# Multi-Layer Perceptron

This study combines phase-amplitude reduction with nonlinear machine-learning sparse-sensor reconstruction to estimate phase and amplitude sensitivity fields from only three airfoil-surface sensors and analytically derive optimal actuation waveforms for fast synchronization of post-stall airfoil wake shedding; the identified optimal forcing alters wake frequency eight times faster than standard sinusoidal actuation, and the amplitude-penalized waveform suppresses lift-coefficient fluctuation relative to the optimal forcing without amplitude penalty.

This study combines phase-amplitude reduction with nonlinear machine-learning sparse-sensor reconstruction to estimate phase and amplitude sensitivity fields from only three airfoil-surface sensors and analytically derive optimal actuation waveforms for fast synchronization of post-stall airfoil wake shedding; the identified optimal forcing alters wake frequency eight times faster than standard sinusoidal actuation, and the amplitude-penalized waveform suppresses lift-coefficient fluctuation relative to the optimal forcing without amplitude penalty.

The study collected user reviews of the EV Charging/SPKLU feature in PLN Mobile from Google Play Store, labeled sentiment using ratings as weak labels, applied algorithm-specific class weighting on the training data to address class imbalance, and compared seven classifiers—Logistic Regression, SVM, Naive Bayes, MLP, RNN, BiLSTM, and DistilBERT—finding that MLP achieved the highest validation macro F1 (67.80%) and was selected for final testing, where it reached 89.16% accuracy and 59.42% macro F1 on 286 test instances, indicating high overall classification performance with uneven performance across sentiment classes.

The study collected user reviews of the EV Charging/SPKLU feature in PLN Mobile from Google Play Store, labeled sentiment using ratings as weak labels, applied algorithm-specific class weighting on the training data to address class imbalance, and compared seven classifiers—Logistic Regression, SVM, Naive Bayes, MLP, RNN, BiLSTM, and DistilBERT—finding that MLP achieved the highest validation macro F1 (67.80%) and was selected for final testing, where it reached 89.16% accuracy and 59.42% macro F1 on 286 test instances, indicating high overall classification performance with uneven performance across sentiment classes.

The study proposes a "gravity-awareness" framework in which two models trained on parabolic-flight literature — CorticalG, a Fourier-feature-augmented multilayer perceptron predicting g-dependent EEG band-power change, and PhysioG, eleven independent Gaussian processes predicting heart-rate variability, electrodermal activity and motor measures — are complemented by Claude 3.5 Sonnet generating first-person narratives of awareness at 0 g, lunar 0.16 g, Martian 0.38 g and hypergravity; results show alpha (DMN) and mu (SMN) suppression in microgravity, elevated beta (PFC) and gamma in hypergravity, a V-shaped electrodermal response (right-hand conductance rising over 200% at 1.8 g) and an inverted-U trunk-activity pattern (dropping over 50% at 0 g).

The study proposes a "gravity-awareness" framework in which two models trained on parabolic-flight literature — CorticalG, a Fourier-feature-augmented multilayer perceptron predicting g-dependent EEG band-power change, and PhysioG, eleven independent Gaussian processes predicting heart-rate variability, electrodermal activity and motor measures — are complemented by Claude 3.5 Sonnet generating first-person narratives of awareness at 0 g, lunar 0.16 g, Martian 0.38 g and hypergravity; results show alpha (DMN) and mu (SMN) suppression in microgravity, elevated beta (PFC) and gamma in hypergravity, a V-shaped electrodermal response (right-hand conductance rising over 200% at 1.8 g) and an inverted-U trunk-activity pattern (dropping over 50% at 0 g).