Peng-Hung Tsai Data-verified

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Researcher

Last publication 2025 Last refreshed 2026-05-16

faculty

2 h-index 10 pubs 29 cited

Biography and Research Information

OverviewAI-generated summary

Peng-Hung Tsai's research focuses on quantitative technology forecasting and trend extrapolation methods. He investigates the prediction of future trends by analyzing historical data, particularly in the context of space exploration technology and satellite lifespans. His work explores the application of advanced techniques, such as Long Short-Term Memory (LSTM) neural networks, to analyze trends in satellite lifetime data and technology forecasting. Tsai also examines the nature of technological progress, questioning whether it follows a random walk pattern based on space travel data. His research extends to healthcare, with a publication on a method for analyzing trends in kidney cancer survival by integrating recent data. Tsai has a notable record of collaboration, with numerous shared publications with Daniel Berleant at the University of Arkansas at Little Rock and Richard S. Segall at Arkansas State University.

Metrics

  • h-index: 2
  • Publications: 10
  • Citations: 29

Selected Publications

  • Start Time End Time Integration (STETI): Method for Including Recent Data to Analyze Trends in Kidney Cancer Survival (2025)
    1 citation DOI OpenAlex
  • Predicting Future Participation of Women in Space by Analyzing Past Trends (2024)
  • Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods (2023)
    20 citations DOI OpenAlex
  • Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives (2022)
    2 citations DOI OpenAlex
  • Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives (2022)
    1 citation DOI OpenAlex
  • Is technological progress a random walk? Examining data from space travel (2021)
    1 citation DOI OpenAlex
  • Spacecraft for Deep Space Exploration: Combining Time and Budget to Model the Trend in Lifespan (2021)
    3 citations DOI OpenAlex

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Collaboration Network

10 Collaborators 3 Institutions 1 Country

Top Collaborators

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