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Article: Hybrid Connectionist Symbolic Model for Morphologic Recognition by Tactile Sensing

TitleHybrid Connectionist Symbolic Model for Morphologic Recognition by Tactile Sensing
Authors
KeywordsNetworked sensor fusion and decisions
soft computing with sensor data
sensor model analysis verification
smart sensor systems
Issue Date2020
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7361
Citation
IEEE Sensors Journal, 2020, v. 21 n. 5, p. 6497-6509 How to Cite?
AbstractMorphology and texture detection, which are important components of tactile sensing, augment the response of human beings to external stimuli. Similarly, tactile sensing-based information acquisition systems in robots can help enhance the interactions of robots with the surroundings. The main drawback of morphology and texture sensing methods is their inability to explain and quantify sensing information, which makes it difficult to utilize prior knowledge and necessitates a new training process to fit the new task, even if the changes between the existing and new tasks are minuscule. Another drawback is its dependence on large datasets. To solve these problems, a hybrid connectionist symbolic model (HCSM) is proposed herein that combines historic symbolic knowledge and end-to-end neural networks. The symbolic model requires a smaller dataset and possesses an improved transferability of detection. Neural networks can be easily established and exhibit better fault tolerance for non-ideal samples. The HCSM model combines these advantages. Experiments with the tactile-based morphology and texture detection demonstrated that the new method can transfer the detection ability to fit new tasks without requiring additional retraining and has a 16% higher recognition precision than a convolutional neural network, LeNet, AlexNet, VGG16, and ResNet. The HCSM method with these features can broaden the range of applications of tactile sensing.
Persistent Identifierhttp://hdl.handle.net/10722/309329
ISSN
2023 Impact Factor: 4.3
2023 SCImago Journal Rankings: 1.084
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHe, K-
dc.contributor.authorXi, N-
dc.contributor.authorYu, P-
dc.contributor.authorWang, W-
dc.contributor.authorZhao, L-
dc.contributor.authorYang, T-
dc.contributor.authorElhajj, IH-
dc.contributor.authorLiu, L-
dc.date.accessioned2021-12-29T02:13:33Z-
dc.date.available2021-12-29T02:13:33Z-
dc.date.issued2020-
dc.identifier.citationIEEE Sensors Journal, 2020, v. 21 n. 5, p. 6497-6509-
dc.identifier.issn1530-437X-
dc.identifier.urihttp://hdl.handle.net/10722/309329-
dc.description.abstractMorphology and texture detection, which are important components of tactile sensing, augment the response of human beings to external stimuli. Similarly, tactile sensing-based information acquisition systems in robots can help enhance the interactions of robots with the surroundings. The main drawback of morphology and texture sensing methods is their inability to explain and quantify sensing information, which makes it difficult to utilize prior knowledge and necessitates a new training process to fit the new task, even if the changes between the existing and new tasks are minuscule. Another drawback is its dependence on large datasets. To solve these problems, a hybrid connectionist symbolic model (HCSM) is proposed herein that combines historic symbolic knowledge and end-to-end neural networks. The symbolic model requires a smaller dataset and possesses an improved transferability of detection. Neural networks can be easily established and exhibit better fault tolerance for non-ideal samples. The HCSM model combines these advantages. Experiments with the tactile-based morphology and texture detection demonstrated that the new method can transfer the detection ability to fit new tasks without requiring additional retraining and has a 16% higher recognition precision than a convolutional neural network, LeNet, AlexNet, VGG16, and ResNet. The HCSM method with these features can broaden the range of applications of tactile sensing.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7361-
dc.relation.ispartofIEEE Sensors Journal-
dc.rightsIEEE Sensors Journal. Copyright © IEEE.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectNetworked sensor fusion and decisions-
dc.subjectsoft computing with sensor data-
dc.subjectsensor model analysis verification-
dc.subjectsmart sensor systems-
dc.titleHybrid Connectionist Symbolic Model for Morphologic Recognition by Tactile Sensing-
dc.typeArticle-
dc.identifier.emailXi, N: xining@hku.hk-
dc.identifier.authorityXi, N=rp02044-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/JSEN.2020.3041058-
dc.identifier.scopuseid_2-s2.0-85100707362-
dc.identifier.hkuros331225-
dc.identifier.volume21-
dc.identifier.issue5-
dc.identifier.spage6497-
dc.identifier.epage6509-
dc.identifier.isiWOS:000616329300100-
dc.publisher.placeUnited States-

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