Observations of breakup processes of liquid jets using real-time X-ray radiography

To unravel the liquid-jet breakup process in the nondilute region, a newly developed system of real-time X-ray radiography, an advanced digital image processor, and a high-speed video camera were used. Based upon recorded X-ray images, the inner structure of a liquid jet during breakup was observed. The jet divergence angle, jet breakup length, and fraction distributions along the axial and transverse directions of the liquid jets were determined in the near-injector region. Both wall- and free-jet tests were conducted to study the effect of wall friction on the jet breakup process

Hamiltonian and measuring time for analog quantum search

We derive in this study a Hamiltonian to solve with certainty the analog quantum search problem analogue to the Grover algorithm. The general form of the initial state is considered. Since the evaluation of the measuring time for finding the marked state by probability of unity is crucially important in the problem, especially when the Bohr frequency is high, we then give the exact formula as a function of all given parameters for the measuring time.Comment: 5 page

Space charge enhanced plasma gradient effects on satellite electric field measurements

It has been recognized that plasma gradients can cause error in magnetospheric electric field measurements made by double probes. Space charge enhanced Plasma Gradient Induced Error (PGIE) is discussed in general terms, presenting the results of a laboratory experiment designed to demonstrate this error, and deriving a simple expression that quantifies this error. Experimental conditions were not identical to magnetospheric conditions, although efforts were made to insure the relevant physics applied to both cases. The experimental data demonstrate some of the possible errors in electric field measurements made by strongly emitting probes due to space charge effects in the presence of plasma gradients. Probe errors in space and laboratory conditions are discussed, as well as experimental error. In the final section, theoretical aspects are examined and an expression is derived for the maximum steady state space charge enhanced PGIE taken by two identical current biased probes

Network Inference via the Time-Varying Graphical Lasso

Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability

Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios.Comment: This revised version fixes two small typos in the published versio

Research on an expert system for database operation of simulation-emulation math models. Volume 2, Phase 1: Results

A reference manual is provided for NESS, a simulation expert system. This manual gives user information regarding starting and operating NASA expert simulation system (NESS). This expert system provides an intelligent interface to a generic simulation program for spacecraft attitude control problems. A menu of the functions the system can perform is provided. Control repeated returns to this menu after executing each user request

Research on an expert system for database operation of simulation-emulation math models. Volume 1, Phase 1: Results

The results of the first phase of Research on an Expert System for Database Operation of Simulation/Emulation Math Models, is described. Techniques from artificial intelligence (AI) were to bear on task domains of interest to NASA Marshall Space Flight Center. One such domain is simulation of spacecraft attitude control systems. Two related software systems were developed to and delivered to NASA. One was a generic simulation model for spacecraft attitude control, written in FORTRAN. The second was an expert system which understands the usage of a class of spacecraft attitude control simulation software and can assist the user in running the software. This NASA Expert Simulation System (NESS), written in LISP, contains general knowledge about digital simulation, specific knowledge about the simulation software, and self knowledge

The activation energy for GaAs/AlGaAs interdiffusion

Copyright 1997 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. This article appeared in Journal of Applied Physics 82, 4842 (1997) and may be found at