pskcci/DX-01
Nov 2024 – Dec 2024
2 Months active
Languages Used
MarkdownPython
Technical Skills
ANNCNNCode OrganizationData AnalysisData AugmentationData Science
eunho
PROFILE
Eunho
Worked on the pskcci/DX-01 repository to deliver hands-on machine learning, computer vision, and factory automation features over two months. Developed and organized Python-based homework modules covering neural networks, transfer learning, and time series forecasting using TensorFlow and Keras, while improving documentation and contributor attribution for easier onboarding. Introduced RNN-based stock prediction notebooks and implemented OpenCV-driven image processing tasks. In December, built a real-time factory automation system with OpenVINO, enabling concurrent video analysis and hardware control through threading. Addressed documentation reliability by updating presentation formats and fixing broken links, resulting in a more maintainable and accessible project structure.
Overall Statistics
Feature vs Bugs
86%Features
Repository Contributions
10Total
Bugs
1
Commits
10
Features
6
Lines of code
1,225
Activity Months2
Your Network
1142 peopleShared Repositories
11
Work History
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 focused on delivering automated, real-time inspection capabilities in pskcci/DX-01 and tightening project documentation. Key feature delivered is the Factory Automation System with Real-time Video Analysis, introducing hw1_led.py for LED/conveyor control and hw2_factory.py for motion/color detection using OpenVINO, implemented with a threading-based pipeline for concurrent video processing and actuator control. Documentation reliability was improved by fixing a broken README link and updating the 04_pkg_ceh project to a current presentation format (PPTX), replacing an obsolete PDF.
2 Commits • 1 Features
Dec 1, 2024December 2024 focused on delivering automated, real-time inspection capabilities in pskcci/DX-01 and tightening project documentation. Key feature delivered is the Factory Automation System with Real-time Video Analysis, introducing hw1_led.py for LED/conveyor control and hw2_factory.py for motion/color detection using OpenVINO, implemented with a threading-based pipeline for concurrent video processing and actuator control. Documentation reliability was improved by fixing a broken README link and updating the 04_pkg_ceh project to a current presentation format (PPTX), replacing an obsolete PDF.
December 2024
November 2024
8 Commits • 5 Features
Nov 1, 2024November 2024 (DX-01) focused on onboarding, maintainability, and expanding hands-on ML/vision/time-series content. Delivered documentation scaffolding and corrected contributor metadata to improve attribution and onboarding in new ceh homework directories. Added numerical methods and basic programming scripts to practice optimization, visualization, tuples, patterns, and image manipulation. Expanded ML and computer vision tasks with transfer learning, CNN/ANN models, simple perceptron gates, and OpenCV edge detection. Cleaned up and reorganized homework files to improve navigation and maintainability. Introduced RNN-based stock price prediction notebooks (SimpleRNN, GRU, LSTM) with data fetching, preprocessing, training, and visualization to enable time-series experimentation. No major defects detected; minor metadata corrections and structural cleanup reduced navigation friction. Overall impact: stronger learning experience, clearer attribution, and lower maintenance burden, enabling faster onboarding and experimentation across ML/vision/time-series domains.
November 2024
8 Commits • 5 Features
Nov 1, 2024November 2024 (DX-01) focused on onboarding, maintainability, and expanding hands-on ML/vision/time-series content. Delivered documentation scaffolding and corrected contributor metadata to improve attribution and onboarding in new ceh homework directories. Added numerical methods and basic programming scripts to practice optimization, visualization, tuples, patterns, and image manipulation. Expanded ML and computer vision tasks with transfer learning, CNN/ANN models, simple perceptron gates, and OpenCV edge detection. Cleaned up and reorganized homework files to improve navigation and maintainability. Introduced RNN-based stock price prediction notebooks (SimpleRNN, GRU, LSTM) with data fetching, preprocessing, training, and visualization to enable time-series experimentation. No major defects detected; minor metadata corrections and structural cleanup reduced navigation friction. Overall impact: stronger learning experience, clearer attribution, and lower maintenance burden, enabling faster onboarding and experimentation across ML/vision/time-series domains.
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance88.0%
AI Usage28.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
ANNCNNCode OrganizationComputer VisionData AnalysisData AugmentationData ScienceData VisualizationDeep LearningDocumentationFactory AutomationFile ManagementGRUHardware ControlImage Processing
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