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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4

A Framework to Debug Diagnostic Matrices

Diagnostics is an important concept in system health and monitoring of space operations. Many of the existing diagnostic algorithms utilize system knowledge in the form of diagnostic matrix (D-matrix, also popularly known as diagnostic dictionary, fault signature matrix or reachability matrix) gleaned from physical models. But, sometimes, this may not be coherent to obtain high diagnostic performance. In such a case, it is important to modify this D-matrix based on knowledge obtained from other sources such as time-series data stream (simulated or maintenance data) within the context of a framework that includes the diagnostic/inference algorithm. A systematic and sequential update procedure, diagnostic modeling evaluator (DME) is proposed to modify D-matrix and wrapper logic considering least expensive solution first. This iterative procedure includes conditions ranging from modifying 0s and 1s in the matrix, or adding/removing the rows (failure sources) columns (tests). We will experiment this framework on datasets from DX challenge 2009.

Model-based↗

A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems’ Worldviews

Autonomous multi-robot systems, where a team of robots shares information to perform tasks that are beyond an individual robot’s abilities, hold great promise for a number of applications, such as planetary exploration missions. Each robot in a multi- robot system autonomously schedules which robots should perform a given task and when, using its worldview–the robot’s internal representation of its belief about the environment and other robots’ states. A key problem for operators is that robots’ worldviews can fall out of sync (often due to weak communication links), leading to desynchronization of the robots’ scheduling decisions and inconsistent emergent behavior (e.g., tasks not performed, or performed by multiple robots). Operators face the time-consuming and difficult task of making sense of the robots’ scheduling decisions, detecting de-synchronizations, and pinpointing their cause by comparing every robot’s worldview. To address these challenges, we introduce MOSAIC Viewer, a visual analytics system that helps operators (i) make sense of the robots’ schedules and (ii) detect and conduct a root cause analysis of the robots’ desynchronized worldviews. Over a year-long partnership with roboticists at the NASA Jet Propulsion Laboratory, a formative study was performed to identify the necessary system design requirements, which supports the design of the system. A qualitative study with 12 roboticists reveals that MOSAIC Viewer is faster- and easier-to-use than the users’ current approaches, and it allows them to stitch low-level details to formulate a high-level understanding of the robots’ schedules and detect and pin-point the cause of desynchronized worldviews.

Ma, Kwan-Liu↗

Method and apparatus to debug an integrated circuit chip via synchronous clock stop and scan

An apparatus and method for evaluating a state of an electronic or integrated circuit (IC), each IC including one or more processor elements for controlling operations of IC sub-units, and each the IC supporting multiple frequency clock domains. The method comprises: generating a synchronized set of enable signals in correspondence with one or more IC sub-units for starting operation of one or more IC sub-units according to a determined timing configuration; counting, in response to one signal of the synchronized set of enable signals, a number of main processor IC clock cycles; and, upon attaining a desired clock cycle number, generating a stop signal for each unique frequency clock domain to synchronously stop a functional clock for each respective frequency clock domain; and, upon synchronously stopping all on-chip functional clocks on all frequency clock domains in a deterministic fashion, scanning out data values at a desired IC chip state. The apparatus and methodology enables construction of a cycle-by-cycle view of any part of the state of a running IC chip, using a combination of on-chip circuitry and software.

47 OTHER INSTRUMENTATION↗

Automata-based Debugging for Dissimilar Parallel Tasks

AutomaDeD is a tool that automatically diagnoses performance and correctness faults in MPI applications. It creates time and control-flow models of processes in an MPI program, and when a failure occus, these models are analyzed probabilistically to find the origin of the failure. The output of the tool is MPI processes ranked by the abnormality degree and regions of code where faults are first manifested. The granularity of code region is an MPI call or a computation region between two MPI calls.

Source record↗

Per-instruction energy debugging using instruction sampling hardware

A processor utilizes instruction based sampling to generate sampling data sampled on a per instruction basis during execution of an instruction. The sampling data indicates what processor hardware was used due to the execution of the instruction. Software receives the sampling data and generates an estimate of energy used by the instruction based on the sampling data. The sampling data may include microarchitectural events and the energy estimate utilizes a base energy amount corresponding to the instruction executed along with energy amounts corresponding to the microarchitectural events in the sampling data. The sampling data may include switching events associated with hardware blocks that switched due to execution of the instruction and the energy estimate for the instruction is based on the switching events and capacitance estimates associated with the hardware blocks.

Wei, Shijia↗

Deep functional analysis of synII, a 770-kilobase synthetic yeast chromosome

INTRODUCTION Although much effort has been devoted to studying yeast in the past few decades, our understanding of this model organism is still limited. Rapidly developing DNA synthesis techniques have made a “build-to-understand” approach feasible to reengineer on the genome scale. Here, we report on the completion of a 770-kilobase synthetic yeast chromosome II (synII). SynII was characterized using extensive Trans-Omics tests. Despite considerable sequence alterations, synII is virtually indistinguishable from wild type. However, an up-regulation of translational machinery was observed and can be reversed by restoring the transfer RNA (tRNA) gene copy number. RATIONALE Following the “design-build-test-debug” working loop, synII was successfully designed and constructed in vivo. Extensive Trans-Omics tests were conducted, including phenomics, transcriptomics, proteomics, metabolomics, chromosome segregation, and replication analyses. By both complementation assays and SCRaMbLE (synthetic chromosome rearrangement and modification by loxP -mediated evolution), we targeted and debugged the origin of a growth defect at 37°C in glycerol medium. RESULTS To efficiently construct megabase-long chromosomes, we developed an I- Sce I–mediated strategy, which enables parallel integration of synthetic chromosome arms and reduced the overall integration time by 50% for synII. An I- Sce I site is introduced for generating a double-strand break to promote targeted homologous recombination during mitotic growth. Despite hundreds of modifications introduced, there are still regions sharing substantial sequence similarity that might lead to undesirable meiotic recombinations when intercrossing the two semisynthetic chromosome arm strains. Induction of the I- Sce I–mediated double-strand break is otherwise lethal and thus introduced a strong selective pressure for targeted homologous recombination. Since our strategy is designed to generate a markerless synII and leave the URA3 marker on the wild-type chromosome, we observed a tenfold increase in URA3 -deficient colonies upon I- Sce I induction, meaning that our strategy can greatly bias the crossover events toward the designated regions. By incorporating comprehensive phenotyping approaches at multiple levels, we demonstrated that synII was capable of powering the growth of yeast indistinguishably from wild-type cells (see the figure), showing highly consistent biological processes comparable to the native strain. Meanwhile, we also noticed modest but potentially significant up-regulation of the translational machinery. The main alteration underlying this change in expression is the deletion of 13 tRNA genes. A growth defect was observed in one very specific condition—high temperature (37°C) in medium with glycerol as a carbon source—where colony size was reduced significantly. We targeted and debugged this defect by two distinct approaches. The first approach involved phenotype screening of all intermediate strains followed by a complementation assay with wild-type sequences in the synthetic strain. By doing so, we identified a modification resulting from PCRTag recoding in TSC10 , which is involved in regulation of the yeast high-osmolarity glycerol (HOG) response pathway. After replacement with wild-type TSC10 , the defect was greatly mitigated. The other approach, debugging by SCRaMbLE, showed rearrangements in regions containing HOG regulation genes. Both approaches indicated that the defect is related to HOG response dysregulation. Thus, the phenotypic defect can be pinpointed and debugged through multiple alternative routes in the complex cellular interactome network. CONCLUSION We have demonstrated that synII segregates, replicates, and functions in a highly similar fashion compared with its wild-type counterpart. Furthermore, we believe that the iterative “design-build-test-debug” cycle methodology, established here, will facilitate progression of the Sc2.0 project in the face of the increasing synthetic genome complexity. SynII characterization. ( A ) Cell cycle comparison between synII and BY4741 revealed by the percentage of cells with separated CEN2-GFP dots, metaphase spindles, and anaphase spindles. ( B ) Replication profiling of synII (red) and BY4741 (black) expressed as relative copy number by deep sequencing. ( C ) RNA sequencing analysis revealed that the significant up-regulation of translational machinery in synII is induced by the deletion of tRNA genes in synII.

Shen, Yue↗