🧠 Daily Study Log [2025-08-11]
A well-rounded day spanning idea generation, SQL learning, competition modeling, project iteration, and deep dives into research papers and novel RL-based evaluation concepts.

💡 Idea Generation — Mind Scope

Documented the concept for a self-insight + mindfulness tool that combines cognitive state tracking with guided meditation.
The goal is to help users identify recurring worry patterns and address them with real-time mindfulness prompts.

🔗 View idea

📘 SQL Study

Focused on multi-row functions and their applications in data aggregation and group-level analysis.

🏆 Competition — Electricity Forecasting

Currently running an Optuna-tuned ensemble experiment:

• Segmented by Time (Night/Day/Evening) × Building Type
• Models: XGB, LGBM, GBR
• Tuning both hyperparameters and Voting weights
• Engineered features refined from Private_3rd version
  ⚠️ Results pending due to extended training time.

🎧 Personal Project — Bass Seeker

Completed the bass_seeker prototype, but initial results didn’t meet expectations.
Considering adjustments to feature extraction and similarity weighting for better alignment with project goals.

🔗 View project

📖 Paper Review — Few-Shot Grounding DINO for Agriculture

Reviewed and documented the Few-Shot Grounding DINO adaptation for agricultural object detection.
Analyzed how BERT embeddings were modified for class-specific understanding with minimal data.

🔗 GitHub – Paper Review

📚 Paper Study — Arm Wave Quality Evaluation (RL-based)

Outlined a novel RL-driven framework for arm wave performance scoring:

• Goal: Classify wave quality (good/bad) and score (0–100)
• Two-model RL architecture:
  • Reward Model (RM): Human preference–based pairwise loss
  • Value Model: Offline RL for stable absolute scoring
• Combines pose-based physical metrics (amplitude, smoothness, phase consistency) with learned embeddings
• Generates frame-level quality heatmaps and segment feedback
• Metrics and scoring designed to be model-agnostic and reusable

Pipeline:

1. Pose extraction (MediaPipe/BlazePose, 33 keypoints)
2. Normalization & DTW standardization
3. Feature generation (physics-based + learned embeddings)
4. RM → Frame/segment rewards
5. Value Model → Final score (0–100)
6. Feedback generation (“phase lag instability”, “jerk spikes”, etc.)

Planned Next Steps:

• Define & formalize 3–4 wave-specific metrics
• Build labeling UI for preference pairs
• Week 1 MVP: data pipeline, RM prototype, basic scoring & visualization

✅ TL;DR

📍 Mind Scope: Mindfulness + self-insight app concept
📍 SQL: Learned multi-row functions
📍 Competition: Running Optuna-tuned time/building ensemble (results pending)
📍 Bass Seeker: Prototype done, needs refinement
📍 Few-Shot Grounding DINO: Reviewed agricultural adaptation
📍 Arm Wave RL Evaluation: Defined novel 2-model RL quality scoring pipeline