Power Aggregation Operators of Simplified Neutrosophic Sets and Their Use in Multi-attribute Group Decision Making

The simplified neutrosophic set(SNS) is a useful generalization of the fuzzy set that is designed for some practical situations in which each element has different truth membership function, indeterminacy membership function and falsity membership function. In this paper, we develop a series of power aggregation operators called simplified neutrosophic number power weighted averaging(SNNPWA) operator, simplified neutrosophic number power weighted geometric(SNNPWG) operator, simplified neutrosophic number power ordered weighted averaging(SNNPOWA) operator and simplified neutrosophic number power ordered weighted geometric(SNNPOWG) operator. We present some useful properties of the operators and discuss the relationships among them. Moreover, an approach to multiattribute group decision making(MAGDM) within the framework of SNSs is developed by the above aggregation operators.Finally, a practical application of the developed approach to deal with the problem of investment is given, and the result shows that our approach is reasonable and effective in dealing with uncertain decision making problems.

The design of metamaterial cloaks embedded in anisotropic medium

By using coordinate transformation method, this paper obtains an useful equation of designing meta-material cloaks embedded in anisotropic medium. This equation is the generalization of what was introduced early by Pendry et al （2006 Science 312 1780） and can be more widely used. As an example of its applications, this paper deduces the material parameter equation for cylinder cloaks embedded in anisotropic medium, and then offers the numerical simulation. The results show that such a cylinder cloak has perfect cloaking performance and therefore verifies the method proposed in this paper.

Fixed-Time Neural Control of a Quadrotor UAV With Input and Attitude Constraints

Dear Editor, This letter is concerned with the attitude control of a quadrotor unmanned aerial vehicle(UAV) subject to the input constraint, attitude constraint and model uncertainty. Firstly, we construct an auxiliary system to eliminate the adverse impact of the input saturation.Secondly, we introduce the nonlinear state-dependent function to deal with the attitude constraint directly. Thirdly, the neural network is utilized to identify the unknown terms in the system. Finally.

Dynamic Event-Triggered Control of Continuous-Time Systems With Random Impulses

In this paper,the networked control problem under event-triggered schemes is considered for a class of continuoustime linear systems with random impulses.In order to save communication costs and lighten communication burden,a dynamic event-triggered scheme whose threshold parameter is dynamically adjusted by a given evolutionary rule,is employed to manage the transmission of data packets.Moreover,the evolution of the threshold parameter only depends on the sampled measurement output,and hence eliminates the influence of impulsive signals on the event-triggered mechanism.Then,with the help of a stochastic analysis method and Lyapunov theory,the existence conditions of desired controller gains are received to guarantee the corresponding input-to-state stability of the addressed system.Furthermore,according to the semi-definite programming property,the desired controller gains are calculated by resorting to the solution of three linear matrix inequalities.In the end,the feasibility and validity of the developed control strategy are verified by a simulation example.

Influence of pumping laser bandwidth on the quantum fluctuation chracteristic of a non-degenerate optical parametric amplifier

Usually the quantum fluctuation characteristic of a non-degenerate optical parametric amplifier is analysed under the assumption of monochromatic pumping. However, in experiments, a driving beam with finite bandwidth is used to obtain the non-degenerate signal and idler beam amplifications. On account of that, we derive an analytical solution for the non-degenerate optical parametric amplification system with finite bandwidth laser pumping, and evaluate the associated quantum fluctuation. Finally, the application of the V1 criterion to bipartite entanglement is discussed.

Effects of A-site non-stoichiometry on the structural and electrical properties of 0.96K_(0.5)Na_(0.5)NbO_3-0.04LiSbO_3 lead-free piezoelectric ceramics

Effects of A-site non-stoichiometry on the structural and electrical properties of 0.96K0.5＋xNa0.5＋xNbO3- 0.04LiSbO3 lead-free piezoelectric ceramics were examined for 0 ≤ x ≤0.02. The piezoelectric coefficients exhibited a maximum, d33 = 187 pC/N at x = 0.0075, coinciding with the maximum of the grain size and the apparent density at x = 0.0075. The apparent density and the piezoelectric coefficients decreased with increasing x at higher x which was likely due to the crystal geometrical distortion of 0.96K0.5＋xNa0.5＋xNbO3-0.04LiSbO3. In addition, super-large grains were found and this may be due to liquid phase sintering. Excess （K＋＋Na＋） attracted a sum of space charges to keep the charge neutral, resulting in charge leakage during the course of ceramic polarization, influencing the piezoelectric and ferroelectric properties. These findings are of importance for guiding the design of Ko.sNao.sNbO3-based lead-free ceramics with enhanced electrical properties.

The propagation characteristic of the EPR entanglement for a composite non-degenerate parametric optical amplification system

This paper applies the minimum variance V1 criterion to monitor the evolution of signal and idler modes of a composite non-degenerate optical parametric amplification （NOPA） system. The analytics and numerical calculation show the influence of the transition time, the vacuum fluctuations, and the thermal noise level on the EPR entanglement of the composite NOPA system. It finds that the entanglement and the squeezing degrade as the minimum variance V1 increases.

Distributed Adaptive Asymptotic Tracking of 2-D Vehicular Platoon Systems With Actuator Faults and Spacing Constraints

Dear Editor,This letter investigates the adaptive asymptotic tracking slidingmode control for nonlinear 2-D vehicular platoon systems subject to actuator faults.Firstly,by using the Nussbaum function,the disadvantageous factors brought by the unknown direction actuator faults can be attenuated.

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

Generative Multi-Modal Mutual Enhancement Video Semantic Communications

Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints

Abstract--In this paper, an adaptive neural network （NN） control approach is proposed for nonlinear pure-feedback sys- tems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions （BLFs） with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closed- loop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach. Index TermsmAdaptive control, neural networks （NNs）, non- linear pure-feedback systems, time-varying constraints.

The simplified material parameter equation for elliptical cylinder cloaks

We simplify the material parameter equation for elliptical cylinder cloaks under transverse-electric and transverse- magnetic models, respectively, and confirm these simplified equations by numerical simulations. As a result, the number of the component parameters is reduced from three to two, which simplifies the design of meta-materials and thus opens up the possibility of achieving elliptical cylinder cloaks in an easy way.

Near Optimal Output Feedback Control of Nonlinear Discrete-time Systems Based on Reinforcement Neural Network Learning

In this paper, the output feedback based finitehorizon near optimal regulation of nonlinear affine discretetime systems with unknown system dynamics is considered by using neural networks(NNs) to approximate Hamilton-JacobiBellman(HJB) equation solution. First, a NN-based Luenberger observer is proposed to reconstruct both the system states and the control coefficient matrix. Next, reinforcement learning methodology with actor-critic structure is utilized to approximate the time-varying solution, referred to as the value function, of the HJB equation by using a NN. To properly satisfy the terminal constraint, a new error term is defined and incorporated in the NN update law so that the terminal constraint error is also minimized over time. The NN with constant weights and timedependent activation function is employed to approximate the time-varying value function which is subsequently utilized to generate the finite-horizon near optimal control policy due to NN reconstruction errors. The proposed scheme functions in a forward-in-time manner without offline training phase. Lyapunov analysis is used to investigate the stability of the overall closedloop system. Simulation results are given to show the effectiveness and feasibility of the proposed method.

STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.

Optimal Formation Control for Second-Order Multi-Agent Systems With Obstacle Avoidance

Dear Editor,The optimal formation control design problem is studied for a class of second-order multi-agent systems(MASs) with obstacle avoidance.Based on the actor-critic framework, an optimized formation controller is proposed by constructing a novel performance index function. Furthermore, the stability of MAS is proved by constructing the Lyapunov function. The simulation results are provided to depict the effectiveness of the proposed strategies.

Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity:A Survey

With the booming of cyber attacks and cyber criminals against cyber-physical systems(CPSs),detecting these attacks remains challenging.It might be the worst of times,but it might be the best of times because of opportunities brought by machine learning(ML),in particular deep learning(DL).In general,DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data.DL models are adopted quickly to cyber attacks against CPS systems.In this survey,a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context.A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems.The methodology includes CPS scenario analysis,cyber attack identification,ML problem formulation,DL model customization,data acquisition for training,and performance evaluation.The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules.Moreover,excellent performance is achieved partly because of several highquality datasets that are readily available for public use.Furthermore,challenges,opportunities,and research trends are pointed out for future research.

Planar Metamaterial Absorber Based on Lumped Elements

We present the design of a planar metamaterial absorber based on lumped elements, which shows a wide-band polarization-insensitive and wide-angle strong absorption. This absorber consists of metal electric resonators, the dielectric substrate, the metal film and lumped elements. The simulated absorbances under two different loss conditions indicate that high absorbance in the absorption band is mainly due to lumped resistances. The simulated absorbances under three different load conditions indicate that the local resonance circuit （lumped resistance and capacitance） could boost up the resonance of the whole RLC circuit. The simulated voltage in lumped elements indicates that the transformation efficiency from electromagnetic energy to electric energy in the absorption band is high, and electric energy is subsequently consumed by lumped resistances. This absorber may have potential applications in many military fields.

Multiband terahertz metamaterial absorber

This paper reports the design of a multiband metamaterial （MM） absorber in the terahertz region. Theoretical and simulated results show that the absorber has four distinct and strong absorption points at 1.69, 2.76, 3.41 and 5.06 THz, which are consistent with ＇fingerprints＇ of some explosive materials. The retrieved material parameters show that the impedance of MM could be tuned to match approximately the impedance of the free space to minimise the reflectance at absorption frequencies and large power loss exists at absorption frequencies. The distribution of the power loss indicates that the absorber is an excellent electromagnetic wave collector： the wave is first trapped and reinforced in certain specific locations and then consumed. This multiband absorber has applications in the detection of explosives and materials characterisation.

Improvement in the piezoelectric temperature stability of(K_(0.5)Na_(0.5))NbO_3 ceramics

Through an analysis of the temperature stability of(K0.5Na0.5)NbO3(KNN)based ceramics and KNN solid solutions,we propose a method to enhance the temperature stability of KNN materials.These materials are valuable for their piezoelectric properties.To verify the feasibility of this method,0.9(K1-xNax)NbO3-0.06LiNbO3-0.04CaTiO3(KNLN-CaTiO3)ceramics were designed,and their structure and properties were studied.The results show that KNLN-CaTiO3(x=0.54)ceramics have a good temperature stability over a wide temperature range(25-320°C).Also,they have good piezoelectric properties(d33=152 pC/N in x=0.54).This result confirms the feasibility of our proposed solution for improving the piezoelectric properties of KNN-based ceramics that have poor temperature stability.

Direct Urca Processes with Hyperon-Hyperon Interactions

With a weak hyperon-hyperon （YY） interaction deduced from the new experimental data of AA potential, this paper has performed a calculation of the direct Urca （DURCA） processes in the framework of the relativistic mean field theory （RMFT） including σ＊ and φ mesons, in comparison with the results calculated with the strong YY interaction and with no （σ＊ ,φ ） mesons included. In neutron star matter, the weak YY interaction supplies the additional repulsive interaction between hyperons while the strong YY interaction supplies the attractive one. With the weak YY interaction, the effective masses of hyperons are larger than those with the strong YY interaction while smaller than those with no （σ＊ ,φ） mesons included. The threshold star masses for the DURCA processes involving nucleons and A are not sensitive to the strength of the YY interaction. The weak YY interaction leads to larger threshold masses for the DURCA process involving [1]1 and [1]0 than the other two cases. The process involving [1]0 can take place in the neutron star only when the weak YY interaction is used. The weak YY interaction is able to bring in the agreement with the observation of the neutron star with larger mass and faster cooling than the strong YY interaction.