INL Control System Situational Awareness Technology Annual Report 2012

The overall goal of this project is to develop an interoperable set of tools to provide a comprehensive, consistent implementation of cyber security and overall situational awareness of control and sensor network implementations. The operation and interoperability of these tools will fill voids in current technological offerings and address issues that remain an impediment to the security of control systems. This report provides an FY 2012 update on the Sophia, Mesh Mapper, Intelligent Cyber Sensor, and Data Fusion projects with respect to the year-two tasks and annual reporting requirements of the INL Control System Situational Awareness Technology report (July 2010)

Assessing the readiness to implement lean in healthcare institutions – a case study

We develop a lean readiness framework and an assessment methodology to quantify the readiness of healthcare institutions for implementing lean. We use stakeholder theory and work with a lean implementation team responsible for process improvement in a healthcare group to develop the framework. The framework uses fuzzy based input derived from the stakeholders of the healthcare institution to generate an overall ranking through ideal solution technique. The assessment method derives input from the readiness scores shared by various stakeholders. The ranking suggests future improvement areas to prepare the healthcare institution for a lean implementation project. We provide an alternative perspective of assessing the lean readiness of healthcare institutions before beginning a lean implementation project for both researchers and practitioners. Our research is the first to develop a lean readiness framework for healthcare institutions and demonstrate it using an assessment technique

System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

The interplay between automatic and controlled processes: experimental contributions to dual-process theories of cognition

Since its beginnings, psychological science has frequently used dichotomous categories to describe behavior and mental phenomena. The most traditional dual models have impactfully equipped both the scientific and folkloristic psychological vocabularies of such dichotomies (e.g., conscious vs. unconscious, logic vs. creative, rational vs. emotional). However, while offering an affordable account of how the human cognitive system works, these models appear too simplistic. Substantially, they are grounded upon the findings obtained in decades of results in almost all the psychological fields, from perception to social processes, which have been later merged into a broad systemic theory of human cognition. However, this dual-system theory, which proposed to unify all cognitive dualities into System 1 (automatic, unconscious, fast, effortless, intuitive, and so on) and System 2 (controlled, conscious, slow, effortful, rational, and so on) entities, lacks a systematic investigation of its basic assumptions: for instance, that the features are aligned within and complementary between the two systems. These properties are essential for the tenets of the theory since a systemic theory should postulate the interdependence and interrelation of the elements constituting a system. In this view, the central thread linking all the experimental contributions in the present work is that the dual-system theory should resist when investigating cognitive performance either at low- and at high-level of complexity (complexity defined as the variety of mechanisms implicated in the phenomena of interest). Through seven studies conducted in three research lines, addressing temporal attention, task-switching, and decision-making, the interaction between automatic and controlled features in each process has shown to be the rule rather than the exception. Thus, the results presented in this work support the idea that the dual-system theory current formulation has a weak explanatory power, suggesting that decomposition approaches aimed at disentangling the contribution of qualitatively and quantitatively different mechanisms in each cognitive process are needed to advance or put aside dual-process theories