No abstract

No abstract

A model based on PCA (principal component analysis) and a neural network is proposed for the multi-fault diagnosis of sensor systems. Firstly, predicted values of sensors are computed by using historical data measured under fault-free conditions and a PCA model. Secondly, the squared prediction error (SPE) of the sensor system is calculated. A fault can then be detected when the SPE suddenly increases. If more than one sensor in the system is out of order, after combining different sensors and reconstructing the signals of combined sensors, the SPE is calculated to locate the faulty sensors. Finally, the feasibility and effectiveness of the proposed method is demonstrated by simulation and comparison studies, in which two sensors in the system are out of order at the same time.

Patients with larynx cancer often lose their voice following total laryngectomy. Current methods for postlaryngectomy voice restoration are all unsatisfactory due to different reasons: requires frequent replacement due to biofilm growth (tracheo-oesoephageal valve), speech sounds gruff and masculine (oesophageal speech) or robotic (electro-larynx) and, in general, are difficult to master (oesophageal speech and electro-larynx). In this work we investigate an alternative approach for voice restoration in which speech articulator movement is converted into audible speech using a speaker-dependent transformation learned from simultaneous recordings of articulatory and audio signals. To capture articulator movement, small magnets are attached to the speech articulators and the magnetic field generated while the user 'mouths' words is captured by a set of sensors. Parallel data comprising articulatory and acoustic signals recorded before laryngectomy are used to learn the mapping between the articulatory and acoustic domains, which is represented in this work as a mixture of factor analysers. After laryngectomy, the learned transformation is used to restore the patient's voice by transforming the captured articulator movement into an audible speech signal. Results reported for normal speakers show that the proposed system is very promising.

Featured Application: Mining valuable knowledge in the literature helps to save time for readers to understand the content and direction of the literature quickly.Abstract: The subjects of literature are the direct expression of the author's research results. Mining valuable knowledge helps to save time for the readers to understand the content and direction of the literature quickly. Therefore, the co-occurrence network of high-frequency words in the bioinformatics literature and its structural characteristics and evolution were analysed in this paper. First, 242,891 articles from 47 top bioinformatics periodicals were chosen as the object of the study. Second, the co-occurrence relationship among high-frequency words of these articles was analysed by word segmentation and high-frequency word selection. Then, a co-occurrence network of high-frequency words in bioinformatics literature was built. Finally, the conclusions were drawn by analysing its structural characteristics and evolution. The results showed that the co-occurrence network of high-frequency words in the bioinformatics literature was a small-world network with scale-free distribution, rich-club phenomenon and disassortative matching characteristics. At the same time, the high-frequency words used by authors changed little in 2-3 years but varied greatly in four years because of the influence of the state-of-the-art technology.