Autonomous power expert system

The goal of the Autonomous Power System (APS) program is to develop and apply intelligent problem solving and control technologies to the Space Station Freedom Electrical Power Systems (SSF/EPS). The objectives of the program are to establish artificial intelligence/expert system technology paths, to create knowledge based tools with advanced human-operator interfaces, and to integrate and interface knowledge-based and conventional control schemes. This program is being developed at the NASA-Lewis. The APS Brassboard represents a subset of a 20 KHz Space Station Power Management And Distribution (PMAD) testbed. A distributed control scheme is used to manage multiple levels of computers and switchgear. The brassboard is comprised of a set of intelligent switchgear used to effectively switch power from the sources to the loads. The Autonomous Power Expert System (APEX) portion of the APS program integrates a knowledge based fault diagnostic system, a power resource scheduler, and an interface to the APS Brassboard. The system includes knowledge bases for system diagnostics, fault detection and isolation, and recommended actions. The scheduler autonomously assigns start times to the attached loads based on temporal and power constraints. The scheduler is able to work in a near real time environment for both scheduling and dynamic replanning

An autonomous fault detection, isolation, and recovery system for a 20-kHz electric power distribution test bed

Future space explorations will require long term human presence in space. Space environments that provide working and living quarters for manned missions are becoming increasingly larger and more sophisticated. Monitor and control of the space environment subsystems by expert system software, which emulate human reasoning processes, could maintain the health of the subsystems and help reduce the human workload. The autonomous power expert (APEX) system was developed to emulate a human expert's reasoning processes used to diagnose fault conditions in the domain of space power distribution. APEX is a fault detection, isolation, and recovery (FDIR) system, capable of autonomous monitoring and control of the power distribution system. APEX consists of a knowledge base, a data base, an inference engine, and various support and interface software. APEX provides the user with an easy-to-use interactive interface. When a fault is detected, APEX will inform the user of the detection. The user can direct APEX to isolate the probable cause of the fault. Once a fault has been isolated, the user can ask APEX to justify its fault isolation and to recommend actions to correct the fault. APEX implementation and capabilities are discussed

Autonomous power system intelligent diagnosis and control

The Autonomous Power System (APS) project at NASA Lewis Research Center is designed to demonstrate the abilities of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution hardware. Knowledge-based software provides a robust method of control for highly complex space-based power systems that conventional methods do not allow. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis and control, the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space based power system. The operation of the Autonomous Power System as a whole is described and the responsibilities of the three elements - APEX, AIPS, and Brassboard - are characterized. A discussion of the methodologies used in each element is provided. Future plans are discussed for the growth of the Autonomous Power System

A Model-Based Expert System for Space Power Distribution Diagnostics

When engineers diagnose system failures, they often use models to confirm system operation. This concept has produced a class of advanced expert systems that perform model-based diagnosis. A model-based diagnostic expert system for the Space Station Freedom electrical power distribution test bed is currently being developed at the NASA Lewis Research Center. The objective of this expert system is to autonomously detect and isolate electrical fault conditions. Marple, a software package developed at TRW, provides a model-based environment utilizing constraint suspension. Originally, constraint suspension techniques were developed for digital systems. However, Marple provides the mechanisms for applying this approach to analog systems such as the test bed, as well. The expert system was developed using Marple and Lucid Common Lisp running on a Sun Sparc-2 workstation. The Marple modeling environment has proved to be a useful tool for investigating the various aspects of model-based diagnostics. This report describes work completed to date and lessons learned while employing model-based diagnostics using constraint suspension within an analog system

Design and Testing of Space Telemetry SCA Waveform

A Software Communications Architecture (SCA) Waveform for space telemetry is being developed at the NASA Glenn Research Center (GRC). The space telemetry waveform is implemented in a laboratory testbed consisting of general purpose processors, field programmable gate arrays (FPGAs), analog-to-digital converters (ADCs), and digital-to-analog converters (DACs). The radio hardware is integrated with an SCA Core Framework and other software development tools. The waveform design is described from both the bottom-up signal processing and top-down software component perspectives. Simulations and model-based design techniques used for signal processing subsystems are presented. Testing with legacy hardware-based modems verifies proper design implementation and dynamic waveform operations. The waveform development is part of an effort by NASA to define an open architecture for space based reconfigurable transceivers. Use of the SCA as a reference has increased understanding of software defined radio architectures. However, since space requirements put a premium on size, mass, and power, the SCA may be impractical for today s space ready technology. Specific requirements for an SCA waveform and other lessons learned from this development are discussed

Autonomous power expert system

The Autonomous Power Expert (APEX) system was designed to monitor and diagnose fault conditions that occur within the Space Station Freedom Electrical Power System (SSF/EPS) Testbed. APEX is designed to interface with SSF/EPS testbed power management controllers to provide enhanced autonomous operation and control capability. The APEX architecture consists of three components: (1) a rule-based expert system, (2) a testbed data acquisition interface, and (3) a power scheduler interface. Fault detection, fault isolation, justification of probable causes, recommended actions, and incipient fault analysis are the main functions of the expert system component. The data acquisition component requests and receives pertinent parametric values from the EPS testbed and asserts the values into a knowledge base. Power load profile information is obtained from a remote scheduler through the power scheduler interface component. The current APEX design and development work is discussed. Operation and use of APEX by way of the user interface screens is also covered

Climate Science, Development Practice, and Policy Interactions in Dryland Agroecological Systems

The literature on drought, livelihoods, and poverty suggests that dryland residents are especially vulnerable to climate change. However, assessing this vulnerability and sharing lessons between dryland communities on how to reduce vulnerability has proven difficult because of multiple definitions of vulnerability, complexities in quantification, and the temporal and spatial variability inherent in dryland agroecological systems. In this closing editorial, we review how we have addressed these challenges through a series of structured, multiscale, and interdisciplinary vulnerability assessment case studies from drylands in West Africa, southern Africa, Mediterranean Europe, Asia, and Latin America. These case studies adopt a common vulnerability framework but employ different approaches to measuring and assessing vulnerability. By comparing methods and results across these cases, we draw out the following key lessons: (1) Our studies show the utility of using consistent conceptual frameworks for vulnerability assessments even when quite different methodological approaches are taken; (2) Utilizing narratives and scenarios to capture the dynamics of dryland agroecological systems shows that vulnerability to climate change may depend more on access to financial, political, and institutional assets than to exposure to environmental change; (3) Our analysis shows that although the results of quantitative models seem authoritative, they may be treated too literally as predictions of the future by policy makers looking for evidence to support different strategies. In conclusion, we acknowledge there is a healthy tension between bottom-up/ qualitative/place-based approaches and top-down/quantitative/generalizable approaches, and we encourage researchers from different disciplines with different disciplinary languages, to talk, collaborate, and engage effectively with each other and with stakeholders at all levels

A novel pathway producing dimethylsulphide in bacteria is widespread in soil environments

The volatile compound dimethylsulphide (DMS) is important in climate regulation, the sulphur cycle and signalling to higher organisms. Microbial catabolism of the marine osmolyte dimethylsulphoniopropionate (DMSP) is thought to be the major biological process generating DMS. Here we report the discovery and characterisation of the first gene for DMSP-independent DMS production in any bacterium. This gene, mddA, encodes a methyltransferase that methylates methanethiol (MeSH) and generates DMS. MddA functions in many taxonomically diverse bacteria including sediment-dwelling pseudomonads, nitrogen-fixing bradyrhizobia and cyanobacteria, and mycobacteria, including the pathogen Mycobacterium tuberculosis. The mddA gene is present in metagenomes from varied environments, being particularly abundant in soil environments, where it is predicted to occur in up to 76% of bacteria. This novel pathway may significantly contribute to global DMS emissions, especially in terrestrial environments, and could represent a shift from the notion that DMSP is the only significant precursor of DMS

Prolonged institutional rearing is associated with atypically large amygdala volume and difficulties in emotion regulation

Early adversity, for example poor caregiving, can have profound effects on emotional development. Orphanage rearing, even in the best circumstances, lies outside of the bounds of a species-typical caregiving environment. The long-term effects of this early adversity on the neurobiological development associated with socio-emotional behaviors are not well understood. Seventy-eight children, who include those who have experienced orphanage care and a comparison group, were assessed. Magnetic resonance imaging (MRI) was used to measure volumes of whole brain and limbic structures (e.g. amygdala, hippocampus). Emotion regulation was assessed with an emotional go-nogo paradigm, and anxiety and internalizing behaviors were assessed using the Screen for Child Anxiety Related Emotional Disorders, the Child Behavior Checklist, and a structured clinical interview. Late adoption was associated with larger corrected amygdala volumes, poorer emotion regulation, and increased anxiety. Although more than 50% of the children who experienced orphanage rearing met criteria for a psychiatric disorder, with a third having an anxiety disorder, the group differences observed in amygdala volume were not driven by the presence of an anxiety disorder. The findings are consistent with previous reports describing negative effects of prolonged orphanage care on emotional behavior and with animal models that show long-term changes in the amygdala and emotional behavior following early postnatal stress. These changes in limbic circuitry may underlie residual emotional and social problems experienced by children who have been internationally adopted

Measuring category intuitiveness in unconstrained categorization tasks

What makes a category seem natural or intuitive? In this paper, an unsupervised categorization task was employed to examine observer agreement concerning the categorization of nine different stimulus sets. The stimulus sets were designed to capture different intuitions about classification structure. The main empirical index of category intuitiveness was the frequency of the preferred classification, for different stimulus sets. With 169 participants, and a within participants design, with some stimulus sets the most frequent classification was produced over 50 times and with others not more than two or three times. The main empirical finding was that cluster tightness was more important in determining category intuitiveness, than cluster separation. The results were considered in relation to the following models of unsupervised categorization: DIVA, the rational model, the simplicity model, SUSTAIN, an Unsupervised version of the Generalized Context Model (UGCM), and a simple geometric model based on similarity. DIVA, the geometric approach, SUSTAIN, and the UGCM provided good, though not perfect, fits. Overall, the present work highlights several theoretical and practical issues regarding unsupervised categorization and reveals weaknesses in some of the corresponding formal models