Acoustic feature extraction and internal defect detection of hardwood logs based on DOS-VMD

Acoustic feature extraction and internal defect detection of hardwood logs based on DOS-VMD | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Acoustic feature extraction and internal defect detection of hardwood logs based on DOS-VMD Xucheng Li, Shixiang Wang, Feng Xu, Yin Wu, Xiping Wang, Robert J. Ross This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8793775/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Accurate detection of internal defects in hardwood logs is a prerequisite for the efficient allocation of wood resources and the maximization of their value. To address the limitations of existing acoustic testing methods—specifically, the reliance on empirical experience for Variational Mode Decomposition (VMD) parameter selection and the inability of single-objective optimization to balance mode independence with feature significance, resulting in poor robustness—this study proposes a method termed Dual-Objective SPEA2-Optimized Variational Mode Decomposition (DOS-VMD). A dual-objective fitness function incorporating the Index of Orthogonality (IO) and Mean Envelope Entropy (MEE) was constructed to balance decomposition completeness with defect signal sparsity. By utilizing the Strength Pareto Evolutionary Algorithm 2 (SPEA2) to search for the global optimal compromise solution on the Pareto front, the adaptive and precise optimization of key VMD parameters ( K and α ) was achieved. Effective modes of DOS-VMD were selected based on a Comprehensive Index (CI) that fuses the energy ratio and sample entropy. Subsequently, the Min-Max normalized frequency band distribution and energy ratio of these effective modes were extracted as characteristic parameters to represent defect signals, enabling precise detection of the internal quality of hardwood logs. Experimental results demonstrated that the DOS-VMD method effectively overcame the mode mixing problem. The prediction accuracies for major defect types and the primary-secondary order of defects reached 93.3% and 71.4%, respectively, significantly outperforming traditional parametric detection methods. The feature extraction framework proposed in this study possesses strong physical interpretability, providing a reliable basis for the development of intelligent identification systems for log defects. SPEA2 algorithm DOS-VMD Variational Mode Decomposition Hardwood logs Defect detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 31 Mar, 2026 Reviews received at journal 25 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers invited by journal 25 Feb, 2026 Editor assigned by journal 25 Feb, 2026 Submission checks completed at journal 08 Feb, 2026 First submitted to journal 05 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8793775","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597189501,"identity":"66855703-2510-4aa4-9bb6-c981d2a049f2","order_by":0,"name":"Xucheng Li","email":"","orcid":"","institution":"Nanjing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xucheng","middleName":"","lastName":"Li","suffix":""},{"id":597189505,"identity":"0cfd92e4-66bd-40be-88b2-35254955fc82","order_by":1,"name":"Shixiang Wang","email":"","orcid":"","institution":"Nanjing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Shixiang","middleName":"","lastName":"Wang","suffix":""},{"id":597189506,"identity":"f29f8466-979f-4e33-96f9-ae471b1df5d9","order_by":2,"name":"Feng Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBAC9gYGxgcMPHIgtgFxWngOMDAbMPAYk6aFTYKBgSQtEjlm1QUyBokN7M3bJBhq7hChheeM2e0ZPEAtPMfKJBiOPSOsxZ69x+w2D8+fxAagdRKMDYeJsIWZx6yYB2SL/BtitQBtYQZrkeAhVgvPsWJpoBbjNp60YouEY8RokUje+Jm3x0C2n/3wxhsfaojQAgaMPQwMbCBGApEagOAH8UpHwSgYBaNgBAIALrIucafDR9MAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Xu","suffix":""},{"id":597189509,"identity":"d59e4ba0-d307-4e27-b9c7-71b732e8f363","order_by":3,"name":"Yin Wu","email":"","orcid":"","institution":"Nanjing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Yin","middleName":"","lastName":"Wu","suffix":""},{"id":597189511,"identity":"ffa9b83e-e1d7-4644-ac3e-a47ef19ec956","order_by":4,"name":"Xiping Wang","email":"","orcid":"","institution":"Forest Products Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Xiping","middleName":"","lastName":"Wang","suffix":""},{"id":597189512,"identity":"04b446a7-d594-45bd-baae-bc14a4d891fe","order_by":5,"name":"Robert J. 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To address the limitations of existing acoustic testing methods\u0026mdash;specifically, the reliance on empirical experience for Variational Mode Decomposition (VMD) parameter selection and the inability of single-objective optimization to balance mode independence with feature significance, resulting in poor robustness\u0026mdash;this study proposes a method termed Dual-Objective SPEA2-Optimized Variational Mode Decomposition (DOS-VMD). A dual-objective fitness function incorporating the Index of Orthogonality (IO) and Mean Envelope Entropy (MEE) was constructed to balance decomposition completeness with defect signal sparsity. By utilizing the Strength Pareto Evolutionary Algorithm 2 (SPEA2) to search for the global optimal compromise solution on the Pareto front, the adaptive and precise optimization of key VMD parameters (\u003cem\u003eK\u003c/em\u003e and \u003cem\u003eα\u003c/em\u003e) was achieved. Effective modes of DOS-VMD were selected based on a Comprehensive Index (CI) that fuses the energy ratio and sample entropy. Subsequently, the Min-Max normalized frequency band distribution and energy ratio of these effective modes were extracted as characteristic parameters to represent defect signals, enabling precise detection of the internal quality of hardwood logs. Experimental results demonstrated that the DOS-VMD method effectively overcame the mode mixing problem. The prediction accuracies for major defect types and the primary-secondary order of defects reached 93.3% and 71.4%, respectively, significantly outperforming traditional parametric detection methods. The feature extraction framework proposed in this study possesses strong physical interpretability, providing a reliable basis for the development of intelligent identification systems for log defects.\u003c/p\u003e","manuscriptTitle":"Acoustic feature extraction and internal defect detection of hardwood logs based on DOS-VMD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 06:35:39","doi":"10.21203/rs.3.rs-8793775/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-31T14:21:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T04:45:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156483347746638710799969349662765506770","date":"2026-03-12T11:30:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215822626143869235815299258054994150539","date":"2026-02-25T22:58:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-25T16:45:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-25T16:32:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-09T02:30:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wood Science and Technology","date":"2026-02-05T07:29:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wood-science-and-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wsat","sideBox":"Learn more about [Wood Science and Technology](http://link.springer.com/journal/226)","snPcode":"226","submissionUrl":"https://submission.nature.com/new-submission/226/3","title":"Wood Science and Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"72134eea-95cb-4469-b5c4-3dfea6cc2feb","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T06:35:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 06:35:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8793775","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8793775","identity":"rs-8793775","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}