Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cloud Control Robotics Platform Based on Intelligent Deployment of Micro-services

Cloud robotics has become a strategic focus of the development of the information technology industry in recent years. More and more cloud robotics platforms have been developed and applied in the field of robotics. Although its emergence has brought powerful computing and storage capabilities to the native robot system, the existing cloud robot platform architecture is relatively single and jumbled, and it is difficult to be efficiently expanded and updated. Inspired by the idea of cloud control system, this paper proposes a cloud control robot system architecture based on micro-services. We designed a Proxy module, which realizes the common function of the network layer by converting ROS system message format into JSON format, and builds a data transmission bridge between the cloud and the edge computing platform. Then we use Kubernetes, Docker, CI/CD and other technologies to realize an automatic deployment of the cloud robotics platform. A method is given to deploy and run ROS instances in the cloud, enabling ROS-based service robots to run computation-intensive tasks. Finally, Gmapping ROS package is used to test on the cloud control robotics platform compared with PC. Experimental results verify the feasibility of cloud control robotics platform.
Coupled Attitude And Orbit Control For Spacecraft Based On Fast Nonsingular Terminal Sliding Mode Without Dual Velocity Information

In order to solve the coupled attitude and orbit control problem of spacecraft, a novel terminal sliding mode controller is proposed in this paper. The relative dynamic model coupling the translational and rotational motion is established by using dual quaternion. To deal with the problem of unmeasurable dual velocity information, a dual velocity observer is designed based on the extended state observer. The finite-time stability of the controller and observer is proven by the Lyapunov stability theory. The simulation result shows that the proposed method is effective.
An Algorithm to Verify a Class of Matrix Inequalities in the Global Stability of Nonlinear Systems

The global stability and stabilization for nonlinear systems is a challenging problem in control theory. The conditions like R(t) < -E0I or R (x) < -E0I often appeared in many related literature. Obviously, to verify this condition is not easy, partly because there is not a general method to find the precise value of its roots by radicals for a univariate polynomial with degree more than 4. In this paper, we mainly discuss the case which all entries in R are polynomials and give a feasible algorithm to verify the condition R(t) <-E0I or R(x) < -E0I. The algorithm is imposed on the coefficients of the polynomials only and the analysis technique is based on real algebraic geometry and mathematics mechanization method. Finally, the authors will give some examples to show the application of our results.
Exploring the Nature of Gravity

I clarify the differences between various approaches in the literature which attempt to link gravity and thermodynamics. I then describe a new perspective based on the following features: (1) As in the case of any other matter field, the gravitational field equations should also remain unchanged if a constant is added to the Lagrangian; in other words, the field equations of gravity should remain invariant under the transformation $T^a_b \to T^a_b + \delta^a_b $(constant). (2) Each event of spacetime has a certain number ($f$) of microscopic degrees of freedom (`atoms of spacetime'). This quantity $f$ is proportional to the area measure of an equi-geodesic surface, centered at that event, when the geodesic distance tends to zero. The spacetime should have a zero-point length in order for $f$ to remain finite. (3) The dynamics is determined by extremizing the heat density at all events of the spacetime. The heat density is the sum of a part contributed by matter and a part contributed by the atoms of spacetime, with the latter being $L_P^{-4} f$. The implications of this approach are discussed.
Facebook tops 500 million users

Students analyze items from the media to answer mathematical questions related to the article. This month's clips deal with the growth of Facebook and a geometric algebra problem that appeared in an obituary for George Lenchner, founder of the Math Olympiads. The mathematics involved in these clips includes scientific notation, proportional reasoning, exponents, exponential growth, and quadratic equations.
Research Article Robust Phase Retrieval with Complexity-Guidance for Coherent X-Ray Imaging

Reconstruction of a stable and reliable solution from noisy and incomplete Fourier intensity data is a challenging problem for iterative phase retrieval algorithms. The typical methodology employed in the coherent X-ray imaging (CXI) literature involves thousands of iterations of well-known phase retrieval algorithms, e.g., hybrid input-output (HIO) or relaxed averaged alternating re ﬂ ections (RAAR), that are concluded with a smaller number of error reduction (ER) iterations. Since the single run of this methodology may not provide a reliable solution, hundreds of trial solutions are ﬁ rst obtained by initializing the phase retrieval algorithm with independent random guesses. The resulting trial solutions are then averaged with appropriate phase adjustment, and resolution of the averaged reconstruction is assessed by plotting the phase retrieval transfer function (PRTF). In this work, we examine this commonly used RAAR-ER methodology from the perspective of the complexity parameter introduced by us in recent years. It is observed that the single run of the RAAR-ER algorithm provides a solution with undesirable grainy artifacts that persist to some extent even after averaging the multiple trial solutions. The grainy features are spurious in the sense that they are smaller in size compared to the resolution predicted by the PRTF curve. This inconsistency can be addressed by a novel methodology that we refer to as complexity-guided RAAR (CG-RAAR). The methodology is demonstrated with simulations and experimental data sets from the CXIDB database. In addition to providing consistent solution, CG-RAAR is also observed to require reduced number of independent trials for averaging.
Dual-Stage Path Planning for Active Pose-Graph SLAM by Graph Topology

In this work, we present a dual-stage active pose-graph simultaneous localization and mapping (SLAM) framework for a robot in the three-dimensional(3D) environment. This framework aims to find the best path for rapid unknown environment exploration and efficient loop-closing reducing the uncertainty of pose-graph SLAM estimation. For online evaluating the uncertainty of pose-graph, we draw connections between the Fisher information matrix of pose-graph SLAM on $\mathfrak{so} (3)\times \mathbb{R}^{3}$ and the weighted Laplacian matrix. Based on this relationship, we propose the active SLAM method by extending a two-stage exploration algorithm. The active graph-based SLAM framework incorporates two planning stages - local stage and global stage. In the local stage, the Rapidly-exploring Random Tree (RRT) is used to generate the optimal path for joint objective function. The global stage is for explicitly transiting the robot to different sub-areas in the environment. Simulation and experiment results show that this framework has a good performance on exploration and improving the quality of SLAM simultaneously.
Multi-robot Cooperative Localization Using Anonymous Relative-Bearing Measurements

In this paper, a cooperative localization problem is investigated for a multi-robot system with anonymous relative bearing measurements among robots. A cooperative localization method is proposed to obtain global poses of a group of robots. First, a data association algorithm is developed to determine the identities of each relative bearing measurement. Then a centralized EFK is given to estimate the pose of each robot. Finally, the proposed method is validated by simulation results.
A Fault-tolerant Finite Time Control Method for Tilting Quadrotors

A fault-tolerant finite time control method for tilting quadcopters with actuator faluts is proposed in this research. To begin, a dynamic model of a tilting quadrotor is created. Then, using nonsingular fast terminal sliding mode control (NFTSM), a finite time control method is examined. A fault-tolerant finite time control method (FTFC) is developed by integrating NFTSM, high-order sliding mode(HOSM) and a simple falut diagnosis scheme to prevent actuator failure from negatively influencing the system. HOSM is used to eliminate chattering. A time-delay disturbance observer (TDO) is created to observe actuator failures and a control strategy is provided to accommodate the faults' bad influence in the falut diagnosis scheme. FTFC provides the advantages of non-singularity, high robustness, finite time convergence and low chattering over other control methods. The efficiency of the control method is verified in the simulation.
PROTOSTELLAR JETS ENCLOSED BY LOW-VELOCITY OUTFLOWS

A protostellar jet and outflow are calculated for ∼270 yr following the protostar formation using a three-dimensional magnetohydrodynamics simulation, in which both the protostar and its parent cloud are spatially resolved. A high-velocity (∼100 km s−1) jet with good collimation is driven near the disk's inner edge, while a low-velocity (≲ 10 km s−1) outflow with a wide opening angle appears in the outer-disk region. The high-velocity jet propagates into the low-velocity outflow, forming a nested velocity structure in which a narrow high-velocity flow is enclosed by a wide low-velocity flow. The low-velocity outflow is in a nearly steady state, while the high-velocity jet appears intermittently. The time-variability of the jet is related to the episodic accretion from the disk onto the protostar, which is caused by gravitational instability and magnetic effects such as magnetic braking and magnetorotational instability. Although the high-velocity jet has a large kinetic energy, the mass and momentum of the jet are much smaller than those of the low-velocity outflow. A large fraction of the infalling gas is ejected by the low-velocity outflow. Thus, the low-velocity outflow actually has a more significant effect than the high-velocity jet in the very early phase of the star formation.
Extraction of Reliable and Actionable Information from Social Media During Emergencies

Communities have been shown to coordinate among themselves using public forums such as Twitter and Facebook during disastrous events, and utilize even sparse telecommunication infrastructure to communicate. In this work we design a framework for text mining that will extract crisis situation and location information from tweets about individuals in need. Using these extracted data, we develop a community source map of the disaster. There have been previous efforts to develop real-time disaster maps, but these are limited to manual entry whereas we use AI to create such maps. While much of the prior work focuses on densely populated urban scenarios, we also consider sparsely populated rural contexts where social network updates may be infrequent. We have experimented our proposed solution on around 20 different disaster related tweet databases from different part of the world. The primary results show ~81 percent accuracy in crisis situation information extraction and ~92 percent accuracy in location extraction.
Understanding the Influence of Random Impulse Noise on Visual SLAM

The localization accuracy of visual SLAM depends on the image quality. However, in postdisaster rescue missions, the images obtained by the camera often contain considerable noise, which affects the pose estimation based on visual SLAM. In this paper, we study the influence of random impulse noise in images on the localization accuracy of visual SLAM, and reduce these influences by denoising and removing mismatches. First, the camera image is preprocessed by the traditional image noise reduction method. Aiming at the problem of a large number of mismatches in optical flow tracking due to the influence of residual noise, the improved random sample consensus method is adopted to remove it. Preliminarily judge the correct matching probability of optical flow tracking results by normalized cross-correlation matching before random sampling. Then use guided sampling to select matching points to estimate the camera motion model, to increase the robustness of the SLAM system. Finally, our method is verified in the open-source solution VINS-Fusion. Experiments show that after random impulse noise is added to the KITTI dataset, the pose estimation accuracy of the improved SLAM is higher than the pose estimation accuracy after noise reduction only, and it is also higher than the pose estimation results of the original images in multiple sequences of the KITTI dataset.
Hierarchical Scheme Based Trajectory Tracking Control for 4-Wheel Skid-Steering Mobile Robots With Unknown Disturbances and Motion Constraints

The trajectory tracking problem of 4-wheel skid-steering mobile robots (SSMRs) with unknown disturbances and motion constraints is considered in this paper. A hierarchical control scheme is proposed to solve these problems. The motion constraints are considered in the kinematic layer with the model predictive control (MPC) approach, and the disturbances are estimated and compensated in the dynamics layer by a time-varying gain extended state observer (ESO). In addition, the control allocation is considered to allocate the virtual control inputs obtained by the upper layer controller to make the 4-wheel motors turn as instructed. Finally, the stability and tracking performance of the proposed control scheme are verified by theoretical analysis and comparative numerical simulations.
Quantifying Shot Quality and Predicting the Goal Probability for Football Shots

In the unpredictable world of football, primitive techniques are used to evaluate player performances, recruitment, and to build strategies for opponents. We aim to offer an improved and advanced technique of using predictive data like expected goals rather than descriptive data like shots taken and goals scored to analyse the game. Our goal is to judge teams and players on the basis of their performances instead of the results and generated predictive data for scouting and strategy formation. This was achieved by using fixed parameters on machine learning algorithms. The expected goals method gives a number between 0 and 1 for every shot taken, that number is interpreted as the probability of that shot being converted to goal. So if a shot at a particular location and at a certain angle produces an expected goal value off 0.67 then the probability of that shot to be a goal will be 67% meaning if 100 shots are taken from that same position and angle, 67 of those shots will result in goal and 33 shots will not be converted to goal. This method of using predictive data like expected goals is better than using primitive and orthodox descriptive data like total shots taken and shot on target ratio because this strips down the luck factor and just focuses on pure skill and ability. This will be beneficial while finding out players with real and hidden talents as well as analysing performances in an unbiased manner without the influence of final result.
A Method of Improving Disturbance-Rejection Performance for Equivalent-Input-Disturbance Approach

This paper presents a method of rejecting an estimation error neglected by the conventional equivalent-input-disturbance (EID) approach and presents an improved EID (IEID) approach. The disturbance-rejection performance of the IEID approach is better than the conventional EID approach. Moreover, it is easy to design the IEID approach and to derive its stability conditions. Finally, a comparison shows the validity and superiority of the presented method.
Optimization-Based Design for Data-Driven Control of SISO Nonaffine Nonlinear Systems

For the adaptive tracking of nonaffine nonlinear systems, the unknown nonlinear dynamics is regarded as one of the essential factors that may hinder the effective controller establishment and the tracking precision improvement. To tackle this difficulty for a class of single-input single-output (SISO) nonaffine nonlinear systems, we develop an optimization-based design method for data-driven control to achieve the desired tracking objective in the presence of the time-varying reference trajectory. It is shown that the proposed data-driven controller can render the tracking error to strictly converge, regardless of the SISO unknown nonlinear dynamics. In addition, thanks to a novel grouping-based contraction mapping method and properties of non-negative matrices, the boundedness of the I/O sequence is established. Simulations are implemented to verify that the proposed data-driven controller can realize the anticipated tracking performance for nonlinear systems.
Prediction of Matte grade in Copper Flash Smelting Process based on LSTM and Mechanism Model

Copper flash smelting is the main process method for copper extracting. It is a complex and multi-phase physical and chemical change process with the characteristics of multiple variable, nonlinearity, delayed detection of product quality. Matte grade is a key indicator reflecting the stability of flash furnace smelting conditions, which directly affect the product quality and process level of the flash smelting. With the ‘four highs’ (high oxygen-enriched blast, high matte grade, high feed rate, high thermal strength) technical requirements, previous forecasting models are no longer applicable to the new conditions, so it is necessary to establish a forecasting model that can meet the requirements of the latest process. This paper uses matte grade as an example to estimate the calculation results through the compound and establish a mechanism model, analyze its feasibility. Based on the mechanism model, analyze the characteristics of long short-term memory network (LSTM) and choose to establish MSLSTM model (Semi-supervised LSTM based on mechanism model), and compare those methods, which shows that MSLSTM dynamically analyzes and predicts the non-linear relationship between the multi-parameter time series. The measured data from a smelter in Jiangxi, China are used for verification. The analysis results show that the predictive model proposed in this paper has high accuracy. MSLSTM model can be used for production operation guidance and optimal control of copper flash smelting process.
A Dynamic game approach for multi-objectives regulating in shared energy storage scenarios

The shared energy storage of the new energy power system should be able to meet the regulating demand in multiple scenarios. However, the demand in multiple scenarios is coupled, which makes the existing operation strategies difficult to apply. It restricts the large-scale development of shared energy storage. So, this paper proposes the cooperative operation mode of multi participants in shared energy storage based on Dynamic Game. Firstly, the framework of cooperative operation of shared energy storage and energy interaction mechanism with multiple participants which involved the power grid, wind power stations, and shared energy storage stations is designed. Secondly, considering the frequency regulation and peak shaving, shared energy storage income, etc, a dynamic game model is established. Finally, the strategies of each participant in the equilibrium of the dynamic game model are analyzed, and a cooperative operation scheme considering the regulation demand and the economy of shared energy storage is obtained. The simulation results of an example verify that the proposed cooperative operation strategy can coordinate the output plan of wind power stations and frequency of the power grid, and improve the economic benefits of the energy storage system at the same time.
Errors-In-Variables Hammerstein-Wiener model identification

This paper concerns the parameter identification problem for Errors-In-Variables(EIV) Hammerstein-Wiener systems using available noisy measurements. Based on the least squares method and consistency, a Bias-Correction Least Squares(BCLS) algorithm is proposed to estimate EIV Hammerstein-Wiener systems. The major contribution of this study is to derive the estimation bias of the least squares method and to recursively represent the monomial of noiseless measurements as available measurements. The effectiveness of the algorithm is demonstrated through the identification of simulation example.
Finite-time Prescribed Performance Control with RBFNN for the HFV Longitudinal Model

A method for designing the finite-time prescribed performance controller with RBFNN (Radial Basis Function Networks) is proposed for the HFV (Hypersonic Flight Vehicle) longitudinal attitude systems with parameters uncertainties and time-varying disturbance. Firstly, the appointed-time prescribed performance functions for outer-loop and inner-loop are designed respectively, which aims to restrain the transient and steady-state performance of command tracking errors. Compared with the traditional prescribed performance function, the proposed method can guarantee that the errors convergence to the set stability precision at the appointed time. Furthermore, the error-transform function is applied to transform the guidance command errors into un-limited boundary, which can ensure the error satisfy the constraint condition by means of controlling the transformed errors. Then, the virtual control law of outer-loop subsystem and the actual control law of inner-loop subsystem are designed independently, and the lumped disturbances are estimated by the RBFNN, a novel finite-time tracking differentiator is applied to avoid the 'explosion of terms' problem in the control law design process. Ultimately, comparative simulations with parametric uncertainties and time-varying disturbance are performed to demonstrate that the proposed method in this paper can effectively satisfy the prescribed performance constraints.
Finite-Time Orbit-Attitude Coupling Control for Flying Around Non-cooperative Targets

In this paper, the attitude and orbit coupling dynamics of the spacecraft is modelled in the sight coordinate, and the joint attitude and orbit controller is designed based on the method of adding a power integral, which can guarantee the finite-time stability of the spacecraft with the expected precision satisfied to fly around non-cooperative targets. It is shown that the proposed algorithm has robustness to the external disturbances. Finally, an numeral example is included to illustrate the effectiveness of the obtained results.
A Method for High-Voltage Electric Field to Control Inkjet 3D Printing

Aiming at the problem of low printing resolution due to the excessively fast droplet falling speed of traditional inkjet 3D printing, a new method of high-voltage electric field controlled inkjet 3D printing was proposed. By applying a high-voltage electric field during the printing process, the droplets are subjected to the reverse electric field force to control the speed of the droplets during printing, there by ensuring high printing resolution and obtaining better results. In this paper, a feasible printing method will be proposed and the feasibility of the method will be verified through confirmatory experiments and simulation studies.
Linear Feedback Stackelberg Equilibrium of Robust Hierarchical Difference Games

We consider a class of discrete-time hierarchical game subject to exogenous disturbance. Different from $H_{2}/H_{\infty}$ control, the disturbance appears not only in the value functional of the leader but also in that of the follower. We introduce a linear feedback information structure to the leader, and provide necessary and sufficient conditions for the existence of the Stackelberg equilibrium. The optimal strategy is explicit obtained in terms of two decoupled and symmetric equations.