18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) [[electronic resource] ] : Salamanca, Spain, September 5–7, 2023, Proceedings, Volume 2 / / edited by Pablo García Bringas, Hilde Pérez García, Francisco Javier Martínez de Pisón, Francisco Martínez Álvarez, Alicia Troncoso Lora, Álvaro Herrero, José Luis Calvo Rolle, Héctor Quintián, Emilio Corchado

• Autore: García Bringas Pablo
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
• Edizione: 1st ed. 2023.
• Descrizione fisica: 1 online resource (376 pages)
• Disciplina: 006.3
• Soggetto topico: Computational intelligence; Industrial engineering; Production engineering; Computational Intelligence; Industrial and Production Engineering
• Altri autori: Pérez GarcíaHilde Martínez de PisónFrancisco Javier Martínez ÁlvarezFrancisco Troncoso LoraAlicia HerreroÁlvaro Calvo RolleJosé Luis QuintiánHéctor CorchadoEmilio
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Special Session 2: Technological Foundations and Advanced Applications of Drone Systems -- Level 3 Data Fusion -- 1 Ontological Foundations -- 2 Level 3 Data Fusion -- 3 DNN Implementation -- 3.1 CoA Recognition -- 3.2 Event Prediction -- 3.3 Forensic Assessment -- 4 Modeling Courses of Action -- 4.1 Probability of Action -- 4.2 CoA Utility Modeling -- 4.3 Evaluating Response Effectiveness -- References -- Image Classification Using Contrastive Language-Image Pre-training: Application to Aerial Views of Power Line Infrastructures -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Selection and Processing -- 3.2 Data Labeling and Caption Generation -- 3.3 CLIP Classifier Selection Methodology -- 4 Results and Discussion -- 4.1 Zero-Shot vs. Few-Shot Classifier -- 4.2 CLIP Fine-Tuning -- 4.3 Final model -- 5 Conclusions -- References -- A Realistic UAS Traffic Generation Tool to Evaluate and Optimize U-Space Airspace Capacity -- 1 Introduction -- 2 Realistic UAS Traffic Generation and Specification -- 2.1 Identification of Common UAS Patterns -- 2.2 Traffic Generation Module -- 2.3 Traffic Specification Module -- 3 Validation of the Traffic Generation and Specification Modules -- 4 Next Steps: Towards a Separation Optimization and Evaluation Framework -- 5 Conclusions -- References -- UAV Airframe Classification Using Acceleration Spectrograms -- 1 Introduction -- 2 State of the Art -- 3 Proposed System -- 3.1 UAV Dataset and Spectrogram Generation -- 3.2 Classification Algorithm -- 4 Experiments and Results -- 5 Conclusions -- References -- Tuning Process Noise in INS/GNSS Fusion for Drone Navigation Based on Evolutionary Algorithms -- 1 Introduction -- 2 INS/GNSS -- 3 Tuning Process -- 4 Case Study -- 4.1 Mission Problem and Simulation Configuration -- 4.2 Filter Parameters. ; 4.3 Optimization Algorithms -- 5 Results -- 5.1 Results Comparison -- 6 Conclusions -- References -- Special Session 3: Soft Computing Methods in Manufacturing and Management Systems -- Digital Twins of Production Systems Based on Discrete Simulation and Machine Learning Algorithms -- 1 Introduction -- 2 Reinforcement Learning -- 3 A Digital Twin Based on Discrete-Event Simulation as a Reinforcement Learning Agent Environment -- 4 Summary -- References -- Edge Architecture for the Integration of Soft Models Based Industrial AI Control into Industry 4.0 Cyber-Physical Systems -- 1 Introduction -- 2 Related Work -- 3 Architecture -- 4 Validation -- 5 Conclusions -- References -- The Use of Line Simplification and Vibration Suppression Algorithms to Improve the Quality of Determining the Indoor Location in RTLSs -- 1 Introduction -- 1.1 Methods and Technologies for Determining Indoor Location -- 2 Decawave as an Example of RTLS Based on UWB Technology -- 3 Algorithms for Improving RTLS Data -- 3.1 Polyline Simplification Algorithms -- 3.2 Location Instability Suppression Algorithm (LISA) -- 4 Research on the Effectiveness of Algorithms -- 4.1 Discussion of Results -- 5 Summary -- References -- Possibilities of Decision Support in Organizing Production Processes -- 1 Introduction -- 2 Industry 4.0 -- 2.1 Technologies of the Industry 4.0 -- 3 Methodology -- 4 Conclusion -- References -- Special Session 4: Efficiency and Explainability in Machine Learning and Soft Computing -- Efficient Short-Term Time Series Forecasting with Regression Trees -- 1 Introduction -- 2 Materials and Method -- 2.1 Dataset -- 2.2 Experimental Setup -- 2.3 Evaluation Procedure -- 3 Results -- 4 Conclusions -- References -- Generating Synthetic Fetal Cardiotocography Data with Conditional Generative Adversarial Networks -- 1 Introduction -- 2 Methodology. ; 2.1 Conditional Generative Adversarial Networks -- 2.2 Classifiers -- 3 Experiments -- 3.1 Dataset -- 3.2 CGAN Parameter Tuning -- 3.3 Classifiers Hyperparameter and Parameter Tuning -- 4 Results and Discussion -- 5 Conclusions -- References -- Olive Oil Fly Population Pest Forecasting Using Explainable Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 ADL -- 3.2 Benchmark Algorithms -- 3.3 Explainability -- 4 Experimentation and Results -- 4.1 Input Data -- 4.2 Results and Discussion -- 4.3 Explainability -- 5 Conclusions and Future Work -- References -- Explaining Learned Patterns in Deep Learning by Association Rules Mining -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Preprocessing -- 3.2 Rules Mining -- 3.3 Calculate Metrics -- 3.4 Classify -- 4 Results and Discussion -- 4.1 Experimental Setting -- 4.2 Metrics -- 4.3 Results -- 5 Conclusions -- References -- Special Session 5: Machine Learning and Computer Vision in Industry 4.0 -- A Deep Learning Ensemble for Ultrasonic Weld Quality Control -- 1 Introduction -- 2 Technology -- 2.1 Manufacturing Process -- 2.2 Deep Learning Models -- 3 Deep Learning Ensemble -- 4 Experimental Results -- 5 Conclusion -- References -- Indoor Scenes Video Captioning -- 1 Introduction -- 2 Related Works -- 2.1 Video Captioning -- 2.2 Indoor Scene Captioning -- 3 Methodology -- 4 Experiments -- 4.1 Charades Dataset -- 4.2 Postprocessing -- 4.3 Setup -- 4.4 Results -- 5 Conclusion -- References -- A Multimodal Dataset to Create Manufacturing Digital Twins -- 1 Introduction -- 2 Related Work -- 2.1 Pose Estimation for Action Recognition -- 3 Experimental Setup and Data Acquisition -- 4 Dataset Discussion -- 4.1 Dataset Top View -- 4.2 Dataset Side View -- 4.3 Dataset Front View -- 5 Datarecords -- 6 Conclusions and Future Work -- References. ; A Modified Loss Function Approach for Instance Segmentation Improvement and Application in Fish Markets -- 1 Introduction -- 2 Dataset -- 3 Proposed Method -- 4 Experiments and Results -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusions -- References -- Parallel Processing Applied to Object Detection with a Jetson TX2 Embedded System -- 1 Introduction -- 2 Methodology -- 3 System Architecture -- 3.1 Software Architecture -- 3.2 Hardware Architecture -- 4 Experimental Results -- 5 Conclusion -- References -- Deep Learning-Based Emotion Detection in Aphasia Patients -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Approach -- 5 Evaluation -- 6 Conclusion -- References -- Defect Detection in Batavia Woven Fabrics by Means of Convolutional Neural Networks -- 1 Introduction and Previous Work -- 2 Related Work -- 3 Case Study -- 4 Methods and Experimentation -- 4.1 Methods -- 4.2 Experimentation -- 5 Results -- 6 Conclusions -- References -- An Image Mosaicing-Based Method for Bird Identification on Edge Computing Devices -- 1 Introduction -- 2 Image Mosaicing-Based Method -- 3 Experiments and Results -- 3.1 Results -- 3.2 Analysis of the Results -- 4 Conclusion -- References -- HoloDemtect: A Mixed Reality Framework for Cognitive Stimulation Through Interaction with Objects -- 1 Introduction -- 2 Related Works -- 3 HoloLens 2 Application -- 3.1 HoloLens 2 API -- 3.2 Implementation Details -- 3.3 Data Collection -- 4 Evaluation of the Proposal -- 4.1 Qualitative Analysis -- 4.2 Quantitative Analysis -- 5 Conclusions -- References -- Accurate Estimation of Parametric Models of the Human Body from 3D Point Clouds -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Obtaining and Preprocessing of the 3D Model. -- 3.2 Estimation of an Intermediate Template Using BPS Neural Network -- 3.3 First Minimization: BPS to SMPL. ; 3.4 Second Minimization: 3D Scan to SMPL -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Results -- 5 Conclusions -- References -- Lightweight Cosmetic Contact Lens Detection System for Iris Recognition at a Distance -- 1 Introduction -- 2 Related Work -- 3 Overview of the IAAD Framework -- 4 The Approach for Cosmetic Contact Lens Detection -- 4.1 BSIF Encoding of an Iris Pattern -- 4.2 Building the Ensemble of Classifiers -- 5 Results -- 5.1 Cross-Dataset Testing -- 6 Conclusions and Future Work -- References -- Vehicle Warning System Based on Road Curvature Effect Using CNN and LSTM Neural Networks -- 1 Introduction -- 2 Road Curvature-Based Dynamics of the Vehicle -- 3 Risky Maneuvers Identification by CNN and LSTM Models -- 3.1 Model's Selection of Variables -- 3.2 Deep Convolutional Neural Network Model -- 4 Results and Discussion -- 5 Conclusions and Future Works -- References -- Special Session 6: Genetic and Evolutionary Computation in Real World and Industry -- Enhancing Time Series Anomaly Detection Using Discretization and Word Embeddings -- 1 Introduction -- 2 Experimental Study -- 2.1 Problem Formulation -- 2.2 Data Preprocessing -- 2.3 Model Architecture -- 2.4 Datasets -- 3 Results -- 4 Conclusions and Future Work -- References -- Multi-objective Optimization for Multi-Robot Path Planning on Warehouse Environments -- 1 Introduction -- 2 Non-Dominated Genetic Algorithm Approach -- 2.1 Route Generation -- 2.2 Initial Population -- 2.3 Crossover -- 2.4 Mutation -- 3 Experimentation Setup -- 4 Results and Discussion -- 5 Conclusion and Future Work -- References -- On the Prediction of Anomalous Contaminant Diffusion -- 1 Introduction -- 2 Bevilacqua-Galeão (BG) Model and Numerical Solution -- 2.1 BG Model -- 2.2 Numerical Solution -- 2.3 Differential Evolution (DE) Method -- 3 Inverse Problem Formulation -- 4 Results. ; 4.1 Direct Problem and Case of Study.
• Sommario/riassunto: This book of Advances in Intelligent and Soft Computing contains accepted papers presented at SOCO 2023 conference held in the beautiful and historic city of Salamanca (Spain) in September 2023. Soft computing represents a collection or set of computational techniques in machine learning, computer science, and some engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena. After a through peer-review process, the 18th SOCO 2023 International Program Committee selected 61 papers which are published in these conference proceedings and represents an acceptance rate of 60%. In this relevant edition, a particular emphasis was put on the organization of special sessions. Seven special sessions were organized related to relevant topics such as: Time Series Forecasting in Industrial and Environmental Applications, Technological Foundations and Advanced Applications of Drone Systems, Soft Computing Methods in Manufacturing and Management Systems, Efficiency and Explainability in Machine Learning and Soft Computing, Machine Learning and Computer Vision in Industry 4.0, Genetic and Evolutionary Computation in Real World and Industry, and Soft Computing and Hard Computing for a Data Science Process Model. The selection of papers was extremely rigorous to maintain the high quality of the conference. We want to thank the members of the Program Committees for their hard work during the reviewing process. This is a crucial process for creating a high-standard conference; the SOCO conference would not exist without their help.
• Titolo autorizzato: 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023)
• ISBN: 3-031-42536-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910743693003321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Networks and Systems, . 2367-3389 ; ; 750