Changshu Institute of Technology

About: Changshu Institute of Technology is a education organization based out in Changshu City, China. It is known for research contribution in the topics: Lithium & Microstructure. The organization has 3324 authors who have published 3027 publications receiving 28578 citations. The organization is also known as: Suzhou Teachers’ College & Chángshúlǐgōng Dàxué.

Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network.

Abstract: Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the heart’s activity. However, automated medical-aided diagnosis with computers usually requires a large volume of labeled clinical data without patients' privacy to train the model, which is an empirical problem that still needs to be solved. To address this problem, we propose a generative adversarial network (GAN), which is composed of a bidirectional long short-term memory(LSTM) and convolutional neural network(CNN), referred as BiLSTM-CNN,to generate synthetic ECG data that agree with existing clinical data so that the features of patients with heart disease can be retained. The model includes a generator and a discriminator, where the generator employs the two layers of the BiLSTM networks and the discriminator is based on convolutional neural networks. The 48 ECG records from individuals of the MIT-BIH database were used to train the model. We compared the performance of our model with two other generative models, the recurrent neural network autoencoder(RNN-AE) and the recurrent neural network variational autoencoder (RNN-VAE). The results showed that the loss function of our model converged to zero the fastest. We also evaluated the loss of the discriminator of GANs with different combinations of generator and discriminator. The results indicated that BiLSTM-CNN GAN could generate ECG data with high morphological similarity to real ECG recordings.

Preparation of Fe3O4 with high specific surface area and improved capacitance as a supercapacitor

Abstract: Here, we report for the first time a facile ultrasonic synthesis of Fe3O4 nanoparticles using FeCl3 and the organic solvent ethanolamine (ETA). The intermediate of the ETA–Fe(II) complex produces Fe3O4 after hydrolysis and hydrothermal treatment. The moderate reduction of ETA and ultrasound play an important role in the synthesis of superfine Fe3O4 particles with a very high specific surface area (165.05 m2 g−1). The Fe3O4 nanoparticles were characterized by X-ray diffraction (XRD), scanning and transmission electron microscopy (SEM and TEM), high-resolution transmission electron microscopy (HRTEM), and ultraviolet-visible absorption spectroscopy (UV-vis). Fe3O4 as an electrode material was fabricated into a supercapacitor and characterized by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and galvanostatic charge–discharge measurements. The as-synthesized Fe3O4 exhibits remarkable pseudocapacitive activities including high specific capacitance (207.7 F g−1 at 0.4 A g−1), good rate capability (90.4 F g−1 at 10 A g−1), and excellent cycling stability (retention 100% after 2000 cycles). This novel synthetic route towards Fe3O4 is a convenient and potential way of producing a secondary energy material which is expected to be applicable in the synthesis of other metal oxide nanoparticles.