Contents of Projects
Battlefield Situation and Data Simulation Technology
[2021-02-16 ~ 2022-11-30, National Defense Research Institute]Objective
Develop a deep learning model that can generate unstructured simulation reports using structured simulation battlefield data.
- Recently, many studies have been conducted to develop an intelligent battlefield recognition system in the military.
- However, it is difficult to collect data for training.
- In particular, there is a limitation in directly annotating reports generated through war games in the military. Therefore, it is intended to develop a deep learning model that generates a report from log data generated through war game simulation.
Data
Develop a deep learning model that can generate unstructured simulation reports using structured simulation battlefield data.
- Recently, many studies have been conducted to develop an intelligent battlefield recognition system in the military.
Related Work
1.Table-to-text Generation by Structure-aware Seq2seq Learning
- In the encoding phase, they utilized the value-field as well as the postion, and proposed a modified encoder, the field-gate encoder.
- In the decoding phase, Dual attention multiplied by word-level attention and field-level attention significantly improved performance.
Proposed Method
Dual attention Seq2Seq with Copy mechanism
- Previous studies have proposed replacing “unk” token with attention distribution as a solution to the OOV(out-of-vocabulary) problem.
- However, this is a method that only applies if print out “unk” token.
- Therefore, we propose a model applying Copy mechanism so that even words that are not in the vocabulary can be output.
Artificial Intelligence Research for Monomer Design
[2021-08-01 ~ 2023-07-31, Samyang]Research Goal: to develop an artificial intelligence technology to design polymer with desired properties and create a structure-property database.
(1) Created a property database by collecting polymer data from public databases and literature.
- Analyzed properties of each database and developed an auto-collecting tool for easy and fast collection. The auto-collecting tool can collect data that is updated later.
(2) Developed an artificial intelligence technology to analyze the relationship between molecule structures and properties.
- Developed a new line notation for organic compounds and created a dataset that is required to train the artificial intelligence model.
(3) Explored the latent space to find a relationship between molecule structures and properties.
- Exploring latent space that represents correlations within properties allows property analysis with low computational costs.
- Attempted to apply VAE (Variational Auto Encoder)
(4) Generative Model-based Structure Prediction
- Developed an artificial intelligence technology that predicts molecule structures with desired properties.
Development of AI-based Project Delay Risk Preview System
[2021-05-20 ~ 2021-10-31, Hyundai NGV, Hyundai Motor Company]Objective
In the construction industry, the construction process is managed based on the completed construction in comparison to the planned construction. With the current construction management system, it is difficult to respond to construction delays in advance, because the construction process rate cannot be estimated quantitatively.
Data
Time Series Construction Data, Weather Forecast Data, Construction Budget Data, Extra-Budgetary Report Data, Subcontractor Data
Related Work
Similarity search and performance prediction of shield tunnels in operation through time series data mining (H.Zhu et al, Automation in Construction, 2020)
Proposed Method
In this paper, we developed an AI-based warning system that proactively predicts the delay in construction based on the completed construction of the past and additional data such as budget, subcontractor, and weather data.
Real-time Object Detection for Lithium Battery
[2021-06-28 ~ 2021-12-09, Korea Electronics Recycling Cooperative]Objective
Lithium battery includes harmful metals (lead, mercury, etc.). Thus, collecting eco-friendly resources and managing hazardous materials are required during the discharge process. We propose a deep learning-based pipeline to select products containing the lithium battery among waste electrical and electronic products on a conveyor belt.
Data
Related Work
Related work: This study aims at the problem that mining conveyor belts are easily damaged under severe working conditions, based on the reclassification and definition of conveyor belt damage types. Conveyor belt damage is detected by the improved Yolov3 algorithm, which considers the impact of model scaling on the detection results [Zhang et al., 2021].
Proposed Method
We trained Electronic and Electronical data based on YOLOv4 with CSPDarknet53 as the backbone and made the pipeline for detecting products containing the lithium battery on a conveyor belt. When waste electrical and electronic products move on a conveyor belt, a line scan camera detects the product, if the product is a lithium battery product. Then, the network signal the reject device to select the product.
Research on Artificial Intelligence Writing Technology Using Natural Language Processing
[2021-07-01 ~ 2021-12-31, Mania Mind (Research collaboration project) ]Objective
Development of Novel Writing Platform based on “user input” through learning of various novel genres.
Data
KoGPT2 fine tuning is performed using novel text data. In the case of Semantic Role Labeling, we use ETRI Semantic Role Labeling Corpus for training SRL model.
Related Work
KoGPT2 is a pretrained language model and optimized for sentence generation so that the next word in a given text can be well predicted. KoGPT2 is a transformer decoder language model that has been learned with more than 40GB of text to overcome insufficient Korean performance.
Proposed Method
It is a structure that combines Generate Layer for novel generation and SRL Layer for reflecting user input. When a sentence is entered, the following sentence is generated through KoGPT2 and the generated sentence is corrected through SRL layer.
AI consultation chatbot
[2021-07-01 ~ 2021-12-31, Korea University Anam Hospital (Research collaboration project)]Objective
Development of a consultation AI chatbot for providing telemedicine counseling solutions
Data
EMR dataset was used for training the chatbot model.
Related Work
GPT-2 is a pretrained language model and optimized for sentence generation so that the next word in a given text can be well predicted. GPT-2 is a transformer decoder language model that has been learned with more than 40GB of text to overcome insufficient Korean performance.
Proposed Method
A Sequence consists of a list of questions and answers and a diagnostic name. It was used for fine-tuning GPT and predicting a diagnostic name. Therefore, it is possible to generate appropriate questions about the patient’s answers and finally predict the diagnostic name.
