Desired Configuration Research Articles

Metafeature Selection via Multivariate Sparse-Group Lasso Learning for Automatic Hyperparameter Configuration Recommendation.

The performance of classification algorithms is mainly governed by the hyperparameter settings deployed in applications, and the search for desirable hyperparameter configurations usually is quite challenging due to the complexity of datasets. Metafeatures are a group of measures that characterize the underlying dataset from various aspects, and the corresponding recommendation algorithm fully relies on the appropriate selection of metafeatures. Metalearning (MtL), aiming to improve the learning algorithm itself, requires development in integrating features, models, and algorithm learning to accomplish its goal. In this article, we develop a multivariate sparse-group Lasso (SGLasso) model embedded with MtL capacity in recommending suitable configurations via learning. The main idea is to select the principal metafeatures by removing those redundant or irregular ones, promoting both efficiency and performance in the hyperparameter configuration recommendation. To be specific, we first extract the metafeatures and classification performance of a set of configurations from the collection of historical datasets, and then, a metaregression task is established through SGLasso to capture the main characteristics of the underlying relationship between metafeatures and historical performance. For a new dataset, the classification performance of configurations can be estimated through the selected metafeatures so that the configuration with the highest predictive performance in terms of the new dataset can be generated. Furthermore, a general MtL architecture combined with our model is developed. Extensive experiments are conducted on 136 UCI datasets, demonstrating the effectiveness of the proposed approach. The empirical results on the well-known SVM show that our model can effectively recommend suitable configurations and outperform the existing MtL-based methods and the well-known search-based algorithms, such as random search, Bayesian optimization, and Hyperband.

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

To achieve recycling of mixed industrial waste toward an advanced sustainable society, waste sorting automation through robots is crucial and urgent. For this purpose, a robot is required to recognize the category, shape, pose, and condition of different waste items and manipulate them according to the category to be sorted. This survey considers three potential difficulties in the sorting automation: 1) End-effector: to robustly grasp and manipulate different waste items with dirt and deformations; 2) Sensor: to recognize the category, shape, and pose of existing objects to be manipulated and the wet and dirty conditions of their surfaces; and 3) Planner: to generate feasible and efficient sequences and trajectories. This survey includes 76 references to studies related to automatic waste sorting and 159 references to worldwide waste recycling attempts. This pioneering investigation reveals the possibility and limitations of conventional systems; thus, providing insights on open issues and potential technologies to achieve a robot-incorporated sorter for the chaotic mixed waste is one of its contributions. This paper further presents a system design policy for readers and discusses future advanced sorters, thereby contributing to the field of robotics and automation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Most automated sorting systems operate for limited target waste items. This study is motivated by the automation of mixed industrial waste treatment facilities using advanced robotic sorters. Emerging advances and increasing functionalities of robot system components will widen system applicability and increase use cases in the chaotic mixed industrial waste domain. This paper surveys the research conducted to date, discusses open issues and potential approaches, and presents user guides that provide practitioners with a system design policy. The user guides created according to the strengths and weaknesses of each system configuration provide future researchers and developers with a useful a priori design policy that has been thus far validated on efficiency, quality, productivity, and reliability. A question-and-answer style guide and a sorting-target-aware previous study reference list allows users to find the desired system configuration, including the investigated components according to their purpose.

Tube Oscillation Drives Transitory Vortices Across Microfluidic Barriers.

Here, the generation of dynamic vortices across microscale barriers using the tube oscillation mechanism is demonstrated. Using a combination of high-speed imaging and computational flow dynamics, the cyclic formation, expansion, and collapse of vortices are studied. The dynamics of vortices across circular , triangular, and blade-shape barriers are investigated at different tube oscillation frequencies. The formation of an array of synchronous vortices across parallel blade-shaped barriers is demonstrated. The transient flows caused by these dynamic vortex arrays are harnessed for the rapid and efficient mixing of blood samples . A circular barrier scribed with a narrow orifice on its shoulder is used to facilitate the injection of liquid into the microfluidic channel, and its rapid mixing with the main flow through the dynamic vortices generated across the barrier. This approach facilitates the generation of vortices with desirable configurations, sizes, and dynamics in a highly controllable, programmable, and predictable manner while operating at low static flow rates.

Integrated Localization and Planning for Cruise Control of UGV Platoons in Infrastructure-Free Environments

This paper investigates the cruise control problem of unmanned ground vehicle (UGV) platoons from the implementation perspective. Unlike most existing works related to platoon cruise control which rely on positioning infrastructures such as lane markings, roadside units, and global navigation satellite systems (GNSS), this paper explores a new problem: platoon cruise control in environments without positioning infrastructures. The introduction of this constraint disables most existing cruise control approaches. To address this problem, an integrated localization and planning framework is proposed, which is composed of three modular algorithms. Firstly, to localize multiple vehicles in a common coordinate system, a collaborative localization algorithm is developed through matching local perceptions of different vehicles. Secondly, to maintain the desired platoon configuration, the historical trajectory of the preceding vehicle is reconstructed, based on which the target state is planned for the following vehicle. Finally, a virtual controller based algorithm is designed to generate feasible trajectories for the following vehicle in real time. The proposed framework has two salient features. Firstly, it does not depend on positioning infrastructures and does not introduce additional positioning sensors, such as GNSS/INS modules, ultra-wideband (UWB) devices, magnetic meters and so on, as long as each vehicle is equipped with a perception sensor (Lidar, radar or camera), which however is essential equipment for nowaday autonomous systems. Secondly, the proposed framework does not depend on direct observations between vehicles to achieve relative localization, making it applicable in non-line-of-sight (non-LOS) situations. Real-world experiments have been conducted to validate the effectiveness, robustness and practicality of the proposed framework.

Sectorial coverage control with load balancing in non-convex hollow environments

It has long been a challenging task for multi-agent systems (MASs) to inexpensively service probable events in non-convex environments. Coverage control provides an efficient framework to address MAS deployment problem for optimizing the cost of tackling unknown events. By means of the divide-and-conquer methodology, this paper proposes a sectorial coverage formulation to configure MASs in non-convex hollow environments while ensuring load balancing among subregions. Thereby, a distributed controller is designed to drive each agent towards a desirable configuration that minimizes the coverage cost by simultaneously adopting sectorial partition mechanism. Theoretical analysis is conducted to ensure the asymptotic stability of closed-loop MASs with exponential convergence of equitable partition. In addition, a circular search algorithm is proposed to identify desirable solutions to such a sectorial coverage problem, which guarantees approximating the optimal deployment of MASs with arbitrarily small tolerance. Finally, both numerical simulations and multi-robot experiments are conducted to substantiate the efficiency of the present sectorial coverage approach.

Fully distributed dynamic event-triggering formation control for multi-agent systems under DoS attacks: Theory and experiment

The fully distributed time-varying formation (TVF) control problem is investigated for multi-agent systems (MASs) subject to Denial-of-Service (DoS) attacks. An asynchronous dynamic event-triggering (DET) mechanism is introduced to alleviate agents’ communication burden suitably, and a fully distributed DET-TVF control protocol is designed. Sufficient conditions and theoretical analysis prove that this protocol can achieve the desired TVF configuration under DoS attacks, and Zeno behavior is excluded. Finally, experimental examples using six quadrotors demonstrate and fully showcase the efficacy of the proposed methods.

A geometric motion planning for a spin-rolling sphere on a plane

The paper deals with motion planning for a spin-rolling sphere when the sphere follows an optimal straight path on a plane. Since the straight line constrains the sphere’s motion, controlling the sphere’s spin motion is essential to converge to a desired full configuration of the sphere. In this paper, we show a new geometric-based planning approach that is based on a full-state description of this nonlinear system. First, the problem statement of the motion planning is posed. Next, we develop a geometric controller implemented as a virtual surface by using the Darboux frame kinematics. This virtual surface generates arc-length-based inputs for controlling the trajectories of the sphere. Then, an iterative algorithm is designed to tune these inputs for the desired configurations. Finally, the feasibility of the proposed approach is verified by simulations.

Cover Image, Volume 5, Number 5, May 2023

Front cover image: Given a rise in the popularity of wearable electronics that are able to deform into desirable configurations while preserving electrochemical functionality, stretchable and flexible supercapacitors (SCs) have become increasingly of interest as innovative energy storage devices. In article number: 10.1002/cey2.320, Amiri et al. investigated various advancements and upcoming prospects of flexible and stretchable SCs including 1D fiber-like SCs, 2D planar SCs, and 3D SCs. The fundamentals of stretchable SCs such as mechanisms, layouts, materials, and configuration designs associated with each configuration and their effects on mechanical and electrochemical properties are studied in details.

Distributed Affine Formation Maneuver Control of Autonomous Surface Vehicles With Event-Triggered Data Transmission Mechanism

This article investigates the distributed affine formation maneuver control for collaborative autonomous surface vehicles (ASVs) systems with the event-triggered data transmission mechanism among inter-ASVs. To achieve preset arbitrary configuration transformations in the case where the formation knowledge is assigned to only a part of ASVs, the affine image is employed to describe desired configurations. To ensure position and attitude configuration rigidities simultaneously, a synthetical constraint composed of the Laplacian matrices and the stress matrices is constructed. Besides, an algebraic relation between the synthetical constraint and the formation tracking error is established. The above tools enable the sliding mode technology to be used to the affine formation maneuver control of collaborative ASVs systems. It follows that a novel distributed event-triggered affine formation maneuver control scheme is developed for the collaborative ASVs systems. The advantages of the proposed control scheme are threefold: 1) the distributed affine formation maneuver control of collaborative second-order vehicles systems is achieved with changeable formation velocities and orientations; 2) the existing literatures adapt to developing the second-order distributed affine formation maneuver control based on the backstepping philosophy. In contrast, the sliding manifold replaces the backstepping philosophy here. Since the complexity explosion is circumvented, the proposed control scheme is relatively simple; and 3) the event-triggered data transmission mechanism among inter-ASVs is introduced, and neighbor velocities and accelerations are omitted for the control implementation. As a consequence, the communication resource usage can be reduced. Finally, the semi-physical simulations are conducted to illustrate the effectiveness of the proposed control scheme.

Physics-regularized neural network of the ideal-MHD solution operator in Wendelstein 7-X configurations

The computational cost of constructing 3D magnetohydrodynamic (MHD) equilibria is one of the limiting factors in stellarator research and design. Although data-driven approaches have been proposed to provide fast 3D MHD equilibria, the accuracy with which equilibrium properties are reconstructed is unknown. In this work, we describe an artificial neural network (NN) that quickly approximates the ideal-MHD solution operator in Wendelstein 7-X (W7-X) configurations. This model fulfils equilibrium symmetries by construction. The MHD force residual regularizes the solution of the NN to satisfy the ideal-MHD equations. The model predicts the equilibrium solution with high accuracy, and it faithfully reconstructs global equilibrium quantities and proxy functions used in stellarator optimization. We also optimize W7-X magnetic configurations, where desirable configurations can be found in terms of fast particle confinement. This work demonstrates with which accuracy NN models can approximate the 3D ideal-MHD solution operator and reconstruct equilibrium properties of interest, and it suggests how they might be used to optimize stellarator magnetic configurations.

Meta‐Module Mutual Assistance: A Bioinspired Design for Self‐Assembly of Modular Space Robot

The theoretical capability of modular robots to organize the overall robot into different structures with different functions has broad prospects in space exploration. Therefore, a novel modular space robot named Space Module is developed, and a self‐assembly strategy inspired by biological cooperative and mutual assistance behaviors is proposed. First, the meta‐module method is utilized to endow nonmobile modules with the mobility. Second, the mutual assistance is presented to achieve position and posture reachability of the assembly unit while minimizing the effect of meta‐modules on granularity. Then, according to the unique motion characteristics of the meta‐module and mutual assistance, an assembly planner is designed to obtain the assembly sequences to realize the self‐manufacturing of desired configurations. Finally, the validity and feasibility of the proposed assembly strategy are further confirmed by demonstrations. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.167264888.81237462/v1.

Machine learning mechanical properties of defect-engineered hexagonal boron nitride

Defect engineering of 2D materials has exhibited promising potentials for designing new properties beyond their pristine forms. However, the structure–property mapping for defect-engineered 2D materials is usually highly complex due to the vast designing space spanned by multiple types of defects, which may coexist and jointly affect the properties of 2D materials. In this work, we propose a general machine learning framework to efficiently predict the mechanical properties of defect-engineered 2D materials with a generic configuration of coexisting types of defects. The mechanical properties of defect-engineered hexagonal boron nitride are taken as examples for demonstration. Through thousands of molecular dynamics simulations, we first reveal the complex mechanical response of hexagonal boron nitride containing both vacancy defects and substitutional doping defects. Then a machine learning model based on convolutional neural network is established and trained to efficiently predict the simulated mechanical properties. We also demonstrate how the neural network can be applied for finding desirable defected configurations with targeted mechanical properties. The proposed methods are expected be applicable to a variety of properties of defect-engineered 2D materials.

Pushing the Limit of Flexible Batteries

Open AccessCCS ChemistryMINI REVIEWS4 Jan 2023Pushing the limit of flexible batteries Chuan Xie, Yanpeng Guo and Zijian Zheng Chuan Xie Google Scholar More articles by this author , Yanpeng Guo Google Scholar More articles by this author and Zijian Zheng Google Scholar More articles by this author https://doi.org/10.31635/ccschem.023.202202635 SectionsAboutPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked InEmail Alongside the rapid development of flexible and wearable electronics, the search for reliable, safe, and highenergy rechargeable flexible batteries (FBs) has become a research hotspot in recent years. Although there are some prototype FBs exhibited by the industry and a rapidly increasing number of publications reported by the academia, most demonstrations are shown on a laboratory scale, and it is still difficult to find real implementation of the technology in the market. This Perspective aims to discuss and analyze the key metrics, including energy density, flexibility, and safety, that is critical to pushing the FBs to a commercially viable level, with a special focus on the lithium batteries and zinc batteries that are mostly reported in the literature. We first compare the figure of merit of flexible batteries (fbFOM) of existing lithium-based and zinc-based FBs with the requirement with market applications. We then analyze the most desired cell configurations towards high flexibility, followed by systematic discussions on features and materials selection towards high-energy-density FBs. Third, we discuss strategies to enhance battery safety. An outlook of the future developments needed to address the gaps between the state-of-the-art demonstrations and the real applications is provided at the end of this article. Download figure Download PowerPoint Next article FiguresReferencesRelatedDetails Issue AssignmentNot Yet Assigned Copyright & Permissions© 2023 Chinese Chemical Society Downloaded 0 times PDF downloadLoading ...

Configuration‐dependent stretchable all‐solid‐state supercapacitors and hybrid supercapacitors

AbstractGiven the rise in the popularity of wearable electronics that are able to deform into desirable configurations while maintaining electrochemical functionality, stretchable and flexible (hybrid) supercapacitors (SCs) have become increasingly of interest as innovative energy storage devices. Their outstanding power density, long lifetime with low capacitance loss, and appropriate energy density, in particular in hybrid cases make them ideal candidates for flexible electronics. The aim of this review paper is to provide an in‐depth discussion of these stretchable and flexible SCs ranging from fabrication to electro‐mechanical properties. This review paper begins with a short overview of the fundamentals of charge storage mechanisms and different types of multivalent metal‐ion hybrid SCs. The research methods leading up to the current state of these stretchable and flexible SCs are then presented. This is followed by an in‐depth presentation of the challenges associated with the fabrication methods for different configurations. Proposed novel strategies to maximize the elastic and electrochemical properties of stretchable/flexible or quasi‐solid‐state SCs are classified and the pros and cons associated with each are shown. The advances in mechanical properties and the expected advancements for the future of these SCs are discussed in the last section.

Layered fully distributed formation-containment tracking control for multiple unmanned surface vehicles

This paper addresses the formation-containment tracking control (FCTC) problem for unmanned surface vehicles (USVs) formation systems swarm with multiple leaders. Unlike the general study of the multi-leader case, communication among leaders is considered to endow the system with broader applicability under different mission requirements. Besides, a layered framework is employed here to distinguish the behaviour and role of agents in the formation system. In the first layer, a formation tracking controller is developed for leaders to track the reference trajectory and achieve the desired formation configuration. Then, a containment controller is proposed for followers in the second layer to drive them into the geometric space formed by leaders. Without loss of generality, the state information transmission between the two layers is considered unidirectional and the directed communication networks are designed in both layers. Meanwhile, considering the limitation that the agents in the USV formation system can only access their local information, the FCTC control strategy is designed in a fully distributed manner. Finally, numerical simulation results are represented to demonstrate the validity and feasibility of the proposed control scheme.

Fast fixed-time nonsingular terminal sliding-mode formation control for autonomous underwater vehicles based on a disturbance observer

In this paper, a novel disturbance observer-based fixed-time formation control method is proposed for autonomous underwater vehicles with actuator faults, model uncertainties and unknown disturbances. Firstly, a fast fixed-time stable system is improved, which has a faster convergence rate than existing stable system. Then, the leader-follower formation control strategy is combined with the path planning strategy based on the artificial potential field to obtain a collision-free formation configuration. Besides, a fast fixed-time disturbance observer is designed to compensate for unknown composite disturbances, which enhances the robustness of the system. Further, based on the disturbance estimation information and the improved nonsingular fast fixed-time terminal sliding-mode surface, a robust fast fixed-time formation control method is presented, which achieves the desired formation configuration. Finally, simulation and comparison results show the effectiveness of the proposed control strategy.

Shape Servoing of Deformable Objects using Adaptive Deformation Model Estimation

In this paper, we propose an adaptive shape servoing method to deform a soft object into a desired 3-D shape. The high dimensional representation and the unknown deformation properties of the soft object pose a challenge to actively manipulate its shape. To address this issue, we develop a method to compute the deformation Jacobian matrix in real-time. The Jacobian is estimated using a set of basis functions and its corresponding parameters to capture the dynamics of the system and relate the applied input motion to changes in the soft object's shape. An integral concurrent learning (ICL) based adaptive update law is derived using Lyapunov analysis to estimate the deformation parameters and prove its convergence. A physics-based simulation is used to validate the proposed method and controller by performing manipulation tasks with different desired configurations. The performance is compared with a standard gradient update law to demonstrate the accuracy and robustness of our approach.

Model predictive control of a serial link robot manipulator

This paper presents a novel approach for model predictive control dynamics applied to a two-link manipulator robot. The technique involves the initial step of linearizing the inherently nonlinear dynamic model of the robot through the application of feedback linearization control. Once the linear model is derived, a predictive control strategy is developed to enhance the robot's overall performance. To achieve this, we introduce a quadratic criterion, and the associated parameters are meticulously computed to induce specific behaviors within the closed-loop system. The primary objective of this study is to effectively control the arm robot, guiding it from an initial configuration to a desired final configuration by employing the predictive control approach, all while minimizing the quadratic criterion. The chosen criterion serves as a crucial metric in evaluating the system's response and optimizing its behavior during the control process. To validate the effectiveness of the proposed method, a series of simulation results are provided, showcasing the system's performance under various conditions. These simulations serve as empirical evidence supporting the feasibility and efficiency of the introduced model predictive control approach for the dynamics of a twolink manipulator robot. The outcomes highlight the method's potential for real-world applications and contribute to the ongoing advancement of control strategies in robotic systems.

Sustainable Droplet Manipulation on Ultrafast Lubricant Self‐Mediating Photothermal Slippery Surfaces

AbstractDesigning intelligent slippery surfaces for droplet manipulation is critical for many applications from drug delivery to bio‐analysis, while is of great challenging in sustainability for inescapable wastage of lubricant layer. Herein, an ultrafast lubricant self‐mediating (self‐replenishing/‐absorbing) photothermal slippery surface is designed that achieves sustainable transport of droplet under the irradiation of near infrared light (NIL) even if the lubricant layer is wiped clean completely, as well as at other man‐made extreme conditions. The ultrafast lubricant self‐mediating performance is caused by synergistic effects of interconnection of porous structure and photothermal expansion of the material. When lubricant on surface is lost, photothermal expansion of material can quickly squeeze the lubricant inside the base to flow into and out of the interconnected porous structure to generate a fresh lubricant layer. Attractively, when the NIL is turned off, the rebuilt lubricant layer can be swiftly self‐absorbed into the porous to inhibit unnecessary wastage. Moreover, an arbitrary split of droplet in desired configurations can be achieved by controlling the NIL irradiating route. This sustainable droplet manipulation induced by ultrafast lubricant self‐mediating can be extensively applied in microfluidics and micro‐reactor settings.

Optimization of the cable forces of completed cable-stayed bridges with differential evolution method

Long-span cable-stayed bridges have been massively erected worldwide in recent decades, and reasonable cable forces play an essential role in achieving the desired configurations of completed bridges. In the current research, a differential evolution (DE) algorithm is adopted to optimize the cable forces considering the counterweight. An asymmetrical cable-stayed bridge is taken as a case study, and the numerical model of the bridge is established with the OpenSees platform. The cable sag effect and geometric nonlinearity are considered in the numerical model. The weighted total bending energy of the girder and pylons is taken as the objective function of the optimization problem. In addition, the constraints of the cable forces, the range and magnitude of the counterweight, and the bearing reaction forces at the transition and auxiliary piers are considered. The numerical results reveal that the proposed framework is efficient and stable for optimizing the cable forces, and several optimum solutions can be obtained. In addition, the performance of the proposed optimum algorithm is less sensitive to the values of the initial parameters (feasible regions and population size). Moreover, the proposed algorithm automatically determines the control parameters (scaling factor and crossover rate) of the DE method, which makes it easy to use in practical engineering. In addition, the proposed algorithm can be used to optimize the cable forces in the final stage and the construction stages.