A Degradation Condition Assessment Technique Based on online_ISSE Degradation Feature and Logistic Regression Model

A degradation assessment technique based on an online improved symbol sequence entropy online_ISSE and a logistic regression model is proposed in this paper. Firstly, the threshold factor is introduced to retain the `coarse graining' information of direction changing and amplitude information, the `sensitivity' of improved symbol sequence entropy (SSE) to impact components is reduced and improved symbol sequence entropy (ISSE) is proposed. Then, a sliding window and Weibull distribution theory are used to effectively filter out the influence of fluctuations in the ISSE feature sequence, forming the degradation feature named online_ISSE. Finally, a logistic regression model is trained and constructed, and the health factor CV is calculated online to assess the degradation condition of the unknown signal samples. The lifetime vibration signal of the hoisting gearbox monitored from #8114 quay crane of the Shanghai Port Container Terminal is introduced for instance analysis. The results show that the proposed ISSE has a better effect in describing the complexity pattern than the SSE algorithm and that the degradation condition can be tracked and assessed accurately based on the technique proposed.

Entropy Based Biological Sequence Study

SARS-CoV-2 virus strains are taken into consideration for the analysis of digitized sequences of information by means of the notions of entropy. The occurrence of a particular pattern in the corona viral sequence is paid a special attention. The incidence of genetic word is represented in a density means. The incidence frequency of the q-gram genetic word is determined with the help of finite impulse response (FIR) filter along the sequence. It is in turn, used for the determination of the probability distribution of the genetic word incidence as the input for the calculation of entropy in the sequence. The sequence entropy is further used for principal component analysis (PCA) to determine the similarity/dissimilarity between the viral sequences. We have considered seven human corona virus sequences. Entropy based similarity study for SARS-CoV-2 strains is presented in this work.

Word Entropy-Based Approach to Detect Highly Variable Genetic Markers for Bacterial Genotyping

Genotyping methods are used to distinguish bacterial strains from one species. Thus, distinguishing bacterial strains on a global scale, between countries or local districts in one country is possible. However, the highly selected bacterial populations (e.g., local populations in hospital) are typically closely related and low diversified. Therefore, currently used typing methods are not able to distinguish individual strains from each other. Here, we present a novel pipeline to detect highly variable genetic segments for genotyping a closely related bacterial population. The method is based on a degree of disorder in analyzed sequences that can be represented by sequence entropy. With the identified variable sequences, it is possible to find out transmission routes and sources of highly virulent and multiresistant strains. The proposed method can be used for any bacterial population, and due to its whole genome range, also non-coding regions are examined.