Intro
Preface
Organization
Contents
Industrial Applications
Comparative Study of Methods for the Real-Time Detection of Dynamic Bottlenecks in Serial Production Lines
1 Introduction
1.1 On the Dynamic Nature of Bottlenecks
1.2 The Need for Real-Time Bottleneck Detection
2 Related Work on Bottleneck Detection
2.1 Detection Using Bottleneck Walk with Buffer Levels
2.2 Detection Using Active Period Method with Machine States
2.3 Detection Using Interdeparture Time Variance with Process Times
3 Design of the Comparative Study for Bottleneck Detection

4 Detection Results using BNW, APM and ITV
4.1 Bottleneck Detection with Bottleneck Walk
4.2 Bottleneck Detection Using the Active Period Method
4.3 Bottleneck Detection Using Interdeparture Time Variances
5 Comparison
5.1 Comparison of 20%-Bottleneck Results
5.2 Results for Varying Bottleneck Process Times (10% to 100%)
6 Conclusion
References
Ultra-short-Term Load Forecasting Model Based on VMD and TGCN-GRU
1 Introduction
2 Methodology
2.1 Variational Mode Decomposition
2.2 Temporal Graph Convolution Network
2.3 VTGG Model

3 Experiments and Discussions
3.1 Data
3.2 Evaluation Method
3.3 Contrast Experimental Model
3.4 Experimental Environment and Parameter Settings
3.5 Experimental Results
4 Conclusion
References
Learning to Match Product Codes
1 Introduction
2 Related Work
3 Data Wrangling
4 Approximate String Matching
5 Deep Learning
6 System Structure Design
7 Experiments and Results
7.1 Exploratory Data Analysis
7.2 Comparison of Approximate String Matching Methods
7.3 Comparison of Deep Learning Methods
8 Conclusion and Future Work
References

ResUnet: A Fully Convolutional Network for Speech Enhancement in Industrial Robots
1 Instruction
2 Related Work
2.1 U-Net
2.2 ResNet
2.3 Huber Loss Function
3 The Proposed Method
3.1 Overview of the Proposed Method
3.2 Structure of Res-Unet
3.3 Optimization Function
4 Experimental Methods
4.1 Dataset
4.2 Feature Transformation
4.3 Training Schemes
4.4 Evaluation Score
5 Experimental Results
6 Conclusion
References
Surface Defect Detection and Classification Based on Fusing Multiple Computer Vision Techniques
1 Introduction

2 Technical Framework
3 Online Defect Detection
3.1 Defect Detection Based on Conventional CV Technology
3.2 Defect Detection Based on CNN
3.3 Detection Result Fusion
4 Offline Defect Classification
5 Case Study and Experiment
5.1 Overall System Architecture
5.2 Data Acquisition
5.3 Online Defect Detection
6 Conclusion
References
Development of a Multiagent Based Order Picking Simulator for Optimizing Operations in a Logistics Warehouse
1 Introduction
2 Order Picking Simulator
2.1 Setting of Simulator
2.2 Cart Behavior Decision Algorithm