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In this research, a hybrid adaptive bionic fuzzy control strategy is developed for a class of complicated nonlinear multiple-input-multiple-output (MIMO) systems with dead-zone input. The first component of the bionic adaptive controller is a general phrase for tunning system parameters depending on the present state, and the second component is a trend-based compensation for adjusting the system parameters. This technique makes the system more intelligent and boosts its anti-interference capabilities. The stability and convergence are analyzed using the Lyapunov synthetic method, and thus the parameter restrictions of the MIMO system are provided. Finally, the strong anti-interference of the system is verified by the simulations. [ABSTRACT FROM AUTHOR]

Crowdsourcing services provide a fast, efficient, and cost-effective way to obtain large labeled data for supervised learning. Unfortunately, the quality of crowdsourced labels cannot satisfy the standards of practical applications. Ground-truth inference, simply called label integration, designs proper aggregation methods to infer the unknown true label of each instance (sample) from the multiple noisy label set provided by ordinary crowd labelers (workers). However, nearly all existing label integration methods focus solely on the multiple noisy label set per individual instance while totally ignoring the intercorrelation among multiple noisy label sets of different instances. To solve this problem, a multiple noisy label distribution propagation (MNLDP) method is proposed in this article. MNLDP at first estimates the multiple noisy label distribution of each instance from its multiple noisy label set and then propagates its multiple noisy label distribution to its nearest neighbors. Consequently, each instance absorbs a fraction of the multiple noisy label distributions from its nearest neighbors and yet simultaneously maintains a fraction of its own original multiple noisy label distribution. Empirical studies on a collection of an artificial dataset, six simulated UCI datasets, and three real-world crowdsourced datasets show that MNLDP outperforms all other existing state-of-the-art label integration methods in terms of the integration accuracy and classification accuracy. [ABSTRACT FROM AUTHOR]

This article investigates the stability and synchronization of nonautonomous reaction–diffusion neural networks with general time-varying delays. Compared with the existing works concerning reaction–diffusion neural networks, the main innovation of this article is that the network coefficients are time-varying, and the delays are general (which means that fewer constraints are posed on delays; for example, the commonly used conditions of differentiability and boundedness are no longer needed). By Green’s formula and some analytical techniques, some easily checkable criteria on stability and synchronization for the underlying neural networks are established. These obtained results not only improve some existing ones but also contain some novel results that have not yet been reported. The effectiveness and superiorities of the established criteria are verified by three numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, the dual common-mode (CM) resonance technique is proposed for lowering the phase noise (PN) and flicker PN corner of LC voltage-controlled oscillators (VCOs) across wide tuning range. The dual CM resonator can provide high and resistive input impedance across a wide $2^{\mathbf {nd}}$ harmonic band without dedicated tuning. It can both prevent the tank-loading effect and reshape the output waveform. Novel implementation methods are demonstrated to realize dual CM resonance at high frequencies. For comparison, both the dual CM resonance VCO and a reference one with a tail filter are fabricated in 65-nm CMOS, achieving the measured frequency range of 8-11.15 and 7.98-11.3 GHz, respectively. In 1/f2 region, the proposed VCO shows an average PN of −139 dBc/Hz and the FOM of 190-192.3 dBc/Hz across the band, leading to an excellent FOM $_{\mathbf {T}}$ of larger than 200.3 dBc/Hz. The reference VCO shows the average PN and FOM of -137.7 and 190.5 dBc/Hz. In 1/f3 region, the former exhibits a low flicker PN corner of 280–350 kHz while the latter’s flicker PN corner varies largely from 290 to 950 kHz across the band. Both VCOs’ cores consume about 5 mW at 1 V supply and occupy approximately 0.13 mm2. [ABSTRACT FROM AUTHOR]

This article proposes a broadband high-efficiency parallel-circuit (PC) class-EF power amplifier (PA) along with a quarter-wavelength stub for the first time. By adding a reactive element ${X}$ , the design space is expanded so that the impedance terminations of the fundamental and harmonics are no longer fixed points. Based on the broadband capability of the proposed structure, using a GaN HEMT transistor, a proof-of-concept circuit implemented with the Chebyshev impedance transformation network is designed and fabricated. Experimental results demonstrate a high-efficiency broadband PA operating from 2.6-3.9 GHz, with the average efficiency of 71.5%, the measured output power (${P}_{\textit {out}}$) of 38.8-40.9 dBm and the power gain of 8.5-10.6 dB. [ABSTRACT FROM AUTHOR]

This article investigates the optimally distributed consensus control problem for discrete-time multiagent systems with completely unknown dynamics and computational ability differences. The problem can be viewed as solving nonzero-sum games with distributed reinforcement learning (RL), and each agent is a player in these games. First, to guarantee the real-time performance of learning algorithms, a data-based distributed control algorithm is proposed for multiagent systems using offline system interaction data sets. By utilizing the interactive data produced during the run of a real-time system, the proposed algorithm improves system performance based on distributed policy gradient RL. The convergence and stability are guaranteed based on functional analysis and the Lyapunov method. Second, to address asynchronous learning caused by computational ability differences in multiagent systems, the proposed algorithm is extended to an asynchronous version in which executing policy improvement or not of each agent is independent of its neighbors. Furthermore, an actor-critic structure, which contains two neural networks, is developed to implement the proposed algorithm in synchronous and asynchronous cases. Based on the method of weighted residuals, the convergence and optimality of the neural networks are guaranteed by proving the approximation errors converge to zero. Finally, simulations are conducted to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The problem of the stochastic event-based distributed fusion estimation for a class of Gaussian systems is investigated. Considering the deterministic event-triggers destroying the Gaussian property of system states, the stochastic event-triggered mechanisms (SETMs) are used, which also can relieve the network transmission burden. Under the stochastic transmission schedules, a two-step fusion estimation method is developed. The first step, with the consideration of channel fading, the local estimation of each sensor is proposed by using the measurements from itself and its neighbors. The second step, the fusion algorithm is designed to eliminate the disagreements among local estimations of each sensor. Finally, experiment is carried out to demonstrate the advantages of the proposed distributed fusion estimation. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are shown to be excellent solutions to staggering and sophisticated problems in machine learning. A key reason for their success is due to the strong expressive power of function representation. For piecewise linear neural networks (PLNNs), the number of linear regions is a natural measure of their expressive power since it characterizes the number of linear pieces available to model complex patterns. In this article, we theoretically analyze the expressive power of PLNNs by counting and bounding the number of linear regions. We first refine the existing upper and lower bounds on the number of linear regions of PLNNs with rectified linear units (ReLU PLNNs). Next, we extend the analysis to PLNNs with general piecewise linear (PWL) activation functions and derive the exact maximum number of linear regions of single-layer PLNNs. Moreover, the upper and lower bounds on the number of linear regions of multilayer PLNNs are obtained, both of which scale polynomially with the number of neurons at each layer and pieces of PWL activation function but exponentially with the number of layers. This key property enables deep PLNNs with complex activation functions to outperform their shallow counterparts when computing highly complex and structured functions, which, to some extent, explains the performance improvement of deep PLNNs in classification and function fitting. [ABSTRACT FROM AUTHOR]

Orthogonal polyphase sequence sets with desirable properties have numerous applications in sensing fields, e.g., a monostatic radar transmitting such waveforms can effectively counter the coherent repeater jamming (CRJ). However, due to imperfections in practical systems, the delicately designed sequences may suffer significant distortion and reduction in performance when transmitted as radar waveforms. In this paper, we focus on the problem of designing polyphase sequence sets with good correlation properties under spectral distortion. To address this issue, we first introduce a new metric called distortion correlation properties (DCorr), which quantifies the quality of such sequence sets. Subsequently, a majorization-minimization (MM)-based iterative algorithm is developed to solve the problem of minimizing DCorr. A number of numerical examples are provided to demonstrate the effectiveness of the proposed method in improving the correlation properties of polyphase sequence sets under spectral distortion. The results also validate that our proposed algorithms can effectively mitigate the performance reduction caused by the imperfections in practical radar systems simply. [ABSTRACT FROM AUTHOR]

In this paper, a novel watermarking algorithm based on quaternion wavelet transform(QWT) and quaternion singular value decomposition(QSVD) is proposed for copyright protection. Firstly, color images are converted from RGB space to YCbCr space. Then one-level QWT transform is applied to the luminance component Y. The LL component is split to 4 × 4 non-overlapping blocks. Every block is decomposed by QSVD, and the bits of watermark are embedded in singular vectors or singular values. Moreover, 2D Chebyshev-Logistic map is proposed to encrypt the watermarks and scramble the embedding positions. In the watermarking extraction process, both blind and semi-blind watermarking extraction methods are introduced. Experiments show that proposed watermarking algorithm has good robust against common attacks and has good invisibility. [ABSTRACT FROM AUTHOR]

In recent years, many methods have been developed for discovering causal relationships from observed data. However, as an important kind of causes existing in many causal systems, combined causes (e.g. multi-factor causes consisting of two or more component variables that individually might not be a cause) have not received enough attention. The existing approach includes both individual and combined variables in the causal discovery process using constraint-based methods, can neither distinguish a set of Markov equivalence classes nor identify a combined cause containing one (or more) individual cause(s), therefore can output only some combined causes, instead of all combined causes. In this paper, we first subsume all possible combined causes into three types and give them formal definitions, then extend the additive noise model (ANM) to infer combined causes. We show that if a candidate variable set X w.r.t. a target Y satisfies: (1) allowing ANM for only the forward direction X → Y , and (2) no disturbance variable is contained in X , i.e., removing any component of X will weaken the causal relationship between X and Y , then X forms a combined cause. Based on this finding, we develop an efficient method to discover combined causes. Furthermore, we also conduct extensive experiments to validate the proposed method on both synthetic and real-world data sets. [ABSTRACT FROM AUTHOR]

Integrating Deep Neural Networks (DNNs) into the Internet of Thing (IoT) devices could result in the emergence of complex sensing and recognition tasks that support a new era of human interactions with surrounding environments. However, DNNs are power-hungry, performing billions of computations in terms of one inference. Spatial DNN accelerators in principle can support computation-pruning techniques compared to other common architectures such as systolic arrays. Energy-efficient DNN accelerators skip bit-wise or word-wise sparsity in the input feature maps (ifmaps) and filter weights which means ineffectual computations are skipped. However, there is still room for pruning the effectual computations without reducing the accuracy of DNNs. In this paper, we propose a novel real-time architecture and dataflow by decomposing multiplications down to the bit level and pruning identical computations in spatial designs while running benchmark networks. The proposed architecture prunes identical computations by identifying identical bit values available in both ifmaps and filter weights without changing the accuracy of benchmark networks. When compared to the reference design, our proposed design achieves an average per layer speedup of ×1.4 and an energy efficiency of × 1.21 per inference while maintaining the accuracy of benchmark networks. [ABSTRACT FROM AUTHOR]

This article presents a 22–47 GHz wideband low-noise amplifier (LNA) with coupled L-type interstage matching inductors. The coupled inductors extend the bandwidth of LNA by moving zeros to lower frequencies to cancel the effect of poles. Meanwhile, asymmetric L-type inductors with different inductances can be designed separately to achieve better gain flatness. In addition, the quality factor of coupled inductors is also slightly improved without significantly changing their inductance. The LNA is fabricated in a 0.13- $\mu \text{m}$ SiGe BiCMOS technology, and it occupies a core area of 0.13 mm2. The LNA achieves a peak gain of 22.2 dB with the 3dB bandwidth of 22–47 GHz (fractional bandwidth up to 72.5%). The measured NF varies between 3.0 and 4.3 dB from 22 to 47 GHz. The measured input 1-dB gain compression point is stable from −23.9 to −22.6 dBm in the entire 3-dB gain bandwidth. The chip consumes a total power of 9.5 mW from a 1.2 V supply. [ABSTRACT FROM AUTHOR]

The deep learning methods powerfully enhance the identification performance by retrieving the deep data features in many fields, which can be used in automatic modulation classification (AMC) work for the better results in the acoustic underwater communication. A novel hybrid time series network structure is scheduled for AMC in this paper. It can accommodate the variable-length signal datas to match the fixed-length input request in the common neural network, and there is the ability to suitably deal with the zero data in the signal sequence to alleviate the effect losses. The proposed network has the mix of two time series network styles to enrich the extracted signal modulation classification features, and dramatically improves the recognition capability and owns the low computation complexity. In the meanwhile, the internal network structure is optimized by the well-designed cascading order, which acquires more hidden signal data representations to increase the accuracy. The simulation experiments shows that the proposed network is more effective and robust than the conventional deep learning methods to identify ten modulation modes in the serious interference communication environment. [ABSTRACT FROM AUTHOR]

• A deep neural network (DNN) is presented to model a 1000 MW USC unit. • Stacked denoising auto-encoder (SDAE) is used to obtain the intrinsic features of the USC unit from the input big data. • The LSTM network is incorporated to learn the long-term dependencies caused by the time delay existed in the USC unit. • A reasonable intensity of added noise is identified in the sense of Lyapunov stability method to guarantee the convergence of the network. The ultra-supercritical (USC) coal fired boiler-turbine unit is an advanced power generation system with low emissions and high efficiency. It is also a typical multivariable nonlinear system with great inertia. Generally, building an accurate analytic model using the conventional system identification methods are quite difficult. However, the big data generated by the monitoring system can reflect the USC unit's operation status and reveal the internal mechanism, if appropriate data analysis methods are developed. A deep neural network (DNN) is proposed in this paper to model a 1000 MW USC unit. In this DNN, stacked denoising auto-encoder is adopted to obtain the intrinsic features from the input data, while the long short-term memory network is in charge of outputting the expected normal behaviors of USC system along the time axis. Furthermore, to guarantee the convergence of this network, a reasonable intensity of added noise is identified via Lyapunov stability method. The DNN model is compared with the traditional multi-layer perception network, the stacked denoising auto-encoder, and two other random neural networks, to show the advantages in forecasting the dynamic behavior of USC unit. [ABSTRACT FROM AUTHOR]

Analog-to-digital and digital-to-analog conversion devices for signals with very large bandwidth are not always available due to technical or cost issues. This limits the realization of very high data-rate digital communication systems. In this paper, we propose a dual pulse shaping (DPS) transmission scheme, which can achieve full Nyquist rate transmission with only a half of the sampling rate for each of the two data streams. Two classes of ideal complementary Nyquist pulses are formulated assuming raised-cosine (RC) pulse shaping. The condition for cross-symbol interference (CSI) free transmission is derived and validated for the proposed pulses. Structures of the DPS transmitter and receiver are described and low-complexity equalization techniques tailored to DPS are proposed. With DPS, a millimeter wave system with commercially available and affordable data conversion devices is exemplified for achieving high-speed low-cost wireless communications. Simulation results with two sets of practical dual spectral shaping pulses are provided. The results verify the effectiveness of the proposed scheme, with comparison to the benchmark conventional Nyquist pulse shaping system. [ABSTRACT FROM AUTHOR]

In this paper, we mainly investigate two issues for sequence labeling, namely, label imbalance and noisy data that are commonly seen in the scenario of named entity recognition (NER) and are largely ignored in the existing works. To address these two issues, a new method termed robust sequence labeling (RoSeq) is proposed. Specifically, to handle the label imbalance issue, we first incorporate label statistics in a novel conditional random field (CRF) loss. In addition, we design an additional loss to reduce the weights of overwhelming easy tokens for augmenting the CRF loss. To address the noisy training data, we adopt an adversarial training strategy to improve model generalization. In experiments, the proposed RoSeq achieves the state-of-the-art performances on CoNLL and English Twitter NER—88.07% on CoNLL-2002 Dutch, 87.33% on CoNLL-2002 Spanish, 52.94% on WNUT-2016 Twitter, and 43.03% on WNUT-2017 Twitter without using the additional data. [ABSTRACT FROM AUTHOR]

This paper proposes a penalty-based adaptive secure estimation method for multi-area power systems under false data injection (FDI) attacks, the adaptive secure estimation method specifically takes the characteristics of FDI attacks into account. Firstly, a new measurement modeling is delicately constructed for subarea of power systems, in which both the state and FDI attack information are well considered. Secondly, for convenient solving of constructed mixed variational inequality (MVI), series virtual nodes are effectively introduced to transfer the multi-area power systems with boundary nodes to a boundary system. Then, a penalty-based distributed estimation method is proposed to estimate the state of multi-area power systems under FDI attacks, where the penalty parameter can be adaptively adjusted based on the dynamic internal error and boundary error. Compared with some existing methods, the efficiency and accuracy of proposed method are improved, and the state and attack signals can be estimated simultaneously. Finally, a case study shows the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of a multi-rate networked system estimation with considering random and malicious packet losses. Three different rates are used: system sampling rate, measurement updating rate and estimation updating rate. Thus, the network energy can be saved. Since the plant and filtering are connected via network channel, the data packet losses unavoidably happen. In order to study the combination of the random and malicious packet losses, the probabilistic characterization for the link failures is applied, which leads to a stochastic estimation error system. The almost surely exponentially stability criteria is applied to guarantee this stochastic system stable. Finally, a cloud-aided vehicle suspension system is applied to verify the theoretical finding. [ABSTRACT FROM AUTHOR]

The pseudo-randomness exhibited by chaotic systems has proven valuable not only in plaintext information encryption of images with varying dimensions but also as a key signal for protecting image copyright information. Image copyright protection is a crucial part of image information protection. Currently, digital watermarking technology is predominantly employed for copyright protection of digital images. However, most of the existing watermarking technologies primarily focus on the improvement of watermarking performance, neglecting the security of the watermarking system itself. Drawing inspiration from the Latin square rule, this paper proposes a novel hyperchaotic mapping modulation framework, which is employed to construct a new 3D hyperchaotic mapping and enhance the security of the fragile watermark and the zero-watermark generation process. Additionally, it is integrated into the classical image encryption framework of scrambling-diffusion to safeguard meaningful copyright information. Moreover, a functional watermarking system is designed based on the RSA asymmetric encryption algorithm, least significant bit (LSB), and polar harmonic Fourier moments (PHFMs). The proposed system enables identity verification, tampering location, and copyright authentication simultaneously. During the generation of zero watermarks, the binarization threshold of the PHFMs information matrix is determined by the method of maximum variance. The experimental results demonstrate that this scheme achieves a high level of security, better robustness to both conventional and geometric attacks, and precise localization for different types of tampering attacks. [ABSTRACT FROM AUTHOR]

This paper deals with the $H_{\infty }$ fault detection (FD) problem for a class of buck converter with the transmission-induced multiplicative noises and the event-triggered scheme under the network environment. The model of buck converter is approximated by a nonlinear model, and it is further analyzed based on the electricity knowledge. Due to the dynamic characteristics of network transmission, multiplicative noises need to be considered, which are described as a product of some uncorrelated standardized normal distribution and the transmitted signal. Meanwhile, an event-triggered mechanism is utilized for the purpose of reducing the energy consumption, and an FD observer is designed to detect the fault presented in buck converter. Some novel sufficient conditions are given in terms of linear matrix inequalities to guarantee the robust stability of the FD system, and the $H_{\infty }$ optimal fault tracking performance index is also achieved. Finally, an illustrative example is provided to show the validity and merit of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract A comparative study on cold supercritical water (SCW) passing over a hot sphere is carried out using the particle-resolved direct numerical simulation (PR-DNS) to examine the wall effects. Different working conditions are considered to obtain the drag coefficient (Cd) and average Nusselt number (Nu) of hot spheres in SCW whose physical properties significantly change with temperature and pressure. The effects of wall factors on Cd and Nu under two different boundary conditions, namely a stationary one for the fluidized sphere problem and a moving one for the settling sphere problem, are compared and discussed. Numerical results show that Cd and Nu of a sphere in SCW cannot fit the previous correlations for conventional fluids well. Therefore, these correlations are improved for SCW, which are good tools to understand the basic knowledge of SCW. [ABSTRACT FROM AUTHOR]

This paper considers the tracking synchronization problem for a class of coupled reaction–diffusion neural networks (CRDNNs) with undirected topology. For the case where the tracking trajectory has identical individual dynamic as that of the network nodes, the edge-based and vertex-based adaptive strategies on coupling strengths as well as adaptive controllers, which demand merely the local neighbor information, are proposed to synchronize the CRDNNs to the tracking trajectory. To reduce the control costs, an adaptive pinning control technique is employed. For the case where the tracking trajectory has different individual dynamic from that of the network nodes, the vertex-based adaptive strategy is proposed to drive the synchronization error to a relatively small area, which is adjustable according to the parameters of the adaptive strategy. This kind of adaptive design can enhance the robustness of the network against the external disturbance posed on the tracking trajectory. The obtained theoretical results are verified by two representative examples. [ABSTRACT FROM AUTHOR]

Abstract This paper considers solving a class of optimization problems over a network of agents, in which the cost function is expressed as the sum of individual objectives of the agents. The underlying communication graph is assumed to be undirected and connected. A distributed algorithm in which agents employ time-varying and heterogeneous step-sizes is proposed by combining consensus of multi-agent systems with gradient tracking technique. The algorithm not only drives the agents' iterates to a global and consensual minimizer but also finds the optimal value of the cost function. When the individual objectives are convex and smooth, we prove that the algorithm converges at a rate of O (1 / t) if the homogeneous step-size does not exceed some upper bound, and it accelerates to O (1/ t) if the homogeneous step-size is sufficiently small. When at least one of the individual objectives is strongly convex and all are smooth, we prove that the algorithm converges at a linear rate of O (λt) with 0 < λ < 1 even though the step-sizes are time-varying and heterogeneous. Two numerical examples are provided to demonstrate the efficiency of the proposed algorithm and to validate the theoretical findings. [ABSTRACT FROM AUTHOR]

• Momentum and heat transfer of spheroids in supercritical water were studied. • Effects of incident angle, particle shape and Reynolds number were discussed. • Correlations for Cd and Nu of spheroids in SCW were established. This paper studies the effect of the incident angle (θ) on momentum and heat transfer of spheroids in supercritical water (SCW) based on numerical simulations. Both prolate and oblate spheroids are considered with the aspect ratio (Ar) ranging from 0.5 to 2.5. Numerical results show that both the drag coefficient (Cd) and average Nusselt number (Nu) of the oblate spheroid (Ar < 1) in SCW decrease with the increase of θ. Opposite trend is found for the prolate spheroid (Ar > 1). When θ ≤ 45° , both Cd and Nu of the two kinds of spheroids decrease with the increase of Ar. When θ > 45° , both Cd and Nu of the prolate spheroid increases with Ar. Nu of the oblate spheroid also increases with Ar but its Cd is dependent on the competition of friction and pressure. The effect of θ on Nu can be enhanced by either lifting Re or ∣ Ar-1 ∣. At last, the effect of θ is directly considered in the correlations of Cd and Nu for spheroids in SCW which can improve the accuracy of the multi-phase flow modellings of SCW. [ABSTRACT FROM AUTHOR]

This paper investigates the cooperative output regulation problem for heterogeneous multiagent systems (MASs) under switching topology. Two novel distributed event-triggered adaptive control strategies based on state feedback and output feedback are developed, which can avoid using the minimal nonzero eigenvalue of Laplacian matrix associated with global system topologies. It is shown that under the proposed control protocols, MASs could achieve asymptotic tracking and disturbance rejection, and meanwhile, the amount of transmission data and communication cost among agents can be reduced. Then, the leader-following consensus problem of MASs is given as an application of our main results. Finally, an example is presented to verify the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

This paper is concerned with the guaranteed cost control problem for a class of Markov jump discrete-time neural networks (NNs) with event-triggered mechanism, asynchronous jumping, and fading channels. The Markov jump NNs are introduced to be close to reality, where the modes of the NNs and guaranteed cost controller are determined by two mutually independent Markov chains. The asynchronous phenomenon is considered, which increases the difficulty of designing required mode-dependent controller. The event-triggered mechanism is designed by comparing the relative measurement error with the last triggered state at the process of data transmission, which is used to eliminate dispensable transmission and reduce the networked energy consumption. In addition, the signal fading is considered for the effect of signal reflection and shadow in wireless networks, which is modeled by the novel Rice fading models. Some novel sufficient conditions are obtained to guarantee that the closed-loop system reaches a specified cost value under the designed jumping state feedback control law in terms of linear matrix inequalities. Finally, some simulation results are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the synchronization issue of coupled reaction–diffusion neural networks with directed topology via an adaptive approach. Due to the complexity of the network structure and the presence of space variables, it is difficult to design proper adaptive strategies on coupling weights to accomplish the synchronous goal. Under the assumptions of two kinds of special network structures, that is, directed spanning path and directed spanning tree, some novel edge-based adaptive laws, which utilized the local information of node dynamics fully are designed on the coupling weights for reaching synchronization. By constructing appropriate energy function, and utilizing some analytical techniques, several sufficient conditions are given. Finally, some simulation examples are given to verify the effectiveness of the obtained theoretical results. [ABSTRACT FROM PUBLISHER]

Noncontact vital sign detection is widely used in finding victims in post-disaster search and rescue, through-wall surveillance (TWS), and medical diagnosis and monitoring. Human subject can be remotely sensed by extracting life activities such as respiration and heartbeat. However, the signal to noise ratio (SNR) is often low, particularly in complex environments, which results in errors in both range and respiration frequency (RF) estimation. To improve the accuracy, an improved system for the vital sign detection is presented which is based on impulse ultra-wideband (UWB) radar. The range is determined based on the short-time Fourier transform (STFT) of the standard deviation of the received signals. Further, the ensemble empirical mode decomposition (EEMD) based frequency accumulation (FA) technique is presented to determine RF. Performance results are presented which show that the proposed method is superior to several well-known techniques. [ABSTRACT FROM AUTHOR]

This paper studies the cluster synchronization of Boolean network with open-loop control. Both synchronous Boolean network and asynchronous Boolean network are considered. Firstly, the basic cluster synchronization concept in synchronous Boolean network is provided and necessary and sufficient conditions are derived. Secondly, with different update scheme, necessary and sufficient conditions for cluster synchronization to reference trajectories are provided and open-loop cycle controllers are designed. Finally, some numerical examples are provided and results show the efficiency of cluster synchronization methods in Boolean network. [ABSTRACT FROM AUTHOR]

Multi-time scales modeling of hydro-turbine governing system is crucial in precise modeling of hydropower plant and provides support for the stability analysis of the system. Considering the inertia and response time of the hydraulic servo system, the hydro-turbine governing system is transformed into the fast-slow hydro-turbine governing system. The effects of the time-scale on the dynamical behavior of the system are analyzed and the fast-slow dynamical behaviors of the system are investigated with different time-scale. Furthermore, the theoretical analysis of the stable regions is presented. The influences of the time-scale on the stable region are analyzed by simulation. The simulation results prove the correctness of the theoretical analysis. More importantly, the methods and results of this paper provide a perspective to multi-time scales modeling of hydro-turbine governing system and contribute to the optimization analysis and control of the system. [ABSTRACT FROM AUTHOR]

This paper studies the projective synchronization of neural network in complex-valued domain. Both projective factors and neuron state variables are set as complex values in the synchronization process. In our study, unknown network structure and time-varying delays are considered. With the projective synchronization, the network structure will be identified and the problem of bounded time delays can be solved. With Lyapunov–Krasovskii stability theory and adaptive feedback scheme, controllers are designed and the complex projective synchronization is achieved. In the numerical simulation, several complex-valued neural network examples are provided showing the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

In this work, a unified phase field theory for two coupled fracture types is proposed. Compared with the existing unified phase field theory for single fracture type, it considers the coupling behavior between different fracture types and is consistent with the single fracture type theory when one type of fracture is suppressed. Based on a new form of energy decomposition, that is, the strain energy is decomposed into deviatoric, tensile volumetric and compressive volumetric parts, and specifying the driving energy for each fracture type, a double phase-field model for coupled spall and adiabatic shear banding, which are two kinds of typical dynamic ductile fracture, is realized with the proposed unified phase field theory for two coupled fracture types. This coupled model can be used to predict the potential spall and adiabatic shear banding failures in ductile metals under dynamic loading, revealing the corresponding fracture types under different loadings without any additional criteria. The entire damage and fracture evolution (that is, from damage evolution, through crack expansion, and to fragmentation) are captured with this unified phase-field modeling framework. In addition, as an advantage of the double phase-field model, the complex multicrack distributions of spall and adiabatic shear bands in ductile metals can be presented with the proposed model. Based on these features, the expanding characteristics of complex cracks in metallic expanding shells are numerically studied. It is found that, if multilayer spall occurs, the specimen tends to fracture into three layers, that is, a complete spall layer, fragmentations in multilayer areas, and the main body, which is consistent with the experimental observations under certain conditions and is not the regular multiple layers predicted by the multilayer spall theory with ideal material assumption. For multiple adiabatic shear bands (ASBs) in the shell, the spiral patterns of ASBs' distributions, the initial positions, and the propagating path are clarified for the collapsed and expanding shells. The collapsed shell tends to form a single-direction spiral pattern whereas the expanding shell tends to form a double-direction spiral pattern. In addition to the inner surface, the intersection points of two ASBs can initiate cracks, and, as the intersection points have the chance of being distributed on the outer surface, the expanding shell has a greater potential to initiate a crack on the outer surface than the collapsed shell. The propagating path will be deflected after interactions of two ASBs, and the quantitative relationship between the deflection angle and the stress state before the interactions is given, which explains why the deflection angle is significantly larger in the collapsed shell compared with that in the expanding shell. Based on the above results and analysis, four typical stages of fractures in the expanding shell under internal explosive loading are identified. [ABSTRACT FROM AUTHOR]

The problem of observer-based decentralized event-triggered H ∞ control for networked systems is investigated in this paper. The system measurements and control inputs are grouped into several nodes. An event-triggered transmission scheme is introduced to choose those necessary signals to be transmitted from the plant to the observer and from the controller to the actuator, where signals from control input nodes and from measurement nodes are released not necessarily synchronously. Under the decentralized event-triggered transmission scheme, H ∞ performance analysis for the resultant closed-loop system is made, and an algorithm to designing suitable observers and controllers is presented as well. Moreover, a sufficient condition is derived such that the inter-event time is strictly greater than zero. Finally, two examples are given to show the effectiveness of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper is concerned with the consensus problem for multi-agent systems with switching topology. A novel distributed control strategy is proposed to reduce the frequency of controller update and save network resources. Based on communication uncertainty of practical networks, partial information exchange and switching topology which subjects to a heterogeneous Markov chain are considered in controller design. An H ∞ consensus criterion is derived by using linear matrix inequality and Lyapunov methods. According to this consensus criterion, a sufficient condition on designing H ∞ state-feedback controller is presented. Finally, a simulation example is given to illustrate the effectiveness of the proposed event-based control strategy. [ABSTRACT FROM AUTHOR]

The end-to-end speech translation (ST) model usually adopts the encoder-decoder structure, which takes the speech of the source language as input and directly outputs its translation result in the target language. Since the model performs cross-modal translation, it needs to extract semantically rich representations from speech. Compared with text, speech is fine-grained and contains more noise, which puts a great burden on the encoder of the ST model. The modal gap between speech and text usually causes the ST model to perform inferior to the corresponding machine translation (MT) model. To bridge the cross-modal gap, this paper proposes to use adversarial training to relieve the burden on the ST encoder by providing internal supervision signals. In this approach, the encoder in the ST model can extract representations that contain rich semantics, which greatly improves performance. Experiments on the datasets of Augmented Librispeech English-French and MuST-C English-German show the effectiveness of our method. Further analysis indicates that our proposed method can perform well in low-resource conditions compared to strong baselines. [ABSTRACT FROM AUTHOR]

This paper addresses the bearing-only distributed localization, which is more accurate and more reliable than traditional localization technologies. A new bearing-only distributed localization framework is proposed to solve localization problems for both static sensor networks and mobile multi-agent systems (MASs). Based on the complex coordinate representation, a novel bearing-only distributed localization algorithm for sensor networks is proposed. The algorithm combines the orientation estimation and the position estimation to solve the orientation alignment problem, which makes the compasses no longer needed for the localized networks. The localization for the mobile MASs is also studied and the corresponding localization algorithm is designed, which is more general and more challenging. The key to obtaining positions of moving agents, is the velocity estimator in the proposed localization algorithm which makes the estimated positions and velocities converge to the true value simultaneously. A distinctive advantage of the localization framework proposed in this paper is that the bearing-only localization algorithms can be applied to more general systems, such as sensor networks and MASs. Simulations and experiment are presented to verify the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the consensus problem of a class of multi-agent systems with single-integrator. Two novel distributed event-triggered control schemes are developed, which can reduce the frequency of the controller updates and the communication load. It is shown that under the proposed control protocols, consensus can be reached if the underlying communication graph of the multi-agent systems is connected and both the computation cost and communication cost are saved. Finally, the effectiveness of the theoretical results is illustrated by a simulation example. [ABSTRACT FROM AUTHOR]

In this study, parallelization of a Discrete Element Method (DEM) code titled Trubal was carried out based on the CPU–GPU heterogeneous architecture where both two- and three-dimensional cases were assessed. In Trubal, the particle–particle/wall interaction rules are governed by the theoretical contact mechanics which enable the direct use of real physical material properties in the calculation. We reconstructed Trubal in two steps: reconstruction of the static storage structure; essential parallelism on the relative newer version code. Numerical simulations were implemented to present the benefits of this research. Firstly, two simulations of die filling with a moving shoe involving 6000 and 60,000 two-dimensional particles were conducted under (i) NVIDIA Tesla C2050 card together with Intel Core-Duo 2.93 GHz CPU and (ii) NVIDIA Tesla K40c card along with Intel Xeon 3.00 GHz CPU. Average speedups of (i) 4.69 and 12.78 as well as (ii) 6.52 and 18.60 in computational time were obtained, respectively. Then, a simulation of die filling with a stationary shoe containing 20,000 three-dimensional particles was carried out under the same conditions where average speedups of (i) 12.90 and (ii) 19.66 in computational time were obtained, respectively. It is shown that the final version parallel code gave a substantial acceleration on the original Trubal. [ABSTRACT FROM AUTHOR]

In this paper, a high-speed and multi-code-rate low density parity check(LDPC) encoder architecture is proposed for the LDPC codes used in high-speed satellite-ground data transmission of the Chinese high resolution earth observation satellite. Synthesized with Taiwan semiconductor manufacturing company ( TSMC ) 130 nm complementary oxide metal semiconductor (COMS) standard cell library, the designed multi-code-rate LDPC encoder can achieve 1. 6 Gbps throughput in 200 MHz clock frequency, the die size of which is only 5. 495 mm2, and the dynamic power dissipation of which is only 184. 3 mW. Compared with the encoder designed by the traditional LDPC encoder architecture, the memory size, the die size, and the power consumption of the new encoder can be reduced by 18. 52%, 20. 3% and 83. 3% respectively. [ABSTRACT FROM AUTHOR]

The vertical gap flow in the wing-leading-edge of the hypersonic vehicle is investigated in this paper. By analyzing the structure of the leading-edge shield, a geometrical model for the leading-edge gap is established. The flow field structure is obtained by solving the 3D Navier-Stokes equations. The spatial distribution fetature of the 3D vortex induced by gap and corresponding relations between the vortex motion and heat-flux distribution are analyzed. The numerical result shows that in the circular section, the rounding zone of the gap shoulder is a local high heat flux region because of strong 3D-effect normally, and the reattachment of the primary vortex in the gap leads to a "non-normal" high heat flux region on the side wall of the gap. In the straight section, there is a strong vortex region above the gap induced by span wise flow. Because of the effect of vortex separation, the original local high heat flux region at the rounding zone of the gap shoulder turn to local low heat flux region, and local high heat-flux region exists in the side wall region. [ABSTRACT FROM AUTHOR]

Highlights: [•] A novel combined LBM–IBM–DEM scheme is presented. [•] The method combines the good features of the LBM and the IBM by using two meshes. [•] The collision rules enable particles to be specified by material properties. [•] The method eliminates the need for determination of artificial parameter. [•] The collision scheme is important in the particulate flow simulation. [ABSTRACT FROM AUTHOR]

This paper proposes a dimensionless approach to analyze the multi-parametric stability behavior of switching converters, which can be characterized by a nonlinear time-periodic (NTP) system. The main objective is to analyze how multiple circuit parameters affect the stability patterns of the derived NTP system and to simplify the parametric complexity of such NTP system. In contrast to previous work, the proposed method focuses on the parametric resultant relationships of the NTP system in the sense of topological equivalence, and investigates its stability in terms of the homeomorphic NTP system. Firstly, an equivalent stability theory of NTP systems is proposed. Then, based on the equivalent theory, a normalized map is introduced and various interesting properties are derived so as to formulate the dimensionless approach. Moreover, the approximate solution of the NTP system in dimensionless parameter space is calculated by using the Galerkin method, and its stability pattern is identified with the help of eigenvalue analysis approach. Finally, a case study of one-cycle controlled Zeta PFC converter is discussed in detail to exemplify the application of the proposed method. These analytical results agree well with those ones obtained from experimental measurements. [ABSTRACT FROM AUTHOR]

This article is concerned with the problem of output feedback control for a class of continuous-time hidden semi-Markov jump systems with time delays. Due to the limitations of the actual environment, system modes are usually undetectable, which are called hidden modes. The controller modes are described as observable modes. Emission probabilities are used to establish the relationship between abovementioned two concepts. The jump parameters are governed by the hidden semi-Markov process, which can better describe the asynchronous information between the controller modes and the system modes. Besides, time delays are considered to be time-varying and dependent on the hidden modes. By employing some mathematical transformation and constructing a novel Lyapunov–Krasovskii functional, some new parameter-dependent sufficient stabilization conditions can be obtained by designing an observed-mode-dependent static output-feedback controller. Finally, a practical example is provided to illustrate the effectiveness and merits of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, control problems are investigated for a class of networked control systems. An improved networked control system model is proposed and the effects of random packet dropout, delay and sensor fault are considered simultaneously. The packet dropout process is modeled as a Markov chain, and the delays are bounded and occurred in a random way in this paper. The fault for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution. The resulting closed-loop system is transformed to a Markovian switching system. Sufficient conditions for stochastic stability of the closed-loop system are given in terms of linear matrix inequality. A mode-independent controller is designed such that the closed-loop system is stochastically stable and achieves the given disturbance attenuation level. Finally, the simulation of the inverted pendulum control is given to illustrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

A new method for realizing hovering formation in close proximity is investigated with main focus on design of open-loop and close-loop control law. The new method utilizes inter-spacecraft coulomb forces to control relative motion. Hence, plume contamination caused by classical thrusters could be mitigated. First, a mathematical description of the Coulomb hovering formation is established by using polar coordinates. Then, an open-loop control law is provided with respect to the case of elliptic reference orbit. For the case of circular reference orbit, linearized dynamics is obtained by linearizing the model about a nominal hovering formation, and then is used to analyze close-loop feedback control law. Numerical examples are provided to demonstrate the performance of the control laws. The approach proposed in the paper could also be used in other scenario of formation flying in high earth orbit. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with two types of bidirectional Hopf bifurcation phenomena in two-stage cascade DC–DC buck converter, which is a popular design solution for obtaining larger conversion rates in distributed power systems (DPS). By means of “exact” cycle-by-cycle numerical simulations, it is found that two types of bidirectional Hopf bifurcations that never occur in standalone buck converter are observed under the variations of some particular circuit parameters. Based on the derived averaged model, theoretical analysis is performed to identify the two types of bidirectional bifurcations by judging in what way the eigenvalue loci of the Jacobian move, and further to reveal their underlying mechanisms. In addition, some behavior boundaries are given in parameter space, and some rules are proposed to avoid the occurrence of the bidirectional Hopf bifurcations. Finally, experimental results are presented for verification purposes. [Copyright &y& Elsevier]

Abstract: This paper deals with the stability and dissipative problem of a class of stochastic hybrid system. The system under study involves Markovian jump, impulsive effects and time delay, which are often encountered in practice and are the sources of instability. Our attention is focused on analysis of whether the stochastic hybrid system with time-delay is stochastically asymptotically stable and strictly (Q, S, R) dissipative. By introducing an extra artificial time instance, the equivalent system is obtained and the sufficient conditions are derived by using linear matrix inequality (LMI) techniques. The main results of this paper unify the existing results on control. [Copyright &y& Elsevier]

Abstract: This paper presents a method for stability analysis of N-cell cascade step-down buck converters, which is a kind of complex nonlinear system. A nonlinear model in the form of time-variant state equations is derived, and then the loop gain describing the overall system is introduced. With the help of them, we obtain the equations for phase cross over frequency and gain margin by small-signal perturbation technique, harmonic balance method, and inverse iteration method. Based on the equations, the overall cascade system stability can be analyzed regardless of the number of converters cascaded. Finally, the cascade buck converter with three power stages is used to demonstrate the effectiveness of the proposed stability analysis method. [Copyright &y& Elsevier]

• A new algorithm (CS-ESN) is proposed to optimize the ESN reserve pool using compressed sensing to improve the prediction speed and accuracy. • We compare and analyze ESN model and CS-ESN model. • We simulate the ESN model and CS-ESN model with chaotic data and stock data, and analyze simulation results. Echo State Network (ESN) is often used for time series prediction and chaotic series prediction. But in the training process of ESN model, the reserve pool will involve a large number of calculations, so the reserve pool may have node redundancy, resulting in inaccurate training model. It is very important to select the active nodes in the reserve pool because the predicted nodes are very few compared with all the nodes in the reserve pool. Based on the characteristics of the original vector, compressed sensing can reduce the dimension of the vector so as to obtain the activated nodes. In this paper, the ESN model is added with compressed sensing, which simplifies the reserve pool part of the ESN model. The simulation results show that our method can not only reduce the calculation amount of training effectively, but also improve the accuracy. [ABSTRACT FROM AUTHOR]