Summary of Stochastic Stem Bucking Using Mixture Density Neural Networks, by Simon Schmiedel
Stochastic stem bucking using mixture density neural networks
by Simon Schmiedel
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to improve bucking decisions made by forest harvesters using a stochastic bucking method. A Long Short-Term Memory (LSTM) neural network is developed to predict parameters of a Gaussian distribution conditioned on known stem profiles, generating multiple samples for unknown parts. The stochastic algorithm optimizes bucking decisions using these predictions. Compared to two benchmark models – a polynomial model and a deterministic LSTM – the stochastic LSTM performs best, demonstrating its effectiveness in improving bucking decisions for four coniferous species prevalent in eastern Canada. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Forest harvesters can make better decisions about what logs to keep or discard by using a new method that uses artificial intelligence. The method predicts what stems will look like based on parts that have already been measured. This helps forest harvesters choose the best logs, which is important for making products like paper and wood. The method was tested on different types of trees and showed it could make better decisions than other methods. |
Keywords
* Artificial intelligence * Lstm * Neural network
