Mitre Corporation

About: Mitre Corporation is a company organization based out in Bedford, Massachusetts, United States. It is known for research contribution in the topics: Air traffic control & National Airspace System. The organization has 4884 authors who have published 6053 publications receiving 124808 citations. The organization is also known as: Mitre & MITRE.

Chaos: a real phenomenon in power electronics

Abstract: An introductory tutorial on chaotic behavior in DC-DC convertors is presented. Chaos is characterized by an emipirical spectrum which has a continuous component, and may even have no discrete components. Chaotic behavior frequently occurs when a power converter operates in a protective mode such as in a short-circuit or overload condition. Chaotic behavior in power converters is described in terms of phase-plane (state-space) trajectories. A description is given of a particular form of buck regulator circuit without PWM (pulse-width modulation) drive and without current sensing, i.e. a form of ripple-regulator. Simulation and experimental data for the circuit are presented and discussed. Two other similar circuits exhibiting chaotic behavior are also considered. >

A Safety Standard Approach for Fully Autonomous Vehicles

Abstract: Assuring the safety of self-driving cars and other fully autonomous vehicles presents significant challenges to traditional software safety standards both in terms of content and approach. We propose a safety standard approach for fully autonomous vehicles based on setting scope requirements for an overarching safety case. A viable approach requires feedback paths to ensure that both the safety case and the standard itself co-evolve with the technology and accumulated experience. An external assessment process must be part of this approach to ensure lessons learned are captured, as well as to ensure transparency. This approach forms the underlying basis for the UL 4600 initial draft standard.

A fast normal random number generator

Abstract: A method is presented for generating pseudorandom numbers with a normal distribution. The technique uses the ratio of uniform deviates method discovered by Kinderman and Monahan with an improved set of bounding curves. An optimized quadratic fit reduces the expected number of logarithm evaluations to 0.012 per normal deviate. The method gives a theoretically correct distribution and can be implemented in 15 lines of FORTRAN. Timing and source size comparisons are made with other methods for generating normal deviates. The proposed algorithm compares favorably with some of the better algorithms.

Confirmation Bias in Complex Analyses

Abstract: Most research works investigating the confirmation bias has used abstract experimental tasks where participants drew inferences from just a few items of evidence. The experiment reported in this paper investigated the confirmation bias in a complex analysis task that is more characteristic of law enforcement investigations, financial analysis, and intelligence analysis. Participants were professionals, half of whom had intelligence analysis experience. The effectiveness of a procedure designed to mitigate the confirmation bias, called analysis of competing hypotheses (ACH), was tested. Results showed a confirmation bias for both experience groups, but ACH significantly reduced bias only for participants without intelligence analysis experience. Confirmation bias manifested as a weighting bias, not as an interpretation bias. Participants tended to agree on the interpretation of evidence (i.e., whose hypothesis was supported by the evidence) but tended to disagree on the importance of the evidence-giving more weight to the evidence that supported their preferred hypothesis and less weight to evidence that disconfirmed it.

Method and system for finding similar records in mixed free-text and structured data

Abstract: A technique for data mining where the available data contains both structured as well as unstructured (free-text) data. The present invention combines together the information available from different types of data to provide a single similarity score indicating the degree of similarity between records. Thus, a data evaluation application selects two records from a database and compares corresponding fields from the two records. The application determines whether to apply a nominal matching process, an ordinal matching process, or a vector-space matching process depending on the type of data in each pair of corresponding fields. The application sums the matching scores for all the fields in the records to compute the similarity score.