Deriving safety cases for hierarchical structure in model-based development

Model-based development and automated code generation are increasingly used for actual production code, in particular in mathematical and engineering domains. However, since code generators are typically not qualified, there is no guarantee that their output satisfies the system requirements, or is even safe. Here we present an approach to systematically derive safety cases that argue along the hierarchical structure in model-based development. The safety cases are constructed mechanically using a formal analysis, based on automated theorem proving, of the automatically generated code. The analysis recovers the model structure and component hierarchy from the code, providing independent assurance of both code and model. It identifies how the given system safety requirements are broken down into component requirements, and where they are ultimately established, thus establishing a hierarchy of requirements that is aligned with the hierarchical model structure. The derived safety cases reflect the results of the analysis, and provide a high-level argument that traces the requirements on the model via the inferred model structure to the code. We illustrate our approach on flight code generated from hierarchical Simulink models by Real-Time Worksho

Performance assessment and diagnosis of refinery control loops

This paper discusses the application of control loop performance assessment (Desborough and Harris, 1992) in a refinery setting. In a large process it is not feasible to tailor the parameters of the algorithm to every individual control loop. A procedure is illustrated for selecting default values which make it possible to implement the technology on a refinery-wide scale. For instance, it is shown that the prediction horizon perameter in the CLPA algorithm can be set so that the analysis is sensitive to the persistent signals that cause loss of performance. Default values are suggested for refinery applications.A frequent cause of loss of performance in a control loop is a persistent oscillation due to a valve nonlinearity or a tuning fault. The paper presents an operational signatures in the form of an estimate of the closed loop impulse response that suggest the causes of such oscillations

Design and implementation of an ultrasonic sensor for rapid monitoring of industrial malolactic fermentation of wines

Ultrasound is an emerging technology that can be applied to monitor food processes. However, ultrasonic techniques are usually limited to research activities within a laboratory environment and they are not extensively used in industrial processes. The aim of this paper is to describe a novel ultrasonic sensor designed to monitor physical–chemical changes that occur in wines stored in industrial tanks. Essentially, the sensor consists of an ultrasonic transducer in contact with a buffer rod, mounted inside a stainless steel tube section. This structure allows the ultrasonic sensor to be directly installed in stainless steel tanks of an industrial plant. The operating principle of this design is based on the measurement of ultrasonic velocity of propagation. To test its proper operation, the sensor has been used to measure changes of concentration in aqueous samples and to monitor the progress of a malolactic fermentation of red wines in various commercial wineries. Results show the feasibility of using this sensor for monitoring malolactic fermentations in red wines placed in industrial tanks.Postprint (author's final draft

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI

Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Methods for Reducing False Alarms in Searches for Compact Binary Coalescences in LIGO Data

The LIGO detectors are sensitive to a variety of noise transients of non-astrophysical origin. Instrumental glitches and environmental disturbances increase the false alarm rate in the searches for gravitational waves. Using times already identified when the interferometers produced data of questionable quality, or when the channels that monitor the interferometer indicated non-stationarity, we have developed techniques to safely and effectively veto false triggers from the compact binary coalescences (CBCs) search pipeline