Peng-Hung Tsai
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Researcher
Faculty Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Peng-Hung Tsai's research focuses on quantitative technology forecasting and trend extrapolation methods. He has investigated methods for predicting the lifespan of spacecraft and satellites, utilizing historical data to model future trends. His work includes developing and applying quantitative methods to analyze trends in technological progress, such as the integration of recent data to analyze trends in kidney cancer survival. Tsai has also explored the application of LSTM neural networks for trend analysis in space exploration vessels and examined whether technological progress follows a random walk using space travel data.
Tsai collaborates with researchers at the University of Arkansas at Little Rock, including Daniel Berleant and Michael Howell, as well as Richard S. Segall from Arkansas State University and Shi‐Ming Tu from the University of Arkansas for Medical Sciences. His scholarship metrics include an h-index of 2, with 10 total publications and 29 total citations.
Metrics
- h-index: 2
- Publications: 10
- Citations: 29
Selected Publications
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Start Time End Time Integration (STETI): Method for Including Recent Data to Analyze Trends in Kidney Cancer Survival (2025)
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Predicting Future Participation of Women in Space by Analyzing Past Trends (2024)
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Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods (2023)
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Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives (2022)
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Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives (2022)
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Is technological progress a random walk? Examining data from space travel (2021)
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Spacecraft for Deep Space Exploration: Combining Time and Budget to Model the Trend in Lifespan (2021)
Collaboration Network
Top Collaborators
- Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods
- Spacecraft for Deep Space Exploration: Combining Time and Budget to Model the Trend in Lifespan
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
- Is technological progress a random walk? Examining data from space travel
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
Showing 5 of 7 shared publications
- Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods
- Spacecraft for Deep Space Exploration: Combining Time and Budget to Model the Trend in Lifespan
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
- Is technological progress a random walk? Examining data from space travel
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
Showing 5 of 6 shared publications
- Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods
- Spacecraft for Deep Space Exploration: Combining Time and Budget to Model the Trend in Lifespan
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
- Is technological progress a random walk? Examining data from space travel
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
- Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods
- Spacecraft for Deep Space Exploration: Combining Time and Budget to Model the Trend in Lifespan
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
- Future Satellite Lifetime Prediction From the Historical Trend in Satellite Half-Lives
- Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods
- Spacecraft for Deep Space Exploration: Combining Time and Budget to Model the Trend in Lifespan
- Is technological progress a random walk? Examining data from space travel
- Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods
- Predicting Future Participation of Women in Space by Analyzing Past Trends
- Start Time End Time Integration (STETI): Method for Including Recent Data to Analyze Trends in Kidney Cancer Survival
- Start Time End Time Integration (STETI): Method for Including Recent Data to Analyze Trends in Kidney Cancer Survival
- Start Time End Time Integration (STETI): Method for Including Recent Data to Analyze Trends in Kidney Cancer Survival
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