Publication:
Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module

Simple item page
dc.citedby0
dc.contributor.authorKoh J.S.P.en_US
dc.contributor.authorAris I.B.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorBashi S.M.en_US
dc.contributor.authorMarhaban M.H.en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid6603306751en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid6603053704en_US
dc.contributor.authorid57211599538en_US
dc.date.accessioned2023-12-28T08:57:38Z
dc.date.available2023-12-28T08:57:38Z
dc.date.issued2006
dc.description.abstractThis research presents a new approach to optimise the performance of a multiple-beam optical scanning system in terms of its marking combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the marking tasks need to be segregated and assigned for each scanner head and path planning where the best combinatorial paths for each scanner are determined in order to minimise the total motion of marking time. The knowledge acquired by the process is interpreted and mapped into vectors, which are kept in the database and used by the system to guide its reasoning process. The main motivation for this study is to introduce and evaluate an advance new customized GA. Comparison results of different combinatorial operators and tests with different probability factors are shown. Also, proposed are the new modifications to existing crossover operator called DPPC (Dynamic Pre-Populated Crossover) together with modification of a simple crossover selection method, called BCS (Bi-Cycle Selection Method). The performance of the new operator called GA_INSP (GA Inspection Module) for a better evolutionary approach to the time-based problem has been discussed in the study. The representation approach has been implemented via a computer program in order to achieve optimized marking performance. This algorithm has been tested and implemented successfully via a dual-beam optical scanning module. � 2006 Asian Network for Scientific Information.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.3923/jas.2006.2201.2208
dc.identifier.epage2208
dc.identifier.issue10
dc.identifier.scopus2-s2.0-33749128068
dc.identifier.spage2201
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-33749128068&doi=10.3923%2fjas.2006.2201.2208&partnerID=40&md5=117dbe1946f6cb225bfb3386f5cfb213
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29778
dc.identifier.volume6
dc.pagecount7
dc.relation.ispartofAll Open Access; Bronze Open Access; Green Open Access
dc.sourceScopus
dc.sourcetitleJournal of Applied Sciences
dc.subjectGenetic algorithm
dc.subjectMultiple-head optical scanner
dc.subjectArtificial intelligence
dc.subjectLaser recording
dc.subjectMotion planning
dc.subjectOptimization
dc.subjectScanning
dc.subjectArtificial intelligent
dc.subjectComparison result
dc.subjectCrossover operator
dc.subjectEvolutionary approach
dc.subjectOptical scanners
dc.subjectOptical scanning systems
dc.subjectPerformance optimizations
dc.subjectReasoning process
dc.subjectGenetic algorithms
dc.titleDesign, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning moduleen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS