices and the phase-out of conventional heavy machines (e.g., thermal power...

journal article 2022

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A Hybrid Spatial-temporal Sequence-to-one Neural Network Model for Lane Detection

Dong, Y. (author), Patil, Sandeep (author), Farah, H. (author), van Arem, B. (author)

Reliable and accurate lane detection is of vital importance for the safe performance of Lane Keeping Assistance and Lane Departure Warning systems. However, under certain challenging peculiar circumstances (e.g., marking degradation, serious vehicle occlusion), it is quite difficult to get satisfactory performance in accurately detecting the...

poster 2022

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Long Short-Term Memory Network Based Trajectory Prediction Incorporating Air Traffic Dynamics

Overkamp, Jean-Luc (author)

Accurate 4D trajectory predictions are required for the implementation of Trajectory Based Operations. In addition, decentralized, free routing can make medium- to long-term flight trajectories more difficult to predict. Novel trajectory prediction techniques are needed, independent of waypoint-to-waypoint navigation and air traffic control...

master thesis 2021

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Multi-step ahead ultra-short-term wind power forecasting: A forecast quality and value comparison between proposed deep learning models and an operational numerical weather prediction based model

Homsma, Thom (author)

The ongoing large scale adoption of wind power increases the associated risks related to the variability. An essential way to mitigate these risks for a utility company is to forecast production accurately. This study aims to create insight into the potential of deep learning models for both forecast quality and value on the ultra-short-term...

master thesis 2021