{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":34,"n_cols":5,"rows_kept":34,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[21,2,21,3],[22,2,22,3],[24,2,24,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910841855903321"]
[null,null,"Titolo",null,"Advances in Intelligent Manufacturing and Robotics : Selected Articles from ICIMR 2023; 22-23 August, Suzhou, China / / edited by Andrew Tan, Fan Zhu, Haochuan Jiang, Kazi Mostafa, Eng Hwa Yap, Leo Chen, Lillian J. A. Olule, Hyun Myung"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"981-9984-98-X"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (566 pages)"]
[null,null,"Collana",null,"Lecture Notes in Networks and Systems, , 2367-3389 ; ; 845"]
[null,null,"Disciplina",null,"670.28563"]
[null,null,"Soggetti",null,"Automatic control"]
[{"$nulls":4},"Robotics"]
[{"$nulls":4},"Automation"]
[{"$nulls":4},"Industrial engineering"]
[{"$nulls":4},"Microtechnology"]
[{"$nulls":4},"Microelectromechanical systems"]
[{"$nulls":4},"Computational intelligence"]
[{"$nulls":4},"Signal processing"]
[{"$nulls":4},"Control, Robotics, Automation"]
[{"$nulls":4},"Industrial Automation"]
[{"$nulls":4},"Microsystems and MEMS"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Digital and Analog Signal Processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Contents -- Springback Prediction Using Gated Recurrent Unit and Data Augmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Background -- 3.2 Data Augmentation -- 3.3 Springback Prediction with GRU -- 3.4 Implementation Detail -- 4 Evaluation -- 4.1 Experiment Result -- 4.2 Evaluation Metrics -- 4.3 What Does the Proposed System Compare with the Previous Work? -- 4.4 What is the Best Step Value? -- 4.5 What is the Best Grid Size? -- 5 Conclusion -- References -- Road Signage and Road Obstacle Detection Using Deep Learning Method -- 1 Introduction -- 1.1 Related Works -- 2 Methodology -- 2.1 Data Collection -- 2.2 Image Annotation -- 2.3 Dataset Splitting -- 2.4 Transfer Learning -- 2.5 "]
[{"$nulls":4},"Evaluation -- 3 Result and Discussion -- 3.1 Object Detection Result -- 3.2 Confusion Matrix -- 3.3 Precision-Recall Curve -- 3.4 F1 Score Graph -- 3.5 Discussion -- 4 Conclusion -- References -- Advancing Mass Customization Through GPT Language Models: A Multidimensional Analysis of Market, Technological, and Managerial Innovations -- 1 Introduction -- 1.1 Introduction to Intelligent Manufacturing -- 1.2 Introduction to ChatGPT -- 1.3 Introduction to Manufacturing and Mass Customization -- 2 Concept of GPT Innovation in Mass Customization -- 2.1 Innovation in New Business Model -- 2.2 Innovation in New Production Environment -- 2.3 Innovation in New Business Process System -- 3 ChatGPT in Mass Customization -- 4 Mass Customization of Luxury Brands Products -- 5 Limitation of GPT Development in Mass Customization -- 6 Conclusion -- 7 Future Work -- References -- CenterNet: A Transfer Learning Approach for Human Presence Detection -- 1 Introduction -- 2 Methodology -- 2.1 Image Acquisition -- 2.2 Image Annotation -- 2.3 Dataset Preparation -- 2.4 Transfer Learning-Fine-Tuning -- 2.5 Learning Curve Inspection."]
[{"$nulls":4},"2.6 Performance Evaluation -- 3 Result and Discussion -- 4 Conclusion -- References -- Deep Learning Algorithms for Recognition of Badminton Strokes: A Study Using SDNN, RNN, and RNN-GRU Models with Off-Court Video Capture -- 1 Introduction -- 2 Methodology -- 3 Results and Discussion -- 4 Conclusion -- References -- Bibliometric Analysis of Image Segmentation with Deep Learning: An Analytical Study -- 1 Introduction -- 2 Method -- 3 Results and Discussion -- 3.1 Analysis of Publication and Document Classifications -- 3.2 Analysis of Journals and Cited Articles -- 3.3 Analysis of Countries and Institutions -- 3.4 Popular Research Topics and Keyphrase Analysis -- 4 Conclusion -- References -- Unsupervised Learning of Time-Series Classification Using Machine Learning Through Fertigation System -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Monitoring Device -- 2.2 Feature Extraction -- 2.3 k-means Clustering -- 2.4 Event Identification -- 3 Results -- 3.1 k-means Clustering -- 3.2 Event Identification -- 3.3 Comparison of Machine Learning Models -- 4 Conclusion -- References -- Design Optimization Study of the Temperature Uniformity in Air-Cooled Freezers -- 1 Introduction -- 2 Methodology -- 2.1 Physical Model -- 2.2 Governing Equation -- 2.3 Solution Algorithm -- 2.4 Optimization Process -- 3 Results -- 3.1 Grid Independence -- 3.2 Problems of Temperature Uniformity -- 3.3 Optimization Result -- 4 Conclusion and Future Work -- References -- Enhancing Elderly Well-Being Through the Adoption of Medication Adherence System -- 1 Introduction -- 2 Literature Review and Hypothesis Development -- 2.1 Medication Adherence -- 2.2 Perceived Risk -- 2.3 Trust -- 2.4 Health Consciousness -- 2.5 Perceived Usefulness -- 2.6 Perceived Ease of Use -- 2.7 Perceived Behavioural Control -- 2.8 Attitudes -- 2.9 Subjective Norm -- 3 Methodology."]
[{"$nulls":4},"4 Findings and Results -- 5 Discussion and Implications -- 6 Conclusion -- References -- Deep Learning-Based Silicon Wafer Defect Classification: A Performance Comparison of Pretrained Networks -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Acquisition and Preprocessing -- 2.2 Transfer Learning of Pretrained Networks -- 2.3 Hyperparameter Settings -- 2.4 Performance Evaluations -- 3 Performance Comparison Results -- 4 Conclusions -- References -- A Modified African Vultures Optimization Algorithm for Enhanced Feature Selection -- 1 Introduction -- 2 Feature Selection Optimization Problem -- 3 Proposed MCAVOA as Feature Selection Algorithm -- 4 Performance Analysis -- 4.1 Simulation Settings -- 4.2 Comparison Between Different MSAs in Feature Selection -- 5 Conclusion -- "]
[{"$nulls":4},"References -- Convolutional Neural Network-Based Identifying Gender of Kiwifruit Flowers in Autonomous Pollination for Future Farming -- 1 Introduction -- 1.1 Literature Review -- 1.2 Motivation, Aims, and Objective -- 2 Methodology -- 2.1 Process Brief -- 2.2 Methodology -- 3 Location Detection (YOLOv5) -- 3.1 Data Collection and Labelling -- 3.2 Model Training -- 3.3 Result -- 4 Gender Recognition (LeNet and AlexNet and ResNet) -- 4.1 Data Pre-processing -- 4.2 Model Comparison and Evaluation -- 5 Conclusion -- References -- Hyperparameter Optimization of Deep Learning Model: A Case Study of COVID-19 Diagnosis -- 1 Introduction -- 2 Related Works -- 3 COVID-19 Diagnosis with Proposed SSOCNN -- 3.1 Data Acquisition and Preprocessing -- 3.2 Network Selection and Training -- 3.3 Hyperparameter Optimization in SSOCNN -- 4 Performance Evaluation -- 5 Conclusion -- References -- Predicting the Impact of Restaurant Automation and Food Safety in China: Identifying Key Factors for Smart Dining Experience -- 1 Introduction -- 1.1 Problem Statements -- 1.2 Hypotheses."]
[{"$nulls":4},"2 Literature Review -- 2.1 Smart Dining Experience -- 2.2 Intelligent Restaurant -- 2.3 Food Safety -- 2.4 Relationship Quality -- 3 Framework -- 4 Conclusion -- References -- Development of Intelligent EWC with Autonomous and Interactive Behavior -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Module Overview -- 3.2 Motor Closed Loop Control -- 3.3 Serial Interface -- 3.4 ROS Driver for Arduino -- 3.5 LiDAR -- 3.6 Real-Time SLAM -- 3.7 Navigation -- 3.8 Speech Recognition -- 4 Results -- 5 Conclusion -- References -- Character Recognition Based on k-Nearest Neighbor, Simple Logistic Regression, and Random Forest -- 1 Introduction -- 2 Literature Review -- 3 Design Methodology -- 3.1 Out-of-Bag Data Error from Random Forest -- 3.2 Slow Training Speed -- 3.3 Large Data Dimension -- 3.4 The Random Forest Algorithm Consumes an Excessive Amount of Memory and is Unable to Process Large Datasets -- 4 Results and Discussion -- 5 Conclusion -- References -- The Integration of Artificial Intelligence in the Fashion Industry and Its Impact on Sustainable Fashion: A Systematic Literature Review -- 1 Introduction -- 2 Literature Review -- 2.1 Understanding Artificial Intelligence in Modern Digital Era -- 2.2 Sustainability in Fashion -- 2.3 Integration of Artificial Intelligence in Fashion Industry -- 3 Methodology -- 3.1 Systematic Literature Review -- 3.2 Search Strategy and Selection Criteria -- 4 Result -- 4.1 Types of AI Technologies Utilized in the Fashion Industry and Their Applications -- 4.2 The Impact of AI on Sustainability Practices in the Fashion Industry -- 4.3 Challenges and Opportunities Associated with the Implementation of AI in the Fashion Industry -- 5 Conclusion -- References -- Technical, Environmental, and Economical Assessment of Photovoltaics Technologies in Malaysia -- 1 Introduction -- 2 Literature Review."]
[{"$nulls":4},"2.1 Technical Aspects -- 2.2 Environmental Aspects -- 2.3 Economical Aspects -- 2.4 Schemes and Tools Established in Malaysia and Singapore -- 3 Methodology -- 3.1 Site Selection -- 3.2 Data Collection -- 3.3 Simulation Using PVsyst -- 4 Results -- 4.1 Tilt and Orientation Factor -- 4.2 PV System Energy Production -- 4.3 Performance Evaluation -- 4.4 Environmental Assessment -- 4.5 Economic Assessment -- 5 Proposed Initiatives -- 6 Conclusion -- References -- An Intelligent Robotic Grasping and Manipulation System with Sensor Fusion -- 1 Introduction -- 2 System Configuration -- 2.1 Control System -- 2.2 Experiment Objects -- 2.3 Object Detection and Pose Estimation -- 2.4 Grasping Pose Strategy -- 2.5 Safe Grasping Force Framework -- 3 Results -- 3.1 Object Detection and Pose Estimation -- 3.2 Grasping Pose Strategy -- 3.3 Safe "]
[{"$nulls":4},"Grasping Force Framework -- 4 Conclusion -- References -- Deep Learning for Breast Cancer Detection from Mammograms Images -- 1 Introduction -- 2 Methodology -- 2.1 Dataset -- 2.2 Data Preprocessing -- 2.3 Transfer Learning -- 2.4 Model Training -- 3 Results -- 3.1 Model Evaluation -- 3.2 Discussion -- 4 Conclusion -- References -- Object Detection in Autonomous Vehicles: A Performance Analysis -- 1 Introduction -- 1.1 Object Detection -- 1.2 Autonomous Vehicles -- 1.3 Faster R-CNN -- 1.4 SSD -- 2 Methodology -- 2.1 Data Acquisition -- 2.2 Data Pre-processing -- 2.3 Data Augmentation -- 2.4 Simulation Settings -- 2.5 Model Selection -- 2.6 Model Training -- 2.7 Training Stage -- 2.8 Loss Function -- 2.9 Performance Evaluation -- 3 Results and Discussion -- 4 Conclusion -- References -- Enhancing E-commerce Recommendation Accuracy Using KNN and Hybrid Approaches: An Empirical Study -- 1 Introduction -- 2 Methods -- 2.1 Data Collection and Preprocessing -- 2.2 Feature Extraction -- 2.3 Model Building -- 2.4 Evaluation."]
[{"$nulls":4},"3 Results and Analysis."]
[null,null,null,"Sommario/riassunto","This book presents selected peer reviewed articles from the International Conference on Intelligent Manufacturing and Robotics (ICIMR 2023) held on 22-23 August at the Xi’an Jiaotong-Liverpool University in China. The book deliberates on the key challenges, engineering and scientific discoveries, innovations, and advances in intelligent manufacturing and robotics that are non-trivial through the lens of Industry 4.0. In this book, traditional and modern solutions that are employed across the spectrum of various intelligent manufacturing and robotics contexts are discussed. The book provides an insightful view on the current trends, issues, mitigating factors as well as proposed solutions."]