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In an experiment conducted during a Mars mission, a rover propels a projectile with an initial velocity, and the projectile rebounds after colliding with the Martian surface. To ascertain the maximum height attained by the projectile after this collision, the known restitution coefficient and acceleration due to gravity are employed.
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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Projectile motion is commonly observed in our day-to-day life. For example, a basketball thrown by a player, an arrow shot from a bow, and kids jumping into the pool, all undergo projectile motion.
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Consider a coffee mug hanging on a hook in a pantry. If the mug gets knocked, it oscillates back and forth like a pendulum until the oscillations die out.
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The theory of projectile motion is very useful for players of several sports to improve their performance. For example, a javelin thrower needs to throw their javelin in such a way that it travels as far as possible. The javelin thrower takes a short run-up to increase the initial speed of the javelin. The range of a projectile is at its maximum at a 45° angle so javelin throwers try to angle their throw as close to 45° as possible.
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Sensor Data Prediction in Missile Flight Tests.

Sang-Gyu Ryu1,2, Jae Jin Jeong3, David Hyunchul Shim1

  • 1Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning and wavelet analysis system to accurately predict missing missile sensor data. The method effectively reconstructs complex data patterns, improving reliability for valuable flight test information.

Keywords:
deep learninglong short-term memorymissing data imputationrecurrent neural networksensor data predictiontime series predictionwavelet reconstruction

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Area of Science:

  • Aerospace Engineering
  • Data Science
  • Signal Processing

Background:

  • Missile flight tests generate valuable sensor data, but data loss due to sensor detachment or failure is common.
  • Incomplete and complex correlations in remotely acquired missile sensor data make accurate prediction challenging.
  • Existing methods struggle to reliably reconstruct missing sensor data, impacting test analysis.

Purpose of the Study:

  • To develop and evaluate a deep learning-based prediction network combined with wavelet analysis for accurate missile sensor data imputation and prediction.
  • To address the challenges posed by missing data and complex correlations in missile flight sensor datasets.
  • To enhance the reliability and completeness of sensor data for missile test analysis.

Main Methods:

  • A hybrid approach combining wavelet transform for data decomposition and Generative Adversarial Networks (GANs) for detail reconstruction in the imputer network.
  • Utilizing a prediction network composed of Long Short-Term Memory (LSTM) with attention and dilation mechanisms for precise forecasting.
  • Evaluating the system's performance on actual missile flight sensor data across various missing data rates.

Main Results:

  • The proposed system demonstrated superior accuracy in predicting missile sensor data compared to existing methods, even with significant data loss.
  • Frequency analysis confirmed that the system's predictions closely matched the frequency responses of actual sensors.
  • The system accurately predicted sensor data in terms of both tendency and frequency characteristics.

Conclusions:

  • The integrated deep learning and wavelet analysis system effectively overcomes the limitations of incomplete missile sensor data.
  • This approach provides a robust solution for accurate data imputation and prediction, crucial for valuable missile test insights.
  • The method shows significant promise for enhancing the analysis of complex sensor data in aerospace applications.