LEADER 04512nam 22007335 450 001 996418218203316 005 20230403144241.0 010 $a3-030-43859-7 024 7 $a10.1007/978-3-030-43859-3 035 $a(CKB)4100000011223228 035 $a(MiAaPQ)EBC6173643 035 $a(DE-He213)978-3-030-43859-3 035 $a(PPN)24376085X 035 $a(EXLCZ)994100000011223228 100 $a20200409d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence in Music, Sound, Art and Design$b[electronic resource] $e9th International Conference, EvoMUSART 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15?17, 2020, Proceedings /$fedited by Juan Romero, Anikó Ekárt, Tiago Martins, João Correia 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (238 pages) $cillustrations 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v12103 311 $a3-030-43858-9 320 $aIncludes bibliographical references and index. 327 $aA deep learning neural network for classifying good and bad photos -- Adapting and Enhancing Evolutionary Art for Casual Creation -- Comparing Fuzzy Rule Based Approaches for Music Genre Classification -- Quantum Zentanglement: Combining Picbreeder and Wave Function Collapse to Create Zentangles -- Emerging Technology System Evolution -- Fusion of Hilbert-Huang Transform and Deep Convolutional Neural Network for Predominant Musical Instruments Recognition -- Genetic Reverb: Synthesizing Artificial Reverberant Fields Via Genetic Algorithms -- Portraits of No One: An Interactive Installation -- Understanding Aesthetic Evaluation with Deep Learning -- An Aesthetic-Based Fitness Measure and a Framework for Guidance of Evolutionary Design in Architecture -- Objective Evaluation of Tonal Fitness for Chord Progressions -- Coevolving Artistic Images Using OMNIREP -- Sound Cells in Genetic Improvisation: An Evolutionary Model for Improvised Music -- Controlling Self-Organization in Generative Creative Systems -- Emulation Games. See and Be Seen, a Subjective Approach to Analog Computational Neuroscience. 330 $aThis book constitutes the refereed proceedings of the 9th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EuroGP, EvoCOP and EvoApplications. The 15 revised full papers presented were carefully reviewed and selected from 31 submissions. The papers cover a wide spectrum of topics and application areas, including generative approaches to music and visual art, deep learning, and architecture. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v12103 606 $aComputer science 606 $aComputer networks 606 $aCompilers (Computer programs) 606 $aImage processing?Digital techniques 606 $aComputer vision 606 $aSignal processing 606 $aTheory of Computation 606 $aComputer Communication Networks 606 $aCompilers and Interpreters 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aSignal, Speech and Image Processing 615 0$aComputer science. 615 0$aComputer networks. 615 0$aCompilers (Computer programs). 615 0$aImage processing?Digital techniques. 615 0$aComputer vision. 615 0$aSignal processing. 615 14$aTheory of Computation. 615 24$aComputer Communication Networks. 615 24$aCompilers and Interpreters. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aSignal, Speech and Image Processing . 676 $a005.11 702 $aRomero$b Juan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aEkárt$b Anikó$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMartins$b Tiago$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCorreia$b João$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418218203316 996 $aArtificial Intelligence in Music, Sound, Art and Design$92257587 997 $aUNISA LEADER 05223nam 2200481 450 001 9910760257903321 005 20230929034655.0 010 $a3-031-41428-4 035 $a(MiAaPQ)EBC30745846 035 $a(Au-PeEL)EBL30745846 035 $a(EXLCZ)9928234560700041 100 $a20230929d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplication of Troubleshooting Tools in the Monitored Production Processes /$fPetr Baron, Marek Kocisko, and Anton Panda 205 $aFirst edition. 210 1$aCham, Switzerland :$cSpringer,$d[2024] 210 4$d©2024 215 $a1 online resource (169 pages) 225 1 $aManagement and Industrial Engineering Series 311 08$aPrint version: Baron, Petr Application of Troubleshooting Tools in the Monitored Production Processes Cham : Springer,c2023 9783031414275 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Vote of Thanks -- Contents -- About the Authors -- Symbols and Abbreviations -- 1 Introduction-Proactive Maintenance Methods -- References -- 2 Technical Diagnostics -- References -- 3 Measurement and Assessment of Technical Systems' Vibrations -- 3.1 Balancing of Rotating Parts of Machines -- 3.1.1 Machine Imbalance -- 3.1.2 Diagnostic Symptoms of Imbalance -- 3.1.3 General Principles for Rotor Balancing -- 3.1.4 Operational Balancing Methods -- 3.1.5 Balancing Rigid Rotors -- 3.1.6 Analysis of the Operating Condition of the Furnace Exhaust Fan Depending on Its Impeller Alignment -- 3.2 Diagnostic of Rolling Bearings -- 3.2.1 Diagnostics of Roller Bearings-Crest Factor -- 3.2.2 Diagnostics of Roller Bearings-HF (High Frequency Emission) -- 3.2.3 Roller Bearing Diagnosis-Kurtosis Factor -- 3.2.4 Diagnostics of Roller Bearings-Envelope Analysis -- 3.2.5 The Correlation of Parameters Measured on Rotary Machine After Reparation of Equipment of the Pulp Production -- 3.2.6 Verification of the Operating Condition of Stationary Industrial Gearbox Through Analysis of Dynamic Signal, Measured on the Pinion Bearing Housing -- 3.2.7 The Dynamic Parameters Correlation Assessment of the Textile Machine High-Speed Bearings in Changed Technological Conditions -- 3.3 Combination the Diagnostic Methods as Suitable Tool for Increasing an Effectivity of Determination the State of Mechanical Nodes -- 3.3.1 The trend's Measurement of Vibrations -- 3.3.2 Tribotechnical Diagnostic -- 3.3.3 The Surface Analysis -- 3.3.4 The Measurement of Roughness -- 3.3.5 The Measurement of Roundness -- 3.3.6 Results of Analyses and Discussion -- 3.4 Application of Methods of Technical Diagnostics by Assessment of Oil Filling Condition in the Process of Running-In of Planetary Gearbox -- 3.4.1 Materials and Methodology. 327 $a3.4.2 Results of the Measurements and Experiments -- 3.4.3 Results and Discussion -- 3.5 The Parameter Correlation of Acoustic Emission and High-Frequency Vibrations in the Assessment Process of the Operating State of the Technical System -- 3.5.1 Description of the Measuring-Characteristics of the Machines and Measuring Methods -- References -- 4 Tribotechnical Diagnostics -- 4.1 Classification of Lubricants -- 4.2 Research and Correlation of Diagnostic Methods for Assessment of the State of Oil Filling in Cycloid Gearbox -- 4.2.1 Correlation, Quantification of Measured Parameters, Recommended Limits -- 4.2.2 Discussion of Realized Experiments -- References -- 5 Application of Technical Diagnostics Tools in the Reductors Test Operation -- 5.1 Determination of Methodology and Research of the Influence of the Trial Run of High-Precision Reducers on the Change of Their Characterizing Properties -- 5.1.1 Parameters Characteristic of High-Precision Reducers -- 5.1.2 Description of the Investigated Problem -- 5.1.3 Characteristics of the Diagnostic Methods Applied -- 5.1.4 Conducting Measurements of Characteristic Properties of Bearing Reducers During Their Trial Run -- 5.1.5 Evaluation of Results and Qualitative Assessment of the Impact of the Load During the Trial Run Mode -- 5.1.6 Discussion of the Study Mentioned Above -- 5.2 Design and Implementation of a Diagnostic System for Measuring High-Precision Reducers -- 5.2.1 Design of a Mechatronic Diagnostic System for Measuring High-Precision Reducers -- 5.2.2 Design of Diagnostic Equipment -- 5.2.3 Results and Discussion -- References -- 6 Conclusion -- References -- Index. 410 0$aManagement and industrial engineering. 606 $aFault location (Engineering) 606 $aReliability (Engineering) 615 0$aFault location (Engineering) 615 0$aReliability (Engineering) 676 $a620.0044 700 $aBaron$b Petr$01438678 702 $aKocisko$b Marek 702 $aPanda$b 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