Solution processed flexible organic thin film back-gated transistors based on polyimide dielectric films

An organic thin film back-gated transistor (OBGT) was fabricated and characterized. The gate electrode was printed on the back side of substrate, and the dielectric layer was omitted by substituting the dielectric layer with the polyimide (PI) film substrate. Roll-to-roll (R2R) gravure printing, doctor blading, and drop casting methods were used to fabricate the OBGT. The printed OBGT device shows better performance compared with an OTFT device based on dielectric layer of BaTiO3. Additionally, a calendering process enhanced the performance by a factor of 3 to 7 (mobility: 0.016 cm2/V·s, on/off ratio: 9.17×103). A bending test was conducted to confirm the flexibility and durability of the OBGT device. The results show the fabricated device endures 20000-cyclic motions. The realized OBGT device was successfully fabricated and working, which is meaningful for production engineering from the viewpoint of process development

Weakly Labeled Sound Event Detection using Tri-training and Adversarial Learning

This paper considers a semi-supervised learning framework for weakly labeled polyphonic sound event detection problems for the DCASE 2019 challenge's task4 by combining both the tri-training and adversarial learning. The goal of the task4 is to detect onsets and offsets of multiple sound events in a single audio clip. The entire dataset consists of the synthetic data with a strong label (sound event labels with boundaries) and real data with weakly labeled (sound event labels) and unlabeled dataset. Given this dataset, we apply the tri-training where two different classifiers are used to obtain pseudo labels on the weakly labeled and unlabeled dataset, and the final classifier is trained using the strongly labeled dataset and weakly/unlabeled dataset with pseudo labels. Also, we apply the adversarial learning to reduce the domain gap between the real and synthetic dataset. We evaluated our learning framework using the validation set of the task4 dataset, and in the experiments, our learning framework shows a considerable performance improvement over the baseline model.18418