Statistical Data Analysis & Quality Design Lab

Introduction to the laboratory


Quality control and improvement are critical business strategies for companies across various industries, including manufacturing, healthcare, finance, and more. Companies that can provide high-quality products or services gain a competitive advantage in the market. Our laboratory focuses on developing concepts, theories, and methods to enhance quality and productivity. Our research areas include statistical process control, spatiotemporal statistical analysis, reliability engineering, process mining, artificial intelligence, and system simulation. We apply statistical techniques to achieve process improvement goals.

Field of research

Statistical Process Control (SPC):
Research topics include the application of Six Sigma or artificial intelligence techniques to quality control, manufacturing processes, or supplier selection issues, tool replacement strategies, and the statistical inference of process capability indices.

Spatiotemporal Statistical Analysis:
This research focuses on methods for detecting changes in spatiotemporal pattern data and applying them to the analysis of spatiotemporal data in fields such as industry, public health, and the environment. The research also involves identifying the time and region where changes occur in the data. After receiving a signal, the monitoring process continues by classifying abnormal patterns and diagnosing their causes, with the aim of further controlling anomalies or determining appropriate strategies.

Reliability Engineering:
Research topics include the validation of failure modes, the application of genetic algorithms to dynamic reliability, opportunistic age-replacement strategies for multi-component systems, and the evaluation of the quality and reliability of satellite communication shipboard receiving systems.

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