Circular Formation Flight Control for Unmanned Aerial Vehicles With Directed Network and External Disturbance

This paper proposes a new distributed formation flight protocol for unmanned aerial vehicles(UAVs)to perform coordinated circular tracking around a set of circles on a target sphere.Different from the previous results limited in bidirectional networks and disturbance-free motions,this paper handles the circular formation flight control problem with both directed network and spatiotemporal disturbance with the knowledge of its upper bound.Distinguishing from the design of a common Lyapunov fiunction for bidirectional cases,we separately design the control for the circular tracking subsystem and the formation keeping subsystem with the circular tracking error as input.Then the whole control system is regarded as a cascade connection of these two subsystems,which is proved to be stable by input-tostate stability(ISS)theory.For the purpose of encountering the external disturbance,the backstepping technology is introduced to design the control inputs of each UAV pointing to North and Down along the special sphere(say,the circular tracking control algorithm)with the help of the switching function.Meanwhile,the distributed linear consensus protocol integrated with anther switching anti-interference item is developed to construct the control input of each UAV pointing to east along the special sphere(say,the formation keeping control law)for formation keeping.The validity of the proposed control law is proved both in the rigorous theory and through numerical simulations.

Distributed Fault-Tolerant Containment Control for Nonlinear Multi-Agent Systems Under Directed Network Topology via Hierarchical Approach

This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on the global information about the communication topology consists of two layers.Different from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault propagation.Based on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer.Finally,simulation results are given to demonstrate the effectiveness of the proposed control protocol.

Bounded consensus tracking of second-order multi-agent systems with sampling delay under directed networks

The bounded consensus tracking problems of second-order multi-agent systems under directed networks with sam- pling delay are addressed in this paper. When the sampling delay is more than a sampling period, new protocols based on sampled-data control are proposed so that each agent can track the time-varying reference state of the virtual leader. By using the delay decomposition approach, the augmented matrix method, and the frequency domain analysis, necessary and sufficient conditions are obtained, which guarantee that the bounded consensus tracking is realized. Furthermore, some numerical simulations are presented to demonstrate the effectiveness of the theoretical results.

Global Synchronization of Directed Networks with Fast Switching Topologies

Global synchronization of a class of directed dynamical networks with switching topologies is investigated.It is found that if there is a directed spanning tree in the fixed time-average of network topology and the time-averageis achieved sufficiently fast,then the network will reach global synchronization for sufficiently large coupling strength.

Fixed-Time Output Consensus Tracking for High-Order Multi-Agent Systems With Directed Network Topology and Packet Dropout

This paper studies the problem of fixed-time output consensus tracking for high-order multi-agent systems(MASs)with directed network topology with consideration of data packet dropout.First,a predictive compensation based distributed observer is presented to compensate for packet dropout and estimate the leader’s states.Next,stability analysis is conducted to prove fixed time convergence of the developed distributed observer.Then,adaptive fixed-time dynamic surface control is designed to counteract mismatched disturbances introduced by observation error,and stabilize the tracking error system within a fixed time,which overcomes explosion of complexity problem and singularity problem.Finally,simulation results are provided to verify the effectiveness and superiority of the consensus tracking strategy proposed.The contribution of this paper is to provide a fixed-time distributed observer design method for high-order MAS under directed graph subject to packet dropout,and a novel fixed-time control strategy which can handle mismatched disturbances and overcome explosion of complexity and singularity problem.

Distributed Asymptotic Consensus in Directed Networks of Nonaffine Systems With Nonvanishing Disturbance

In this paper the distributed asymptotic consensus problem is addressed for a group of high-order nonaffine agents with uncertain dynamics,nonvanishing disturbances and unknown control directions under directed networks.A class of auxiliary variables are first introduced which forms second-order filters and induces all measurable signals of agents’states.In view of this property,a distributed robust integral of the sign of the error(DRISE)design combined with the Nussbaum-type function is presented that guarantees not only the desired asymptotic consensus,but also the uniform boundedness of all closed-loop variables.Compared with the traditional sliding mode control(SMC)technique,the main feature of our approach is that the integral operation in the proposed control algorithm is designed to be adopted in a continuous manner and ensures less chattering behavior.Simulation results for a group of Duffing-Holmes chaotic systems are employed to verify our theoretical analysis.

Global behavior of dynamical agents in directed network

This paper investigates the global behavior of controlled dynamical agents in directed networks. The agents are Lyapunov stable, are distributed in a line, and communicate through a directed network. The communication topology of the network is characterized by a directed graph and the control protocol is designed in simple linear decentralized feedback law. We study the different conditions under which agents will achieve aggregation, and critical and divergent trajectories, respectively. Our investigation on the dynamical agent system under network is extended to the time-delay network case. Furthermore, we study the case with two pre-specified virtual leaders in the system. Numerical simulations are given and demonstrate that our theoretical results are effective.

Feedback arcs and node hierarchy in directed networks

Directed networks such as gene regulation networks and neural networks are connected by arcs（directed links）. The nodes in a directed network are often strongly interwound by a huge number of directed cycles, which leads to complex information-processing dynamics in the network and makes it highly challenging to infer the intrinsic direction of information flow. In this theoretical paper, based on the principle of minimum-feedback, we explore the node hierarchy of directed networks and distinguish feedforward and feedback arcs. Nearly optimal node hierarchy solutions, which minimize the number of feedback arcs from lower-level nodes to higher-level nodes, are constructed by belief-propagation and simulated-annealing methods. For real-world networks, we quantify the extent of feedback scarcity by comparison with the ensemble of direction-randomized networks and identify the most important feedback arcs. Our methods are also useful for visualizing directed networks.

Search for Directed Networks by Different Random Walk Strategies

A comparative study is carried out on the effciency of five different random walk strategies searching on directed networks constructed based on several typical complex networks.Due to the difference in search effciency of the strategies rooted in network clustering,the clustering coeFfcient in a random walker's eye on directed networks is defined and computed to be half of the corresponding undirected networks.The search processes are performed on the directed networks based on Erd(o)s-Rényi model,Watts-Strogatz model,Barabási-Albert model and clustered scale-free network model.It is found that self-avoiding random walk strategy is the best search strategy for such directed networks.Compared to unrestricted random walk strategy,path-iteration-avoiding random walks can also make the search process much more effcient.However,no-triangle-loop and no-quadrangle-loop random walks do not improve the search effciency as expected,which is different from those on undirected networks since the clustering coefficient of directed networks are smaller than that of undirected networks.

Spontaneous Recovery in Directed Dynamical Networks

Complex networked systems,which range from biological systems in the natural world to infrastructure systems in the human-made world,can exhibit spontaneous recovery after a failure;for example,a brain may spontaneously return to normal after a seizure,and traffic flow can become smooth again after a jam.Previous studies on the spontaneous recovery of dynamical networks have been limited to undirected networks.However,most real-world networks are directed.To fill this gap,we build a model in which nodes may alternately fail and recover,and we develop a theoretical tool to analyze the recovery properties of directed dynamical networks.We find that the tool can accurately predict the final fraction of active nodes,and the prediction accuracy decreases as the fraction of bidirectional links in the network increases,which emphasizes the importance of directionality in network dynamics.Due to different initial states,directed dynamical networks may show alternative stable states under the same control parameter,exhibiting hysteresis behavior.In addition,for networks with finite sizes,the fraction of active nodes may jump back and forth between high and low states,mimicking repetitive failure-recovery processes.These findings could help clarify the system recovery mechanism and enable better design of networked systems with high resilience.

Review on laser directed energy deposited aluminum alloys

Lightweight aluminum(Al)alloys have been widely used in frontier fields like aerospace and automotive industries,which attracts great interest in additive manufacturing(AM)to process high-value Al parts.As a mainstream AM technique,laser-directed energy deposition(LDED)shows good scalability to meet the requirements for large-format component manufacturing and repair.However,LDED Al alloys are highly challenging due to their inherent poor printability(e.g.low laser absorption,high oxidation sensitivity and cracking tendency).To further promote the development of LDED high-performance Al alloys,this review offers a deep understanding of the challenges and strategies to improve printability in LDED Al alloys.The porosity,cracking,distortion,inclusions,element evaporation and resultant inferior mechanical properties(worse than laser powder bed fusion)are the key challenges in LDED Al alloys.Processing parameter optimizations,in-situ alloy design,reinforcing particle addition and field assistance are the efficient approaches to improving the printability and performance of LDED Al alloys.The underlying correlations between processes,alloy innovation,characteristic microstructures,and achievable performances in LDED Al alloys are discussed.The benchmark mechanical properties and primary strengthening mechanism of LDED Al alloys are summarized.This review aims to provide a critical and in-depth evaluation of current progress in LDED Al alloys.Future opportunities and perspectives in LDED high-performance Al alloys are also outlined.

Fixed-Time Cluster Consensus for Multi-Agent Systems with Objective Optimization on Directed Networks

This paper studies the cluster consensus of multi-agent systems(MASs)with objective optimization on directed and detail balanced networks,in which the global optimization objective function is a linear combination of local objective functions of all agents.Firstly,a directed and detail balanced network is constructed that depends on the weights of the global objective function,and two kinds of novel continuous-time optimization algorithms are proposed based on time-invariant and timevarying objective functions.Secondly,by using fixed-time stability theory and convex optimization theory,some sufficient conditions are obtained to ensure that all agents'states reach cluster consensus within a fixed-time,and asymptotically converge to the optimal solution of the global objective function.Finally,two examples are presented to show the efficacy of the theoretical results.

Percolation transitions in edge-coupled interdependent networks with directed dependency links

We propose a model of edge-coupled interdependent networks with directed dependency links(EINDDLs)and develop the theoretical analysis framework of this model based on the self-consistent probabilities method.The phase transition behaviors and parameter thresholds of this model under random attacks are analyzed theoretically on both random regular(RR)networks and Erd¨os-Renyi(ER)networks,and computer simulations are performed to verify the results.In this EINDDL model,a fractionβof connectivity links within network B depends on network A and a fraction(1-β)of connectivity links within network A depends on network B.It is found that randomly removing a fraction(1-p)of connectivity links in network A at the initial state,network A exhibits different types of phase transitions(first order,second order and hybrid).Network B is rarely affected by cascading failure whenβis small,and network B will gradually converge from the first-order to the second-order phase transition asβincreases.We present the critical values ofβfor the phase change process of networks A and B,and give the critical values of p andβfor network B at the critical point of collapse.Furthermore,a cascading prevention strategy is proposed.The findings are of great significance for understanding the robustness of EINDDLs.

Printability disparities in heterogeneous material combinations via laser directed energy deposition:a comparative study

Additive manufacturing provides achievability for the fabrication of bimetallic and multi-material structures;however,the material compatibility and bondability directly affect the parts’formability and final quality.It is essential to understand the underlying printability of different material combinations based on an adapted process.Here,the printability disparities of two common and attractive material combinations(nickel-and iron-based alloys)are evaluated at the macro and micro levels via laser directed energy deposition(DED).The deposition processes were captured using in situ high-speed imaging,and the dissimilarities in melt pool features and track morphology were quantitatively investigated within specific process windows.Moreover,the microstructure diversity of the tracks and blocks processed with varied material pairs was comparatively elaborated and,complemented with the informative multi-physics modeling,the presented non-uniformity in mechanical properties(microhardness)among the heterogeneous material pairs was rationalized.The differences in melt flow induced by the unlike thermophysical properties of the material pairs and the resulting element intermixing and localized re-alloying during solidification dominate the presented dissimilarity in printability among the material combinations.This work provides an in-depth understanding of the phenomenological differences in the deposition of dissimilar materials and aims to guide more reliable DED forming of bimetallic parts.

Effect of hot isostatic pressure on the microstructure and tensile properties of γ'-strengthened superalloy fabricated through induction-assisted directed energy deposition

The microstructure characteristics and strengthening mechanism of Inconel738LC(IN-738LC) alloy prepared by using induction-assisted directed energy deposition(IDED) were elucidated through the investigation of samples subjected to IDED under 1050℃ preheating with and without hot isostatic pressing(HIP,1190℃,105 MPa,and 3 h).Results show that the as-deposited sample mainly consisted of epitaxial columnar crystals and inhomogeneously distributed γ’ phases in interdendritic and dendritic core regions.After HIP,grain morphology changed negligibly,whereas the size of the γ’ phase became increasingly even.After further heat treatment(HT,1070℃,2 h + 845℃,24 h),the γ’ phase in the as-deposited and HIPed samples presented a bimodal size distribution,whereas that in the as-deposited sample showed a size that remained uneven.The comparison of tensile properties revealed that the tensile strength and uniform elongation of the HIP + HTed sample increased by 5% and 46%,respectively,due to the synergistic deformation of bimodal γ’phases,especially large cubic γ’ phases.Finally,the relationship between phase transformations and plastic deformations in the IDEDed sample was discussed on the basis of generalized stability theory in terms of the trade-off between thermodynamics and kinetics.

Behavior and mechanism of pre-oxidation improvement on fluidization in the fluidized reduction of titanomagnetite

The direct reduction process is an important development direction of low-carbon ironmaking and efficient comprehensive utilization of poly-metallic iron ore,such as titanomagnetite.However,the defluidization of reduced iron particles with a high metallization degree at a high temperature will seriously affect the operation of fluidized bed reduction.Coupling the pre-oxidation enhancing reduction and the particle surface modification of titanomagnetite,the behavior and mechanism of pre-oxidation improvement on fluidization in the fluidized bed reduction of titanomagnetite are systematically studied in this paper.Pre-oxidation treatment of titanomagnetite can significantly lower the critical stable reduction fluidization gas velocity to 0.17 m/s,which is reduced by 56%compared to that of titanomagnetite reduction without pre-oxidation,while achieving a metallization degree of>90%,Corresponding to the different reduction fluidization behaviors,three pre-oxidation operation regions have been divided,taking oxidation degrees of 26%and 86%as the boundaries.Focusing on the particle surface morphology evolution in the pre-oxidation-reduction process,the relationship between the surface morphology of pre-oxidized ore and the reduced iron with fluidization properties is built.The improving method of pre-oxidation on the reduction fluidization provides a novel approach to prevent defluidization by particle surface modification,especially for the fluidized bed reduction of poly-metallic iron ore.

A Non-parametric Gradient-Based Shape Optimization Approach for Solving Inverse Problems in Directed Self-Assemblyof Block Copolymers

We consider the inverse problem of finding guiding pattern shapes that result in desired self-assembly morphologies of block copolymer melts.Specifically,we model polymer selfassembly using the self-consistent field theory and derive,in a non-parametric setting,the sensitivity of the dissimilarity between the desired and the actual morphologies to arbitrary perturbations in the guiding pattern shape.The sensitivity is then used for the optimization of the confining pattern shapes such that the dissimilarity between the desired and the actual morphologies is minimized.The efficiency and robustness of the proposed gradient-based algorithm are demonstrated in a number of examples related to templating vertical interconnect accesses(VIA).

MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot.However,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper.The main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale features.The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively.Secondly,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature information.Thirdly,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different directions.To verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried out.In the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,respectively.In the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,respectively.This model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

Estimation of the anisotropy of hydraulic conductivity through 3D fracture networks using the directional geological entropy

With an extension of the geological entropy concept in porous media,the approach called directional entrogram is applied to link hydraulic behavior to the anisotropy of the 3D fracture networks.A metric called directional entropic scale is used to measure the anisotropy of spatial order in different directions.Compared with the traditional connectivity indexes based on the statistics of fracture geometry,the directional entropic scale is capable to quantify the anisotropy of connectivity and hydraulic conductivity in heterogeneous 3D fracture networks.According to the numerical analysis of directional entrogram and fluid flow in a number of the 3D fracture networks,the hydraulic conductivities and entropic scales in different directions both increase with spatial order(i.e.,trace length decreasing and spacing increasing)and are independent of the dip angle.As a result,the nonlinear correlation between the hydraulic conductivities and entropic scales from different directions can be unified as quadratic polynomial function,which can shed light on the anisotropic effect of spatial order and global entropy on the heterogeneous hydraulic behaviors.

Nonlinear Registration of Brain Magnetic Resonance Images with Cross Constraints of Intensity and Structure

Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook semantic consistency,limiting their performance.To address these issues,we present a novel approach for medical image registration called theDual-VoxelMorph,featuring a dual-channel cross-constraint network.This innovative network utilizes both intensity and segmentation images,which share identical semantic information and feature representations.Two encoder-decoder structures calculate deformation fields for intensity and segmentation images,as generated by the dual-channel cross-constraint network.This design facilitates bidirectional communication between grayscale and segmentation information,enabling the model to better learn the corresponding grayscale and segmentation details of the same anatomical structures.To ensure semantic and directional consistency,we introduce constraints and apply the cosine similarity function to enhance semantic consistency.Evaluation on four public datasets demonstrates superior performance compared to the baselinemethod,achieving Dice scores of 79.9%,64.5%,69.9%,and 63.5%for OASIS-1,OASIS-3,LPBA40,and ADNI,respectively.