Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive neural network H∞ tracking control for a class of nonlinear fractional order systems

The H∞ tracking control problem is investigated for a class of incommensurate fractional order strict feedback nonlinear systems with unknown nonlinear functions and external disturbances in this paper. The adaptive neural network H∞ tracking controller of the system is designed by combining backstepping technique, adaptive neural network technique with H∞ control theory and utilizing the frequency distributed model to construct Lyapunov functions. The H∞ tracking controller proposed in this paper can guarantee that the tracking error and all the signals in the closed-loop system are bounded, and the system has an H∞ performance for external disturbances. Finally, the simulation results verify the feasibility and effectiveness of the proposed control method.
Research on the High-Precision Unmanned Operation Control Algorithm of the Vibroseis Vehicle

In order to meet the demand for high-precision operation of the vibroseis vehicle in the case of unmanned driving, an unmanned operation system that meets the working characteristics of the vibroseis vehicle is designed, and the key technical problems are studied, so that the vibroseis vehicle can operate in the complex environment of the field and improve the efficiency of geological exploration greatly. Firstly, the coordinate system and the kinematic model without sideslip of the articulated vehicle are established successively. Secondly, in order to improve the path tracking accuracy of the vibroseis vehicle, a model predictive control algorithm based on the kinematic model is designed for vehicle path tracking. In order to improve the positioning accuracy of the working point, a compensation algorithm for positioning error is proposed to reduce the positioning error caused by terrain factors. Finally, the unmanned operation platform is built and the path tracking algorithm and the compensation algorithm for positioning error are verified on the platform. The real vehicle test data shows that the path tracking algorithm proposed in this paper can control the average lateral error of the path tracking of the vibroseis vehicle within 22 cm. Compared with the PID control method, its path tracking accuracy can be improved by 59%. The average positioning error of working point is controlled within about 15 cm by adopting the compensation algorithm for positioning error.
Distributed No-Regret Learning for Stochastic Aggregative Games over Networks

In this work, we propose a distributed no-regret learning for stochastic aggregative games over time-varying networks. We consider a finite set of players repeatedly playing the network aggregative game in stochastic regimes, where each player has an expectation-valued objective function depending on its own strategy and the aggregate of all player strategies, and the players can estimate gradients of their objective functions up to a zero-mean error with bounded variance. We consider the scenario in which players cannot directly obtain the aggregate value, but they are able to share their estimates of the aggregate with their neighbors without disclosing their own strategies. We design a distributed learning algorithm based on the mirror descent and dynamical averaging tracking. We then provide analysis for both the variational regret and the cost regret for aggregative games with player-specific problem being convex, and show that the expected regret bounds can be $O\left( {\sqrt K } \right)$ for specific step-sizes. These analyses indicate the key correlations between the regret bounds, the network connectivity, and game structures, etc. In addition, we validate the almost sure convergence to the Nash equilibrium for the class of strictly monotone games. Finally, we present preliminary numerics by applying the proposed scheme to the Nash-Cournot competition problem.
Adaptive Tracking Control of Autonomous Underwater Vehicle Under Stochastic Environmental Disturbances

Many practical tasks in the ocean depend on autonomous underwater vehicle (AUV). In order to accomplish these tasks, tracking control is the most basic and critical application of AUV. However, due to the influence of stochastic environmental disturbances, it is very difficult for AUV to achieve accurate trajectory tracking in practical application. In this paper, the problem of trajectory tracking control under stochastic environmental disturbances is studied. Considering this problem, we establish an AUV model with stochastic environmental disturbances. Based on the backstepping method, the adaptive tracking controller is designed to deal with the uncertain part of stochastic disturbances. By using the Lyapunov stability analysis, it is proved that tracking errors converge to a small neighborhood of zero. This shows that the AUV can track the desired trajectory accurately. Finally, the effectiveness of the proposed theoretical method is verified by simulation.
Attacking Massive MIMO Cognitive Radio Networks by Optimized Jamming

Massive multiple-input multiple-output (MaMIMO) and cognitive radio networks (CRNs) are two promising technologies for improving spectral efficiency of next-generation wireless communication networks. In this paper, we investigate the problem of physical layer security in the networks that jointly use both technologies, named MaMIMO-CRN. Specifically, to investigate the vulnerability of this network, we design an optimized attacking scenario to MaMIMO-CRNs by a jammer. For having the most adversary effect on the uplink transmission of the legitimate MaMIMO-CRN, we propose an efficient method for power allocation of the jammer. The legitimate network consists of a training and a data transmission phase, and both of these phases are attacked by the jammer using an optimized power split between them. The resulting power allocation problem is non-convex. We thus propose three different efficient methods for solving this problem, and we show that under some assumptions, a closed-form solution can also be obtained. Our results show the vulnerability of the MaMIMO-CRN to an optimized jammer. It is also shown that increasing the number of antennas at the legitimate network does not improve the security of the network.
Neural-Network-Based Self-Learning Disturbance Rejection Design for Continuous-Time Nonlinear Constrained Systems

In this paper, we adopt a neural-network-based adaptive dynamic programming (ADP) method to solve the disturbance rejection problem for continuous-time nonlinear systems with control constraints. First, we define a suitable function and then derive the optimal control law with control constraints and the worst disturbance law. Besides, the constrained Hamilton-Jacobi-Isaacs equation for continuous-time nonlinear constrained systems is derived. Then, only one critic neural network is used to approximate the optimal cost function. Consequently, the approximate optimal control law and the approximate worst disturbance law are obtained. Additionally, a new updating rule is developed in the process of neural critic learning. Finally, the simulation results show that the self-learning optimal control is realized and the effectiveness of the disturbance rejection is verified by using the neural-network-based ADP method.
Fully distributed event-triggered leader-follower tracking control for multi-agent systems over directed switching networks

This paper investigates the event-triggered leader-follower tracking control for the general linear multi-agent systems over directed switching networks. We first propose a distributed event-triggered observer to estimate the state of the leader. We guarantee that the estimation errors of the observer are contained within a certain bound and the observer system does not exhibit Zeno behavior The observer is then used to construct an event-triggered controller in a fully distributed manner to achieve the bounded tracking errors. A numerical example is presented to illustrate the efficacy of the theoretical results.
Multiple Model Adaptive Control of Quadrotor with Uncertain Load

This article describes the adaptive control problem of quadrotor with varying load, and the main purpose is to propose an efficient controls scheme such that the quadrotor will remain stable when the load varies as soon as possible. First of all, the modeling analysis of quadrotor is presented. The fixed and adaptive controllers for altitude and attitude racking are discussed. Then, the multiple model adaptive control scheme is proposed to increase the convergence speed and transient performance, where both the switching scheme and second level adaptation scheme are adopted. The design details of identification model set, switching scheme, second level adaptation law are presented, along with the stability analysis. One simulation is carried out to show the feasibility of multiple model adaptive control scheme.
Mechanism Design and Performance Analysis of Bionic Quadruped Robot With Spine

In this paper, the mechanical structure of a bionic quadruped robot with a spine is designed and the corresponding performances of different spines are analyzed. Firstly, the mechanisms of leg and spine are designed. And then the kinematics analysis of the leg mechanism is carried out by listing the closed-loop vector of the mechanism using the integral method. Secondly, taking the planning trajectory based on the Bezier curve as the target trajectory of foot end effector, 12 parameters of leg mechanism are optimized to make the error between target and optimized trajectory as small as possible. Finally, a simplified simulation model of quadruped robot is established in ADAMS software, and the performances of the quadruped robot with pure rigid, rigid-flexible coupled passive spine and active spine are compared and analyzed. Comparative simulation shows that the application of rigid-flexible coupled spine could improve the motion speed and reduce the impact force of foot end effector efficiently. This research provides an insight for the locomotion control of legged robots.
A Novel Deep Neural Network based Method for House Price Prediction

Nowadays, house price is extremely essential for human living and plays an important part in the financial market, especially in the real estate industry. In this paper, we proposed a deep learning based network for house price prediction. Firstly, a brief introduction and related work are discussed. Then, Linear Regression, Random Forest Regression, XGBoost, and SVM are all detailed explained, developed and tested. In addition, several types of densely connected based neural network are proposed and developed. Finally, all methods are evaluated on a publicly available dataset, Boston house price dataset. Our method performs competitively performance compared with other classical methods including Linear Regression, Random Forest Regression, XGBoost, and SVM, and evaluated with a variety of evaluation methods including R2, Adjustable R2, MAE, MSE, RMSE.
A Microwave Power Transmission System Using Sequential Phase Ring Antenna and Inverted Class F Rectenna

A high-efficiency microwave power transmission (MPT) system based on an inverted class F (<inline-formula> <tex-math notation="LaTeX">$\text{F}^{-1 }$ </tex-math></inline-formula>) rectifier for microwave wireless charging applications is presented in this paper. A left-hand circular polarization (LHCP) transmitting antenna (Tx) is designed based on a modified sequential phase rotation (SPR) divider integrated with a <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> array. The proposed Tx exhibits compact size with LHCP maximum gain of 11.85 dBi at 5.8 GHz. Furthermore, the receiver is composed of an LHCP receiving antenna (Rx) and a microwave <inline-formula> <tex-math notation="LaTeX">$\text{F}^{-1}$ </tex-math></inline-formula> rectifier. To realize the power radiated region of the Tx, an Rx with a wide beamwidth for minimizing distance loss is proposed, which has a 3-dB axial ratio (AR) beamwidth of 165.55° and 175.17° in the <inline-formula> <tex-math notation="LaTeX">$x - z$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$y - z$ </tex-math></inline-formula> planes, respectively. In addition, to improve the RF to DC conversion efficiency (<inline-formula> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula>), the class <inline-formula> <tex-math notation="LaTeX">$\text{F}^{-1}$ </tex-math></inline-formula> harmonic processing network is utilized at the load of the rectifier that can process the voltage and current waveforms without using a DC pass filter. The proposed <inline-formula> <tex-math notation="LaTeX">$\text{F}^{-1}$ </tex-math></inline-formula> rectifier circuit occupies a compact area of <inline-formula> <tex-math notation="LaTeX">$15.3\times12.7$ </tex-math></inline-formula> mm<sup>2</sup>, and it exhibits an average <inline-formula> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula> of 50% for the input power range from 4 to 20 dBm with a peak efficiency of 77.9% at 18 dBm. Overall, the experimental results show that our proposed system achieves a maximum power transmission efficiency (PTE) of 8.8% for wirelessly charging low-power multiple devices at a distance of 60–200 mm.
Light scattering in spin orbit coupling dominated systems

Strong spin-orbit coupling (SOC) in solids has recently been identified as leading to novel electronic states with unconventional phases. Examples are topological insulators and unconventional magnets. Sr2IrO4, Sr3Ir2O7, BaIrO3, and Na4Ir3O8 are candidates and model systems for such phases. Recent Resonant X-Ray and IR absorption studies on Sr2IrO4 show anomalies that are related to the proximity of charge and spin modes. This proximity is further investigated in the following thesis based on Raman scattering with different incident Laser energies (Resonant Raman scattering). Pronounced resonances show up in intensity and linewidths of phonon and magnetic scattering. Sr3Ir2O7 is another interesting case of spin orbit physics. Despite the similar crystal structure to Sr2IrO4, the electronic structure and magnetic structure of Sr3Ir2O7 differs significantly from Sr2IrO4 which is evident in our magnetic Raman scattering data. BaIrO3 is a quasi-1D system which exhibits peculiar transitions. A charge density wave (CDW) formation accompanied by structural distortions is evident in our Raman scattering data in terms of electronic scattering and phonon anomalies. Another novel case of spin-orbit physics is given in Na4Ir3O8 which is reported to be a spin liquid. First Raman data of this special compound are discussed.
A Study on Fake and Real Lane Change Behavior Prediction Based on Discrete Hidden Markov Model

In advanced assisted driving, an effective understanding of the driver’s behavior can effectively improve the safety of the driving process. In this paper, we use discrete hidden Markov models to predict lane change and real lane change behavior by collecting natural driving data from a driving simulator. First, we build a lane change road on the Prescan software, collect data from 12 groups of drivers and filter the data, select whether the four characteristic parameters of steering wheel angle, steering wheel angular velocity, yaw rate and yaw acceleration are true for lane change perform analysis. Then, on the effective data set training, the viterbi algorithm is used to predict the fake lane change and the real lane change behavior of the discrete hidden Markov model. The prediction results show that the model can use the above four characteristic parameters to predict fake lane change and real lane change behavior. The accuracy of the prediction respectively is 89.83% ,91.20% ,88.52% ,80.99%, and it has good accuracy.
Distributed adaptive protocols design based load frequency control for multi-area interconnected power system

This paper studies the load frequency control (LFC) problem of the multi-area interconnected power system. A fully distributed adaptive design based LFC scheme is used based on the idea of multi-agent system (MAS). Firstly, a model transformation on the multi-area interconnected power system is proposed so that a new system model with external disturbances is obtained in term of MAS. Then, an adaptive leader-following consensus protocol is used, which do not need any information about communication topology and the upper bound of external disturbances. Finally, the case studies are performed to verify the effectiveness of the proposed schemes.
Pose estimation of non-cooperative spacecraft based on Convelutional Neural Network

On-orbit proximity operations such as rendezvous need to obtain high-precision pose information. However, the pose estimation of space target is difficult, because the space target is greatly affected by the illumination and the earth background. In order to solve the above problems, a Convolutional Neural Networks (CNN) based pose estimation method for known non-cooperative spacecraft is proposed. Three branches CNNs are designed to estimate orientation, position and spacecraft category respectively, and the loss function of three tasks is balanced by the method of automatic learning coefficient. Because CNN needs a lot of training data, the manual annotation method will bring huge workload. In this paper, we use 3D Max to generate spacecraft rendering data quickly without manual annotation. The experimental results show that the method can accurately predict the position and orientation of spacecraft, and classify spacecraft types at the same time.
Remaining Useful Life Prediction for Turbofan Engine using SAE-TCN Model

Turbofan engines are known as the heart of the aircraft, as important equipment of the aircraft, the health state of the engine determines the aircraft’s operational status. Therefore, the equipment monitoring and maintenance of the engine is an important part of ensuring the healthy and stable operation of the aircraft, and the remaining useful life (RUL) prediction of the engine is an important part of it. The monitoring data of turbofan engines have a high dimension and a long time span, which brings difficulties to predicting the remaining useful life of the engine. This paper proposes a residual life prediction model based on Autoencoder and temporal convolutional network (TCN). Among them, Autoencoder is used to reduce the dimension of the data and extract features from the engine monitoring data. The obtained low-dimensional data is trained in the TCN network to predict the remaining useful life. The model mentioned in this article is verified on the NASA public dataset(C-MAPSS) and compared with common machine learning methods and other deep neural networks. The experimental results show that the model proposed in this paper performs best in the evaluation methods, and this conclusion has important implications for engine health.
An Effective Network Repair Strategy Against Both Random and Malicious Edge Attacks

Attack vulnerability and protection of large-scale complex networks are of great significance in academic and engineering communities. In this study, the problem of structural vulnerability of complex networks induced by attacks on edges is addressed. In order to restore network’s performance after attacks, a simple two-stage attack-recovery model based on activating backup edges is presented. Moreover, with the consideration of two different edge attack strategies, the joint influence of repair cost (i.e. the extent of edge redundancy) and repair targets are extensively investigated. Numerical simulations are implemented on artificially generated random networks and real-world networks, which demonstrate the effectiveness of proposed repair model. The research results can provide some useful suggestion on constructing robust infrastructure systems, also help to better understand the structural complexity of large-scale networks.
CNT-anchored cellulose fluorescent nanofiber membranes as a fluorescence sensor for Cu2+ and Cr3+

1,4-Dihydroxyanthraquinone (1,4-DHAQ, a fluorophore) doped carbon nanotubes@cellulose (1,4-DHAQ-doped CNTs@CL) nanofibrous membranes have been prepared via electrospinning and subsequent deacetylation in this work. They have been successfully applied for highly sensitive detection of Cu2+ in aqueous solution. The surface area per unit mass (S/M) ratio of the nanofibrous membranes was enhanced by incorporating the CNTs into cellulose. Moreover, the excellent metal ion sorption capability of the CNTs improved the sensitivity of the sensing materials. Both of the two points mentioned above significantly improved the lower detection limit of fluorescence sensing as expected. As the resultant byproduct of Cu2+-contaminated 1,4-DHAQ-doped CNTs@CL nanofibrous membranes showed recovered fluorescence by extra addition of Cr3+ in solution, 1,4-DHAQ and Cu2+ co-doped CNTs@CL (denoted as 1,4-DHAQ/Cu2+ co-doped CNTs@CL) microporous nanofibrous membranes were further fabricated for the fluorescence enhancement detection of Cr3+ in aqueous solution. It was found that the fluorescence intensity of the 1,4-DHAQ-doped CNTs@CL nanofibrous membranes linearly decreased with Cu2+ concentration in the range of 0–2.2 × 10−8 M, while that of the 1,4-DHAQ/Cu2+ co-doped CNTs@CL nanofibrous membranes linearly increased with Cr3+ concentration in the range of 0–2.4 × 10−8 M. The excellent fluorescence sensing properties of the 1,4-DHAQ-doped CNTs@CL and 1,4-DHAQ/Cu2+ co-doped CNTs@CL nanofibrous membranes for Cu2+ and Cr3+ were further demonstrated by using polluted lake waters, proving their potential applications in environmental monitoring areas. The design concept mentioned in this work might also open a door to the design of other effective fluorescence probes with high sensitivity.
Feature Extraction and Classification of Steel Plate Surface Defects Based on Principal Component Analysis

In connection with low detection rate and accuracy of steel plate defects currently, the steel plate defect detection system based on machine vision is proposed. It can realize the effective detection and classification of steel plate defects. Based on image enhancement of histogram, the image retain the defect details of the steel plate by improved median filtering algorithm, as it removed speckle noise and salt pepper noise. Image segmentation was realized by adaptive and iterative algorithms. The geometric features, gray features and texture features of steel plate defect images were extracted and selected effectively. The steel plate defects were classified and identified effectively based on BP network, the experimental results show that the accuracy of the proposed steel plate defect detection method is improved.
Orthogonal Versus Zero-Forced Beamforming in Multibeam Antenna Systems: Review and Challenges for Future Wireless Networks

Orthogonality in multibeam antennas is revisited. The difference between orthogonal beamforming and zero-forced beamforming is highlighted. The intriguing relation between orthogonality, reciprocity and losses is recapitulated. Different approaches on the design of orthogonal beamforming networks and implementation of zero-forced beamforming strategies are shown with various examples from the antenna-research-oriented literature. The use of orthogonal and zero-forced beamforming is discussed from the communication system perspective with relevant studies from diverse disciplines. Some of the future research challenges and potential benefits are outlined for the next generation satellite and cellular communication applications.