Optimization-based mode selection scheme for OAM millimeter wave system in the off-axis misalignment case

Millimeter-wave transmission combined with Orbital Angular Momentum(OAM)has the advantage of reducing the loss of beam power and increasing the system capacity.However,to fulfill this advantage,the antennas at the transmitter and receiver must be parallel and coaxial;otherwise,the accuracy of mode detection at the receiver can be seriously influenced.In this paper,we design an OAM millimeter-wave communication system for overcoming the above limitation.Specifically,the first contribution is that the power distribution between different OAM modes and the capacity of the system with different mode sets are analytically derived for performance analysis.The second contribution lies in that a novel mode selection scheme is proposed to reduce the total interference between different modes.Numerical results show that system performance is less affected by the offset when the mode set with smaller modes or larger intervals is selected.

HgaNets:Fusion of Visual Data and Skeletal Heatmap for Human Gesture Action Recognition

Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data,failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility toefficiently process both uniformand disparate input patterns.Thus, in this paper, an attention-enhanced pseudo-3Dresidual model is proposed to address the GAR problem, called HgaNets. This model comprises two independentcomponents designed formodeling visual RGB (red, green and blue) images and 3Dskeletal heatmaps, respectively.More specifically, each component consists of two main parts: 1) a multi-dimensional attention module forcapturing important spatial, temporal and feature information in human gestures;2) a spatiotemporal convolutionmodule that utilizes pseudo-3D residual convolution to characterize spatiotemporal features of gestures. Then,the output weights of the two components are fused to generate the recognition results. Finally, we conductedexperiments on four datasets to assess the efficiency of the proposed model. The results show that the accuracy onfour datasets reaches 85.40%, 91.91%, 94.70%, and 95.30%, respectively, as well as the inference time is 0.54 s andthe parameters is 2.74M. These findings highlight that the proposed model outperforms other existing approachesin terms of recognition accuracy.

CWoT-Share: Context-Based Web of Things Resource Sharing in Blockchain Environment

Web of Things(WoT)resources are not only numerous,but also have a wide range of applications and deployments.The centralized WoT resource sharing mechanism lacks flexibility and scalability,and hence cannot satisfy requirement of distributed resource sharing in large-scale environment.In response to this problem,a trusted and secure mechanism for WoT resources sharing based on context and blockchain(CWoT-Share)was proposed.Firstly,the mechanism can respond quickly to the changes of the application environment by dynamically determining resource access control rules according to the context.Then,the flexible resource charging strategies,which reduced the fees paid by the users who shared more resources and increased the fees paid by users who frequently used resources maliciously,were used to fulfill efficient sharing of WoT resources.Meanwhile,the charging strategies also achieve load balancing by dynamic selection of WoT resources.Finally,the open source blockchain platform Ethereum was used for the simulation and the simulation results show that CWoT-Share can flexibly adapt to the application environment and dynamically adjust strategies of resource access control and resource charging.

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

Synthesis and Microstructure Analysis of Autoclaved Aerated Concrete with Carbide Slag Addition

Synthesis of autoclaved aerated concrete （AAC） has been carried out with carbide slag addition, and the carbide slag could be used as a main material to produce the AAC with the compressive strength about 2 MPa and the density below 0.6 g.cm-3. In this study, quartz sand acted as frame structure phase in the matrix, and quartz addition also influenced the Si/Ca of starting material. Tobermorite and CSH gel were formed readily at 62%, which seemed to enhance the compressive strength of samples. Curing time seemed to affect the morphology of phase produced, and specimen with the plate-like tobermorite formed at 10 h appeared to have a better compressive strength development than the fiber-like one at 18 h. The higher curing temperature seemed to favor the tobermorite and CSH gel formation, which also exerted a significant effect on the strength development of the samples. On the micro-scale, the formed CSH gel was filled in the interface of the matrix, and the tobermorite appeared to grow in internal-surface of the pores and interstices. The tobermorite or/and CSH formation seemed to densify the matrix, and therefore enhanced the strength of the samples.

Distributed event-triggered consensus tracking of second-order multi-agent systems with a virtual leader

This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which is intermittently examined at constant sampling instants. Only partial neighbor information and local measurements are required for event detection. Then the corresponding event-triggered consensus tracking protocol is presented to guarantee second-order multi-agent systems to achieve consensus tracking. Numerical simulations are given to illustrate the effectiveness of the proposed strategy.

Optimization of rice wine fermentation process based on the simultaneous saccharification and fermentation kinetic model

Chinese rice wine making is a typical simultaneous saccharification and fermentation （SSF） process. During the fermentation process, temperature is one of the key parameters which decide the quality of Chinese rice wine. To optimize the SSF process for Chinese rice wine brewing, the effects of temperature on the kinetic parameters of yeast growth and ethanol production at various temperatures were determined in batch cultures using a mathematical model. The kinetic parameters as a function of temperature were evaluated using the software Origin8.0. Combing these functions with the mathematical model, an appropriate form of the model equations for the SSF considering the effects of temperature were developed. The kinetic parameters were found to fit the experimental data satisfactorily with the developed temperature-dependent model. The temperature profile for maximizing the ethanol production for rice wine fermentation was determined by genetic algorithm. The optimum temperature profile began at a low temperature of 26℃ up to 30 h. The operating temperature increased rapidly to 31.9 ℃, and then decreased slowly to 18℃ at 65 h. Thereafter, the temperature was maintained at 18 ℃ until the end of fermentation. A maximum ethanol production of 89.3 g.L 1 was attained. Conceivably, our model would facilitate the improvement of Chinese rice wine production at the industrial scale.

Performance Analysis and Comparison of Two Fault-Tolerant Model Predictive Control Methods for Five-Phase PMSM Drives

Model predictive current control(MPCC)and model predictive torque control(MPTC)are two derivatives of model predictive control.These two control methods have demonstrated their strengths in the fault-tolerant control of multiphase motor drives.To explore the inherent link,the pros and cons of two strategies,the performance analysis and comparative investigation of MPCC and MPTC are conducted through a five-phase permanent magnet synchronous motor with open-phase fault.In MPCC,the currents of fundamental and harmonic subspaces are simultaneously employed and constrained for a combined regulation of the open-circuit fault drive.In MPTC,apart from the torque and the stator flux related to fundamental subspace,the x-y currents are also considered and predicted to achieve the control of harmonic subspace.The principles of two methods are demonstrated in detail and the link is explored in terms of the cost function.Besides,the performance by two methods is experimentally assessed in terms of steady-state,transition,and dynamic tests.Finally,the advantages and disadvantages of each method are concluded.

Research on Data Extraction and Analysis of Software Defect in IoT Communication Software

Software defect feature selection has problems of feature space dimensionality reduction and large search space.This research proposes a defect prediction feature selection framework based on improved shuffled frog leaping algorithm(ISFLA).Using the two-level structure of the framework and the improved hybrid leapfrog algorithm's own advantages,the feature values are sorted,and some features with high correlation are selected to avoid other heuristic algorithms in the defect prediction that are easy to produce local The case where the convergence rate of the optimal or parameter optimization process is relatively slow.The framework improves generalization of predictions of unknown data samples and enhances the ability to search for features related to learning tasks.At the same time,this framework further reduces the dimension of the feature space.After the contrast simulation experiment with other common defect prediction methods,we used the actual test data set to verify the framework for multiple iterations on Internet of Things(IoT)system platform.The experimental results show that the software defect prediction feature selection framework based on ISFLA is very effective in defect prediction of IoT communication software.This framework can save the testing time of IoT communication software,effectively improve the performance of software defect prediction,and ensure the software quality.

Fault-tolerant finite-time dynamical consensus of double-integrator multi-agent systems with partial agents subject to synchronous self-sensing function failure

This paper investigates fault-tolerant finite-time dynamical consensus problems of double-integrator multi-agent systems(MASs)with partial agents subject to synchronous self-sensing function failure(SSFF).A strategy of recovering the connectivity of network topology among normal agents based on multi-hop communication and a fault-tolerant finitetime dynamical consensus protocol with time-varying gains are proposed to resist synchronous SSFF.It is proved that double-integrator MASs with partial agents subject to synchronous SSFF using the proposed strategy of network topology connectivity recovery and fault-tolerant finite-time dynamical consensus protocol with the proper time-varying gains can achieve finite-time dynamical consensus.Numerical simulations are given to illustrate the effectiveness of the theoretical results.

A multi-resource scheduling scheme of Kubernetes for IIoT

With the rapid development of data applications in the scene of Industrial Internet of Things(IIoT),how to schedule resources in IIoT environment has become an urgent problem to be solved.Due to benefit of its strong scalability and compatibility,Kubernetes has been applied to resource scheduling in IIoT scenarios.However,the limited types of resources,the default scheduling scoring strategy,and the lack of delay control module limit its resource scheduling performance.To address these problems,this paper proposes a multi-resource scheduling(MRS)scheme of Kubernetes for IIoT.The MRS scheme dynamically balances resource utilization by taking both requirements of tasks and the current system state into consideration.Furthermore,the experiments demonstrate the effectiveness of the MRS scheme in terms of delay control and resource utilization.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises and sampled-data

This paper is concerned with the stochastic bounded consensus tracking problems of leader-follower multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbours or the virtual leader with a time-varying reference state, and the measurements are corrupted by random noises. The probability limit theory and the algebra graph theory are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking. It is shown that the maximum allowable upper boundary of the sampling period simultaneously depends on the constant feedback gains and the network topology. Furthermore, the effects of the sampling period on the tracking performance are analysed. It turns out that from the view point of the sampling period, there is a trade-off between the tracking speed and the static tracking error. Simulations are provided to demonstrate the effectiveness of the theoretical results.

Efficient Deployment of Multiple UAVs for IoT Communication in Dynamic Environment

The application of unmanned aerial vehicle(UAV)-mounted base stations is emerging as an effective solution to provide wireless communication service for a target region containing some smart objects(SOs)in internet of things(IoT).This paper investigates the efficient deployment problem of multiple UAVs for IoT communication in dynamic environment.We first define a measurement of communication performance of UAVto-SO in the target region which is regarded as the optimization objective.The state of one SO is active when it needs to transmit or receive the data;otherwise,silent.The switch of two different states is implemented with a certain probability that results in a dynamic communication environment.In the dynamic environment,the active states of SOs cannot be known by UAVs in advance and only neighbouring UAVs can communicate with each other.To overcome these challenges in the deployment,we leverage a game-theoretic learning approach to solve the position-selected problem.This problem is modeled a stochastic game,which is proven that it is an exact potential game and exists the best Nash equilibria(NE).Furthermore,a distributed position optimization algorithm is proposed,which can converge to a pure-strategy NE.Numerical results demonstrate the excellent performance of our proposed algorithm.

Fundamental Limits of Doppler Shift-Based, ToA-Based,and TDoA-Based Underwater Localization

Dear Editor,This paper is concerned with the underwater localization based on acoustic signals. Specifically, we will focus on the search of an underwater target that can constantly broadcast a beacon signal, such as a black box. Common measurements for localization are Doppler shift [1], time of arrival(ToA) [2]–[4], time difference of arrival(TDoA) [5], [6], angle of arrival(AoA) [7], etc.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with general sampling delay

In this paper we provide a unified framework for consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with a general sampling delay. First, a stochastic bounded consensus tracking protocol based on sampled data with a general sampling delay is presented by employing the delay decomposition technique. Then, necessary and sufficient conditions are derived for guaranteeing leader-follower multi-agent systems with measurement noises and a time-varying reference state to achieve mean square bounded consensus tracking. The obtained results cover no sampling delay, a small sampling delay and a large sampling delay as three special cases. Last, simulations are provided to demonstrate the effectiveness of the theoretical results.

Effects of Strong Turbulence on the Spiral Plane Mode of Whittaker-Gaussian Beam through Terrene-Atmosphere

The analytic formulae of probability distribution of spiral plane modes for the Whittaker-Gaussian （WG） beams with orbital angular momentum （OAM） in strong turbulence regime are modeled based on the modified Rytov approximation. Numerical results show that the erosstalk range of OAM modes in the vicinity of signal mode increases with the increasing refractive-index construction parameter. However, effects of change of the width of the Gaussian envelope and the parameter Wo of WG beams on normalization energy weight of signal mode can be ignored. We find theoretically that signal spiral plane mode of WG beams at each OAM level approximatively has the same normalization energy weight, implying that the channels with WG （pseudo non-diffraction） beam have higher channel capacity than the channels with the Laguerre-Gaussian beam.

Coverage-optimization based guidance of mobile agents for improved control of distributed parameter systems

The control problem of a class of parabolic distributed parameter systems (DPSs) is investigated by using mobile agents with capabilities of sensing and actuating. The guidance strategies of mobile agents based on coverage optimization methods are proposed to improve the control performance of the system and make the state norm of the system converge to zero faster. The coverage optimization problems are constructed based on the measurement of each agent. By solving the coverage optimization problems, the local optimal moving direction of each agent can be obtained. Then the gradient-based agent motion control laws are established. With the indicator function and the surface delta function, this method is generalized to n-dimensional space, and suitable for any sensing region with piecewise smooth boundaries. The stability and control performance of the system are analyzed. Numerical simulations show the effectiveness of the proposed methods.

Asymptotic bounded consensus tracking of double-integrator multi-agent systems with bounded-jerk target based on sampled-data without velocity measurements

This paper investigates asymptotic bounded consensus tracking（ABCT） of double-integrator multi-agent systems（MASs） with an asymptotically-unbounded-acceleration and bounded-jerk target（AUABJT） available to partial agents based on sampled-data without velocity measurements. A sampled-data consensus tracking protocol（CTP） without velocity measurements is proposed to guarantee that double-integrator MASs track an AUABJT available to only partial agents.The eigenvalue analysis method together with the augmented matrix method is used to obtain the necessary and sufficient conditions for ABCT. A numerical example is provided to illustrate the effectiveness of theoretical results.

Consensus of heterogeneous multi-agent systems based on sampled data with a small sampling delay

In this paper, consensus problems of heterogeneous multi-agent systems based on sampled data with a small sampling delay are considered. First, a consensus protocol based on sampled data with a small sampling delay for heterogeneous multi-agent systems is proposed. Then, the algebra graph theory, the matrix method, the stability theory of linear systems, and some other techniques are employed to derive the necessary and sufficient conditions guaranteeing heterogeneous multi-agent systems to asymptotically achieve the stationary consensus. Finally, simulations are performed to demonstrate the correctness of the theoretical results.

A novel model and behavior analysis for a swarm of multi-agent systems with finite velocity

Inspired by the fact that in most existing swarm models of multi-agent systems the velocity of an agent can be infinite, which is not in accordance with the real applications, we propose a novel swarm model of multi-agent systems where the velocity of an agent is finite. The Lyapunov function method and LaSalle＇s invariance principle are employed to show that by using the proposed model all of the agents eventually enter into a bounded region around the swarm center and finally tend to a stationary state. Numerical simulations are provided to demonstrate the effectiveness of the theoretical results.