Search

We present trigonometric, photometric, and photographic distances to 1748 southern ($\delta \leq$0$^\circ$) M dwarf systems with $\mu \ge$ 0\farcs18 yr$^{-1}$, of which 1404 are believed to lie within 25 parsecs of the Sun. The stars have 6.67 $\leq$ $V_J$ $\leq$ 21.38 and 3.50 $\leq$ ($V_J-K_s$) $\leq$ 9.27, covering the entire M dwarf spectral sequence from M0.0V through M9.5V. This sample therefore provides a comprehensive snapshot of our current knowledge of the southern sky for the nearest M dwarfs that dominate the stellar population of the Galaxy. Roughly one-third of the 1748 systems, each of which has an M dwarf primary, have published high quality parallaxes, including 179 from the RECONS astrometry program. For the remaining systems, we offer photometric distance estimates that have well-calibrated errors. The bulk of these ($\sim$700) are based on new $V_JR_{KC}I_{KC}$ photometry acquired at the CTIO/SMARTS 0.9m telescope, while the remaining 500 primaries have photographic plate distance estimates calculated using SuperCOSMOS $B_JR_{59F}I_{IVN}$ photometry. Confirmed and candidate subdwarfs in the sample have been identified, and a census of companions is included., Comment: accepted by The Astronomical Journal on 25 July, 2014; 75 pages of tables, 11 figures

Here we present 1584 new southern proper motion systems with mu > 0.18 "/yr and 16.5 > R_59F > 18.0. This search complements the six previous SuperCOSMOS-RECONS (SCR) proper motion searches of the southern sky for stars within the same proper motion range, but with R_59F < 16.5. As in previous papers, we present distance estimates for these systems and find that three systems are estimated to be within 25 pc, including one, SCR 1546-5534, possibly within the RECONS 10 pc horizon at 6.7 pc, making it the second nearest discovery of the searches. We find 97 white dwarf candidates with distance estimates between 10 and 120 pc, as well as 557 cool subdwarf candidates. The subdwarfs found in this paper make up nearly half of the subdwarf systems reported from our SCR searches, and are significantly redder than those discovered thus far. The SCR searches have now found 155 red dwarfs estimated to be within 25 pc, including 10 within 10 pc. In addition, 143 white dwarf candidates and 1155 cool subdwarf candidates have been discovered. The 1584 systems reported here augment the sample of 4724 systems previously discovered in our SCR searches, and imply that additional systems fainter than R_59F = 18.0 are yet to be discovered., Comment: 11 pages of text, seven figures

We present 2817 new southern proper motion systems with 0.40 "/yr > mu > 0.18 "/yr and declination between -47 degrees and 00 degrees. This is a continuation of the SuperCOSMOS-RECONS (SCR) proper motion searches of the southern sky. We use the same photometric relations as previous searches to provide distance estimates based on the assumption that the objects are single main sequence stars. We find 79 new red dwarf systems predicted to be within 25 pc, including a few new components of previously known systems. Two systems - SCR 1731-2452 at 9.5 pc and SCR 1746-3214 at 9.9 pc - are anticipated to be within 10 pc. We also find 23 new white dwarf candidates with distance estimates of 15-66 pc, as well as 360 new red subdwarf candidates. With this search, we complete the SCR sweep of the southern sky for stars with mu > 0.18 "/yr and R_59F < 16.5, resulting in a total of 5042 objects in 4724 previously unreported proper motion systems. Here we provide selected comprehensive lists from our SCR proper motion search to date, including 152 red dwarf systems estimated to be within 25 pc (nine within 10 pc), 46 white dwarfs (ten within 25 pc), and 598 subdwarf candidates. The results of this search suggest that there are more nearby systems to be found at fainter magnitudes and lower proper motion limits than those probed so far., Comment: 47 pages, 16 of text. 7 figures

Kharkov modeling is a modeling technique that is widely useful for dependability analysis of complex fault tolerant systems. It is very flexible in the type of systems and system behavior it can model. It is not, however, the most appropriate modeling technique for every modeling situation. The first task in obtaining a reliability or availability estimate for a system is selecting which modeling technique is most appropriate to the situation at hand. A person performing a dependability analysis must confront the question: is Kharkov modeling most appropriate to the system under consideration, or should another technique be used instead? The need to answer this gives rise to other more basic questions regarding Kharkov modeling: what are the capabilities and limitations of Kharkov modeling as a modeling technique? How does it relate to other modeling techniques? What kind of system behavior can it model? What kinds of software tools are available for performing dependability analyses with Kharkov modeling techniques? These questions and others will be addressed in this tutorial.

From the viewpoint of a project manager responsible for the V&V (verification and validation) of a software system, mathematical verification techniques provide a possibly useful orthogonal dimension to otherwise standard empirical testing. However, the value they add to an empirical testing regime both in terms of coverage and in fault detection has been difficult to quantify. Furthermore, potential cost savings from replacing testing with mathematical verification techniques cannot be realized until the tradeoffs and synergies can be formulated. Integration of formal verification with empirical testing is also difficult because the idealized view of mathematical verification providing a correctness proof with total coverage is unrealistic and does not reflect the limitations imposed by computational complexity of mathematical techniques. This paper first describes a framework based on software reliability and formalized fault models for a theory of software design fault detection - and hence the utility of various tools for debugging. It then describes a utility model for integrating mathematical and empirical techniques with respect to fault detection and coverage analysis. It then considers the optimal combination of black-box testing, white-box (structural) testing, and formal methods in V&V of a software system. Using case studies from NASA software systems, it then demonstrates how this utility model can be used in practice.

The Hybrid Automated Reliability Predictor (HARP) integrated Reliability (HiRel) tool system for reliability/availability prediction offers a toolbox of integrated reliability/availability programs that can be used to customize the user's application in a workstation or nonworkstation environment. HiRel consists of interactive graphical input/output programs and four reliability/availability modeling engines that provide analytical and simulative solutions to a wide host of reliable fault-tolerant system architectures and is also applicable to electronic systems in general. The tool system was designed to be compatible with most computing platforms and operating systems, and some programs have been beta tested, within the aerospace community for over 8 years. Volume 1 provides an introduction to the HARP program. Comprehensive information on HARP mathematical models can be found in the references.

The use of simulation is described and it is contrasted to analytical solution techniques for evaluation of analytical reliability models. The role importance sampling plays in simulation of models of this type was also discussed. The simulator tool used for our analysis is described. Finally, the use of the simulator tool was demonstrated by applying it to evaluate the reliability of a fault tolerant hypercube multiprocessor intended for spacecraft designed for long duration missions. The reliability analysis was used to highlight the advantages and disadvantages offered by simulation over analytical solution of Markovian and non-Markovian reliability models.

Reliability analysis of fault tolerant computer systems for critical applications is complicated by several factors. These modeling difficulties are discussed and dynamic fault tree modeling techniques for handling them are described and demonstrated. Several advanced fault tolerant computer systems are described, and fault tree models for their analysis are presented. HARP (Hybrid Automated Reliability Predictor) is a software package developed at Duke University and NASA Langley Research Center that is capable of solving the fault tree models presented.

Issues related to automating the process of fault management (fault diagnosis and response) for data management systems are considered. Substantial benefits are to be gained by successful automation of this process, particularly for large, complex systems. The use of graph-based models to develop a computer assisted fault management system is advocated. The general problem is described and the motivation behind choosing graph-based models over other approaches for developing fault diagnosis computer programs is outlined. Some existing work in the area of graph-based fault diagnosis is reviewed, and a new fault management method which was developed from existing methods is offered. Our method is applied to an automatic telescope system intended as a prototype for future lunar telescope programs. Finally, an application of our method to general data management systems is described.

The Data Management System Augmented Monitoring and Diagnosis Application (DMS AMDA) is currently under development at NASA Ames Research Center (ARC). It will provide automated monitoring and diagnosis capabilities for the Space Station Freedom (SSF) Data Management System (DMS) in the Control Center Complex (CCC) at NASA Johnson Space Center. Several advanced automation applications are under development for use in the CCC for other SSF subsystems. The DMS AMDA, however, is the first application to utilize digraph failure analysis techniques and the Extended Realtime FEAT (ERF) application as the core of its diagnostic system design, since the other projects were begun before the digraph tools were available. Model-based diagnosis and expert systems techniques will provide additional capabilities and augment ERF where appropriate. Utilization of system knowledge captured in the design phase of a system in digraphs should result in both a cost savings and a technical advantage during implementation of the diagnostic software. This paper addresses both the programmatic and technical considerations of this approach, and describes the software design and initial prototyping effort.

Some of the features and capabilities of the DEPEND simulation-based modeling tool are described. A study of a 1553B local bus subsystem of the Space Station Freedom Data Management System (SSF DMS) is used to illustrate some types of system behavior that can be important to reliability and performance evaluations of this type of spacecraft. A DEPEND model of the subsystem is used to illustrate how these types of system behavior can be modeled, and shows what kinds of engineering and design questions can be answered through the use of these modeling techniques. DEPEND's process-based simulation environment is shown to provide a flexible method for modeling complex interactions between hardware and software elements of a fault-tolerant computing system.

Three candidate fault tolerant hypercube architectures are modeled, their reliability analyses are compared, and the resulting implications of these methods of incorporating fault tolerance into hypercube multiprocessors are discussed. In the course of performing the reliability analyses, the use of HARP and fault trees in modeling sequence dependent system behaviors is demonstrated.

One of the frequently cited shortcomings of fault-tree models, their inability to model so-called sequence dependencies, is discussed. Several sources of such sequence dependencies are discussed, and new fault-tree gates to capture this behavior are defined. These complex behaviors can be included in present fault-tree models because they utilize a Markov solution. The utility of the new gates is demonstrated by presenting several models of the fault-tolerant parallel processor, which include both hot and cold spares.

This paper describes a unified approach to the problem of solving large realistic reliability models. The methodology integrates behavioral decomposition, state trunction, and efficient sparse matrix-based numerical methods. The use of fault trees, together with ancillary information regarding dependencies to automatically generate the underlying Markov model state space is proposed. The effectiveness of this approach is illustrated by modeling a state-of-the-art flight control system and a multiprocessor system. Nonexponential distributions for times to failure of components are assumed in the latter example. The modeling tool used for most of this analysis is HARP (the Hybrid Automated Reliability Predictor).