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Advances in Data-Driven Computing and Intelligent Systems : Selected Papers from ADCIS 2024, Volume 3
| Advances in Data-Driven Computing and Intelligent Systems : Selected Papers from ADCIS 2024, Volume 3 |
| Autore | Bansal Jagdish Chand |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2025 |
| Descrizione fisica | 1 online resource (661 pages) |
| Disciplina | 006.33 |
| Altri autori (Persone) |
SahaSnehanshu
Coello CoelloCarlos A RathoreHemant |
| Collana | Lecture Notes in Networks and Systems Series |
| ISBN | 981-9653-70-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911028653503321 |
Bansal Jagdish Chand
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| Singapore : , : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in Data-Driven Computing and Intelligent Systems : Selected Papers from ADCIS 2024, Volume 2
| Advances in Data-Driven Computing and Intelligent Systems : Selected Papers from ADCIS 2024, Volume 2 |
| Autore | Bansal Jagdish Chand |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2025 |
| Descrizione fisica | 1 online resource (760 pages) |
| Disciplina | 006.33 |
| Altri autori (Persone) |
SahaSnehanshu
Coello CoelloCarlos A RathoreHemant |
| Collana | Lecture Notes in Networks and Systems Series |
| ISBN | 981-9645-36-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911028661803321 |
|
Bansal Jagdish Chand
|
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| Singapore : , : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Data-Driven Computing and Intelligent Systems : Selected Papers from ADCIS 2022, Volume 1 / / edited by Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Jagdish Chand Bansal
| Advances in Data-Driven Computing and Intelligent Systems : Selected Papers from ADCIS 2022, Volume 1 / / edited by Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Jagdish Chand Bansal |
| Autore | Das Swagatam |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (885 pages) |
| Disciplina | 006.33 |
| Altri autori (Persone) |
SahaSnehanshu
Coello CoelloCarlos A BansalJagdish Chand |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Telecommunication
Electronic circuits Cloud computing Artificial intelligence Signal processing Communications Engineering, Networks Electronic Circuits and Systems Cloud Computing Artificial Intelligence Signal, Speech and Image Processing |
| ISBN |
9789819932504
9819932505 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Editors and Contibutors -- Adaptive Volterra Noise Cancellation Using Equilibrium Optimizer Algorithm -- 1 Introduction -- 2 Problem Formulation -- 3 Proposed Equilibrium Optimizer Algorithm-Based Adaptive Volterra Noise Cancellation -- 3.1 Gbest -- 3.2 Exploration Stage (F) -- 3.3 Exploitation Stage (Rate of Generation G) -- 4 Simulation Outcomes -- 4.1 Qualitative Performance Analysis -- 4.2 Quantitative Performance Analysis -- 5 Conclusion and Scope -- References -- SHLPM: Sentiment Analysis on Code-Mixed Data Using Summation of Hidden Layers of Pre-trained Model -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 BERT -- 3.2 RoBERTa -- 3.3 SHLPM -- 4 Implementation Details -- 4.1 Dataset and Pre-processing -- 4.2 SHLPM-BERT -- 4.3 SHLPM-XLM-RoBERTa -- 5 Results and Discussion -- 6 Conclusion -- References -- Comprehensive Analysis of Online Social Network Frauds -- 1 Introduction -- 1.1 Statistics of Online Social Network Frauds -- 2 Interrelationship between OSN Frauds, Social Network Threats, and Cybercrime -- 3 Types of Frauds in OSN -- 3.1 Social Engineering Frauds (SEF) -- 3.2 Human-Targeted Frauds (Child/Adults) -- 3.3 False Identity -- 3.4 Misinformation -- 3.5 E-commerce Fraud (Consumer Frauds) -- 3.6 Case Study for Facebook Security Fraud -- 4 OSN Frauds Detection Using Machine Learning -- 4.1 Pros and Cons -- 5 Conclusion -- References -- Electric Vehicle Control Scheme for V2G and G2V Mode of Operation Using PI/Fuzzy-Based Controller -- 1 Introduction -- 2 Motivation -- 3 System Description -- 4 Mathematical Model Equipments Used -- 4.1 Bidirectional AC-DC Converter -- 4.2 Bidirectional Buck-Boost Converter -- 4.3 Battery Modeling -- 4.4 Control of 1-∅-Based Bidirectional AC-DC Converter Strategy -- 5 Fuzzy Logic Controller -- 6 Control Strategy -- 6.1 Constant Voltage Strategy.
6.2 Constant Current Strategy -- 7 Results and Discussion -- 7.1 PI Controller -- 7.2 Fuzzy Logic Controller -- 7.3 Comparison of Harmonic Profile -- 8 Conclusion -- References -- Experimental Analysis of Skip Connections for SAR Image Denoising -- 1 Introduction -- 2 Related Works -- 2.1 Residual Network -- 2.2 Existing ResNet-Based Denoising Works -- 3 Implementation of the Different Patterns of Skip Connections -- 3.1 Datasets and Pre-processing -- 3.2 Loss Function -- 4 Results and Discussions -- 4.1 Denoising Results on Synthetic Images -- 4.2 Denoising Results on Real SAR Images -- 5 Conclusion -- References -- A Proficient and Economical Approach for IoT-Based Smart Doorbell System -- 1 Introduction -- 2 Literature Review -- 3 System Design and Implementation -- 3.1 System Design -- 3.2 Implementation -- 4 Results and Discussion -- 4.1 Performance Results -- 4.2 Comparison with an Existing System -- 4.3 Cost Analysis -- 5 Limitations -- 6 Conclusion -- References -- Predicting Word Importance Using a Support Vector Regression Model for Multi-document Text Summarization -- 1 Introduction -- 2 Related Work -- 3 Description of Dataset -- 4 Proposed Methodology -- 4.1 Preprocessing -- 4.2 Word Importance Prediction Using Support Vector Regression Model -- 4.3 Sentence Scoring -- 4.4 Summary Generation -- 5 Evaluation, Experiment, and Results -- 5.1 Evaluation -- 5.2 Experiment -- 5.3 Results -- 6 Conclusion and Future Works -- References -- A Comprehensive Survey on Deep Learning-Based Pulmonary Nodule Identification on CT Images -- 1 Introduction -- 2 Datasets and Experimental Setup -- 2.1 LIDC/IDRI Dataset -- 2.2 LUNA16 Dataset -- 2.3 NLST Dataset -- 2.4 KAGGLE DATA SCIENCE BOWL (KDSB) Dataset -- 2.5 VIA/I-ELCAP -- 2.6 NELSON -- 2.7 Others -- 3 CAD System Structure -- 3.1 Data Acquisition -- 3.2 Preprocessing -- 3.3 Lung Segmentation. 3.4 Candidate Nodule Detection -- 3.5 False Positive Reduction -- 3.6 Nodule Categorization -- 4 CNN -- 4.1 Overview -- 4.2 CNN Architectures for Medical Imaging -- 4.3 Unique Characteristics of CNNs -- 4.4 CNN Software and Hardware Equipment -- 4.5 CNNs versus Conventional Models -- 5 Discussion -- 5.1 Research Trends -- 5.2 Challenges and Future Directions -- 6 Conclusion -- References -- Comparative Study on Various CNNs for Classification and Identification of Biotic Stress of Paddy Leaf -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Proposed Methods -- 3 Experimental Results -- 3.1 Hardware Setup -- 3.2 Time Analysis with respect to GPU and CPU -- 3.3 Performance Analysis for Keras and PyTorch -- 3.4 Performance Analysis of CNN Models -- 3.5 Comparison of the Proposed CNN with Other State-of-the-Art Works -- 4 Conclusion -- References -- Studies on Machine Learning Techniques for Multivariate Forecasting of Delhi Air Quality Index -- 1 Introduction -- 2 Materials and Methodology -- 2.1 Delhi AQI Multivariate Data -- 2.2 Methodology -- 3 Experimental Setup and Simulation Results -- 4 Contrast Analysis Considering Dimensionality Reduction -- 5 Conclusions -- References -- Fine-Grained Voice Discrimination for Low-Resource Datasets Using Scalogram Images -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 3.1 Collection of Voice Dataset -- 3.2 Preprocessing of Available Dataset to Increase the Trainable Samples -- 3.3 Classification of Phonemes Using Deep Convolutional Neural Network (DCNN)-Based Image Classifiers -- 4 Implementation Result and Analysis -- 5 Conclusion and Future Work -- References -- Sign Language Recognition for Indian Sign Language -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset -- 3.2 Data Preprocessing -- 3.3 Data Splitting -- 3.4 Data Augmentation -- 3.5 Model Compilation. 3.6 Model Training and Testing -- 4 Results -- 5 Novelty and Future Work -- 6 Conclusion -- References -- Buffering Performance of Optical Packet Switch Consisting of Hybrid Buffer -- 1 Introduction -- 2 Literature Survey -- 3 Description of the Optical Packet Switch -- 4 Simulation Results -- 4.1 Bernoulli Process -- 4.2 Results -- 5 Conclusions -- References -- Load Balancing using Probability Distribution in Software Defined Network -- 1 Introduction -- 2 Related Work -- 3 Grouping of Controllers in SDN -- 4 Load Balancing in SDN -- 4.1 Simulation and Evaluation Result -- 5 Conclusion -- References -- COVID Prediction Using Different Modality of Medical Imaging -- 1 Introduction -- 2 Principles of Support Vector Machine (SVM) -- 2.1 Linear Case -- 2.2 Nonlinear Case -- 3 Material and Methods -- 3.1 CT Image Dataset -- 3.2 X-Ray Image Dataset -- 3.3 Ultrasound Image Dataset -- 4 The Proposed Model -- 5 Experimental Result -- 6 Conclusion -- References -- Optimizing Super-Resolution Generative Adversarial Networks -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 3.1 Training Dataset -- 3.2 Test Dataset -- 4 Proposed Methodology -- 5 Performance Metrics -- 5.1 Peak Signal-to-Noise Ratio (PSNR) -- 5.2 Structural Similarity Index (SSIM) -- 6 Results and Discussion -- 7 Conclusion -- References -- Prediction of Hydrodynamic Coefficients of Stratified Porous Structure Using Artificial Neural Network (ANN) -- 1 Introduction -- 2 Stratified Porous Structure -- 3 Experimental Setup -- 4 Artificial Neural Network -- 4.1 Dataset Used for ANN -- 4.2 ANN Model -- 5 Results and Discussions -- 6 Conclusions -- References -- Performance Analysis of Machine Learning Algorithms for Landslide Prediction -- 1 Introduction -- 2 Literature Survey -- 3 Methodology of the Performance Analysis Work -- 3.1 Data Acquisition Layer -- 3.2 Fog Layer -- 3.3 Cloud Layer. 4 Performance Analysis and Results -- 5 Conclusion -- References -- Brain Hemorrhage Classification Using Leaky ReLU-Based Transfer Learning Approach -- 1 Introduction -- 2 Related Works -- 3 Materials and Method -- 3.1 Dataset -- 3.2 Transfer Learning -- 3.3 ResNet50 -- 4 Proposed Methodology -- 4.1 Input Dataset -- 4.2 Pre-processing -- 4.3 Network Training -- 4.4 Transfer Learning-Based Feature Extraction -- 5 Results -- 6 Conclusion -- References -- Factors Affecting Learning the First Programming Language of University Students -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Collection -- 3.2 Experimental Design -- 3.3 Data Analysis -- 4 Result -- 4.1 Findings -- 5 Decision and Conclusion -- References -- Nature-Inspired Hybrid Virtual Machine Placement Approach in Cloud -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Proposed Framework -- 4.1 Intelligent Water Drops (IWD) Algorithm -- 4.2 Water Cycle Algorithm (WCA) -- 4.3 Intelligent Water Drop Cycle Algorithm (IWDCA) -- 5 Result -- 5.1 Experiment Setup -- 5.2 Simulation Analysis of IWDCA -- 6 Conclusion -- References -- Segmented ε-Greedy for Solving a Redesigned Multi-arm Bandit Environment -- 1 Introduction -- 2 Previous Works -- 3 Methodology -- 4 Results -- 5 Conclusion and Future Work -- References -- Data-Based Time Series Modelling of Industrial Grinding Circuits -- 1 Introduction -- 2 Formulation -- 2.1 Grinding Circuit -- 2.2 Least Square Support Vector Regression -- 2.3 Proposed Algorithm -- 3 Results and Discussions -- 3.1 Results of Proposed Algorithm -- 3.2 LS-SVR Model Performance -- 3.3 Comparison with Arbitrarily Selected Model -- 4 Conclusions -- References -- Computational Models for Prognosis of Medication for Cardiovascular Diseases -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Results -- 5 Conclusion -- References. Develop a Marathi Lemmatizer for Common Nouns and Simple Tenses of Verbs. |
| Record Nr. | UNINA-9910736981503321 |
|
Das Swagatam
|
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in Data-Driven Computing and Intelligent Systems : Selected Papers from ADCIS 2023, Volume 4 / / edited by Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Hemant Rathore, Jagdish Chand Bansal
| Advances in Data-Driven Computing and Intelligent Systems : Selected Papers from ADCIS 2023, Volume 4 / / edited by Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Hemant Rathore, Jagdish Chand Bansal |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (517 pages) |
| Disciplina | 006.33 |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Telecommunication
Electronic circuits Cloud computing Artificial intelligence Signal processing Communications Engineering, Networks Electronic Circuits and Systems Cloud Computing Artificial Intelligence Signal, Speech and Image Processing |
| ISBN | 981-9995-31-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910847582703321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in intelligent data analysis VII : 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, September 6-8, 2007, proceedings / / Michael R. Berthold, John Shawe-Taylor, Nada Lavrač (editors)
| Advances in intelligent data analysis VII : 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, September 6-8, 2007, proceedings / / Michael R. Berthold, John Shawe-Taylor, Nada Lavrač (editors) |
| Edizione | [1st ed. 2007.] |
| Pubbl/distr/stampa | Berlin ; ; Heidelberg : , : Springer, , [2007] |
| Descrizione fisica | 1 online resource (XIV, 382 p.) |
| Disciplina | 006.33 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Mathematical statistics
Mathematical statistics - Data processing Expert systems (Computer science) |
| ISBN | 3-540-74825-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Statistical Data Analysis -- Compact and Understandable Descriptions of Mixtures of Bernoulli Distributions -- Multiplicative Updates for L 1–Regularized Linear and Logistic Regression -- Learning to Align: A Statistical Approach -- Transductive Reliability Estimation for Kernel Based Classifiers -- Bayesian Approaches -- Parameter Learning for Bayesian Networks with Strict Qualitative Influences -- Tree Augmented Naive Bayes for Regression Using Mixtures of Truncated Exponentials: Application to Higher Education Management -- Clustering Methods -- DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation -- Visualising the Cluster Structure of Data Streams -- Relational Topographic Maps -- Ensemble Learning -- Incremental Learning with Multiple Classifier Systems Using Correction Filters for Classification -- Combining Bagging and Random Subspaces to Create Better Ensembles -- Two Bagging Algorithms with Coupled Learners to Encourage Diversity -- Ranking -- Relational Algebra for Ranked Tables with Similarities: Properties and Implementation -- A New Way to Aggregate Preferences: Application to Eurovision Song Contests -- Trees -- Conditional Classification Trees Using Instrumental Variables -- Robust Tree-Based Incremental Imputation Method for Data Fusion -- Sequence/ Time Series Analysis -- Making Time: Pseudo Time-Series for the Temporal Analysis of Cross Section Data -- Recurrent Predictive Models for Sequence Segmentation -- Sequence Classification Using Statistical Pattern Recognition -- Knowledge Discovery -- Subrule Analysis and the Frequency-Confidence Diagram -- A Partial Correlation-Based Algorithm for Causal Structure Discovery with Continuous Variables -- Visualization -- Visualizing Sets of Partial Rankings -- A Partially Supervised Metric Multidimensional Scaling Algorithm for Textual Data Visualization -- Landscape Multidimensional Scaling -- Text Mining -- A Support Vector Machine Approach to Dutch Part-of-Speech Tagging -- Towards Adaptive Web Mining: Histograms and Contexts in Text Data Clustering -- Does SVM Really Scale Up to Large Bag of Words Feature Spaces? -- Bioinformatics -- Noise Filtering and Microarray Image Reconstruction Via Chained Fouriers -- Motif Discovery Using Multi-Objective Genetic Algorithm in Biosequences -- Soft Topographic Map for Clustering and Classification of Bacteria -- Applications -- Fuzzy Logic Based Gait Classification for Hemiplegic Patients -- Traffic Sign Recognition Using Discriminative Local Features -- Novelty Detection in Patient Histories: Experiments with Measures Based on Text Compression. |
| Altri titoli varianti |
Advances in intelligent data analysis 7
Advances in intelligent data analysis seven |
| Record Nr. | UNISA-996465748403316 |
| Berlin ; ; Heidelberg : , : Springer, , [2007] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in Intelligent Data Analysis VII : 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, September 6-8, 2007, Proceedings / / edited by Michael R. Berthold, John Shawe-Taylor, Nada Lavrač
| Advances in Intelligent Data Analysis VII : 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, September 6-8, 2007, Proceedings / / edited by Michael R. Berthold, John Shawe-Taylor, Nada Lavrač |
| Edizione | [1st ed. 2007.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2007 |
| Descrizione fisica | 1 online resource (XIV, 382 p.) |
| Disciplina | 006.33 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Database management
Artificial intelligence Information storage and retrieval systems Computer science - Mathematics Mathematical statistics Pattern recognition systems Information technology - Management Database Management Artificial Intelligence Information Storage and Retrieval Probability and Statistics in Computer Science Automated Pattern Recognition Computer Application in Administrative Data Processing |
| ISBN | 3-540-74825-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Statistical Data Analysis -- Compact and Understandable Descriptions of Mixtures of Bernoulli Distributions -- Multiplicative Updates for L 1–Regularized Linear and Logistic Regression -- Learning to Align: A Statistical Approach -- Transductive Reliability Estimation for Kernel Based Classifiers -- Bayesian Approaches -- Parameter Learning for Bayesian Networks with Strict Qualitative Influences -- Tree Augmented Naive Bayes for Regression Using Mixtures of Truncated Exponentials: Application to Higher Education Management -- Clustering Methods -- DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation -- Visualising the Cluster Structure of Data Streams -- Relational Topographic Maps -- Ensemble Learning -- Incremental Learning with Multiple Classifier Systems Using Correction Filters for Classification -- Combining Bagging and Random Subspaces to Create Better Ensembles -- Two Bagging Algorithms with Coupled Learners to Encourage Diversity -- Ranking -- Relational Algebra for Ranked Tables with Similarities: Properties and Implementation -- A New Way to Aggregate Preferences: Application to Eurovision Song Contests -- Trees -- Conditional Classification Trees Using Instrumental Variables -- Robust Tree-Based Incremental Imputation Method for Data Fusion -- Sequence/ Time Series Analysis -- Making Time: Pseudo Time-Series for the Temporal Analysis of Cross Section Data -- Recurrent Predictive Models for Sequence Segmentation -- Sequence Classification Using Statistical Pattern Recognition -- Knowledge Discovery -- Subrule Analysis and the Frequency-Confidence Diagram -- A Partial Correlation-Based Algorithm for Causal Structure Discovery with Continuous Variables -- Visualization -- Visualizing Sets of Partial Rankings -- A Partially Supervised Metric Multidimensional Scaling Algorithmfor Textual Data Visualization -- Landscape Multidimensional Scaling -- Text Mining -- A Support Vector Machine Approach to Dutch Part-of-Speech Tagging -- Towards Adaptive Web Mining: Histograms and Contexts in Text Data Clustering -- Does SVM Really Scale Up to Large Bag of Words Feature Spaces? -- Bioinformatics -- Noise Filtering and Microarray Image Reconstruction Via Chained Fouriers -- Motif Discovery Using Multi-Objective Genetic Algorithm in Biosequences -- Soft Topographic Map for Clustering and Classification of Bacteria -- Applications -- Fuzzy Logic Based Gait Classification for Hemiplegic Patients -- Traffic Sign Recognition Using Discriminative Local Features -- Novelty Detection in Patient Histories: Experiments with Measures Based on Text Compression. |
| Record Nr. | UNINA-9910484403103321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in statistical multisource-multitarget information fusion / / Ronald P.S. Mahler
| Advances in statistical multisource-multitarget information fusion / / Ronald P.S. Mahler |
| Autore | Mahler Ronald P. S. |
| Pubbl/distr/stampa | Boston : , : Artech House, , ©2014 |
| Descrizione fisica | 1 online resource (1167 p.) |
| Disciplina | 006.33 |
| Collana | Artech House electronic warfare library |
| Soggetto topico |
Expert systems (Computer science)
Multisensor data fusion - Mathematics Bayesian statistical decision theory |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-60807-799-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; Acknowledgments; Chapter 1 Introduction to the Book; 1.1 OVERVIEW OF FINITE-SET STATISTICS; 1.2 RECENT ADVANCES IN FINITE-SET STATISTICS; 1.3 ORGANIZATION OF THE BOOK; Part I Elements of Finite-Set Statistics; Chapter 2 Random Finite Sets; 2.1 INTRODUCTION; 2.2 SINGLE-SENSOR, SINGLE-TARGET STATISTICS; 2.3 RANDOM FINITE SETS (RFSs); 2.4 MULTIOBJECT STATISTICS IN A NUTSHELL; Chapter 3 Multiobject Calculus; 3.1 INTRODUCTION; 3.2 BASIC CONCEPTS; 3.3 SET INTEGRALS; 3.4 MULTIOBJECT DIFFERENTIAL CALCULUS; 3.5 KEY FORMULAS OF MULTIOBJECT CALCULUS.
Chapter 4 Multiobject Statistics4.1 INTRODUCTION; 4.2 BASIC MULTIOBJECT STATISTICAL DESCRIPTORS; 4.3 IMPORTANT MULTIOBJECT PROCESSES; 4.4 BASIC DERIVED RFSs; Chapter 5 Multiobject Modeling and Filtering; 5.1 INTRODUCTION; 5.2 THE MULTISENSOR-MULTITARGET BAYES FILTER; 5.3 MULTITARGET BAYES OPTIMALITY; 5.4 RFS MULTITARGET MOTION MODELS; 5.5 RFS MULTITARGET MEASUREMENT MODELS; 5.6 MULTITARGET MARKOV DENSITIES; 5.7 MULTISENSOR-MULTITARGET LIKELIHOOD FUNCTIONS; 5.8 THE MULTITARGET BAYES FILTER IN p.g.fl -- FORM; 5.9 THE FACTORED MULTITARGET BAYES FILTER; 5.10 APPROXIMATE MULTITARGET FILTERS. Chapter 6 Multiobject Metrology6.1 INTRODUCTION; 6.2 MULTIOBJECT MISS DISTANCE; 6.3 MULTIOBJECT INFORMATION FUNCTIONALS; Part II RFS Filters: StandardMeasurement Model; Chapter 7 Introduction to Part II; 7.1 SUMMARY OF MAJOR LESSONS LEARNED; 7.2 STANDARD MULTITARGET MEASUREMENT MODEL; 7.3 AN APPROXIMATE STANDARD LIKELIHOOD FUNCTION; 7.4 STANDARD MULTITARGET MOTION MODEL; 7.5 STANDARD MOTION MODEL WITH TARGET SPAWNING; 7.6 ORGANIZATION OF PART II; Chapter 8 Classical PHD and CPHD Filters; 8.1 INTRODUCTION; 8.2 A GENERAL PHD FILTER; 8.3 ARBITRARY-CLUTTER PHD FILTER; 8.4 CLASSICAL PHD FILTER. 8.5 CLASSICAL CARDINALIZED PHD (CPHD) FILTER8.6 ZERO FALSE ALARMS (ZFA) CPHD FILTER; 8.7 PHD FILTER FOR STATE-DEPENDENT POISSON CLUTTER; Chapter 9 Implementing Classical PHD/CPHDFilters; 9.1 INTRODUCTION; 9.2 "SPOOKY ACTION AT A DISTANCE"; 9.3 MERGING AND SPLITTING FOR PHD FILTERS; 9.4 MERGING AND SPLITTING FOR CPHD FILTERS; 9.5 GAUSSIAN MIXTURE (GM) IMPLEMENTATION; 9.6 SEQUENTIAL MONTE CARLO (SMC) IMPLEMENTATION; Chapter 10 Multisensor PHD and CPHD Filters; 10.1 INTRODUCTION; 10.2 THE MULTISENSOR-MULTITARGET BAYES FILTER; 10.3 THE GENERAL MULTISENSOR PHD FILTER. 10.4 THE MULTISENSOR CLASSICAL PHD FILTER10.5 ITERATED-CORRECTOR MULTISENSOR PHD/CPHD FILTERS; 10.6 PARALLEL COMBINATION MULTISENSOR PHD AND CPHD FILTERS; 10.7 AN ERRONEOUS "AVERAGED" MULTISENSOR PHD FILTER; 10.8 PERFORMANCE COMPARISONS; Chapter 11 Jump-Markov PHD/CPHD Filters; 11.1 INTRODUCTION; 11.2 JUMP-MARKOV FILTERS: A REVIEW; 11.3 MULTITARGET JUMP-MARKOV SYSTEMS; 11.4 JUMP-MARKOV PHD FILTER; 11.5 JUMP-MARKOV CPHD FILTER; 11.6 VARIABLE STATE SPACE JUMP-MARKOV CPHD FILTERS; 11.7 IMPLEMENTING JUMP-MARKOV PHD/CPHD FILTERS; 11.8 IMPLEMENTED JUMP-MARKOV PHD/CPHD FILTERS. |
| Record Nr. | UNINA-9910460377903321 |
|
Mahler Ronald P. S.
|
||
| Boston : , : Artech House, , ©2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in statistical multisource-multitarget information fusion / / Ronald P.S. Mahler
| Advances in statistical multisource-multitarget information fusion / / Ronald P.S. Mahler |
| Autore | Mahler Ronald P. S. |
| Pubbl/distr/stampa | Boston : , : Artech House, , ©2014 |
| Descrizione fisica | 1 online resource (1167 p.) |
| Disciplina | 006.33 |
| Collana | Artech House electronic warfare library |
| Soggetto topico |
Expert systems (Computer science)
Multisensor data fusion - Mathematics Bayesian statistical decision theory |
| ISBN | 1-60807-799-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; Acknowledgments; Chapter 1 Introduction to the Book; 1.1 OVERVIEW OF FINITE-SET STATISTICS; 1.2 RECENT ADVANCES IN FINITE-SET STATISTICS; 1.3 ORGANIZATION OF THE BOOK; Part I Elements of Finite-Set Statistics; Chapter 2 Random Finite Sets; 2.1 INTRODUCTION; 2.2 SINGLE-SENSOR, SINGLE-TARGET STATISTICS; 2.3 RANDOM FINITE SETS (RFSs); 2.4 MULTIOBJECT STATISTICS IN A NUTSHELL; Chapter 3 Multiobject Calculus; 3.1 INTRODUCTION; 3.2 BASIC CONCEPTS; 3.3 SET INTEGRALS; 3.4 MULTIOBJECT DIFFERENTIAL CALCULUS; 3.5 KEY FORMULAS OF MULTIOBJECT CALCULUS.
Chapter 4 Multiobject Statistics4.1 INTRODUCTION; 4.2 BASIC MULTIOBJECT STATISTICAL DESCRIPTORS; 4.3 IMPORTANT MULTIOBJECT PROCESSES; 4.4 BASIC DERIVED RFSs; Chapter 5 Multiobject Modeling and Filtering; 5.1 INTRODUCTION; 5.2 THE MULTISENSOR-MULTITARGET BAYES FILTER; 5.3 MULTITARGET BAYES OPTIMALITY; 5.4 RFS MULTITARGET MOTION MODELS; 5.5 RFS MULTITARGET MEASUREMENT MODELS; 5.6 MULTITARGET MARKOV DENSITIES; 5.7 MULTISENSOR-MULTITARGET LIKELIHOOD FUNCTIONS; 5.8 THE MULTITARGET BAYES FILTER IN p.g.fl -- FORM; 5.9 THE FACTORED MULTITARGET BAYES FILTER; 5.10 APPROXIMATE MULTITARGET FILTERS. Chapter 6 Multiobject Metrology6.1 INTRODUCTION; 6.2 MULTIOBJECT MISS DISTANCE; 6.3 MULTIOBJECT INFORMATION FUNCTIONALS; Part II RFS Filters: StandardMeasurement Model; Chapter 7 Introduction to Part II; 7.1 SUMMARY OF MAJOR LESSONS LEARNED; 7.2 STANDARD MULTITARGET MEASUREMENT MODEL; 7.3 AN APPROXIMATE STANDARD LIKELIHOOD FUNCTION; 7.4 STANDARD MULTITARGET MOTION MODEL; 7.5 STANDARD MOTION MODEL WITH TARGET SPAWNING; 7.6 ORGANIZATION OF PART II; Chapter 8 Classical PHD and CPHD Filters; 8.1 INTRODUCTION; 8.2 A GENERAL PHD FILTER; 8.3 ARBITRARY-CLUTTER PHD FILTER; 8.4 CLASSICAL PHD FILTER. 8.5 CLASSICAL CARDINALIZED PHD (CPHD) FILTER8.6 ZERO FALSE ALARMS (ZFA) CPHD FILTER; 8.7 PHD FILTER FOR STATE-DEPENDENT POISSON CLUTTER; Chapter 9 Implementing Classical PHD/CPHDFilters; 9.1 INTRODUCTION; 9.2 "SPOOKY ACTION AT A DISTANCE"; 9.3 MERGING AND SPLITTING FOR PHD FILTERS; 9.4 MERGING AND SPLITTING FOR CPHD FILTERS; 9.5 GAUSSIAN MIXTURE (GM) IMPLEMENTATION; 9.6 SEQUENTIAL MONTE CARLO (SMC) IMPLEMENTATION; Chapter 10 Multisensor PHD and CPHD Filters; 10.1 INTRODUCTION; 10.2 THE MULTISENSOR-MULTITARGET BAYES FILTER; 10.3 THE GENERAL MULTISENSOR PHD FILTER. 10.4 THE MULTISENSOR CLASSICAL PHD FILTER10.5 ITERATED-CORRECTOR MULTISENSOR PHD/CPHD FILTERS; 10.6 PARALLEL COMBINATION MULTISENSOR PHD AND CPHD FILTERS; 10.7 AN ERRONEOUS "AVERAGED" MULTISENSOR PHD FILTER; 10.8 PERFORMANCE COMPARISONS; Chapter 11 Jump-Markov PHD/CPHD Filters; 11.1 INTRODUCTION; 11.2 JUMP-MARKOV FILTERS: A REVIEW; 11.3 MULTITARGET JUMP-MARKOV SYSTEMS; 11.4 JUMP-MARKOV PHD FILTER; 11.5 JUMP-MARKOV CPHD FILTER; 11.6 VARIABLE STATE SPACE JUMP-MARKOV CPHD FILTERS; 11.7 IMPLEMENTING JUMP-MARKOV PHD/CPHD FILTERS; 11.8 IMPLEMENTED JUMP-MARKOV PHD/CPHD FILTERS. |
| Record Nr. | UNINA-9910797934303321 |
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Mahler Ronald P. S.
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| Boston : , : Artech House, , ©2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in statistical multisource-multitarget information fusion / / Ronald P.S. Mahler
| Advances in statistical multisource-multitarget information fusion / / Ronald P.S. Mahler |
| Autore | Mahler Ronald P. S. |
| Pubbl/distr/stampa | Boston : , : Artech House, , ©2014 |
| Descrizione fisica | 1 online resource (1167 p.) |
| Disciplina | 006.33 |
| Collana | Artech House electronic warfare library |
| Soggetto topico |
Expert systems (Computer science)
Multisensor data fusion - Mathematics Bayesian statistical decision theory |
| ISBN | 1-60807-799-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; Acknowledgments; Chapter 1 Introduction to the Book; 1.1 OVERVIEW OF FINITE-SET STATISTICS; 1.2 RECENT ADVANCES IN FINITE-SET STATISTICS; 1.3 ORGANIZATION OF THE BOOK; Part I Elements of Finite-Set Statistics; Chapter 2 Random Finite Sets; 2.1 INTRODUCTION; 2.2 SINGLE-SENSOR, SINGLE-TARGET STATISTICS; 2.3 RANDOM FINITE SETS (RFSs); 2.4 MULTIOBJECT STATISTICS IN A NUTSHELL; Chapter 3 Multiobject Calculus; 3.1 INTRODUCTION; 3.2 BASIC CONCEPTS; 3.3 SET INTEGRALS; 3.4 MULTIOBJECT DIFFERENTIAL CALCULUS; 3.5 KEY FORMULAS OF MULTIOBJECT CALCULUS.
Chapter 4 Multiobject Statistics4.1 INTRODUCTION; 4.2 BASIC MULTIOBJECT STATISTICAL DESCRIPTORS; 4.3 IMPORTANT MULTIOBJECT PROCESSES; 4.4 BASIC DERIVED RFSs; Chapter 5 Multiobject Modeling and Filtering; 5.1 INTRODUCTION; 5.2 THE MULTISENSOR-MULTITARGET BAYES FILTER; 5.3 MULTITARGET BAYES OPTIMALITY; 5.4 RFS MULTITARGET MOTION MODELS; 5.5 RFS MULTITARGET MEASUREMENT MODELS; 5.6 MULTITARGET MARKOV DENSITIES; 5.7 MULTISENSOR-MULTITARGET LIKELIHOOD FUNCTIONS; 5.8 THE MULTITARGET BAYES FILTER IN p.g.fl -- FORM; 5.9 THE FACTORED MULTITARGET BAYES FILTER; 5.10 APPROXIMATE MULTITARGET FILTERS. Chapter 6 Multiobject Metrology6.1 INTRODUCTION; 6.2 MULTIOBJECT MISS DISTANCE; 6.3 MULTIOBJECT INFORMATION FUNCTIONALS; Part II RFS Filters: StandardMeasurement Model; Chapter 7 Introduction to Part II; 7.1 SUMMARY OF MAJOR LESSONS LEARNED; 7.2 STANDARD MULTITARGET MEASUREMENT MODEL; 7.3 AN APPROXIMATE STANDARD LIKELIHOOD FUNCTION; 7.4 STANDARD MULTITARGET MOTION MODEL; 7.5 STANDARD MOTION MODEL WITH TARGET SPAWNING; 7.6 ORGANIZATION OF PART II; Chapter 8 Classical PHD and CPHD Filters; 8.1 INTRODUCTION; 8.2 A GENERAL PHD FILTER; 8.3 ARBITRARY-CLUTTER PHD FILTER; 8.4 CLASSICAL PHD FILTER. 8.5 CLASSICAL CARDINALIZED PHD (CPHD) FILTER8.6 ZERO FALSE ALARMS (ZFA) CPHD FILTER; 8.7 PHD FILTER FOR STATE-DEPENDENT POISSON CLUTTER; Chapter 9 Implementing Classical PHD/CPHDFilters; 9.1 INTRODUCTION; 9.2 "SPOOKY ACTION AT A DISTANCE"; 9.3 MERGING AND SPLITTING FOR PHD FILTERS; 9.4 MERGING AND SPLITTING FOR CPHD FILTERS; 9.5 GAUSSIAN MIXTURE (GM) IMPLEMENTATION; 9.6 SEQUENTIAL MONTE CARLO (SMC) IMPLEMENTATION; Chapter 10 Multisensor PHD and CPHD Filters; 10.1 INTRODUCTION; 10.2 THE MULTISENSOR-MULTITARGET BAYES FILTER; 10.3 THE GENERAL MULTISENSOR PHD FILTER. 10.4 THE MULTISENSOR CLASSICAL PHD FILTER10.5 ITERATED-CORRECTOR MULTISENSOR PHD/CPHD FILTERS; 10.6 PARALLEL COMBINATION MULTISENSOR PHD AND CPHD FILTERS; 10.7 AN ERRONEOUS "AVERAGED" MULTISENSOR PHD FILTER; 10.8 PERFORMANCE COMPARISONS; Chapter 11 Jump-Markov PHD/CPHD Filters; 11.1 INTRODUCTION; 11.2 JUMP-MARKOV FILTERS: A REVIEW; 11.3 MULTITARGET JUMP-MARKOV SYSTEMS; 11.4 JUMP-MARKOV PHD FILTER; 11.5 JUMP-MARKOV CPHD FILTER; 11.6 VARIABLE STATE SPACE JUMP-MARKOV CPHD FILTERS; 11.7 IMPLEMENTING JUMP-MARKOV PHD/CPHD FILTERS; 11.8 IMPLEMENTED JUMP-MARKOV PHD/CPHD FILTERS. |
| Record Nr. | UNINA-9910816058003321 |
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Mahler Ronald P. S.
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| Boston : , : Artech House, , ©2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in System-Integrated Intelligence : Proceedings of the 6th International Conference on System-Integrated Intelligence (SysInt 2022), September 7-9, 2022, Genova, Italy / / edited by Maurizio Valle, Dirk Lehmhus, Christian Gianoglio, Edoardo Ragusa, Lucia Seminara, Stefan Bosse, Ali Ibrahim, Klaus-Dieter Thoben
| Advances in System-Integrated Intelligence : Proceedings of the 6th International Conference on System-Integrated Intelligence (SysInt 2022), September 7-9, 2022, Genova, Italy / / edited by Maurizio Valle, Dirk Lehmhus, Christian Gianoglio, Edoardo Ragusa, Lucia Seminara, Stefan Bosse, Ali Ibrahim, Klaus-Dieter Thoben |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (745 pages) |
| Disciplina | 006.33 |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Cooperating objects (Computer systems)
Machine learning Industrial engineering Automation Cyber-Physical Systems Machine Learning Industrial Automation |
| ISBN | 3-031-16281-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- 6th International Conference on System-Integrated Intelligence (SysInt2022) -- General Chair -- Organizing Committee -- International Scientific Committee -- Technical Program Chairs -- Track and Special Session Chairs -- Organizers -- Sponsors -- Contents -- Artificial Intelligence -- Towards Challenges and Proposals for Integrating and Using Machine Learning Methods in Production Environments -- 1 Introduction -- 1.1 Motivation -- 2 General Overview -- 3 Identified Challenges and Proposals -- 3.1 Social Challenges and Human Factors -- 3.2 Sensors and Data Sources -- 3.3 Computational and Processing Capacity -- 3.4 Software Dependencies -- 3.5 Model Availability and System Failures -- 3.6 Adaptation of Business Processes and Model Adjustments -- 4 Summary and Conclusion -- References -- Autonomous Driving Based on Imitation and Active Inference -- 1 Introduction -- 2 Proposed Framework -- 2.1 Offline Learning Phase -- 2.2 Online Learning Phase -- 3 Experimental Evaluation -- 3.1 Offline Learning Phase -- 3.2 Online Learning Phase -- 4 Conclusion -- References -- Machine Learning Based Reconstruction of Process Forces -- 1 Introduction -- 2 Methods -- 2.1 Experimental Setup -- 2.2 Data Preprocessing -- 2.3 Algorithms and Model Training -- 3 Results -- 3.1 Data Complexity -- 3.2 Generalizability -- 3.3 Comparing MILLTAP700 and HSC30 -- 4 Summary and Conclusion -- References -- A Novel Rule-Based Modeling and Control Approach for the Optimization of Complex Water Distribution Networks -- 1 Introduction -- 2 Related Work -- 3 Rule Based Control -- 3.1 Modeling -- 3.2 Control -- 4 Results -- 5 Discussion -- References -- Graph-Based Segmentation and Markov Random Field for Covid-19 Infection in Lung CT Volumes -- 1 Introduction -- 2 Materials and Methods -- 2.1 Overview -- 2.2 Dataset -- 2.3 Lung Masking and Region Segmentation.
2.4 Parametric Model Fitting -- 2.5 Markov Random Field Modelling -- 3 Results -- 4 Conclusion -- References -- Image Based Classification of Methods-Time Measurement Operations in Assembly Using Recurrent Neuronal Networks -- 1 Introduction -- 2 Classification of Assembly Operations -- 3 Experimental Design and Data Analysis -- 3.1 Architecture of the Neuronal Network -- 3.2 Analysis of the Data Set -- 3.3 Data Processing -- 4 Results -- 5 Summary -- References -- Pervasive and Ubiquitous Intelligence -- FPGA-Based Road Crack Detection Using Deep Learning -- 1 Introduction -- 2 Dataset and Crack Detection Network Architecture -- 3 FPGA Implementation and Deployment -- 3.1 Implementation Results -- 4 Performance Analysis -- 4.1 Detection Accuracy -- 4.2 Throughput and Energy Efficiency -- 5 Conclusion -- References -- Simple Non Regressive Informed Machine Learning Model for Prescriptive Maintenance of Track Circuits in a Subway Environment -- 1 Introduction -- 2 Problem Formalization and Available Data -- 3 Simple Non Regressive Informed Data Driven Model -- 4 Experimental Results -- 5 Conclusions -- References -- Embedded Implementation of an Algorithm for Online Inertia Estimation in Power Grids -- 1 Introduction -- 2 Inertia Estimation Algorithm -- 2.1 Mesh Adaptive Direct Search -- 3 Results -- 4 Conclusions -- References -- Random Weights Neural Network for Low-Cost Readout of Colorimetric Reactions: Accurate Detection of Antioxidant Levels -- 1 Introduction -- 2 Preliminaries -- 2.1 Related Works -- 2.2 Random Based Neural Networks -- 3 Case Study: Accurate Measure of Antioxidant Level in Saliva Using Colorimetric Reactions -- 4 Experiments -- 5 Conclusion -- References -- Resource-Constrained Implementation of Deep Learning Algorithms for Dynamic Touch Modality Classification -- 1 Introduction -- 2 Related Works. 3 Deep Networks for Touch Modality Classification -- 3.1 1-D Convolutional Neural Networks -- 3.2 Recurrent Neural Networks -- 4 Experimental Setup -- 4.1 Dataset -- 4.2 Implementation -- 5 Experimental Results -- 6 Conclusion -- References -- Human Recognition for Resource-Constrained Mobile Robot Applied to Covid-19 Disinfection -- 1 Introduction -- 2 Related Works -- 3 Proposed Safety Mechanism -- 4 Design and Test of the Artificial Intelligence Application -- 5 On-Field Experiments -- 6 Conclusion and Future Works -- References -- Data-Driven Methods for Aviation Safety: From Data to Knowledge -- 1 Introduction -- 2 Scope of the Work -- 3 Data Description -- 3.1 CEANITA LoS Reports -- 3.2 ENAIRE-CRIDA Contextual Information -- 4 Methods -- 5 Experimental Results -- 5.1 Automatic Information Extraction from Free Text -- 5.2 Automatic Contribution Assessment -- 6 Conclusions -- References -- Design and Deployment of an Efficient Landing Pad Detector -- 1 Introduction -- 2 Related Works -- 3 Proposal: Design of a DNN for Landing Detection -- 4 Experiments -- 4.1 Generalization Performance Validation -- 4.2 Model Optimization -- 5 Conclusions -- References -- Towards a Trade-off Between Accuracy and Computational Cost for Embedded Systems: A Tactile Sensing System for Object Classification -- 1 Introduction -- 2 Related Works -- 3 Proposal -- 3.1 Loss Function -- 3.2 Signal Conditioning -- 3.3 Feature Extraction -- 3.4 Predictors -- 4 Experimental Setup -- 4.1 System Setup -- 4.2 Objects -- 4.3 Data Collection -- 4.4 Training Strategy -- 5 Results -- 6 Conclusion -- References -- An Optimized Heart Rate Detection System Based on Low-Power Microcontroller Platforms for Biosignal Processing -- 1 Introduction and Related Work -- 2 Methodology -- 2.1 System Architecture -- 2.2 Algorithm Description -- 3 Results -- 3.1 Implementation on the PULP Platform. 3.2 Performance Analysis and Energy Consumption -- 3.3 Algorithm Accuracy -- 4 Conclusion -- References -- Sensors and Sensing Systems -- A Non-Hilbertian Inversion Technique for the Diagnosis of Faulty Elements in Antenna Arrays -- 1 Introduction -- 2 Mathematical Formulation -- 3 Results of Numerical Simulations -- 4 Conclusion -- References -- A Passive, Wireless Sensor Node for Material-Integrated Strain and Temperature Measurements in Glass Fiber Reinforced Composites -- 1 Introduction -- 1.1 Curing Temperature -- 1.2 Mechanical Loading -- 1.3 State of the Art -- 1.4 Conducted Work -- 2 System Concept -- 2.1 Temperature Measurement -- 2.2 Strain Measurement -- 3 Implementation and Fabrication -- 3.1 Strain Sensor -- 4 Results -- 4.1 Antenna and Overall System -- 4.2 Temperature Tests -- 4.3 Bending Tests -- 5 Conclusions -- References -- Multi-camera Metrology System for Shape and Position Correction of Large Fuselage Components in Aircraft Assembly -- 1 Introduction and State of the Art -- 1.1 Aircraft Section Assembly -- 1.2 Large Fuselage Panel Metrology -- 2 Multi-camera System Calibration -- 2.1 System Setup -- 2.2 Calibration Method -- 2.3 Data Acquisition -- 2.4 Numerical Rank Analysis -- 2.5 Calibration Result -- 3 Shell Deformation and Shape Adjustment -- 3.1 Measurement of Rivet Groups as Features and Setup -- 3.2 Deformation and Iterative Shape Adjustment -- 4 Conclusion and Outlook -- References -- Management of Research Field Data Within the Concept of Digital Twin -- 1 Introduction -- 2 Data Management -- 2.1 Research Data Management -- 2.2 Product Data Management and Product Life Cycle Management -- 2.3 Digital Twin -- 3 Digital Twin in Research Data Management -- 3.1 Concept of a Digital Twin for RDM -- 3.2 Derivation of Suitable Applications for Digital Twin in RDM -- 4 Exemplary Implementation of a Digital Twin for RDM. 5 Conclusion -- References -- Feed-Forward SNN for Touch Modality Prediction -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Two Layers Spiking Neural Network -- 2.3 Synapse Model -- 3 Simulation Results -- 3.1 Synaptic Weights Learning -- 3.2 Touch Modality Classification -- 4 Conclusion -- References -- Smart Factory and Logistic Systems -- Ansaldo Energia Progetto LHP (OR6.3) -- 1 Introduction -- 2 Literature Review (State of the Art) -- 3 Case Study -- 3.1 Team Building -- 3.2 Scenario AS-IS (Current Methodology) -- 3.3 Scenario TO-BE (Innovative Methodology Proposed by the Team) -- 3.4 Benchmarking of Technologies -- 3.5 Feasibility and Sustainability Study -- 3.6 Framework of the Project Steps -- 3.7 Design of the Operational Process -- 3.8 IIOT (Industrial Internet of Things) -- 3.9 Application for Smartphones -- 3.10 Pilot Project -- 4 Results and Benefits -- 5 Conclusions -- References -- An Application of Engineering 4.0 to Hospitalized Patients -- 1 Premise -- 2 Introduction -- 3 Literature Review -- 4 Description of the Device -- 5 Other Features -- 5.1 Stasis Monitoring -- 5.2 Tremor Monitoring -- 5.3 Weight Tracking -- 5.4 Standardization -- 5.5 Software Features -- 6 Innovation -- 7 Benefits -- 8 Future Development -- 9 Case Study and Economic Sustainability -- 10 Conclusions -- References -- A DT-Based System for Predicting Process Behavior -- 1 Introduction -- 2 Conceptual Framework of the System -- 3 Development of Digital Twin System -- 3.1 Modelling of Grinding Machine -- 3.2 Modelling of Grinding Process -- 4 Installation of Developed System -- 5 Conclusion -- References -- Optimal Robot Workpiece Placement for Maximized Repeatability -- 1 Introduction -- 2 State of the Art -- 3 Approach -- 4 Experiments and Results -- 5 Discussion -- References. Enhancing Vendor Managed Inventory with the Application of Blockchain Technology. |
| Record Nr. | UNINA-9910627268703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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