The development of deep learning has led to great success in the bearing fault diagnosis. However, the issue of limited fault samples impedes the extensive application of most fault diagnosis approaches based on deep learning. To address this challenge, a new few-shot fault diagnosis method based on mutual centralized learning (MCL) and simple and parameter-free attention (SimAM) is put forward in this paper. First, MCL is adopted to diagnose the bearing fault with small samples, which employs a bidirectional approach rather than the traditional unidirectional method to better learn mutual affiliations between the fault features, having better few-shot classification ability. Furthermore, a new feature extractor module is constructed through the SimAM to improve the feature extraction capability of the MCL model by providing better feature maps for classification. The effectiveness of the proposed method is tested on CWRU bearing dataset and our own bearing dataset. The experimental results show that the proposed MCL-SimAM model can effectively recognize the bearing fault with few samples. Additionally, the comparison experiments demonstrate that the proposed model is superior to the comparable models [relation network (RN), prototypical network (PN), and matching network (MN), deep subspace networks (DSN), and ridge regression differentiable discriminator (R2D2)], which has a better recognition accuracy in few-shot scenarios.

This paper addresses the application of a deep convolutional fuzzy system (DCFS) for the fault diagnosis of rolling element bearings. The limitations of the conventional deep convolutional neural network (CNN) are the huge computational load of training the tones of parameters and the lack of interpretability for the corresponding parameters. In this paper, a DCFS-based bearing fault diagnosis method under variable working conditions is proposed. The DCFS on a high dimensional input space is a multilayer connection of many low dimensional fuzzy systems, which can overcome the computational and interpretability problems of the traditional CNN. Moreover, to improve the identification efficiency and diagnosis accuracy, the infinite feature selection (Inf-FS) algorithm is employed to select the most informative fault features. The proposed approach is experimentally demonstrated to be able to identify the different fault types and fault severities of rolling bearings under variable running states.

Aiming at the problems of weak keys,low security and many loopholes in the lightweight cryptography and authentica-tion protocols used in most authentication models of the Internet of Things,a multi-factor enhanced authentication model was pro-posed based on zero-knowledge proof.The model was composed of one-way one-time computing type tasks,and combines vari-ous consensus verification technologies to form a multi-factor consensus verification method.And through the distributed consen-sus mechanism of the blockchain architecture,the enhanced processing function of multi-factor one-time difficult tasks had been re-alized.At the same time,flexible access control and privacy protection were realized by dynamically setting the type of evidence in the token.Finally,a simulation experiment was carried out to verify,and a prototype scene was constructed for the offline smart lock scene.Experimental results show that the model implements concise peer-to-peer verification of dynamic difficulty policies,exhibiting millisecond-level high performance and flexibility.Compared with other IoT authentication models,it has better securi-ty and practicality.

This article focuses on how to effectively make full use of the storage resources in vehicular cloud. A trust mechanism called DI-Trust (Trust Model Based on Dynamic Incentive Mechanism) is proposed to schedule vehicular cloud storage resource. The discussion is under the scenario of the parking lot where vehicle nodes are in static state. The model can reasonably arrange the suitable scheduling algorithm according to the attribute characteristics of different kind of service requirements. The trust value of a vehicle is updated according to the model to fully utilize the vehicle idle storage resources. The simulation experiment results show that the model can work effectively. It can objectively evaluate trust values of vehicle nodes, and construct the effective resource schedule of the vehicular cloud storage resource to meet needs from users.

Diesel engine anti-shock performance is important for navy ships. The calculation method is a fast and economic way compared to underwater explosion trial in this field. Researchers of diesel engine anti-shock performance mainly use the spring damping model to simulate the main bearings of a diesel engine. The elastohydrodynamic lubrication method has been continuously used in the main bearings of diesel engines in normal working conditions. This research aims at using the elastohydrodynamic lubrication method in the main bearings of the diesel engine in external shock conditions. The main bearing elastohydrodynamic lubrication and diesel engine multi-body dynamics analysis is based on AVL EXCITE Power Unite software. The external shock is equivalent to the interference on the elastohydrodynamic lubrication calculation. Whether the elastohydrodynamic lubrication algorithm can complete the calculation under interference is the key to the study. By adopting a very small calculation step size, a high number of iterations, and increasing the stiffness of the thrust bearing, the elastohydrodynamic lubrication algorithm can be successfully completed under the external impact environment. The calculation results of the accelerations on engine block feet have a similar trend as the experiment results. Diesel engines with and without shock absorbers in external shock conditions are calculated. This calculation model can also be used for diesel engine dynamics calculations and main bearing lubrication calculations under normal working conditions.

The paper proposed a trust model based on mathematical statistic method to enhance the safety of a VANET. By applying the statistical principles such as significance test, hypothesis test and confidence interval, the model can help the reputation management center to calculate trust values of all vehicle nodes, can help vehicle nodes to judge whether an event message can be trust or not. The simulation experiment results show that the model can effectively identify false event messages, improve the accuracy of data and enhance the information security of a VANET.

Cognitive Computing (CC) is a contemporary field of fundamental intelligence theories and general AI technologies triggered by the transdisciplinary development in intelligence, computer, brain, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part II) on the theoretical foundations of CI/CC and recent breakthroughs in AI engineering reported in the 20th IEEE International ICCI*CC Conference (ICCI*CC'21). The latest advances in CI and CC towards general AI are presented by twenty-two distinguished panelists. Strategic AI engineering applications in CI, CC, and cognitive systems are elaborated for abstract intelligence, cognitive robots, autonomous systems, intelligent vehicles, and safety-and-mission-critical systems.

Wi-Fi signals have broader spectrum and are more susceptible to multipath interference, resulting in the decline of the recognition accuracy based on radio frequency fingerprint (RFF) features. This paper proposes a novel power spectrum based Wi-Fi RF fingerprint extraction method which can eliminate the impact of multipath channels and achieve purer RFF characteristics. The experimental results show that the recognition rate of 27 devices can reach 93.3% when random forest model is used.

Previous studies have shown that the twin-plate breakwater has good performance in wave dissipation, in particular under deep water conditions. The performance of twin-plate breakwater is subjected to several parameters and some of these parameters are coupled to the others. Thus far, there was no simple and effective method to calculate the dissipation coefficient of the twin-plate breakwater. The objective of the present study is to address the mechanism of wave dissipation and apply the developed RN number to evaluate the dissipating performance on the twin-plate breakwater. The experimental results showed that the transmission, reflection and dissipation coefficients were strongly depending on the developed RN number. Based on various data fitting, an optimal empirical equation is obtained to predict the dissipation of the twin-plate breakwater through the developed RN number. The empirical equation has been verified by the Chi-square test and hence can be used for bulk estimation for transmission coefficient under normal and oblique waves.

The Heisenberg–Robertson uncertainty relation bounds the product of the variances in the two possible measurement outcomes in terms of the expectation of the commutator of the observables. Notably, it does not capture the concept of incompatible observables because it can be trivial, i.e., the lower bound can be null even for two noncompatible observables. Here, we give two stronger uncertainty relations, relating to the sum of variances with respect to density matrix, whose lower bounds are guaranteed to be nontrivial whenever the two observables are incompatible on the state of the system; moreover, two stronger uncertainty relations in terms of the product of the variances of two observables are established. Also, several stronger uncertainty relations for three observables are established, relating to the sum and product of variances with respect to density matrix, respectively.

Previous studies indicated that the occurrence of freak wave has a significant impact on the time-domain characteristics of a moored floater dynamic response. In this study, extensive investigations have been performed to figure out further dynamic response characteristics of a moored square cylinder under freak wave in frequency-domain. In the experiments, the wave sequences with and without the freak wave are defined as freak and random waves, respectively. The results show that, the freak wave parameter α1 and spectral peak period have significant impacts on the dynamic response characteristics of the moored floater in frequency-domain. The effect of freak wave on surge and mooring tension of the floater is figured out through the low frequency components (0–0.5fp) which leads to significant increase with α1. Followed the occurrence of freak wave, surge increases sharply and oscillates for longer than 20 wave periods in low frequency. Without the occurrence of freak wave, however, both amplitude and oscillating of surge is significantly weaker. The mooring tension response is subjected to surge and hence the trend of variation is similar to surge.

The purpose here is to make online users more aware of the problem, of fake emails and highlight the problems that it could cause.

Multi-scale fuzzy entropy (MFE) is a recently developed non-linear dynamic parameter for measuring the complexity of vibration signals of rolling element bearing over different scales. However, the calculation of fuzzy entropy (FuzzyEn) in each scale ignores the sequence’s global characteristics while the bearing vibration signals’ global fluctuation may vary as the bearing runs under different states. Therefore, in this paper, the multi-scale global fuzzy entropy (MGFE) method is put forward for extracting the fault features from the bearing vibration signals. After the feature extraction, multiple class feature selection (MCFS) method is introduced to select the most informative features from the high-dimensional feature vector. Then, a new rolling element bearing fault diagnosis approach is proposed based on MGFE, MCFS and support vector machine (SVM). The experimental results indicate that the proposed approach can effectively fulfill the fault diagnosis of rolling element bearing and has good classification performance.

Fuzzy measure entropy (FuzzyMEn) is a recently improved non-linear dynamic parameter for evaluating the signals’ complexity. In comparison with fuzzy entropy (FuzzyEn), which only emphasizes the local characteristics of the signal but neglects its global trend, FuzzyMEn can reflect not only the local but also the global characteristics of the signal. Therefore, by calculating the FuzzyMEn values in different scales, the multi-scale fuzzy measure entropy (MFME) method is put forward in this paper and used for extracting the fault features from vibration signals of rolling bearing. After the feature extraction, the newly developed infinite feature selection (Inf-FS) method is employed to choose the most representative features from the original ones of high dimension. Finally, a new rolling bearing fault diagnosis approach is presented based on MFME, Inf-FS and support vector machine (SVM). The experimental analysis indicates that the presented approach can realize the rolling bearing fault diagnosis effectively.

Heart disease is the leading cause of death all around the world. And heart sound monitoring is a commonly used diagnostic method. This method can obtain vital physiological and pathological evidence about health. Many existing techniques are not suitable for long-term dynamic heart sound monitoring since their large size, high-cost and uncomfortable to wear. This paper proposes a small, low-cost and wearable piezoelectric heart sound sensor, which is suitable for long-term dynamic monitoring and provides technical support for preliminary diagnosis of heart disease. First, the theoretical analysis and finite element method (FEM) simulation have been carried out to determine the optimum structure size of piezoelectric sensor. Subsequently, the sensor is embedded into the fabric-based chest strap to verify the detection performance in wearable scenarios. An existing piezoelectric sensor (TSD108) is used as reference. The designed sensor can acquire complete heart sound signals, and its signal-to-noise ratio is 2 dB higher than that of TSD108.

In quantum mechanics, it is well known that the Heisenberg–Schrödinger uncertainty relations hold for two non-commutative observables and density operator. Recently some people start to focus on the uncertainty relations for two non-commutative non-Hermitian operators and density operator. In this paper, we introduce the generalized metric adjusted skew information, generalized metric adjusted correlation measure and the related quantities for non-Hermitian operators. Various properties of them are discussed. Finally, we establish several generalizations of uncertainty relation expressed in terms of the generalized metric adjusted skew information and obtain several results including previous results which can be given as corollaries of our non-Hermitian extensions of Heisenberg-type or Schrödinger-type uncertainty relations.

In this paper, we propose a scheme allow vehicles in a Vehicular Ad hoc Network share running information among them. We developed a mechanism that can help a vehicle to judge the trustworthiness of a message. We classify information into two types: one is emergency warning message, and the other is event reporting message. By collecting reports from other vehicles who pass through the place where an event occurs on claimed by a message, a vehicle can make a decision whether the message is true or false by using algorithms proposed in the paper. Simulation experiments show the scheme can work well and efficiently. The scheme can resist certain attacks that are hardly identified by other schemes as it avoids using vehicle's ID. It can also protect a driver's privacy in some extent which is a hot issue in recent research.

For an active distribution system (ADS) that integrates high levels of distributed generators (DGs), the control dimension of such a wide and dynamic set of resources would become overwhelming. This study proposes a novel pinning group consensus (PGC)-based distributed coordination control to simplify ADS control by using the virtual clusters building block concept. A given ADS is rethought as coupled small virtual clusters, including virtual microgrid clusters and virtual power plant clusters; accordingly, the ADS can be coordinated by controlling a small number of selected pinning agents instead of a huge number of DGs. The predefining of the PGC values for these pinning agents, which comprehensively considers both clusters features and DG capacities, is the most distinguishing work for the proposed control scheme and can lead to effective global coordination in a distributed manner. Simulation cases under normal/disturbed/emergency conditions verify the effectiveness and advantages of the proposed scheme.

In this paper, we propose a new characterization of non-Markovian quantum evolution based on the covariance matrix. The fundamental properties of covariance matrices are elucidated. The measure captures quite directly the characteristics of non-Markovianity from the perspective of uncertainty. We consider several typical examples and compare the covariance matrix characterization of quantum non-Markovianity with Fisher-information matrix, divisibility and the Breuer-Laine-Piilo characterization of quantum non-Markovianity.

Based on the fact that both nonlocality and contextuality are resource theories, it is natural to ask how to amplify them more efficiently. In this paper, we present a contextuality distillation protocol which produces an n-cycle box B ∗ B ′ from two given n-cycle boxes B and B ′. It works efficiently for a class of contextual n-cycle (n ≥ 4) boxes which we termed as “the generalized correlated contextual n-cycle boxes”. For any two generalized correlated contextual n-cycle boxes B and B ′, B ∗ B ′ is more contextual than both B and B ′. Moreover, they can be distilled toward to the maximally contextual box C H n as the times of iteration goes to infinity. Among the known protocols, our protocol has the strongest approximate ability and is optimal in terms of its distillation rate. What is worth noting is that our protocol can witness a larger set of nonlocal boxes that make communication complexity trivial than the protocol in Brunner and Skrzypczyk (Phys. Rev. Lett. 102, 160403 2009), this might be helpful for exploring the problem that why quantum nonlocality is limited.