Uncertainty Management Approach Research Articles

Uncertainty Management in Assessment of FMEA Expert Based on Negation Information and Belief Entropy.

The failure mode and effects analysis (FMEA) is a commonly adopted approach in engineering failure analysis, wherein the risk priority number (RPN) is utilized to rank failure modes. However, assessments made by FMEA experts are full of uncertainty. To deal with this issue, we propose a new uncertainty management approach for the assessments given by experts based on negation information and belief entropy in the Dempster-Shafer evidence theory framework. First, the assessments of FMEA experts are modeled as basic probability assignments (BPA) in evidence theory. Next, the negation of BPA is calculated to extract more valuable information from a new perspective of uncertain information. Then, by utilizing the belief entropy, the degree of uncertainty of the negation information is measured to represent the uncertainty of different risk factors in the RPN. Finally, the new RPN value of each failure mode is calculated for the ranking of each FMEA item in risk analysis. The rationality and effectiveness of the proposed method is verified through its application in a risk analysis conducted for an aircraft turbine rotor blade.

Multivendor fully automatic uncertainty management approaches for the intuitive representation of DME fluid accumulations in OCT images

Diabetes represents one of the main causes of blindness in developed countries, caused by fluid accumulations in the retinal layers. The clinical literature defines the different types of diabetic macular edema (DME) as cystoid macular edema (CME), diffuse retinal thickening (DRT), and serous retinal detachment (SRD), each with its own clinical relevance. These fluid accumulations do not present defined borders that facilitate segmentational approaches (specially the DRT type, usually not taken into account by the state of the art for this reason) so a diffuse paradigm is used for its detection and visualization. In this paper, we propose three novel approaches for the representation and characterization of these types of DME. A baseline proposal, using a convolutional neural network as backbone, another based on transfer learning from a general domain, and a third approach exploiting information of regions without a defined label. Overall, our baseline proposal obtained an AUC of 0.9583 ± 0.0093, the approach pretrained with a general-domain dataset an AUC of 0.9603 ± 0.0087, and the approach pretrained in the domain taking advantage of uncertainty, an AUC of 0.9619 ± 0.0073.

"I wish that there was more info": characterizing the uncertainty experienced by carriers of pathogenic ATM and/or CHEK2 variants.

Little is known about what uncertainties patients experience after being identified to carry a pathogenic variant in a moderate-risk cancer gene as a result of undergoing multigene panel testing for cancer susceptibility. Data regarding cancer risk estimates and effectiveness of risk management strategies for these variants continues to evolve, which has the potential to evoke uncertainty. Acknowledging uncertainty during pre- and post-test discussions is imperative to helping individuals to adapt to their results. A better understanding of this population's experience of uncertainty is needed to facilitate such discussions and is the aim of the current study. Semi-structured interviews (30-60min in length), informed by Han and colleagues' taxonomy of uncertainty in clinical genomic sequencing, were conducted to assess motivations to pursue genetic testing, areas of perceived uncertainty, and strategies for managing uncertainty among 20 carriers of pathogenic variants in two moderate-risk genes, ATM and CHEK2. We found that participants pursue genetic testing with the expectation that results will clarify cancer risks and approaches to management. Participants experience uncertainties aligning with Han's taxonomy relating to the ambiguity of specific cancer risk estimates and effectiveness of certain risk management strategies. These uncertainties influenced decisions around the uptake of risk management strategies, which were additionally impacted by clinicians' uncertainty towards such strategies. Participants employ a variety of uncertainty management approaches to cope with their anxieties. Clinicians may wish to use these findings to facilitate patient adaptation to the implications of multigene panel testing for cancer susceptibility during both pre- and post-test counseling sessions.

Innovation intelligence and its role in environmental uncertainty management: a conceptual framework

PurposeOrganizations need actionable knowledge to cope with environmental uncertainty, make effective decisions and develop innovation strategies. Since innovation evolves through generations, the present study aims to unravel and define innovation intelligence, considering this transformation, and discuss how environmental uncertainty is resolved in each one.Design/methodology/approachThis article is a conceptual paper that employs a typology and model approach in its research designFindingsContexts are categorized into ordered and unordered (according to the Cynefin framework), in which intelligence with prediction and control approaches are applied for uncertainty management, respectively. Also, the three generations of innovation management, namely, technology push, market pull and a combination of these two (hybrid), intelligence benefit from a prediction approach, and in the networked (collaborative) generation, intelligence takes advantage of a control approach.Research limitations/implicationsThe conceptual approach adopted in this research is limited to, and focused on, understanding intelligence, innovation intelligence and presenting preliminary insights into their relationship with uncertainty management.Practical implicationsThis research guides decision-makers to adopt the appropriate intelligence approach to manage uncertainty during their innovation management process and illustrate it by the industry uncertainty matrix and COVID-19 pandemic situation.Originality/valueThis study proposes a typology of intelligence based on different knowledge pyramids. Also, it introduces innovation intelligence and its relation to knowledge management and environmental uncertainty management that has not yet been clearly addressed in the literature. Moreover, it determines the uncertainty management approaches for each variant of innovation intelligence.

Ontology-based uncertainty management approach in designing of robust decision workflows

The formal representation and capturing of uncertainty knowledge are always essential but difficult. Typically, the uncertainties due to incompleteness and inaccuracies of model information in engineering design necessitate the designing of robust decision workflows to improve the quality of process/product in variations. This requests extending a designer's abilities in managing various uncertainties in system design and making decisions that are robust, flexible, and comprehensive. To enable the management of various uncertainties, in this paper, we propose an ontology for robust design and a template-based ontological method that is employed to design decision workflows. We achieve the aforementioned goals through the identification of: (1) procedural knowledge – defining a procedure of designing robust decision workflows, including the sequence of activities, to determine the right combination of design information for a specific type of uncertainty, and (2) declarative knowledge – developing a frame-based ontology for the formal representation of tacit knowledge to capture and document the re-usable information of a robust design by utilising the process templates. We demonstrate the efficacy by carrying out the robust design of the hot rod rolling process based on the analysis and synthesis of the processing-microstructure (cooling module) and the microstructure-mechanical (rod module) simulation models.

A Novel Uncertainty Management Approach for Air Combat Situation Assessment Based on Improved Belief Entropy.

Uncertain information exists in each procedure of an air combat situation assessment. To address this issue, this paper proposes an improved method to address the uncertain information fusion of air combat situation assessment in the Dempster–Shafer evidence theory (DST) framework. A better fusion result regarding the prediction of military intention can be helpful for decision-making in an air combat situation. To obtain a more accurate fusion result of situation assessment, an improved belief entropy (IBE) is applied to preprocess the uncertainty of situation assessment information. Data fusion of assessment information after preprocessing will be based on the classical Dempster’s rule of combination. The illustrative example result validates the rationality and the effectiveness of the proposed method.

Assessing climate change adaptation strategies—the case of drought and heat wave in the French nuclear sector

Nuclear energy is a very important component of overall power supply in France. If the effects of future extreme weather events or climate shifts are not addressed, energy systems will be highly vulnerable to extreme weather events or shifts in weather patterns, such as changes in precipitation. Because of the deep uncertainties involved in climate projections and response strategies, any strategy implementation should perform adequately regardless of which scenario actually materialises. In this paper, we analyse the effects of drought and heat wave in the French nuclear energy sector using the Strategy Robustness Visualisation Method. The key feature of the method is the modelling of uncertainty of the quantitative indicators by (min, max) values plotted on radar plots such that each strategy option’s performance can be visually inspected for robustness. The method can be utilised as a “module” of its own in different uncertainty management approaches. Based on the case study, the presented adaptation strategies “Maintaining industrial production and final demand” and “Smart grid infrastructure” were more robust than the “No planned or automatic adaptation”.

An improved belief entropy–based uncertainty management approach for sensor data fusion

In real applications, sensors may work in complicated environments; thus, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. To address this issue, an improved belief entropy–based uncertainty management approach for sensor data fusion is proposed in this article. First, the sensor report is modeled as the body of evidence in Dempster–Shafer framework. Then, the uncertainty measure of each body of evidence is based on the subjective uncertainty represented as the evidence sufficiency and evidence importance, and the objective uncertainty measure is expressed as the improved belief entropy. Evidence modification of conflict sensor data is based on the proposed uncertainty management approach before evidence fusion with Dempster’s rule of combination. Finally, the fusion result can be applied in real applications. A case study on sensor data fusion for fault diagnosis is presented to show the rationality of the proposed method.

A Context-Driven Framework for Proactive Decision Support With Applications

Major challenges anticipated in the future C4ISR (command, control, communications, computers, intelligence, surveillance, and reconnaissance) operations involve rapid mission planning/ re-planning in highly dynamic, asymmetric, unpredictable, and network-centric environments. Developing decision support for such complex mission environments requires automated processing, interpretation, and development of proactive decisions using large volumes of structured, unstructured, and semi-structured data, while simultaneously decreasing the time necessary to arrive at a decision. To overcome this data deluge, there is a need for mastering information dominance via acquisition, fusion, and transfer of the right data/information/knowledge from the right sources in the right mission context to the right decision-maker (DM) at the right time for the right purpose (6R). The fundamental challenge in achieving the 6R is to conceive a generic framework that encompasses the dynamics of relevant contextual elements, their interdependence and correlation to the current and evolving situation, while taking into account the cognitive status of the DM. In this paper, we propose a context-driven proactive decision support (PDS) framework that comprises: 1) adaptive model-based dynamic graph models (e.g., Dynamic Hierarchical Bayesian Networks) and the concomitant inference algorithms for context representation, inference, and forecasting, 2) information selection, valuation, and prioritization methods for context-driven operations, including uncertainty management approaches, and 3) application of PDS concepts for courses of action recommendations across representative maritime operations.

Modeling uncertainties involved with software development with a stochastic Petri net

Abstract: Due to the rapid growth of the IT industry as well as the high failure rate of software development projects, it is highly desirable to develop a qualitative approach for dealing with uncertainties in the software development process. In order to manage risk and uncertainty, a software project uncertainty management approach based on a generalized stochastic Petri net is proposed. It consists of two main components: model building based on a Petri net and a Markov process algorithm. The technique is presented with an example. Finally, the proposed model is demonstrated with a real-world case.