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2830-2021 : IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning / / Institute of Electrical and Electronics Engineers
| 2830-2021 : IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | New York, NY, USA : , : IEEE, , 2021 |
| Descrizione fisica | 1 online resource (23 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Machine learning
Deep learning (Machine learning) Reinforcement learning Computational learning theory |
| ISBN | 1-5044-7724-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910503501803321 |
| New York, NY, USA : , : IEEE, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
2830-2021 : IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning / / Institute of Electrical and Electronics Engineers
| 2830-2021 : IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | New York, NY, USA : , : IEEE, , 2021 |
| Descrizione fisica | 1 online resource (23 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Machine learning
Deep learning (Machine learning) Reinforcement learning Computational learning theory |
| ISBN | 1-5044-7724-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996574913703316 |
| New York, NY, USA : , : IEEE, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
3D imaging, multidimensional signal processing and deep learning : 3D images, graphics and information technologies. Volume 1 / / editors: Lakhmi C. Jain, [and three others]
| 3D imaging, multidimensional signal processing and deep learning : 3D images, graphics and information technologies. Volume 1 / / editors: Lakhmi C. Jain, [and three others] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (262 pages) |
| Disciplina | 006.693 |
| Collana | Smart innovation, systems, and technologies |
| Soggetto topico |
Three-dimensional imaging
Deep learning (Machine learning) Signal processing |
| ISBN | 981-19-2448-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Editors -- 1 Color Restoration of RGB-NIR Images in Low-Light Environment Using CycleGAN -- 1.1 Introduction -- 1.2 CycleGAN Structure -- 1.2.1 The Overall Structure -- 1.3 Objective Function -- 1.3.1 The Basic Objective Function of GANs -- 1.3.2 Objective Function of Cyclic Transformation Consistency -- 1.3.3 Total Objective Function of CycleGAN -- 1.4 Experiment and Evaluation -- 1.4.1 Image Data Set in Low-Light Environment -- 1.4.2 Training Method -- 1.5 Quantitative and Qualitative Evaluation -- 1.6 Conclusion and Discussion -- References -- 2 Dynamic Grey Wolf Optimization Algorithm Based on Quasi-Opposition Learning -- 2.1 Introduction -- 2.2 Grey Wolf Optimization Algorithm -- 2.3 Improved Grey Wolf Algorithm -- 2.3.1 Opposition-Based Learning (OBL) -- 2.3.2 Dynamic Search Strategy -- 2.3.3 Improved the Grey Wolf Algorithm to Optimize the Process -- 2.4 Numerical Experiment and Result Analysis -- 2.4.1 Influence of Improved Strategy on GWO -- 2.4.2 Compared with Other Swarm Intelligence Optimization Algorithms -- 2.5 Conclusion -- References -- 3 Image Recognition Methods Based on Deep Learning -- 3.1 Introduction -- 3.1.1 Overview of Image Recognition -- 3.1.2 Preprocessing in Image Recognition -- 3.1.3 Feature Extraction in Image Recognition -- 3.2 Deep Learning Model for Image Recognition -- 3.2.1 Feedforward Neural Network (FNN) -- 3.2.2 Convolutional Neural Network (CNN) -- 3.2.3 Recursive Neural Network (RNN) -- 3.2.4 U-Net Convolutional Neural Network -- 3.2.5 Long Short-Term Memory Network -- 3.2.6 Auto-Encoder Neural Network -- 3.2.7 Generative Adversarial Network (GAN) -- 3.2.8 Deep Belief Network (DBN) -- 3.3 The Application of Deep Learning in Image Recognition -- 3.3.1 Face Recognition -- 3.3.2 Traffic Image Recognition -- 3.3.3 Medical Image Recognition.
3.4 Train a Convolutional Neural Network from a Small Data Set -- 3.4.1 Data Sorting and Network Building -- 3.4.2 Data Processing and Reading -- 3.4.3 Image Processing -- 3.5 Conclusion -- References -- 4 Longitudinal Structure Analysis and Segmentation Algorithm of Dongba Document -- 4.1 Overview of Dongba Hieroglyphics -- 4.2 Structure of Dongba Document Image -- 4.3 Preprocessing of Dongba Documents -- 4.4 Automatic Segmentation and Recognition of Columns -- 4.5 Experiment -- 4.6 Conclusion -- References -- 5 Overview of SAR Image Change Detection Based on Segmentation -- 5.1 Introduction -- 5.2 Traditional Image Change Detection Methods -- 5.2.1 Image Difference Method -- 5.2.2 Image Ratio Method -- 5.2.3 Correlation Coefficient Method -- 5.2.4 Image Regression Method -- 5.3 SAR Image Change Detection Method Based on Segmentation -- 5.4 Simulation Results -- 5.5 Summary and Expectation -- References -- 6 Full-Focus Imaging Detection of Ship Ultrasonic-Phased Array Based on Directivity Function -- 6.1 Introduction -- 6.2 Method -- 6.2.1 Principle Analysis -- 6.2.2 Full-Focusing Algorithm in Frequency-Wave Number Domain -- 6.3 Result Analysis -- 6.3.1 System Introduction -- 6.3.2 Test Experiment -- 6.4 Conclusion -- References -- 7 A Novel Space Division Rough Set Model for Feature Selection -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Our Approach -- 7.4 Experiments -- 7.5 Conclusion -- References -- 8 Development of Mobile Food Recognition System Based on Deep Convolutional Network -- 8.1 Introduction -- 8.2 Related Work -- 8.2.1 Food Recognition Models -- 8.2.2 Mobile Food Recognition System -- 8.3 Methods -- 8.3.1 Deep Convolutional Neural Network -- 8.3.2 Training Food Recognition Models -- 8.3.3 Deploying the Recognition Application on Android Side -- 8.4 Experiment Results -- 8.4.1 "ChineseFood80" Dataset. 8.4.2 Train and Select Food Recognition Models -- 8.4.3 Food Recognition Application on Android Device -- 8.5 Conclusion -- References -- 9 Water Environmental Quality Assessment and Effect Prediction Based on Artificial Neural Network -- 9.1 Introduction of Artificial Neural Network Model for Water Environmental Quality Assessment -- 9.2 Prediction Model Based on Levenberg-Marquardt Optimization Algorithm -- 9.2.1 Time Series -- 9.2.2 Algorithm of Prediction Model -- 9.2.3 Defining the Grid Structure -- 9.2.4 Sample Selection and Training Methods -- 9.3 Predictive Analysis -- 9.4 Conclusion -- References -- 10 Network Intrusion Detection Based on Apriori-Kmeans Algorithm -- 10.1 Introduction -- 10.2 Research Status -- 10.3 Apriori-Kmeans Algorithm -- 10.4 Intrusion Detection Model Based on Apriori-Kmeans Algorithm -- 10.5 Simulation Experiment -- 10.6 Summary -- References -- 11 A Fast Heuristic k-means Algorithm Based on Nearest Neighbor Information -- 11.1 Introduction -- 11.1.1 Optimization of the Selection of Initial Centroids -- 11.1.2 Accelerate Approximate K-means -- 11.1.3 Accelerate Exact K-means -- 11.1.4 Our Contribution -- 11.2 A Heuristic K-means Algorithm -- 11.2.1 Narrow the Search Space of Sample Points -- 11.2.2 Reduce the Number of Sample Points for Reallocation -- 11.2.3 Algorithm Flow Chart -- 11.3 Experiments -- 11.4 Conclusion -- References -- 12 Global Analysis of Discrete SIR and SIS Systems -- 12.1 Introduction -- 12.2 Continuous Model -- 12.3 Discretization of Continuous Models -- 12.4 The Second Discrete Model -- 12.5 Stability Analysis -- 12.6 Globally Asymptotically Stable -- 12.7 Conclusion -- References -- 13 Image-Based Physics Rendering for 3D Surface Reconstruction: A Survey -- 13.1 Introduction -- 13.2 Research Status of 3D Reconstruction Based on Image -- 13.3 Image-Based 3D Surface Reconstruction. 13.3.1 Laser Scanning Method -- 13.3.2 Time-Of-Flight Method -- 13.3.3 Structured Light Method -- 13.3.4 Shape from Shading Method -- 13.3.5 Shape from Silhouettes Method -- 13.3.6 Shape-from-Motion Method -- 13.3.7 Shape-from-Texture Method -- 13.3.8 Shape-from-Focus Method -- 13.3.9 Photometric Stereo -- 13.4 Summary -- References -- 14 Insulator Detection Study Based on Improved Faster-RCNN -- 14.1 Introduction -- 14.2 Sample Expansion -- 14.3 Insulator Identification and Positioning -- 14.3.1 Faster RCNN Detection Principle -- 14.3.2 Improved RPN -- 14.3.3 Residual Networks (ResNet) -- 14.3.4 Multi-Scale Training -- 14.3.5 Comparison of Different Detection Methods -- 14.4 Detection of Defective Insulators -- 14.5 Improved Faster-RCNN -- 14.6 Experimental Verifications -- 14.6.1 Conduct a Comparative Experiment -- 14.6.2 Compare Experimental Results -- 14.7 Summary -- References -- 15 Citrus Positioning Method Based on Camera and Lidar Data Fusion -- 15.1 Introduction -- 15.2 Method -- 15.2.1 The Citrus Positioning Algorithm -- 15.2.2 Preliminary Positioning of Pixel Coordinates of Citrus -- 15.2.3 Camera and Lidar Joint Calibration -- 15.2.4 Camera and Lidar Data Fusion -- 15.2.5 Conversion of Citrus Pixel Coordinates to Three-Dimensional Space Coordinates -- 15.3 Experiments -- 15.3.1 Environment of System -- 15.3.2 Detection Effects of Citrus -- 15.3.3 The Results of Camera Internal Parameter Calibration -- 15.3.4 The Results of Camera and Lidar Joint Calibration -- 15.3.5 The Results of Citrus Positioning -- 15.4 Conclusion -- References -- 16 Comparative Analysis of Automatic Poetry Generation Systems Based on Different Recurrent Neural Networks -- 16.1 Introduction -- 16.2 Problem Formation -- 16.3 The Invariant Testbed -- 16.4 The Internal Logic of RNN Modules -- 16.5 Results -- 16.6 Analysis and Expectation -- References. 17 Grid False Data Intrusion Detection Method Based on Edge Computing and Federated Learning -- 17.1 Introduction -- 17.2 Research Status -- 17.3 Principles of False Data Injection Attacks -- 17.4 Design of Intrusion Detection Model Based on Edge Computing and Federated Learning -- 17.4.1 Edge Computing -- 17.4.2 Federated Learning -- 17.4.3 Framework Based on Edge Computing and Federated Learning -- 17.4.4 CNN-LSTM Joint Detection Model -- 17.5 Case Analysis -- 17.6 Summary -- References -- 18 Innovative Design of Traditional Arts and Crafts Based on 3D Digital Technology -- 18.1 Introduction -- 18.2 Application Steps -- 18.2.1 Preparation Period -- 18.2.2 Design Period -- 18.2.3 Optimization Period -- 18.3 Application Direction -- 18.3.1 Ceramic -- 18.4 Conclusion -- References -- 19 Research on the Simulation of Informationized Psychological Sand Table Based on 3D Scene -- 19.1 Introduction -- 19.2 Method -- 19.2.1 Design and Application -- 19.2.2 Hardware Design -- 19.2.3 Infrared Scanning Design -- 19.2.4 Software Design -- 19.3 Result Analysis -- 19.4 Conclusion -- References -- 20 Research on Graphic Design of Digital Media Art Based on Computer Aided Algorithm -- 20.1 Introduction -- 20.2 Method -- 20.2.1 Digital Media Art Graphic Design Content -- 20.2.2 Shape Interpolation -- 20.3 Result Analysis -- 20.4 Conclusion -- References -- 21 Research on Visual Communication of Graphic Design Based on Machine Vision -- 21.1 Introduction -- 21.2 Method -- 21.2.1 Design Process -- 21.2.2 Visual Analysis -- 21.3 Result Analysis -- 21.4 Conclusion -- References -- 22 Research on the Adaptive Matching Mechanism of Graphic Design Elements Based on Visual Communication Technology -- 22.1 Introduction -- 22.2 Method -- 22.2.1 Hardware Design -- 22.2.2 Software Design -- 22.3 Result Analysis -- 22.4 Conclusion -- References. 23 Design of Intelligent Recognition English Translation Model Based on Improved Machine Translation Algorithm. |
| Record Nr. | UNINA-9910735392003321 |
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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3D imaging--multidimensional signal processing and deep learning . Volume 2. : multidimensional signals, video processing and applications / / edited by Srikanta Patnaik [and three others]
| 3D imaging--multidimensional signal processing and deep learning . Volume 2. : multidimensional signals, video processing and applications / / edited by Srikanta Patnaik [and three others] |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (283 pages) |
| Disciplina | 060.68 |
| Collana | Smart Innovation, Systems and Technologies |
| Soggetto topico |
Deep learning (Machine learning)
Signal processing Three-dimensional imaging |
| ISBN | 981-9911-45-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Editors -- 1 Prediction Based on Sentiment Analysis and Deep Learning -- 1.1 First Section -- 1.2 Benchmark Prediction -- 1.2.1 Capture Data of Stock Comments from www.guba.eastmoney.com [1] -- 1.2.2 Build a Model for False News Judgment -- 1.2.3 Test Stock Comments for False News -- 1.2.4 Build a Sentiment Classification Model for Stock Comment -- 1.2.5 Build an Index from the Analysis Results -- 1.2.6 Capture and Load Data -- 1.2.7 A Subsection Sample -- 1.3 Conclusion -- References -- 2 A Survey on Time Series Forecasting -- 2.1 Introduction -- 2.2 Traditional Machine Learning-Based Method -- 2.2.1 Feature Extraction -- 2.2.2 Feature Selection -- 2.2.3 Model Training -- 2.2.4 Rolling Time Series Forecasting -- 2.3 Deep Learning-Based Method -- 2.3.1 RNN -- 2.3.2 LSTM -- 2.3.3 GRU -- 2.4 Experiment Results -- 2.4.1 Machine Learning Results -- 2.4.2 Deep Learning Results -- 2.5 Conclusion -- References -- 3 Research and Development of Visual Interactive Performance Test Methods and Equipment for Intelligent Cockpit -- 3.1 Introduction -- 3.2 System Overview -- 3.2.1 Visual Bionic Robot -- 3.2.2 Main Case of Bionic Robot -- 3.2.3 Binocular High-Frame Camera -- 3.2.4 Software -- 3.3 Head Visual Tracking -- 3.3.1 Self-Stabilizing Function of the Head -- 3.3.2 High-Precision Servo Motor -- 3.3.3 Servo Encoder -- 3.3.4 Self-Stabilizing PID Algorithm for the Cradle Head -- 3.3.5 Precision Test of Self-Stabilizing Function of Head -- 3.4 System Effect -- 3.5 Conclusion -- References -- 4 Design and Validation of Automated Inspection System Based on 3D Laser Scanning of Rocket Segments -- 4.1 Introduction -- 4.2 3D Scanning Measurement Principle -- 4.2.1 Three-Dimensional Laser Scanning Equipment -- 4.2.2 Lifting Platform System -- 4.2.3 Checking Standard Device -- 4.2.4 Measurement Software Design.
4.3 Measurement System Accuracy Verification -- 4.3.1 Verification of Length Splicing Accuracy -- 4.3.2 Verification of Geometric Element Detection Accuracy -- 4.4 Conclusion -- 4.5 Discussion -- References -- 5 Research and Implementation of Electric Equipment Connectivity Data Analysis Model Based on Graph Database -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Research on the Method and Algorithm of Electric Data Modeling -- 5.3.1 Electric Data -- 5.3.2 Electric Data Modeling -- 5.4 Implementation of Electric Data Model Based on Graph Database -- 5.4.1 Electric Data Relation Processing -- 5.4.2 Implementation and Construction of Power Data Model -- 5.4.3 Electric Equipment Connectivity Analysis Based on Power Grid Data Model -- 5.5 Application Results -- References -- 6 Improving CXR Self-Supervised Representation by Pretext Task and Cross-Domain Synthetic Data -- 6.1 Introduction -- 6.2 Related Works -- 6.2.1 Overview of CXR Classification -- 6.2.2 Self-Supervision and Contrastive Learning -- 6.2.3 Pretext Task and Data Augmentation -- 6.3 Problem Definition -- 6.3.1 Contrastive Learning Pretext Task -- 6.3.2 Supervised Multi-class Linear Evaluation -- 6.4 Method -- 6.4.1 Selection of Candidate Transformations -- 6.4.2 XR-Augment -- 6.4.3 Pseudo-CXR Generation -- 6.5 Experiment -- 6.5.1 Data -- 6.5.2 Settings -- 6.5.3 Result and Analysis -- 6.6 Conclusion and Future Research -- References -- 7 Research on Dynamic Analysis Technology of Quantitative Control Oriented to Characteristics of Power Grid Digital Application Scenarios -- 7.1 Introduction -- 7.2 Quantitative Control Dynamic Analysis Technique -- 7.3 Dynamic Analysis of Quantitative Control of Power Network -- 7.4 Function Analysis of Power Grid Digitalization Project. 7.5 Research on Influencing Factor Set of Target Feature Quantification in Digital Application Scene Based on Expert Scoring Method -- 7.6 Research on Quantitative Impact Index Set of Digital Application Scene Features Based on Fuzzy Analytic Hierarchy Process -- 7.7 Dynamic Identification Technology of Quantitative Control Based on Bayesian Network -- 7.8 Conclusion -- References -- 8 Research on Detection of Fungus Image Based on Graying -- 8.1 Introduction -- 8.2 Fungus Image Gray Processing -- 8.2.1 Graying of Fungus Pictures -- 8.2.2 Threshold Method -- 8.2.3 Problems with Testing -- 8.3 Realization of Single Chip Microcomputer -- 8.3.1 Selection of Single Chip Microcomputer -- 8.3.2 Total Process of Single Chip Microcomputer -- 8.3.3 Selection of Filter -- 8.3.4 Detection Function Module -- 8.4 Summary -- References -- 9 Secondary Frequency Regulation Control Strategy of Battery Energy Storage with Improved Consensus Algorithm -- 9.1 Introduction -- 9.2 Optimal Control Method of Secondary Frequency -- 9.2.1 Energy Storage Output Control Structure -- 9.2.2 Secondary Frequency Modulation Objective Function of Power Grid -- 9.3 Secondary Frequency Modulation Based on Consistency Algorithm -- 9.3.1 Iterative Calculation Method of Frequency Response Consistency -- 9.3.2 Double-Layer Cooperative Control of Secondary Frequency Modulation for Battery Energy Storage -- 9.4 Simulation Verification -- 9.5 Conclusions -- References -- 10 Application of Deep Learning for Registration Between SAR and Optical Images -- 10.1 Introduction -- 10.2 Methodology -- 10.2.1 Using CNN for Feature Extraction -- 10.2.2 Improved Euclidean Distance for Matching -- 10.3 Experimental Results and Analysis -- 10.4 Conclusion -- References -- 11 Research on Digital Architecture of Power Grid and Dynamic Analysis Technology of Digital Project -- 11.1 Introduction. 11.2 Enterprise Middle Office Architecture -- 11.3 Architecture Design of Power Grid Digital Service -- 11.4 Architecture Design of Power Grid Digitalization Technology -- 11.5 Midrange Architecture of Power Grid Enterprises -- 11.6 Dynamic Construction and Calculation of Digital Project Evaluation Index Based on Grid Middle Platform Architecture -- 11.7 Conclusions -- References -- 12 Research on Characteristics and Architecture Application Technology of Power Grid Digital System -- 12.1 Introduction -- 12.2 Enterprise Architecture Theory -- 12.3 Research on Characteristics of Power Grid Digital System -- 12.4 Digital Architecture Design of Power Grid -- 12.5 Technical and Economic Dynamic Analysis of Digital Projects Based on Power Grid Architecture -- 12.6 Conclusion -- References -- 13 Investigation of Vessel Segmentation by U-Net Based on Numerous Datasets -- 13.1 Introduction -- 13.2 Introduction to Deep Learning U-Net Model -- 13.3 Construction and Training of U-Net Model -- 13.3.1 Datasets -- 13.3.2 Data Processing -- 13.3.3 Evaluation Indexes of the U-Net Model -- 13.4 Predictive Generation of Fundus Vessel Segmentation Images -- 13.5 Conclusion -- References -- 14 Design of License Plate Recognition System Based on OpenCV -- 14.1 Introduction -- 14.2 Experimental Principle -- 14.2.1 License Plate Location Method Based on License Plate Color -- 14.2.2 License Plate Location Method Based on Edge Detection -- 14.2.3 License Plate Correction Methods -- 14.2.4 Character Recognition Algorithm Based on Template Matching -- 14.3 Implementation and Results -- 14.3.1 License Plate Positioning Based on License Plate Color -- 14.3.2 License Plate Location Based on License Plate Edge Detection -- 14.3.3 Character Segmentation Method Based on Projection -- 14.3.4 SVM-Based Character Recognition Method -- 14.4 Conclusion -- References. 15 Traveling Wave Solutions of the Nonlinear Gardner Equation with Variable-Coefficients Arising in Stratified Fluids -- 15.1 Introduction -- 15.2 Application of Trial Equation Method -- 15.3 Exact Solutions of Eq. (15.1) -- 15.4 Conclusions -- References -- 16 Research on the Construction of Food Safety Standards Training System Based on 3D Virtual Reality Technology -- 16.1 Introduction -- 16.2 Main Technologies of Foods Safety Standards Comprehensive Platform -- 16.2.1 3D Virtual Simulation Technology -- 16.2.2 Text Mining Technology -- 16.2.3 Knowledge Mapping Technology -- 16.3 Design of Foods Safety Standards Comprehensive Platform System -- 16.4 Functions of Foods Safety Standards Comprehensive Platform System -- 16.4.1 Foods Safety Standards Human Machine Interaction Question-Answering Subsystem -- 16.4.2 Intelligent Scene-Specific Foods Safety Standards Training and Implementation Evaluation Subsystem of Foods Safety Supervisors -- 16.4.3 Intelligent Scene-Specific Foods Safety Standards Training and Implementation Evaluation Subsystem of Foods Practitioners -- 16.4.4 Foods Safety Standards Knowledge Library Information-Based Management Subsystem -- 16.5 Conclusions -- References -- 17 Online Fault Diagnosis of Chemical Processes Based on Attention-Enhanced Encoder-Decoder Network -- 17.1 Introduction -- 17.2 LSTM Network -- 17.3 AEDN Method for Sequential Fault Diagnosis -- 17.4 Case Study on Benchmark Process -- 17.4.1 TE Process Dataset -- 17.4.2 Diagnostic Results and Discussion -- 17.5 Conclusion -- References -- 18 Micro-nano Satellite Novel Spatial Temperature Measurement Method and Experimental Study -- 18.1 Introduction -- 18.2 Temperature Measurement Principle on DS18B20 -- 18.3 A New Temperature Measurement Experiment of Micro-nano Satellite -- 18.3.1 Thermoscope System Design on DS18B20. 18.3.2 Design of Temperature Measurement Cable Net. |
| Record Nr. | UNINA-9910720082903321 |
| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced analytics and deep learning models / / Shaveta Malik, Amit Kumar Tyagi and Archana Mire, editors
| Advanced analytics and deep learning models / / Shaveta Malik, Amit Kumar Tyagi and Archana Mire, editors |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] |
| Descrizione fisica | 1 online resource (375 pages) |
| Disciplina | 006.31 |
| Collana | Next Generation Computing and Communication Engineering Ser. |
| Soggetto topico |
Big data
Deep learning (Machine learning) Artificial intelligence |
| ISBN |
1-119-79243-6
1-119-79241-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910573097503321 |
| Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced analytics and deep learning models / / Shaveta Malik, Amit Kumar Tyagi and Archana Mire, editors
| Advanced analytics and deep learning models / / Shaveta Malik, Amit Kumar Tyagi and Archana Mire, editors |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] |
| Descrizione fisica | 1 online resource (375 pages) |
| Disciplina | 006.31 |
| Collana | Next Generation Computing and Communication Engineering Ser. |
| Soggetto topico |
Big data
Deep learning (Machine learning) Artificial intelligence |
| ISBN |
1-119-79243-6
1-119-79241-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910642849403321 |
| Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced analytics and deep learning models / / Shaveta Malik, Amit Kumar Tyagi and Archana Mire, editors
| Advanced analytics and deep learning models / / Shaveta Malik, Amit Kumar Tyagi and Archana Mire, editors |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] |
| Descrizione fisica | 1 online resource (375 pages) |
| Disciplina | 006.31 |
| Collana | Next Generation Computing and Communication Engineering |
| Soggetto topico |
Deep learning (Machine learning)
Artificial intelligence Big data |
| ISBN |
1-119-79243-6
1-119-79241-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910830819303321 |
| Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced Machine Learning and Deep Learning Approaches for Remote Sensing / / edited by Gwanggil Jeon
| Advanced Machine Learning and Deep Learning Approaches for Remote Sensing / / edited by Gwanggil Jeon |
| Pubbl/distr/stampa | [Place of publication not identified] : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023 |
| Descrizione fisica | 1 online resource (362 pages) |
| Disciplina | 621.3678 |
| Soggetto topico |
Deep learning (Machine learning)
Machine learning Remote sensing |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910734348503321 |
| [Place of publication not identified] : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances and Applications in Deep Learning / / edited by Marco Antonio Aceves-Fernandez
| Advances and Applications in Deep Learning / / edited by Marco Antonio Aceves-Fernandez |
| Pubbl/distr/stampa | London : , : IntechOpen, , 2020 |
| Descrizione fisica | 1 online resource (xiii, 122 pages) : illustrations |
| Disciplina | 006.31 |
| Collana | Artificial Intelligence |
| Soggetto topico | Deep learning (Machine learning) |
| ISBN | 1-83962-879-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910431355603321 |
| London : , : IntechOpen, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances and Applications in Deep Learning / / edited by Marco Antonio Aceves-Fernandez
| Advances and Applications in Deep Learning / / edited by Marco Antonio Aceves-Fernandez |
| Pubbl/distr/stampa | London, England : , : IntechOpen, , 2020 |
| Descrizione fisica | 1 online resource (122 pages) |
| Disciplina | 006.31 |
| Collana | Artificial Intelligence |
| Soggetto topico |
Deep learning (Machine learning)
Artificial intelligence |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910688212303321 |
| London, England : , : IntechOpen, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
