Flexible distributed fencing control for Euler–Lagrange multi‐agent systems with moving target

This letter studies the distributed adaptive consensus problem for multi-agent systems (MASs) subject to nonlinearly parameterized uncertainties. In the existing research, an adaptive consensus scheme was developed for the same scenario but under the assumption that the uncertainties must be linearly parameterized in their unknown parameters. In this letter, the assumption is removed by using a novel distributed adaptive update law, which makes the scheme applicable for more general nonlinear MASs. This letter contains a general design paradigm using the certainty equivalence principle and specific implementation of it on nonlinear second-order MASs. The results are supported by rigorous mathematical analysis and numerical simulation.

A method for the sound ranging of explosions is described, in which the direction-angle of the explosion is given in terms of the ratio of the differences in the arrival times at two pairs of microphones, geophones, or hydrophones. It is unnecessary to know either the phase-velocity in the intervening medium or the absolute values of the time differences. However, the paths and the phase-velocities must be such that the time differences are the same as they would be for a wave-front traveling from explosion to the microphones at some constant velocity. A rapid graphical method for determining the location of the explosion is described. The precision of the determinations is discussed. Applications of the method to the scoring of bomb hits on bombing practice areas are suggested. The effect of the motion of the medium is considered.

Crowdsourcing has become an effective tool to utilize human intelligence to perform tasks that are challenging for machines. In the integrated crowdsourcing systems, the requesters are non- monopolistic and may show preferences over the workers. We are the first to design the incentive mechanisms, which consider the issue of stimulating the biased requesters in the competing crowdsourcing market. In this paper, we explore truthful task assignment mechanisms to maximize the total value of accomplished tasks for this new scenario. We present three models of crowdsourcing, which take the preferences of the requesters and the workload constraints of the workers into consideration. We design a task assignment mechanism, which follows the matching approach to solve the Valuation Maximizing Assignment (VMA) problem for each of the three models. Through both rigorous theoretical analyses and extensive simulations, we demonstrate that the proposed assignment mechanisms achieve computational efficiency, workload feasibility, preference (universal) truthfulness and constant approximation.

Data is an extremely important asset in a modern scientific and commercial society. The life force behind powerful artificial intelligence (AI) or machine learning (ML) algorithms is data, especially lots of data, which makes data trading significantly essential to unlocking the power of AI or ML. Data owners who offer crowdsourced data and data consumers who request data blocks negotiate with each other to make an agreement on data assignment and trading prices via a data trading platform; consequently, both sides gain profit from the process of data trading. A great many existing studies have investigated various kinds of data sharing or trading as well as protecting data privacy or constructing a decentralized data trading platform due to mistrust issues. However, existing studies neglect an important characteristic, i.e., dynamics of both data owners and data requests in trading crowdsourced data collected by IoT devices. To this end, we first construct an auction-based model to formulate the data trading process and then propose a near-optimal online data trading algorithm that not only resolves the problem of matching dynamic data owners and randomly generated data requests but also determines the data trading price of each data block. The proposed algorithm achieves several good properties, such as a constant competitive ratio for near-optimal social efficiency, incentive compatibility, and individual rationality of participants, via rigorous theoretical analysis and extensive simulations. We further design a decentralized data trading platform in order to construct a practical data trading process incorporating the proposed data trading algorithm.

Heterogeneity is found widely in populations, e.g. different individuals have diverse personalities and a different willingness to accept novel ideas or behaviors. Whereas population heterogeneity is rarely considered in studying the social contagions on complex networks, especially on multiplex networks. To explore the effect of population heterogeneity on the dynamics of social contagions, a novel model based on double-layer multiplex networks is proposed, in which information diffuses synchronously on the two layers, and each layer is assigned with different adoption thresholds. Meanwhile, populations are classified into the activists and conservatives according to their willingness to adopt new behaviors. To qualitatively understand the effect of population heterogeneity on social contagions, a generalized edge-based compartmental theory is proposed. Through rigorous theoretical analysis and extensive simulations, we find the activists in the two layers promote the adoption of behavior. More interestingly, the crossover phenomena in phase transition are found in the growth of the final adopted size when increasing the information transmission rate. When the proportion of activists is relatively large, the phase transition exhibits continuous pattern. Reducing the proportion of activists induces a hybrid transition, in which the final adoption size first grows continuously at the first threshold, and then increases discontinuously at the second threshold. In addition, we find that the network heterogeneity will change the crossover phenomena in phase transition.

Nonlinear phenomena are often encountered in various practical systems, and most of the nonlinear problems in science and engineering can be simply described by nonlinear equation, effectively solving nonlinear equation (NE) has aroused great interests of the academic and industrial communities. In this paper, a robust zeroing neural network (RZNN) activated by a new power versatile activation function (PVAF) is proposed and analyzed for finding the solutions of dynamic nonlinear equations (DNE) within fixed time in noise polluted environment. As compared with the previous ZNN model activated by other commonly used activation functions (AF), the main improvement of the presented RZNN model is the fixed-time convergence even in the presence of noises. In addition, the convergence time of the proposed RZNN model is irrelevant to its initial states, and it can be computed directly. Both the rigorous mathematical analysis and numerical simulation results are provided for the verification of the effectiveness and robustness of the proposed RZNN model. Moreover, a successful robotic manipulator path tracking example in noise polluted environment further demonstrates the practical application prospects of the proposed RZNN models.

We find the conditions for the existence of trapped modes in planar periodic particle arrays. Confined excitations of TE and TM symmetry are observed in symmetric environments, originating in lattice resonances that are signalled by the onset of new diffraction beams. This mechanism of mode formation is shown to be inhibited by the presence of a dielectric interface in an asymmetric configuration. Modes can still exist above a threshold finite distance from the interface. Both rigorous numerical simulation and analytical modeling are used to elucidate the origin and systematics of this unexpected difference in the behavior of trapped modes in self-standing and supported particle arrays.

Rabi splitting and Fano resonance are well-known physical phenomena in conventional quantum systems as atoms and quantum dots, arising from strong interaction between two quantum states. In recent years similar features have been observed in various nanophotonic and nanoplasmonic systems. Yet, realization of strong interaction between two or more Fano resonance states has not been accomplished either in quantum or in optical systems. Here we report the observation of Rabi splitting of two strongly coupled surface plasmon Fano resonance states in a three-dimensional plasmonic nanostructure consisting of vertical asymmetric split-ring resonators. The plasmonic system stably supports triple Fano resonance states and double Rabi splittings can occur between lower and upper pairs of the Fano resonance states. The experimental discovery agrees excellently with rigorous numerical simulations, and is well explained by an analytical three-oscillator model. The discovery of Fano resonance Rabi splitting could provide a stimulating insight to explore new fundamental physics in analogous atomic systems and could be used to significantly enhance light-matter interaction for optical sensing and detecting applications.

In this article, we consider physical-layer security (PLS) of the Internet-of-Things (IoT) communications based on hybrid automatic repeat request (HARQ) protocols in multiple-input single-output single-eavesdropper (MISOSE) channels. Owing to the reciprocity of wireless channels, a negative acknowledge frame can be used to obtain the channel state information (CSI) from a source with multiple antennas to an IoT destination device, and this CSI of a legitimate link is used for beamforming for the next transmission round (TR). Therefore, a higher secrecy rate can be expected from retransmissions with beamforming based on the CSI as compared to the first TR without CSI. To evaluate the secrecy throughput under time-invariant and time-varying channels, connection and secrecy outage probabilities (OPs) are derived. By exploiting the characteristics of the OPs, we suggest an approximated secrecy throughput. We then consider the optimization of the approximated secrecy throughput considering the rate parameters for legitimate and eavesdropping links. With an alternating optimization approach, an algorithm to find convergent system parameters, i.e., the rates for legitimate and eavesdropping links, is proposed. Moreover, an asymptotic analysis with the large number of transmit antennas indicates an interesting behavior of the secrecy rate with regard to the number of TRs. The analytical results are verified by rigorous numerical simulations and they can serve as a useful guideline to those involved in the design of secure IoT communication systems with HARQ in MISOSE channels.

We investigate the propagation properties of terahertz plasmon of a metallic nanowire with sub-skin-depth diameter. By taking the small radius and the huge relative permittivity into account, we establish an approximate analytical description for this kind of surface plasmon. It is shown that the main propagation properties are closely related to the product of the radius of the metallic nanowire and the complex wave number of the metal. In addition, when the radius of the metal wire is smaller than the skin-depth, the size of the modal field is simply proportional to the radius of the metal wire. We also carefully verify these analytical predictions with rigorous numerical simulations.

The coupled-mode theory (CMT) for optical waveguides is reviewed, with emphasis on the analysis of coupled optical waveguides. A brief account of the recent development of the CMT for coupled optical waveguides is given. Issues raised in the debates of the 1980’s on the merits and shortcomings of the conventional as well as the improved coupled-mode formulations are discussed. The conventional coupled-mode formulations are set up in a simple, intuitive way. The rigorous CMT is established on the basis of a linear superposition of the modes for individual waveguides. The cross-power terms appear logically as a result of modal nonorthogonality. The cross power is necessary for the self-consistency of the CMT for dissimilar waveguides. The nonorthogonal CMT, though more complicated, yields more-accurate results than the conventional orthogonal CMT for most practical applications. It also leads to the prediction of cross talk in directional couplers. The conventional orthogonal CMT is, however, reliably accurate for describing the power coupling between two weakly coupled, nearly identical waveguides. For dissimilar waveguides, a self-consistent orthogonal CMT can be derived by a redefinition of the coupling coefficients, and it predicts the coupling length and therefore the power exchange between the waveguides accurately if the two waveguides are far apart. Three typical coupler configurations—the uniform, the grating-assisted, and the tapered—are examined in detail. The accuracy, scope of validity, limitations, and extensions of the coupled-mode formulations are discussed in conjunction with each configuration. To verify the arguments in the discussions, comparisons with the exact analytical solutions and the rigorous numerical simulations are made.

Tunnel field-effect-transistor (TFET) based biosensor is proposed, and it is shown that they can surpass by several orders, the performance of those based on conventional FET (CFET) and hence, can potentially revolutionize the biosensing applications. Analytical formula is derived for the sensitivity and response time to provide physical insights in terms of material bandgap and operation regime of the TFET biosensor for achieving optimal results. At the same time, rigorous numerical simulations have been performed in order to obtain accurate values of sensitivity for both biomolecule and pH sensing operations. The time dependent response of the biosensors has also been discussed through analytical and numerical solutions. It is shown that while the CFET biosensors suffer from fundamental limitations on the maximum sensitivity and minimum detection time achievable, TFET biosensors, with their fundamentally different current injection mechanism in the form of band-to-band tunneling, can overcome such limitations and lead to over four orders of magnitude higher sensitivity and over an order of magnitude lower response time.

A dynamical change of material properties induces a special type of reflection and refraction at a temporal discontinuity. Here, we study the interaction of graphene plasmons with single and double temporal discontinuities or shocks, leading to controlled in-plane scattering. We analytically determine the Fresnel-like coefficients for graphene plasmons at these boundaries, and validate our results by rigorous numerical simulations. Temporally controlled doping of two-dimensional materials such as graphene thus leads to a new mechanism for planar and compact plasmonic devices.

Exploration and development of coalbed methane (CBM) reservoirs is ongoing in the Asia Pacific region, with low permeability, undersaturated coals being the target in some areas. Horizontal wells are typically required to produce low permeability CBM reservoirs at commercial rates, and may experience a period of single-phase (water) transient flow, followed by a transition to two-phase (water+gas) transient flow. Wells exhibiting this behavior are particularly difficult to model analytically, yet simple analytical and semi-analytical tools are desirable for making quick, but reasonably accurate forecasts. The purpose of this work is to therefore provide a new forecasting methodology for low permeability, undersaturated CBM wells. The new method for forecasting CBM wells introduced in this work is based on the concept of a dynamic drainage area (DDA), recently adapted to forecast tight black oil and gas condensate wells exhibiting two-phase flow. A critical advancement provided in the current work is modeling the transition from single-phase flow of water to two-phase flow of gas and water during the transient linear flow period, which was not attempted for the black oil/gas condensate cases. Above desorption pressure, material balance, performed on the dynamic drainage area to determine average pressure in the distance of investigation, is combined with the linear flow productivity index equation to forecast water production at each time step. For two-phase flow below desorption pressure, the two-phase version of the linear productivity equation and material balance equations for water and gas are solved iteratively for pressure, saturation and fluid production rate within the DDA. The gas material balance equation accounts for desorption effects. In order to calculate the DDA at each time step, the linear flow distance of investigation equation is used. Importantly, using the DDA method, variable operating conditions (flowing pressures) may be accounted for, which is critical for CBM wells during early stages of production. The new semi-analytical DDA model is used to history-match production data from an actual horizontal well completed in a low permeability, undersaturated, western Canadian CBM reservoir. Further, the match is compared to that obtained from rigorous numerical simulation. The history-match of the field data is reasonable with the new model and comparable to numerical simulation. The new model is however easier to setup and less data-intensive than numerical simulation, providing a practical alternative to CBM reservoir engineers who are responsible for generating a large number of forecasts.

With the development of technology and improved understanding of nuclear spin-spin interactions and their behavior in static/oscillating magnetic fields, NMR spectroscopy has emerged as a powerful tool for characterizing molecular structure in a wide range of systems of chemical, physical and biological relevance. Here in this article, we revisit the important connection between "Secular-Approximation" (a well-known fundamental concept) and NMR spectroscopy. Employing recent experimental results as the background, an alternate interpretation of the secular approximation is presented for describing and understanding the nuances of Multi-Quantum (MQ) NMR spectroscopy of quadrupolar nuclei. Since MQ NMR spectroscopy of quadrupolar nuclei forms the basis of the structural characterization of inorganic solids and clusters, we believe that the analytic theory presented herein would be beneficial both in the understanding and design of MQ NMR experiments. Additionally, the analytic results are corroborated with rigorous numerical simulations and could be employed in the quantitative interpretation of experimental results.