Search

Radix Puerariae (RP) is an important medicinal and edible food in China. The country has an abundant RP output and a large number of RP consumers. Isoflavones are the most abundant and bioactive ingredients in RP. This article comprehensively reviews the composition and structure of isoflavones from RP, and systematically expounds their functions such as anti-hypertension, anti-hyperlipidemia, anti-hyperglycemia and antioxidant activity, alleviating a hangover, liver protection, alleviating post-menopausal osteoporosis and improving cognition as well as the underlying mechanism of action. The current research hotspots and problems existing in the field of research on RP isoflavones and their significance to the development of drugs and functional foods containing RP isoflavones are also proposed. We believe that this review will provide useful ideas and guidance for the basic research on RP and the commercialization of research achievements in this field.

Within the cloud environment, the availability of storage, as well as bandwidth, can be effectively preserved in virtue of data de-duplication. However, refraining redundancy from additional storage or communication is not trivial due to security concerns. Though intensive researches have been addressed on a convergent cryptosystem for secure data de-duplication, the conflicts amongst functionality, confidentiality, and authority remain unbalanced. More concretely, although data are obfuscated under convergent encryption, a violent dictionary attack is still efficacious since the whole pseudorandom process relies heavily on plaintexts. As for data ownership, the download privilege, which depends on hash value, may also be infringed due to the same reason. To dispose of these problems, we presented a conspiracy-free data de-duplication protocol based on a threshold blind signature in this article. With the help of multiple key servers, the outsourced file and de-duplication label will be computationally indistinguishable from random strings. We used the Boom filter as a tool to implement a proof of ownership, ensuring that the ownership claims made by users are real. It effectively prevents the attacker from using the stolen tag to get the whole file to gain file access without authorization. The most significant innovation of this article is to use homomorphism computation to aggregate and generate partial signature tags, and to introduce a secret sharing mechanism based on The Chinese Remainder Theorem to hide signature keys, thus balancing the security concerns of cloud and client. Compared with existing schemes, both communication and computation performances are preferable in our protocol. As far as we know, our scheme is the only data de-duplication scheme that satisfies the semantic security of ciphertext and label.

In the real environment of industrial equipment, the vibration signals of essential components show deviations due to the fault and noise. Notably, the noise in the signal will interfere with the diagnosis process of the signal and reduce the accuracy of fault diagnosis. Based on the above problem, adaptive filtering (AF) is used as an excellent method to attenuate noise without specifying the noise type. However, how to define the most appropriate length and type of morphological filter element is the most inherent problem which needs to be solved first. This paper proposed a cooperative diagnosis method of rolling bearings vibration signal based on improved adaptive filtering and joint distribution adaptation (JDA). First, the kurtosis under different element types and lengths is calculated as an index. The structural element corresponding to maximum kurtosis is selected as the most suitable morphological filter element because the different morphological filter elements reflect the effect of feature extraction. Then, JDA aims to improve both the marginal distribution and the conditional distribution to solve the chaotic distribution of time-domain features under variable working conditions. Finally, the improved least squares support vector machine (LSSVM) verified the effectiveness and improvement of the proposed method under bearing acceleration signal. At the same time, the comparative experiment proved that the proposed method not only directly corrects the most appropriate elements greatly optimizes the feature structure, but also enhances the accuracy of fault diagnosis.

The zero-trust framework is a potential solution to address complex dynamic behaviors, information interactions, complex network topologies, and environmental security threats to groups, such as the safety of the intelligent connected vehicle (ICV) platoon. In addition, it addresses the challenges faced due to increased traffic of intelligent vehicles, communication in critical infrastructure, and malicious attacks between vehicles. Motivated by these observations, we have investigated the zero-trust theory and framework based on the authorization and confirm processes among connected nodes in a group. Furthermore, we have analyzed and summarized the information perception, full information chain communication, and fault-tolerant control mechanism of ICV systems under a zero-trust framework, in addition to the methods of trust value calculation and heterogeneous vehicle platoons. The results indicated that zero-trust theory has a strong potential in group reliability research. [ABSTRACT FROM AUTHOR]

To solve the problem that fault diagnosis accuracy of complex equipment bearings is not high due to the complexity of its structure and the environment, a cooperative algorithm for fault diagnosis of complex equipment bearings based on ensemble empirical mode decomposition (EEMD) and support vector machine (SVM) is proposed. First, the vibration signal of the bearings is decomposed by EEMD. Second, the correlation coefficient and kurtosis value are selected as the evaluation indexes for the intrinsic mode function (IMF) components after decomposition, and the weights of the parameters are set dynamically by the mean-guided weight method. Then, the IMF components are filtered by an improved genetic algorithm to obtain the optimal IMF component combination, which can effectively eliminate redundant components and retain as much fault information as possible. Next, using the orthogonality of IMF components, the energy distribution of the selected IMF components is calculated as the Eigenvector. Finally, using the advantage of accurate classification of SVM in small samples, the fault status of complex equipment bearings can be identified. The effectiveness of the algorithm model is proven by example simulation data, and the model has certain scalability and applicability in engineering.

Since the characteristics of the incipient faults for the rolling bearing under the sectional jumping speed are difficult to be extracted, the conventional fault diagnostic approaches usually with poor monitoring performance and low diagnostic accuracy (i.e., the ratio of samples correctly classified to total samples). This paper proposes a novel cooperative diagnostic approach for the incipient faults of the rolling bearing based on the optimized local mean decomposition (LMD) and support vector machine (SVM). First, to resolve the problem of selecting the appropriate number of product function (PF) components, an optimally weighted fusion model of PF components is established by introducing a genetic algorithm, aiming to maximize the correlation with the original incipient fault signal. Besides, in this model, a novel rule is set to calculate the weight coefficients of fusion. Second, considering the sparsity of the incipient fault characteristics caused by the sectional jumping speed, from the perspective of energy distribution, a novel characteristic extraction model is constructed based on the equal interval energy projection. This characteristic extraction model can correctly extract the fault characteristics and effectively eliminate redundant information. Moreover, the SVM has collaborated with the above-mentioned characteristic extraction model to diagnosis incipient faults. Finally, the effectiveness and correctness of the proposed approach are verified by the experimental simulation results. The comparison and analysis show that the proposed algorithm cannot only correctly extract fault characteristics but also have a high accuracy of fault characteristics recognition with good operability and scalability.

The stacker is an important part of automatic storage. The safety and health of the running track are the primary premise to ensure the stable operation of the stacker. In this paper, a new combinational method to locate abnormal interval for running track of stacker is proposed. First, the interval smoothing method and data interpolation method are used for preprocessing of the original current data sampled from stackers; second, the box plot method for outlier detection is applied to the smoothed data; at the same time, in order to make up for the defect of the box plot method that can only detect the extreme points, failing to detect abrupt change points, the box plot method is combined with the wavelet transformation method to construct a combinational anomaly interval location method; finally, the validity of the algorithm proposed in this paper is verified by the running current data of the stacker in the intelligent operation and maintenance warehouse of the national power grid.

A fault observer of the MIMO (multiple input multiple output) feedback fault system with measurement error and system error is designed and analysed based on the linear time-invariant system. First, the disturbance system with system error and measurement error in the case of failure is given. Second, the expression form of system error is obtained, and the system error is divided into the random error part and the error part generated by the system error at the last moment. Then, a fault diagnosis observer is given for a fault system with both system error and measurement error. Finally, simulation results are presented to show the effectiveness of the proposed method.

Aiming at the difficulty of extracting information for incipient fault symptoms from rolling bearings with strong background noise, an improved incipient fault detection method based on modified recursive least squares (RLS) adaptive equalization, and a local mean decomposition (LMD) algorithm is proposed. First, an efficient RLS de-noising model is established by introducing a momentum factor together with a forgotten factor to de-noise the incipient fault signal of the bearings. Then, the LMD algorithm is used to decompose the pre-processed signal to obtain an effective PF component, and complete the envelope demodulation to extract information from the incipient fault. Based on the above algorithm, an improved RLS and LMD identifying algorithm for incipient faults can thus be achieved. Finally, some actual fault signals of a large unit rolling bearing are used to simulate and verify the accuracy and efficiency of the proposed algorithm. The experimental comparison indicated that our algorithm can not only improve the de-noising effect, but also correctly extract the features of the incipient fault and identify them with good engineering operability and expansibility.