Unknown Surface Research Articles

Tactile Exploration Using Unified Force-Impedance Control

Tactile robots can perform complex interaction skills, e.g., polishing. Such robots should therefore be designed to be adaptive to environmental uncertainties such as changing geometry and contact-loss. To address this, we propose a tactile exploration technique to observe the local curvatures of the physical constraints such as corners, edges, etc. for updating predefined tactile skill policies accordingly. First, we develop a unified force-impedance control approach in which the force controller significantly improves the geometry following performance due to the ensured contact. Second, we use the proposed controller to autonomously investigate the unknown environment via the local curvature observer, designed to be a dynamic process. Finally, the exploration performance of the proposed controller is demonstrated by using a polishing skill on an unknown 3D surface, where the robot is observed to autonomously investigate the unknown surface from top to bottom along the edges and corners.

НАСКАЛЬНЫЕ ИЗОБРАЖЕНИЯ ХОДЖИКЕНТА

The limestone rock with petroglyphs in the village of Khodzikent (Uzbek: Xo'jakent) is one of the most well-known and accessible rock art sites of Uzbekistan, with a rich history of discovery and study. The first mentions of signs on the rock have been known in print since 1879, but the systematic study of rock images has been organized by many researchers since the middle of the 20th century, such as H.A. Alpysbaev, G.V. Parfenov, G.V. Shatsky, D. Kabirov, M.M. Khuzhanazarov, etc. In addition to the published data, this work includes unique archival materials that shed new light on the research of the Khodzikent petroglyphs by the Bostandyk archaeological team led by G.V. Parfenov. The analysis of historiographical materials reveals a number of controversial issues related primarily to the quality of fixation of pictorial sources and discrepancies in their chronological attribution. A continuous survey of promising sites and a documentation technique, developed taking into account the specifics of the Khodzikent petroglyphs (the smoothness of their contours), allowed the authors to obtain qualitatively new results: to identify a previously unknown surface with the image of a “rosette”, fix 119 rock images on the “central” rock and determine the sequence of their creation using palimpsests and planigraphy analysis, reflect in digital drawings an integral image of the site of rock art. The relative chronology and analysis of iconography and the pictorial series indicate a possible broad chronological framework for the creation of Khodzikent rock images: from the Eneolithic to the early Middle Ages.

Using pseudo-absence models to test for environmental selection in marine movement ecology: the importance of sample size and selection strength

BackgroundUnderstanding the selection of environmental conditions by animals requires knowledge of where they are, but also of where they could have been. Presence data can be accurately estimated by direct sampling, sightings, or through electronic tag deployments. However, absence data are harder to determine because absences are challenging to measure in an uncontrolled setting. To address this problem, ecologists have developed different methods for generating pseudo-absence data relying on theoretical movement models. These null models represent the movement of environmentally naive individuals, creating a set of locations that animals could have been if they were not exhibiting environmental selection.MethodsHere, we use four different kinds of null animal movement models—Brownian motion, Lévy walks, Correlated random walks, and Joint correlated random walks to test the ability and power of each of these null movement models to serve as appropriate animal absence models. We use Kolmogorov-Smirnov tests to detect environmental selection using two data sets, one of simulated animal tracks biased towards warmer sea surface temperatures, and one of 57 observed blue shark tracks of unknown sea surface temperature selection.ResultsThe four different types of movement models showed minimal difference in the ability to serve as appropriate null models for environmental selection studies. Selection strength and sample size were more important in detecting true environmental selection. We show that this method can suffer from high false positive rates, especially in the case where animals are not selecting for specific environments. We provide estimates of test accuracy at different sample sizes and selection strengths to avoid false positives when using this method.ConclusionWe show how movement models can be used to generate pseudo-absences and test for habitat selection in marine organisms. While this approach efficiently detects environmental selection in marine organisms, it cannot detect the underlying mechanisms driving this selection.

Analytically Regularized Evaluation of the Coupling of Planar Concentric Conducting Rings

In this paper, an accurate and efficient method for the analysis of coupled perfectly conducting annular rings is presented. The problem is first formulated as a couple of Integral Equation (IEs) in the Vector Hankel Transform (VHT) domain, considered as unknowns in the cylindrical harmonics of the unknown surface current density. As a second step, Galerkin’s method is applied with suitable expansion functions. The selected functions have two main properties: they reconstruct the expected physical behavior of the nth cylindrical harmonic at the edges of the annular rings, and their VHT transform is analytical and can be expressed in closed-form. Consequently, the method is effective and the problem is regularized, as testified by the truncation error. Comparisons with the commercial software CST Microwave Studio have been carried out and are presented to validate the method.

Denoising scanning tunneling microscopy images of graphene with supervised machine learning

Machine learning (ML) methods are extraordinarily successful at denoising photographic images. The application of such denoising methods to scientific images is, however, often complicated by the difficulty in experimentally obtaining a suitable expected result as an input to training the ML network. Here, we propose and demonstrate a simulation-based approach to address this challenge for denoising atomic-scale scanning tunneling microscopy (STM) images, which consists of training a convolutional neural network on STM images simulated based on a tight-binding electronic structure model. As model materials, we consider graphite and its mono- and few-layer counterpart, graphene. With the goal of applying it to any experimental STM image obtained on graphitic systems, the network was trained on a set of simulated images with varying characteristics such as tip height, sample bias, atomic-scale defects, and nonlinear background. Denoising of both simulated and experimental images with this approach is compared to that of commonly used filters, revealing a superior outcome of the ML method in the removal of noise as well as scanning artifacts---including on features not simulated in the training set. An extension to larger STM images is further discussed, along with intrinsic limitations arising from training set biases that discourage application to fundamentally unknown surface features. The approach demonstrated here provides an effective way to remove noise and artifacts from typical STM images, yielding the basis for further feature discernment and automated processing.

Wide-field optical imaging of electrical charge and chemical reactions at the solid–liquid interface

From molecules and particles to macroscopic surfaces immersed in fluids, chemical reactions often endow interfaces with electrical charge which in turn governs surface interactions and interfacial phenomena. The ability to measure the electrical properties of a material immersed in any solvent, as well as to monitor the spatial heterogeneity and temporal variation thereof, has been a long-standing challenge. Here, we describe an optical microscopy-based approach to probe the surface charge distribution of a range of materials, including inorganic oxide, polymer, and polyelectrolyte films, in contact with a fluid. The method relies on optical visualization of the electrical repulsion between diffusing charged probe molecules and the unknown surface to be characterized. Rapid image-based measurements enable us to further determine isoelectric points of the material as well as properties of its ionizable chemical groups. We further demonstrate the ability to optically monitor chemically triggered surface charge changes with millisecond time resolution. Finally, we present a scanning-surface probe technique capable of diffraction-limited imaging of spatial heterogeneities in chemical composition and charge over large areas. This technique will enable facile characterization of the solid-liquid interface with wide-ranging relevance across application areas from biology to engineering.

Localization and topological observability analysis of a moored floating structure using mooring line tension measurements

Mooring is a widely used stationkeeping approach for floating structures. The main contribution of this paper is to propose a novel localization scheme, which provides additional real-time position references and estimates the unknown anchor positions to a moored structure simultaneously using tension measurements from the mooring monitoring system. The proposed method enhances the safety of moored floating structures by extracting redundant position information from tension measurements instead of using satellite signals. A line-of-sight model that transforms tension measurements into range signals is developed. Fairleads, turret dynamics, and slow-varying current profiles are considered to provide a more realistic and robust tension-based localization scheme than early research. A distance-only sensor network is constructed, and graph rigidity theory is applied to analyze its topological observability. Two simplified models are presented for some specific applications: (1) to find the broken anchors offline with stored data for any vessels equipped with tension cells and (2) to position the moored structures with known anchor positions. Sensitivity studies show that the influence of unknown surface current is small, while the effects of unknown current profiles are larger. The positioning accuracy can be improved by increasing the number of virtual vessels.

Adaptive Dynamic Surface Control of Strict-Feedback Fractional-Order Nonlinear Systems with Input Quantization and External Disturbances

In this work, an adaptive dynamic surface control law for a type of strict-feedback fractional-order nonlinear system is proposed. The considered system contained input quantization and unknown external disturbances. The virtual control law is presented by utilizing a dynamic surface control approach at each step, where the nonlinear compensating term with the estimation of unknown bounded parameters is introduced to overcome the influence of unknown external disturbances and surface errors. Meanwhile, the adaptive laws of relevant parameters are also designed. In addition, an improved fractional-order nonlinear filter is developed to deal with the explosion of complexity raised by the recursive process. In the last step, an adaptive dynamic surface control law is proposed to ensure the convergence of tracking error, in which the Nussbaum gain function is applied to solve the problem of the unknown control gain generated by input quantization. Then, the fractional Lyapunov stability theory is applied to verify the stability of the proposed control law. Finally, simulation examples are given to illustrate the effectiveness of the proposed control law.

Autonomous Sensor System for Determining Instrument Position Relative to Unknown Surfaces Utilized on Mars Rover

In some applications, a robot arm has to position instrumentation/payloads accurately relative to an unknown surface. This article describes an optical sensor system for doing so. The sensor is based on a number of different image processing techniques combined with optical stimulation. More specifically, the system utilizes a camera, floodlight, structured light illumination, terrain relative navigation (TRN), camera pose estimation, and circle finding. The sensor has been developed for the Planetary Instrument for X-Ray Lithochemistry (PIXL) instrument on NASA’s Mars Perseverance rover. The sensor also has other applications such as exploration of polluted or inaccessible areas or locating a robotic arm relative to an unknown surface. The sensor can aid in placing an instrument relative to the unknown surface with an accuracy of 0.5 mm and maintaining a 3-D position lock utilizing TRN. This article will describe the sensor design and performance.

Identifying distinct plastics in hyperspectral experimental lab-, aircraft-, and satellite data using machine/deep learning methods trained with synthetically mixed spectral data

The growing production and use of plastics are becoming a serious progressive issue and people pay increasing attention to the effects of plastics on ecosystems and human health. The availability of hyperspectral data from space sensors inspired us to study the feasibility to detect and identify different types of plastics in aircraft -, Goafen-5 (GF-5) and PRISMA satellite data by means of deep -, and machine learning models trained with spectral signatures. In this context, various inhouse and public spectral libraries are used to create a comprehensive database with mixed pixels of different plastic and non-plastic materials. The endmembers of plastic types involved in this study are polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), polyethylene terephthalate (PET) and polystyrene (PS), covering 95% of the global production. Additionally, some important varieties of industrial plastics types such as acrylonitrile butadiene styrene (ABS), ethylene vinyl acetate (EVA), polyamide (PA), polycarbonate (PC), and polymethyl methacrylate (PMMA) were included in the investigations. Different samples with varying optical properties (color, brightness, transmissivity) have been selected for each plastic type. As non-plastic materials we have chosen spectra of vegetation, rocks, soils and minerals contained in the public US libraries (ECOSTRESS and USGS). The number of spectra for the training of the deep learning and machine learning models was enlarged by a random linear mixing method and the resulting database was separated into a training and a test group for subsequent multi-label classification. Algorithms selected are a convolutional neural network (CNN), random forest (RF) and support vector machine (SVM). To investigate the transferability to any hyperspectral image data obtained by air-, and spacecraft sensors, we opted for a unification of the spectral response functions (SRF) and the spectral sampling intervals of all data. Validation is accomplished based on the test group of the spectral database, and tested by controlled laboratory and aircraft experiments recorded over surfaces with varying background materials. Results are further analyzed for the influence of different noise quantities and abundance levels. The performance of the three models is roughly balanced for the validation of the spectral data with an overall accuracy of 97%, 96%, and 95% for the CNN, RF, and SVM, models respectively. In the controlled lab experiments, various accuracy indicators, such as the recall rates and the comprehensive metrics F1-score, OA, and Kappa suggest the RF classifier as the most robust one, followed by the SVM and CNN models. As for the evaluation of the aircraft data from controlled experiments, the RF further outperforms the other two models, behaving most robustly and reliably against conditions with unknown plastics and unknown background surfaces. Thus, the RF was used to classify the ten types of plastics mentioned above in one GF-5 and two PRISMA satellite recordings of the same area. In comparison of both sensor systems, the RF produced high quality and transferable results for detecting plastic mainly related to greenhouses, sport fields, photovoltaic constructions and industrial sites that are discussed in detail in this paper.

SMC-based model-free tracking control of unknown autonomous surface vehicles

This paper investigates the robust tracking control problem of disturbed unknown autonomous surface vehicles (ASVs), and whereby a sliding-mode-control-based model-free tracking control (SMTC) approach by the combination of sliding-mode control and data-driven backstepping techniques is innovatively devised. By deploying a data-driven backstepping sliding-mode surface, a robust model-free adaptive controller is designed to achieve strong adaptability and robustness to unknown couplings, uncertainties and disturbances. Besides, a data-driven adaptive law based on disturbance observer and feedforward control strategies is effectively developed to estimate these unknowns, and thereafter the estimation is served as the compensation within the controller. Rigorous analysis proves that asymptotic tracking performance and strong robustness can be guaranteed theoretically. Lastly, simulation studies for the ASV are explored to demonstrate the validity and superiority of the devised SMTC approach in terms of disturbance attenuation, nonlinearity adaption, and high accurate tracking.

HSC70 is a novel binding partner involved in the capture of immunoglobulins on B cells in the NOD mouse

B cells have been shown to be essential for Type 1 diabetes development in the non-obese diabetic mouse, where their contribution as antigen presenting cells has been emphasised. Other important functions for B cells include surface capture of immunoglobulins and transportation of immune complexes, with subsequent endocytosis, antigen processing and antigen presentation. We have previously demonstrated that NOD B cells capture IgM and IgG immune complexes through an unknown surface molecule. In this study, we revealed the presumptive immunoglobulin-binding molecule to be HSC70. Moreover, we detected increased levels of HSC70 on NOD B cells. HSC70 has been shown to play a role in antigen processing and presentation as well as being important in several autoimmune diseases, including rheumatoid arthritis and systemic lupus erythematosus. Due to its protein stabilising properties, increased HSC70 could contribute to enhanced self-antigen collection and presentation and thereby contribute to the development of Type 1 diabetes.

Low Cost Linear Variable Differential Transformer Construction and Calibration

The Low Cost Linear Variable Differential Transformer (LVDT) sensor's fabrication and calibration are discussed in this study. The iron core of the built LVDT measures 8 inches in length and 1 inch in diameter, while the probe is 4.5 inches long and 0.5 inches in diameter. By using a 1 cm known surface displacement to measure the voltage at 6.35 V, the performance of the LVDT was determined. The unknown surface waviness about voltage is measured using the constant of the known surface displacement from this. This sensor's performance accuracy ranges from 97.6% to 99.96%. When compared to other market sensors, the manufactured LVDT costs only about $28, which is little.

Adam SPGD algorithm in freeform surface in-process interferometry.

The adaptive interferometer has been recently proposed to realize the metrology of unknown freeform surfaces with several restructured algorithms for feedback control. The adaptive moment estimation (Adam) stochastic parallel gradient descent (SPGD) algorithm is employed in this paper for fringes release. The proposed algorithm makes considerable progress in relieving conflict of the convergence rate, speed, and parameters intervention. Simulations and experiments show its 37% time saving and 99% convergence rate, with arbitrarily configured parameter increment, compared with the SPGD algorithm. It would have great potential in in-process tests in freeform surface fabrication or large-volume testing.

A multi-temporal and multi-angular approach for systematically retrieving soil moisture and vegetation optical depth from SMOS data

Passive microwave remote sensing of soil moisture is an underdetermined problem, as observed microwave emission from the landscape is affected by a variety of unknown surface parameters. Increasing observation information is an effective means to make retrievals more robust. In this study, a multi-temporal and multi-angular (MTMA) approach is proposed using SMOS (Soil Moisture and Ocean Salinity) satellite L-band data for retrieving vegetation optical depth (VODp, p indicates the polarization with H for horizontal and V for vertical)), effective scattering albedo (ωpeff), soil surface roughness (Zps), and soil moisture (SMp). The advantage of the MTMA approach is that it does not need auxiliary data as inputs or constraints. SMOS polarization-dependent VOD are produced and compared at a global scale for the first time, and it is found that the polarization difference of vegetation effects should not be ignored in the SM retrieval algorithm. The MTMA VOD retrievals are found to have a reasonable global spatial distribution, which is generally consistent with the VOD retrievals obtained from the SMOS Level 3 (SMOS-L3) and SMOS-IC Version 2 (V2) (referred to as SMOS-IC), except for showing relatively lower values over densely vegetated areas compared with the other two SMOS products. The spatial distribution of retrieved ωpeff generally shows a dependence on both VOD and land cover types. In addition, the values of MTMA-ωVeff are higher than that of MTMA-ωHeff, indicating stronger microwave scattering of V-pol in the vegetation layer than that of H-pol. The retrieved surface roughness parameter (Zps) ranges from 0.04 to 0.22 cm, and its spatial distribution is partially different from the existing roughness products/auxiliary data from SMOS and SMAP. The retrieved MTMA SM shows generally high correlations with in-situ measurements (11 dense observation networks) with overall correlation coefficients of > 0.75. The overall ubRMSE of MTMA-SMH and MTMA-SMV are < 0.055 m3/m3 and lower than that of SMOS-IC and SMOS-L3 products. SMOS-IC generally presents higher correlation coefficients compared to MTMA in most sites outside China; in China, RFI filtering is crucial and makes it very difficult when comparing algorithms based on different brightness temperature products. The number of effective retrievals of MTMA-SMH and MTMA-SMV ranges from 1409 to 1640 and 1104 to 1603 respectively, which is more than that from SMOS-IC (from 236 to 1358) over the selected 11 networks. Therefore, it is concluded that by incorporating multi-temporal SMOS data, the proposed method of MTMA can be used to systematically retrieve SM, VOD and additional surface parameters (effective scattering albedo and surface roughness) with comparable or better performance of SM than that of SMOS-IC and SMOS-L3. Moreover, this paper for the first time produced a polarization-dependent SMOS VOD product at a global scale.

The Influence of Characteristic Sea State Parameters on the Accuracy of Irregular Wave Field Simulations of Different Complexity

The accurate description of the complex genesis and evolution of ocean waves, as well as the associated kinematics and dynamics is indispensable for the design of offshore structures and the assessment of marine operations. In the majority of cases, the water-wave problem is reduced to potential flow theory on a somehow simplified level. However, the nonlinear terms in the surface boundary conditions and the fact that they must be fulfilled on the unknown water surface make the boundary value problem considerably complex. Hereby, the contrary objectives with respect to a very accurate representation of reality and numerical efficiency must be balanced wisely. This paper investigates the influence of characteristic sea state parameters on the accuracy of irregular wave field simulations of different complexity. For this purpose, the high-order spectral method was applied and the underlying Taylor series expansion was truncated at different orders so that numerical simulations of different complexity can be investigated. It is shown that, for specific characteristic sea state parameters, the boundary value problem can be significantly reduced while providing sufficient accuracy.

Response of rigid circular plate in a functionally graded transversely isotropic, half-space under horizontal steady-state excitation

In this paper, a theoretical formulation for lateral translation and impedance function of a rigid foundation in a transversely isotropic functionally graded (TIFG) half-space is presented. Accordingly, the medium is considered linear elastic and the elastic constants of materials vary exponentially along the depth for considering the effect of elastic non-homogeneities. The relax treatment of mixed boundary value problem is formulated by integrating the transformed kernels of Green’s functions over the foundation area with unknown surface traction function, which can be reduced into a Fredholm integral equation. The results represent the impact and treatment of frequency and material anisotropy through the medium. In addition, static and dynamic distribution of tangential shear stress over the foundation for surface and embedded disk extracted and the cutoff frequency is found out explicitly. The proposed solution gives exact concurrence with accessible literature.

Multimodal Unknown Surface Material Classification and Its Application to Physical Reasoning

Unknown surface material classification (SMC) can inform a robot about material properties, enabling it to interact with environments appropriately. Recent research has leveraged multimodal data using deep learning to improve the performance of SMC. In this article, we present a deep learning model, multimodal temporal convolutional neural network (MTCNN), which integrates energy spectrum, dilated convolutions, and sequence poolings into a unified network architecture. The proposed model can learn material representations from auditory and multitactile (i.e., acceleration, normal force, and friction force) data generated by dragging a tool along surfaces, and distinguish unknown object surface materials into categories. For surface material data collection, a tool is also designed to detect different object surfaces. The performance of MTCNN is evaluated on a public dataset and the highest classification accuracy is 87.55%. A robotic curling example is provided to illustrate how the presented model helps the robot in manipulation.

Carriage interior noise-based inspection for rail corrugation on high-speed railway track

The presence of rail corrugation enlarges the wheel-rail impact and exacerbates the failure of track components, and the situation becomes even worse under high train speed, which promotes the urgent need for an efficient and easily accessible inspection method. Conventional diagnosis approaches such as axle box acceleration (ABA) and image recognition measurements, however, require complex instrumentations on the running gear, restricting their applications on a wide range of operational trains. In this study, we investigate the capability of carriage interior noise in diagnosing rail corrugation on the high-speed railway (HSR). For this purpose, train-borne vibration & noise measurements were integrated with in-situ rail surface irregularity tests, to extract the characteristic carriage interior responses induced by rail corrugation. The measurements were conducted on two HSR tracks with different corrugation geometries, and the time–frequency distributions of interior noise were identified under different train speeds and with different track radii. Afterward, an interior noise-based inspection algorithm was proposed by proper correlation of the gained data, and was then demonstrated on a third HSR track with an unknown rail surface condition. The comparison between the proposed inspection algorithm and the widely-recognized ABA measurements indicates that the interior noise succeeded in identifying the position, typical wavelength and severity of rail corrugation under varying train speeds. The work advances a cost-effective and easily accessible way for the condition monitoring of railway tracks.

I AM A.I. Gradient Descent – an Open-Source Digital Game for Inquiry-Based CLIL Learning

We present an interactive online workshop for K-12 students, which aims in familiarizing students with core concepts of AI. The workshop consists of a variety of resources, inspired by inquiry-based learning techniques, of which we present in detail one module, centered around a browser-based game called Gradient Descent. This module introduces the mathematical concepts behind a gradient descent-based optimization algorithm through the computer game of a treasure hunt at an unknown sea surface landscape. Finally, we report on student feedback for the module in a series of content and language integrated learning in German (CLiLiG) workshops for students aged 14-17 in 30 countries.