{"id":{"repo_id":"siu-theses","oai_identifier":"oai:opensiuc.lib.siu.edu:dissertations-1152"},"canonical_url":"https://search.dev.ndltd.org/etd/siu-theses/oai:opensiuc.lib.siu.edu:dissertations-1152","repository":{"repo_id":"siu-theses","name":"Southern Illinois University","base_url":"https://opensiuc.lib.siu.edu/do/oai/"},"display":{"title":"NOVEL APPROACHES FOR STATISTICAL PROCESS CONTROL CHARTS PATTERN RECOGNITION","abstract":"Fast and accurate recognition of the Statistical Control Chart Patterns (SPCCP) is significant for supervising manufacturing processes to accomplish better control and to make high value products. SPCCP can display eight kinds of patterns: normal, stratification, systematic, increasing trend, decreasing trend, up shift, down shift and cyclic. With the exception of the natural pattern, all other patterns indicate that the supervised manufacturing process is not performing properly and actions need to be taken to correct the problems. This research proposes new approaches, neural networks and neural-fuzzy systems, to the (SPCCP) recognition. This dissertation also investigates the use of features extracted from statistical analysis for simple patterns, and wavelet analysis for concurrent patterns as the components of the input vectors. Results based on simulated data show that the proposed approaches perform better than conventional approaches. Our work concluded that the extracted features improve the performance of the proposed recognizer systems.","abstract_html":"Fast and accurate recognition of the Statistical Control Chart Patterns (SPCCP) is significant for supervising manufacturing processes to accomplish better control and to make high value products. SPCCP can display eight kinds of patterns: normal, stratification, systematic, increasing trend, decreasing trend, up shift, down shift and cyclic. With the exception of the natural pattern, all other patterns indicate that the supervised manufacturing process is not performing properly and actions need to be taken to correct the problems. This research proposes new approaches, neural networks and neural-fuzzy systems, to the (SPCCP) recognition. This dissertation also investigates the use of features extracted from statistical analysis for simple patterns, and wavelet analysis for concurrent patterns as the components of the input vectors. Results based on simulated data show that the proposed approaches perform better than conventional approaches. Our work concluded that the extracted features improve the performance of the proposed recognizer systems.","abstract_has_math":false,"creators":["el homani, Abdellatif"],"institution":null,"degree_name":"Doctor of Philosophy","degree_level":"Campus Only Dissertation","degree_discipline":"Engineering Science","degree_department":null,"school":null,"contributors":["Aouadi, Samir"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-05-01T07:00:00Z","date_published":"2010-05-01T07:00:00Z","updated_at":"2026-07-24T04:33:31Z","subjects":["chart","control","fuzzy logic","pattern","process","wavelet"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://opensiuc.lib.siu.edu/dissertations/152","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Aouadi, Samir"]},{"key":"dc:creator","label":"Author","values":["el homani, Abdellatif"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Campus Only Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["chart","control","fuzzy logic","pattern","process","wavelet"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://opensiuc.lib.siu.edu/dissertations/152"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Fast and accurate recognition of the Statistical Control Chart Patterns (SPCCP) is significant for supervising manufacturing processes to accomplish better control and to make high value products. SPCCP can display eight kinds of patterns: normal, stratification, systematic, increasing trend, decreasing trend, up shift, down shift and cyclic. With the exception of the natural pattern, all other patterns indicate that the supervised manufacturing process is not performing properly and actions need to be taken to correct the problems. This research proposes new approaches, neural networks and neural-fuzzy systems, to the (SPCCP) recognition. This dissertation also investigates the use of features extracted from statistical analysis for simple patterns, and wavelet analysis for concurrent patterns as the components of the input vectors. Results based on simulated data show that the proposed approaches perform better than conventional approaches. Our work concluded that the extracted features improve the performance of the proposed recognizer systems."]},{"key":"dc:title","label":"Title","values":["NOVEL APPROACHES FOR STATISTICAL PROCESS CONTROL CHARTS PATTERN RECOGNITION"]}]}],"canonical_facts":{"dc:contributor":["Aouadi, Samir"],"dc:creator":["el homani, Abdellatif"],"dc:description.abstract":["Fast and accurate recognition of the Statistical Control Chart Patterns (SPCCP) is significant for supervising manufacturing processes to accomplish better control and to make high value products. SPCCP can display eight kinds of patterns: normal, stratification, systematic, increasing trend, decreasing trend, up shift, down shift and cyclic. With the exception of the natural pattern, all other patterns indicate that the supervised manufacturing process is not performing properly and actions need to be taken to correct the problems. This research proposes new approaches, neural networks and neural-fuzzy systems, to the (SPCCP) recognition. This dissertation also investigates the use of features extracted from statistical analysis for simple patterns, and wavelet analysis for concurrent patterns as the components of the input vectors. Results based on simulated data show that the proposed approaches perform better than conventional approaches. Our work concluded that the extracted features improve the performance of the proposed recognizer systems."],"dc:identifier":["https://opensiuc.lib.siu.edu/dissertations/152"],"dc:subject":["chart","control","fuzzy logic","pattern","process","wavelet"],"dc:title":["NOVEL APPROACHES FOR STATISTICAL PROCESS CONTROL CHARTS PATTERN RECOGNITION"],"thesis:degree_discipline":["Engineering Science"],"thesis:degree_level":["Campus Only Dissertation"],"thesis:degree_name":["Doctor of Philosophy"]},"updated_at":"2026-07-24T04:33:31Z"}