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--- emrp:ws2025:amt [2026/02/26 21:20] – [6. References] 37554_students.hsrw
+++ emrp:ws2025:amt [2026/02/26 21:31] (current) – 37554_students.hsrw
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 **Model v2 Architecture**
-The v2 architecture is an updated 1D-CNN designed for three-class classification and TFLite deployment on Raspberry Pi. Figure 21 shows the full architecture.
+The v2 architecture is an updated 1D-CNN designed for three-class classification and TFLite deployment on Raspberry Pi One-dimensional CNNs have been shown to be effective for classifying temporal sensor signals with limited training data [R3]. Figure 21 shows the full architecture.
 {{ :emrp:ws2025:model_v2_pipeline.png?direct&600 |}}
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 **Experiment 1 — Batch Normalization**
-Batch Normalization (BN) layers were added immediately after each of the three Conv1D layers. BN normalises the activations of each mini-batch during training, which stabilises gradient flow and reduces sensitivity to the initial learning rate. The change was three additional lines in the model definition.
+Batch Normalization (BN) layers were added immediately after each of the three Conv1D layers. Batch normalization stabilizes the distribution of layer activations during training and can reduce the need for Dropout regularization [R4]. In our experiments, adding BatchNorm halved convergence time from ~80 to ~37 epochs.
 **Result:** EarlyStopping triggered at epoch ~37 compared to ~80 for the baseline — convergence speed was more than halved. Test accuracy remained unchanged at 95.7%. Validation loss showed slightly more stable behaviour. The significant reduction in training time with no accuracy cost made this an easy decision.