High Degree Of Freedom Research Articles

Multi-mode industrial soft sensor method based on mixture Laplace variational auto-encoder

The industrially collected process data usually exhibit non-Gaussian and multi-mode characteristics. Due to sensor failures, irregular disturbances, and transmission problems, there are unavoidable outliers that make the data exhibit heavy-tailed characteristics. To this end, a variational auto-encoder regression method based on the mixture Laplacian distribution (MLVAER) is proposed, by introducing a type-II multivariate Laplacian distribution in the latent variable space for robust modeling, and further extending it to the mixture form to accommodate multi-mode processes, the corresponding reparameterization trick is finally proposed for the mixture form of this distribution for neural network gradient descent training. The model based on this distribution assumption has higher degrees of freedom than the model based on the traditional multivariate Laplace distribution assumption when the network structure is the same. Numerical simulation and experiments on two industrial examples demonstrate that the proposed algorithm reduces the root mean square error by over 15% compared to other algorithms.

Continuous Gesture Control of a Robot Arm: Performance Is Robust to a Variety of Hand-to-Robot Maps.

Despite advances in human-machine-interface design, we lack the ability to give people precise and fast control over high degree of freedom (DOF) systems, like robotic limbs. Attempts to improve control often focus on the static map that links user input to device commands; hypothesizing that the user's skill acquisition can be improved by finding an intuitive map. Here we investigate what map features affect skill acquisition. Each of our 36 participants used one of three maps that translated their 19-dimensional finger movement into the 5 robot joints and used the robot to pick up and move objects. The maps were each constructed to maximize a different control principle to reveal what features are most critical for user performance. 1) Principal Components Analysis to maximize the linear capture of finger variance, 2) our novel Egalitarian Principal Components Analysis to maximize the equality of variance captured by each component and 3) a Nonlinear Autoencoder to achieve both high variance capture and less biased variance allocation across latent dimensions Results: Despite large differences in the mapping structures there were no significant differences in group performance. Participants' natural aptitude had a far greater effect on performance than the map. Robot-user interfaces are becoming increasingly common and require new designs to make them easier to operate. Here we show that optimizing the map may not be the appropriate target to improve operator skill. Therefore, further efforts should focus on other aspects of the robot-user-interface such as feedback or learning environment.

Synergy between heterogeneous catalysis and homogeneous radical reactions for pharmaceutical waste destruction: Perspective

Enormous quantities of pharmaceuticals are consumed by humans leading to a growing and continuous release of harmful components into the environment. Existing conventional water treatment plants are designed mainly for eliminating biodegradable organics and nutrients and cannot degrade pharmaceuticals and personal care products efficiently enough due to their chemical stability. Advanced oxidation processes using radicals generated from ozone can be efficiently combined with heterogeneous catalysis for treatment of wastewater containing pharmaceuticals and personal care products. From the technology viewpoint, elimination of pharmaceuticals from water by heterogeneously catalyzed ozonation should done in a continuous fixed bed reactor. The structured catalysts can be prepared by additive manufacturing using 3D-direct printing of supports/catalysts allowing a high degree of freedom in both the composition and design of the final catalytic material for a fixed bed heterogeneous-homogeneous reaction. Structured materials can exhibit non-periodic structure, such as for example semi-ordered structures, inspired by nature. For periodic and semi-periodic structures, heat and mass transfer should be investigated using computational fluid dynamics and flow imaging methods, guiding further design of novel architectures and subsequently allowing in combination with the materials development efficient control of activity and selectivity. The innovative catalytic reactor engineering should include experimental and numerical investigation of the stage wise injection of the oxidation agent, variation of the reactor cross-section size, changing the distance between the catalyst beds and introduction of the recycle loops.

Direct trajectory optimization of macro-micro robotic system using a Gauss pseudospectral framework

Trajectory planning is a crucial aspect of macro-micro robotic systems (MMRSs), especially when the system has high degrees of freedom (DOFs). In the field of robotic polishing, the MMRS is usually composed of an industrial robot and an end-effector, which is responsible for polishing force control. Therefore, the compliance of the macro-robot can be minimized by trajectory planning to reduce its impact on the micro-robot. This study proposes a trajectory planning strategy based on Gauss pseudospectral method for a 9-DOF MMRS. Different from traditional sequential solution strategies, it can be used to obtain an approximate global optimal trajectory. Firstly, the velocity-level kinematics model of MMRS is built, which comprehensively considers the workpiece placement pose and task redundancy. Secondly, an optimal control model for trajectory planning is developed through an effective variable allocation. On the premise of considering traditional trajectory smoothness constraints, a constraint on manipulability is additionally analyzed to avoid reaching a singular configuration during compliance optimization. Thirdly, a Gauss pseudospectral framework based on the optimal control model is proposed, and the costate mapping theorem is proved. The latter provides a theoretical basis for the efficiency and accuracy of the proposed method. Finally, comparison results demonstrate the effectiveness of the proposed method.

Deciphering the structure of deep eutectic solvents: A computational study from the solute's viewpoint

In the last few years, Deep Eutectic Solvents (DESs) have emerged as sustainable alternatives to traditional organic solvents. The high degree of freedom when tailoring DES structures represents, at the same time, their most praised and most complicated feature. Indeed, given the enormous number of possible combinations, the selection of the most suitable DES for a given application cannot be based on a trial-and-error approach; therefore, reliable computational tools are needed to fully exploit DESs potentialities. In this work, we propose the first computational protocol to investigate absorption properties of solutes dissolved in DES. The protocol combines an accurate sampling of the solute-solvent phase-space by means of Molecular Dynamics (MD) and a fully atomistic Quantum Mechanics/Molecular Mechanics (QM/MM) approach to describe intermolecular interactions and spectral properties. In this way, specific interactions such as hydrogen bonding, which characterize DES complex interaction patterns, are properly modelled. The robustness and reliability of the method are proved by comparing computed data with experimental spectra of 2-hydroxymethylfurfural and syringic, vanillic, p-coumaric, gallic, and caffeic acids dissolved in DES and water. These molecules have been chosen because of their relevance in the frame of biomass valorization. Remarkably, the protocol allows getting insights into DES/water mixtures and opens up to the prediction of aqueous DES behaviour of the water threshold which causes the switch from water-in-DES to aqueous electrolyte-like environments.

Comprehensive profile and areal calibration by additively manufactured material measures

Abstract The calibration of surface texture measuring instruments is standardized with two distinct types of material measures. ISO 25178-70 categorizes material measures that feature a profile and an areal surface topography. The result is that different types of measuring instruments like profilers on the one hand and areal surface topography measuring instruments on the other hand may require different material measures whose scope of application may be limited to only one of the named instrument types. The reason is that most manufacturing principles allow either a linear or circular extrusion of a geometry limiting the possibilities to manufacture material measures that are suitable for both, profile and areal surface topography measuring instruments. Since a comparability is desired for as many different measuring instruments as possible, we examine to what degree profile and areal material measures of ISO 25178-70 can be adapted and combined to possibly allow a calibration of all types of surface topography measuring instruments. Additive manufacturing with direct laser writing (DLW) is characterized by a high degree of freedom in the design of material measures. An enhancement of structures that can be imaged either in multiple lateral directions or extruded to circular geometries is possible, allowing both, a profile sampling in different directions, just as well as an areal measurement. In the present publication, a modification of the ISO 25178-70 material measures is described including the design process, the manufacturing and the measurement with areal and profile surface topography measuring instruments to practically demonstrate the feasibility of a multifunctional calibration that considers the possible effects of directionality. We show that it is possible to combine different profile and areal geometries by linear and/or areal extrusion of the corresponding profile-based geometry. By aligning multiple material measures onto one sample, it can also be demonstrated that a comprehensive calibration of an optical profiler is enabled with only one measurement.

ZrO2 Holographic Metasurfaces for Efficient Optical Trapping in The Visible Range

AbstractHolographic Metasurfaces (HMs) in the visible range enable advanced imaging applications in compact, planarized systems. The ability to control and structure light with high accuracy and a high degree of freedom is particularly relevant in lab‐on‐chip biophotonic applications. Pushing the operation wavelength into the blue region and below is an open challenge. Here, it is demonstrated that Zirconium dioxide (ZrO2) metasurfaces (MSs) are particularly well‐suited to satisfy these requirements. Specifically, MSs are designed for optical trapping applications with a high numerical aperture (NA = 1.2) at a wavelength as low as 488 nm, with trap stiffness >200 pN (µmW)−1.

Language as a medium for inclusion and exclusion: Supporting multilingualism in a Swedish minority language ECEC setting in Finland

Language awareness and multilingualism in early childhood education has received much attention in recent years. The aim of this article is to shed light on challenges and tensions in supporting multilingualism in a minority language ECEC setting in Finland. Of special interest are situations where children not yet proficient in Swedish or Finnish are included or at risk of being excluded. The study was conducted in Southern Finland where many children attending Swedish ECEC come from bilingual Finnish-Swedish homes. The qualitative data consists of interviews with 74 teachers and staff working in 18 Swedish ECEC. Also, participant observations of children aged 3–5 interacting with peers were conducted. The results show that children are aware of language differences and use language as a means for inclusion and exclusion of peers during play. The high degree of freedom in children’s free play, without the participation of adults, makes it difficult for staff to create a common praxis or linguistic strategies. Furthermore, the results indicate that ECEC staff need more guidelines on how to actively support the children’s development of Swedish while supporting children’s bilingualism or multilingualism.

G-code generation for deposition of continuous glass fibers on curved surfaces using material extrusion-based 3D printing

Improving the mechanical properties of 3D printed parts produced through a material extrusion-based 3D printer with continuous fibers (carbon, glass, and aramid) has been a focal point for numerous researchers. Given the layered nature of additive manufacturing (AM) processes, wherein parts are built up layer by layer, most studies involve the deposition of continuous fibers onto a 2D surface. Cases involving curved surfaces have employed robots with high degrees of freedom. This research introduces a method for depositing continuous glass fibers onto curved surfaces, implemented on a cost-effective material extrusion-based 3D printer. The presented approach involves G-code modification, the incorporation of a rotating axis for the nozzle, and the application of computer-aided design and manufacturing techniques. Experimental results affirm the efficacy of this method for depositing continuous fibers onto curved surfaces. The developed technique enables the production of free-form composite shells with a thermoplastic matrix and continuous fiber reinforcement. Lastly, through 3D scanning of the printed sample and subsequent comparison with the 3D model, the degree of surface form deviation and tolerance is determined. The maximum deviation identified in this study is 0.1 mm, a tolerable amount considering the inherent characteristics and behaviors of thermoplastic materials (shrinkage and warpage) during production processes.

4D Printed Non-Euclidean-Plate Jellyfish Inspired Soft Robot in Diverse Organic Solvents.

Soft robots have the potential to assist and complement human exploration of extreme and harsh environments (i.e., organic solvents). However, soft robots with stable performance in diverse organic solvents are not developed yet. In the current research, a non-Euclidean-plate under-liquid soft robot inspired by jellyfish based on phototropic liquid crystal elastomers is fabricated via a 4D-programmable strategy. Specifically, the robot employs a 3D-printed non-Euclidean-plate, designed with Archimedean orientation, which undergoes autonomous deformation to release internal stress when immersed in organic solvents. With the assistance of near-infrared light illumination, the organic solvent inside the robot vaporizes and generates propulsion in the form of bubble streams. The developed NEP-Jelly-inspired soft robot can swim with a high degree of freedom in various organic solvents, for example, N, N-dimethylformamide, N, N-dimethylacetamide, tetrahydrofuran, dichloromethane, and trichloromethane, which is not reported before. Besides bionic jellyfish, various aquatic invertebrate-inspired soft robots can potentially be prepared via a similar 4D-programmable strategy.

Carbon performance and financial debt: Effect of formal and informal institutions

AbstractClimate change has led to a transformation of the economy, with institutions such as the European Commission pushing for decarbonization. Using a sample of 4607 European‐listed firms from 2005 to 2019, we find evidence that political and financial factors moderate the relationship between carbon performance and financial debt. Environmentally responsible firms operating in countries with better democratic values, associated with higher degrees of freedom and law enforcement, are favored with more access to debt. Similarly, better carbon performers obtain more debt in countries with concentrated banking markets, greater financial stability, and lower government debt. Furthermore, the cultural factors uncertainty avoidance and long‐term orientation moderate the relationship between carbon performance and financial debt, as well as the effect of political and financial institutions on this relationship. These results show the relevant role formal and informal institutions play in facilitating the decarbonization of firms, as well as the importance of coordinating European policies.

A liquid crystal-based multi-bit terahertz reconfigurable intelligent surface

Recently, the growing interest in reconfigurable intelligent surface (RIS) technology has spurred extensive research on its utilization in the terahertz (THz) regime. The reconfiguration of the THz field empowered by the RIS holds great significance for various practical RIS-aided implementations at THz frequencies. In this study, we present a multi-bit liquid crystal-based RIS that allows for the programmable control of THz waves. The proposed RIS is characterized by an achievable 3-bit working state as well as a near 270° maximum phase shift around 0.28 THz. This high degree of freedom in manipulating the phase of the reflected field provides flexibility in terahertz spatial beam reconfigurations. We show that the terahertz single-beam pattern can be steered continuously from 5° to 55° toward the desired angles while also allowing the adjustment of the beam number and beamwidth. Through this demonstration, we aim to contribute to the advancement of RIS technologies in the terahertz regime, paving the way for various RIS-aided applications such as THz wireless communications and beyond.

Learning Playing Piano with Bionic-Constrained Diffusion Policy for Anthropomorphic Hand.

Anthropomorphic hand manipulation is a quintessential example of embodied intelligence in robotics, presenting a notable challenge due to its high degrees of freedom and complex inter-joint coupling. Though recent advancements in reinforcement learning (RL) have led to substantial progress in this field, existing methods often overlook the detailed structural properties of anthropomorphic hands. To address this, we propose a novel deep RL approach, Bionic-Constrained Diffusion Policy (Bio-CDP), which integrates knowledge of human hand control with a powerful diffusion policy representation. Our bionic constraint modifies the action space of anthropomorphic hand control, while the diffusion policy enhances the expressibility of the policy in high-dimensional continuous control tasks. Bio-CDP has been evaluated in the simulation environment, where it has shown superior performance and data efficiency compared to state-of-the-art RL approaches. Furthermore, our method is resilient to task complexity and robust in performance, making it a promising tool for advanced control in robotics.

MAR and UAR: Solutions for Depth Profile Reconstruction of MFL Inspection With High Degree of Freedoms

MAR and UAR: Solutions for Depth Profile Reconstruction of MFL Inspection With High Degree of Freedoms

Data-driven reduced-order modeling for nonlinear aerodynamics using an autoencoder neural network

The design of commercial air transportation vehicles heavily relies on understanding and modeling fluid flows, which pose computational challenges due to their complexity and high degrees of freedom. To overcome these challenges, we propose a novel approach based on machine learning (ML) to construct reduced-order models (ROMs) using an autoencoder neural network coupled with a discrete empirical interpolation method (DEIM). This methodology combines the interpolation of nonlinear functions identified based on selected interpolation points using DEIM with an ML-based clustering algorithm that provides accurate predictions by spanning a low-dimensional subspace at a significantly lower computational cost. In this study, we demonstrate the effectiveness of our approach by the calculation of transonic flows over the National Advisory Committee of Aeronautics 0012 airfoil and the National Aeronautics and Space Administration Common Research Model wing. All the results confirm that the ROM captures high-dimensional parameter variations efficiently and accurately in transonic regimes, in which the nonlinearities are induced by shock waves, demonstrating the feasibility of the ROM for nonlinear aerodynamics problems with varying flow conditions.

Topological structures of vibration responses for dual-rotor aeroengine

Many advanced aeroengines are typically designed with coaxial coupling dual-rotor structures. As a result, the vibration responses of the rotor system are characterized by complex multifrequency oscillations. In the paper, the topological structures of the nonlinear vibration responses of the dual-rotor system are revealed, providing a better understanding of the complex structural dynamics of many advanced aeroengines. A nonlinear dynamic model of the structural system of a dual-rotor-aeroengine experimental rig is established using the finite element (FE) method. This model takes into the blade-casing rubbing, misalignment, and the nonlinearities of the supporting rolling element bearings. The component mode synthesis (CMS) method is used to reduce the order of the dynamic model with high-degrees of freedom (DOFs) in order to enhance the computational efficiency. We first discover that long periodic structures exist in the multi-harmonic vibration responses of the dual-rotor system. The structures of Poincare mappings and bifurcation diagrams provide clearer and more concise information when obtained through long periodic sampling. The numerical periodic vibration response structures matched the measurements. These results help uncover the inherent structural characteristics of the complex nonlinear vibrations of a dual-rotor aeroengine.

Single Benzene Yellow Fluorophore: Anodic Synthesis of Tetrahydrobenzodifuran

Fluorophores have proven to be indispensable tools in fields ranging from physics, chemistry, and biology. Organic fluorophores have high degrees of freedom in their molecular designs, which is essential for fundamental applications such as imaging probes. The strategies to produce organic fluorophores with improved physicochemical properties are two-fold, including an extension of aryl systems and a construction of push-pull motifs, where electron-donating and -withdrawing groups are conjugated. When used in bio-related research fields, the construction of push-pull motifs is preferred in terms of water solubility and molecular size, since the extension of aryl systems produces relatively large hydrophobic, planar, and thus highly crystalline, structures.The early work by Shimizu in 2009 triggered explosive growth in the development of new single-benzene fluorophores. From the viewpoints of synthetic practicality and unique and tunable photophysical properties, installing diagonally symmetrical substitution patterns with the same electron-donating and -withdrawing groups is the simplest strategy to create new single-benzene fluorophores.During the course of our studies focusing on electrochemical cycloadditions, we found that dihydrobenzofuran derivatives armed with electron-donating and -withdrawing groups exhibited bright fluorescence. In order to suppress molecular motions that cause non-radiative transitions, formation of a furan ring is effective in restricting bond rotation. Furthermore, we found that symmetrically substituted dialkoxy-dicarbonylbenzenes exhibited unique bright fluorescence. In this study, we successfully demonstrate that constructing symmetrical push-pull motifs in combination with restricting bond rotations is the key to creating small yet brightly emitting fluorophores. The anodically constructed tetrahydrobenzodifuran moiety has proven to be an effective core architecture to realize unique and powerful single-benzene fluorophores with high quantum yields and large Stokes shifts.

Multifunctional Metasurface Inverse Design Based on Ultra‐Wideband Spectrum Prediction Neural Network

AbstractMultifunctional metasurfaces have demonstrated extensive potential in various fields. During the design of metasurfaces, optimization of components for polarization, amplitude distribution, phase distribution, and other factors is necessary. This process typically demands expert involvement and is time‐consuming. In this paper, a metasurface inverse design method is introduced that combines a high‐precision ultra‐wideband spectrum forward prediction utilizing a neural network and a genetic algorithm. A neural network is constructed and trained to accurately predict the amplitude and phase of a 16 × 16 discrete grid structure with high degrees of freedom in the frequency range of 0.5–2 THz. Leveraging the neural network's ultra‐fast spectrum prediction capabilities (producing approximately 1000 spectra per second), the average optimization time for a single component is reduced to 1.5 min. Finally, the effectiveness of this inverse design method is validated through the design and simulation of multifunctional reflective deflection metasurfaces with two sets of 3‐bit frequency multiplexing and polarization multiplexing. The proposed metasurface inverse design method offers a new approach for the rapid design of components in complex application scenarios and holds significant reference value for metasurface designers.

Solder fatigue life modeling of QFN components based on design of experiments

Shorter development times and increasing cost-efficiency make it necessary to predict the reliability of electronic assemblies at an early stage. Within this framework, this work focuses on the influence of load and geometric parameters on the solder fatigue life of Quad Flat No Leads (QFN) components.Following a Design of Experiments (DoE) test plan, the following design parameters affecting the solder fatigue life are investigated: the number of leads and the pitch, the orientation of the component on the Printed Circuit Board (PCB), the constant ambient temperature and the surface strain. A test setup is used in which each parameter can be varied individually.From the resulting lifetimes, a multiple regression model and a physical-based model are derived. The regression model shows a slightly better fit quality due to its higher degree of freedom and is not subject to any physical constraints. The physical-based model is a combination of different proven solder fatigue life models and avoids any overfits which can occur in a regression model. Based on these models, the influence of the parameters on the solder fatigue life and the relationship between the parameters and the solder fatigue life is evaluated.With these results, it is possible to predict the lifetime with respect to mechanical load cases depending on size, orientation and surface strain as well as thermal load. This opens up completely new possibilities in PCB design by significantly improving the quality of solder fatigue life prediction and significantly reducing the restricted areas for component placement.

The NeuGRID Workflow: a “Swiss Army knife” for MRI processing

AbstractBackgroundThe advent of big data and artificial intelligence has energized the neuroimaging community with powerful processing tools and the need for large‐scale computational resources for data analysis has increased. Not all neuroscientists have access to extensive resources or have sufficient expertise to configure these libraries, making them difficult to use.MethodA collection of popular MRI scan processing software, (i.e.: Fsl, FreeSurfer, Ants, SPM, Adaboost, Neuromorphometrics, LPA, Tracula) was selected as a proof of concept to be included in the NeuGRID Workflow to produce an advanced tool for group analysis and biomarkers assessment.ResultThe NeuGRID Workflow was designed to allow the user to build a custom MRI processing pipeline by means of a layered structure (Fig.1) available for users at the URL https://neugrid2.eu/index.php/workflowgroup. In the first layer, the user must upload a compressed file containing T13D, FLAIR, and DWI scans of subjects. In the second layer, the user selects which kind of MRI sequence to process. If DICOMs were uploaded, the third layer provides tools to convert DICOMs into NIFTI, the default format for all subsequent workflow processes. All DICOMs are anonymized upon upload. The fourth layer allows the user to choose how images are preprocessed, and the preprocessing pipelines can be customized for different modalities. In the final layer, the user can choose how to analyze the images to calculate volume, thickness, area and curvature of brain regions, quantify white‐matter lesions and reconstruct major white‐matter pathways. The triggered job is efficiently parallelized using a Sun Grid Engine scheduler to reduce computation time. After the scans are processed in the NeuGRID cluster (450 CPUs, 2.5TB memory, 400TB storage), the user is notified by email that the job is finished and the results can be downloaded.ConclusionThe NeuGRID Workflow is a practical tool that allows researchers without computational resources and/or expertise to design a custom pipeline to process large amounts of data from anywhere using a traditional laptop or personal computer. The collection of heterogeneous tools provides a high degree of freedom in pipeline design, making NeuGRID Workflow a Swiss Army knife for MRI processing