Deep learning: algorithms and applications:
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
[2020]
|
| Schriftenreihe: | Studies in computational intelligence
volume 865 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xii, 360 Seiten Illustrationen, Diagramme, Karte (überwiegend farbig) |
| ISBN: | 9783030317591 |
| ISSN: | 1860-949X |
Internformat
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Datensatz im Suchindex
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| adam_text | Contents Activation Functions ......................................................................................... Mohit Goyái, Rajan Goyái, P. Venkatappa Reddy and Brejesh Lall 1 Introduction..................................................................................................... 1.1 Drawbacks of Fixed Activation Functions........................................ 2 Existing Activation Functions...................................................................... 2.1 Linear Activation.................................................................................. 2.2 Binary Step Activation......................................................................... 2.3 Sigmoid and Softmax........................................................................... 2.4 Tanh (Hyperbolic Tan)......................................................................... 2.5 The ReLU Family................................................................................ 2.6 Softplus................................................................................................... 2.7 Maxout................................................................................................... 2.8 Swish Activation Function.................................................................. 3 Comparison of Activation Functions........................................................... 4 Learning Activation Functions...................................................................... 4.1 Learning Adaptive Piecewise Linear (APL) Activation
Functions................................................................................................ 4.2 SLAF: Learning Non-linear Approximation of Activation Functions................................................................................................ 5 Conclusion....................................................................................................... 6 Future Works................................................................................................... References.............................................................................................................. Adversarial Examples in Deep Neural Networks: An Overview............. Emilio Rafael Baida, Arash Behboodi and Rudolf Mathar 1 Introduction..................................................................................................... 1.1 Notation and Preliminaries.................................................................. 2 Adversarial Perturbation Design .................................................................. 2.1 White-Box Attacks................................................................................ 2.2 Black-Box Attacks and Universal Adversarial Perturbations......... 1 1 4 4 4 5 6 8 10 15 16 17 18 20 20 21 27 28 28 31 32 33 34 37 44 vii
viii Contents 3 Theoretical Explanations of the Nature of Adversarial Examples........... 3.1 Linearity Hypothesis and Curvature of Decision Boundaries......... 3.2 Boundary Tilting and Other Explanations ......................................... 3.3 Feature Selection and No Free Lunch Theorems for Adversarial Robustness............................................................................................. 3.4 Generalization Bounds for Adversarial Examples.............................. 4 Defenses Against Adversarial Attacks...................................................... 4.1 Obfuscated Gradients and Adversarial Training................................ 4.2 Robust Regularization........................................................................... 5 Future Directions....................................................................................... References.......................................................................................................... 47 49 52 Representation Learning in Power Time Series Forecasting..................... Janosch Henze, Jens Schreiber and Bernhard Sick 1 Introduction................................................................................................ 2 Regression in Power Time Series Forecasting ........................................ 2.1 Renewable Power Time Series Forecasting .................................... 2.2 Challenges of Power Time Series Forecasting............................... 3 Foundations of Representation Learning.................................................... 3.1 Traditional
Dimensionality Reduction Techniques ......................... 3.2 Deep Architectures for Latent Feature Extraction........................... 4 Evaluation of Representation Learning in Regression Tasks.................. 4.1 Evaluation of a Regression Model.................................................... 4.2 Evaluation of the Learned Feature Representation........................... 5 Representation Learning Applied in Power TimeSeries Forecasting ... 5.1 The Power Time Series..................................................................... 5.2 Representation Learning Experiments ............................................. 5.3 Principal Component Analysis for Feature Extraction.................... 5.4 Deep Architectures for Feature Extraction...................................... 5.5 Fine-Tuning for Power Forecasting................................................. 5.6 Key Insights....................................................................................... 6 Conclusion................................................................................................... 6.1 How to Build a Representation Learning Model............................. References......................................................................................................... 67 Deep Learning Application: Load Forecasting in Big Data of Smart Grids.................................................................................................................. Abdulaziz Almalaq and Jun Jason Zhang 1
Introduction................................................................................................ 2 Background................................................................................................ 2.1 Electrical Load................................................................................... 2.2 Load Forecasting.............................................................................. 2.3 Influential Factors of Load Forecasting Models............................. 54 56 58 58 59 61 62 68 69 70 71 72 73 76 81 82 83 85 85 86 88 92 95 98 98 99 99 103 104 106 107 107 109
Contents 3 Forecasting Modeling Issues in Smart Grids.............................................. 3.1 General Issues with Load Forecasting Modeling.............................. 3.2 Traditional Load Forecasting Models................................................. 3.3 Traditional Load Forecasting Models................................................. 4 Solutions and Recommendations................................................................. 4.1 Guidelines and Solutions to Modeling Issues................................... 4.2 Case Study............................................................................................. 5 Conclusions and Future Trends................................................................... References............................................................................................................. Fast and Accurate SeismicTomography via Deep Learning...................... Mauricio Araya-Рою, Amir Adler, Stuart Farris and Joseph Jennings 1 Introduction.................................................................................................... 2 The Seismic Tomography Problem............................................................ 2.1 Seismic Data........................................................................................ 2.2 Seismic Tomography.......................................................................... 3 Seismic Tomography via Deep Learning................................................... 3.1 Deep Neural Networks for Inverse Imaging Problems..................... 3.2 Velocity
Semblance as Input Feature for Deep Networks.............. 4 Semblance-Based CNN Results................................................................... 4.1 Experimental Setup............................................................................... 4.2 Quantitative Metrics............................................................................ 4.3 Results and Analysis............................................................................ 5 Industry Baseline: Full Waveform Inversion.............................................. 5.1 Industry VMB Methods...................................................................... 5.2 Full Waveform Inversion................................................................... 5.3 Baseline Comparison Setup................................................................. 5.4 Experiments........................................................................................... 5.5 Results.................................................................................................... 5.6 Discussion............................................................................................. 6 Feature Extraction-Free Results................................................................... 7 Conclusions.................................................................................................... References................................................................................................................ Traffic Light and Vehicle Signal Recognition with High Dynamic Range Imaging andDeep
Learning.................................................................. Jian-Gang Wang and Lu-Bing Zhou 1 Introduction.................................................................................................... 2 HDR Imaging Traffic Light Detection........................................................ 2.1 HDR Imaging ...................................................................................... 2.2 Dark Images for Detecting Traffic Light Candidates....................... 2.3 Saliency Map Filtering........................................................................ 2.4 Auto Exposure for Uncontrolled Illumination................................... 2.5 Region of Interest (ROI)...................................................................... ix 110 Ill Ill 117 118 118 121 125 126 129 130 131 131 133 135 135 138 139 139 141 141 143 143 144 146 147 147 148 152 154 154 157 158 160 I60 161 163 164 165
x Contents 3 167 167 168 170 172 172 173 183 185 185 185 186 187 189 190 Traffic Light Recognition with Deep Learning......................................... 3.1 Dual Channel Mechanism................................................................. 3.2 Customized Convolutional Neural Network.................................... 4 Temporal Trajectory Analysis.................................................................... 5 Experimental Results................................................................................. 5.1 Evaluation of Performance............................................................... 5.2 Comparison with State of the ArtAlgorithms................................... 6 HDR Imaging Vehicle Signal Recognition............................................... 6.1 Related Approaches.......................................................................... 6.2 Two-Stage Vehicle Signal Recognition........................................... 6.3 Vehicle Detection............................................................................... 6.4 Brake Light Pattern and Recognition............................................... 6.5 Experimental Results........................................................................ 7 Conclusion and Future Work...................................................................... References.......................................................................................................... The Application of Deep Learning in MarineSciences.............................. 193 Miguel Martin-Abadal, Ana Ruiz-Frau,
Hilmar Hinz and Yolanda Gonzalez-Cid 1 Introduction................................................................................................ 194 2 Methodology.............................................................................................. 195 3 Seagrass Segmentation.............................................................................. 197 3.1 Deep Learning Approach................................................................. 199 3.2 Experimental Framework................................................................. 202 3.3 Classification Results........................................................................ 207 4 Jellyfish Detection and Identification........................................................ 213 4.1 Deep Learning Approach................................................................. 214 4.2 Experimental Framework................................................................. 218 4.3 Results................................................................................................ 223 5 Conclusions................................................................................................ 226 References......................................................................................................... 227 Deep Learning Case Study on Imbalanced Training Data for Automatic Bird Identification................................................................. Juha Niemi and Juha T. Tanttu 1 Introduction................................................................................................ 2
The System................................................................................................ 3 Related Work.............................................................................................. 4 Data........................................................................................................... 4.1 Data Augmentation............................................................................ 4.2 Grouping Data.................................................................................. 5 Classification.............................................................................................. 5.1 Hyperparameter Selection................................................................ 5.2 Feature Extraction ............................................................................ 231 232 233 234 235 237 238 240 241 241
Contents 5.3 Tests for Deeper CNN Model............................................................. 5.4 Dealing with Imbalanced Data............................................................. 5.5 Hybrid of Hierarchical and Cascade Model..................................... 5.6 Top-Level Classification...................................................................... 5.7 Classification of Waterfowl................................................................. 5.8 Classification of Gulls and Terns........................................................ 6 Results.............................................................................................................. 7 Discussion....................................................................................................... References.............................................................................................................. xi 244 244 246 248 252 253 256 260 261 Deep Learning forPersonRe-identificationin SurveillanceVideos .... 263 Swathi Jamjala Narayanan, Boominathan Peramai, Sangeetha Saman and Aditya Pratap Singh 1 Introduction..................................................................................................... 264 2 Preliminaries of Deep Neural Networks...................................................... 265 3 Person Re-identification Datasets................................................................. 280 4 Deep Learning Architectures for Person Re-identification........................ 280 5 Experimental
Setup......................................................................................... 289 6 Conclusions and Future Work...................................................................... 292 References.............................................................................................................. 293 Deep Learningin Gait Analysis for Securityand Healthcare...................... Omar Costilla-Reyes, Ruben Vera-Rodriguez, Abdullah S. Alharthi, Syed U. Yunas and Krikor B. Ozanyan 1 Introduction.................................................................................................... 2 Gait Analysis Review..................................................................................... 2.1 Non-wearable Sensors........................................................................... 2.2 Wearable Sensors.................................................................................. 2.3 A Review of Floor Sensor Systems and Datasets for Gait Analysis.................................................................................................. 3 Deep Learning for Gait Analysis................................................................. 3.1 Convolutional Neural Networks.......................................................... 4 Deep Learning in Healthcare: A Case Study in Dual-Tasks..................... 4.1 Aims and General Method................................................................. 4.2 Background........................................................................................... 4.3
Methodology......................................................................................... 4.4 Experiments for Age-Related Classification...................................... 4.5 Spatio-Temporal Deep Learning Model............................................ 4.6 Results for Age-Related Differences ................................................. 4.7 Analysis of Experiments Three and Seven........................................ 4.8 Discussion............................................................................................. 4.9 Future Directions.................................................................................. 299 300 301 303 305 306 307 309 311 311 312 312 313 314 315 317 317 320
xii Contents 5 Deep Learning in Security: A Case Study in Biometrics........................... 5.1 Aims and General Method................................................................... 5.2 Footstep Data as a Biometric .............................................................. 5.3 Deep Residual Network Model............................................................ 5.4 Spatial and Temporal Architectures..................................................... 5.5 Verification System Evaluation............................................................ 5.6 Results...................................................................................................... 5.7 Home Scenario: Benchmark B3............................................................ 5.8 Discussion............................................................................................... 6 Conclusions....................................................................................................... References................................................................................................................ Deep Learning for Building Occupancy Estimation Using Environmental Sensors........................................................................................ Zhenghua Chen, Chaoyang Jiang, Mustafa K. Masood, Yeng Chat Soh, Min Wu and Xiaoli Li 1 Introduction...................................................................................................... 2 Literature
Review............................................................................................. 3 Methodology.................................................................................................... 3.1 Overview.................................................................................................. 3.2 Convolutional Operation....................................................................... 3.3 Deep Bi-directional LSTM................................................................... 3.4 Occupancy Inference Layers................................................................ 3.5 Training Process of the CDBLSTM.................................................... 4 Evaluation Results.......................................................................................... 4.1 Data Collection..................................................................................... 4.2 Evaluation Setup................................................................................... 4.3 Evaluation Results................................................................................. 4.4 HyperParameters ................................................................................... 4.5 The Impact of Noise.............................................................................. 4.6 Generalization Performance.................................................................. 4.7 Additional Evaluation with Data from Another Environment......... 5
Conclusion........................................................................................................ References............................................................................................................... Index 321 321 322 323 325 326 326 327 327 329 330 335 336 337 338 338 340 341 344 344 345 346 347 348 351 352 353 354 355 355 359
This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
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| spelling | Deep learning: algorithms and applications Witold Pedrycz, Shyi-Ming Chen, editors Cham Springer [2020] © 2020 xii, 360 Seiten Illustrationen, Diagramme, Karte (überwiegend farbig) txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence volume 865 1860-949X Computational Intelligence Artificial Intelligence Computational intelligence Artificial intelligence Deep learning (DE-588)1135597375 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Maschinelles Lernen (DE-588)4193754-5 s Deep learning (DE-588)1135597375 s Algorithmus (DE-588)4001183-5 s DE-604 Pedrycz, Witold 1953- (DE-588)122838203 edt Chen, Shyi-Ming (DE-588)1097439267 edt Erscheint auch als Online-Ausgabe 978-3-030-31760-7 Studies in computational intelligence volume 865 (DE-604)BV020822171 865 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031655077&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031655077&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Deep learning: algorithms and applications Studies in computational intelligence Computational Intelligence Artificial Intelligence Computational intelligence Artificial intelligence Deep learning (DE-588)1135597375 gnd Algorithmus (DE-588)4001183-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4001183-5 (DE-588)4193754-5 (DE-588)4143413-4 |
| title | Deep learning: algorithms and applications |
| title_auth | Deep learning: algorithms and applications |
| title_exact_search | Deep learning: algorithms and applications |
| title_full | Deep learning: algorithms and applications Witold Pedrycz, Shyi-Ming Chen, editors |
| title_fullStr | Deep learning: algorithms and applications Witold Pedrycz, Shyi-Ming Chen, editors |
| title_full_unstemmed | Deep learning: algorithms and applications Witold Pedrycz, Shyi-Ming Chen, editors |
| title_short | Deep learning: algorithms and applications |
| title_sort | deep learning algorithms and applications |
| topic | Computational Intelligence Artificial Intelligence Computational intelligence Artificial intelligence Deep learning (DE-588)1135597375 gnd Algorithmus (DE-588)4001183-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Computational Intelligence Artificial Intelligence Computational intelligence Artificial intelligence Deep learning Algorithmus Maschinelles Lernen Aufsatzsammlung |
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