scholarly journals An automated system analysis and design tool for spacecrafts ceas space journal

2021 ◽  
Author(s):  
Manfred Ehresmann ◽  
Georg Herdrich ◽  
Stefanos Fasoulas
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Degrees Of Freedom ◽  
System Analysis ◽  
Propulsion System ◽  
Optimal System ◽  
Automated System ◽  
Processing Unit ◽  
Data Set ◽  
Analysis And Design

AbstractIn this paper, a generic full-system estimation software tool is introduced and applied to a data set of actual flight missions to derive a heuristic for system composition for mass and power ratios of considered sub-systems. The capability of evolutionary algorithms to analyse and effectively design spacecraft (sub-)systems is shown. After deriving top-level estimates for each spacecraft sub-system based on heuristic heritage data, a detailed component-based system analysis follows. Various degrees of freedom exist for a hardware-based sub-system design; these are to be resolved via an evolutionary algorithm to determine an optimal system configuration. A propulsion system implementation for a small satellite test case will serve as a reference example of the implemented algorithm application. The propulsion system includes thruster, power processing unit, tank, propellant and general power supply system masses and power consumptions. Relevant performance parameters such as desired thrust, effective exhaust velocity, utilised propellant, and the propulsion type are considered as degrees of freedom. An evolutionary algorithm is applied to the propulsion system scaling model to demonstrate that such evolutionary algorithms are capable of bypassing complex multidimensional design optimisation problems. An evolutionary algorithm is an algorithm that uses a heuristic to change input parameters and a defined selection criterion (e.g., mass fraction of the system) on an optimisation function to refine solutions successively. With sufficient generations and, thereby, iterations of design points, local optima are determined. Using mitigation methods and a sufficient number of seed points, a global optimal system configurations can be found.

scholarly journals Kinematics Analysis of 5R axis using Evolutionary Algorithms

2019 ◽  
Vol 8 (3) ◽  
pp. 7028-7033
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Degrees Of Freedom ◽  
Artificial Bee Colony ◽  
Robotic Manipulators ◽  
Absolute Error ◽  
Kinematics Analysis ◽  
Bee Colony ◽  
Revolute Joints ◽  
Minimization Of Errors

In this paper, we have represented the kinematics solution of five degrees of freedom articulated arm with five revolute joints using Evolutionary algorithm. DH parameters are used to obtain the kinematics analysis of the manipulator. Simulations are performed on the MATLAB to show the workspace of the robotic manipulators. Firefly and artificial bee colony algorithms (ABC) have been used for the minimization of errors. The position error and absolute error have been minimized to the acceptable level.

scholarly journals Modular Impulsive Green Monopropellant Propulsion System (MIMPS-G): For CubeSats in LEO and to the Moon

Aerospace ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 169
Author(s):  
Ahmed E. S. Nosseir ◽  
Angelo Cervone ◽  
Angelo Pasini
Keyword(s):  
High Performance ◽  
System Analysis ◽  
Propulsion System ◽  
Propulsion Systems ◽  
Small Satellites ◽  
Analysis And Design ◽  
Performance Space ◽  
Green Propellants ◽  
Commercial Off The Shelf ◽  
High Thrust

Green propellants are currently considered as enabling technology that is revolutionizing the development of high-performance space propulsion, especially for small-sized spacecraft. Modern space missions, either in LEO or interplanetary, require relatively high-thrust and impulsive capabilities to provide better control on the spacecraft, and to overcome the growing challenges, particularly related to overcrowded LEOs, and to modern space application orbital maneuver requirements. Green monopropellants are gaining momentum in the design and development of small and modular liquid propulsion systems, especially for CubeSats, due to their favorable thermophysical properties and relatively high performance when compared to gaseous propellants, and perhaps simpler management when compared to bipropellants. Accordingly, a novel high-thrust modular impulsive green monopropellant propulsion system with a micro electric pump feed cycle is proposed. MIMPS-G500mN is designed to be capable of delivering 0.5 N thrust and offers theoretical total impulse Itot from 850 to 1350 N s per 1U and >3000 N s per 2U depending on the burnt monopropellant, which makes it a candidate for various LEO satellites as well as future Moon missions. Green monopropellant ASCENT (formerly AF-M315E), as well as HAN and ADN-based alternatives (i.e., HNP225 and LMP-103S) were proposed in the preliminary design and system analysis. The article will present state-of-the-art green monopropellants in the (EIL) Energetic Ionic Liquid class and a trade-off study for proposed propellants. System analysis and design of MIMPS-G500mN will be discussed in detail, and the article will conclude with a market survey on small satellites green monopropellant propulsion systems and commercial off-the-shelf thrusters.

scholarly journals Versatility of constrained CRB for system analysis and design

2014 ◽  
Vol 50 (3) ◽  
pp. 1841-1863 ◽  
Author(s):  
Tarek Menni ◽  
Jerome Galy ◽  
Eric Chaumette ◽  
Pascal Larzabal
Keyword(s):  
System Analysis ◽  
System Analysis And Design ◽  
Analysis And Design

scholarly journals A Traveler’s Guide to the Multiverse: Promises, Pitfalls, and a Framework for the Evaluation of Analytic Decisions

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592095492
Author(s):  
Marco Del Giudice ◽  
Steven W. Gangestad
Keyword(s):  
Degrees Of Freedom ◽  
A Priori ◽  
Simulated Data ◽  
Style Analysis ◽  
Data Set ◽  
Scant Attention ◽  
Equivalence Type ◽  
The Impact ◽  
Biased Estimates

Decisions made by researchers while analyzing data (e.g., how to measure variables, how to handle outliers) are sometimes arbitrary, without an objective justification for choosing one alternative over another. Multiverse-style methods (e.g., specification curve, vibration of effects) estimate an effect across an entire set of possible specifications to expose the impact of hidden degrees of freedom and/or obtain robust, less biased estimates of the effect of interest. However, if specifications are not truly arbitrary, multiverse-style analyses can produce misleading results, potentially hiding meaningful effects within a mass of poorly justified alternatives. So far, a key question has received scant attention: How does one decide whether alternatives are arbitrary? We offer a framework and conceptual tools for doing so. We discuss three kinds of a priori nonequivalence among alternatives—measurement nonequivalence, effect nonequivalence, and power/precision nonequivalence. The criteria we review lead to three decision scenarios: Type E decisions (principled equivalence), Type N decisions (principled nonequivalence), and Type U decisions (uncertainty). In uncertain scenarios, multiverse-style analysis should be conducted in a deliberately exploratory fashion. The framework is discussed with reference to published examples and illustrated with the help of a simulated data set. Our framework will help researchers reap the benefits of multiverse-style methods while avoiding their pitfalls.

scholarly journals Drift Analysis and Evolutionary Algorithms Revisited

2018 ◽  
Vol 27 (4) ◽  
pp. 643-666 ◽  
Author(s):  
J. LENGLER ◽  
A. STEGER
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Simple Algorithm ◽  
Linear Functions ◽  
Search Point ◽  
Drift Analysis ◽  
Fixed Constant ◽  
Greedy Optimization

One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a function f: {0,1}n → ℝ. The algorithm starts with a random search point ξ ∈ {0,1}n, and in each round it flips each bit of ξ with probability c/n independently at random, where c > 0 is a fixed constant. The thus created offspring ξ' replaces ξ if and only if f(ξ') ≥ f(ξ). The analysis of the runtime of this simple algorithm for monotone and for linear functions turned out to be highly non-trivial. In this paper we review known results and provide new and self-contained proofs of partly stronger results.

scholarly journals Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data

2021 ◽  
Vol 14 ◽  
pp. 263177452199062
Author(s):  
Benjamin Gutierrez Becker ◽  
Filippo Arcadu ◽  
Andreas Thalhammer ◽  
Citlalli Gamez Serna ◽  
Owen Feehan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ulcerative Colitis ◽  
Clinical Trials ◽  
Deep Learning ◽  
Mayo Clinic ◽  
Automated System ◽  
Phase Iii ◽  
Plain Language ◽  
Data Set ◽  
Endoscopic Videos

Introduction: The Mayo Clinic Endoscopic Subscore is a commonly used grading system to assess the severity of ulcerative colitis. Correctly grading colonoscopies using the Mayo Clinic Endoscopic Subscore is a challenging task, with suboptimal rates of interrater and intrarater variability observed even among experienced and sufficiently trained experts. In recent years, several machine learning algorithms have been proposed in an effort to improve the standardization and reproducibility of Mayo Clinic Endoscopic Subscore grading. Methods: Here we propose an end-to-end fully automated system based on deep learning to predict a binary version of the Mayo Clinic Endoscopic Subscore directly from raw colonoscopy videos. Differently from previous studies, the proposed method mimics the assessment done in practice by a gastroenterologist, that is, traversing the whole colonoscopy video, identifying visually informative regions and computing an overall Mayo Clinic Endoscopic Subscore. The proposed deep learning–based system has been trained and deployed on raw colonoscopies using Mayo Clinic Endoscopic Subscore ground truth provided only at the colon section level, without manually selecting frames driving the severity scoring of ulcerative colitis. Results and Conclusion: Our evaluation on 1672 endoscopic videos obtained from a multisite data set obtained from the etrolizumab Phase II Eucalyptus and Phase III Hickory and Laurel clinical trials, show that our proposed methodology can grade endoscopic videos with a high degree of accuracy and robustness (Area Under the Receiver Operating Characteristic Curve = 0.84 for Mayo Clinic Endoscopic Subscore ⩾ 1, 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 2 and 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 3) and reduced amounts of manual annotation. Plain language summary Patient, caregiver and provider thoughts on educational materials about prescribing and medication safety Artificial intelligence can be used to automatically assess full endoscopic videos and estimate the severity of ulcerative colitis. In this work, we present an artificial intelligence algorithm for the automatic grading of ulcerative colitis in full endoscopic videos. Our artificial intelligence models were trained and evaluated on a large and diverse set of colonoscopy videos obtained from concluded clinical trials. We demonstrate not only that artificial intelligence is able to accurately grade full endoscopic videos, but also that using diverse data sets obtained from multiple sites is critical to train robust AI models that could potentially be deployed on real-world data.

Manufacturing Plant Management Information System Analysis and Design Based on UML

2010 ◽  
Vol 455 ◽  
pp. 237-241
Author(s):  
X.Y. Yang ◽  
H.B. Zheng ◽  
Z.W. Zhang
Keyword(s):  
Asset Management ◽  
System Analysis ◽  
Structural Model ◽  
System Development ◽  
Unified Modeling Language ◽  
Object Oriented ◽  
Plant Management ◽  
Management Information ◽  
Static Structure ◽  
Analysis And Design

With the development of manufacturing automation and intelligent increasing speed, the construction in plant management information has been important tasks to promote business innovation ability, improve competitiveness and manufacturing execution. In this paper, UML (Unified Modeling Language) and object-oriented modeling technology were applied to model the static structure and dynamic behavior of the plant management information from requirement analysis to system implementation, including functional requirement model, static structural model, asset management time sequence chart, system physical model and so on. The visualized system analysis method and technology better planned the system design and improved the efficiency of the system development. It will play a guiding role in the object-oriented software development.

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