Status Of Spacecraft Research Articles

Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit

IntroductionDigital twins, a form of artificial intelligence, are virtual representations of the physical world. In the past 20 years, digital twins have been utilized to track wind turbines' operations, monitor spacecraft's status, and even create a model of the Earth for climate research. While digital twins hold much promise for the neurocritical care unit, the question remains on how to best establish the rules that govern these models. This model will expand on our group’s existing digital twin model for the treatment of sepsis.MethodsThe authors of this project collaborated to create a Direct Acyclic Graph (DAG) and an initial series of 20 DELPHI statements, each with six accompanying sub-statements that captured the pathophysiology surrounding the management of acute ischemic strokes in the practice of Neurocritical Care (NCC). Agreement from a panel of 18 experts in the field of NCC was collected through a 7-point Likert scale with consensus defined a-priori by ≥ 80% selection of a 6 (“agree”) or 7 (“strongly agree”). The endpoint of the study was defined as the completion of three separate rounds of DELPHI consensus. DELPHI statements that had met consensus would not be included in subsequent rounds of DELPHI consensus. The authors refined DELPHI statements that did not reach consensus with the guidance of de-identified expert comments for subsequent rounds of DELPHI. All DELPHI statements that reached consensus by the end of three rounds of DELPHI consensus would go on to be used to inform the construction of the digital twin model.ResultsAfter the completion of three rounds of DELPHI, 93 (77.5%) statements reached consensus, 11 (9.2%) statements were excluded, and 16 (13.3%) statements did not reach a consensus of the original 120 DELPHI statements.ConclusionThis descriptive study demonstrates the use of the DELPHI process to generate consensus among experts and establish a set of rules for the development of a digital twin model for use in the neurologic ICU. Compared to associative models of AI, which develop rules based on finding associations in datasets, digital twin AI created by the DELPHI process are easily interpretable models based on a current understanding of underlying physiology.

291: DEVELOPING DELPHI EXPERT CONSENSUS RULES FOR A DIGITAL TWIN MODEL OF THE NEURO CRITICAL CARE UNIT

Introduction: In the past 20 years, the concept of the digital twin has been utilized in other fields to track the operations of wind turbines, monitor the status of spacecraft, and even create a model of the Earth for climate research While artificial intelligence holds much promise for the neurocritical care unit, these models should be clearly based on an understanding of underlying physiological variables rather than be opaque “black box” models. Our group has previously created such a digital twin model to predict acute response to treatment of sepsis. This project expands on our previous work by developing a similar model for the critical care of acute ischemic stroke. Methods: A panel of 18 experts in the field of critical care reviewed the clinical practice and pathophysiological statements related to neurology critical care. The core group of investigators developed statements based on a Directed Acyclic Graph (DAG) describing pathophysiology surrounding acute neurological issues encountered in the practice of Neurocritical Care (NCC). A modified Delphi method (3 rounds) was used to gauge agreement on 20 statements (120 sub statements) using a 7-point Likert scale. Agreement was defined a-priori by >80% selection of a 6 (“agree”) or 7 (“strongly agree”). Results: Consensus was achieved on 58 (48.3%) expert statements after completion of 2 rounds. After completion of 3rd round, the level of consensus increased to 93 (77.5%) expert statements. Some statements garnered 100% consensus in the first round of DELPHI such as “Reperfusion of ischemic stroke can lead to improvement of stroke.” Other statements required revision to capture the nuances of clinical practice before gaining consensus. For example, when the severity of infection was incorporated into the statement “Infection can lead to low blood pressure.” consensus increased from 56% to 100%. Conclusions: Our study demonstrated the feasibility of application of the DELPHI process to generate consensus among experts for use in development of a “digital twin” artificial intelligence model for use in the Neuro ICU. Compared to other models which rely on “black-box” associative artificial intelligence, our proposed causal AI model is based on a solid foundation of expert rules and casual mechanisms.

OBJECTIVES AND STRUCTURE OF SMART MONITORING OF SPACECRAFT STATUS IN THE COURSE OF MISSION CONTROL

The paper discusses objectives and structure of smart monitoring of spacecraft status in the course of mission control. It defines the place of the monitoring and the notion of analysis used in the context of solving mission control problems. It summarizes the current state of monitoring techniques used in the course of controlling the missions of modern spacecraft and orbital complexes. It provides data on the results of the use of computerized data analysis techniques for our country’s space assets that are currently in operation. It articulates major drawbacks in the monitoring process, which are getting more exacerbated with the current trends in space programs development. It proposes a remedy for the drawbacks by means of development and introduction of procedures that will make telemetry data analysis smarter. The paper introduces the notion of smart analysis and its key advantages in technical applications. It identifies the key prerequisites for expanding the computerization of the monitoring process involved in the spacecraft mission control. It outlines basic operational principles of a smart monitoring system taking into account its operational peculiarities. Key words: spacecraft, mission control, introduction of smart technologies, status analysis, monitoring system.

Objectives and structure of smart monitoring of spacecraft status in the course of mission control

The paper discusses objectives and structure of smart monitoring of spacecraft status in the course of mission control. It defines the place of the monitoring and the notion of analysis used in the context of solving mission control problems. It summarizes the current state of monitoring techniques used in the course of controlling the missions of modern spacecraft and orbital complexes. It provides data on the results of the use of computerized data analysis techniques for our country’s space assets that are currently in operation. It articulates major drawbacks in the monitoring process, which are getting more exacerbated with the current trends in space programs development. It proposes a remedy for the drawbacks by means of development and introduction of procedures that will make telemetry data analysis smarter. The paper introduces the notion of smart analysis and its key advantages in technical applications. It identifies the key prerequisites for expanding the computerization of the monitoring process involved in the spacecraft mission control. It outlines basic operational principles of a smart monitoring system taking into account its operational peculiarities. Key words: spacecraft, mission control, introduction of smart technologies, status analysis, monitoring system.

On-Board Telemetry Monitoring via Support Vector Machine with Application to Philae Solar Generator

Recent developments in machine learning for anomaly detection make it possible to use spacecraft status telemetry to produce sophisticated system health monitoring applications, that can run autonomously on-board. Both archived and simulated data are used to train intelligent algorithms to automatically detect and classify anomalous time series of the produced telemetry. The focus of this work is the application of a Support Vector Machine (SVM) based classifier to monitor the status of an interplanetary probe photovoltaic system with a minimum set of available measurements. SVM is a popular machine learning method for classification and regression and it has outstanding generalization performance. The Rosetta lander Philae is considered as test case. A complete model of power production subsystem has been developed to simulate the real telemetry. Training data, generated for nominal and faulty cases, are used to train the SVM model, with the goal of classifying permanent and temporary power loss conditions. The simulated telemetry is then used to test its performance and to identify the minimum number of measurements that is necessary for a successful classification of the failures of interest.

An industrial information integration approach to in-orbit spacecraft

ABSTRACTTo operate an in-orbit spacecraft, the spacecraft status has to be monitored autonomously by collecting and analysing real-time data, and then detecting abnormities and malfunctions of system components. To develop an information system for spacecraft state detection, we investigate the feasibility of using ontology-based artificial intelligence in the system development. We propose a new modelling technique based on the semantic web, agent, scenarios and ontologies model. In modelling, the subjects of astronautics fields are classified, corresponding agents and scenarios are defined, and they are connected by the semantic web to analyse data and detect failures. We introduce the modelling methodologies and the resulted framework of the status detection information system in this paper. We discuss system components as well as their interactions in details. The system has been prototyped and tested to illustrate its feasibility and effectiveness. The proposed modelling technique is generic which can be extended and applied to the system development of other large-scale and complex information systems.

Интеллектуальный метод анализа для автоматизированного прогнозирования состояния КА

Интеллектуальный метод анализа для автоматизированного прогнозирования состояния КА

Hipparcos on-orbit environment and performance

Hipparcos was launched on 8 August 1989 for astrometry mission on geostationary orbit. Several unsuccessful attempts to ignite the ABM resulted in a revised mission on an eccentric orbit (500 km/36,000 km). Compared with the planned geostationary orbit, this one induced a more severe environment from physical and operational points of view. It resulted in major drawbacks for the radiation environment, since the radiation belts are crossed twice per orbit (i.e. roughly 4 times per day), and the eclipse duration increased up to 50%, for an orbit period divided by a factor two. Although the ground segment was extended to include additional ground stations, the revised orbit does not allow permanent contact between the ground stations and the spacecraft. The flexibility of the on-board software has been used for programming the main housekeeping functions during the non visibility periods. The spacecraft has been operating for several months, and after the payload calibration, the scientific mission started in November 1989. The results are very encouraging and the scientific returns will reach an unexpected level compared with the initial predictions after the ABM failure. The final performance relies on two major parameters. The payload characteristics measured on orbit are very well in line with ground measurements and simulations; however, radiation affects the photometry by the optics darkening effect. The mission lifetime will depend on resources and possible failures; the power budget was considered as a limiting factor, due to the solar generator degradation, but the last predictions, which are correlated with the on-orbit behaviour, indicate a possible lifetime of 3 years; however, the severe radiation levels could cause other problems which cannot be predicted. This paper presents the spacecraft status and payload performances as observed after 10 months of scientific mission. The first, astrometry performances will be known at that time, since the complete celestial sphere will have been covered, and the expected mission performance predictions will be re-evaluated more accurately. The last available results at the time of the congress could also be presented, i.e. after almost 1 year of mission.

Letter to the Editor: UOSAT spacecraft status report

Letter to the Editor: UOSAT spacecraft status report