Qualitative Fault Detection and Hazard Analysis Based on Signed Directed Graphs for Large-Scale Complex Systems

Nowadays in modern industries, the scale and complexity of process systems are increased continuously. These systems are subject to low productivity, system faults or even hazards because of various conditions such as mis-operation, equipment quality change, externa

Integration of Multiple Temporal Qualitative Probabilistic Networks in Time Series Environments

Abstract: The integration of uncertain information from different time sources is a crucial issue in various applications. In this paper, we propose an integration method of multiple Temporal Qualitative Probabilistic Networks (TQPNs) in time series environments. First, we present the method for learning TQPN from time series data. The TQPN's structure is constructed using Dynamic Bayesian Networks learning based on Markov Chain Monte Carlo. Furthermore, the corresponding qualitative influences are obtained by the conditional probabilities. Secondly, based on rough set theory, we integrate multiple TQPNs into a single QPN that preserves as much information as possible. Specifically, we take the rough-set-based dependency degree as the strength of qualitative influence, and then make the rules to solve the ambiguities reduction and cycles deletion problems which arise from the integration of different TQPNs. Finally, we verify the feasibility of the integration method by the simulation experiments

Grasping Causality for the Explanation of Criticality for Automated Driving

The verification and validation of automated driving systems at SAE levels 4 and 5 is a multi-faceted challenge for which classical statistical considerations become infeasible. For this, contemporary approaches suggest a decomposition into scenario classes combined with statistical analysis thereof regarding the emergence of criticality. Unfortunately, these associational approaches may yield spurious inferences, or worse, fail to recognize the causalities leading to critical scenarios, which are, in turn, prerequisite for the development and safeguarding of automated driving systems. As to incorporate causal knowledge within these processes, this work introduces a formalization of causal queries whose answers facilitate a causal understanding of safety-relevant influencing factors for automated driving. This formalized causal knowledge can be used to specify and implement abstract safety principles that provably reduce the criticality associated with these influencing factors. Based on Judea Pearl's causal theory, we define a causal relation as a causal structure together with a context, both related to a domain ontology, where the focus lies on modeling the effect of such influencing factors on criticality as measured by a suitable metric. As to assess modeling quality, we suggest various quantities and evaluate them on a small example. As availability and quality of data are imperative for validly estimating answers to the causal queries, we also discuss requirements on real-world and synthetic data acquisition. We thereby contribute to establishing causal considerations at the heart of the safety processes that are urgently needed as to ensure the safe operation of automated driving systems

Bayesian participatory-based decision analysis : an evolutionary, adaptive formalism for integrated analysis of complex challenges to social-ecological system sustainability

Includes bibliographical references (pages. 379-400).This dissertation responds to the need for integration between researchers and decision-makers who are dealing with complex social-ecological system sustainability and decision-making challenges. To this end, we propose a new approach, called Bayesian Participatory-based Decision Analysis (BPDA), which makes use of graphical causal maps and Bayesian networks to facilitate integration at the appropriate scales and levels of descriptions. The BPDA approach is not a predictive approach, but rather, caters for a wide range of future scenarios in anticipation of the need to adapt to unforeseeable changes as they occur. We argue that the graphical causal models and Bayesian networks constitute an evolutionary, adaptive formalism for integrating research and decision-making for sustainable development. The approach was implemented in a number of different interdisciplinary case studies that were concerned with social-ecological system scale challenges and problems, culminating in a study where the approach was implemented with decision-makers in Government. This dissertation introduces the BPDA approach, and shows how the approach helps identify critical cross-scale and cross-sector linkages and sensitivities, and addresses critical requirements for understanding system resilience and adaptive capacity

From Inverse Problems in Mathematical Physiology to Quantitative Differential Diagnoses

The improved capacity to acquire quantitative data in a clinical setting has generally failed to improve outcomes in acutely ill patients, suggesting a need for advances in computer-supported data interpretation and decision making. In particular, the application of mathematical models of experimentally elucidated physiological mechanisms could augment the interpretation of quantitative, patient-specific information and help to better target therapy. Yet, such models are typically complex and nonlinear, a reality that often precludes the identification of unique parameters and states of the model that best represent available data. Hypothesizing that this non-uniqueness can convey useful information, we implemented a simplified simulation of a common differential diagnostic process (hypotension in an acute care setting), using a combination of a mathematical model of the cardiovascular system, a stochastic measurement model, and Bayesian inference techniques to quantify parameter and state uncertainty. The output of this procedure is a probability density function on the space of model parameters and initial conditions for a particular patient, based on prior population information together with patient-specific clinical observations. We show that multimodal posterior probability density functions arise naturally, even when unimodal and uninformative priors are used. The peaks of these densities correspond to clinically relevant differential diagnoses and can, in the simplified simulation setting, be constrained to a single diagnosis by assimilating additional observations from dynamical interventions (e.g., fluid challenge). We conclude that the ill-posedness of the inverse problem in quantitative physiology is not merely a technical obstacle, but rather reflects clinical reality and, when addressed adequately in the solution process, provides a novel link between mathematically described physiological knowledge and the clinical concept of differential diagnoses. We outline possible steps toward translating this computational approach to the bedside, to supplement today's evidence-based medicine with a quantitatively founded model-based medicine that integrates mechanistic knowledge with patient-specific information

Network Analysis with Stochastic Grammars

Digital forensics requires significant manual effort to identify items of evidentiary interest from the ever-increasing volume of data in modern computing systems. One of the tasks digital forensic examiners conduct is mentally extracting and constructing insights from unstructured sequences of events. This research assists examiners with the association and individualization analysis processes that make up this task with the development of a Stochastic Context -Free Grammars (SCFG) knowledge representation for digital forensics analysis of computer network traffic. SCFG is leveraged to provide context to the low-level data collected as evidence and to build behavior profiles. Upon discovering patterns, the analyst can begin the association or individualization process to answer criminal investigative questions. Three contributions resulted from this research. First , domain characteristics suitable for SCFG representation were identified and a step -by- step approach to adapt SCFG to novel domains was developed. Second, a novel iterative graph-based method of identifying similarities in context-free grammars was developed to compare behavior patterns represented as grammars. Finally, the SCFG capabilities were demonstrated in performing association and individualization in reducing the suspect pool and reducing the volume of evidence to examine in a computer network traffic analysis use case

A diagnostic investigation and a corrective model for implementing change in response to innovation

Organizational change can be described as a series of activities oriented towards modifying behaviors and structures within the organization. These series of activities are interconnected internally and externally and are affected by human, operational and environmental factors that dynamically influence decisions and processes in the organization. There has been a significant amount of work in organizational change, using both behavioral and systemic approaches. Moreover it has been argued that research in change processes should include also the dynamic relationship between change processes and outcomes to detect how organizational change context, processes and the pace of change affect performance outcomes. Despite the amount of research, there is a need for more profound studies exploring the contexts, content, and processes involved in a change initiative. This research proposes a model to help organizations implement change initiatives with an increased likelihood of success. The Influence Model for Organizational Change – IMOC - was developed with the hope of better demonstrating the dynamics that take place in the organization by using a systems engineering view. As an exercise to verify the relationships that govern IMOC a systems dynamic simulation model was partially developed. The dynamic simulation confirmed the impact of variables such as employees’ and management participation, environment and delay in implementing policies on the level of resistance to change existing in the organization.Organizational change can be described as a series of activities oriented towards modifying behaviors and structures within the organization. These series of activities are interconnected internally and externally and are affected by human, operational and environmental factors that dynamically influence decisions and processes in the organization. There has been a significant amount of work in organizational change, using both behavioral and systemic approaches. Moreover it has been argued that research in change processes should include also the dynamic relationship between change processes and outcomes to detect how organizational change context, processes and the pace of change affect performance outcomes. Despite the amount of research, there is a need for more profound studies exploring the contexts, content, and processes involved in a change initiative. This research proposes a model to help organizations implement change initiatives with an increased likelihood of success. The Influence Model for Organizational Change – IMOC - was developed with the hope of better demonstrating the dynamics that take place in the organization by using a systems engineering view. As an exercise to verify the relationships that govern IMOC a systems dynamic simulation model was partially developed. The dynamic simulation confirmed the impact of variables such as employees’ and management participation, environment and delay in implementing policies on the level of resistance to change existing in the organization

Probabilistic analysis of supply chains resilience based on their characteristics using dynamic Bayesian networks

Previously held under moratorium from 14 December 2016 until 19 January 2022There is an increasing interest in the resilience of supply chains given the growing awareness of their vulnerabilities to natural and man-made hazards. Contemporary academic literature considers, for example, so-called resilience enablers and strategies, such as improving the nature of collaboration and flexibility within the supply chain. Efforts to analyse resilience tend to view the supply chain as a complex system. The present research adopts a distinctive approach to the analysis of supply resilience by building formal models from the perspective of the responsible manager. Dynamic Bayesian Networks (DBNs) are selected as the modelling method since they are capable of representing the temporal evolution of uncertainties affecting supply. They also support probabilistic analysis to estimate the impact of potentially hazardous events through time. In this way, the recovery rate of the supply chain under mitigation action scenarios and an understanding of resilience can be obtained. The research is grounded in multiple case studies of manufacturing and retail supply chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each case involves building models to estimate the resilience of the supply chain given uncertainties about, for example, business continuity, lumpy spare parts demand and operations of critical infrastructure. DBNs have been developed by using relevant data from historical empirical records and subjective judgement. Through the modelling practice, It has been found that some SC characteristics (i.e. level of integration, structure, SC operating system) play a vital role in shaping and quantifying DBNs and reduce their elicitation burden. Similarly, It has been found that the static and dynamic discretization methods of continuous variables affect the DBNs building process. I also studied the effect of level of integration, visibility, structure and SC operating system on the resilience level of SCs through the analysis of DBNs outputs. I found that the influence of the integration intensity on supply chain resilience can be revealed through understanding the dependency level of the focal firm on SC members resources. I have also noticed the relationship between the span of integration and the level of visibility to SC members. This visibility affects the capability of SC managers in the focal firm to identify the SC hazards and their consequences and, therefore, improve the planning for adverse events. I also explained how some decision rules related to SC operating system such as the inventory strategy could influence the intermediate ability of SC to react to adverse events. By interpreting my case data in the light of the existing academic literature, I can formulate some specific propositions.There is an increasing interest in the resilience of supply chains given the growing awareness of their vulnerabilities to natural and man-made hazards. Contemporary academic literature considers, for example, so-called resilience enablers and strategies, such as improving the nature of collaboration and flexibility within the supply chain. Efforts to analyse resilience tend to view the supply chain as a complex system. The present research adopts a distinctive approach to the analysis of supply resilience by building formal models from the perspective of the responsible manager. Dynamic Bayesian Networks (DBNs) are selected as the modelling method since they are capable of representing the temporal evolution of uncertainties affecting supply. They also support probabilistic analysis to estimate the impact of potentially hazardous events through time. In this way, the recovery rate of the supply chain under mitigation action scenarios and an understanding of resilience can be obtained. The research is grounded in multiple case studies of manufacturing and retail supply chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each case involves building models to estimate the resilience of the supply chain given uncertainties about, for example, business continuity, lumpy spare parts demand and operations of critical infrastructure. DBNs have been developed by using relevant data from historical empirical records and subjective judgement. Through the modelling practice, It has been found that some SC characteristics (i.e. level of integration, structure, SC operating system) play a vital role in shaping and quantifying DBNs and reduce their elicitation burden. Similarly, It has been found that the static and dynamic discretization methods of continuous variables affect the DBNs building process. I also studied the effect of level of integration, visibility, structure and SC operating system on the resilience level of SCs through the analysis of DBNs outputs. I found that the influence of the integration intensity on supply chain resilience can be revealed through understanding the dependency level of the focal firm on SC members resources. I have also noticed the relationship between the span of integration and the level of visibility to SC members. This visibility affects the capability of SC managers in the focal firm to identify the SC hazards and their consequences and, therefore, improve the planning for adverse events. I also explained how some decision rules related to SC operating system such as the inventory strategy could influence the intermediate ability of SC to react to adverse events. By interpreting my case data in the light of the existing academic literature, I can formulate some specific propositions

On the Theoretical Foundations and Principles of Organizational Safety Risk Analysis

This research covers a targeted review of relevant theories and technical domains related to the incorporation of organizational factors into technological systems risk. In the absence of a comprehensive set of principles and modeling guidelines rooted in theory and empirical studies, all models look equally good, or equally poor, with very little basis to discriminate and build confidence. Therefore, this research focused on the possibility of improving the theoretical foundations and principles for the field of Organizational Safety Risk Analysis. Also, a process for adapting a hybrid modeling technique, in order to operationalize the theoretical organizational safety frameworks, is proposed. Candidate ingredients are techniques from Risk Assessment, Human Reliability, Social and Behavioral Science, Business Process Modeling, and Dynamic Modeling. Then, as a realization of aforementioned modeling principles, an organizational safety risk framework, named Socio-Technical Risk Analysis (SoTeRiA)is developed. The proposed framework considers the theoretical relation between organizational safety culture, organizational safety structure/practices, and organizational safety climate, with specific distinction between safety culture and safety climate. A systematic view of safety culture and safety climate fills an important gap in modeling complex system safety risk, and thus the proposed organizational safety risk theory describing the theoretical relation between two concepts to bridge this gap. In contrast to the current safety causal models which do not adequately consider the multilevel nature of the issue, the proposed multilevel causal model explicitly recognizes the relationships among constructs at multiple levels of analysis. Other contributions of this research are in implementing the proposed organizational safety framework in the aviation domain, particularly the airline maintenance system. The US Federal Aviation Administration (FAA), which has sponsored this research over the past three years, has recognized the issue of organizational factors as one of the most critical questions in the quest to achieve 80% reduction in aviation accidents. An example of the proposed hybrid modeling environment including an integration of System Dynamics (SD), Bayesian Belief Network (BBN), Event Sequence Diagram (ESD), and Fault Tree (FT), is also applied in order to demonstrate the value of hybrid frameworks. This hybrid technique integrates deterministic and probabilistic modeling perspectives, and provides a flexible risk management tool

Causality Management and Analysis in Requirement Manuscript for Software Designs

For software design tasks involving natural language, the results of a causal investigation provide valuable and robust semantic information, especially for identifying key variables during product (software) design and product optimization. As the interest in analytical data science shifts from correlations to a better understanding of causality, there is an equal task focused on the accuracy of extracting causality from textual artifacts to aid requirement engineering (RE) based decisions. This thesis focuses on identifying, extracting, and classifying causal phrases using word and sentence labeling based on the Bi-directional Encoder Representations from Transformers (BERT) deep learning language model and five machine learning models. The aim is to understand the form and degree of causality based on their impact and prevalence in RE practice. Methodologically, our analysis is centered around RE practice, and we considered 12,438 sentences extracted from 50 requirement engineering manuscripts (REM) for training our machine models. Our research reports that causal expressions constitute about 32% of sentences from REM. We applied four evaluation metrics, namely recall, accuracy, precision, and F1, to assess our machine models’ performance and accuracy to ensure the results’ conformity with our study goal. Further, we computed the highest model accuracy to be 85%, attributed to Naive Bayes. Finally, we noted that the applicability and relevance of our causal analytic framework is relevant to practitioners for different functionalities, such as generating test cases for requirement engineers and software developers and product performance auditing for management stakeholders