Encoder-Decoder Model for Population State Determination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting abnormal population changes in an area lack accuracy due to their inability to consider population changes over time.
Innovation Solution
A population state determination system that utilizes an encoder-decoder model to compress and reconstruct population data in real-time, allowing for accurate comparison and determination of population states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical methods based on average and variance are used for population detection, then the detection method is simple, but the detection accuracy is insufficient because population changes are not considered
Solution Approach 1:
The patent transforms the population detection approach by changing from simple statistical parameters (average, variance) to a sequence-based representation that captures temporal dynamics. The population data is converted into sequences that reflect changes over time, enabling the detection system to analyze population trends rather than just static statistics.
Solution Approach 2:
The patent introduces an encoder-decoder model as an intermediary between the raw population data and the abnormality detection process. This neural network model acts as a mediator that learns to reconstruct normal population patterns and identifies deviations from these patterns, thereby improving detection accuracy without requiring complex manual feature engineering.
2Measurement precision
If encoder-decoder model is used to analyze population changes, then the detection accuracy is improved, but the system complexity increases
Solution Approach 1:
The encoder-decoder model operates with an inherent feedback mechanism where the decoder attempts to reconstruct the input sequence. The difference between the original and reconstructed sequences provides feedback that indicates abnormality. This feedback loop enables the system to automatically identify deviations from normal population patterns without requiring external validation.
Solution Approach 2:
The system performs preliminary training of the encoder-decoder model using historical population data before actual detection begins. During this preliminary phase, the model learns normal population patterns and dynamics. Once trained, the model can quickly detect abnormalities in real-time without requiring complex analysis during the detection phase itself.
Data Source
AI summary
A population state determination system includes an acquisition unit configured to acquire population information indicating a population in a time series in an area that is a population state determination target, a model calculation unit configured to perform calculation by inputting the population information acquired by the acquisition unit to a pre-stored encoder-decoder model for compressing and reconstructing input data and obtain an output from the encoder-decoder model, and a determination unit configured to determine a state of the population in the area by comparing the population information acquired by the acquisition unit-11 with the output obtained by the model calculation unit.