Browsing by Author "Huuhtanen, Timo"

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  • Anomaly Location Detection with Electrical Impedance Tomography Using Multilayer Perceptrons
    (2020-09-23) Huuhtanen, Timo; Jung, Alex
     A4 Artikkeli konferenssijulkaisussa
    Electrical impedance tomography (EIT) does imaging by solving a nonlinear ill-posed inverse problem. Recently, there has been an increasing interest in solving this problem with artificial neural networks. However, a systematic understanding of the optimal neural network architecture for this problem is still lacking. This paper compares the performance of different multilayer perceptron algorithms for detecting the location of an anomaly on a sensing surface by solving the EIT inverse problem. We generate synthetic data with varying anomaly sizes/locations and compare a wide range of multilayer perceptron algorithms by simulations. Our results indicate that increasing the dimensions of the perceptron improves performance, but this improvement saturates soon. The best performance is achieved when using the multilayer perceptron for regression and Gaussian noise addition as the regularization method.
  • Applying Machine Learning to Root Cause Analysis in Agile CI/CD Software Testing Environments
    (2019-01-28) Kahles Bastida, Julen
     Perustieteiden korkeakoulu | Master's thesis
    This thesis evaluates machine learning classification and clustering algorithms with the aim of automating the root cause analysis of failed tests in agile software testing environments. The inefficiency of manually categorizing the root causes in terms of time and human resources motivates this work. The development and testing environments of an agile team at Ericsson Finland are used as this work's framework. The author of the thesis extracts relevant features from the raw log data after interviewing the team's testing engineers (human experts). The author puts his initial efforts into clustering the unlabeled data, and despite obtaining qualitative correlations between several clusters and failure root causes, the vagueness in the rest of the clusters leads to the consideration of labeling. The author then carries out a new round of interviews with the testing engineers, which leads to the conceptualization of ground-truth categories for the test failures. With these, the human experts label the dataset accordingly. A collection of artificial neural networks that either classify the data or pre-process it for clustering is then optimized by the author. The best solution comes in the form of a classification multilayer perceptron that correctly assigns the failure category to new examples, on average, 88.9\% of the time. The primary outcome of this thesis comes in the form of a methodology for the extraction of expert knowledge and its adaptation to machine learning techniques for test failure root cause analysis using test log data. The proposed methodology constitutes a prototype or baseline approach towards achieving this objective in a corporate environment.
  • Automating Root Cause Analysis via Machine Learning in Agile Software Testing Environments
    (2019-04-01) Kahles, Julen; Torronen, Juha; Huuhtanen, Timo; Jung, Alexander
     A4 Artikkeli konferenssijulkaisussa
    We apply machine learning to automate the root cause analysis in agile software testing environments. In particular, we extract relevant features from raw log data after interviewing testing engineers (human experts). Initial efforts are put into clustering the unlabeled data, and despite obtaining weak correlations between several clusters and failure root causes, the vagueness in the rest of the clusters leads to the consideration of labeling. A new round of interviews with the testing engineers leads to the definition of five ground-truth categories. Using manually labeled data, we train artificial neural networks that either classify the data or pre-process it for clustering. The resulting method achieves an accuracy of 88.9%. The methodology of this paper serves as a prototype or baseline approach for the extraction of expert knowledge and its adaptation to machine learning techniques for root cause analysis in agile environments.
  • Design of Multidimensional Filters for Sampling Structure Conversion of Video Signals
    (1993) Huuhtanen, Timo
     Helsinki University of Technology | Licentiate thesis
  • Fall detection with machine learning
    (2020-09-01) Piippo, Alisa
     Perustieteiden korkeakoulu | Bachelor's thesis
  • Machine learning in human gait analysis
    (2021-05-14) Ruley, Brian
     Sähkötekniikan korkeakoulu | Bachelor's thesis
  • Neural Networks in Electrical Impedance Tomography
    (2019-12-04) Purhonen, Mikko
     Sähkötekniikan korkeakoulu | Bachelor's thesis
  • Outlier Detection from Non-Smooth Sensor Data
    (2019-09-05) Huuhtanen, Timo; Ambos, Henrik; Jung, Alex
     A4 Artikkeli konferenssijulkaisussa
    Outlier detection is usually based on smooth assumption of the data. Most existing approaches for outlier detection from spatial sensor data assume the data to be a smooth function of the location. Spatial discontinuities in the data, such as arising from shadows in photovoltaic (PV) systems, may cause outlier detection methods based on the spatial smoothness assumption to fail. In this paper, we propose novel approaches for outlier detection of non-smooth spatial data. The methods are evaluated by numerical experiments involving PV panel measurements as well as synthetic data.
  • Search of New Applications for Graph Based Machine Learning Algorithms
    (2018-05-03) Lohi, Pauli
     Perustieteiden korkeakoulu | Bachelor's thesis
  • Sellumarkkinoiden perusvoimiin vaikuttavat tekijät ja niiden toiminta
    (1993) Huuhtanen, Timo
     School of Business | Master's thesis
  • Target Tracking on Sensing Surface with Electrical Impedance Tomography
    (2021-01-24) Huuhtanen, Timo; Lankinen, Antti; Jung, Alex
     A4 Artikkeli konferenssijulkaisussa
    An emerging class of applications uses sensing surfaces, where sensor data is collected from a 2-dimensional surface covering a large spatial area. Sensing surface applications range from observing human activity to detecting failures of construction materials. Electrical impedance tomography (EIT) is an imaging technology, which has been successfully applied to imaging in several important application domains such as medicine, geophysics, and process industry. EIT is a low-cost technology offering high temporal resolution, which makes it a potential technology sensing surfaces. In this paper, we evaluate the applicability of EIT algorithms for tracking a small moving object on a 2D sensing surface. We compare standard EIT algorithms for this purpose and develop a method which models the movement of a small target on a sensing surface using hidden Markov models (HMM). Existing EIT methods are geared towards high image quality instead of smooth target trajectories, which makes them suboptimal for target tracking. Numerical experiments indicate that our proposed method outperforms existing EIT methods in target tracking accuracy.
  • VLSI design of digital signal processing modules
    (1988) Huuhtanen, Timo
     Helsinki University of Technology | Master's thesis