A feedback shift-register scaler.
Scaler using ferrite-core shift register with linear-logic feedback developed for spacecraft computer clock
ERROR CORRECTING DEVICE↗
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Results for “SEQUENTIAL MACHINE”
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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Structure theory for the realization of finite state automata Progress report, 1 Nov. 1966 - 30 Apr. 1967
Structure theory for realization of finite state automata
STRUCTURE↗
Design and preliminary performance tests of a flexible, high speed sequential decoder for deep space channels
High speed sequential decoding machine design and performance test for deep space channels
Layland, J. W.↗
A flexible high-speed sequential decoder for deep space channels.
This paper describes a sequential decoding machine built at the Jet Propulsion Laboratory (JPL), which uses a 3-bit quantization of the code symbols and achieves a computation rate of MHz. This machine is flexible and can be programmed to decode any complementary convolutional code with rates down to 1/4 and constraint lengths up to 32. In addition, metric programmability is provided for optimization of decoder performance with respect to channel parameter variations.
Layland, J. W.↗
Implicit hybrid schemes for the flux-difference split, three-dimensional Navier-Stokes equations
Implicit hybrid algorithms employing symmetric planar Gauss-Seidel (SPGS) relaxation and either block-tridiagonally structured coefficient matrices (AF-SPGS) or block-triangular coefficient matrices (LU-SPGS) are derived to solve the flux-difference-split Navier-Stokes equations for three-dimensional incompressible flow in an upwind scheme. The physical basis of the approach is discussed, and results for problems involving vortex flow around a thin delta wing at Reynolds numbers 900,000 and 10,000 are presented graphically. It is found that AF-SPGS converges faster on vector computers which depend on long vector lengths to achieve optimum performance, whereas LU-SPGS is preferable on sequentially operating machines and vector computers using shorter vector lengths.
Hartwich, P.-M.↗
Practical applications of digital integrated circuits. Part 3: Practical sequential theory and synchronous circuits
Here, the 7400 line of transistor to transistor logic (TTL) devices is emphasized almost exclusively where hardware is concerned. However, it should be noted that the logic theory contained herein applies to all hardware. Discussed here are synchronous binary UP counters, synchronous DOWN and UP/DOWN counters, integrated circuit counters, shift registers, sequential techniques, and designing sequential counting machines.
Source record↗
Fault tolerant sequential circuits using sequence invariant state machines
The idea of introducing redundancy to improve the reliability of digital systems originates from papers published in the 1950's. Since then, redundancy has been recognized as a realistic means for constructing reliable systems. A method using redundancy to reconfigure the Sequency Invariant State Machine (SISM) to achieve fault tolerance is introduced. This new architecture is most useful in space applications, where recovery rather than replacement of faulty modules is the only means of maintenance.
Alahmad, M.↗
Towards informatics-driven design of nuclear waste forms
Informatics-driven approaches, such as machine learning and sequential experimental design, have shown the potential to drastically impact next-generation materials discovery and design.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Micromachined Silicon Waveguides
Components that handle millimeter and submillimeter wavelengths fabricated conveniently. Micromachining rectangular waveguide involves standard steps of masking, etching, and deposition of metal on silicon. Parts made assembled into half-waveguide and finally into full waveguide. Silicon-micromachining approach enables simultaneous fabrication of several versions of waveguide, with variations in critical parameter, on single wafer of silicon. Performances of versions compared and optimized more quickly and at lower cost than is possible if different versions are fabricated sequentially, by conventional machining techniques.
Mcgrath, William R.↗
Asynchronous finite-state machines - A novel control system class.
Asynchronous finite state sequential nonlinear controller synthesis with few flip-flops for dynamic space vehicle systems
CONTROL DEVICE↗
Uncertainty Propagation within Chained Models for Machine Learning Reconstruction of Neutrino-LAr Interactions
Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are particularly useful when a task is separable into distinct steps with a hierarchy of meaningful intermediate representations. In reliability-critical tasks, it is important to quantify the confidence of model inferences. However, chained models pose an additional challenge for uncertainty quantification, especially when input uncertainties need to be propagated. In such cases, a fully uncertainty-aware chain of models is required, where each step accepts a probability distribution over the input space, and produces a probability distribution over the output space. In this work, we present a case study for adapting a single model within an existing chain, designed for reconstruction within neutrino-Argon interactions, developed for neutrino oscillation experiments such as MicroBooNE, ICARUS, and the future DUNE experiment. We test the performance of an input uncertainty-enabled model against an uncertainty-blinded model using a method for generating synthetic noise. By comparing these two, we assess the increase in inference quality achieved by exposing models to upstream uncertainty estimates.
97 MATHEMATICS AND COMPUTING↗
Fault Tolerant State Machines
State machines are commonly used to control sequential logic in FPGAs and ASKS. An errant state machine can cause considerable damage to the device it is controlling. For example in space applications, the FPGA might be controlling Pyros, which when fired at the wrong time will cause a mission failure. Even a well designed state machine can be subject to random errors us a result of SEUs from the radiation environment in space. There are various ways to encode the states of a state machine, and the type of encoding makes a large difference in the susceptibility of the state machine to radiation. In this paper we compare 4 methods of state machine encoding and find which method gives the best fault tolerance, as well as determining the resources needed for each method.
encoding↗
Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide
Understanding the structure and properties of refractory oxides is critical for high temperature applications. In this work, a combined experimental and simulation approach uses an automated closed loop via an active learner, which is initialized by x-ray and neutron diffraction measurements, and sequentially improves a machine-learning model until the experimentally predetermined phase space is covered. Furthermore, a multiphase potential is generated for a canonical example of the archetypal refractory oxide, HfO 2 , by drawing a minimum number of training configurations from room temperature to the liquid state at similar to 2900 degrees C. The method significantly reduces model development time and human effort.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
By how much can closed-loop frameworks accelerate computational materials discovery?
A combination of task automation, calculation runtime improvements, machine learning surrogatization, and sequential learning-guided candidate selection within a closed-loop computational workflow can accelerate materials discovery by up to 20×.
Kavalsky, Lance↗
A Deep Learning Filter for the Intraseasonal Variability of the Tropics
Abstract This paper presents a novel application of convolutional neural network (CNN) models for filtering the intraseasonal variability of the tropical atmosphere. In this deep learning filter, two convolutional layers are applied sequentially in a supervised machine learning framework to extract the intraseasonal signal from the total daily anomalies. The CNN-based filter can be tailored for each field similarly to fast Fourier transform filtering methods. When applied to two different fields (zonal wind stress and outgoing longwave radiation), the index of agreement between the filtered signal obtained using the CNN-based filter and a conventional weight-based filter is between 95% and 99%. The advantage of the CNN-based filter over the conventional filters is its applicability to time series with the length comparable to the period of the signal being extracted. Significance Statement This study proposes a new method for discovering hidden connections in data representative of tropical atmosphere variability. The method makes use of an artificial intelligence (AI) algorithm that combines a mathematical operation known as convolution with a mathematical model built to reflect the behavior of the human brain known as artificial neural network. Our results show that the filtered data produced by the AI-based method are consistent with the results obtained using conventional mathematical algorithms. The advantage of the AI-based method is that it can be applied to cases for which the conventional methods have limitations, such as forecast (hindcast) data or real-time monitoring of tropical variability in the 20–100-day range.
Stan, Cristiana↗
Development and assessment of prognosis digital twin in a NAMAC system
The nearly autonomous management and control (NAMAC) system is a comprehensive control system to assist plant operations by furnishing control recommendations to operators. Prognosis digital twin (DT-P) is a critical component in NAMAC for predicting action effects and supporting NAMAC decision-making during normal and accident scenarios. To quantifying and reducing uncertainty of machine-learning-based DT-Ps in multi-step predictions, this work investigates and derives insights from the application of three techniques for optimizing the performance of DT-P by long short-term memory recurrent neural networks, including manual search, sequential model-based optimization, and physics-guided machine learning. Finally, sequential model-based optimization and physics-guide machine learning result in smallest errors when the predicting transients are similar to the training data.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Nonlinear Ensemble Filtering with Diffusion Models: Application to the Surface Quasigeostrophic Dynamics
The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here, we explore another promising solution in which diffusion models are used to formulate a robust nonlinear ensemble filter for sequential data assimilation. Unlike standard machine learning methods, the proposed ensemble score filter (EnSF) is completely training free and can efficiently generate a set of analysis ensemble members. Here, in this study, we apply the EnSF to a surface quasigeostrophic model and compare its performance against the popular local ensemble transform Kalman filter (LETKF), which makes Gaussian assumptions in the analysis step. Numerical tests demonstrate that EnSF maintains stable performance in the absence of localization and for a variety of experimental settings. We find that while LETKF maintains optimal performance in the case of linear observations of the entire state and a perfect model, EnSF shows improvements over LETKF when nonlinear observations are assimilated and the system is subject to unexpected model errors. A spectral decomposition of the analysis results in this nonlinear observation regime shows that the largest improvements over LETKF occur at large scales (small wavenumbers), where LETKF lacks sufficient ensemble spread. Overall, this initial application of EnSF to a geophysical model of intermediate complexity motivates further development of the algorithm for more realistic problems.
Artificial intelligence↗
Sequence invariant state machines
A synthesis method and new VLSI architecture are introduced to realize sequential circuits that have the ability to implement any state machine having N states and m inputs, regardless of the actual sequence specified in the flow table. A design method is proposed that utilizes BTS logic to implement regular and dense circuits. A given state sequence can be programmed with power supply connections or dynamically reallocated if stored in a register. Arbitrary flow table sequences can be modified or programmed to dynamically alter the function of the machine. This allows VLSI controllers to be designed with the programmability of a general purpose processor but with the compact size and performance of dedicated logic.
Whitaker, S.↗