Deep Learning Model for Cosmetic Gel Classification Based on a Short-Time Fourier Transform and Spectrogram.

Cosmetics and topical medications, such as gels, foams, creams, and lotions, are viscoelastic substances that are applied to the skin or mucous membranes. The human perception of these materials is complex and involves multiple sensory modalities. Traditional panel-based sensory evaluations have limitations due to individual differences in sensory receptors and factors such as age, race, and gender. Therefore, this study proposes a deep-learning-based method for systematically analyzing and effectively identifying the physical properties of cosmetic gels. Time-series friction signals generated by rubbing the gels were measured. These signals were preprocessed through short-time Fourier transform (STFT) and continuous wavelet transform (CWT), respectively, and the frequency factors that change over time were distinguished and analyzed. The deep learning model employed a ResNet-based convolution neural network (CNN) structure with optimization achieved through a learning rate scheduler. The optimized STFT-based 2D CNN model outperforms the CWT-based 2D and 1D CNN models. The optimized STFT-based 2D CNN model also demonstrated robustness and reliability through k-fold cross-validation. This study suggests the potential for an innovative approach to replace traditional expert panel evaluations and objectively assess the user experience of cosmetics.

Analysis of Thermal Characteristics of AlGaN/GaN Heterostructure Field-Effect Transistors Using Micro-Raman Spectroscopy.

We investigated the heat dissipation in heterostructure field-effect transistors (HFETs) using microRaman measurement of the temperature in active AIGaN/GaN. By varying the gate structure, the heat dissipation through the gate was clearly revealed. The temperature increased to 120 °C at the flat gate device although the inserted gate increased to only 37 °C. Our results showed that the inserted gate structure reduced the self-heating effect by three times compared to the flat gate structure. Temperature mapping using micro-Raman measurement confirmed that the temperature of the near gate area was lower than that of the near drain area. This indicated that the inserted gate electrode structure effectively prohibited self-heating effects.