Browsing by Author "Rousu, Juho, Prof., Aalto University, Department of Information and Computer Science, Finland"

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    Advances in Wireless Damage Detection for Structural Health Monitoring
    (Aalto University, 2014) Toivola, Janne; Hollmén, Jaakko, Dr., Aalto University, Department of Information and Computer Science, Finland; Tietojenkäsittelytieteen laitos; Department of Information and Computer Science; Parsimonious Modelling; Perustieteiden korkeakoulu; School of Science; Rousu, Juho, Prof., Aalto University, Department of Information and Computer Science, Finland
    One of the fundamental tasks in structural health monitoring is to extract relevant information about a structure, such as a bridge or a crane, and reach statistical decisions about the existence of damages in the structure. Recent advances in wireless sensor network technology has offered new possibilities for acquiring and processing structural health monitoring data automatically.  The purpose of this dissertation is to explore various data processing methods for detecting previously unobserved deviation in measurements from accelerometer sensors, based on natural vibration of structures. Part of the processing is projected to be performed on resource constrained wireless sensors to ultimately reduce the cost of measurements.  Data processing in the proposed detection systems is divided into following general stages: feature extraction, dimensionality reduction, novelty detection, and performance assessment. Several methods in each of the stages are proposed and benchmarked in offline experiments with multiple accelerometer data sets. The methods include, for example, the Goertzel algorithm, random projection, tensor decomposition, collaborative filtering, nearest neighbor classification, and evaluating detection accuracy in terms of receiver operating characteristic curves.  Significant reductions are achieved in the amount of data transmitted from sensors and input to statistical classifiers, while maintaining some of the classification accuracy. However, the sensitivity and specificity in detection are worse than those of centralized methods operating on raw sensor data.  The work proposed and evaluated several combinations of data processing stages for wireless damage detection. While better than random detection accuracy can be achieved with very small amount of data per accelerometer sensor, challenges remain in reaching specificity required in practical applications. 
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    Computational methods for comparison and exploration of event sequences
    (Aalto University, 2013) Lijffijt, Jefrey; Mannila, Heikki, Prof., Aalto University, Department of Information and Computer Science, Finland; Tietojenkäsittelytieteen laitos; Department of Information and Computer Science; Perustieteiden korkeakoulu; School of Science; Rousu, Juho, Prof., Aalto University, Department of Information and Computer Science, Finland
    Many types of data, e.g., natural language texts, biological sequences, or time series of sensor data, contain sequential structure. Analysis of such sequential structure is interesting for various reasons, for example, to detect that data consists of several homogeneous parts, that data contains certain recurring patterns, or to find parts that are different or surprising compared to the rest of the data. The main question studied in this thesis is how to identify global and local patterns in event sequences. Within this broad topic, we study several subproblems. The first problem that we address is how to compare event frequencies across event sequences and databases of event sequences. Such comparisons are relevant, for example, to linguists who are interested in comparing word counts between two corpora to identify linguistic differences, e.g., between groups of speakers, or language change over time. The second problem that we address is how to find areas in an event sequence where an event has a surprisingly high or low frequency. More specifically, we study how to take into account the multiple testing problem when looking for local frequency deviations in event sequences. Many algorithms for finding local patterns in event sequences require that the person applying the algorithm chooses the level of granularity at which the algorithm operates, and it is often not clear how to choose that level. The third problem that we address is which granularities to use when looking for local patterns in an event sequence. The main contributions of this thesis are computational methods that can be used to compare and explore (databases of) event sequences with high computational efficiency, increased accuracy, and that offer new perspectives on the sequential structure of data. Furthermore, we illustrate how the proposed methods can be applied to solve practical data analysis tasks, and describe several experiments and case studies where the methods are applied on various types of data. The primary focus is on natural language texts, but we also study DNA sequences and sensor data. We find that the methods work well in practice and that they can efficiently uncover various types of interesting patterns in the data.