Business Cases

• Change point detection of time series - technical survey

  Surveyed theories and technologies of detecting change points on a given time series in some meaning and summarized as a report document.

  These technologies are generally called "change point detection", "anomaly detection", or "motif discovery", and many methods have been developed. The fields in need of these technologies are wide, not only signal processing but also security system, bioinformatics, multimedia processing and so on.

  Mainly surveyed the following methods.

  • Symbolic Aggregation Approximation Method
  • Random Projection Pursuit Method
  • Markov-Suffix Tree Method
  • Normalized Edit Distance Method
  • Isomap Nonlinear Dimension Reduction Method
  • Kolmogorov Complexity Anomaly Detection Method
  • Motif Discovery by PCA and MDL Principal

• Validation of the accuracy of a real-time situation assessment model using time-series sensor data - application development

  We have developed an application that analyzes time-series data measured by physical sensors, estimates a prediction model to determine the situation in real time, and verifies the accuracy of the prediction.

  Situation judgments were made using binary values. To learn to judge the situation, we set a threshold value for the frequency distribution of each sensor data and developed a annotation support application to classify positive and negative cases.

  We estimated two types of situation judgment prediction models: a logistic regression model and an SVM learning model. In order to verify the prediction accuracy of the situation judgment prediction model, we developed an application that synchronizes sensor data with corresponding video data, and visually verified the prediction accuracy in real time.

  Technologies

  • R
  • C++/CLI
  • Feature Selection
  • Logistic Regression
  • SVM
  • Cross Validation

• Machine design parameters analysis - tool development

  We have developed a set of tools to analyze and visualize machine design parameters.

  Self-Organizing Map

  The information necessary for 2D drawing by SOM is output and visualized by ESOM.

  Clustering

  The parameters are clustered by K-means, and the data for graphical display is output by automatically extracting the two axes for which the representative points of the clusters are easily visible.

  Least-Squares

  Given 𝒚 and 𝑿, we obtain 𝒘 which satisfies 𝒚≈𝑿𝒘 by least-squares method. QR decomposition by Gram-Schmidt orthogonalization was used to compute the inverse matrix.

  

  Technologies

  • Self-Organizing Map
  • ESOM
  • K-means
  • Gram-Schmidt
  • QR Decomposition
  • Java

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