APPLICATION OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS FOR MULTIDIMENSIONAL SENSORY DATA PREDICTION AND RESOURCE SCHEDULING IN SMART CITY DESIGN.
Saved in:
| Title: | APPLICATION OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS FOR MULTIDIMENSIONAL SENSORY DATA PREDICTION AND RESOURCE SCHEDULING IN SMART CITY DESIGN. |
|---|---|
| Authors: | LIYA LIU1 Liya_Liu23@outlook.com |
| Source: | Scalable Computing: Practice & Experience. Jul2024, Vol. 25 Issue 4, p2973-2984. 12p. |
| Subjects: | Smart cities, Multidimensional databases, Evolutionary algorithms, City traffic, Traffic monitoring, Texture mapping, Scheduling |
| Abstract: | Multidimensional sensory data prediction and resource scheduling are paramount challenges in the design of smart cities. This paper delves into the utilization of multi-objective evolutionary algorithms to enhance the accuracy and efficiency of target detection through optimized YOLO_v3 network models. By integrating the YOLO_v3 model with the K-means++ algorithm for Anchor_Box generation, the novel approach exhibits superior adaptability and flexibility, particularly in handling variable-sized feature pattern mappings. This adaptability better caters to the detection of targets of diverse sizes, thus elevating the performance and precision of target detection algorithms. To further scrutinize the YOLO-v3 joint algorithm's performance in urban traffic detection, P-R curves were plotted for various loss types on the NEU-DET dataset. Comparative analysis of these curves highlights the optimized algorithms' superiority in detecting various types of losses in urban model completeness. Additionally, practical application analysis revealed that the optimized monitoring results outperform the detection time of the original YOLO-v3_means++ network model on FP_GA. Notably, post-processing with C-FENCE can reduce average single-frame image detection time to 2.01 seconds, while convolutional degree-level fusion with the BN layer cuts it down to 2.25 seconds. In summary, the FP_GA-based YOLO-v3_means++ network algorithm offers superior detection capabilities, and the multi-objective evolutionary algorithm's optimization of the YOLO-v3 model enhances target detection performance and precision. [ABSTRACT FROM AUTHOR] |
| Copyright of Scalable Computing: Practice & Experience is the property of Scalable Computing: Practice & Experience and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 177937625 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: APPLICATION OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS FOR MULTIDIMENSIONAL SENSORY DATA PREDICTION AND RESOURCE SCHEDULING IN SMART CITY DESIGN. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22LIYA+LIU%22">LIYA LIU</searchLink><relatesTo>1</relatesTo><i> Liya_Liu23@outlook.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Scalable+Computing%3A+Practice+%26+Experience%22">Scalable Computing: Practice & Experience</searchLink>. Jul2024, Vol. 25 Issue 4, p2973-2984. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Smart+cities%22">Smart cities</searchLink><br /><searchLink fieldCode="DE" term="%22Multidimensional+databases%22">Multidimensional databases</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22City+traffic%22">City traffic</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+monitoring%22">Traffic monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Texture+mapping%22">Texture mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Multidimensional sensory data prediction and resource scheduling are paramount challenges in the design of smart cities. This paper delves into the utilization of multi-objective evolutionary algorithms to enhance the accuracy and efficiency of target detection through optimized YOLO_v3 network models. By integrating the YOLO_v3 model with the K-means++ algorithm for Anchor_Box generation, the novel approach exhibits superior adaptability and flexibility, particularly in handling variable-sized feature pattern mappings. This adaptability better caters to the detection of targets of diverse sizes, thus elevating the performance and precision of target detection algorithms. To further scrutinize the YOLO-v3 joint algorithm's performance in urban traffic detection, P-R curves were plotted for various loss types on the NEU-DET dataset. Comparative analysis of these curves highlights the optimized algorithms' superiority in detecting various types of losses in urban model completeness. Additionally, practical application analysis revealed that the optimized monitoring results outperform the detection time of the original YOLO-v3_means++ network model on FP_GA. Notably, post-processing with C-FENCE can reduce average single-frame image detection time to 2.01 seconds, while convolutional degree-level fusion with the BN layer cuts it down to 2.25 seconds. In summary, the FP_GA-based YOLO-v3_means++ network algorithm offers superior detection capabilities, and the multi-objective evolutionary algorithm's optimization of the YOLO-v3 model enhances target detection performance and precision. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Scalable Computing: Practice & Experience is the property of Scalable Computing: Practice & Experience and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=177937625 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.12694/scpe.v25i4.2929 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 2973 Subjects: – SubjectFull: Smart cities Type: general – SubjectFull: Multidimensional databases Type: general – SubjectFull: Evolutionary algorithms Type: general – SubjectFull: City traffic Type: general – SubjectFull: Traffic monitoring Type: general – SubjectFull: Texture mapping Type: general – SubjectFull: Scheduling Type: general Titles: – TitleFull: APPLICATION OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS FOR MULTIDIMENSIONAL SENSORY DATA PREDICTION AND RESOURCE SCHEDULING IN SMART CITY DESIGN. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: LIYA LIU IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 18951767 Numbering: – Type: volume Value: 25 – Type: issue Value: 4 Titles: – TitleFull: Scalable Computing: Practice & Experience Type: main |
| ResultId | 1 |
