エンドツーエンドの AI ワークフロー

要件の定義、データの準備、深層学習ネットワークの学習、ロバスト性の検証、Simulink へのネットワークの統合、モデルの展開を行う。 (R2024b 以降)

• ステップ 1: Define Requirements for Battery State of Charge Estimation
• ステップ 2: Prepare Data for Battery State of Charge Estimation Using Deep Learning
• ステップ 3: Train Deep Learning Network for Battery State of Charge Estimation
• ステップ 4: Compress Deep Learning Network for Battery State of Charge Estimation
• ステップ 5: Test Deep Learning Network for Battery State of Charge Estimation
• ステップ 6: Integrate AI Model into Simulink for Battery State of Charge Estimation
• ステップ 7: Generate Code for Battery State of Charge Estimation Using Deep Learning

Train and compress a sequence classification network using pruning, projection, and quantization to meet a fixed memory requirement. (R2025a 以降)

• ステップ 1: Train Sequence Classification Network for Road Damage Detection
• ステップ 2: Compress Sequence Classification Network for Road Damage Detection
• ステップ 3: Tune Compression Parameters for Sequence Classification Network for Road Damage Detection
• ステップ 4: Generate Simulink Model from Sequence Classification Network for Road Damage Detection

This example shows how to verify a deep learning system for airborne applications and is based on the work in [5,6,7], which includes the development and verification activities required by DO-178C [1], ARP4754A [2], and prospective EASA and FAA guidelines [3,4]. To verify that the system complies with aviation industry standards and prospective guidelines, including activities such as requirements tracing, testing and reporting, see Runway Sign Classifier: Certify an Airborne Deep Learning System (DO Qualification Kit). (R2023b 以降)