Jimei University

About: Jimei University is a education organization based out in Amoy, China. It is known for research contribution in the topics: Computer science & Population. The organization has 4645 authors who have published 4293 publications receiving 42239 citations. The organization is also known as: Jíměi Dàxué.

Tea stalks – a novel agro-residue for the production of tannase under solid state fermentation by Aspergillus niger JMU-TS528

Abstract: A novel agro-residue, tea stalks, was tested for the production of tannase under solid-state fermentation (SSF) using Aspergillus niger JMU-TS528. Maximum yield of tannase was obtained when SSF was carried out at 28 °C, pH 6.0, liquid-to-solid ratio (v/w) 1.8, inoculum size 2 ml (1 × 108 spores/ml), 5 % (w/v) ammonium chloride as nitrogen source and 5 % (w/v) lactose as additional carbon source. Under optimum conditions, tannase production reached 62 U/g dry substrate after 96 h of fermentation. Results from the study are promising for the economic utilization and value addition of tea stalks.

Thin thermally grown oxide thickness detection in thermal barrier coatings based on SWT-BP neural network algorithm and terahertz technology.

Abstract: Terahertz time-domain spectroscopy is a contactless and nondestructive testing technique that is often used to measure the thickness of layered materials. However, the technique presents limited thickness detection resolution, especially in the thin thermally grown oxide (TGO) of thermal barrier coatings whose thickness is below 30 µm. In this study, an SWT-BP algorithm combining a stationary wavelet transform (SWT) and a backpropagation (BP) neural network was proposed, and the regression coefficient of SWT-detailed results was 0.92. The prediction results were in good agreement with the real-time results; it demonstrated that the proposed algorithm was able to achieve a thickness prediction of up to 1–29 µm of the TGO. The proposed algorithm is suitable for thin thickness detection of the TGO.

Security Analysis of Public-key Encryption Scheme Based on Neural Networks and Its Implementing

Abstract: A Diffie-Hellman public-key cryptography based on chaotic attractors of neural networks is described in the paper. There is a one-way function between chaotic attractors and initial states in an Overstoraged Hopfield Neural Network (OHNN). If the synaptic matrix of OHNN is changed, each attractor and its corresponding domain of initial state attraction will be changed. Then, we regard the neural synaptic matrix as a trap door, and change it with commutative random permutation matrix. A new Diffie-Hellman public-key cryptosystem can be implemented, namely keeping the random permutation operation of the neural synaptic matrix as the secret key, and the neural synaptic matrix after permutation as public-key. In order to explain the practicability of the encryption scheme, Security and encryption efficient of the scheme are discussed. The scheme of application for Internet secure communications is implemented by using Java program. The experimental results show that the proposed cryptography is feasible, and has a good performance of encryption and decryption speed to ensure the real time of IPng secure communications.