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AbstractMajor industrial accidents occurring at so‐called major hazard installations may cause domino accidents which are among the most destructive industrial accidents existing at present. As there may be many hazard installations in an area, a primary accident scenario may potentially propagate from one installation to another, and correlations exist in probability calculations of domino effects. In addition, during the propagation of a domino effect, accidents of diverse types may occur, some of them having a synergistic effect, while others do not. These characteristics make the analytical formulation of domino accidents very complex. In this work, a simple matrix‐based modeling approach for domino effect analysis is proposed. Matrices can be used to represent the mutual influences of different escalation vectors between installations. On this basis, an analysis approach for accident propagation as well as a simulation‐based algorithm for probability calculation of accidents and accident levels is provided. The applicability and flexibility of this approach is discussed while applying it to estimate domino probabilities in a case study.

AbstractHistorical data analysis shows that escalation accidents, so‐called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent‐based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent‐based modeling technique explains the domino effects from a bottom‐up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher‐level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large‐scale complicated cases.

Intro -- Contents -- 1 Applications of UAVs and Machine Learning in Agriculture -- 1.1 Introduction -- 1.2 Types of UAVs -- 1.3 Examples of UAV-Based Agricultural Applications -- 1.4 Artificial Intelligence and Machine Learning -- 1.5 Conclusion -- References -- 2 Robot Operating System Powered Data Acquisition for Unmanned Aircraft Systems in Digital Agriculture -- 2.1 Introduction -- 2.2 ROS-Based Data Acquisition System -- 2.2.1 Basic Concepts and Components in ROS -- 2.2.2 Connecting with Other UAS Components -- 2.2.3 Examples for Representative Sensors -- 2.3 A Case Study for Industrial Hemp Phenotyping -- 2.3.1 UAS Data Acquisition System -- 2.3.2 Plant Materials and Experimental Design -- 2.3.3 Data Acquisition and Ground-Truth Measurements -- 2.3.4 Data Processing Pipeline for Extracting Morphological and Vegetation Traits -- 2.3.5 Measurement Accuracy -- 2.4 Discussion -- 2.5 Summary -- References -- 3 Unmanned Aerial Vehicle (UAV) Applications in Cotton Production -- 3.1 Introduction -- 3.1.1 Precision Agriculture Technology in Agricultural Production -- 3.1.2 UAV-Based Remote Sensing (RS) for Crop Monitoring -- 3.1.3 UAV Imagery Data Processing Pipeline -- 3.2 UAV Systems in Cotton Production -- 3.2.1 Field Management for Cotton Production -- 3.2.2 Cotton Emergence Assessment -- 3.2.3 Cotton Growth Monitoring Using UAV-Based RS -- 3.2.4 Cotton Yield Estimation -- 3.3 Summary -- References -- 4 Time Effect After Initial Wheat Lodging on Plot Lodging Ratio Detection Using UAV Imagery and Deep Learning -- 4.1 Introduction -- 4.2 Materials and Methods -- 4.2.1 Experimental Field and Data Collection -- 4.2.2 Data Pre-Processing and Auto Dataset Generation -- 4.2.3 Handcrafted Features -- 4.2.4 Deep Features -- 4.2.5 Classifier -- 4.3 Results and Discussion -- 4.3.1 Deep Learning Model Selection for Deep Feature Extraction.