Artificial Intelligence Enhanced Environmental Detection System

Artificial Intelligence Enhanced Environmental Detection System | 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Article Artificial Intelligence Enhanced Environmental Detection System Xiaoyin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5189895/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper presents a novel approach to improve the accuracy of environmental detection and prediction by incorporating artificial intelligence (AI) technology into existing detection systems. At the heart of our approach lies the combination of a complex AI model with the hardware and software components of the inspection system. This combined approach can significantly improve the accuracy of detection systems through greater ability to predict environmental changes and events, underscoring the superior performance of hardware and software combined with AI technology. This paper delves into the details of hardware and software design, and discusses measurement implementation methods using a build-down machine. We also explore the practical application of AI models within the framework described above. In addition, this paper also describes the implementation of communication protocols to ensure the effective data exchange between the system network and the artificial intelligence model. These protocols are essential for the real-time processing and analysis of environmental data, enabling systems to respond quickly to detected changes. Applied Statistics Artificial Intelligence Environmental Detection System Network Interface Chip 1 Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-5189895","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":361307295,"identity":"67ada2a7-6eff-443b-81f0-77e1ceb9ec6e","order_by":0,"name":"Xiaoyin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYDACZhBhAGYyPnjABqITiNfCbJBAlBYkwCZBlBaD48wPH/MU3Ensn91+rSKh7DADP3uOAXPFH9xaJJvZjI15DJ4lzrhzpuxGwrnDDJI9bwwYz7bh1sLPzGAmzWNwOLfhRk7ajcS2wwwGN3IMGBsb8Lifmf0bWMt8oJYCkBZ7kJYGPA7jZ+aB2LLhRvoxBrAtEiAtbPj8wlNsOMfgcP3GGznMEgnn0nkkzjwrONiIxy8G549vfPDmz2FjuRvpDz98KLOW429P3vgQn8NAgIkHTPGAIrQZzD6AXwMwofwAU+wPgEQdIcWjYBSMglEwAgEAEXRT1bafsG0AAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-10-01 23:18:14","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-5189895/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5189895/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65805185,"identity":"32c98655-4af2-4d15-93be-7603c6ed441c","added_by":"auto","created_at":"2024-10-03 02:26:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":208362,"visible":true,"origin":"","legend":"","description":"","filename":"ArtificialIntelligenceEnhancedEnvironmentalDetectionSystem.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5189895/v1_covered_83ed190c-1ea6-4533-b5e0-20f627f8d05a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eArtificial Intelligence Enhanced Environmental\u003c/p\u003e\n\u003cp\u003eDetection System\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"East China Normal University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Environmental Detection System, Network Interface Chip 1","lastPublishedDoi":"10.21203/rs.3.rs-5189895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5189895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a novel approach to improve the accuracy of environmental detection and prediction by incorporating artificial intelligence (AI) technology into existing detection systems. 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