1. Company introduction

EHR&C Co., Ltd. has technology for ecological and chemical toxicity in the environmental field and risk assessment of products, and is a research firm that conducts R&D and academic research in the field.

2. Problem Background and Summary

Explore appropriate mathematical methods to derive a distribution of collective exposure that reflects individual usage patterns (time, frequency of use, usage) and usage of individuals' products

Learn the distribution of personal exposure data for the product and use it to generate data to create a usage pattern matrix

Appropriate methods of filling missing values of exposure data by product for 3,000 people who responded to the survey and methods of interpreting generated data

3. Solving Process

Provides a statistical review of the parametric estimation methods used by existing companies estimating the usage pattern survey response distribution for each product

Using the deep learning algorithm Variational Autoencoder, it presents a nonparametric way to learn the distribution of exposure and to generate data

Provide a deion of the distribution distances that can measure the different degrees of data distribution and share information on measuring the distance between the data generated and the existing data

Suggest how to interpret the generated distribution and test its independence from other product exposure distributions

4. Ripple effects and future plans

Generates an individual product usage pattern matrix by learning the distribution of data more easily than traditional parametric methods used by the enterprise. This matrix has been used immediately for the research the enterprise is conducting. Additional discussions can be made to think about the problem of exploring which matrix converges when a product usage pattern matrix is created in a bootstrap manner.