000777085 000__ 04850cam\a2200553Ii\4500 000777085 001__ 777085 000777085 005__ 20230306142656.0 000777085 006__ m\\\\\o\\d\\\\\\\\ 000777085 007__ cr\nn\nnnunnun 000777085 008__ 160823t20162017sz\a\\\\ob\\\\001\0\eng\d 000777085 019__ $$a957615447$$a961205503 000777085 020__ $$a9783319402109$$q(electronic book) 000777085 020__ $$a3319402102$$q(electronic book) 000777085 020__ $$z9783319402093 000777085 020__ $$z3319402099 000777085 035__ $$aSP(OCoLC)ocn957156891 000777085 035__ $$aSP(OCoLC)957156891$$z(OCoLC)957615447$$z(OCoLC)961205503 000777085 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dIDEBK$$dEBLCP$$dGW5XE$$dYDX$$dN$T$$dOCLCF$$dIDB$$dUAB$$dIOG 000777085 049__ $$aISEA 000777085 050_4 $$aTA169.6 000777085 050_4 $$aTA1-2040 000777085 08204 $$a620/.0045$$223 000777085 08204 $$a620 000777085 24500 $$aKnowledge-driven board-level functional fault diagnosis /$$cFangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu. 000777085 264_1 $$aSwitzerland :$$bSpringer,$$c[2016]. 000777085 264_4 $$c©2017 000777085 300__ $$a1 online resource (xiii, 147 pages) :$$billustrations. 000777085 336__ $$atext$$btxt$$2rdacontent 000777085 337__ $$acomputer$$bc$$2rdamedia 000777085 338__ $$aonline resource$$bcr$$2rdacarrier 000777085 504__ $$aIncludes bibliographical references and index. 000777085 5050_ $$aPreface; Acknowledgments; Contents; 1 Introduction; 1.1 Introduction to Manufacturing Test; 1.1.1 System and Tests; 1.1.2 Testing in the Manufacturing Line; 1.2 Introduction to Board-Level Diagnosis; 1.2.1 Review of State-of-the-Art; 1.2.2 Automation in Diagnosis System; 1.2.3 New Directions Enabled by Machine Learning; 1.2.4 Challenges and Opportunities; 1.3 Outline of Book; References; 2 Diagnosis Using Support Vector Machines (SVM); 2.1 Background and Chapter Highlights; 2.2 Diagnosis Using Support Vector Machines; 2.2.1 Support Vector Machines; 2.2.2 SVM Diagnosis Flow 000777085 5058_ $$a2.3 Multi-kernel Support Vector Machines and Incremental Learning2.3.1 Multi-kernel Support Vector Machines; 2.3.2 Incremental Learning; 2.4 Results; 2.4.1 Evaluation of MK-SVM-Based Diagnosis System; 2.4.2 Evaluation of Incremental SVM-Based Diagnosis System; 2.4.3 Evaluation of Incremental MK-SVM-Based Diagnosis System; 2.5 Chapter Summary; References; 3 Diagnosis Using Multiple Classifiers and Majority-Weighted Voting (WMV); 3.1 Background and Chapter Highlights; 3.2 Artificial Neural Networks (ANN); 3.2.1 Architecture of ANNs; 3.2.2 Demonstration of ANN-Based Diagnosis System 000777085 5058_ $$a3.3 Comparison Between ANNs and SVMs3.4 Diagnosis Using Weighted-Majority Voting; 3.4.1 Weighted-Majority Voting; 3.4.2 Demonstration of WMV-Based Diagnosis System; 3.5 Results; 3.5.1 Evaluation of ANNs-Based Diagnosis System; 3.5.2 Evaluation of SVMs-Based Diagnosis System; 3.5.3 Evaluation of WMV-Based Diagnosis System; 3.6 Chapter Summary; References; 4 Adaptive Diagnosis Using Decision Trees (DT); 4.1 Background and Chapter Highlights; 4.2 Decision Trees; 4.2.1 Training of Decision Trees; 4.2.2 Example of DT-Based Training and Diagnosis; 4.3 Diagnosis Using Incremental Decision Trees 000777085 5058_ $$a4.3.1 Incremental Tree Node4.3.2 Addition of a Case; 4.3.3 Ensuring the Best Splitting; 4.3.4 Tree Transposition; 4.4 Diagnosis Flow Based on Incremental Decision Trees; 4.5 Results; 4.5.1 Evaluation of DT-Based Diagnosis System; 4.5.2 Evaluation of Incremental DT-Based Diagnosis System; 4.6 Chapter Summary; References; 5 Information-Theoretic Syndrome and Root-Cause Evaluation; 5.1 Background and Chapter Highlights; 5.2 Evaluation Methods for Diagnosis Systems; 5.2.1 Subset Selection for Syndromes Analysis; 5.2.2 Class-Relevance Statistics; 5.3 Evaluation and Enhancement Framework 000777085 5058_ $$a5.3.1 Evaluation and Enhancement Procedure5.3.2 An Example of the Proposed Framework; 5.4 Results; 5.4.1 Demonstration of Syndrome Analysis; 5.4.2 Demonstration of Root-Cause Analysis; 5.5 Chapter Summary; References; 6 Handling Missing Syndromes; 6.1 Background and Chapter Highlights; 6.2 Methods to Handle Missing Syndromes; 6.2.1 Missing-Syndrome-Tolerant Fault Diagnosis Flow; 6.2.2 Missing-Syndrome-Preprocessing Methods; 6.2.3 Feature Selection; 6.3 Results; 6.3.1 Evaluation of Label Imputation; 6.3.2 Evaluation of Feature Selection in Handling Missing Syndromes 000777085 506__ $$aAccess limited to authorized users. 000777085 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 30, 2016). 000777085 650_0 $$aFault location (Engineering) 000777085 650_0 $$aFault tolerance (Engineering) 000777085 7001_ $$aYe, Fangming,$$eauthor. 000777085 7001_ $$aZhang, Zhaobo,$$eauthor. 000777085 7001_ $$aChakrabarty, Krishnendu,$$eauthor. 000777085 7001_ $$aGu, Xinli,$$eauthor. 000777085 77608 $$iPrint version:$$z9783319402093$$z3319402099$$w(OCoLC)950953424 000777085 852__ $$bebk 000777085 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-40210-9$$zOnline Access$$91397441.1 000777085 909CO $$ooai:library.usi.edu:777085$$pGLOBAL_SET 000777085 980__ $$aEBOOK 000777085 980__ $$aBIB 000777085 982__ $$aEbook 000777085 983__ $$aOnline 000777085 994__ $$a92$$bISE