1,721,085 research outputs found
Evaluating model mismatch impacting CACC controllers in mixed traffic using a driving simulator
No full textUnsupervised approaches for time-evolving graph embeddings with application to human microbiome
Full text linkMore and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, and even some types of cancer. Advances in high-throughput omics technologies have made it possible to directly analyze the human microbiome and its impact on human health and physiology. Microbial composition is usually observed over long periods of time and the interactions between their members are explored. Numerous studies have used microbiome data to accurately differentiate disease states and understand the differences in microbiome profiles between healthy and ill individuals. However, most of them mainly focus on various statistical approaches, omitting microbe-microbe interactions among a large number of microbiome species that, in principle, drive microbiome dynamics. Constructing and analyzing time-evolving graphs is needed to understand how microbial ecosystems respond to a range of distinct perturbations, such as antibiotic exposure, diseases, or other general dynamic properties. This becomes especially challenging due to dozens of complex interactions among microbes and metastable dynamics.
The key to addressing this challenge lies in representing time-evolving graphs constructed from microbiome data as fixed-length, low-dimensional feature vectors that preserve the original dynamics. Therefore, we propose two unsupervised approaches that map the time-evolving graph constructed from microbiome data into a low-dimensional space where the initial dynamic, such as the number of metastable states and their locations, is preserved. The first method relies on the spectral analysis of transfer operators, such as the Perron--Frobenius or Koopman operator, and graph kernels. These components enable us to extract topological information such as complex interactions of species from the time-evolving graph and take into account the dynamic changes in the human microbiome composition. Further, we study how deep learning techniques can contribute to the study of a complex network of microbial species. The method consists of two key components: 1) the Transformer, the state-of-the-art architecture used in the sequential data, that learns both structural patterns of the time-evolving graph and temporal changes of the microbiome system and 2) contrastive learning that allows the model to learn the low-dimensional representation while maintaining metastability in a low-dimensional space.
Finally, this thesis will address an important challenge in microbiome data, specifically identifying which species or interactions of species are responsible for or affected by the changes that the microbiome undergoes from one state (healthy) to another state (diseased or antibiotic exposure). Using interpretability techniques of deep learning models, which, at the outset, have been used as methods to prove the trustworthiness of a deep learning model, we can extract structural information of the time-evolving graph pertaining to particular metastable states
Design and experimental validation of a cooperative driving system in the grand cooperative driving challenge
No full textModelling and Controlling an Offset Lithographic Printing Process
No full textThe objective of this thesis is to provide methodsfor print quality enhancements in an offset lithographic printing process. Various parameters characterising the print quality are recognised, however, in this work print quality is defined as the deviation of the amount of ink in a sample image from the reference print.The methods developed are model-based and historical datacollected at the printing press are used to build the models. Inherent in the historical process data are outliers owing to sensor faults, measurement errors and impurity of the materials used. It is essential to detect and remove these outliers to avoid using them to update the process models. A process model-based outlier detection tool has been proposed. Several diagnostic measures are combined via a neural network to achieve robust data categorisation into inlier and outlier classes.To cope with the slow variation in printing process data, aSOM-based data mining and adaptive modelling technique has been proposed. The technique continuously updates the data set characterising the process and the process models if they becomeout-of-date. A SOM-based approach to model combination has been proposed to permit the creation of adaptive -data dependent-committees.A multiple models-based controller, which employs the process models developed, is combined with an integrating controller to achieve robust ink feed control. Results have shown that the robust ink feed controller is capable of controlling the ink feed in the newspaper printing press according to the desired process output. Based on the process modelling, techniques have also been developed for initialising the printing press in order to reduce the time needed to achieve the desired print quality. The use of the developed methods and tools at a print shop in Halmstad, Sweden, resulted in higher print quality and lower ink and paper waste
Action intention recognition of cars and bicycles in intersections
No full textCopyright © 2020 Inderscience Enterprises Ltd.Action intention recognition is becoming increasingly important in the road vehicle automation domain. Autonomous vehicles must be aware of their surroundings if we are to build safe and efficient transport systems. This paper presents a method for predicting the action intentions of road users based on sensors in the road infrastructure. The scenarios used for demonstration are recorded on two different public road sections. The first scenario includes bicyclists and the second includes cars that are driving in a road approaching an intersection where they are either leaving or continuing straight. A 3D camera-based data acquisition system is used to collect trajectories of the road users that are used as input for models trained to predict the action intention of the road users. The proposed system enables future connected and automated vehicles to receive collision warnings from an infrastructure-based sensor system well in advance to enable better planning.</p
Modelling and Controlling an Offset Lithographic Printing Process
No full textThe objective of this thesis is to provide methods
for print quality enhancements in an offset lithographic printing process. Various parameters characterising the print quality are recognised, however, in this work print quality is defined as the deviation of the amount of ink in a sample image from the reference print.
The methods developed are model-based and historical data
collected at the printing press are used to build the models. Inherent in the historical process data are outliers owing to sensor faults, measurement errors and impurity of the materials used. It is essential to detect and remove these outliers to avoid using them to update the process models. A process model-based outlier detection tool has been proposed. Several diagnostic measures are combined via a neural network to achieve robust data categorisation into inlier and outlier classes.
To cope with the slow variation in printing process data, a
SOM-based data mining and adaptive modelling technique has been proposed. The technique continuously updates the data set characterising the process and the process models if they become
out-of-date. A SOM-based approach to model combination has been proposed to permit the creation of adaptive -data dependent-committees.
A multiple models-based controller, which employs the process models developed, is combined with an integrating controller to achieve robust ink feed control. Results have shown that the robust ink feed controller is capable of controlling the ink feed in the newspaper printing press according to the desired process output. Based on the process modelling, techniques have also been developed for initialising the printing press in order to reduce the time needed to achieve the desired print quality. The use of the developed methods and tools at a print shop in Halmstad, Sweden, resulted in higher print quality and lower ink and paper waste
Aware and intelligent infrastructure for action intention recognition of cars and bicycles
No full textAction intention recognition is becoming increasingly important in the road vehicle automation domain. Autonomous vehicles must be aware of their surroundings if we are to build safe and efficient transport systems. This paper explores methods for predicting the action intentions of road users based on an aware and intelligent 3D camera-based sensor system. The collected data contains trajectories of two different scenarios. The first one includes bicyclists and the second cars that are driving in a road approaching an intersection where they are either turning or continuing straight. The data acquisition system is used to collect trajectories of the road users that are used as input for models trained to predict the action intention of the road users.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
