Search

The main elements of the statistical model of packed hole assemblies (PHA) design for drilling holding sections in conditions of information uncertainty are formalized and described. A constraint system has been given for the angle maintenance conditions of wellbore direction and dynamic stability of bottom hole assembly (BHA) lateral vibrations. The influence of the information uncertainty of some factors (angle, parameters of the drilling practice, presence of local caverns, etc.) on risk indicators has been analyzed according to the results of the numerical calculations. It has been determined that the risks of angle maintenance disturbance are significantly influenced by the angle and weight on the bit (WOB), and dynamic stability conditions, such as angle and rotation frequency. Risks of multi-supported BHAs which have been designed for conditions of minimizing bit side force, dynamic stability and include 4-6 stabilizers. [ABSTRACT FROM AUTHOR]

The design process involves multiple stages to provide the solution for an actual or perceived need. The process needs different information at different stages to generate the desired output, but most of the time, the information is uncertain. Information uncertainty almost always makes design decision critical. The present work aims to reduce this information uncertainty in product design by reusing previous design knowledge. Reusing an existing ontology is an obvious way to minimize information uncertainty, and consequently, it can shorten the manufacturing lead time. However, the existing techniques have a few gaps that are still unaddressed. To fill these gaps, a methodology based on bionic engineering is proposed in this work for the purpose of design reuse in product development. Here, for the representation and manipulation of the knowledge, bionic-based reasoning approach is used for the development of the new product with the help of bionic reverse engineering. The proposed methodology is validated by illustrating an example of shoe design suitable to maneuver ill-conditioned roads. This work would be useful to the design and manufacturing practitioner in product development and decision making. [ABSTRACT FROM AUTHOR]

Because of the rapid growth in the scale and complexity of information networks, self-organizing systems are increasingly being used to realize novel network control systems that are highly scalable, adaptable, and robust. However, the uncertainty of information (with regard to incompleteness, vagueness, and dynamics) in self-organizing systems makes it difficult for them to work appropriately in accordance with the network state. In this study, we apply a model of the collective decision-making of animal groups to enable self-organizing control mechanisms to adapt to information uncertainty. Specifically, we apply a mathematical model of collective decision-making that is known as the effective leadership model (ELM). In the ELM, informed individuals (those who are experienced or well-informed) take the role of leading the others. In contrast, uninformed individuals (those who perceive only local information) follow neighboring individuals. As a result of the collective behavior of informed/uninformed individuals, the animal group achieves consensus. We consider a self-organizing control mechanism using potential-based routing with an optimal control, and propose a mechanism for determining a data-packet forwarding scheme based on the ELM. Through evaluation by simulation, we show that, in a situation in which the perceived information is incomplete and dynamic, nodes can forward data packets in accordance with the network state by applying the ELM. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer among Canadian women and the second cause of death from cancer. Fine needle aspirate (FNA) is a technology used to investigate early breast tumors to detect cancer. In this paper, we demonstrate the application of a new ordered weighted averaging operator (OWA) to the problem of breast tumor classification. The OWA operator employs the Laplace distribution to calculate the weight vector to aggregate the uncertain information about the breast tumors. The aggregated information is used along with the tumor label, i.e., benign or malignant, to train a nearest neighbor, support vector machine, and logistic regression classifiers. The result of this study based on the nearest neighbor classifier achieves 99.71% accuracy that outperforms other studies that utilize other OWA operators using the same dataset. [ABSTRACT FROM AUTHOR]

The iterative and developing nature of designing a building involves the specification and handling of vague, imprecise, and incomplete information. A crucial factor for mitigating the impact of these uncertainties on the decision-making process is to effectively quantify and communicate them among the project stakeholders. The interactive visualization of 3D building models provides great support for evaluating building designs. However, the currently available visualization methods of the available authoring tools do not incorporate the potential uncertainties associated with the geometric and semantic information of building elements. Currently, building models appear precise and certain, even in the early design stages, which can lead to false assumptions and model evaluations, affecting the decisions made throughout the design stages. Hence, this paper presents a set of visualization approaches, including intrinsic, extrinsic, animation, and walkthroughs, that have been developed to present the uncertainties associated with the building elements' information. The efficiency of the approaches developed in this study was evaluated through an online survey and interviews. More specifically, the approaches were compared in terms of intuitiveness, applicability, and acceptance. The evaluation results positively indicated the participants' ability to understand the amount and impact of the uncertainties on the design by using the developed approaches. [ABSTRACT FROM AUTHOR]