Contactless Medical Equipment AI Big Data Risk Control and Quasi Thinking Iterative Planning

Contactless Medical Equipment AI Big Data Risk Control and Quasi Thinking Iterative Planning | 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 Contactless Medical Equipment AI Big Data Risk Control and Quasi Thinking Iterative Planning zhu rongrong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1100366/v2 This work is licensed under a CC BY 4.0 License Status: Under Review Version 2 posted 11 You are reading this latest preprint version Show more versions Abstract Contactless medical equipment AI big data risk control and quasi thinking iterative planning，The tanh equilibrium state of heavy core clustering based on hierarchical fuzzy clustering system based on differential incremental equilibrium theory is adopted. Successfully control the parameter group of CT / MR machine internal data, big data AI mathematical model risk. The polar graph of high-dimensional heavy core clustering processing data is regular and scientific. Compared with the discrete characteristics of the polar graph of the original data. So as to correctly detect and control the dynamic change process of CT / MR in the whole life cycle. It provides help for the predictive maintenance of early pre inspection and orderly maintenance of the medical system. It also puts forward and designs the big data depth statistics of AI risk control medical equipment, and establishes the standardized model software. Scientifically evaluated the exposure time and heat capacity MHU% of CT tubes, as well as the internal law of MR (nuclear magnetic resonance ), and processed big data twice and three times in heavy nuclear clustering. After optimizing the algorithm, hundreds of thousands of nonlinear random vibrations are carried out in the operation and maintenance database every second, and at least 30 concurrent operations are formed, which greatly improves and shortens the operation time. Finally, after adding micro vibration quasi thinking iterative planning to the uncertain structure of AI operation, we can successfully obtain the scientific and correct results required by high-dimensional information and images. This kind of AI big data risk control improves the intelligent management ability of medical institutions, establishes the software for predictable maintenance of AI big data, which is cross platform and embedded into the web system. AI data risk control Quasi thinking iterative planning Heavy core clustering tanh equilibrium Heavy core clustering lens RBF complex variable feature space kernel Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 2 posted Editorial decision: Major revision 30 Mar, 2022 Reviews received at journal 29 Mar, 2022 Reviewers agreed at journal 06 Mar, 2022 Reviews received at journal 24 Jan, 2022 Reviewers agreed at journal 13 Jan, 2022 Reviewers agreed at journal 05 Jan, 2022 Reviewers invited by journal 05 Jan, 2022 Editor assigned by journal 05 Jan, 2022 Editor invited by journal 05 Jan, 2022 Submission checks completed at journal 05 Jan, 2022 First submitted to journal 18 Dec, 2021 You are reading this latest preprint version Show more versions 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. 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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-1100366","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[{"code":1,"date":"2021-12-01 14:58:18","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-machine-intelligence","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natmachintell","sideBox":"Learn more about [Nature Machine Intelligence](http://www.nature.com/natmachintell/)","snPcode":"","submissionUrl":"","title":"Nature Machine Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":77541524,"identity":"7cef11d6-f983-4a6f-84f3-a9f3eb60c36a","order_by":0,"name":"zhu rongrong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYFADCQaGAx8qbOTY2NsPEK2F8eCMM2nGfDxnEojWwnyYt+Vw4jwJBwO8Cs3bew+/rqi4Yzd/do/BYd6Gw+ltEgwJDD8qtuHUInPmXJrlmTPPkhvnnDE4OHdHem6bdOMBxp4zt3E7RyLHzLCx7XAys0SOwYG3Z6xz22QOJDAzthGhhQ2khbeNOZ1NIsGAkBbjh0AtdjxALQd525wTCGvhOWPG2HDmcIKERFoBKJAN24CBfBCvX9h7jD82VBy2l5+RvPkDMCrl5dvbDz74UYFbCxCwAeOdIbEBWegAPvVAwPwBSNgTUDQKRsEoGAUjGQAAnIRdc+alymsAAAAASUVORK5CYII=","orcid":"","institution":"Fudan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"zhu","middleName":"","lastName":"rongrong","suffix":""}],"badges":[],"createdAt":"2021-11-21 10:55:29","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-1100366/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-1100366/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":17463087,"identity":"9cb35254-d6f6-4a6d-8977-66f08e4441b1","added_by":"auto","created_at":"2022-01-19 14:05:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1039341,"visible":true,"origin":"","legend":"","description":"","filename":"ContactlessMedicalEquipmentAIBigDataRiskControlandQuasiThinkingIterativePlanningNEW.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1100366/v2_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Contactless Medical Equipment AI Big Data Risk Control and Quasi Thinking Iterative Planning","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1100366/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AI data risk control, Quasi thinking iterative planning, Heavy core clustering tanh equilibrium, Heavy core clustering lens, RBF complex variable feature space kernel","lastPublishedDoi":"10.21203/rs.3.rs-1100366/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1100366/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Contactless medical equipment AI big data risk control and quasi thinking iterative planning，The tanh equilibrium state of heavy core clustering based on hierarchical fuzzy clustering system based on differential incremental equilibrium theory is adopted. Successfully control the parameter group of CT / MR machine internal data, big data AI mathematical model risk. The polar graph of high-dimensional heavy core clustering processing data is regular and scientific. Compared with the discrete characteristics of the polar graph of the original data. So as to correctly detect and control the dynamic change process of CT / MR in the whole life cycle. It provides help for the predictive maintenance of early pre inspection and orderly maintenance of the medical system. It also puts forward and designs the big data depth statistics of AI risk control medical equipment, and establishes the standardized model software. Scientifically evaluated the exposure time and heat capacity MHU% of CT tubes, as well as the internal law of MR (nuclear magnetic resonance ), and processed big data twice and three times in heavy nuclear clustering. After optimizing the algorithm, hundreds of thousands of nonlinear random vibrations are carried out in the operation and maintenance database every second, and at least 30 concurrent operations are formed, which greatly improves and shortens the operation time. Finally, after adding micro vibration quasi thinking iterative planning to the uncertain structure of AI operation, we can successfully obtain the scientific and correct results required by high-dimensional information and images. This kind of AI big data risk control improves the intelligent management ability of medical institutions, establishes the software for predictable maintenance of AI big data, which is cross platform and embedded into the web system.","manuscriptTitle":"Contactless Medical Equipment AI Big Data Risk Control and Quasi Thinking Iterative Planning","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-01-19 14:05:20","doi":"10.21203/rs.3.rs-1100366/v2","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-03-30T08:15:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-03-29T09:40:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c0dbffd3-6dd5-4417-bb07-e5f37b73c125","date":"2022-03-07T03:38:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-01-25T00:52:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"05cffc87-ca81-4fec-adeb-3a9fcedfddf6","date":"2022-01-13T14:22:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3b86df5e-2688-48d8-bfba-1796fbbdee5c","date":"2022-01-05T22:42:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-01-05T11:22:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-01-05T11:21:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-01-05T10:35:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-01-05T08:57:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2021-12-18T06:20:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"600c908d-5815-46c3-8efd-974ac989cbb5","owner":[],"postedDate":"January 19th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-08-18T06:44:27+00:00","versionOfRecord":[],"versionCreatedAt":"2022-01-19 14:05:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-1100366","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1100366","identity":"rs-1100366","version":["v2"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}