The jaakkotalonen’s Podcast

Self-organizing map based visualization techniques and their assessment. International Journal of Computing (2012)

The authors explore the application of SOM in dynamic systems, particularly within the context of a Finnish nuclear power plant, Olkiluoto, using both plant and training simulator data. They discuss various user interface and visualization assessment criteria and present a case study demonstrating the SOM method's information value in process visualization.

The Finnish Car Rejection Reasons Shown in an Interactive SOM Visualization Tool. In Workshop on Self Organizing Maps (2012)

The authors highlight the integration of Collaborative Filtering (CF) as a preprocessing step to address missing values and filter discrete data, enhancing the effectiveness of SOM training. This combined approach allows for deeper insights into car rejection reasons, including their temporal relationships and dependencies.

Network Visualization of Car Inspection Data using Graph Layout (2012)

The researchers utilized data from A-Katsastus, a major vehicle inspection provider in Northern Europe, to aggregate extensive statistical tables into a single visual network. They compare this novel network visualization, implemented using the Gephi platform and ForceAtlas2 algorithm, with a Principal Component Analysis (PCA) approach previously explored.

Analyzing Parliamentary Elections Based on Voting Advice Application Data (2011)

A methodology for modeling the values of Finnish citizens and Members of Parliament (MP) by combining voting advice application (VAA) data with the results of the 2011 parliamentary elections. The authors preprocess the qualitative VAA data into a high-dimensional matrix, which is then reduced to two principal components using Principal Component Analysis (PCA) for visualization.

Modelling Power Output at Nuclear Power Plant by Neural Networks (2010)

Welcome to 'Nuclear Insights' where we explore cutting-edge advancements in nuclear technology. In today’s episode, we dive into neural network innovations for industrial forecasting. Using real data from a boiling water reactor in Olkiluoto, Finland, we compare Feed-Forward and Elman Neural Networks for power output prediction. With techniques like cross-correlation analysis and metrics such as NMSE, discover why ENN stands out and the challenges of tackling nonstationary processes.

Generated Control Limits as a Basis of Operator-Friendly Process Monitoring (2009)

This academic paper from the IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems introduces a novel method for process monitoring and fault detection in complex industrial environments, specifically nuclear power plants.

Abnormal Process State Detection by Cluster Center Point Monitoring in BWR Nuclear Power Plant (2009)

Discussion about method, which focuses on monitoring the movement of cluster center points in real-time, classifying process signals into "slow" and "fast" categories using the K-means algorithm. By extracting statistical features like absolute difference and moving standard deviation, the system aims to provide early detection of potential faults, thereby enhancing plant safety and reducing operational costs.

Leakage Detection by Adaptive Process Modeling (2008)

The authors propose an adaptive linear approach for time series modeling, employing the Weighted Recursive Least Squares (WRLS) method. To ensure a robust model, they utilize Principal Component Analysis (PCA) to select linearly correlated interpretive variables, examining eigenvalues and eigenvectors.

Fault Detection in Nuclear Power Plants by Adaptive Modeling (2007)

Master's thesis that explores fault detection in nuclear power plants (NPPs) using adaptive process modeling. The author, Jaakko Talonen, from Helsinki University of Technology, details a methodology involving data mining (DM), principal component analysis (PCA), and weighted recursive least squares (WRLS).