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An AI researcher with extensive experience in AI image processing and data analysis. I have successfully executed projects across various domains, including computer vision, natural language processing, and time-series data analysis.
AI Researcher (April 2023 - Present)
- As a member of the AI Image Team, I was responsible for developing various computer vision models for classification, object detection, and OCR.
Period: April 2023 - July 2023
Project Details
- Objective: To accurately extract information from in-store product price tags by capturing photos.
- Key Achievements:
- Collected image data of price tags within stores.
- Detected the location of price tags in images using Azure Computer Vision (Object Detection).
- Extracted text from within the price tags using Azure OCR.
Period: July 2023 - November 2023
Project Details
- Objective: To detect and classify products on snack shelves in stores.
- Key Achievements:
- Collected image data of snack shelves in stores.
- Detected snack objects on the shelves using the DINO model (detrex library).
- Classified the detected objects to identify product names using the ViT model (timm library).
Period: November 2023 - August 2024
Project Details
- Objective: To verify the consistency between photos of commercial vehicles and their entered information using AI.
- Key Achievements:
- Collected 4 vehicle images (front, back, left, right) and 3 pieces of vehicle information (type, license plate, VIN) via an app.
- Performed vehicle classification by comprehensively analyzing the four images using the Swin-Transformer V2 model.
- Applied a semi-supervised learning-based anomaly detection model.
- Recognized text from license plates and Vehicle Identification Numbers (VIN) using PaddleOCR.
Period: November 2023 - December 2025
Project Details
- Objective: To develop a solution for predicting and reducing power consumption to improve energy efficiency in automotive parts forging lines.
- Key Achievements:
- Collected and analyzed time-series data (power, temperature, etc.) from the forging lines.
- Predicted future power consumption using Time-Series Forecasting models.
- Identified energy-inefficient sections and proposed reduction measures based on the prediction model.
AI Researcher (July 2020 - November 2021)
- As a founding member of the startup, I collaborated with the CEO on project planning and coordination of external projects.
- Led and executed core development tasks.
Project Details
- Objective: To automatically recognize the type and license plate of vehicles entering electric vehicle charging stations.
- Key Achievements:
- Collected image data using a Raspberry Pi 4.
- Generated synthetic electric vehicle license plate data using Image Augmentation techniques.
- Developed a vehicle license plate detection model based on YOLO v4 and v5.
- Developed an OCR model based on Bidirectional LSTM.
- Link: https://github.com/forallx94/Electronic-Car-Generate
Project Details
- Objective: To detect the condition of grass and the presence of diseases by analyzing drone-captured images of a golf course.
- Key Achievements:
- Extracted image frames from drone-recorded videos.
- Detected grass diseases using the EfficientNet B5 model.
- Managed and stored the analysis results in MongoDB.
Project Details
- Objective: To provide information on suitable clinical trials for new patients by leveraging cancer clinical trial data.
- Key Achievements:
- Preprocessed and structured clinical trial data.
- Performed text embedding using TF-IDF, Word2Vec, and BERT.
- Conducted network analysis and clustering using HDBSCAN and Hierarchical Clustering.
- Developed a core keyword extraction algorithm.
Project Details
- Objective: To develop an unsupervised learning model that predicts anomalies in advance using IoT data from factory equipment.
- Key Achievements:
- Retrieved data from factory compressors and rectifiers from MySQL.
- Performed time-series data preprocessing and feature engineering.
- Developed an unsupervised anomaly detection model using Machine Learning, Prophet, and Autoencoder.
- Link: https://github.com/forallx94/Sequential_Anomaly_detecion
Project Details
- Objective: To predict energy consumption in homes and buildings, including temperature, humidity, electricity usage, and hot water usage.
- Key Achievements:
- Preprocessed energy consumption data for houses and buildings.
- Developed an energy consumption forecasting model using Prophet and Residual LSTM.
- Uploaded the prediction results to MongoDB.
AI Researcher (March 2020 - June 2020)
- Responsible for preprocessing brain MRI data to improve the performance of a brain tumor detection model (U-Net).
Project Details
- Key Achievements:
- Applied and comparatively analyzed six preprocessing techniques, including Z-score normalization and White Stripe, on brain MRI data.
- Insight:
- Despite various preprocessing methods, performance improvement was minimal. A comparative analysis with the original data was conducted.
- The analysis revealed that the delivered data was not the original and had already undergone some preprocessing.
- This experience highlighted the importance of data originality and the preprocessing pipeline, contributing to the improvement of the company's data handling processes.