Design of Multi-Coupled Laminates with Extension-Twisting Coupling for Application in Adaptive Structures

The multiple coupling of composite laminates has a unique advantage in improving the macro mechanical properties of composite structures.A total of three hygro-thermally stablemulti-coupled laminates with extensiontwisting coupling were presented,which were conducive to the formation of passive adaptive structures.Then,the multi-coupled laminates were used to design the bending-twisting coupled box structure,in which the configuration of laminate and box structure could be extended to variable cross-section configuration.The optimal design of stacking sequence was realized,the optimization objectives of which were to maximize bending-twisting coupling of box structure and extension-twisting coupling of laminate,respectively.The effects of multiple coupling on hygro-thermal stability,coupling,failure strength,buckling load,robustness and other comprehensive mechanical properties of laminates and box structures were analyzed by parametric modeling method.The results show that the extension-twisting coupling of laminate and the bending-twisting coupling of box structures can be greatly improved by 450%and 260%at maximum,respectively.Meanwhile,it would have a negative impact on the failure strength and buckling load,which,however,can be minimized by a reasonable paving method.Multicoupled laminates have good robustness,and the bending-twisting coupling helps improve robustness.Finally,the hygro-thermal stability and mechanical properties were verified by numerical simulation with finite element method.

Dynamic Response of Truss Adaptive Structures

It is pointed out that the damping matrix deduced by active members in the finite element vibration equation of a truss adaptive structure generally can not be decoupled, which leads to the difficulty in the process of modal analysis by classical superposition method. This paper focuses on the computational method of the dynamic response for truss adaptive structures. Firstly, a new technique of state vector approach is applied to study the dynamic response of truss adaptive structures. It can make the coeffic lent matrix of first derivative of state vector a symmetric positive definite matrix, and particularly a diagonal matrix provided that mass matrix is derived by lumped method, so the coefficient matrix of the first derivative of state vector can be exactly decomposed by CHOLESKY method. In this case, the proposed technique not only improves the calculation accuracy, but also saves the computing time. Based on the procedure mentioned above, the mathematical formulation for the system response of truss adaptive structures is systematically derived in theory. Thirdly, by using FORTRAN language, a program system for computing dynamic response of truss adaptive structures is developed. Fourthly, a typical 18 bar space truss adaptive structure has been chosen as test numerical examples to show the feasibility and effectiveness of the proposed method. Finally, some good suggestions, such as how to choose complex mode shapes practically in determining the dynamic response are also given. The new approach can be extended to calculate the dynamic response of general adaptive structures.

Optimal Placement of Active Members in Truss Adaptive Structures

The mathematical model of optimal placement of active members in truss adaptive structures is essentially a nonlinear multi-objective optimization problem with mixed variables. It is usually much difficult and costly to be solved. In this paper, the optimal location of active members is treated in terms of （0, 1） discrete variables. Structural member sizes, control gains, and （0, 1） placement variables are treated simultaneously as design variables. Then, a succinct and reasonable compromise scalar model, which is transformed from original multi-objective optimization, is established, in which the （0, 1） discrete variables are converted into an equality constraint. Secondly, by penalty function approach, the subsequent scalar mixed variable compromise model can be formulated equivalently as a sequence of continuous variable problems. Thirdly, for each continuous problem in the sequence, by choosing intermediate design variables and temporary critical constraints, the approximation concept is carried out to generate a sequence of explicit approximate problems which enhance the quality of the approximate design problems. Considering the proposed method, a FORTRAN program OPAMTAS2.0 for optimal placement of active members in truss adaptive structures is developed, which is used by the constrained variable metric method with the watchdog technique （CVMW method）. Finally, a typical 18 bar truss adaptive structure as test numerical examples is presented to illustrate that the design methodology set forth is simple, feasible, efficient and stable. The established scalar mixed variable compromise model that can avoid the ill-conditioned possibility caused by the different orders of magnitude of various objective functions in optimization process, therefore, it enables the optimization algorithm to have a good stability. On the other hand, the proposed novel optimization technique can make both discrete and continuous variables be optimized simultaneously.

CHARACTERISTICS OF STRENGTH CONTROL OF ADAPTIVE STRUCTURE WITH ELECTROMECHANICAL COUPLING

Based on the programming method, an electromechanical coupling adaptive statically indeterminate truss structure is controlled for increasing its load capacity. Several main parameters during the process of design of the adaptive structure are selected for a study of its characteristic during the control stage. The curves of each parameter for the effect of control results are plotted and corresponding conclusions are drawn. Thus, the theoretical basis is presented for optimal design, manufacture and control of the adaptive structure.

SYSTEM IDENTIFICATION OF ADAPTIVE TRUSS STRUCTURES USING PIEZOELECTRIC ACTIVE MEMBERS

Adaptive truss structures are a new kind of structures with integrated active members,whose dynamic characteristies can be beneficially modified to meet mission requirements.Active members containing actuating and sensing units are the major components of adaptive truss structures.Modeling of adaptive truss structures is a key step to analyze the structural dynamic characteristics.A new experimental modal analysis approach,in which active members are used as excitatiDn sources for modal test,has been proposed in this paper.The excitation forces generated by the active members, which are different from the excitation forces exerted on structures in the conventional modal test,are internal forces for the truss structures.The relation between internal excitation forces and external forces is revealed such that the traditional identification method can be adopted to obtain modal parameters of adaptive structures.Placement problem of the active member in adaptive truss structures is also discussed in this work. Modal test and analysis are conducted with a planar adaptive truss structure by using piezoelectric active members in order to verify the feasibility and effectiveness of the proposed method.

Compensation for secondary uncertainty in electro-hydraulic servo system by gain adaptive sliding mode variable structure control

Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.

Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor

In soft sensor field, just-in-time learning(JITL) is an effective approach to model nonlinear and time varying processes. However, most similarity criterions in JITL are computed in the input space only while ignoring important output information, which may lead to inaccurate construction of relevant sample set. To solve this problem, we propose a novel supervised feature extraction method suitable for the regression problem called supervised local and non-local structure preserving projections(SLNSPP), in which both input and output information can be easily and effectively incorporated through a newly defined similarity index. The SLNSPP can not only retain the virtue of locality preserving projections but also prevent faraway points from nearing after projection,which endues SLNSPP with powerful discriminating ability. Such two good properties of SLNSPP are desirable for JITL as they are expected to enhance the accuracy of similar sample selection. Consequently, we present a SLNSPP-JITL framework for developing adaptive soft sensor, including a sparse learning strategy to limit the scale and update the frequency of database. Finally, two case studies are conducted with benchmark datasets to evaluate the performance of the proposed schemes. The results demonstrate the effectiveness of LNSPP and SLNSPP.

Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain.In tactical Mobile Ad-hoc Network(MANET),hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out,self-mending and self-administration.Clustering in the routing process is one of the key aspects to increase MANET performance by coordinat-ing the pathways using multiple criteria and analytics.We present a Group Adaptive Hybrid Routing Algorithm(GAHRA)for gathering portability,which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile situations.Based on this aspect,the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach,with the objectives of enhancing the out-put of MANET routing computation in each hub.Simulation analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.

AHLNet:Adaptive Multihead Structure and Lightweight Feature Pyramid Network for Detection of Live Working in Substations

With the increasing demand for power in society,there is much live equipment in substations,and the safety and standardization of live working of workers are facing challenges.Aiming at these problems of scene complexity and object diversity in the real-time detection of the live working safety of substation workers,an adaptive multihead structure and lightweight feature pyramid-based network(AHLNet)is proposed in this study,which is based on YOLOV3.First,we take AH-Darknet53 as the backbone network of YOLOV3,which can introduce an adaptive multihead(AMH)structure,reduce the number of network parameters,and improve the feature extraction ability of the backbone network.Second,to reduce the number of convolution layers of the deeper feature map,a lightweight feature pyramid network(LFPN)is proposed,which can perform feature fusion in advance to alleviate the problem of feature imbalance and gradient disappearance.Finally,the proposed AHLNet is evaluated on the datasets of 16 categories of substation safety operation scenarios,and the average prediction accuracy MAP_(50)reaches 82.10%.Compared with YOLOV3,MAP_(50)is increased by 2.43%,and the number of parameters is 90 M,which is only 38%of the number of parameters of YOLOV3.In addition,the detection speed is basically the same as that of YOLOV3,which can meet the real-time and accurate detection requirements for the safe operation of substation staff.

Salience adaptive morphological structuring element construction method based on minimum spanning tree

Classical mathematical morphology operations use a fixed size and shape structuring element to process the whole image.Due to the diversity of image content and the complexity of target structure,for processed image,its shape may be changed and part of the information may be lost.Therefore,we propose a method for constructing salience adaptive morphological structuring elements based on minimum spanning tree(MST).First,the gradient image of the input image is calculated,the edge image is obtained by non-maximum suppression(NMS)of the gradient image,and then chamfer distance transformation is performed on the edge image to obtain a salience map(SM).Second,the radius of structuring element is determined by calculating the maximum and minimum values of SM and then the minimum spanning tree is calculated on the SM.Finally,the radius is used to construct a structuring element whose shape and size adaptively change with the local features of the input image.In addition,the basic morphological operators such as erosion,dilation,opening and closing are redefined using the adaptive structuring elements and then compared with the classical morphological operators.The simulation results show that the proposed method can make full use of the local features of the image and has better processing results in image structure preservation and image filtering.

Few-shot node classification via local adaptive discriminant structure learning

Node classification has a wide range of application scenarios such as citation analysis and social network analysis.In many real-world attributed networks,a large portion of classes only contain limited labeled nodes.Most of the existing node classification methods cannot be used for few-shot node classification.To train the model effectively and improve the robustness and reliability of the model with scarce labeled samples,in this paper,we propose a local adaptive discriminant structure learning(LADSL)method for few-shot node classification.LADSL aims to properly represent the nodes in the attributed graphs and learn a metric space with a strong discriminating power by reducing the intra-class variations and enlargingginter-classdifferences.Extensiveexperiments conducted on various attributed networks datasets demonstrate that LADSL is superior to the other methods on few-shot node classification task.

RESPONSE SUPPRESSION OF COMPOSITES INDUCED BY BUFFETING AERODYNAMIC LOAD

One of the aerodynamic phenomena associated with high performance aircraft is the high frequency vortex induced buffeting. The buffeting load can lead to high cyclic strain and stress,dramatically reduce the fatigue life of composite structures. In this paper, piezoelectric patches are bonded on the surface of composite panel. The dynamic response of the structure is measured by using bonded piezoelectric sensors. Filtered adaptive control algorithm is used to control the strain of piezoelectric actuators actively, so as to increase the modal damping coefficient of the composite panel, suppress the dynamic response and improve the fatigue performance of the structure. The feasibility of this method is verified in model experiments.

Static control method using gradient-genetic algorithm for grillage adaptive beam string structures based on minimal internal force

The grillage adaptive beam string structure(GABSS)is a new type of smart structure that can self-adjust its deformation and internal forces through a group of active struts(actuators)in response to changes in environmental conditions.In this paper,an internal force control method based on a gradient–genetic algorithm(GGA)is proposed for the static control of a tensioned structure(especially the GABSS).Specifically,an optimization model of the GABSS is established in which the adjustment values of the actuators are set as the control variables,and the internal force of the beam is set as the objective function.The improved algorithm has the advantage of the global optimization ability of the genetic algorithm and the local search ability of the gradient algorithm.Two examples are provided to illustrate the application of the GGA method.The results show that the proposed method is practical for solving the internal force control problem of the GABSS.

Fuzzy Adaptive Variable Structure Guidance with Application to Space Interception

The adaptive variable structure guidance (AVSG) is robust to target maneuvers, but apt to induce unwanted chattering .Fuzzy technique is used to implementthe AVSGfor eliminating chattering ,and then a new guidance method called fuzzy AVSGis produced .The fuzzy AVSGis applied to the space interception problem .Simulationresults demonstratethe effectiveness ofthe new guidance method .

Woofer–tweeter adaptive optical structured illumination microscopy

A woofer–tweeter adaptive optical structured illumination microscope(AOSIM) is presented. By combining a low-spatial-frequency large-stroke deformable mirror(woofer) with a high-spatial-frequency low-stroke deformable mirror(tweeter), we are able to remove both large-amplitude and high-order aberrations. In addition, using the structured illumination method, as compared to widefield microscopy, the AOSIM can accomplish highresolution imaging and possesses better sectioning capability. The AOSIM was tested by correcting a large aberration from a trial lens in the conjugate plane of the microscope objective aperture. The experimental results show that the AOSIM has a point spread function with an FWHM that is 140 nm wide(using a water immersion objective lens with NA=1.1) after correcting a large aberration(5.9 μm peak-to-valley wavefront error with 2.05 μm RMS aberration). After structured light illumination is applied, the results show that we are able to resolve two beads that are separated by 145 nm, 1.62× below the diffraction limit of 235 nm. Furthermore, we demonstrate the application of the AOSIM in the field of bioimaging. The sample under investigation was a green-fluorescentprotein-labeled Drosophila embryo. The aberrations from the refractive index mismatch between the microscope objective, the immersion fluid, the cover slip, and the sample itself are well corrected. Using AOSIM we were able to increase the SNR for our Drosophila embryo sample by 5×.

A Hybrid Heuristic Algorithm for Solving COVID-19’s Social Distancing at Universities Campus

Social distancing during COVID-19 has become one of the most important measures in reducing the risks of the spread of the virus. Implementing thesemeasures at universities is crucial and directly related to the physical attendance ofthe populations of students, professors, employees, and other members on campus. This research proposes an automated scheduling approach that can help universities and schools comply with the social distancing regulations by providingassistance in avoiding huge assemblages of people. Furthermore, this paper proposes a novel course timetable-scheduling scheme based on four main constraints.First, a distance of two meters must be maintained between each student inside theclassroom. Second, no classrooms should contain more than 20% of their regularcapacity. Third, there would be no back-to-back classes. Lastly, no lectures shouldbe held simultaneously in adjacent classrooms. The proposed approach wasimplemented using a variable neighborhood search (VNS) approach with an adaptive neighborhood structure (AD-NS) to resolve the problem of scheduling coursetimetables at Al-Ahlyyia Amman University. However, the experimental resultsshow that the proposed techniques outperformed the standard VNS tested on university course timetabling benchmark dataset ITC2007-Track3. Meanwhile, theapproach was tested using datasets collected from the faculty of information technology at Al-Ahlyyia Amman University (Jordan). Where the results showed that,the proposed technique could help educational institutes to resume their regularoperations while complying with the social distancing guidelines.

SARISTU:Adaptive Trailing Edge Device(ATED)design process review

SARISTU was a big cooperation project granted by the European Commission,7th Framework Programme,carried out between 2011 and 2015.It dealt with smart aeronautic structures,both morphing and sensored;its main target was to demonstrate the feasibility of designing,manufacturing and operating in representative environment,instrumented structures.Till now,it represents the major effort carried out within the European Union on the development of adaptive architectures for air systems.Inside that big activity,the realization of an Adaptive Trailing Edge Device(ATED)for wing camber adaptations aimed at compensating the weight reduction following the fuel consumption during cruise was addressed.It made the core of investigations target variable geometry aircraft components together with two other analyses concerning the development of shape-changing winglet and droop nose.ATED activities were conducted by the Italian Aerospace Research Centre(CIRA)in tight cooperation with the University of Napoli,"Federico II",who coordinated a group of 12 different partners from 8 different nations(France,Germany,Greece,the Netherlands,Israel,Spain,Turkey,and Italy).In this paper,an integral synthesis of that work is reported,with a focus on the definition and realization of the components of the presented device.The publication is in fact meant as the first part of a series that is aimed at overviewing the whole adaptive trailing edge development,till wind tunnel tests execution.Such a concise report is a critical and harmonized review of what have been performed by many colleagues spread all over Europe,all of which are duly recalled in the reported bibliography where the reader may access more detailed information and descriptions.In detail,the paper starts with a general introduction of the concept and its aims,to move to the specs definition immediately after.Then,it deals with a short but comprehensive description of the main ATED components:structural skeleton,skin,actuation and sensing systems.It is worth remarking that the paragraph dedicated to the body frame includes some discussion about aeroelastic assessment and manufacture,seen as complementation for a complete assessment of the design constraints.

Distributed event-triggered adaptive control for second-order nonlinear uncertain multi-agent systems

In this paper,the event-triggered consensus control problem for nonlinear uncertain multi-agent systems subject to unknown parameters and external disturbances is considered.The dynamics of subsystems are second-order with similar structures,and the nodes are connected by undirected graphs.The event-triggered mechanisms are not only utilized in the transmission of information from the controllers to the actuators,and from the sensors to the controllers within each agent,but also in the communication between agents.Based on the adaptive backstepping method,extra estimators are introduced to handle the unknown parameters,and the measurement errors that occur during the event-triggered communication are well handled by designing compensating terms for the control signals.The presented distributed event-triggered adaptive control laws can guarantee the boundness of the consensus tracking errors and the Zeno behavior is avoided.Meanwhile,the update frequency of the controllers and the load of communication burden are vastly reduced.The obtained control protocol is further applied to a multi-input multi-output second-order nonlinear multi-agent system,and the simulation results show the effectiveness and advantages of our proposed method.

Ranking with Adaptive Neighbors

Retrieving the most similar objects in a large-scale database for a given query is a fundamental building block in many application domains, ranging from web searches, visual, cross media, to document retrievals. Stateof-the-art approaches have mainly focused on capturing the underlying geometry of the data manifolds. Graphbased approaches, in particular, define various diffusion processes on weighted data graphs. Despite success,these approaches rely on fixed-weight graphs, making ranking sensitive to the input affinity matrix. In this study,we propose a new ranking algorithm that simultaneously learns the data affinity matrix and the ranking scores.The proposed optimization formulation assigns adaptive neighbors to each point in the data based on the local connectivity, and the smoothness constraint assigns similar ranking scores to similar data points. We develop a novel and efficient algorithm to solve the optimization problem. Evaluations using synthetic and real datasets suggest that the proposed algorithm can outperform the existing methods.

An integrated approach to modeling and adaptive control

In the book(Adaptive Identification,Prediction and Control-Multi Level Recursive Approach),the concept of dynamical linearization of nonlinear systems has been presented.This dynamical linearization is formal only,not a real linearization.From the linearization procedure,we can find a new approach of system identification,which is on-line real-time modeling and real-time feedback control correction.The modeling and real-time feedback control have been integrated in the identification approach,with the parameter adaptation model being abandoned.The structure adaptation of control systems has been achieved,which avoids the complex modeling steps.The objective of this paper is to introduce the approach of integrated modeling and control.