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Suspicious mass traffic constantly evolves, making network behaviour tracing and structure more complex. Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them. They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results. Artificial neural network (ANN) offers optimal solutions in classifying and clustering the various reels of data, and the results obtained purely depend on identifying a problem. In this research work, the design of optimized applications is presented in an organized manner. In addition, this research work examines theoretical approaches to achieving optimized results using ANN. It mainly focuses on designing rules. The optimizing design approach of neural networks analyzes the internal process of the neural networks. Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters. The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues. The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors. The proposed ANN offered optimal results in real-world application problems, and the results were obtained using MATLAB. [ABSTRACT FROM AUTHOR]

Feedforward deep neural networks (DNNs) commonly involve layer-wise linear operations and subsequent nonlinear operations, which are repeated through all layers. The nonlinear operations by nonlinear activations in each layer remarkably enhance the expressiveness of DNNs, resulting in the great success in a variety of application domains. Although the necessity of layer-wise nonlinear operations is agreed, the optimal nonlinearity for each layer in a given DNN is not clear. In this regard, we propose an easy-to-use method to layer-wise measure the optimal nonlinearity for a given DNN using its replica termed a linear-nonlinear network (LiNLNet). The key to the LiNLNet is the use of linear-nonlinear units (LiNLUs) whose degree of nonlinearity is parameterized by a trainable parameter p. The parameter p is shared among all LiNLUs in a given layer, thus indicating the layer-wise optimal nonlinearity. This method allows layer-level pruning such that the layers that do not require nonlinearity are merged into the subsequent layers, reducing computational complexity. For proofs of concept, we applied the proposed method to a MLP, AlexNet, VGG16, and ResNet18 on CIFAR-10 and ImageNet. The results commonly indicate the last hidden layer as a linear layer that may be merged into the output layer, reducing memory usage by 27% while maintaining the accuracy for LiNL-AlexNet on ImageNet. [ABSTRACT FROM AUTHOR]

A method for estimating the current flowing through a non-linear power inductor operating in a DC/DC converter is proposed. The knowledge of such current, that cannot be calculated in closed form as for the linear inductor, is crucial for the design of the converter. The proposed method is based on a third-order polynomial model of the inductor, already developed by the authors; it is exploited to solve the differential equation of the inductor and to implement a flux model in a circuit simulator. The method allows the estimation of the current up to saturation, intended as the point at which the differential inductance is reduced to half of its maximum value. The current profile depends also on the inductor temperature. Based on this, the influence of core temperature on the conduction time of the power switch was determined. This study shows that the exploitation of saturation requires a proper value of the conduction time value that depends on the temperature. The theoretical analysis has been experimentally verified on a boost converter and is valid for the entire class of DC-DC converters in which the power inductor is subjected to a constant voltage for a given time. The simulations agree with the experimental data from a case study concerning conduction time and temperature. [ABSTRACT FROM AUTHOR]

The permanent-electro magnetic suspension (PEMS) technology takes advantage of the attractive magnetic force between the magnet and the iron core and reduces the power consumption eventually to zero. However, the active current in the electromagnet of the zero-power PEMS system fluctuates around zero due to external disturbances and suffers from the electronic nonlinearity of the driving circuit, such as the full-bridge pulse-width-modulation (PWM) inverter. This work presents that the 2 $\mu \text{s}$ turn-off delay (one electronic defect) of the integrated circuit L298N (one commercial full-bridge PWM inverter produced by STMicroelectronics) leads to the nonlinear current-duty cycle characteristic, which undermines the control stability and limits the PWM frequency of the zero-power PEMS system. Moreover, the nonlinear mechanism is experimentally and theoretically analyzed for the critical PWM frequency and the sensitivity transition with respect to the $2~\mu \text{s}$ turn-off delay. Besides, the critical PWM frequency is of great significance for the energy efficiency and the dynamic performance of the high-speed PEMS transportation system.

Distinguishing between direct and indirect frequency coupling is an important aspect of functional connectivity analyses because this distinction can determine if two brain regions are directly connected. Although partial coherence quantifies partial frequency coupling in the linear Gaussian case, we introduce a general framework that can address even the nonlinear and non-Gaussian case. Our technique, partial generalized coherence (PGC), expands prior work by allowing pairwise frequency coupling analyses to be conditioned on other processes, enabling model-free partial frequency coupling results. By taking advantage of recent advances in conditional mutual information estimation, we are able to implement our technique in a way that scales well with dimensionality, making it possible to condition on many processes and produce a partial frequency coupling graph. We analyzed both linear Gaussian and nonlinear simulated networks. We then performed PGC analysis of calcium recordings from mouse olfactory bulb glomeruli under anesthesia and quantified the dominant influence of breathing-related activity on the pairwise relationships between glomeruli for breathing-related frequencies. Overall, we introduce a technique capable of eliminating indirect frequency coupling in a model-free way, empowering future research to correct for potentially misleading frequency interactions in functional connectivity analyses. [ABSTRACT FROM AUTHOR]

This study deals with a single soft fault diagnosis of non‐linear circuits having multiple DC solutions. The key problem of the diagnosis is arranging a measurement test. Creation of the test for the above‐defined class of circuits is discussed in detail. It is assumed that only input and output terminals are accessible in the circuit. The test is created based on some single‐valued input or transfer characteristics traced in a modified circuit. The modification does not disturb the integrity of the circuit and is realised by connecting some components to the accessible terminals. A very fast method for tracing the characteristics is developed. Having appropriate characteristics several values of the exciting voltage are chosen and the corresponding values of voltage or current defined by the characteristic are measured in the course of testing. The method for arranging the measurement test is illustrated using four non‐linear circuits having multiple DC solutions and three of them were built in bipolar and complementary metal‐oxide‐semiconductor (CMOS) technology. One of the circuits is comprehensibly diagnosed using a method for detecting, locating, and estimating values of different faulty parameters exploiting the proposed test. [ABSTRACT FROM AUTHOR]

One of the popular area of research for the past decade in the academia as well as in the industry is networked control systems (NCS). Variable time delays induced by the network and data packet dropout during transmission of data are among the problems encountered in these type of systems. Researchers proposed and developed various control strategies over the past years to deal with the above-mentioned problem. Fuzzy logic control (FLC) is a widely used technique for dealing with control problems, and the most commonly used one is type-1 FLC. However, the interval type-2 (IT2) is proven to be better at handling uncertainties compared to the type-1 FLC. In this paper, a nonlinear NCS with delays is considered, and an observer-based IT2 FLC is designed in order to improve the control of NCS due to the presence of uncertainties and delays. The developed scheme includes the design of a state feedback controller based on IT2 FLC, and an observer-based IT2 FLC using Lyapunov–Krasovskii theory. To investigate the effectiveness of the proposed scheme, simulations are performed considering various network delays. Firstly, the results of IT2 FLCs are compared with that of type-1 FLCs and improvement is observed. Secondly, the newly developed observer-based IT2 FLC is compared with the IT2 state feedback FLC developed in the literature. Results show faster and more effective response using the newly developed technique. [ABSTRACT FROM AUTHOR]

The synchronization control problem for a class of nonlinear complex dynamical networks with network attacks and actuator faults is addressed in this paper. The main contributions are as follows: (1) The proposed controller can be used to compensate for nonlinearity and actuator faults simultaneously; (2) in contrast to existing network attacks results, in which an identity input matrix is considered, the input matrix considered in this paper is an arbitrary one and is more general. In particular, a new adaptive controller is developed by introducing a fuzzy logic system (FLS) to approximate the nonlinear uncertainty. By introducing an adaptive mechanism to estimate the norm of weight vector in the FLS, the proposed FLS-based controller can compensate for the effect of nonlinearity under actuator faults. By using matrix factorization technique and sliding mode control method, the developed method is shown to be resilient to network attacks. Moreover, it is proven that the proposed control method can guarantee asymptotic convergence of synchronization errors. Finally, an illustrative example is used to demonstrate the efficiency and effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

Recent progress on salient object detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs). Semantic segmentation and salient object detection algorithms developed lately have been mostly based on Fully Convolutional Neural Networks (FCNs). There is still a large room for improvement over the generic FCN models that do not explicitly deal with the scale-space problem. The Holistically-Nested Edge Detector (HED) provides a skip-layer structure with deep supervision for edge and boundary detection, but the performance gain of HED on saliency detection is not obvious. In this paper, we propose a new salient object detection method by introducing short connections to the skip-layer structures within the HED architecture. Our framework takes full advantage of multi-level and multi-scale features extracted from FCNs, providing more advanced representations at each layer, a property that is critically needed to perform segment detection. Our method produces state-of-the-art results on 5 widely tested salient object detection benchmarks, with advantages in terms of efficiency (0.08 seconds per image), effectiveness, and simplicity over the existing algorithms. Beyond that, we conduct an exhaustive analysis of the role of training data on performance. We provide a training set for future research and fair comparisons. [ABSTRACT FROM AUTHOR]

Resonant converters under zero current switching control strategy can exhibit coexistence of attractors, making it difficult the startup of the system from zero initial conditions. In this brief, the problem of multiple coexisting attractors in parallel resonant converters is addressed. Appropriate modifications of the switching decision with the aim of converting undesired attractors into virtual ones are proposed. A suitable control signal is generated from the state variables of the system and used to adjust the switching decision. Numerical simulations corroborate the proposed solutions and the simplest one was finally verified by measurements from a laboratory prototype. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to sensor-based feature evaluation and selection using a self-organizing map and spatial statistics as a combined technique applied to tool condition monitoring of the turning process. This approach takes advantage of the unique features of unsupervised neural networks combined with spatial statistics to perform analyses into the contributions of the different sensor-based features, carrying large quantities of noise, to achieve a classification of tool wear and a quantitative measure of each feature's suitability. This method does not assume a prior direct correlation between features avoiding misconstructions inherent to common approaches that assume that only obviously correlated features should be considered for condition monitoring. Instead, and taking advantage of neural networks ability to perform non-linear modeling, it has allowed a prior modeling of the process and then analyzed each feature's contribution toward classification. It was found that some of the commonly used features have proven to have a significant contribution to the classification of cutting tool wear, whereas others adversely affect classification performance. Further, it is demonstrated that the proposed combined technique can be used extensively to quantitatively evaluate the contribution of different features toward system monitoring in the presence of noisy data. [ABSTRACT FROM AUTHOR]

Abstract We present a novel nonlinear adaptive path following controller that compensates for drift forces though time-varying sideslip for amphibious hovercrafts. The sideslip arises and changes with time during path following when the hovercraft is subject to drift forces caused by ocean disturbances or control forces. The time-varying sideslip of hovercraft is estimated online and independent of the tracking error by a bounded-gain-forgetting (BGF) adaptive estimator. Then, a line-of-sight (LOS) guidance law based on BGF is designed to follow the desired path accurately in the presence of ocean disturbances, in which the parameter estimation and the tracking errors are both proven to be bounded. Furthermore, the closed-loop cascade BGF-LOS guidance system, which is composed of tracking subsystem and sideslip parameter estimation subsystem, is proven to be input-to-state stable (ISS) according to the stability theory of cascaded system. The proposed guidance law is intended for maneuvering of an amphibious hovercraft in the horizontal-plane to follow a predefined parametrized curve without time constraints. The theoretical results are supported by two cases studies of an amphibious hovercraft. [ABSTRACT FROM AUTHOR]

Abstract As industrial systems expand and complex systems are developed, it is no longer effective to minimize hazards and risks for industrial safety using the technological solutions limited to a single industry. Thus, to resolve complicated problems, safety technology has been developed by promoting technology innovation and convergence. In this respect, this study aims at monitoring major safety fields and technologies through patent analysis to identify the trends in technology development that prevent the risks of various industrial systems. Patent information is effectively used for analyzing technology descriptions, which include the purpose and newness of technology. Using this patent information, we propose the major safety fields and related technology keywords using the following two techniques: (1) latent Dirichlet allocation (LDA), which aims to extract the latent topics and main keywords contained in documents, and (2) network analysis, which is useful for monitoring change patterns and relations. Further, the convergence trajectories of safety technology are identified to provide insights about the technology trends in safety fields. The results are expected to enable safety managers and engineers to effectively find relevant technology trends for reducing hazardous factors. Highlights • This study aims at monitoring major safety fields and technologies. • Patent information is used to identify trends of safety fields and technologies. • LDA-based network analysis is proposed to examine safety fields and their convergence. • This study helps safety managers and engineers to find technology trends. [ABSTRACT FROM AUTHOR]

Nonlinear transmission lines (NLTLs) are passive networks with the capability of modulating baseband pulses. These networks are used for generating RF pulses at high-power levels. NLTLs have a unique performance in high-power RF pulse generation among all the generators based on the solid-state technology. A high-power rectangular pulse is injected to the line, gradually evolved into an oscillatory pulse while propagating through the network, and finally, in the form of a high-power RF pulse, is delivered to the load. Properly terminating an NLTL, in order to transfer the maximum available RF power to the load, is an important problem. In this paper, an accurate analysis is presented, which gives a closed-form formula for the output RF power in lossy NLTLs. Using this analysis, optimal resistive terminating load for RF applications of NLTLs is extracted. The output central frequency is also investigated in this paper. All the obtained theoretical results are verified by SPICE simulations and measurements in two implemented NLTLs. [ABSTRACT FROM AUTHOR]

This paper contributes to the empirical identification of the structural positions in network through an exploration of the interactions between R&D activities (patents) and technological areas of patents (technological classifications). Affiliation network analysis was conducted to study the affiliation between patents and technological classifications. We identified some key patents and explored the differentiated positions of the core and periphery. The results of network analysis was further verified by statistical analysis. We found that the impacts of the centralities in position were significant. Technological implications and foresights are discussed in the conclusion. [ABSTRACT FROM AUTHOR]

Nonlinear network coding is researched here. For determined network, Jaggi-Sanders algorithm is generalized to its nonlinear form for the the multicast network. Precise details of the algorithm implementation and the proof on the algorithm existence are given. The error-correcting codes for determined network coding is also proposed based on the traditional error-correcting codes for network coding. For random network, the nonlinear network coding scheme is proposed. It shows the nonlinear network coding has advantages in solving the so-called “all-or-nothing” problem caused by errors. Some interesting mathematical concepts such as shared agreements, composite functions and n-dimensional maximal independent set based on combinatorics are proposed. These new concepts may offer beneficial lessons for further research on nonlinear network coding. [ABSTRACT FROM AUTHOR]

This paper deals with transient stability in interconnected micro-grids. The main challenge is to understand the impact of the connectivity of the graph and model nonlinearities on transient and steady-state behavior of the system as a whole. The contribution of this paper is three-fold. First, we provide a robust classification of transient dynamics for different intervals of the parameters for a single micro-grid. We prove that underdamped dynamics and oscillations arise when the damping coefficient is below a certain threshold which we calculate explicitly as function of the product between the inertia coefficient and the synchronization parameter. Second, for interconnected micro-grids, under the hypothesis of homogeneity, we prove that the transient dynamics mimics a consensus dynamics. We provide bounds on the damping coefficient characterizing underdamped and overdamped consensus. Third, we extend the study to the case of disturbed measurements due to hackering or parameter uncertainties. We introduce model nonlinearities and prove absolute stability. [ABSTRACT FROM AUTHOR]

Multilayer bootstrap network builds a gradually narrowed multilayer nonlinear network from bottom up for unsupervised nonlinear dimensionality reduction. Each layer of the network is a nonparametric density estimator. It consists of a group of k-centroids clusterings. Each clustering randomly selects data points with randomly selected features as its centroids, and learns a one-hot encoder by one-nearest-neighbor optimization. Geometrically, the nonparametric density estimator at each layer projects the input data space to a uniformly-distributed discrete feature space, where the similarity of two data points in the discrete feature space is measured by the number of the nearest centroids they share in common. The multilayer network gradually reduces the nonlinear variations of data from bottom up by building a vast number of hierarchical trees implicitly on the original data space. Theoretically, the estimation error caused by the nonparametric density estimator is proportional to the correlation between the clusterings, both of which are reduced by the randomization steps. [ABSTRACT FROM AUTHOR]

In this paper, a new combined electro-optic (EO) and pulsed nonlinear vector network analyzer-based (NVNA) load–pull measurement system for distributed multiharmonic electric field ( $E$ -field) measurements is presented. The system uses an external EO probe to measure cross-frequency phase-coherent multiharmonic vector $E$ -fields with an 8- $\mu \text{m}$ spatial resolution and 20 MHz–20 GHz bandwidth. We demonstrate the performance of the distributed phase-coherent $E$ -field measurements of $E_{x}$ , $E_{y}$ , and $E_{z}$ components with three harmonics above a commercially available large periphery, packaged, laterally diffused metal-oxide-semiconductor transistor. The transistor was measured at 2.2 GHz under pulsed conditions with 10- $\mu \text{s}$ pulse and 10% duty cycle, while outputting 55.1 dBm of power. The measured $E$ -fields of the operating transistor are animated for the first time and reveal complex nonuniform operation at harmonic frequencies. [ABSTRACT FROM AUTHOR]

In this paper, the synthesized phase reference standard is introduced as a new viable alternative to comb generator-based phase references for nonlinear vector network analyzer measurements. Emphasis is put on the achievable higher specific output power of a frequency picket of interest for a denser spacing in the reference frequency grid. Supporting calculations on network analyzer IF filter bandwidths and limits for a denser grid spacing for comb-based phase references are presented. The concept is evaluated with a reference prototype covering the frequency range of 54 MHz to 6.8 GHz requiring only a 10-MHz reference signal and no network analyzer generators for stimulus, thus enabling even 2-port network analyzers without direct receiver and generator access to perform nonlinear reflection measurements. Furthermore, a method for generating a low-jitter trigger signal for the oscilloscope in absence of the comb generator pulse is shown. The synthesized phase standard implementation and the characterization setup are described in high detail to facilitate reproducibility of the results. Characterization of the new phase standard is performed in a frequency hopping pattern for amplitude and phase values of the reference pickets with a sampling oscilloscope-based setup. Startup and steady state measurements are provided. A steady-state relative phase repeatability better than 2° over power cycles and drift of 0.089 °/h at 4 GHz referenced to a synthesized 1-GHz fundamental is achieved. [ABSTRACT FROM AUTHOR]

The versatility of cellular nonlinear network (CNN) made it attractive in various scientific and engineering applications. The strength of solving problems using CNN comes from the fact that it deals with nonlinear dynamic systems. In this paper, feasible criteria for adaptive hardware approximation, automated method to switch between high and low precisions, is proposed to efficiently reduce energy consumption of a digital CNN. Two different criteria, state- and sign-dependent control, are presented and compared by simulating discrete-time CNN with various applications: image processing, associative memory, and scientific computing. Simulation results show that the state-dependent control is better suited for automatically introducing approximate computing in CNN accelerator. Even further, this control method allows more aggressive power reduction by stopping the computations for those cells in the saturation region of CNN dynamics for applications like associative memory. As a result, adaptive hardware approximation is effective in reducing energy consumption of a digital CNN compared with simply injecting predetermined approximation for entire CNN computations. The experimental results also show that the acceptable level of energy reduction differs from the application, equivalently the type of CNN. [ABSTRACT FROM AUTHOR]

This paper presents a design principle of a wound field synchronous motor (WFSM). Because the main flux path is sensitive to magnetic saturation, the WFSM has to be designed considering the latter, especially for applications requiring high torque density such as electric vehicles (EVs). Thus, a magnetic equivalent circuit (MEC) should be constructed considering the nonlinearity of the electrical steel sheets. A nonlinearMEC for the WFSMis constructed and solved by the Newton-Raphson method. Then, using the nonlinearMEC, the design of aWFSMis presented under some given constraints. The result of the final model for an EV is compared to finite-element analysis to verify its accuracy, and experiments on the final designed model are performed. [ABSTRACT FROM AUTHOR]

Improved understanding of molecular systems has only emphasised the sophistication of networks within the cell. Simultaneously, the advance of nucleic acid nanotechnology, a platform within which reactions can be exquisitely controlled, has made the development of artificial architectures and devices possible. Vital to this progress has been a solid foundation in the thermodynamics of molecular systems. In this pedagogical review and perspective, we discuss how thermodynamics determines both the overall potential of molecular networks, and the minute details of design. We then argue that, in turn, the need to understand molecular systems is helping to drive the development of theories of thermodynamics at the microscopic scale. [ABSTRACT FROM AUTHOR]

With the development of aeronautical telecommunication and unmanned aerial vehicles (UAVs), wireless networks will be extended to three-dimensional (3-D) space. Besides, 3-D wireless networks have been widely deployed in battlefields, which consist of aircraft, UAVs, ground troops, and fleets. When nodes in these wireless networks form swarm and cooperate with each other, there will be social behavior among them and 3-D wireless social networks emerge. Although the study of wireless social networks has been initiated, the capacity of 3-D wireless social networks is unknown. In this paper, we derive the achievable capacity of 3-D wireless social networks. It shows that the capacity is a function of the pathloss exponent, the number of nodes, the social group concentration, the contact concentration, and the size of social group. When the social group concentration or the contact concentration exceeds a threshold, the wireless social network is scalable and the capacity of wireless social network is much larger than that of the wireless network without social behavior. Besides, we discover the existence of a singular point for social group concentration and contact concentration, where the network capacity skips to a larger value than the neighborhood. The results reveal the interplay between wireless communication and social connection in 3-D space, which brings an insight into the design of 3-D wireless networks. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic model of a motor-gear system is proposed. The model combines a nonlinear permeance network model (PNM) of a squirrel-cage induction motor and a coupled lateral-torsional dynamic model of a planetary geared rotor system. The external excitations including voltage transients and load saltations, as well as the internal excitations such as spatial effects, magnetic circuits topology and material nonlinearity in the motor, and time-varying mesh stiffness and damping in the planetary gear system are considered in the proposed model. Then, the simulation results are compared with those predicted by the electromechanical model containing a dynamic motor model with constant inductances. The comparison showed that the electromechanical system model with the PNM motor model yields more reasonable results than the electromechanical system model with the lumped-parameter electric machine. It is observed that electromechanical coupling effect can induce additional and severe gear vibrations. In addition, the external conditions, especially the voltage transients, will dramatically affect the dynamic characteristics of the electromechanical system. Finally, some suggestions are offered based on this analysis for improving the performance and reliability of the electromechanical system. [ABSTRACT FROM AUTHOR]

A simple diode-based nonlinear verification device (NVD) design for nonlinear vector network analyzers is presented together with an improved figure of merit (FOM) parameter that is insensitive to impedance match and isolates variation of the device’s nonlinear parameters. The stability over 84 h and load–pull performance of this new design have been evaluated. [ABSTRACT FROM PUBLISHER]

Motivated by missing data recovery in power system monitoring, we study the problem of recovering missing entries of high-dimensional signals that exhibit low-dimensional nonlinear structures. We propose a novel model, termed as “union and sums of subspaces,” to characterize practical nonlinear datasets. In this model, each data point belongs to either one of a few low-dimensional subspaces or the sum of a subset of subspaces. We propose convex-optimization-based methods to recover missing entries under this model. We theoretically analyze the recovery guarantee of our proposed methods with both noiseless and noisy measurements. Numerical experiments on synthetic data and simulated power system data are conducted to verify the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

The nonlinear complexity of a periodic sequence s is the length of the shortest feedback shift register that can generate s, and its value is upper bounded by the least period of s minus 1. In this paper, a recursive approach that generates all periodic sequences with maximum nonlinear complexity is presented, and the total number of such sequences is determined. The randomness properties of these sequences are also examined. [ABSTRACT FROM PUBLISHER]

A new generalised Lorenz three‐dimensional integer‐order non‐linear system is constructed, and integer‐order derivatives are simply replaced by fractional‐order ones. Transfer functions in Laplace domain have been calculated and compared for a set of fractional orders qi in (0, 1) with decrement of 0.025 for the maximum error Y = 1 dB/2 dB/3 dB. A novel basic fractional‐order unit circuit named 'Assemble Type' is designed consulting to the four existing unit circuits, which are 'Chain Type', 'Tree Type', 'Mixed Type' and 'New Type'. Meanwhile, it is compared with the four existing unit circuits in circuit theory and applications. The novel fractional‐order unit circuit is applied to the circuit simulation of Lorenz system combined with the other four unit circuits, and circuit simulation shows that they have very resemble chaotic behaviours, actually, the scheme can be applied in any tiny values of qi, which shows that the fractional‐order system has better effectiveness, flexibility and universality. Finally, using stability analytical method based on two stability theorems and necessary conditions to exhibit validity and feasibility of this novel‐type design. [ABSTRACT FROM AUTHOR]

This paper is concerned with the event-triggered H\infty control problem for discrete-time nonlinear networked control systems with unreliable communication links. First, an event-triggered scheme is proposed to determine whether the sampled data should be released into the network or not. Second, when the released data is transmitted in the network, a Bernoulli process is employed to model the phenomenon of data losses. Third, considering the instants at which the sampled data is not released or data losses occur, a new random process is first developed to model the input data sequence of the controller under the effect of the buffer. Consequently, a novel method is presented to address the stability analysis and control synthesis problems based on the polynomial fuzzy model approach. Finally, some simulation results are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Reluctance network (RN) method is suitable for the modeling of complex magnetic field. In traditional RN models, the meshes cover the whole cross-sectional area of the electric machines, so much time is required. In order to improve the modeling efficiency, a new modular RN structure and two mesh generation methods are proposed in this paper. The size and shape of the mesh elements are identical and their permeance calculations are unified. These characteristics can greatly enhance the modeling flexibility of the RN. Then, the proposed methods and the simple magnetic circuit method are combined to analyze the performances of a linear permanent magnet vernier machine. Finally, finite-element analysis and experimental tests are carried out to evaluate the proposed method. Results show that the proposed method has high accuracy and high modeling speed. [ABSTRACT FROM PUBLISHER]

High-speed demand driven by bandwidth-consuming applications, such as video streaming and cloud computing, is exceeding available network capacity, forcing operators to implement innovative technologies to increase the throughput offered to end users. In particular, passive optical networks (PONs) with a full-duplex capacity of 10 Gb/s over a 40-km span are expected to be developed shortly. However, the range of such systems is severely penalized due to fiber and splitter losses. To increase the transmission distance of PONs, coherent optical (CO) communications have regained attention. Orthogonal frequency division multiplexing (OFDM) has been proposed as a modulation format due to its robustness to chromatic dispersion, high spectral efficiency, and flexibility. The high peak-to-average ratio of OFDM signals, however, makes them very vulnerable to fiber nonlinear distortion. In this paper, we propose what we believe is a novel, low-complexity equalizer based on the inverse Hammerstein model to partially compensate for the nonlinear distortion in CO-OFDM PONs. Numerical simulations using the split-step Fourier transform method reveal a potential link increase of 20 and 5 km for bit rates of 10 and 40 Gb/s, respectively, when compared to linear equalization. [ABSTRACT FROM PUBLISHER]

This paper is concerned with event-triggered H ∞ control for a class of nonlinear networked control systems. An event-triggered transmission scheme is introduced to select 'necessary' sampled data packets to be transmitted so that precious communication resources can be saved significantly. Under the event-triggered transmission scheme, the closed-loop system is modeled as a system with an interval time-varying delay. Two novel integral inequalities are established to provide a tight estimation on the derivative of the Lyapunov-Krasovskii functional. As a result, a novel sufficient condition on the existence of desired event-triggered H ∞ controllers is derived in terms of solutions to a set of linear matrix inequalities. No parameters need to be tuned when controllers are designed. The proposed method is then applied to the robust stabilization of a class of nonlinear networked control systems, and some linear matrix inequality-based conditions are formulated to design both event-triggered and time-triggered H ∞ controllers. Finally, two numerical examples are given to demonstrate the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Feature extraction is the key technique for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP). Traditional feature extraction algorithms usually utilize shallow architectures, which result in the limited capability to characterize HRRP data and restrict the generalization performance for RATR. Aiming at those issues, in this paper deep networks are built up for HRRP target recognition by adopting multi-layered nonlinear networks for feature learning. To learn the stable structure and correlation of targets from unlabeled data, a deep network called Stacked Corrective Autoencoders (SCAE) is further proposed via taking the advantage of the HRRP's properties. As an extension of deep autoencoders, SCAE is stacked by a series of Corrective Autoencoders (CAE) and employs the average profile of each HRRP frame as the correction term. The covariance matrix of each HRRP frame is considered for establishing an effective loss function under the Mahalanobis distance criterion. We use the measured HRRP data to show the effectiveness of our methods. Furthermore, we demonstrate that with the proper optimization procedure, our model is also effective even with a moderately incomplete training set. [ABSTRACT FROM AUTHOR]

In many social contexts, social influence seems to be inescapable: the behavior of others influences us to modify ours, and vice-versa. However, social psychology is full of examples of phenomena where individuals experience a discrepancy between their public behavior and their private opinion. This raises two central questions. First, how does an individual reason about the behavior of others and their private opinions in situations of social influence? And second, what are the laws of the resulting information dynamics? In this paper, we address these questions by introducing a formal framework for representing reasoning about an individual's private opinions and public behavior under the dynamics of social influence in social networks. Moreover, we dig deeper into the involved information dynamics by modeling how individuals can learn about each other based on this reasoning. This compels us to introduce a new formal notion of reflective social influence. Finally, we initialize the work on proof theory and automated reasoning for our framework by introducing a sound and complete tableaux system for a fragment of our logic. Furthermore, this constitutes the first tableau system for the 'Facebook logic' of J. Seligman, F. Liu, and P. Girard. [ABSTRACT FROM AUTHOR]

In this paper, we investigate global-exponential stabilisation of a class of nonlinear networked control systems. The network-induced delays are assumed to be random and significantly smaller than the sampling period. First, sufficient conditions are presented to ensure global-exponential stability for a class of hybrid systems with time delay. Then, the networked control systems are modelled as the hybrid systems with time delay. By the techniques of adding a power integrator and a recursive argument, a sampled-data state feedback control law is presented. Sufficient conditions are given to ensure global-exponential stability of the closed-loop system by constructing a Lyapunov–Krasovskii function. Finally, a numerical example is presented to show the validity of the new methods. [ABSTRACT FROM AUTHOR]

While the capacity, feasibility, and methods to obtain codes for network coding problems are well studied, the decoding procedure and complexity have not garnered much attention. In this paper, we pose the decoding problem at a sink node in a network as a marginalize product function (MPF) problem over the Boolean semiring and use the sum product (SP) algorithm on a suitably constructed factor graph to perform iterative decoding. The number of operations required to perform SP decoding is reduced using traceback. The number of operations required to perform SP decoding with and without traceback is obtained. For nonlinear network codes, we define fast decodability of a network code at sinks demanding all the messages and identify a sufficient condition for the same. Next, we consider the network function computation problem wherein the sink nodes demand a function of the messages. We present an MPF formulation for function computation at the sink nodes and use the SP algorithm to obtain the value of the demanded function. Though the proposed method can be used for decoding both linear and nonlinear network codes, it is advantageous only for the case of nonlinear network codes. [ABSTRACT FROM PUBLISHER]

Presents the table of contents for this issue of this publication. [ABSTRACT FROM PUBLISHER]

The problem of fuzzy observer-based controller design is investigated for nonlinear networked control systems subject to imperfect communication links and parameter uncertainties. The nonlinear networked control systems with parameter uncertainties are modeled through an interval type-2 (IT2) Takagi–Sugeno (T-S) model, in which the uncertainties are handled via lower and upper membership functions. The measurement loss occurs randomly, both in the sensor-to-observer and the controller-to-actuator communication links. Specially, a novel data compensation strategy is adopted in the controller-to-actuator channel. The observer is designed under the unmeasurable premise variables case, and then, the controller is designed with the estimated states. Moreover, the conditions for the existence of the controller can ensure that the resulting closed-loop system is stochastically stable with the predefined disturbance attenuation performance. Two examples are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]