TOT, a Fast Multivariate Public Key Cryptosystem with Basic Secure Trapdoor

In this paper, we design a novel one-way trapdoor function, and then propose a new multivariate public key cryptosystem called $\rm TOT$, which can be used for encryption, signature and authentication. Through analysis, we declare that $\rm TOT$ is secure, because it can resist current known algebraic attacks if its parameters are properly chosen. Some practical implementations for $\rm TOT$ are also given, and whose security level is at least $2^{90}$. The comparison shows that $\rm TOT$ is more secure than $\rm HFE$, $\rm HFEv$ and $\rm Quartz$ (when $n \ge 81$ and $D_{HFE} \ge 129$, $\rm HFE$ is still secure), and it can reach almost the same speed of computing the secret map by $\rm C^\ast$ and $\rm Sflash^{v2}$ (even though $\rm C^\ast$ was broken, its high speed has been affirmed)

Protein phosphatase 5 and the tumor suppressor p53 down-regulate each other's activities in mice

Protein phosphatase 5 (PP5), a serine/threonine phosphatase, has a wide range of biological functions and exhibits elevated expression in tumor cells. We previously reported that pp5-deficient mice have altered ataxia-telangiectasia mutated (ATM)-mediated signaling and function. However, this regulation was likely indirect, as ATM is not a known PP5 substrate. In the current study, we found that pp5-deficient mice are hypersensitive to genotoxic stress. This hypersensitivity was associated with the marked up-regulation of the tumor suppressor tumor protein p53 and its downstream targets cyclin-dependent kinase inhibitor 1A (p21), MDM2 proto-oncogene (MDM2), and phosphatase and tensin homolog (PTEN) in pp5-deficient tissues and cells. These observations suggested that PP5 plays a role in regulating p53 stability and function. Experiments conducted with p53 +/- pp5 +/- or p53 +/- pp5 -/- mice revealed that complete loss of PP5 reduces tumorigenesis in the p53 +/- mice. Biochemical analyses further revealed that PP5 directly interacts with and dephosphorylates p53 at multiple serine/threonine residues, resulting in inhibition of p53-mediated transcriptional activity. Interestingly, PP5 expression was significantly up-regulated in p53-deficient cells, and further analysis of pp5 promoter activity revealed that p53 strongly represses PP5 transcription. Our results suggest a reciprocal regulatory interplay between PP5 and p53, providing an important feedback mechanism for the cellular response to genotoxic stress

ROLLING BEARING FAULT DIAGNOSIS BASED ON ADAPTIVE RCGMVMFE AND MANIFOLD LEARNING

Multi-scale fuzzy entropy can well measure the complexity of the vibration signal, but it lacks the effective use of other channel information. To make full use of the vibration information of other channels, the multivariate sample entropy theory that characterizes the multivariate complexity of synchronized multi-channel data is applied to the bearing fault diagnosis. To accurately extract fault features of bearing signals, a bearing multi-fault diagnosis method based on complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN） and refined composite generalized multivariate multiscale fuzzy entropy（RCGmvMFE） is proposed. First, CEEMDAN is used to decompose multi-channel raw signals to obtain IMF without mode mixing. Then the correlation analysis method is used to screen the IMF components, and the IMF sensitive to the fault characteristics is selected as the multi-channel data to constitute the multivariate variable, and the RCGmvMFE is calculated to constitute the fault feature. Then, t-distributed stochastic neighbor embedding（t-SNE） is used to reduce the dimensionality of high-dimensional features. Finally, the whale optimization algorithm（WOA）is used to optimize the kernel extreme learning machine（WOA-KELM） so as to classify the low-dimensional fault features. Experimental results show that this method can effectively diagnose different fault severity of bearings, and provides a supplementary method for fault diagnosis of rolling bearings

Fault Diagnosis of Hydraulic Pumps Using PSO-VMD and Refined Composite Multiscale Fluctuation Dispersion Entropy

Multiscale fluctuation dispersion entropy (MFDE) has been proposed to measure the dynamic features of complex signals recently. Compared with multiscale sample entropy (MSE) and multiscale fuzzy entropy (MFE), MFDE has higher calculation efficiency and better performance to extract fault features. However, when conducting multiscale analysis, as the scale factor increases, MFDE will become unstable. To solve this problem, refined composite multiscale fluctuation dispersion entropy (RCMFDE) is proposed and used to improve the stability of MFDE. And a new fault diagnosis method for hydraulic pumps using particle swarm optimization variational mode decomposition (PSO-VMD) and RCMFDE is proposed in this paper. Firstly, PSO-VMD is adopted to process the original vibration signals of hydraulic pumps, and the appropriate components are selected and reconstructed to get the denoised vibration signals. Then, RCMFDE is adopted to extract fault information. Finally, particle swarm optimization support vector machine (PSO-SVM) is adopted to distinguish different work states of hydraulic pumps. The experiments prove that the proposed method has higher fault recognition accuracy in comparison with MSE, MFE, and MFDE

RESEARCH ON FAULT DIAGNOSIS METHOD OF ROTATING MACHINERY BASED ON REFINED IMPROVED MULTISCALE FAST SAMPLE ENTROPY (MT)

To solve the problems of low computational efficiency and missing amplitude information existing in the current multiscale sample entropy（MSE） method when extracting features of complex series, refined improved multiscale fast sample entropy（RIMFSE） is presented. Firstly, fast sample entropy is employed to substitute traditional sample entropy, and the calculation cost is greatly reduced by improving the matching mechanism of reconstructed vectors. After that, the improved multiscale expansion method is applied to replace the traditional coarse-grained method, thereby avoiding the loss of amplitude information. Based on this, a new rotating machinery fault diagnosis method is proposed in combination with the max-relevance and min-redundancy（mRMR） method and the support vector machine（SVM） classifier. Two fault data sets of gearbox and bearing are used to verify the performance of the presented method; meanwhile, the presented method is compared with existing methods such as MSE, composite MSE（CMSE） and refined composite MSE（RCMSE）. The results show that compared with MSE, CMSE and RCMSE, the proposed method enjoys significant advantages in terms of robustness, calculation efficiency and recognition accuracy, thereby providing a new idea for rotating machinery fault diagnosis based on entropy feature

An Integrated Health Condition Detection Method for Rotating Machinery Using Refined Composite Multivariate Multiscale Amplitude-Aware Permutation Entropy

With a view to realizing the fault diagnosis of rotating machinery effectively, an integrated health condition detection approach for rotating machinery based on refined composite multivariate multiscale amplitude-aware permutation entropy (RCmvMAAPE), max-relevance and min-redundancy (mRmR), and whale optimization algorithm-based kernel extreme learning machine (WOA-KELM) is presented in this paper. The approach contains two crucial parts: health detection and fault recognition. In health detection stage, multivariate amplitude-aware permutation entropy (mvAAPE) is proposed to detect whether there is a fault in rotating machinery. Afterward, if it is detected that there is a fault, RCmvMAAPE is employed to extract the initial fault features that represent the fault states from the multivariate vibration signals. Based on the multivariate expansion and multiscale expansion of amplitude-aware permutation entropy, RCmvMAAPE enjoys the ability to effectively extract state information on multiple scales from multichannel series, thereby overcoming the defect of information loss in traditional methods. Then, mRmR is adopted to screen the sensitive features so as to form sensitive feature vectors, which are input into the WOA-KELM classifier for fault classification. Two typical rotating machinery cases are conducted to prove the effectiveness of the raised approach. The experimental results demonstrate that mvAAPE shows excellent performance in fault detection and can effectively detect the fault of rotating machinery. Meanwhile, the feature extraction method based on RCmvMAAPE and mRmR, as well as the classifier based on WOA-KELM, shows superior performance in feature extraction and fault recognition, respectively. Compared with other fault identification methods, the raised method enjoys better performance and the average fault recognition accuracy of the two typical cases in this paper can all reach above 98%

Calculation and Performance Evaluation of Text Similarity Based on Strong Classification Features

Based on the strong classification feature recognition algorithm, the calculation algorithm of a text semantic similarity is studied with the performance evaluation in this paper. In order to achieve a general algorithm for this function, the semantic function library based on a semantic recognition code as a comparison object is designed. It drives the algorithm modules of two fuzzy neuron deep convolution machine learning, and between these two processes of machine learning, a rigid algorithm based on Fourier transform frequency domain feature is extracted. Finally, a more complex machine learning general algorithm is realized by the use of external data fuzzy algorithm and de-fuzzy algorithm before and after the algorithm module. It is also a technical innovation in this paper. Through the performance evaluation based on the subjective evaluation of volunteers, it is found that the system focuses on the text semantic similarity evaluation of the Chinese language, and achieves a comparison result of 81.78% of the artificial judgment accuracy rate, and only 5.52% of the volunteers believe that the system judgment result is completely different from that of manual judgment