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Recently, cyclostationary blind deconvolution (CYCBD) is often used in gearbox fault feature extraction, and it is more effective in recovering single periodic pulse. However, in the engineering application of CYCBD, the filter length ( $L$ ) and cycle frequency ( $\alpha$ ) need to be verified and set through a large number of experiments, and the efficiency is very low; Moreover, the effect is not good when it is used to extract composite fault features under the background of strong noise. In order to overcome the above limitations, empirical mode decomposition (EEMD) is used to preprocess the composite fault. EEMD can remove the high-frequency noise and weak correlation components in the sampled signal. The strong correlation component is reconstructed to obtain the modal function closer to the fault frequency. The chimpanzee intelligent algorithm is applied to the determination of $L$ and $\alpha $ of CYCBD by optimization to form an adaptive CYCBD. Adaptive CYCBD takes the dispersion entropy of envelope spectrum as the fitness function of chimpanzee optimization algorithm (CHOA), and finds the optimal $L$ and $\alpha $ through iteration. The optimal parameter value is applied to CYCBD, and the reconstructed modal function is deconvoluted to obtain the optimal inverse filter, so as to accurately separate the fault characteristic components. Simulation and experimental results show that this method is effective for gearbox composite fault diagnosis and extraction under strong noise background.

This paper proposes a blockchain-based ceramic anti-counterfeiting technology identification method. The method mainly extracts the size and position of the underglaze bubbles formed when the ceramic is fired in the kiln to form the ceramic fingerprint, and constructs the ceramic fingerprint and the ceramic fingerprint. The only correspondence that cannot be forged. In order to eliminate the malicious operation of ceramic fingerprints by a key third party in the storage and sharing process, the non-tamperable modification of the data on the blockchain is used to combine ceramic fingerprints and blockchain technology, so that each ceramic can form its own unique Proof of identity. Experiments have proved that this method can creatively solve the various shortcomings of the current anti-counterfeiting traceability technology applied to ceramics. Through the new technology and new model of anti-counterfeiting traceability of ceramics, ceramic fingerprints and blockchain, it has subverted the current market The anti-counterfeiting traceability system enables everyone to implement ceramic anti-counterfeiting traceability, which is a fundamental technological innovation in the field of anti-counterfeiting traceability.

In order to address the inconvenience and opaqueness issues existing in the scenario of ordering resources for manufacturing companies, in this paper, we design a supply chain resource management system based on the blockchain technology. A deserving resource smart contract (DRSC) and a punishment smart contract (PSC) are developed to deal with the environment of transactions, and a resource allocation method is proposed to obtain an optimal strategy. Finally, simulation is carried out to verify the feasibility of the designed system and illustrate a final allocation strategy maximizing the overall profits.

This paper investigates data-driven control methods based on function approximation with binomial coefficients and sampling techniques. We propose two approximate models and corresponding controllers formed from sampled data. It shows that the information truncated in the first approximate model is saved in the latter with parameter estimation method. Simulation results validate the effectiveness of proposed approaches.

Traditional regularization algorithms for electromagnetic tomography (EMT) reconstruction merely obtains the approximate estimation value of a single conductivity, and the model information provided is limited. A large number of reasonable model parameter estimates are obtained by statistical methods. In this paper, we propose an EMT image reconstruction method based on Bayesian statistical model. According to the sparsity of defect distribution, the solved conductivity can be divided into a series of block structures. With the aid of sparse Bayesian learning (SBL) framework, statistical information, including the prior probability of the conductivity sparse distribution and the noise information in the measurement data, is taken into account. Hence the full statistical description of the conductivity distribution can be obtained. Both simulation and experimental results show that this method effectively improves the quality and accuracy of the defect image.

Constant current power supply system is often used for power supply of underwater information network because of its better ability to resist short-circuit faults and simple conversion circuit. In the constant current power supply system, the current main technical problems mainly focus on its electric energy conversion technology. This paper designs an isolated half-bridge constant current/constant current (CC/CC) converter, analyzes it through the state-space averaging method, and simulates it with pspice simulation software. The result shows that the circuit is feasible. On this basis, the advantages and disadvantages of an isolated push-pull CC/CC converter are compared.

Currently, network security accidents occur frequently, and traditional passive defense technologies can no longer deal with unknown network security threats. In response to this problem, we constructed a dynamic evolutionary game model based on incomplete information combined with the actual situation of network offense and defense. Based on the assumption of bounded rationality of both parties in the game, we designed the optimal active defense strategy selection algorithm, the replicator dynamics equation is introduced to analyze the evolutionary trend of the strategies of both offense and defense under various attack modes. And by comparing with existing research methods, the superiority and practicability of this method are confirmed, and it has theoretical value and practical guiding significance for the research of new network active defense technology.

Multiple-input and multiple-output (MIMO) radars are vulnerable to deceptive jamming launched by false target generators that send jamming signals with the goal of pretending that the radar echo signals are reflected by faked targets. In this paper, we present a reinforcement learning based energy efficient power control scheme to detect deceptive jamming for frequency diverse array MIMO radars. This scheme enables a radar to choose the transmit power over the antennas without relying on the known deceptive jamming model. Instead, based on the emergency level, the battery level, the echo signal quality, the antenna phase differences, the received jamming power and the previous detection error rate, this scheme improves the detection accuracy and the energy efficiency, and uses a Dyna architecture to train the learning parameters with the simulated jamming detection experiences for faster optimization in the dynamic game against deceptive jamming. Simulation results show that this scheme effectively improves the deceptive jamming detection accuracy and saves the radar energy. Index Terms–MIMO, radar, reinforcement learning, deceptive jamming.

This paper investigates linear multi-agent systems under switching topology. A novel event-triggered consensus formation controller is proposed. Sufficient conditions are presented to ensure the boundedness of formation error. Zeno behavior can also be avoided in the event-triggered mechanism. A numerical simulation based on hexagon formation of six robots is carried out to validate the results.

This paper investigates the problem of dynamic sliding-mode control through piecewise affine systems (PWAs). An uncertain piecewise affine system is utilized to equivalently represent a general nonlinear system on a compact region around the origin. A piecewise dynamic sliding-mode control law is then constructed for robustly stabilizing the uncertain piecewise affine system and, consequently, for asymptotic stabilization of the corresponding non-affine continuous-time nonlinear system. The constructed approach holds a favorable feature: it removes some assumptions on the system matrices, thus the developed scheme is more relaxed compared with the published approaches. The simplicity and advantages of the developed scheme is illustrated by a numerical simulation.

With the development of swarm intelligence in last decades, consensus problem of multi-agent systems has been attracting much attention. To reveal the inherent mechanism of leader-following consensus in multi-agent systems with stochastic dynamics, PI control protocols are designed in this paper. Owing to the stochastic analysis techniques, algebraic graph theory, and constructing appropriate Lyapunov function, it is proved that the leader-following consensus of nonlinear stochastic dynamical multi-agent systems can be reached in mean square. Sufficient condition are deduced to select the PI control protocol parameters. Finally, the theoretical results are demonstrated through a simulation example.

Constant current to constant current (CC/CC) converter technology is one of the key technologies of cabled underwater information network. An isolated full-bridge CC/CC conversion circuit is designed. The model of the circuit is established by state-space average method to calculate its steady-state operating point of DC. At the same time, the simulation is carried out by using saber simulation software to verify the feasibility and rationality of the designed circuit. The results show that the output current of the circuit is independent of the output load, the duty cycle and the phase shift angle of the two sets of switching tube control signals, and has constant current (CC) output characteristic. Its power conversion efficiency is positively correlated with the output load and the phase shift angle between the two sets of switching tube control signals while independent of the duty ratio.

With the development of computer vision research, the architecture of convolutional neural network becomes more and more complex to reach the state-of-the-art performance. Is the complexity of the model necessarily proportional to its accuracy? To answer this, the compression of the network has attracted much attention in the academy and industry. Existing network pruning methods mostly rely on the scoring mechanism of complexity or diversity of kernels to compress the network, and then build the network model after removing the kernels by tuning or training on the input data. These methods are cumbersome and depend on a well-trained pre-trained model. In this paper, we propose an end-to-end block pruning method based on kernel and feature stability by pruning blocks efficiently. To accomplish this, we firstly introduce a mask to scale the output of the blocks, and the L1 regularization term to monitor the mask update. Second, we introduce the Center Loss to guarantee that the feature does not deviate greatly during learning. To converge fast, we introduce fast iterative shrinkage-thresholding algorithm (FISTA) to optimize the mask, by which a more fast and reliable pruning process is achieved. We implement experiments on different datasets, including CIFAR-10 and ImageNet ILSVRC2012. All the experiments have achieved the state-of-the-art accuracy.

Security problem in WLAN has always been a hot topic, but traditional assessment methods are often not suitable for WLAN. By deeply analyzing the typical characteristics and security attributes of WLAN, a hierarchical security risk assessment model is established. First analytic hierarchy process is used to determine weight of risk assessment index in the model, and then improved D-S combination rules of evidence theory is used to fuse risk assessment indexes from bottom to top, and eliminate the conflict between different evaluation values, next final risk value of WLAN is obtained with a comprehensive consideration of various factors including asset value, vulnerability severity, and threat frequency. Finally simulation results verify feasibility and validity of the proposed method, which can provide objective basis of decision and pointed guidance for security management of WLAN.

Modern data center applications need high throughput (40Gbps) and ultra-low latency (

With the development of sensor networks in the past two decades, synchronization of complex dynamical networks has received an increasing attention and has been widely studied both in theoretical research and in practical applications. This paper concerns the synchronization protocol design problem of nonlinear dynamical complex networks with stochastic coupling. Proportional-integral (PI) control protocols are designed to guarantee that complex dynamical networks with stochastic coupling can achieve global synchronization. Sufficient condition for choice of PI control gains are derived. Numerical example is included to demonstrate the efficiency of the theoretical results.

This paper investigates the leader-following consensus of a class of multi-agent systems (MASs) with actuator saturations. Different from the traditional literature, the followers in our paper are governed by nonidentical dynamics. With the help of low gain feedback method and parameterized algebraic Riccati equation, we propose design algorithms of the feedback gains and coupling strength. Sufficient conditions are given to ensure the semi-global stability of the tracking error system. Numerical results are also given to verify the effectiveness of our theoretical method.

We experimentally demonstrate an ultrafast terahertz (THz) photonic wireless communication link in the 350 GHz band with up to 100 Gbit/s data rate, with an emphasis on extending its wireless reach. By combining the techniques of using a cutting-edge narrow-band uni-traveling-carrier photodiode (UTC-PD) as the THz photo-mixing emitter, spectrally efficient modulation formats and advanced digital signal processing (DSP) algorithms, we successfully transmit 25 GBaud/s 16-quadrature amplitude modulation (16-QAM) signals (overall 100 Gbit/s data rate) over a 350 GHz wireless link with an extended distance of 2 m. This achievement pushes the accessibility of ultrafast 100 Gbit/s wireless communication beyond the state-of-the-art, and makes 100 Gbit/s ultrafast THz wireless communication systems promising for future smart high speed wireless access services.

In this work, spoof surface plasmon polaritons in a novel subwavelength periodic bilateral cross-metal diaphragm channel waveguide are theoretically and experimentally investigated. It is found that the propagation frequency range can be broadened due to the absence of the bandgap between the fundamental band and the second guiding band by tuning the geometric parameters. In addition, the dispersion curve of the second band can pass through the light line and then enter into the radiation region, leading to a frequency-based beam steering radiation. We also find that, below the light line, there exists an anomalous linear dispersion such that the guiding mode propagation length can be extended up to around 200λ. Good agreement has been achieved between experimental and numerical results.

Consensus of multi-agent systems under random networks has attracted considerable attention. In previous literatures, it is often assumed that the agents communicate through Markov switching networks. However, in some engineering applications, the available network information is the expectation of the Laplacian matrix and the time-varying networks are not proper to be regarded as Markov switching. To solve this problem, we develop a method to cope with the mean square consensus of multi-agent systems. Based on the properties of expectation of Laplacian matrix, we present the design of relevant parameters and establish some conditions to guarantee the mean square consensus. Numerical simulations are also given to validate our approach.

A broadband metasurface Luneburg lens based on glide-symmetric bed of nails is designed. First, the Luneburg lens was re-shaped using quasi-conformal transformation optics. With this technique, the original circular focal curve of the lens is changed to a straight line. Afterwards, the refractive index distribution of the optically transformed lens is realized by changing the height of the pins in a bed of nails configuration. The complete Luneburg lens is simulated in CST Microwave Studio and the results demonstrate that the lens has 8–16 GHz bandwidth.

The design of networked observers has recently received much consideration. This paper is concerned with the design of distributed observers with arbitrarily large communication time delays. A traditional centralized observer assumes access to the full output the plant. In the real world situations, the output is often measured by distributed senors connected through a communication network. We propose the design of a set of distributed observers with time delays in the communication channels. Each observer is regarded as an agent, which obtains local information of the plant and communicates with other observers through an undirected communication network, with arbitrarily large time delays. Conditions are established under which the states of all observers converge exponentially to the plant state. Numerical results are proposed to illustrate our results.

In recent years opinion dynamics has been widely used in political and economy. In this article we present a opinion dynamics model where agents show discrete actions and continuous opinions in a social network. Our model combines bounded confidence and real society network which differs from previous regular networks. In real life, the structure of social networks are often unknown, so uncovering the interacting structure of the underlying network is the key to get final opinions. Based on the sparse of social networks and observed time series, we use compressive sensing, an efficient way to reconstructing the social network where the opinion updates take place. We find that with a smaller threshold a better success rate of recovering is obtained. Also with the threshold increasing more final opinions survive at last. And the density of network also affects the final opinions.

The underwater image segmentation in high quality is one of the important parts in ocean investigations. Markov random field (MRF) based framework has been considered as one powerful statistical estimation approach for the spatial continuity on the basis of the Bayesian theory. However, in the underwater imaging system, poor visibility in the sea and the variations in the illumination, viewpoints, etc., have limited the application of the standard MRF and cause the sensitivity to the speckle noises in underwater image segmentation. Hereby, in this paper, we try to explore one kind of the underwater image segmentation scheme combining the MRF model and Hard C-means(HCM) clustering technique with the regional labeling and the local characteristics concerned, achieving great performances in both robustness and effectiveness.

Traditionally, pinning control design is based on the same input signal. However, in some real world engineering systems, it is difficult or very expensive to obtain the same input signal for all the pinned nodes due to different observations and configuration. To overcome the above essential difficulty, this paper develops a novel cooperative pinning synchronization method for a class of typical undirected complex dynamical networks. In detail, apart from the traditional pinning control, the proposed cooperative pining control approach utilizes different observations to drive all network nodes with the same reference signal. Built on this method, this paper obtains a sufficient condition for pinning synchronization of undirected connected networks. Moreover, this paper further explores the design algorithms for pinning controllers. Finally, the numerical simulations are given to validate the proposed approach.

With the rapid development of sensor networks in the last decade, the cooperative design for networked observers has received an increasing attention from engineering community. This paper aims at developing a unified framework for cooperative design of networked observers to stabilize LTI plants. Apart from the traditional centralized design of MIMO system, the proposed cooperative design approach only utilizes the local information of each sensor. For undirected networks, this paper obtains a sufficient and necessary condition for the existence of the parameters that lead to the stabilization of the LTI plant. In particular, we give the detailed design procedures for the parameters of networked observers, including feedback gains and the coupling strength. The numerical simulation is also given to validate the proposed theoretical results.

Multi-agent systems (MASs) are universal in nature, including biological, physical, and engineering systems. This paper investigates the robust consensus of a class of linear MASs with transmission noise. In detail, we develop a new framework to design an observer-based consensus protocol utilizing sampled data. That is, some sufficient conditions are derived to guarantee the existence of sampled period for the robust consensus of MASs with connected and undirected structure. Finally, numerical simulations are also given to validate our proposed methods.

In traditional control theory, a single observer has access all the measured outputs of the plant to estimates its asymptotically. In many real world engineering systems, it may be difficult to build a single observer that has access to all the measured outputs. One way around this difficulty is to build a network of cooperative observers, each of which obtains a portion of the measurement outputs, that collectively produce an asymptotic estimate of the plant state. In this paper, we construct a network of such observers for a continuous-time linear system. Assuming that these observers are connected through an undirected connected network, we establish a necessary and sufficient condition on the plant parameters under which the network of observers will achieve asymptotic omniscience. A network of cooperative observers is said to achieve asymptotic omniscience if their states all converge to the plant state asymptotically. Numerical simulation results are presented to validate theoretical results. The design of cooperative observers sheds some light on the solution of some other real-world problems, such as the design of networked location-based services and sensor networks.