cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29574739
title: Pre-calculated conductivity Jacobian. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29574739.v2
title: Pre-calculated conductivity Jacobian. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29574742
title: Domain elemental connectivity. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29574742.v1
title: Domain elemental connectivity. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29574739.v1
title: Pre-calculated conductivity Jacobian. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29596436
title: Main code for absolute image with L2 regularization. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29596436.v1
title: Main code for absolute image with L2 regularization. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29596439
title: Solving the CEM forward model. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29596439.v1
title: Solving the CEM forward model. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29596442
title: Calculation of the deformation Jacobian. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29596442.v1
title: Calculation of the deformation Jacobian. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29596445
title: EIT voltage data with strain. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29596445.v1
title: EIT voltage data with strain. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29596448
title: Main code for difference image with L1 regularization. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.6084/m9.figshare.29596448.v1
title: Main code for difference image with L1 regularization. from Full-field mechanics imaging by direct inversion of electrical data

description: Self-sensing materials with deformation-dependent electrical conductivity have been studied in diverse applications. A limitation of self-sensing materials is that they do not directly report on the underlying condition of the material. That is, it would be much more useful to know the actual stresses, strains or damages that give rise to observed conductivity changes. The process of deducing material condition from electrical data is called the <i>self-sensing inverse problem</i> (SSIP). Previous work has required electrical impedance tomography (EIT) as an intermediate step to estimate the conductivity distribution prior to estimating the spatially varying mechanics. But this is undesirable because EIT is an ill-posed inverse problem and is highly dependent on regularization, which renders the SSIP as subject to the same assumptions and limitations of EIT. The contribution of this manuscript is the development of a <i>direct</i> SSIP formulation that omits the EIT step such that mechanics are predicted directly from voltage–current data. The effects of regularization type, regularization norm and formulation (difference versus absolute imaging) are also explored. The direct SSIP formulation is experimentally validated on a soft carbon nanofibre-modified polyurethane pressure sensor and compared to ANSYS-solved mechanics with good agreement.
DOI: 10.15468/dl.pq27hh
title: Occurrence Download

description: A dataset containing 389 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus thunbergii",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 389 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005075-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/2fp-2w4-fnj
title: Plantae

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/czq-8te-14d
title: Plantae

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/afr-c11-g5l
title: Plantae

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/j9v-qkj-ysq
title: Castanea Mill.

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/3sj-3c6-phn
title: †Anthracosphaerium Trueman & Weir, 1946

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/fn3-v93-qqm
title: Voacanga Thouars

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.