The Design of Finite State Machine for Asynchronous Replication Protocol

NASA Astrophysics Data System (ADS)

Wang, Yanlong; Li, Zhanhuai; Lin, Wei; Hei, Minglei; Hao, Jianhua

Data replication is a key way to design a disaster tolerance system and to achieve reliability and availability. It is difficult for a replication protocol to deal with the diverse and complex environment. This means that data is less well replicated than it ought to be. To reduce data loss and to optimize replication protocols, we (1) present a finite state machine, (2) run it to manage an asynchronous replication protocol and (3) report a simple evaluation of the asynchronous replication protocol based on our state machine. It's proved that our state machine is applicable to guarantee the asynchronous replication protocol running in the proper state to the largest extent in the event of various possible events. It also can helpful to build up replication-based disaster tolerance systems to ensure the business continuity.

Classifying Black Hole States with Machine Learning

NASA Astrophysics Data System (ADS)

Huppenkothen, Daniela

2018-01-01

Galactic black hole binaries are known to go through different states with apparent signatures in both X-ray light curves and spectra, leading to important implications for accretion physics as well as our knowledge of General Relativity. Existing frameworks of classification are usually based on human interpretation of low-dimensional representations of the data, and generally only apply to fairly small data sets. Machine learning, in contrast, allows for rapid classification of large, high-dimensional data sets. In this talk, I will report on advances made in classification of states observed in Black Hole X-ray Binaries, focusing on the two sources GRS 1915+105 and Cygnus X-1, and show both the successes and limitations of using machine learning to derive physical constraints on these systems.

High-throughput state-machine replication using software transactional memory

PubMed Central

Yang, William; Zhang, Honglei; Yang, Jack; Luo, Xiong; Zhu, Yueqin; Yang, Mary; Luo, Chaomin

2017-01-01

State-machine replication is a common way of constructing general purpose fault tolerance systems. To ensure replica consistency, requests must be executed sequentially according to some total order at all non-faulty replicas. Unfortunately, this could severely limit the system throughput. This issue has been partially addressed by identifying non-conflicting requests based on application semantics and executing these requests concurrently. However, identifying and tracking non-conflicting requests require intimate knowledge of application design and implementation, and a custom fault tolerance solution developed for one application cannot be easily adopted by other applications. Software transactional memory offers a new way of constructing concurrent programs. In this article, we present the mechanisms needed to retrofit existing concurrency control algorithms designed for software transactional memory for state-machine replication. The main benefit for using software transactional memory in state-machine replication is that general purpose concurrency control mechanisms can be designed without deep knowledge of application semantics. As such, new fault tolerance systems based on state-machine replications with excellent throughput can be easily designed and maintained. In this article, we introduce three different concurrency control mechanisms for state-machine replication using software transactional memory, namely, ordered strong strict two-phase locking, conventional timestamp-based multiversion concurrency control, and speculative timestamp-based multiversion concurrency control. Our experiments show that speculative timestamp-based multiversion concurrency control mechanism has the best performance in all types of workload, the conventional timestamp-based multiversion concurrency control offers the worst performance due to high abort rate in the presence of even moderate contention between transactions. The ordered strong strict two-phase locking

High-throughput state-machine replication using software transactional memory.

PubMed

Zhao, Wenbing; Yang, William; Zhang, Honglei; Yang, Jack; Luo, Xiong; Zhu, Yueqin; Yang, Mary; Luo, Chaomin

2016-11-01

State-machine replication is a common way of constructing general purpose fault tolerance systems. To ensure replica consistency, requests must be executed sequentially according to some total order at all non-faulty replicas. Unfortunately, this could severely limit the system throughput. This issue has been partially addressed by identifying non-conflicting requests based on application semantics and executing these requests concurrently. However, identifying and tracking non-conflicting requests require intimate knowledge of application design and implementation, and a custom fault tolerance solution developed for one application cannot be easily adopted by other applications. Software transactional memory offers a new way of constructing concurrent programs. In this article, we present the mechanisms needed to retrofit existing concurrency control algorithms designed for software transactional memory for state-machine replication. The main benefit for using software transactional memory in state-machine replication is that general purpose concurrency control mechanisms can be designed without deep knowledge of application semantics. As such, new fault tolerance systems based on state-machine replications with excellent throughput can be easily designed and maintained. In this article, we introduce three different concurrency control mechanisms for state-machine replication using software transactional memory, namely, ordered strong strict two-phase locking, conventional timestamp-based multiversion concurrency control, and speculative timestamp-based multiversion concurrency control. Our experiments show that speculative timestamp-based multiversion concurrency control mechanism has the best performance in all types of workload, the conventional timestamp-based multiversion concurrency control offers the worst performance due to high abort rate in the presence of even moderate contention between transactions. The ordered strong strict two-phase locking

Developing a PLC-friendly state machine model: lessons learned

NASA Astrophysics Data System (ADS)

Pessemier, Wim; Deconinck, Geert; Raskin, Gert; Saey, Philippe; Van Winckel, Hans

2014-07-01

Modern Programmable Logic Controllers (PLCs) have become an attractive platform for controlling real-time aspects of astronomical telescopes and instruments due to their increased versatility, performance and standardization. Likewise, vendor-neutral middleware technologies such as OPC Unified Architecture (OPC UA) have recently demonstrated that they can greatly facilitate the integration of these industrial platforms into the overall control system. Many practical questions arise, however, when building multi-tiered control systems that consist of PLCs for low level control, and conventional software and platforms for higher level control. How should the PLC software be structured, so that it can rely on well-known programming paradigms on the one hand, and be mapped to a well-organized OPC UA interface on the other hand? Which programming languages of the IEC 61131-3 standard closely match the problem domains of the abstraction levels within this structure? How can the recent additions to the standard (such as the support for namespaces and object-oriented extensions) facilitate a model based development approach? To what degree can our applications already take advantage of the more advanced parts of the OPC UA standard, such as the high expressiveness of the semantic modeling language that it defines, or the support for events, aggregation of data, automatic discovery, ... ? What are the timing and concurrency problems to be expected for the higher level tiers of the control system due to the cyclic execution of control and communication tasks by the PLCs? We try to answer these questions by demonstrating a semantic state machine model that can readily be implemented using IEC 61131 and OPC UA. One that does not aim to capture all possible states of a system, but rather one that attempts to organize the course-grained structure and behaviour of a system. In this paper we focus on the intricacies of this seemingly simple task, and on the lessons that we

State Machine Modeling of the Space Launch System Solid Rocket Boosters

NASA Technical Reports Server (NTRS)

Harris, Joshua A.; Patterson-Hine, Ann

2013-01-01

The Space Launch System is a Shuttle-derived heavy-lift vehicle currently in development to serve as NASA's premiere launch vehicle for space exploration. The Space Launch System is a multistage rocket with two Solid Rocket Boosters and multiple payloads, including the Multi-Purpose Crew Vehicle. Planned Space Launch System destinations include near-Earth asteroids, the Moon, Mars, and Lagrange points. The Space Launch System is a complex system with many subsystems, requiring considerable systems engineering and integration. To this end, state machine analysis offers a method to support engineering and operational e orts, identify and avert undesirable or potentially hazardous system states, and evaluate system requirements. Finite State Machines model a system as a finite number of states, with transitions between states controlled by state-based and event-based logic. State machines are a useful tool for understanding complex system behaviors and evaluating "what-if" scenarios. This work contributes to a state machine model of the Space Launch System developed at NASA Ames Research Center. The Space Launch System Solid Rocket Booster avionics and ignition subsystems are modeled using MATLAB/Stateflow software. This model is integrated into a larger model of Space Launch System avionics used for verification and validation of Space Launch System operating procedures and design requirements. This includes testing both nominal and o -nominal system states and command sequences.

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

PubMed

Billings, John M; Eder, Maxwell; Flood, William C; Dhami, Devendra Singh; Natarajan, Sriraam; Whitlow, Christopher T

2017-11-01

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances. Copyright © 2017 Elsevier Inc. All rights reserved.

A control technology evaluation of state-of-the-art, perchloroethylene dry-cleaning machines.

PubMed

Earnest, G Scott

2002-05-01

NIOSH researchers evaluated the ability of fifth-generation dry-cleaning machines to control occupational exposure to perchloroethylene (PERC). Use of these machines is mandated in some countries; however, less than 1 percent of all U.S. shops have them. A study was conducted at a U.S. dry-cleaning shop where two fifth-generation machines were used. Both machines had a refrigerated condenser as a primary control and a carbon adsorber as a secondary control to recover PERC vapors during the dry cycle. These machines were designed to lower the PERC concentration in the cylinder at the end of the dry cycle to below 290 ppm. A single-beam infrared photometer continuously monitors the PERC concentration in the machine cylinder, and a door interlock prevents opening until the concentration is below 290 ppm. Personal breathing zone air samples were measured for the machine operator and presser. The operator had time-weighted average (TWA) PERC exposures that were less than 2 ppm. Highest exposures occurred during loading and unloading the machine and when performing routine machine maintenance. All presser samples were below the limit of detection. Real-time video exposure monitoring showed that the operator had peak exposures near 160 ppm during loading and unloading the machine (below the OSHA maximum of 300 ppm). This exposure (160 ppm) is an order of magnitude lower than exposures with more traditional machines that are widely used in the United States. The evaluated machines were very effective at reducing TWA PERC exposures as well as peak exposures that occur during machine loading and unloading. State-of-the-art dry-cleaning machines equipped with refrigerated condensers, carbon adsorbers, drum monitors, and door interlocks can provide substantially better protection than more traditional machines that are widely used in the United States.

Distributed state machine supervision for long-baseline gravitational-wave detectors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rollins, Jameson Graef, E-mail: jameson.rollins@ligo.org

The Laser Interferometer Gravitational-wave Observatory (LIGO) consists of two identical yet independent, widely separated, long-baseline gravitational-wave detectors. Each Advanced LIGO detector consists of complex optical-mechanical systems isolated from the ground by multiple layers of active seismic isolation, all controlled by hundreds of fast, digital, feedback control systems. This article describes a novel state machine-based automation platform developed to handle the automation and supervisory control challenges of these detectors. The platform, called Guardian, consists of distributed, independent, state machine automaton nodes organized hierarchically for full detector control. User code is written in standard Python and the platform is designed to facilitatemore » the fast-paced development process associated with commissioning the complicated Advanced LIGO instruments. While developed specifically for the Advanced LIGO detectors, Guardian is a generic state machine automation platform that is useful for experimental control at all levels, from simple table-top setups to large-scale multi-million dollar facilities.« less

PLA realizations for VLSI state machines

NASA Technical Reports Server (NTRS)

Gopalakrishnan, S.; Whitaker, S.; Maki, G.; Liu, K.

1990-01-01

A major problem associated with state assignment procedures for VLSI controllers is obtaining an assignment that produces minimal or near minimal logic. The key item in Programmable Logic Array (PLA) area minimization is the number of unique product terms required by the design equations. This paper presents a state assignment algorithm for minimizing the number of product terms required to implement a finite state machine using a PLA. Partition algebra with predecessor state information is used to derive a near optimal state assignment. A maximum bound on the number of product terms required can be obtained by inspecting the predecessor state information. The state assignment algorithm presented is much simpler than existing procedures and leads to the same number of product terms or less. An area-efficient PLA structure implemented in a 1.0 micron CMOS process is presented along with a summary of the performance for a controller implemented using this design procedure.

Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem

PubMed Central

Liu, Xunying; Zhang, Chao; Woodland, Phil; Fonteneau, Elisabeth

2017-01-01

There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental ‘machine states’, generated as the ASR analysis progresses over time, to the incremental ‘brain states’, measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain. PMID:28945744

Control of discrete event systems modeled as hierarchical state machines

NASA Technical Reports Server (NTRS)

Brave, Y.; Heymann, M.

1991-01-01

The authors examine a class of discrete event systems (DESs) modeled as asynchronous hierarchical state machines (AHSMs). For this class of DESs, they provide an efficient method for testing reachability, which is an essential step in many control synthesis procedures. This method utilizes the asynchronous nature and hierarchical structure of AHSMs, thereby illustrating the advantage of the AHSM representation as compared with its equivalent (flat) state machine representation. An application of the method is presented where an online minimally restrictive solution is proposed for the problem of maintaining a controlled AHSM within prescribed legal bounds.

Implementing finite state machines in a computer-based teaching system

NASA Astrophysics Data System (ADS)

Hacker, Charles H.; Sitte, Renate

1999-09-01

Finite State Machines (FSM) are models for functions commonly implemented in digital circuits such as timers, remote controls, and vending machines. Teaching FSM is core in the curriculum of many university digital electronic or discrete mathematics subjects. Students often have difficulties grasping the theoretical concepts in the design and analysis of FSM. This has prompted the author to develop an MS-WindowsTM compatible software, WinState, that provides a tutorial style teaching aid for understanding the mechanisms of FSM. The animated computer screen is ideal for visually conveying the required design and analysis procedures. WinState complements other software for combinatorial logic previously developed by the author, and enhances the existing teaching package by adding sequential logic circuits. WinState enables the construction of a students own FSM, which can be simulated, to test the design for functionality and possible errors.

Rosen's (M,R) system as an X-machine.

PubMed

Palmer, Michael L; Williams, Richard A; Gatherer, Derek

2016-11-07

Robert Rosen's (M,R) system is an abstract biological network architecture that is allegedly both irreducible to sub-models of its component states and non-computable on a Turing machine. (M,R) stands as an obstacle to both reductionist and mechanistic presentations of systems biology, principally due to its self-referential structure. If (M,R) has the properties claimed for it, computational systems biology will not be possible, or at best will be a science of approximate simulations rather than accurate models. Several attempts have been made, at both empirical and theoretical levels, to disprove this assertion by instantiating (M,R) in software architectures. So far, these efforts have been inconclusive. In this paper, we attempt to demonstrate why - by showing how both finite state machine and stream X-machine formal architectures fail to capture the self-referential requirements of (M,R). We then show that a solution may be found in communicating X-machines, which remove self-reference using parallel computation, and then synthesise such machine architectures with object-orientation to create a formal basis for future software instantiations of (M,R) systems. Copyright © 2016 Elsevier Ltd. All rights reserved.

Method of Individual Forecasting of Technical State of Logging Machines

NASA Astrophysics Data System (ADS)

Kozlov, V. G.; Gulevsky, V. A.; Skrypnikov, A. V.; Logoyda, V. S.; Menzhulova, A. S.

2018-03-01

Development of the model that evaluates the possibility of failure requires the knowledge of changes’ regularities of technical condition parameters of the machines in use. To study the regularities, the need to develop stochastic models that take into account physical essence of the processes of destruction of structural elements of the machines, the technology of their production, degradation and the stochastic properties of the parameters of the technical state and the conditions and modes of operation arose.

Exponentially-Biased Ground-State Sampling of Quantum Annealing Machines with Transverse-Field Driving Hamiltonians

NASA Technical Reports Server (NTRS)

Mandra, Salvatore

2017-01-01

We study the performance of the D-Wave 2X quantum annealing machine on systems with well-controlled ground-state degeneracy. While obtaining the ground state of a spin-glass benchmark instance represents a difficult task, the gold standard for any optimization algorithm or machine is to sample all solutions that minimize the Hamiltonian with more or less equal probability. Our results show that while naive transverse-field quantum annealing on the D-Wave 2X device can find the ground-state energy of the problems, it is not well suited in identifying all degenerate ground-state configurations associated to a particular instance. Even worse, some states are exponentially suppressed, in agreement with previous studies on toy model problems [New J. Phys. 11, 073021 (2009)]. These results suggest that more complex driving Hamiltonians are needed in future quantum annealing machines to ensure a fair sampling of the ground-state manifold.

Statistical Abstract of the United States: 2012. 131st Edition

ERIC Educational Resources Information Center

US Census Bureau, 2011

2011-01-01

"The Statistical Abstract of the United States," published from 1878 to 2012, is the authoritative and comprehensive summary of statistics on the social, political, and economic organization of the United States. It is designed to serve as a convenient volume for statistical reference, and as a guide to other statistical publications and…

ERGONOMICS ABSTRACTS 48347-48982.

ERIC Educational Resources Information Center

Ministry of Technology, London (England). Warren Spring Lab.

IN THIS COLLECTION OF ERGONOMICS ABSTRACTS AND ANNOTATIONS THE FOLLOWING AREAS OF CONCERN ARE REPRESENTED--GENERAL REFERENCES, METHODS, FACILITIES, AND EQUIPMENT RELATING TO ERGONOMICS, SYSTEMS OF MAN AND MACHINES, VISUAL, AUDITORY, AND OTHER SENSORY INPUTS AND PROCESSES (INCLUDING SPEECH AND INTELLIGIBILITY), INPUT CHANNELS, BODY MEASUREMENTS,…

Learning Extended Finite State Machines

NASA Technical Reports Server (NTRS)

Cassel, Sofia; Howar, Falk; Jonsson, Bengt; Steffen, Bernhard

2014-01-01

We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.

Design Methodology for Automated Construction Machines

DTIC Science & Technology

1987-12-11

along with the design of a pair of machines which automate framework installation.-,, 20. DISTRIBUTION IAVAILABILITY OF ABSTRACT 21. ABSTRACT SECURITY... Development Assistant Professor of Civil Engineering and Laura A . Demsetz, David H. Levy, Bruce Schena Graduate Research Assistants December 11, 1987 U.S...are discussed along with the design of a pair of machines which automate framework installation. Preliminary analysis and testing indicate that these

Optical realization of optimal symmetric real state quantum cloning machine

NASA Astrophysics Data System (ADS)

Hu, Gui-Yu; Zhang, Wen-Hai; Ye, Liu

2010-01-01

We present an experimentally uniform linear optical scheme to implement the optimal 1→2 symmetric and optimal 1→3 symmetric economical real state quantum cloning machine of the polarization state of the single photon. This scheme requires single-photon sources and two-photon polarization entangled state as input states. It also involves linear optical elements and three-photon coincidence. Then we consider the realistic realization of the scheme by using the parametric down-conversion as photon resources. It is shown that under certain condition, the scheme is feasible by current experimental technology.

Optimum and Heuristic Algorithms for Finite State Machine Decomposition and Partitioning

DTIC Science & Technology

1989-09-01

Heuristic Algorithms for Finite State Machine Decomposition and Partitioning Pravnav Ashar, Srinivas Devadas , and A. Richard Newton , T E’,’ .,jpf~s’!i3...94720. Devadas : Department of Electrical Engineering and Computer Science, Room 36-848, MIT, Cambridge, MA 02139. (617) 253-0454. Copyright* 1989 MIT...and reduction, A finite state miachinie is represenutedl by its State Transition Graphi itodlitied froini two-level B ~oolean imiinimizers. Ilist

Support vector machines for nuclear reactor state estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zavaljevski, N.; Gross, K. C.

2000-02-14

Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformedmore » into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm.« less

Robust one-step catalytic machine for high fidelity anticloning and W-state generation in a multiqubit system.

PubMed

Olaya-Castro, Alexandra; Johnson, Neil F; Quiroga, Luis

2005-03-25

We propose a physically realizable machine which can either generate multiparticle W-like states, or implement high-fidelity 1-->M (M=1,2,...infinity) anticloning of an arbitrary qubit state, in a single step. This universal machine acts as a catalyst in that it is unchanged after either procedure, effectively resetting itself for its next operation. It possesses an inherent immunity to decoherence. Most importantly in terms of practical multiparty quantum communication, the machine's robustness in the presence of decoherence actually increases as the number of qubits M increases.

Effect of Built-Up Edge Formation during Stable State of Wear in AISI 304 Stainless Steel on Machining Performance and Surface Integrity of the Machined Part.

PubMed

Ahmed, Yassmin Seid; Fox-Rabinovich, German; Paiva, Jose Mario; Wagg, Terry; Veldhuis, Stephen Clarence

2017-10-25

During machining of stainless steels at low cutting -speeds, workpiece material tends to adhere to the cutting tool at the tool-chip interface, forming built-up edge (BUE). BUE has a great importance in machining processes; it can significantly modify the phenomenon in the cutting zone, directly affecting the workpiece surface integrity, cutting tool forces, and chip formation. The American Iron and Steel Institute (AISI) 304 stainless steel has a high tendency to form an unstable BUE, leading to deterioration of the surface quality. Therefore, it is necessary to understand the nature of the surface integrity induced during machining operations. Although many reports have been published on the effect of tool wear during machining of AISI 304 stainless steel on surface integrity, studies on the influence of the BUE phenomenon in the stable state of wear have not been investigated so far. The main goal of the present work is to investigate the close link between the BUE formation, surface integrity and cutting forces in the stable sate of wear for uncoated cutting tool during the cutting tests of AISI 304 stainless steel. The cutting parameters were chosen to induce BUE formation during machining. X-ray diffraction (XRD) method was used for measuring superficial residual stresses of the machined surface through the stable state of wear in the cutting and feed directions. In addition, surface roughness of the machined surface was investigated using the Alicona microscope and Scanning Electron Microscopy (SEM) was used to reveal the surface distortions created during the cutting process, combined with chip undersurface analyses. The investigated BUE formation during the stable state of wear showed that the BUE can cause a significant improvement in the surface integrity and cutting forces. Moreover, it can be used to compensate for tool wear through changing the tool geometry, leading to the protection of the cutting tool from wear.

Effect of Built-Up Edge Formation during Stable State of Wear in AISI 304 Stainless Steel on Machining Performance and Surface Integrity of the Machined Part

PubMed Central

Fox-Rabinovich, German; Wagg, Terry

2017-01-01

During machining of stainless steels at low cutting -speeds, workpiece material tends to adhere to the cutting tool at the tool–chip interface, forming built-up edge (BUE). BUE has a great importance in machining processes; it can significantly modify the phenomenon in the cutting zone, directly affecting the workpiece surface integrity, cutting tool forces, and chip formation. The American Iron and Steel Institute (AISI) 304 stainless steel has a high tendency to form an unstable BUE, leading to deterioration of the surface quality. Therefore, it is necessary to understand the nature of the surface integrity induced during machining operations. Although many reports have been published on the effect of tool wear during machining of AISI 304 stainless steel on surface integrity, studies on the influence of the BUE phenomenon in the stable state of wear have not been investigated so far. The main goal of the present work is to investigate the close link between the BUE formation, surface integrity and cutting forces in the stable sate of wear for uncoated cutting tool during the cutting tests of AISI 304 stainless steel. The cutting parameters were chosen to induce BUE formation during machining. X-ray diffraction (XRD) method was used for measuring superficial residual stresses of the machined surface through the stable state of wear in the cutting and feed directions. In addition, surface roughness of the machined surface was investigated using the Alicona microscope and Scanning Electron Microscopy (SEM) was used to reveal the surface distortions created during the cutting process, combined with chip undersurface analyses. The investigated BUE formation during the stable state of wear showed that the BUE can cause a significant improvement in the surface integrity and cutting forces. Moreover, it can be used to compensate for tool wear through changing the tool geometry, leading to the protection of the cutting tool from wear. PMID:29068405

Laser assisted machining: a state of art review

NASA Astrophysics Data System (ADS)

Punugupati, Gurabvaiah; Kandi, Kishore Kumar; Bose, P. S. C.; Rao, C. S. P.

2016-09-01

Difficult-to-cut materials have increasing demand in aerospace and automobile industries due to their high yield stress, high strength to weight ratio, high toughness, high wear resistance, high creep, high corrosion resistivity, ability to retain high strength at high temperature, etc. The machinability of these advanced materials, using conventional methods of machining is typical due to the high temperature and pressure at the cutting zone and tool and properties such as low thermal conductivity, high cutting forces and cutting temperatures makes the materials difficult to machine. Laser assisted machining (LAM) is a new and innovative technique for machining the difficult-to-cut materials. This paper deals with a review on the advances in lasers, tools and the mechanism of machining using LAM and their effects.

Limitations Of The Current State Space Modelling Approach In Multistage Machining Processes Due To Operation Variations

NASA Astrophysics Data System (ADS)

Abellán-Nebot, J. V.; Liu, J.; Romero, F.

2009-11-01

The State Space modelling approach has been recently proposed as an engineering-driven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.

Abstraction and art.

PubMed

Gortais, Bernard

2003-07-29

In a given social context, artistic creation comprises a set of processes, which relate to the activity of the artist and the activity of the spectator. Through these processes we see and understand that the world is vaster than it is said to be. Artistic processes are mediated experiences that open up the world. A successful work of art expresses a reality beyond actual reality: it suggests an unknown world using the means and the signs of the known world. Artistic practices incorporate the means of creation developed by science and technology and change forms as they change. Artists and the public follow different processes of abstraction at different levels, in the definition of the means of creation, of representation and of perception of a work of art. This paper examines how the processes of abstraction are used within the framework of the visual arts and abstract painting, which appeared during a period of growing importance for the processes of abstraction in science and technology, at the beginning of the twentieth century. The development of digital platforms and new man-machine interfaces allow multimedia creations. This is performed under the constraint of phases of multidisciplinary conceptualization using generic representation languages, which tend to abolish traditional frontiers between the arts: visual arts, drama, dance and music.

A Simple Universal Turing Machine for the Game of Life Turing Machine

NASA Astrophysics Data System (ADS)

Rendell, Paul

In this chapter we present a simple universal Turing machine which is small enough to fit into the design limits of the Turing machine build in Conway's Game of Life by the author. That limit is 8 symbols and 16 states. By way of comparison we also describe one of the smallest known universal Turing machines due to Rogozhin which has 6 symbols and 4 states.

A New Method for Incremental Testing of Finite State Machines

NASA Technical Reports Server (NTRS)

Pedrosa, Lehilton Lelis Chaves; Moura, Arnaldo Vieira

2010-01-01

The automatic generation of test cases is an important issue for conformance testing of several critical systems. We present a new method for the derivation of test suites when the specification is modeled as a combined Finite State Machine (FSM). A combined FSM is obtained conjoining previously tested submachines with newly added states. This new concept is used to describe a fault model suitable for incremental testing of new systems, or for retesting modified implementations. For this fault model, only the newly added or modified states need to be tested, thereby considerably reducing the size of the test suites. The new method is a generalization of the well-known W-method and the G-method, but is scalable, and so it can be used to test FSMs with an arbitrarily large number of states.

Resting-State Functional Connectivity Underlying Costly Punishment: A Machine-Learning Approach.

PubMed

Feng, Chunliang; Zhu, Zhiyuan; Gu, Ruolei; Wu, Xia; Luo, Yue-Jia; Krueger, Frank

2018-06-08

A large number of studies have demonstrated costly punishment to unfair events across human societies. However, individuals exhibit a large heterogeneity in costly punishment decisions, whereas the neuropsychological substrates underlying the heterogeneity remain poorly understood. Here, we addressed this issue by applying a multivariate machine-learning approach to compare topological properties of resting-state brain networks as a potential neuromarker between individuals exhibiting different punishment propensities. A linear support vector machine classifier obtained an accuracy of 74.19% employing the features derived from resting-state brain networks to distinguish two groups of individuals with different punishment tendencies. Importantly, the most discriminative features that contributed to the classification were those regions frequently implicated in costly punishment decisions, including dorsal anterior cingulate cortex (dACC) and putamen (salience network), dorsomedial prefrontal cortex (dmPFC) and temporoparietal junction (mentalizing network), and lateral prefrontal cortex (central-executive network). These networks are previously implicated in encoding norm violation and intentions of others and integrating this information for punishment decisions. Our findings thus demonstrated that resting-state functional connectivity (RSFC) provides a promising neuromarker of social preferences, and bolster the assertion that human costly punishment behaviors emerge from interactions among multiple neural systems. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

Generating Models of Infinite-State Communication Protocols Using Regular Inference with Abstraction

NASA Astrophysics Data System (ADS)

Aarts, Fides; Jonsson, Bengt; Uijen, Johan

In order to facilitate model-based verification and validation, effort is underway to develop techniques for generating models of communication system components from observations of their external behavior. Most previous such work has employed regular inference techniques which generate modest-size finite-state models. They typically suppress parameters of messages, although these have a significant impact on control flow in many communication protocols. We present a framework, which adapts regular inference to include data parameters in messages and states for generating components with large or infinite message alphabets. A main idea is to adapt the framework of predicate abstraction, successfully used in formal verification. Since we are in a black-box setting, the abstraction must be supplied externally, using information about how the component manages data parameters. We have implemented our techniques by connecting the LearnLib tool for regular inference with the protocol simulator ns-2, and generated a model of the SIP component as implemented in ns-2.

Abstraction and art.

PubMed Central

Gortais, Bernard

2003-01-01

In a given social context, artistic creation comprises a set of processes, which relate to the activity of the artist and the activity of the spectator. Through these processes we see and understand that the world is vaster than it is said to be. Artistic processes are mediated experiences that open up the world. A successful work of art expresses a reality beyond actual reality: it suggests an unknown world using the means and the signs of the known world. Artistic practices incorporate the means of creation developed by science and technology and change forms as they change. Artists and the public follow different processes of abstraction at different levels, in the definition of the means of creation, of representation and of perception of a work of art. This paper examines how the processes of abstraction are used within the framework of the visual arts and abstract painting, which appeared during a period of growing importance for the processes of abstraction in science and technology, at the beginning of the twentieth century. The development of digital platforms and new man-machine interfaces allow multimedia creations. This is performed under the constraint of phases of multidisciplinary conceptualization using generic representation languages, which tend to abolish traditional frontiers between the arts: visual arts, drama, dance and music. PMID:12903659

The application of top-down abstraction learning using prediction as a supervisory signal to cyber security

NASA Astrophysics Data System (ADS)

Mugan, Jonathan; Khalili, Aram E.

2014-05-01

Current computer systems are dumb automatons, and their blind execution of instructions makes them open to attack. Their inability to reason means that they don't consider the larger, constantly changing context outside their immediate inputs. Their nearsightedness is particularly dangerous because, in our complex systems, it is difficult to prevent all exploitable situations. Additionally, the lack of autonomous oversight of our systems means they are unable to fight through attacks. Keeping adversaries completely out of systems may be an unreasonable expectation, and our systems need to adapt to attacks and other disruptions to achieve their objectives. What is needed is an autonomous controller within the computer system that can sense the state of the system and reason about that state. In this paper, we present Self-Awareness Through Predictive Abstraction Modeling (SATPAM). SATPAM uses prediction to learn abstractions that allow it to recognize the right events at the right level of detail. These abstractions allow SATPAM to break the world into small, relatively independent, pieces that allow employment of existing reasoning methods. SATPAM goes beyond classification-based machine learning and statistical anomaly detection to be able to reason about the system, and SATPAM's knowledge representation and reasoning is more like that of a human. For example, humans intuitively know that the color of a car is not relevant to any mechanical problem, and SATPAM provides a plausible method whereby a machine can acquire such reasoning patterns. In this paper, we present the initial experimental results using SATPAM.

FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET.

PubMed

N Ahmed, Malik; Abdullah, Abdul Hanan; Kaiwartya, Omprakash

2016-01-01

Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs) significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS) and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM) for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV) protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-the-art techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks.

Abstract Interpreters for Free

NASA Astrophysics Data System (ADS)

Might, Matthew

In small-step abstract interpretations, the concrete and abstract semantics bear an uncanny resemblance. In this work, we present an analysis-design methodology that both explains and exploits that resemblance. Specifically, we present a two-step method to convert a small-step concrete semantics into a family of sound, computable abstract interpretations. The first step re-factors the concrete state-space to eliminate recursive structure; this refactoring of the state-space simultaneously determines a store-passing-style transformation on the underlying concrete semantics. The second step uses inference rules to generate an abstract state-space and a Galois connection simultaneously. The Galois connection allows the calculation of the "optimal" abstract interpretation. The two-step process is unambiguous, but nondeterministic: at each step, analysis designers face choices. Some of these choices ultimately influence properties such as flow-, field- and context-sensitivity. Thus, under the method, we can give the emergence of these properties a graph-theoretic characterization. To illustrate the method, we systematically abstract the continuation-passing style lambda calculus to arrive at two distinct families of analyses. The first is the well-known k-CFA family of analyses. The second consists of novel "environment-centric" abstract interpretations, none of which appear in the literature on static analysis of higher-order programs.

The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction.

PubMed

Casey, M

1996-08-15

Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attractor structure of such systems is given. This knowledge effectively predicts activation space dynamics, which allows one to understand RNN computation dynamics in spite of complexity in activation dynamics. This theory provides a theoretical framework for understanding finite state machine (FSM) extraction techniques and can be used to improve training methods for RNNs performing FSM computations. This provides an example of a successful approach to understanding a general class of complex systems that has not been explicitly designed, e.g., systems that have evolved or learned their internal structure.

A finite state machine read-out chip for integrated surface acoustic wave sensors

NASA Astrophysics Data System (ADS)

Rakshit, Sambarta; Iliadis, Agis A.

2015-01-01

A finite state machine based integrated sensor circuit suitable for the read-out module of a monolithically integrated SAW sensor on Si is reported. The primary sensor closed loop consists of a voltage controlled oscillator (VCO), a peak detecting comparator, a finite state machine (FSM), and a monolithically integrated SAW sensor device. The output of the system oscillates within a narrow voltage range that correlates with the SAW pass-band response. The period of oscillation is of the order of the SAW phase delay. We use timing information from the FSM to convert SAW phase delay to an on-chip 10 bit digital output operating on the principle of time to digital conversion (TDC). The control inputs of this digital conversion block are generated by a second finite state machine operating under a divided system clock. The average output varies with changes in SAW center frequency, thus tracking mass sensing events in real time. Based on measured VCO gain of 16 MHz/V our system will convert a 10 kHz SAW frequency shift to a corresponding mean voltage shift of 0.7 mV. A corresponding shift in phase delay is converted to a one or two bit shift in the TDC output code. The system can handle alternate SAW center frequencies and group delays simply by adjusting the VCO control and TDC delay control inputs. Because of frequency to voltage and phase to digital conversion, this topology does not require external frequency counter setups and is uniquely suitable for full monolithic integration of autonomous sensor systems and tags.

FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET

PubMed Central

N. Ahmed, Malik; Abdullah, Abdul Hanan; Kaiwartya, Omprakash

2016-01-01

Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs) significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS) and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM) for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV) protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-the-art techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks. PMID:27285146

A grounded theory of abstraction in artificial intelligence.

PubMed Central

Zucker, Jean-Daniel

2003-01-01

In artificial intelligence, abstraction is commonly used to account for the use of various levels of details in a given representation language or the ability to change from one level to another while preserving useful properties. Abstraction has been mainly studied in problem solving, theorem proving, knowledge representation (in particular for spatial and temporal reasoning) and machine learning. In such contexts, abstraction is defined as a mapping between formalisms that reduces the computational complexity of the task at stake. By analysing the notion of abstraction from an information quantity point of view, we pinpoint the differences and the complementary role of reformulation and abstraction in any representation change. We contribute to extending the existing semantic theories of abstraction to be grounded on perception, where the notion of information quantity is easier to characterize formally. In the author's view, abstraction is best represented using abstraction operators, as they provide semantics for classifying different abstractions and support the automation of representation changes. The usefulness of a grounded theory of abstraction in the cartography domain is illustrated. Finally, the importance of explicitly representing abstraction for designing more autonomous and adaptive systems is discussed. PMID:12903672

A grounded theory of abstraction in artificial intelligence.

PubMed

Zucker, Jean-Daniel

2003-07-29

In artificial intelligence, abstraction is commonly used to account for the use of various levels of details in a given representation language or the ability to change from one level to another while preserving useful properties. Abstraction has been mainly studied in problem solving, theorem proving, knowledge representation (in particular for spatial and temporal reasoning) and machine learning. In such contexts, abstraction is defined as a mapping between formalisms that reduces the computational complexity of the task at stake. By analysing the notion of abstraction from an information quantity point of view, we pinpoint the differences and the complementary role of reformulation and abstraction in any representation change. We contribute to extending the existing semantic theories of abstraction to be grounded on perception, where the notion of information quantity is easier to characterize formally. In the author's view, abstraction is best represented using abstraction operators, as they provide semantics for classifying different abstractions and support the automation of representation changes. The usefulness of a grounded theory of abstraction in the cartography domain is illustrated. Finally, the importance of explicitly representing abstraction for designing more autonomous and adaptive systems is discussed.

State-to-State integral cross section for the H+H2O-->H2+OH abstraction reaction.

PubMed

Zhang, Dong H; Xie, Daiqian; Yang, Minghui; Lee, Soo-Y

2002-12-31

The initial state selected time-dependent wave-packet method was extended to calculate the state-to-state integral cross section for the title reaction with H2O in the ground rovibrational state on the potential energy surface of Yang, Zhang, Collins, and Lee. One OH bond length was fixed in the study, which is justifiable for the abstraction reaction, but the remaining 5 degrees of freedom were treated exactly. It was found that the H2 molecule is produced vibrationally cold for collision energy up to 1.6 eV. The OH rotation takes away about 4% of total available energy in the products, while the fraction of energy going to H2 rotation increases with collision energy to about 20% at 1.6 eV.

Toward an implicit measure of emotions: ratings of abstract images reveal distinct emotional states.

PubMed

Bartoszek, Gregory; Cervone, Daniel

2017-11-01

Although implicit tests of positive and negative affect exist, implicit measures of distinct emotional states are scarce. Three experiments examined whether a novel implicit emotion-assessment task, the rating of emotion expressed in abstract images, would reveal distinct emotional states. In Experiment 1, participants exposed to a sadness-inducing story inferred more sadness, and less happiness, in abstract images. In Experiment 2, an anger-provoking interaction increased anger ratings. In Experiment 3, compared to neutral images, spider images increased fear ratings in spider-fearful participants but not in controls. In each experiment, the implicit task indicated elevated levels of the target emotion and did not indicate elevated levels of non-target negative emotions; the task thus differentiated among emotional states of the same valence. Correlations also supported the convergent and discriminant validity of the implicit task. Supporting the possibility that heuristic processes underlie the ratings, group differences were stronger among those who responded relatively quickly.

On the Stability of Jump-Linear Systems Driven by Finite-State Machines with Markovian Inputs

NASA Technical Reports Server (NTRS)

Patilkulkarni, Sudarshan; Herencia-Zapana, Heber; Gray, W. Steven; Gonzalez, Oscar R.

2004-01-01

This paper presents two mean-square stability tests for a jump-linear system driven by a finite-state machine with a first-order Markovian input process. The first test is based on conventional Markov jump-linear theory and avoids the use of any higher-order statistics. The second test is developed directly using the higher-order statistics of the machine s output process. The two approaches are illustrated with a simple model for a recoverable computer control system.

Abstracting Concepts and Methods.

ERIC Educational Resources Information Center

Borko, Harold; Bernier, Charles L.

This text provides a complete discussion of abstracts--their history, production, organization, publication--and of indexing. Instructions for abstracting are outlined, and standards and criteria for abstracting are stated. Management, automation, and personnel are discussed in terms of possible economies that can be derived from the introduction…

Towards a molecular logic machine

NASA Astrophysics Data System (ADS)

Remacle, F.; Levine, R. D.

2001-06-01

Finite state logic machines can be realized by pump-probe spectroscopic experiments on an isolated molecule. The most elaborate setup, a Turing machine, can be programmed to carry out a specific computation. We argue that a molecule can be similarly programmed, and provide examples using two photon spectroscopies. The states of the molecule serve as the possible states of the head of the Turing machine and the physics of the problem determines the possible instructions of the program. The tape is written in an alphabet that allows the listing of the different pump and probe signals that are applied in a given experiment. Different experiments using the same set of molecular levels correspond to different tapes that can be read and processed by the same head and program. The analogy to a Turing machine is not a mechanical one and is not completely molecular because the tape is not part of the molecular machine. We therefore also discuss molecular finite state machines, such as sequential devices, for which the tape is not part of the machine. Nonmolecular tapes allow for quite long input sequences with a rich alphabet (at the level of 7 bits) and laser pulse shaping experiments provide concrete examples. Single molecule spectroscopies show that a single molecule can be repeatedly cycled through a logical operation.

Compositional mining of multiple object API protocols through state abstraction.

PubMed

Dai, Ziying; Mao, Xiaoguang; Lei, Yan; Qi, Yuhua; Wang, Rui; Gu, Bin

2013-01-01

API protocols specify correct sequences of method invocations. Despite their usefulness, API protocols are often unavailable in practice because writing them is cumbersome and error prone. Multiple object API protocols are more expressive than single object API protocols. However, the huge number of objects of typical object-oriented programs poses a major challenge to the automatic mining of multiple object API protocols: besides maintaining scalability, it is important to capture various object interactions. Current approaches utilize various heuristics to focus on small sets of methods. In this paper, we present a general, scalable, multiple object API protocols mining approach that can capture all object interactions. Our approach uses abstract field values to label object states during the mining process. We first mine single object typestates as finite state automata whose transitions are annotated with states of interacting objects before and after the execution of the corresponding method and then construct multiple object API protocols by composing these annotated single object typestates. We implement our approach for Java and evaluate it through a series of experiments.

Compositional Mining of Multiple Object API Protocols through State Abstraction

PubMed Central

Mao, Xiaoguang; Qi, Yuhua; Wang, Rui; Gu, Bin

2013-01-01

API protocols specify correct sequences of method invocations. Despite their usefulness, API protocols are often unavailable in practice because writing them is cumbersome and error prone. Multiple object API protocols are more expressive than single object API protocols. However, the huge number of objects of typical object-oriented programs poses a major challenge to the automatic mining of multiple object API protocols: besides maintaining scalability, it is important to capture various object interactions. Current approaches utilize various heuristics to focus on small sets of methods. In this paper, we present a general, scalable, multiple object API protocols mining approach that can capture all object interactions. Our approach uses abstract field values to label object states during the mining process. We first mine single object typestates as finite state automata whose transitions are annotated with states of interacting objects before and after the execution of the corresponding method and then construct multiple object API protocols by composing these annotated single object typestates. We implement our approach for Java and evaluate it through a series of experiments. PMID:23844378

Technology of machine tools. Volume 4. Machine tool controls

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Technology of machine tools. Volume 3. Machine tool mechanics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tlusty, J.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Technology of machine tools. Volume 5. Machine tool accuracy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hocken, R.J.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

A Machine-Checked Proof of A State-Space Construction Algorithm

NASA Technical Reports Server (NTRS)

Catano, Nestor; Siminiceanu, Radu I.

2010-01-01

This paper presents the correctness proof of Saturation, an algorithm for generating state spaces of concurrent systems, implemented in the SMART tool. Unlike the Breadth First Search exploration algorithm, which is easy to understand and formalise, Saturation is a complex algorithm, employing a mutually-recursive pair of procedures that compute a series of non-trivial, nested local fixed points, corresponding to a chaotic fixed point strategy. A pencil-and-paper proof of Saturation exists, but a machine checked proof had never been attempted. The key element of the proof is the characterisation theorem of saturated nodes in decision diagrams, stating that a saturated node represents a set of states encoding a local fixed-point with respect to firing all events affecting only the node s level and levels below. For our purpose, we have employed the Prototype Verification System (PVS) for formalising the Saturation algorithm, its data structures, and for conducting the proofs.

Heat-machine control by quantum-state preparation: from quantum engines to refrigerators.

PubMed

Gelbwaser-Klimovsky, D; Kurizki, G

2014-08-01

We explore the dependence of the performance bounds of heat engines and refrigerators on the initial quantum state and the subsequent evolution of their piston, modeled by a quantized harmonic oscillator. Our goal is to provide a fully quantized treatment of self-contained (autonomous) heat machines, as opposed to their prevailing semiclassical description that consists of a quantum system alternately coupled to a hot or a cold heat bath and parametrically driven by a classical time-dependent piston or field. Here, by contrast, there is no external time-dependent driving. Instead, the evolution is caused by the stationary simultaneous interaction of two heat baths (having distinct spectra and temperatures) with a single two-level system that is in turn coupled to the quantum piston. The fully quantized treatment we put forward allows us to investigate work extraction and refrigeration by the tools of quantum-optical amplifier and dissipation theory, particularly, by the analysis of amplified or dissipated phase-plane quasiprobability distributions. Our main insight is that quantum states may be thermodynamic resources and can provide a powerful handle, or control, on the efficiency of the heat machine. In particular, a piston initialized in a coherent state can cause the engine to produce work at an efficiency above the Carnot bound in the linear amplification regime. In the refrigeration regime, the coefficient of performance can transgress the Carnot bound if the piston is initialized in a Fock state. The piston may be realized by a vibrational mode, as in nanomechanical setups, or an electromagnetic field mode, as in cavity-based scenarios.

Heat-machine control by quantum-state preparation: From quantum engines to refrigerators

NASA Astrophysics Data System (ADS)

Gelbwaser-Klimovsky, D.; Kurizki, G.

2014-08-01

We explore the dependence of the performance bounds of heat engines and refrigerators on the initial quantum state and the subsequent evolution of their piston, modeled by a quantized harmonic oscillator. Our goal is to provide a fully quantized treatment of self-contained (autonomous) heat machines, as opposed to their prevailing semiclassical description that consists of a quantum system alternately coupled to a hot or a cold heat bath and parametrically driven by a classical time-dependent piston or field. Here, by contrast, there is no external time-dependent driving. Instead, the evolution is caused by the stationary simultaneous interaction of two heat baths (having distinct spectra and temperatures) with a single two-level system that is in turn coupled to the quantum piston. The fully quantized treatment we put forward allows us to investigate work extraction and refrigeration by the tools of quantum-optical amplifier and dissipation theory, particularly, by the analysis of amplified or dissipated phase-plane quasiprobability distributions. Our main insight is that quantum states may be thermodynamic resources and can provide a powerful handle, or control, on the efficiency of the heat machine. In particular, a piston initialized in a coherent state can cause the engine to produce work at an efficiency above the Carnot bound in the linear amplification regime. In the refrigeration regime, the coefficient of performance can transgress the Carnot bound if the piston is initialized in a Fock state. The piston may be realized by a vibrational mode, as in nanomechanical setups, or an electromagnetic field mode, as in cavity-based scenarios.

Automatic Abstraction in Planning

NASA Technical Reports Server (NTRS)

Christensen, J.

1991-01-01

Traditionally, abstraction in planning has been accomplished by either state abstraction or operator abstraction, neither of which has been fully automatic. We present a new method, predicate relaxation, for automatically performing state abstraction. PABLO, a nonlinear hierarchical planner, implements predicate relaxation. Theoretical, as well as empirical results are presented which demonstrate the potential advantages of using predicate relaxation in planning. We also present a new definition of hierarchical operators that allows us to guarantee a limited form of completeness. This new definition is shown to be, in some ways, more flexible than previous definitions of hierarchical operators. Finally, a Classical Truth Criterion is presented that is proven to be sound and complete for a planning formalism that is general enough to include most classical planning formalisms that are based on the STRIPS assumption.

Virtual Machine Language 2.1

NASA Technical Reports Server (NTRS)

Riedel, Joseph E.; Grasso, Christopher A.

2012-01-01

VML (Virtual Machine Language) is an advanced computing environment that allows spacecraft to operate using mechanisms ranging from simple, time-oriented sequencing to advanced, multicomponent reactive systems. VML has developed in four evolutionary stages. VML 0 is a core execution capability providing multi-threaded command execution, integer data types, and rudimentary branching. VML 1 added named parameterized procedures, extensive polymorphism, data typing, branching, looping issuance of commands using run-time parameters, and named global variables. VML 2 added for loops, data verification, telemetry reaction, and an open flight adaptation architecture. VML 2.1 contains major advances in control flow capabilities for executable state machines. On the resource requirements front, VML 2.1 features a reduced memory footprint in order to fit more capability into modestly sized flight processors, and endian-neutral data access for compatibility with Intel little-endian processors. Sequence packaging has been improved with object-oriented programming constructs and the use of implicit (rather than explicit) time tags on statements. Sequence event detection has been significantly enhanced with multi-variable waiting, which allows a sequence to detect and react to conditions defined by complex expressions with multiple global variables. This multi-variable waiting serves as the basis for implementing parallel rule checking, which in turn, makes possible executable state machines. The new state machine feature in VML 2.1 allows the creation of sophisticated autonomous reactive systems without the need to develop expensive flight software. Users specify named states and transitions, along with the truth conditions required, before taking transitions. Transitions with the same signal name allow separate state machines to coordinate actions: the conditions distributed across all state machines necessary to arm a particular signal are evaluated, and once found true, that

Identification of Tool Wear when Machining of Austenitic Steels and Titatium by Miniature Machining

NASA Astrophysics Data System (ADS)

Pilc, Jozef; Kameník, Roman; Varga, Daniel; Martinček, Juraj; Sadilek, Marek

2016-12-01

Application of miniature machining is currently rapidly increasing mainly in biomedical industry and machining of hard-to-machine materials. Machinability of materials with increased level of toughness depends on factors that are important in the final state of surface integrity. Because of this, it is necessary to achieve high precision (varying in microns) in miniature machining. If we want to guarantee machining high precision, it is necessary to analyse tool wear intensity in direct interaction with given machined materials. During long-term cutting process, different cutting wedge deformations occur, leading in most cases to a rapid wear and destruction of the cutting wedge. This article deal with experimental monitoring of tool wear intensity during miniature machining.

State sales tax rates for soft drinks and snacks sold through grocery stores and vending machines, 2007.

PubMed

Chriqui, Jamie F; Eidson, Shelby S; Bates, Hannalori; Kowalczyk, Shelly; Chaloupka, Frank J

2008-07-01

Junk food consumption is associated with rising obesity rates in the United States. While a "junk food" specific tax is a potential public health intervention, a majority of states already impose sales taxes on certain junk food and soft drinks. This study reviews the state sales tax variance for soft drinks and selected snack products sold through grocery stores and vending machines as of January 2007. Sales taxes vary by state, intended retail location (grocery store vs. vending machine), and product. Vended snacks and soft drinks are taxed at a higher rate than grocery items and other food products, generally, indicative of a "disfavored" tax status attributed to vended items. Soft drinks, candy, and gum are taxed at higher rates than are other items examined. Similar tax schemes in other countries and the potential implications of these findings relative to the relationship between price and consumption are discussed.

Two-qubit quantum cloning machine and quantum correlation broadcasting

NASA Astrophysics Data System (ADS)

Kheirollahi, Azam; Mohammadi, Hamidreza; Akhtarshenas, Seyed Javad

2016-11-01

Due to the axioms of quantum mechanics, perfect cloning of an unknown quantum state is impossible. But since imperfect cloning is still possible, a question arises: "Is there an optimal quantum cloning machine?" Buzek and Hillery answered this question and constructed their famous B-H quantum cloning machine. The B-H machine clones the state of an arbitrary single qubit in an optimal manner and hence it is universal. Generalizing this machine for a two-qubit system is straightforward, but during this procedure, except for product states, this machine loses its universality and becomes a state-dependent cloning machine. In this paper, we propose some classes of optimal universal local quantum state cloners for a particular class of two-qubit systems, more precisely, for a class of states with known Schmidt basis. We then extend our machine to the case that the Schmidt basis of the input state is deviated from the local computational basis of the machine. We show that more local quantum coherence existing in the input state corresponds to less fidelity between the input and output states. Also we present two classes of a state-dependent local quantum copying machine. Furthermore, we investigate local broadcasting of two aspects of quantum correlations, i.e., quantum entanglement and quantum discord, defined, respectively, within the entanglement-separability paradigm and from an information-theoretic perspective. The results show that although quantum correlation is, in general, very fragile during the broadcasting procedure, quantum discord is broadcasted more robustly than quantum entanglement.

Humanizing machines: Anthropomorphization of slot machines increases gambling.

PubMed

Riva, Paolo; Sacchi, Simona; Brambilla, Marco

2015-12-01

Do people gamble more on slot machines if they think that they are playing against humanlike minds rather than mathematical algorithms? Research has shown that people have a strong cognitive tendency to imbue humanlike mental states to nonhuman entities (i.e., anthropomorphism). The present research tested whether anthropomorphizing slot machines would increase gambling. Four studies manipulated slot machine anthropomorphization and found that exposing people to an anthropomorphized description of a slot machine increased gambling behavior and reduced gambling outcomes. Such findings emerged using tasks that focused on gambling behavior (Studies 1 to 3) as well as in experimental paradigms that included gambling outcomes (Studies 2 to 4). We found that gambling outcomes decrease because participants primed with the anthropomorphic slot machine gambled more (Study 4). Furthermore, we found that high-arousal positive emotions (e.g., feeling excited) played a role in the effect of anthropomorphism on gambling behavior (Studies 3 and 4). Our research indicates that the psychological process of gambling-machine anthropomorphism can be advantageous for the gaming industry; however, this may come at great expense for gamblers' (and their families') economic resources and psychological well-being. (c) 2015 APA, all rights reserved).

A Technique for Machine-Aided Indexing

ERIC Educational Resources Information Center

Klingbiel, Paul H.

1973-01-01

The technique for machine-aided indexing developed at the Defense Documentation Center (DDC) is illustrated on a randomly chosen abstract. Additional text is provided in coded form so that the reader can more fully explore this technique. (2 references) (Author)

Workshop on Fielded Applications of Machine Learning Held in Amherst, Massachusetts on 30 June-1 July 1993. Abstracts.

DTIC Science & Technology

1993-01-01

engineering has led to many AI systems that are now regularly used in industry and elsewhere. The ultimate test of machine learning , the subfield of Al that...applications of machine learning suggest the time was ripe for a meeting on this topic. For this reason, Pat Langley (Siemens Corporate Research) and Yves...Kodratoff (Universite de Paris, Sud) organized an invited workshop on applications of machine learning . The goal of the gathering was to familiarize

Trends of Occupational Fatalities Involving Machines, United States, 1992–2010

PubMed Central

Marsh, Suzanne M.; Fosbroke, David E.

2016-01-01

Background This paper describes trends of occupational machine-related fatalities from 1992–2010. We examine temporal patterns by worker demographics, machine types (e.g., stationary, mobile), and industries. Methods We analyzed fatalities from Census of Fatal Occupational Injuries data provided by the Bureau of Labor Statistics to the National Institute for Occupational Safety and Health. We used injury source to identify machine-related incidents and Poisson regression to assess trends over the 19-year period. Results There was an average annual decrease of 2.8% in overall machine-related fatality rates from 1992 through 2010. Mobile machine-related fatality rates decreased an average of 2.6% annually and stationary machine-related rates decreased an average of 3.5% annually. Groups that continued to be at high risk included older workers; self-employed; and workers in agriculture/forestry/fishing, construction, and mining. Conclusion Addressing dangers posed by tractors, excavators, and other mobile machines needs to continue. High-risk worker groups should receive targeted information on machine safety. PMID:26358658

Equivalence of restricted Boltzmann machines and tensor network states

NASA Astrophysics Data System (ADS)

Chen, Jing; Cheng, Song; Xie, Haidong; Wang, Lei; Xiang, Tao

2018-02-01

The restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep learning. RBM finds wide applications in dimensional reduction, feature extraction, and recommender systems via modeling the probability distributions of a variety of input data including natural images, speech signals, and customer ratings, etc. We build a bridge between RBM and tensor network states (TNS) widely used in quantum many-body physics research. We devise efficient algorithms to translate an RBM into the commonly used TNS. Conversely, we give sufficient and necessary conditions to determine whether a TNS can be transformed into an RBM of given architectures. Revealing these general and constructive connections can cross fertilize both deep learning and quantum many-body physics. Notably, by exploiting the entanglement entropy bound of TNS, we can rigorously quantify the expressive power of RBM on complex data sets. Insights into TNS and its entanglement capacity can guide the design of more powerful deep learning architectures. On the other hand, RBM can represent quantum many-body states with fewer parameters compared to TNS, which may allow more efficient classical simulations.

Enter the machine

NASA Astrophysics Data System (ADS)

Palittapongarnpim, Pantita; Sanders, Barry C.

2018-05-01

Quantum tomography infers quantum states from measurement data, but it becomes infeasible for large systems. Machine learning enables tomography of highly entangled many-body states and suggests a new powerful approach to this problem.

Time of Flight Estimation in the Presence of Outliers: A Biosonar-Inspired Machine Learning Approach

DTIC Science & Technology

2013-08-29

REPORT Time of Flight Estimation in the Presence of Outliers: A biosonar -inspired machine learning approach 14. ABSTRACT 16. SECURITY CLASSIFICATION OF...installations, biosonar , remote sensing, sonar resolution, sonar accuracy, sonar energy consumption Nathan Intrator, Leon N Cooper Brown University...Presence of Outliers: A biosonar -inspired machine learning approach Report Title ABSTRACT When the Signal-to-Noise Ratio (SNR) falls below a certain

Advanced light source: Compendium of user abstracts and technical reports,1993-1996

DOE Office of Scientific and Technical Information (OSTI.GOV)

None, None

1997-04-01

This compendium contains abstracts written by users summarizing research completed or in progress from 1993-1996, ALS technical reports describing ongoing efforts related to improvement in machine operations and research and development projects, and information on ALS beamlines planned through 1998. Two tables of contents organize the user abstracts by beamline and by area of research, and an author index makes abstracts accessible by author and by principal investigator. Technical details for each beamline including whom to contact for additional information can be found in the beamline information section. Separate abstracts have been indexed into the database for contributions to thismore » compendium.« less

Abstraction Techniques for Parameterized Verification

DTIC Science & Technology

2006-11-01

approach for applying model checking to unbounded systems is to extract finite state models from them using conservative abstraction techniques. Prop...36 2.5.1 Multiple Reference Processes . . . . . . . . . . . . . . . . . . . 36 2.5.2 Adding Monitor Processes...model checking to complex pieces of code like device drivers depends on the use of abstraction methods. An abstraction method extracts a small finite

Fast implementation of the 1\\rightarrow3 orbital state quantum cloning machine

NASA Astrophysics Data System (ADS)

Lin, Jin-Zhong

2018-05-01

We present a scheme to implement a 1→3 orbital state quantum cloning machine assisted by quantum Zeno dynamics. By constructing shortcuts to adiabatic passage with transitionless quantum driving, we can complete this scheme effectively and quickly in one step. The effects of decoherence, including spontaneous emission and the decay of the cavity, are also discussed. The numerical simulation results show that high fidelity can be obtained and the feasibility analysis indicates that this can also be realized in experiments.

Preliminary Development of Real Time Usage-Phase Monitoring System for CNC Machine Tools with a Case Study on CNC Machine VMC 250

NASA Astrophysics Data System (ADS)

Budi Harja, Herman; Prakosa, Tri; Raharno, Sri; Yuwana Martawirya, Yatna; Nurhadi, Indra; Setyo Nogroho, Alamsyah

2018-03-01

The production characteristic of job-shop industry at which products have wide variety but small amounts causes every machine tool will be shared to conduct production process with dynamic load. Its dynamic condition operation directly affects machine tools component reliability. Hence, determination of maintenance schedule for every component should be calculated based on actual usage of machine tools component. This paper describes study on development of monitoring system to obtaining information about each CNC machine tool component usage in real time approached by component grouping based on its operation phase. A special device has been developed for monitoring machine tool component usage by utilizing usage phase activity data taken from certain electronics components within CNC machine. The components are adaptor, servo driver and spindle driver, as well as some additional components such as microcontroller and relays. The obtained data are utilized for detecting machine utilization phases such as power on state, machine ready state or spindle running state. Experimental result have shown that the developed CNC machine tool monitoring system is capable of obtaining phase information of machine tool usage as well as its duration and displays the information at the user interface application.

Design and FPGA Implementation of a Universal Chaotic Signal Generator Based on the Verilog HDL Fixed-Point Algorithm and State Machine Control

NASA Astrophysics Data System (ADS)

Qiu, Mo; Yu, Simin; Wen, Yuqiong; Lü, Jinhu; He, Jianbin; Lin, Zhuosheng

In this paper, a novel design methodology and its FPGA hardware implementation for a universal chaotic signal generator is proposed via the Verilog HDL fixed-point algorithm and state machine control. According to continuous-time or discrete-time chaotic equations, a Verilog HDL fixed-point algorithm and its corresponding digital system are first designed. In the FPGA hardware platform, each operation step of Verilog HDL fixed-point algorithm is then controlled by a state machine. The generality of this method is that, for any given chaotic equation, it can be decomposed into four basic operation procedures, i.e. nonlinear function calculation, iterative sequence operation, iterative values right shifting and ceiling, and chaotic iterative sequences output, each of which corresponds to only a state via state machine control. Compared with the Verilog HDL floating-point algorithm, the Verilog HDL fixed-point algorithm can save the FPGA hardware resources and improve the operation efficiency. FPGA-based hardware experimental results validate the feasibility and reliability of the proposed approach.

Machine tool task force

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutton, G.P.

1980-10-22

The Machine Tool Task Force (MTTF) is a multidisciplined team of international experts, whose mission was to investigate the state of the art of machine tool technology, to identify promising future directions of that technology for both the US government and private industry, and to disseminate the findings of its research in a conference and through the publication of a final report. MTTF was a two and one-half year effort that involved the participation of 122 experts in the specialized technologies of machine tools and in the management of machine tool operations. The scope of the MTTF was limited tomore » cutting-type or material-removal-type machine tools, because they represent the major share and value of all machine tools now installed or being built. The activities of the MTTF and the technical, commercial and economic signifiance of recommended activities for improving machine tool technology are discussed. (LCL)« less

Technology of machine tools. Volume 2. Machine tool systems management and utilization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomson, A.R.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qi, Junjian; Sun, Kai; Wang, Jianhui

In this paper, in order to enhance the numerical stability of the unscented Kalman filter (UKF) used for power system dynamic state estimation, a new UKF with guaranteed positive semidifinite estimation error covariance (UKFGPS) is proposed and compared with five existing approaches, including UKFschol, UKF-kappa, UKFmodified, UKF-Delta Q, and the squareroot UKF (SRUKF). These methods and the extended Kalman filter (EKF) are tested by performing dynamic state estimation on WSCC 3-machine 9-bus system and NPCC 48-machine 140-bus system. For WSCC system, all methods obtain good estimates. However, for NPCC system, both EKF and the classic UKF fail. It is foundmore » that UKFschol, UKF-kappa, and UKF-Delta Q do not work well in some estimations while UKFGPS works well in most cases. UKFmodified and SRUKF can always work well, indicating their better scalability mainly due to the enhanced numerical stability.« less

Energy-efficient algorithm for classification of states of wireless sensor network using machine learning methods

NASA Astrophysics Data System (ADS)

Yuldashev, M. N.; Vlasov, A. I.; Novikov, A. N.

2018-05-01

This paper focuses on the development of an energy-efficient algorithm for classification of states of a wireless sensor network using machine learning methods. The proposed algorithm reduces energy consumption by: 1) elimination of monitoring of parameters that do not affect the state of the sensor network, 2) reduction of communication sessions over the network (the data are transmitted only if their values can affect the state of the sensor network). The studies of the proposed algorithm have shown that at classification accuracy close to 100%, the number of communication sessions can be reduced by 80%.

Machine learning phases of matter

NASA Astrophysics Data System (ADS)

Carrasquilla, Juan; Melko, Roger G.

2017-02-01

Condensed-matter physics is the study of the collective behaviour of infinitely complex assemblies of electrons, nuclei, magnetic moments, atoms or qubits. This complexity is reflected in the size of the state space, which grows exponentially with the number of particles, reminiscent of the `curse of dimensionality' commonly encountered in machine learning. Despite this curse, the machine learning community has developed techniques with remarkable abilities to recognize, classify, and characterize complex sets of data. Here, we show that modern machine learning architectures, such as fully connected and convolutional neural networks, can identify phases and phase transitions in a variety of condensed-matter Hamiltonians. Readily programmable through modern software libraries, neural networks can be trained to detect multiple types of order parameter, as well as highly non-trivial states with no conventional order, directly from raw state configurations sampled with Monte Carlo.

Foundations of the Bandera Abstraction Tools

NASA Technical Reports Server (NTRS)

Hatcliff, John; Dwyer, Matthew B.; Pasareanu, Corina S.; Robby

2003-01-01

Current research is demonstrating that model-checking and other forms of automated finite-state verification can be effective for checking properties of software systems. Due to the exponential costs associated with model-checking, multiple forms of abstraction are often necessary to obtain system models that are tractable for automated checking. The Bandera Tool Set provides multiple forms of automated support for compiling concurrent Java software systems to models that can be supplied to several different model-checking tools. In this paper, we describe the foundations of Bandera's data abstraction mechanism which is used to reduce the cardinality (and the program's state-space) of data domains in software to be model-checked. From a technical standpoint, the form of data abstraction used in Bandera is simple, and it is based on classical presentations of abstract interpretation. We describe the mechanisms that Bandera provides for declaring abstractions, for attaching abstractions to programs, and for generating abstracted programs and properties. The contributions of this work are the design and implementation of various forms of tool support required for effective application of data abstraction to software components written in a programming language like Java which has a rich set of linguistic features.

Human Machine Learning Symbiosis

ERIC Educational Resources Information Center

Walsh, Kenneth R.; Hoque, Md Tamjidul; Williams, Kim H.

2017-01-01

Human Machine Learning Symbiosis is a cooperative system where both the human learner and the machine learner learn from each other to create an effective and efficient learning environment adapted to the needs of the human learner. Such a system can be used in online learning modules so that the modules adapt to each learner's learning state both…

How state taxes and policies targeting soda consumption modify the association between school vending machines and student dietary behaviors: a cross-sectional analysis.

PubMed

Taber, Daniel R; Chriqui, Jamie F; Vuillaume, Renee; Chaloupka, Frank J

2014-01-01

Sodas are widely sold in vending machines and other school venues in the United States, particularly in high school. Research suggests that policy changes have reduced soda access, but the impact of reduced access on consumption is unclear. This study was designed to identify student, environmental, or policy characteristics that modify the associations between school vending machines and student dietary behaviors. Data on school vending machine access and student diet were obtained as part of the National Youth Physical Activity and Nutrition Study (NYPANS) and linked to state-level data on soda taxes, restaurant taxes, and state laws governing the sale of soda in schools. Regression models were used to: 1) estimate associations between vending machine access and soda consumption, fast food consumption, and lunch source, and 2) determine if associations were modified by state soda taxes, restaurant taxes, laws banning in-school soda sales, or student characteristics (race/ethnicity, sex, home food access, weight loss behaviors.). Contrary to the hypothesis, students tended to consume 0.53 fewer servings of soda/week (95% CI: -1.17, 0.11) and consume fast food on 0.24 fewer days/week (95% CI: -0.44, -0.05) if they had in-school access to vending machines. They were also less likely to consume soda daily (23.9% vs. 27.9%, average difference  =  -4.02, 95% CI: -7.28, -0.76). However, these inverse associations were observed primarily among states with lower soda and restaurant tax rates (relative to general food tax rates) and states that did not ban in-school soda sales. Associations did not vary by any student characteristics except for weight loss behaviors. Isolated changes to the school food environment may have unintended consequences unless policymakers incorporate other initiatives designed to discourage overall soda consumption.

How State Taxes and Policies Targeting Soda Consumption Modify the Association between School Vending Machines and Student Dietary Behaviors: A Cross-Sectional Analysis

PubMed Central

Taber, Daniel R.; Chriqui, Jamie F.; Vuillaume, Renee; Chaloupka, Frank J.

2014-01-01

Background Sodas are widely sold in vending machines and other school venues in the United States, particularly in high school. Research suggests that policy changes have reduced soda access, but the impact of reduced access on consumption is unclear. This study was designed to identify student, environmental, or policy characteristics that modify the associations between school vending machines and student dietary behaviors. Methods Data on school vending machine access and student diet were obtained as part of the National Youth Physical Activity and Nutrition Study (NYPANS) and linked to state-level data on soda taxes, restaurant taxes, and state laws governing the sale of soda in schools. Regression models were used to: 1) estimate associations between vending machine access and soda consumption, fast food consumption, and lunch source, and 2) determine if associations were modified by state soda taxes, restaurant taxes, laws banning in-school soda sales, or student characteristics (race/ethnicity, sex, home food access, weight loss behaviors.) Results Contrary to the hypothesis, students tended to consume 0.53 fewer servings of soda/week (95% CI: -1.17, 0.11) and consume fast food on 0.24 fewer days/week (95% CI: -0.44, -0.05) if they had in-school access to vending machines. They were also less likely to consume soda daily (23.9% vs. 27.9%, average difference = -4.02, 95% CI: -7.28, -0.76). However, these inverse associations were observed primarily among states with lower soda and restaurant tax rates (relative to general food tax rates) and states that did not ban in-school soda sales. Associations did not vary by any student characteristics except for weight loss behaviors. Conclusion Isolated changes to the school food environment may have unintended consequences unless policymakers incorporate other initiatives designed to discourage overall soda consumption. PMID:25083906

Cue-Reactive Altered State of Consciousness Mediates the Relationship Between Problem-Gambling Severity and Cue-Reactive Urge in Poker-Machine Gamblers.

PubMed

Tricker, Christopher; Rock, Adam J; Clark, Gavin I

2016-06-01

In order to enhance our understanding of the nature of poker-machine problem-gambling, a community sample of 37 poker-machine gamblers (M age = 32 years, M PGSI = 5; PGSI = Problem Gambling Severity Index) were assessed for urge to gamble (responses on a visual analogue scale) and altered state of consciousness (assessed by the Altered State of Awareness dimension of the Phenomenology of Consciousness Inventory) at baseline, after a neutral cue, and after a gambling cue. It was found that (a) problem-gambling severity (PGSI score) predicted increase in urge (from neutral cue to gambling cue, controlling for baseline; sr (2) = .19, p = .006) and increase in altered state of consciousness (from neutral cue to gambling cue, controlling for baseline; sr (2) = .57, p < .001), and (b) increase in altered state of consciousness (from neutral cue to gambling cue) mediated the relationship between problem-gambling severity and increase in urge (from neutral cue to gambling cue; κ(2) = .40, 99 % CI [.08, .71]). These findings suggest that cue-reactive altered state of consciousness is an important component of cue-reactive urge in poker-machine problem-gamblers.

Relative Effectiveness of Titles, Abstracts, and Subject Headings for Machine Retrieval from the COMPENDEX Services

ERIC Educational Resources Information Center

Byrne, Jerry R.

1975-01-01

Investigated the relative merits of searching on titles, subject headings, abstracts, free-language terms, and combinations of these elements. The combination of titles and abstracts came the closest to 100 percent retrieval. (Author/PF)

Energy Research Abstracts. [DOE abstract journal

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1981-01-01

Energy Research Abstracts (ERA) provides abstracting and indexing coverage of all scientific and technical reports, journal articles, conference papers and proceedings, books, patents, theses, and monographs originated by the US Department of Energy, its laboratories, energy centers, and contractors. ERA also covers other energy information prepared in report form by federal and state government organizations, foreign governments, and domestic and foreign universities and research organizations. ERA coverage of non-report literature is limited to that generated by Department of Energy activity. ERA is comprehensive in its subject scope, encompassing the DOE's research, development, demonstration, and technological programs resulting from its broadmore » charter for energy sources, conservation, safety, environmental impacts, and regulation. Corporate, author, subject, report number, and contract number indexes are included. ERA is available on an exchange basis to universities, research intitutions, industrial firms, and publishers of scientific information. Federal, state, and municipal agencies concerned with energy development, conservation, and usage may obtain ERA free of charge. Inquiries should be directed to the Technical Information Center, P.O. Box 62, Oak Ridge, Tennessee 37830. ERA is available to the public on a subscription basis for 24 semimonthly issues including a semiannual index and an annual index. All citations announced in ERA exist as separate records in the DOE Energy Data Base.« less

An easy-to-use calculating machine to simulate steady state and non-steady-state preparative separations by multiple dual mode counter-current chromatography with semi-continuous loading of feed mixtures.

PubMed

Kostanyan, Artak E; Shishilov, Oleg N

2018-06-01

Multiple dual mode counter-current chromatography (MDM CCC) separation processes with semi-continuous large sample loading consist of a succession of two counter-current steps: with "x" phase (first step) and "y" phase (second step) flow periods. A feed mixture dissolved in the "x" phase is continuously loaded into a CCC machine at the beginning of the first step of each cycle over a constant time with the volumetric rate equal to the flow rate of the pure "x" phase. An easy-to-use calculating machine is developed to simulate the chromatograms and the amounts of solutes eluted with the phases at each cycle for steady-state (the duration of the flow periods of the phases is kept constant for all the cycles) and non-steady-state (with variable duration of alternating phase elution steps) separations. Using the calculating machine, the separation of mixtures containing up to five components can be simulated and designed. Examples of the application of the calculating machine for the simulation of MDM CCC processes are discussed. Copyright © 2018 Elsevier B.V. All rights reserved.

Development of techniques to enhance man/machine communication

NASA Technical Reports Server (NTRS)

Targ, R.; Cole, P.; Puthoff, H.

1974-01-01

A four-state random stimulus generator, considered to function as an ESP teaching machine was used to investigate an approach to facilitating interactions between man and machines. A subject tries to guess in which of four states the machine is. The machine offers the user feedback and reinforcement as to the correctness of his choice. Using this machine, 148 volunteer subjects were screened under various protocols. Several whose learning slope and/or mean score departed significantly from chance expectation were identified. Direct physiological evidence of perception of remote stimuli not presented to any known sense of the percipient using electroencephalographic (EEG) output when a light was flashed in a distant room was also studied.

Boolean Minimization and Algebraic Factorization Procedures for Fully Testable Sequential Machines

DTIC Science & Technology

1989-09-01

Boolean Minimization and Algebraic Factorization Procedures for Fully Testable Sequential Machines Srinivas Devadas and Kurt Keutzer F ( Abstract In this...Projects Agency under contract number N00014-87-K-0825. Author Information Devadas : Department of Electrical Engineering and Computer Science, Room 36...MA 02139; (617) 253-0292. 0 * Boolean Minimization and Algebraic Factorization Procedures for Fully Testable Sequential Machines Siivas Devadas

Trait Mindfulness, Problem-Gambling Severity, Altered State of Awareness and Urge to Gamble in Poker-Machine Gamblers.

PubMed

McKeith, Charles F A; Rock, Adam J; Clark, Gavin I

2017-06-01

In Australia, poker-machine gamblers represent a disproportionate number of problem gamblers. To cultivate a greater understanding of the psychological mechanisms involved in poker-machine gambling, a repeated measures cue-reactivity protocol was administered. A community sample of 38 poker-machine gamblers was assessed for problem-gambling severity and trait mindfulness. Participants were also assessed regarding altered state of awareness (ASA) and urge to gamble at baseline, following a neutral cue, and following a gambling cue. Results indicated that: (a) urge to gamble significantly increased from neutral cue to gambling cue, while controlling for baseline urge; (b) cue-reactive ASA did not significantly mediate the relationship between problem-gambling severity and cue-reactive urge (from neutral cue to gambling cue); (c) trait mindfulness was significantly negatively associated with both problem-gambling severity and cue-reactive urge (i.e., from neutral cue to gambling cue, while controlling for baseline urge); and (d) trait mindfulness did not significantly moderate the effect of problem-gambling severity on cue-reactive urge (from neutral cue to gambling cue). This is the first study to demonstrate a negative association between trait mindfulness and cue-reactive urge to gamble in a population of poker-machine gamblers. Thus, this association merits further evaluation both in relation to poker-machine gambling and other gambling modalities.

An Analysis of Heavy-Ion Single Event Effects for a Variety of Finite State-Machine Mitigation Strategies

NASA Technical Reports Server (NTRS)

Berg, Melanie D.; Label, Kenneth A.; Kim, Hak; Phan, Anthony; Seidleck, Christina

2014-01-01

Finite state-machines (FSMs) are used to control operational flow in application specific integrated circuits (ASICs) and field programmable gate array (FPGA) devices. Because of their ease of interpretation, FSMs simplify the design and verification process and consequently are significant components in a synchronous design.

High-Density Liquid-State Machine Circuitry for Time-Series Forecasting.

PubMed

Rosselló, Josep L; Alomar, Miquel L; Morro, Antoni; Oliver, Antoni; Canals, Vincent

2016-08-01

Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the intrinsic parallelism of these computing systems are more efficiently exploited. Liquid state machines (LSM) have arisen as a strategic technique to implement recurrent designs of SNN with a simple learning methodology. In this work, we show a new low-cost methodology to implement high-density LSM by using Boolean gates. The proposed method is based on the use of probabilistic computing concepts to reduce hardware requirements, thus considerably increasing the neuron count per chip. The result is a highly functional system that is applied to high-speed time series forecasting.

Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

NASA Astrophysics Data System (ADS)

Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.

Architectures for intelligent machines

NASA Technical Reports Server (NTRS)

Saridis, George N.

1991-01-01

The theory of intelligent machines has been recently reformulated to incorporate new architectures that are using neural and Petri nets. The analytic functions of an intelligent machine are implemented by intelligent controls, using entropy as a measure. The resulting hierarchical control structure is based on the principle of increasing precision with decreasing intelligence. Each of the three levels of the intelligent control is using different architectures, in order to satisfy the requirements of the principle: the organization level is moduled after a Boltzmann machine for abstract reasoning, task planning and decision making; the coordination level is composed of a number of Petri net transducers supervised, for command exchange, by a dispatcher, which also serves as an interface to the organization level; the execution level, include the sensory, planning for navigation and control hardware which interacts one-to-one with the appropriate coordinators, while a VME bus provides a channel for database exchange among the several devices. This system is currently implemented on a robotic transporter, designed for space construction at the CIRSSE laboratories at the Rensselaer Polytechnic Institute. The progress of its development is reported.

High speed operation of permanent magnet machines

NASA Astrophysics Data System (ADS)

El-Refaie, Ayman M.

This work proposes methods to extend the high-speed operating capabilities of both the interior PM (IPM) and surface PM (SPM) machines. For interior PM machines, this research has developed and presented the first thorough analysis of how a new bi-state magnetic material can be usefully applied to the design of IPM machines. Key elements of this contribution include identifying how the unique properties of the bi-state magnetic material can be applied most effectively in the rotor design of an IPM machine by "unmagnetizing" the magnet cavity center posts rather than the outer bridges. The importance of elevated rotor speed in making the best use of the bi-state magnetic material while recognizing its limitations has been identified. For surface PM machines, this research has provided, for the first time, a clear explanation of how fractional-slot concentrated windings can be applied to SPM machines in order to achieve the necessary conditions for optimal flux weakening. A closed-form analytical procedure for analyzing SPM machines designed with concentrated windings has been developed. Guidelines for designing SPM machines using concentrated windings in order to achieve optimum flux weakening are provided. Analytical and numerical finite element analysis (FEA) results have provided promising evidence of the scalability of the concentrated winding technique with respect to the number of poles, machine aspect ratio, and output power rating. Useful comparisons between the predicted performance characteristics of SPM machines equipped with concentrated windings and both SPM and IPM machines designed with distributed windings are included. Analytical techniques have been used to evaluate the impact of the high pole number on various converter performance metrics. Both analytical techniques and FEA have been used for evaluating the eddy-current losses in the surface magnets due to the stator winding subharmonics. Techniques for reducing these losses have been

Absorption of language concepts in the machine mind

NASA Astrophysics Data System (ADS)

Kollár, Ján

2016-06-01

In our approach, the machine mind is the applicative dynamic system represented by its algorithmically evolvable internal language. By other words, the mind and the language of mind are synonyms. Coming out from Shaumyan's semiotic theory of languages, we present the representation of language concepts in the machine mind as a result of our experiment, to show non-redundancy of the language of mind. To provide useful restriction for further research, we also introduce the hypothesis of semantic saturation in Computer-Computer communication, which indicates that a set of machines is not self-evolvable. The goal of our research is to increase the abstraction of Human-Computer and Computer-Computer communication. If we want humans and machines comunicate as a parent with the child, using different symbols and media, we must find the language of mind commonly usable by both machines and humans. In our opinion, there exist a kind of calm language of thinking, which we try to propose for machines in this paper. We separate the layers of a machine mind, we present the structure of the evolved mind and we discuss the selected properties. We are concentrating on the representation of symbolized concepts in the mind, that are languages, not just grammars, since they have meaning.

Descriptions and Abstracts of Regular Education Inservice Projects (REGI).

ERIC Educational Resources Information Center

Erwin, Barbara, Comp.; And Others

This description of the Regular Education Inservice (REGI) effort in fiscal year 1981 includes a summary analysis of data from the REGI projects and a state by state compilation of project abstracts. Following the summary analysis of the REGI effort, project abstracts are organized by state or territory. Within each state or territory section,…

Quantum machine learning for quantum anomaly detection

NASA Astrophysics Data System (ADS)

Liu, Nana; Rebentrost, Patrick

2018-04-01

Anomaly detection is used for identifying data that deviate from "normal" data patterns. Its usage on classical data finds diverse applications in many important areas such as finance, fraud detection, medical diagnoses, data cleaning, and surveillance. With the advent of quantum technologies, anomaly detection of quantum data, in the form of quantum states, may become an important component of quantum applications. Machine-learning algorithms are playing pivotal roles in anomaly detection using classical data. Two widely used algorithms are the kernel principal component analysis and the one-class support vector machine. We find corresponding quantum algorithms to detect anomalies in quantum states. We show that these two quantum algorithms can be performed using resources that are logarithmic in the dimensionality of quantum states. For pure quantum states, these resources can also be logarithmic in the number of quantum states used for training the machine-learning algorithm. This makes these algorithms potentially applicable to big quantum data applications.

Allocating dissipation across a molecular machine cycle to maximize flux

PubMed Central

Brown, Aidan I.; Sivak, David A.

2017-01-01

Biomolecular machines consume free energy to break symmetry and make directed progress. Nonequilibrium ATP concentrations are the typical free energy source, with one cycle of a molecular machine consuming a certain number of ATP, providing a fixed free energy budget. Since evolution is expected to favor rapid-turnover machines that operate efficiently, we investigate how this free energy budget can be allocated to maximize flux. Unconstrained optimization eliminates intermediate metastable states, indicating that flux is enhanced in molecular machines with fewer states. When maintaining a set number of states, we show that—in contrast to previous findings—the flux-maximizing allocation of dissipation is not even. This result is consistent with the coexistence of both “irreversible” and reversible transitions in molecular machine models that successfully describe experimental data, which suggests that, in evolved machines, different transitions differ significantly in their dissipation. PMID:29073016

Workshop on Fielded Applications of Machine Learning

DTIC Science & Technology

1994-05-11

This report summaries the talks presented at the Workshop on Fielded Applications of Machine Learning , and draws some initial conclusions about the state of machine learning and its potential for solving real-world problems.

State but not district nutrition policies are associated with less junk food in vending machines and school stores in US public schools.

PubMed

Kubik, Martha Y; Wall, Melanie; Shen, Lijuan; Nanney, Marilyn S; Nelson, Toben F; Laska, Melissa N; Story, Mary

2010-07-01

Policy that targets the school food environment has been advanced as one way to increase the availability of healthy food at schools and healthy food choice by students. Although both state- and district-level policy initiatives have focused on school nutrition standards, it remains to be seen whether these policies translate into healthy food practices at the school level, where student behavior will be impacted. To examine whether state- and district-level nutrition policies addressing junk food in school vending machines and school stores were associated with less junk food in school vending machines and school stores. Junk food was defined as foods and beverages with low nutrient density that provide calories primarily through fats and added sugars. A cross-sectional study design was used to assess self-report data collected by computer-assisted telephone interviews or self-administered mail questionnaires from state-, district-, and school-level respondents participating in the School Health Policies and Programs Study 2006. The School Health Policies and Programs Study, administered every 6 years since 1994 by the Centers for Disease Control and Prevention, is considered the largest, most comprehensive assessment of school health policies and programs in the United States. A nationally representative sample (n=563) of public elementary, middle, and high schools was studied. Logistic regression adjusted for school characteristics, sampling weights, and clustering was used to analyze data. Policies were assessed for strength (required, recommended, neither required nor recommended prohibiting junk food) and whether strength was similar for school vending machines and school stores. School vending machines and school stores were more prevalent in high schools (93%) than middle (84%) and elementary (30%) schools. For state policies, elementary schools that required prohibiting junk food in school vending machines and school stores offered less junk food than

State but not District Nutrition Policies Are Associated with Less Junk Food in Vending Machines and School Stores in US Public Schools

PubMed Central

KUBIK, MARTHA Y.; WALL, MELANIE; SHEN, LIJUAN; NANNEY, MARILYN S.; NELSON, TOBEN F.; LASKA, MELISSA N.; STORY, MARY

2012-01-01

Background Policy that targets the school food environment has been advanced as one way to increase the availability of healthy food at schools and healthy food choice by students. Although both state- and district-level policy initiatives have focused on school nutrition standards, it remains to be seen whether these policies translate into healthy food practices at the school level, where student behavior will be impacted. Objective To examine whether state- and district-level nutrition policies addressing junk food in school vending machines and school stores were associated with less junk food in school vending machines and school stores. Junk food was defined as foods and beverages with low nutrient density that provide calories primarily through fats and added sugars. Design A cross-sectional study design was used to assess self-report data collected by computer-assisted telephone interviews or self-administered mail questionnaires from state-, district-, and school-level respondents participating in the School Health Policies and Programs Study 2006. The School Health Policies and Programs Study, administered every 6 years since 1994 by the Centers for Disease Control and Prevention, is considered the largest, most comprehensive assessment of school health policies and programs in the United States. Subjects/setting A nationally representative sample (n = 563) of public elementary, middle, and high schools was studied. Statistical analysis Logistic regression adjusted for school characteristics, sampling weights, and clustering was used to analyze data. Policies were assessed for strength (required, recommended, neither required nor recommended prohibiting junk food) and whether strength was similar for school vending machines and school stores. Results School vending machines and school stores were more prevalent in high schools (93%) than middle (84%) and elementary (30%) schools. For state policies, elementary schools that required prohibiting junk food

Effect of focusing flow on stationary spot machining properties in elastic emission machining

PubMed Central

2013-01-01

Ultraprecise optical elements are applied in advanced optical apparatus. Elastic emission machining (EEM) is one of the ultraprecision machining methods used to fabricate shapes with 0.1-nm accuracy. In this study, we proposed and experimentally tested the control of the shape of a stationary spot profile by introducing a focusing-flow state between the nozzle outlet and the workpiece surface in EEM. The simulation results indicate that the focusing-flow nozzle sharpens the distribution of the velocity on the workpiece surface. The results of machining experiments verified those of the simulation. The obtained stationary spot conditions will be useful for surface processing with a high spatial resolution. PMID:23680043

Tomography and generative training with quantum Boltzmann machines

NASA Astrophysics Data System (ADS)

Kieferová, Mária; Wiebe, Nathan

2017-12-01

The promise of quantum neural nets, which utilize quantum effects to model complex data sets, has made their development an aspirational goal for quantum machine learning and quantum computing in general. Here we provide methods of training quantum Boltzmann machines. Our work generalizes existing methods and provides additional approaches for training quantum neural networks that compare favorably to existing methods. We further demonstrate that quantum Boltzmann machines enable a form of partial quantum state tomography that further provides a generative model for the input quantum state. Classical Boltzmann machines are incapable of this. This verifies the long-conjectured connection between tomography and quantum machine learning. Finally, we prove that classical computers cannot simulate our training process in general unless BQP=BPP , provide lower bounds on the complexity of the training procedures and numerically investigate training for small nonstoquastic Hamiltonians.

Overview of the Machine-Tool Task Force

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutton, G.P.

1981-06-08

The Machine Tool Task Force, (MTTF) surveyed the state of the art of machine tool technology for material removal for two and one-half years. This overview gives a brief summary of the approach, specific subjects covered, principal conclusions and some of the key recommendations aimed at improving the technology and advancing the productivity of machine tools. The Task Force consisted of 123 experts from the US and other countries. Their findings are documented in a five-volume report, Technology of Machine Tools.

The Machine Scoring of Writing

ERIC Educational Resources Information Center

McCurry, Doug

2010-01-01

This article provides an introduction to the kind of computer software that is used to score student writing in some high stakes testing programs, and that is being promoted as a teaching and learning tool to schools. It sketches the state of play with machines for the scoring of writing, and describes how these machines work and what they do.…

An Autonomous Connectivity Restoration Algorithm Based on Finite State Machine for Wireless Sensor-Actor Networks.

PubMed

Zhang, Ying; Wang, Jun; Hao, Guan

2018-01-08

With the development of autonomous unmanned intelligent systems, such as the unmanned boats, unmanned planes and autonomous underwater vehicles, studies on Wireless Sensor-Actor Networks (WSANs) have attracted more attention. Network connectivity algorithms play an important role in data exchange, collaborative detection and information fusion. Due to the harsh application environment, abnormal nodes often appear, and the network connectivity will be prone to be lost. Network self-healing mechanisms have become critical for these systems. In order to decrease the movement overhead of the sensor-actor nodes, an autonomous connectivity restoration algorithm based on finite state machine is proposed. The idea is to identify whether a node is a critical node by using a finite state machine, and update the connected dominating set in a timely way. If an abnormal node is a critical node, the nearest non-critical node will be relocated to replace the abnormal node. In the case of multiple node abnormality, a regional network restoration algorithm is introduced. It is designed to reduce the overhead of node movements while restoration happens. Simulation results indicate the proposed algorithm has better performance on the total moving distance and the number of total relocated nodes compared with some other representative restoration algorithms.

An Autonomous Connectivity Restoration Algorithm Based on Finite State Machine for Wireless Sensor-Actor Networks

PubMed Central

Zhang, Ying; Wang, Jun; Hao, Guan

2018-01-01

With the development of autonomous unmanned intelligent systems, such as the unmanned boats, unmanned planes and autonomous underwater vehicles, studies on Wireless Sensor-Actor Networks (WSANs) have attracted more attention. Network connectivity algorithms play an important role in data exchange, collaborative detection and information fusion. Due to the harsh application environment, abnormal nodes often appear, and the network connectivity will be prone to be lost. Network self-healing mechanisms have become critical for these systems. In order to decrease the movement overhead of the sensor-actor nodes, an autonomous connectivity restoration algorithm based on finite state machine is proposed. The idea is to identify whether a node is a critical node by using a finite state machine, and update the connected dominating set in a timely way. If an abnormal node is a critical node, the nearest non-critical node will be relocated to replace the abnormal node. In the case of multiple node abnormality, a regional network restoration algorithm is introduced. It is designed to reduce the overhead of node movements while restoration happens. Simulation results indicate the proposed algorithm has better performance on the total moving distance and the number of total relocated nodes compared with some other representative restoration algorithms. PMID:29316702

Spatially Regularized Machine Learning for Task and Resting-state fMRI

PubMed Central

Song, Xiaomu; Panych, Lawrence P.; Chen, Nan-kuei

2015-01-01

Background Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades. New Method A spatially regularized support vector machine (SVM) technique was developed for the reliable brain mapping in task- and resting-state. Unlike most existing SVM-based brain mapping techniques, which implement supervised classifications of specific brain functional states or disorders, the proposed method performs a semi-supervised classification for the general brain function mapping where spatial correlation of fMRI is integrated into the SVM learning. The method can adapt to intra- and inter-subject variations induced by fMRI nonstationarity, and identify a true boundary between active and inactive voxels, or between functionally connected and unconnected voxels in a feature space. Results The method was evaluated using synthetic and experimental data at the individual and group level. Multiple features were evaluated in terms of their contributions to the spatially regularized SVM learning. Reliable mapping results in both task- and resting-state were obtained from individual subjects and at the group level. Comparison with Existing Methods A comparison study was performed with independent component analysis, general linear model, and correlation analysis methods. Experimental results indicate that the proposed method can provide a better or comparable mapping performance at the individual and group level. Conclusions The proposed method can provide accurate and reliable mapping of brain function in task- and resting-state, and is applicable to a variety of quantitative fMRI studies. PMID:26470627

22 CFR 121.10 - Forgings, castings and machined bodies.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Forgings, castings and machined bodies. 121.10... STATES MUNITIONS LIST Enumeration of Articles § 121.10 Forgings, castings and machined bodies. Articles on the U.S. Munitions List include articles in a partially completed state (such as forgings...

Agents Technology Research

DTIC Science & Technology

2010-02-01

multi-agent reputation management. State abstraction is a technique used to allow machine learning technologies to cope with problems that have large...state abstrac- tion process to enable reinforcement learning in domains with large state spaces. State abstraction is vital to machine learning ...across a collective of independent platforms. These individual elements, often referred to as agents in the machine learning community, should exhibit both

Is searching full text more effective than searching abstracts?

PubMed Central

Lin, Jimmy

2009-01-01

Background With the growing availability of full-text articles online, scientists and other consumers of the life sciences literature now have the ability to go beyond searching bibliographic records (title, abstract, metadata) to directly access full-text content. Motivated by this emerging trend, I posed the following question: is searching full text more effective than searching abstracts? This question is answered by comparing text retrieval algorithms on MEDLINE® abstracts, full-text articles, and spans (paragraphs) within full-text articles using data from the TREC 2007 genomics track evaluation. Two retrieval models are examined: bm25 and the ranking algorithm implemented in the open-source Lucene search engine. Results Experiments show that treating an entire article as an indexing unit does not consistently yield higher effectiveness compared to abstract-only search. However, retrieval based on spans, or paragraphs-sized segments of full-text articles, consistently outperforms abstract-only search. Results suggest that highest overall effectiveness may be achieved by combining evidence from spans and full articles. Conclusion Users searching full text are more likely to find relevant articles than searching only abstracts. This finding affirms the value of full text collections for text retrieval and provides a starting point for future work in exploring algorithms that take advantage of rapidly-growing digital archives. Experimental results also highlight the need to develop distributed text retrieval algorithms, since full-text articles are significantly longer than abstracts and may require the computational resources of multiple machines in a cluster. The MapReduce programming model provides a convenient framework for organizing such computations. PMID:19192280

Vending machine policies and practices in Delaware.

PubMed

Gemmill, Erin; Cotugna, Nancy

2005-04-01

Overweight has reached alarming proportions among America's youth. Although the cause of the rise in overweight rates in children and adolescents is certainly the result of the interaction of a variety of factors, the presence of vending machines in schools is one issue that has recently come to the forefront. Many states have passed or proposed legislation that limits student access to vending machines in schools or require that vending machines in schools offer healthier choices. The purposes of this study were (a) to assess the food and beverage vending machine offerings in the public school districts in the state of Delaware and (b) to determine whether there are any district vending policies in place other than the current U.S. Department of Agriculture regulations. The results of this study indicate the most commonly sold food and drink items in school vending machines are of minimal nutritional value. School administrators are most frequently in charge of the vending contract, as well as setting and enforcing vending machine policies. Suggestions are offered to assist school nurses, often the only health professional in the school, in becoming advocates for changes in school vending practices and policies that promote the health and well-being of children and adolescents.

Neural-Network Quantum States, String-Bond States, and Chiral Topological States

NASA Astrophysics Data System (ADS)

Glasser, Ivan; Pancotti, Nicola; August, Moritz; Rodriguez, Ivan D.; Cirac, J. Ignacio

2018-01-01

Neural-network quantum states have recently been introduced as an Ansatz for describing the wave function of quantum many-body systems. We show that there are strong connections between neural-network quantum states in the form of restricted Boltzmann machines and some classes of tensor-network states in arbitrary dimensions. In particular, we demonstrate that short-range restricted Boltzmann machines are entangled plaquette states, while fully connected restricted Boltzmann machines are string-bond states with a nonlocal geometry and low bond dimension. These results shed light on the underlying architecture of restricted Boltzmann machines and their efficiency at representing many-body quantum states. String-bond states also provide a generic way of enhancing the power of neural-network quantum states and a natural generalization to systems with larger local Hilbert space. We compare the advantages and drawbacks of these different classes of states and present a method to combine them together. This allows us to benefit from both the entanglement structure of tensor networks and the efficiency of neural-network quantum states into a single Ansatz capable of targeting the wave function of strongly correlated systems. While it remains a challenge to describe states with chiral topological order using traditional tensor networks, we show that, because of their nonlocal geometry, neural-network quantum states and their string-bond-state extension can describe a lattice fractional quantum Hall state exactly. In addition, we provide numerical evidence that neural-network quantum states can approximate a chiral spin liquid with better accuracy than entangled plaquette states and local string-bond states. Our results demonstrate the efficiency of neural networks to describe complex quantum wave functions and pave the way towards the use of string-bond states as a tool in more traditional machine-learning applications.

Machine learning for science: state of the art and future prospects.

PubMed

Mjolsness, E; DeCoste, D

2001-09-14

Recent advances in machine learning methods, along with successful applications across a wide variety of fields such as planetary science and bioinformatics, promise powerful new tools for practicing scientists. This viewpoint highlights some useful characteristics of modern machine learning methods and their relevance to scientific applications. We conclude with some speculations on near-term progress and promising directions.

Using Multiple Indicators of Cognitive State in Logistic Models that Predict Individual Performance in Machine-Mediated Learning Environments.

ERIC Educational Resources Information Center

Hancock, Thomas E.; And Others

1995-01-01

In machine-mediated learning environments, there is a need for more reliable methods of calculating the probability that a learner's response will be correct in future trials. A combination of domain-independent response-state measures of cognition along with two instructional variables for maximum predictive ability are demonstrated. (Author/LRW)

Cooperating with machines.

PubMed

Crandall, Jacob W; Oudah, Mayada; Tennom; Ishowo-Oloko, Fatimah; Abdallah, Sherief; Bonnefon, Jean-François; Cebrian, Manuel; Shariff, Azim; Goodrich, Michael A; Rahwan, Iyad

2018-01-16

Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human-machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions. Here, we develop an algorithm that combines a state-of-the-art reinforcement-learning algorithm with mechanisms for signaling. We show that this algorithm can cooperate with people and other algorithms at levels that rival human cooperation in a variety of two-player repeated stochastic games. These results indicate that general human-machine cooperation is achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.

An Immanent Machine: Reconsidering Grades, Historical and Present

ERIC Educational Resources Information Center

Tocci, Charles

2010-01-01

At some point the mechanics of schooling begin running of their own accord. Such has become the case with grades (A's, B's, C's, etc.). This article reconsiders the history of grades through the concepts of immanence and abstract machines from the oeuvre of Deleuze and Guattari. In the first section, the history of grades as presently written…

Technical Report on Occupations in Numerically Controlled Metal-Cutting Machining.

ERIC Educational Resources Information Center

Manpower Administration (DOL), Washington, DC. U.S. Employment Service.

At the present time, only 5 percent of the short-run metal-cutting machining in the United States is done by numerically controlled machined tools, but within the next decade it is expected to increase by 50 percent. Numerically controlled machines use taped data which is changed into instructions and directs the machine to do certain steps…

Enhanced Flexibility and Reusability through State Machine-Based Architectures for Multisensor Intelligent Robotics.

PubMed

Herrero, Héctor; Outón, Jose Luis; Puerto, Mildred; Sallé, Damien; López de Ipiña, Karmele

2017-05-31

This paper presents a state machine-based architecture, which enhances the flexibility and reusability of industrial robots, more concretely dual-arm multisensor robots. The proposed architecture, in addition to allowing absolute control of the execution, eases the programming of new applications by increasing the reusability of the developed modules. Through an easy-to-use graphical user interface, operators are able to create, modify, reuse and maintain industrial processes, increasing the flexibility of the cell. Moreover, the proposed approach is applied in a real use case in order to demonstrate its capabilities and feasibility in industrial environments. A comparative analysis is presented for evaluating the presented approach versus traditional robot programming techniques.

Flexible architecture of data acquisition firmware based on multi-behaviors finite state machine

NASA Astrophysics Data System (ADS)

Arpaia, Pasquale; Cimmino, Pasquale

2016-11-01

A flexible firmware architecture for different kinds of data acquisition systems, ranging from high-precision bench instruments to low-cost wireless transducers networks, is presented. The key component is a multi-behaviors finite state machine, easily configurable to both low- and high-performance requirements, to diverse operating systems, as well as to on-line and batch measurement algorithms. The proposed solution was validated experimentally on three case studies with data acquisition architectures: (i) concentrated, in a high-precision instrument for magnetic measurements at CERN, (ii) decentralized, for telemedicine remote monitoring of patients at home, and (iii) distributed, for remote monitoring of building's energy loss.

Acceleration of saddle-point searches with machine learning.

PubMed

Peterson, Andrew A

2016-08-21

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

Microstructural evolution during reheating of A356 machining chips at semisolid state

NASA Astrophysics Data System (ADS)

Wang, Fang; Zhang, Wen-qiang; Xiao, Wen-long; Yamagata, Hiroshi; Ma, Chao-li

2017-08-01

The microstructural evolution of A356 machining chips in the semisolid state was studied at different temperatures and holding times. The results showed that the elongated α-Al grains first recrystallized in the semisolid state and then became globular with a high shape factor (SF). Both the temperature and the holding time clearly affected the grain size and SF. When the heating temperature or holding time was increased, the grain size and SF gradually increased and finally became stable. Moreover, the Vickers hardness of primary α-Al grains gradually decreased with increasing heating temperature. The optimal slurry for semisolid processing, with a good combination of grain size and SF, was obtained when the chips were held at 600°C for 15 min. The semisolid slurry of A356 chips exhibited a lower coarsening rate of α-Al grains than those produced by most of the conventional semisolid processes. The coarsening coefficient was determined to be 436 μm3·s-1 on the basis of the linear Lifshitz-Slyozov-Wagner (LSW) relationship.

Finding Feasible Abstract Counter-Examples

NASA Technical Reports Server (NTRS)

Pasareanu, Corina S.; Dwyer, Matthew B.; Visser, Willem; Clancy, Daniel (Technical Monitor)

2002-01-01

A strength of model checking is its ability to automate the detection of subtle system errors and produce traces that exhibit those errors. Given the high computational cost of model checking most researchers advocate the use of aggressive property-preserving abstractions. Unfortunately, the more aggressively a system is abstracted the more infeasible behavior it will have. Thus, while abstraction enables efficient model checking it also threatens the usefulness of model checking as a defect detection tool, since it may be difficult to determine whether a counter-example is feasible and hence worth developer time to analyze. We have explored several strategies for addressing this problem by extending an explicit-state model checker, Java PathFinder (JPF), to search for and analyze counter-examples in the presence of abstractions. We demonstrate that these techniques effectively preserve the defect detection ability of model checking in the presence of aggressive abstraction by applying them to check properties of several abstracted multi-threaded Java programs. These new capabilities are not specific to JPF and can be easily adapted to other model checking frameworks; we describe how this was done for the Bandera toolset.

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges

PubMed Central

Goldstein, Benjamin A.; Navar, Ann Marie; Carter, Rickey E.

2017-01-01

Abstract Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. PMID:27436868

Research on intelligent machine self-perception method based on LSTM

NASA Astrophysics Data System (ADS)

Wang, Qiang; Cheng, Tao

2018-05-01

In this paper, we use the advantages of LSTM in feature extraction and processing high-dimensional and complex nonlinear data, and apply it to the autonomous perception of intelligent machines. Compared with the traditional multi-layer neural network, this model has memory, can handle time series information of any length. Since the multi-physical domain signals of processing machines have a certain timing relationship, and there is a contextual relationship between states and states, using this deep learning method to realize the self-perception of intelligent processing machines has strong versatility and adaptability. The experiment results show that the method proposed in this paper can obviously improve the sensing accuracy under various working conditions of the intelligent machine, and also shows that the algorithm can well support the intelligent processing machine to realize self-perception.

Bilingual Vocational Education. Project Abstracts 1987-1988.

ERIC Educational Resources Information Center

Office of Vocational and Adult Education (ED), Washington, DC.

Abstracts are provided for 16 bilingual vocational education projects offered in 1987-88. Each abstract provides information on: grantee; state; project title; project director, address, and telephone number; project officer and U.S. Department of Education address; language group(s) served; occupational skills area(s); length of training cycle…

Augmentation of machine structure to improve its diagnosability

NASA Technical Reports Server (NTRS)

Hsieh, L.

1973-01-01

Two methods of augmenting the structure of a sequential machine so that it is diagnosable are presented. The checkable (checking sequences) and repeated symbol distinguishing sequences (RDS) are discussed. It was found that as few as twice the number of outputs of the given machine is sufficient for constructing a state-output augmentation with RDS. Techniques for minimizing the number of states in resolving convergences and in resolving equivalent and nonreduced cycles are developed.

Programming the Navier-Stokes computer: An abstract machine model and a visual editor

NASA Technical Reports Server (NTRS)

Middleton, David; Crockett, Tom; Tomboulian, Sherry

1988-01-01

The Navier-Stokes computer is a parallel computer designed to solve Computational Fluid Dynamics problems. Each processor contains several floating point units which can be configured under program control to implement a vector pipeline with several inputs and outputs. Since the development of an effective compiler for this computer appears to be very difficult, machine level programming seems necessary and support tools for this process have been studied. These support tools are organized into a graphical program editor. A programming process is described by which appropriate computations may be efficiently implemented on the Navier-Stokes computer. The graphical editor would support this programming process, verifying various programmer choices for correctness and deducing values such as pipeline delays and network configurations. Step by step details are provided and demonstrated with two example programs.

A Function-Behavior-State Approach to Designing Human Machine Interface for Nuclear Power Plant Operators

NASA Astrophysics Data System (ADS)

Lin, Y.; Zhang, W. J.

2005-02-01

This paper presents an approach to human-machine interface design for control room operators of nuclear power plants. The first step in designing an interface for a particular application is to determine information content that needs to be displayed. The design methodology for this step is called the interface design framework (called framework ). Several frameworks have been proposed for applications at varying levels, including process plants. However, none is based on the design and manufacture of a plant system for which the interface is designed. This paper presents an interface design framework which originates from design theory and methodology for general technical systems. Specifically, the framework is based on a set of core concepts of a function-behavior-state model originally proposed by the artificial intelligence research community and widely applied in the design research community. Benefits of this new framework include the provision of a model-based fault diagnosis facility, and the seamless integration of the design (manufacture, maintenance) of plants and the design of human-machine interfaces. The missing linkage between design and operation of a plant was one of the causes of the Three Mile Island nuclear reactor incident. A simulated plant system is presented to explain how to apply this framework in designing an interface. The resulting human-machine interface is discussed; specifically, several fault diagnosis examples are elaborated to demonstrate how this interface could support operators' fault diagnosis in an unanticipated situation.

Probabilistic machine learning and artificial intelligence.

PubMed

Ghahramani, Zoubin

2015-05-28

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Probabilistic machine learning and artificial intelligence

NASA Astrophysics Data System (ADS)

Ghahramani, Zoubin

2015-05-01

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Acceleration of saddle-point searches with machine learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peterson, Andrew A., E-mail: andrew-peterson@brown.edu

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the numbermore » of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.« less

The War in Man; Media and Machines.

ERIC Educational Resources Information Center

Wilhelmsen, Frederick D.; Bret, Jane

The authors present a picture of contemporary man torn by conflicting forces, caught in a psychic house divided against itself, a victim of war between media and machines. Machines, they state, represent the rationalistic tradition which has brought man to the brink of psychic and social disaster. The media they see as offering hope--true…

Title I, Higher Education Act Program Abstracts.

ERIC Educational Resources Information Center

Miller, Lorna M., Ed.

The 1979 edition of the Title I, Higher Education Act Program Abstracts is presented. Directed toward state Title I, HEA administrators, the program abstracts are made available in order to encourage nationwide program replication of those tested and evaluated programs that have been conducted with Title I support by institutions of higher…

Modelling Metamorphism by Abstract Interpretation

NASA Astrophysics Data System (ADS)

Dalla Preda, Mila; Giacobazzi, Roberto; Debray, Saumya; Coogan, Kevin; Townsend, Gregg M.

Metamorphic malware apply semantics-preserving transformations to their own code in order to foil detection systems based on signature matching. In this paper we consider the problem of automatically extract metamorphic signatures from these malware. We introduce a semantics for self-modifying code, later called phase semantics, and prove its correctness by showing that it is an abstract interpretation of the standard trace semantics. Phase semantics precisely models the metamorphic code behavior by providing a set of traces of programs which correspond to the possible evolutions of the metamorphic code during execution. We show that metamorphic signatures can be automatically extracted by abstract interpretation of the phase semantics, and that regular metamorphism can be modelled as finite state automata abstraction of the phase semantics.

Machine rates for selected forest harvesting machines

Treesearch

R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

2002-01-01

Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Zhang, Yingchen

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vectormore » regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.« less

Enhanced Flexibility and Reusability through State Machine-Based Architectures for Multisensor Intelligent Robotics

PubMed Central

Herrero, Héctor; Outón, Jose Luis; Puerto, Mildred; Sallé, Damien; López de Ipiña, Karmele

2017-01-01

This paper presents a state machine-based architecture, which enhances the flexibility and reusability of industrial robots, more concretely dual-arm multisensor robots. The proposed architecture, in addition to allowing absolute control of the execution, eases the programming of new applications by increasing the reusability of the developed modules. Through an easy-to-use graphical user interface, operators are able to create, modify, reuse and maintain industrial processes, increasing the flexibility of the cell. Moreover, the proposed approach is applied in a real use case in order to demonstrate its capabilities and feasibility in industrial environments. A comparative analysis is presented for evaluating the presented approach versus traditional robot programming techniques. PMID:28561750

Methods for the design and analysis of power optimized finite-state machines using clock gating

NASA Astrophysics Data System (ADS)

Chodorowski, Piotr

2017-11-01

The paper discusses two methods of design of power optimized FSMs. Both methods use clock gating techniques. The main objective of the research was to write a program capable of generating automatic hardware description of finite-state machines in VHDL and testbenches to help power analysis. The creation of relevant output files is detailed step by step. The program was tested using the LGSynth91 FSM benchmark package. An analysis of the generated circuits shows that the second method presented in this paper leads to significant reduction of power consumption.

Efficient Checkpointing of Virtual Machines using Virtual Machine Introspection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aderholdt, Ferrol; Han, Fang; Scott, Stephen L

Cloud Computing environments rely heavily on system-level virtualization. This is due to the inherent benefits of virtualization including fault tolerance through checkpoint/restart (C/R) mechanisms. Because clouds are the abstraction of large data centers and large data centers have a higher potential for failure, it is imperative that a C/R mechanism for such an environment provide minimal latency as well as a small checkpoint file size. Recently, there has been much research into C/R with respect to virtual machines (VM) providing excellent solutions to reduce either checkpoint latency or checkpoint file size. However, these approaches do not provide both. This papermore » presents a method of checkpointing VMs by utilizing virtual machine introspection (VMI). Through the usage of VMI, we are able to determine which pages of memory within the guest are used or free and are better able to reduce the amount of pages written to disk during a checkpoint. We have validated this work by using various benchmarks to measure the latency along with the checkpoint size. With respect to checkpoint file size, our approach results in file sizes within 24% or less of the actual used memory within the guest. Additionally, the checkpoint latency of our approach is up to 52% faster than KVM s default method.« less

AN EIGHT WEEK SEMINAR IN AN INTRODUCTION TO NUMERICAL CONTROL ON TWO- AND THREE-AXIS MACHINE TOOLS FOR VOCATIONAL AND TECHNICAL MACHINE TOOL INSTRUCTORS. FINAL REPORT.

ERIC Educational Resources Information Center

BOLDT, MILTON; POKORNY, HARRY

THIRTY-THREE MACHINE SHOP INSTRUCTORS FROM 17 STATES PARTICIPATED IN AN 8-WEEK SEMINAR TO DEVELOP THE SKILLS AND KNOWLEDGE ESSENTIAL FOR TEACHING THE OPERATION OF NUMERICALLY CONTROLLED MACHINE TOOLS. THE SEMINAR WAS GIVEN FROM JUNE 20 TO AUGUST 12, 1966, WITH COLLEGE CREDIT AVAILABLE THROUGH STOUT STATE UNIVERSITY. THE PARTICIPANTS COMPLETED AN…

Modelling machine ensembles with discrete event dynamical system theory

NASA Technical Reports Server (NTRS)

Hunter, Dan

1990-01-01

Discrete Event Dynamical System (DEDS) theory can be utilized as a control strategy for future complex machine ensembles that will be required for in-space construction. The control strategy involves orchestrating a set of interactive submachines to perform a set of tasks for a given set of constraints such as minimum time, minimum energy, or maximum machine utilization. Machine ensembles can be hierarchically modeled as a global model that combines the operations of the individual submachines. These submachines are represented in the global model as local models. Local models, from the perspective of DEDS theory , are described by the following: a set of system and transition states, an event alphabet that portrays actions that takes a submachine from one state to another, an initial system state, a partial function that maps the current state and event alphabet to the next state, and the time required for the event to occur. Each submachine in the machine ensemble is presented by a unique local model. The global model combines the local models such that the local models can operate in parallel under the additional logistic and physical constraints due to submachine interactions. The global model is constructed from the states, events, event functions, and timing requirements of the local models. Supervisory control can be implemented in the global model by various methods such as task scheduling (open-loop control) or implementing a feedback DEDS controller (closed-loop control).

Abstracts of Research, July 1973 through June 1974.

ERIC Educational Resources Information Center

Ohio State Univ., Columbus. Computer and Information Science Research Center.

Abstracts of research papers in the fields of computer and information science are given; 72 papers are abstracted in the areas of information storage and retrieval, information processing, linguistic analysis, artificial intelligence, mathematical techniques, systems programing, and computer networks. In addition, the Ohio State University…

A Framework to Guide the Assessment of Human-Machine Systems.

PubMed

Stowers, Kimberly; Oglesby, James; Sonesh, Shirley; Leyva, Kevin; Iwig, Chelsea; Salas, Eduardo

2017-03-01

We have developed a framework for guiding measurement in human-machine systems. The assessment of safety and performance in human-machine systems often relies on direct measurement, such as tracking reaction time and accidents. However, safety and performance emerge from the combination of several variables. The assessment of precursors to safety and performance are thus an important part of predicting and improving outcomes in human-machine systems. As part of an in-depth literature analysis involving peer-reviewed, empirical articles, we located and classified variables important to human-machine systems, giving a snapshot of the state of science on human-machine system safety and performance. Using this information, we created a framework of safety and performance in human-machine systems. This framework details several inputs and processes that collectively influence safety and performance. Inputs are divided according to human, machine, and environmental inputs. Processes are divided into attitudes, behaviors, and cognitive variables. Each class of inputs influences the processes and, subsequently, outcomes that emerge in human-machine systems. This framework offers a useful starting point for understanding the current state of the science and measuring many of the complex variables relating to safety and performance in human-machine systems. This framework can be applied to the design, development, and implementation of automated machines in spaceflight, military, and health care settings. We present a hypothetical example in our write-up of how it can be used to aid in project success.

Classification of fMRI resting-state maps using machine learning techniques: A comparative study

NASA Astrophysics Data System (ADS)

Gallos, Ioannis; Siettos, Constantinos

2017-11-01

We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

Assessing the potential of surface-immobilized molecular logic machines for integration with solid state technology.

PubMed

Dunn, Katherine E; Trefzer, Martin A; Johnson, Steven; Tyrrell, Andy M

2016-08-01

Molecular computation with DNA has great potential for low power, highly parallel information processing in a biological or biochemical context. However, significant challenges remain for the field of DNA computation. New technology is needed to allow multiplexed label-free readout and to enable regulation of molecular state without addition of new DNA strands. These capabilities could be provided by hybrid bioelectronic systems in which biomolecular computing is integrated with conventional electronics through immobilization of DNA machines on the surface of electronic circuitry. Here we present a quantitative experimental analysis of a surface-immobilized OR gate made from DNA and driven by strand displacement. The purpose of our work is to examine the performance of a simple representative surface-immobilized DNA logic machine, to provide valuable information for future work on hybrid bioelectronic systems involving DNA devices. We used a quartz crystal microbalance to examine a DNA monolayer containing approximately 5×10(11)gatescm(-2), with an inter-gate separation of approximately 14nm, and we found that the ensemble of gates took approximately 6min to switch. The gates could be switched repeatedly, but the switching efficiency was significantly degraded on the second and subsequent cycles when the binding site for the input was near to the surface. Otherwise, the switching efficiency could be 80% or better, and the power dissipated by the ensemble of gates during switching was approximately 0.1nWcm(-2), which is orders of magnitude less than the power dissipated during switching of an equivalent array of transistors. We propose an architecture for hybrid DNA-electronic systems in which information can be stored and processed, either in series or in parallel, by a combination of molecular machines and conventional electronics. In this architecture, information can flow freely and in both directions between the solution-phase and the underlying electronics

[A new machinability test machine and the machinability of composite resins for core built-up].

PubMed

Iwasaki, N

2001-06-01

A new machinability test machine especially for dental materials was contrived. The purpose of this study was to evaluate the effects of grinding conditions on machinability of core built-up resins using this machine, and to confirm the relationship between machinability and other properties of composite resins. The experimental machinability test machine consisted of a dental air-turbine handpiece, a control weight unit, a driving unit of the stage fixing the test specimen, and so on. The machinability was evaluated as the change in volume after grinding using a diamond point. Five kinds of core built-up resins and human teeth were used in this study. The machinabilities of these composite resins increased with an increasing load during grinding, and decreased with repeated grinding. There was no obvious correlation between the machinability and Vickers' hardness; however, a negative correlation was observed between machinability and scratch width.

Method and system for controlling a synchronous machine over full operating range

DOEpatents

Walters, James E.; Gunawan, Fani S.; Xue, Yanhong

2002-01-01

System and method for controlling a synchronous machine are provided. The method allows for calculating a stator voltage index. The method further allows for relating the magnitude of the stator voltage index against a threshold voltage value. An offset signal is generated based on the results of the relating step. A respective state of operation of the machine is determined. The offset signal is processed based on the respective state of the machine.

A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.

PubMed

Zhang, Yong; Li, Peng; Jin, Yingyezhe; Choe, Yoonsuck

2015-11-01

This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel VLSI implementation. We use subsets of the TI46 speech corpus to benchmark the bioinspired digital LSM. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks.

Sensing position and speed by recording magnetization transitions on mechanically functional machine members (abstract)

NASA Astrophysics Data System (ADS)

Garshelis, I. J.

1997-04-01

Conventional means of sensing position and speed of moving machine members for control purposes typically requires the use of supplementary, ad hoc devices. Many mechanically functional moving machine members are fabricated from ferromagnetic steels and may, thus, provide an opportunity to themselves carry positionally relevant information in the form of local regions of deliberately instilled remanent magnetization, Mr. To avoid ambiguities associated with the imprecise borders of such regions as well as their possibly preexisting presence, information is more reliably carried in the form of local transitions in the polarity of Mr from a quiescent bias. The presence and physical location of such transitions relative to reference features either on the member itself or on other members undergoing correlated motion constitutes stored information. The presence of a transition is signaled by the transitory appearance of the external field associated with ∇ṡMr as the transition containing region passes by a magnetic-field detecting device fixed to the machine frame. Implanting and removing transitions from parts while in motion is readily accomplished by pulsed currents and biasing magnets. While the whole process of storing, reading, and erasing bits of information in magnetic form follows the concepts and principles of conventional magnetic recording, profoundly different quantitative factors, conditions, and performance requirements affect the implementation of the described sensing system. In particular, the coercivity, Hc, of commonly used steels is 3-30 Oe versus 300-1200 Oe in recording media and both the thickness of the media and the air gaps separating the media surface from the heads used in conventional systems are each 2-3 orders of magnitude smaller than their counterparts in the described system, where speed may also be variable down to zero. While the combined effect of these factors is to greatly diminish the attainable density of recorded

Honors Dissertation Abstracts: A Bounded Qualitative Meta-Study

ERIC Educational Resources Information Center

Holman, Debra K.; Banning, James H.

2012-01-01

A potential source of useful information about undergraduate honors education can be found in doctoral dissertation abstracts that focus on honors. Debra Holman and James Banning of Colorado State University sought to explore this resource by undertaking a bounded qualitative meta-study of such abstracts using document analysis. Three…

The evolution and practical application of machine translation system (1)

NASA Astrophysics Data System (ADS)

Tominaga, Isao; Sato, Masayuki

This paper describes a development, practical applicatioin, problem of a system, evaluation of practical system, and development trend of machine translation. Most recent system contains next four problems. 1) the vagueness of a text, 2) a difference of the definition of the terminology between different language, 3) the preparing of a large-scale translation dictionary, 4) the development of a software for the logical inference. Machine translation system is already used practically in many industry fields. However, many problems are not solved. The implementation of an ideal system will be after 15 years. Also, this paper described seven evaluation items detailedly. This English abstract was made by Mu system.

Experience with abstract notation one

NASA Technical Reports Server (NTRS)

Harvey, James D.; Weaver, Alfred C.

1990-01-01

The development of computer science has produced a vast number of machine architectures, programming languages, and compiler technologies. The cross product of these three characteristics defines the spectrum of previous and present data representation methodologies. With regard to computer networks, the uniqueness of these methodologies presents an obstacle when disparate host environments are to be interconnected. Interoperability within a heterogeneous network relies upon the establishment of data representation commonality. The International Standards Organization (ISO) is currently developing the abstract syntax notation one standard (ASN.1) and the basic encoding rules standard (BER) that collectively address this problem. When used within the presentation layer of the open systems interconnection reference model, these two standards provide the data representation commonality required to facilitate interoperability. The details of a compiler that was built to automate the use of ASN.1 and BER are described. From this experience, insights into both standards are given and potential problems relating to this development effort are discussed.

Energy: Machines, Science (Experimental): 5311.03.

ERIC Educational Resources Information Center

Castaldi, June P.

This unit of instruction was designed as an introductory course in energy involving six simple machines, electricity, magnetism, and motion. The booklet lists the relevant state-adopted texts and states the performance objectives for the unit. It provides an outline of the course content and suggests experiments, demonstrations, field trips, and…

Abstraction and Assume-Guarantee Reasoning for Automated Software Verification

NASA Technical Reports Server (NTRS)

Chaki, S.; Clarke, E.; Giannakopoulou, D.; Pasareanu, C. S.

2004-01-01

Compositional verification and abstraction are the key techniques to address the state explosion problem associated with model checking of concurrent software. A promising compositional approach is to prove properties of a system by checking properties of its components in an assume-guarantee style. This article proposes a framework for performing abstraction and assume-guarantee reasoning of concurrent C code in an incremental and fully automated fashion. The framework uses predicate abstraction to extract and refine finite state models of software and it uses an automata learning algorithm to incrementally construct assumptions for the compositional verification of the abstract models. The framework can be instantiated with different assume-guarantee rules. We have implemented our approach in the COMFORT reasoning framework and we show how COMFORT out-performs several previous software model checking approaches when checking safety properties of non-trivial concurrent programs.

High-speed machining of Space Shuttle External Tank (ET) panels

NASA Technical Reports Server (NTRS)

Miller, J. A.

1983-01-01

Potential production rates and project cost savings achieved by converting the conventional machining process in manufacturing shuttle external tank panels to high speed machining (HSM) techniques were studied. Savings were projected from the comparison of current production rates with HSM rates and with rates attainable on new conventional machines. The HSM estimates were also based on rates attainable by retrofitting existing conventional equipment with high speed spindle motors and rates attainable using new state of the art machines designed and built for HSM.

Making the Abstract Concrete

ERIC Educational Resources Information Center

Potter, Lee Ann

2005-01-01

President Ronald Reagan nominated a woman to serve on the United States Supreme Court. He did so through a single-page form letter, completed in part by hand and in part by typewriter, announcing Sandra Day O'Connor as his nominee. While the document serves as evidence of a historic event, it is also a tangible illustration of abstract concepts…

Technology of machine tools. Volume 1. Executive summary

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutton, G.P.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Constraint-Based Abstract Semantics for Temporal Logic: A Direct Approach to Design and Implementation

NASA Astrophysics Data System (ADS)

Banda, Gourinath; Gallagher, John P.

interpretation provides a practical approach to verifying properties of infinite-state systems. We apply the framework of abstract interpretation to derive an abstract semantic function for the modal μ-calculus, which is the basis for abstract model checking. The abstract semantic function is constructed directly from the standard concrete semantics together with a Galois connection between the concrete state-space and an abstract domain. There is no need for mixed or modal transition systems to abstract arbitrary temporal properties, as in previous work in the area of abstract model checking. Using the modal μ-calculus to implement CTL, the abstract semantics gives an over-approximation of the set of states in which an arbitrary CTL formula holds. Then we show that this leads directly to an effective implementation of an abstract model checking algorithm for CTL using abstract domains based on linear constraints. The implementation of the abstract semantic function makes use of an SMT solver. We describe an implemented system for proving properties of linear hybrid automata and give some experimental results.

Laser machining of explosives

DOEpatents

Perry, Michael D.; Stuart, Brent C.; Banks, Paul S.; Myers, Booth R.; Sefcik, Joseph A.

2000-01-01

The invention consists of a method for machining (cutting, drilling, sculpting) of explosives (e.g., TNT, TATB, PETN, RDX, etc.). By using pulses of a duration in the range of 5 femtoseconds to 50 picoseconds, extremely precise and rapid machining can be achieved with essentially no heat or shock affected zone. In this method, material is removed by a nonthermal mechanism. A combination of multiphoton and collisional ionization creates a critical density plasma in a time scale much shorter than electron kinetic energy is transferred to the lattice. The resulting plasma is far from thermal equilibrium. The material is in essence converted from its initial solid-state directly into a fully ionized plasma on a time scale too short for thermal equilibrium to be established with the lattice. As a result, there is negligible heat conduction beyond the region removed resulting in negligible thermal stress or shock to the material beyond a few microns from the laser machined surface. Hydrodynamic expansion of the plasma eliminates the need for any ancillary techniques to remove material and produces extremely high quality machined surfaces. There is no detonation or deflagration of the explosive in the process and the material which is removed is rendered inert.

Information Leakage Analysis by Abstract Interpretation

NASA Astrophysics Data System (ADS)

Zanioli, Matteo; Cortesi, Agostino

Protecting the confidentiality of information stored in a computer system or transmitted over a public network is a relevant problem in computer security. The approach of information flow analysis involves performing a static analysis of the program with the aim of proving that there will not be leaks of sensitive information. In this paper we propose a new domain that combines variable dependency analysis, based on propositional formulas, and variables' value analysis, based on polyhedra. The resulting analysis is strictly more accurate than the state of the art abstract interpretation based analyses for information leakage detection. Its modular construction allows to deal with the tradeoff between efficiency and accuracy by tuning the granularity of the abstraction and the complexity of the abstract operators.

37 CFR 1.72 - Title and abstract.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 37 Patents, Trademarks, and Copyrights 1 2011-07-01 2011-07-01 false Title and abstract. 1.72 Section 1.72 Patents, Trademarks, and Copyrights UNITED STATES PATENT AND TRADEMARK OFFICE, DEPARTMENT OF... enable the United States Patent and Trademark Office and the public generally to determine quickly from a...

Plan for conducting an international machine tool task force

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutton, G.P.; McClure, E.R.; Schuman, J.F.

1978-08-28

The basic objectives of the Machine Tool Task Force (MTTF) are to characterize and summarize the state of the art of cutting machine tool technology and to identify promising areas of future R and D. These goals will be accomplished with a series of multidisciplinary teams of prominent experts and individuals experienced in the specialized technologies of machine tools or in the management of machine tool operations. Experts will be drawn from all areas of the machine tool community: machine tool users or buyer organizations, builders, and R and D establishments including universities and government laboratories, both domestic and foreign.more » A plan for accomplishing this task is presented. The area of machine tool technology has been divided into about two dozen technology subjects on which teams of one or more experts will work. These teams are, in turn, organized into four principal working groups dealing, respectively, with machine tool accuracy, mechanics, control, and management systems/utilization. Details are presented on specific subjects to be covered, the organization of the Task Force and its four working groups, and the basic approach to determining the state of the art of technology and the future directions of this technology. The planned review procedure, the potential benefits, our management approach, and the schedule, as well as the key participating personnel and their background are discussed. The initial meeting of MTTF members will be held at a plenary session on October 16 and 17, 1978, in Scottsdale, AZ. The MTTF study will culminate in a conference on September 1, 1980, in Chicago, IL, immediately preceeding the 1980 International Machine Tool Show. At this time, our results will be released to the public; a series of reports will be published in late 1980.« less

Journalism Abstracts. Volume 25, 1987.

ERIC Educational Resources Information Center

Wilhoit, Frances Goins, Ed.

Collected from 55 universities in the United States and Canada, and published by the Association for Education in Journalism and Mass Communication, this volume contains 339 abstracts of research dissertations and theses accepted for graduate degrees in journalism and mass communications from July 1, 1986 through June 30, 1987. Only research…

A review of supervised machine learning applied to ageing research.

PubMed

Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

2017-04-01

Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

Method and system employing finite state machine modeling to identify one of a plurality of different electric load types

DOE Office of Scientific and Technical Information (OSTI.GOV)

Du, Liang; Yang, Yi; Harley, Ronald Gordon

A system is for a plurality of different electric load types. The system includes a plurality of sensors structured to sense a voltage signal and a current signal for each of the different electric loads; and a processor. The processor acquires a voltage and current waveform from the sensors for a corresponding one of the different electric load types; calculates a power or current RMS profile of the waveform; quantizes the power or current RMS profile into a set of quantized state-values; evaluates a state-duration for each of the quantized state-values; evaluates a plurality of state-types based on the powermore » or current RMS profile and the quantized state-values; generates a state-sequence that describes a corresponding finite state machine model of a generalized load start-up or transient profile for the corresponding electric load type; and identifies the corresponding electric load type.« less

Gelcasting compositions having improved drying characteristics and machinability

DOEpatents

Janney, Mark A.; Walls, Claudia A. H.

2001-01-01

A gelcasting composition has improved drying behavior, machinability and shelf life in the dried and unfired state. The composition includes an inorganic powder, solvent, monomer system soluble in the solvent, an initiator system for polymerizing the monomer system, and a plasticizer soluble in the solvent. Dispersants and other processing aides to control slurry properties can be added. The plasticizer imparts an ability to dry thick section parts, to store samples in the dried state without cracking under conditions of varying relative humidity, and to machine dry gelcast parts without cracking or chipping. A method of making gelcast parts is also disclosed.

Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms

DTIC Science & Technology

2014-03-27

BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS THESIS Jessica R. Werling, Captain, USAF AFIT-ENG-14-M-81 DEPARTMENT...subject to copyright protection in the United States. AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ...AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS Jessica R. Werling, B.S.C.S. Captain, USAF Approved

Gambling with stimulus payments: feeding gaming machines with federal dollars.

PubMed

Lye, Jenny; Hirschberg, Joe

2014-09-01

In late 2008 and early 2009 the Australian Federal Government introduced a series of economic stimulus packages designed to maintain consumer spending in the early days of the Great Recession. When these packages were initiated the media suggested that the wide-spread availability of electronic gaming machines (EGMs, e.g. slot machines, poker machines, video lottery terminals) in Australia would result in stimulating the EGMs. Using state level monthly data we estimate that the stimulus packages led to an increase of 26 % in EGM revenues. This also resulted in over $60 million in additional tax revenue for State Governments. We also estimate a short-run aggregate income demand elasticity for EGMs to be approximately 2.

An Analysis of Hardware-Assisted Virtual Machine Based Rootkits

DTIC Science & Technology

2014-06-01

certain aspects of TPM implementation just to name a few. HyperWall is an architecture proposed by Szefer and Lee to protect guest VMs from...DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words) The use of virtual machine (VM) technology has expanded rapidly since AMD and Intel implemented ...Intel VT-x implementations of Blue Pill to identify commonalities in the respective versions’ attack methodologies from both a functional and technical

Extracting rate changes in transcriptional regulation from MEDLINE abstracts.

PubMed

Liu, Wenting; Miao, Kui; Li, Guangxia; Chang, Kuiyu; Zheng, Jie; Rajapakse, Jagath C

2014-01-01

Time delays are important factors that are often neglected in gene regulatory network (GRN) inference models. Validating time delays from knowledge bases is a challenge since the vast majority of biological databases do not record temporal information of gene regulations. Biological knowledge and facts on gene regulations are typically extracted from bio-literature with specialized methods that depend on the regulation task. In this paper, we mine evidences for time delays related to the transcriptional regulation of yeast from the PubMed abstracts. Since the vast majority of abstracts lack quantitative time information, we can only collect qualitative evidences of time delays. Specifically, the speed-up or delay in transcriptional regulation rate can provide evidences for time delays (shorter or longer) in GRN. Thus, we focus on deriving events related to rate changes in transcriptional regulation. A corpus of yeast regulation related abstracts was manually labeled with such events. In order to capture these events automatically, we create an ontology of sub-processes that are likely to result in transcription rate changes by combining textual patterns and biological knowledge. We also propose effective feature extraction methods based on the created ontology to identify the direct evidences with specific details of these events. Our ontologies outperform existing state-of-the-art gene regulation ontologies in the automatic rule learning method applied to our corpus. The proposed deterministic ontology rule-based method can achieve comparable performance to the automatic rule learning method based on decision trees. This demonstrates the effectiveness of our ontology in identifying rate-changing events. We also tested the effectiveness of the proposed feature mining methods on detecting direct evidence of events. Experimental results show that the machine learning method on these features achieves an F1-score of 71.43%. The manually labeled corpus of events

Operation of a quantum dot in the finite-state machine mode: Single-electron dynamic memory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klymenko, M. V.; Klein, M.; Levine, R. D.

2016-07-14

A single electron dynamic memory is designed based on the non-equilibrium dynamics of charge states in electrostatically defined metallic quantum dots. Using the orthodox theory for computing the transfer rates and a master equation, we model the dynamical response of devices consisting of a charge sensor coupled to either a single and or a double quantum dot subjected to a pulsed gate voltage. We show that transition rates between charge states in metallic quantum dots are characterized by an asymmetry that can be controlled by the gate voltage. This effect is more pronounced when the switching between charge states correspondsmore » to a Markovian process involving electron transport through a chain of several quantum dots. By simulating the dynamics of electron transport we demonstrate that the quantum box operates as a finite-state machine that can be addressed by choosing suitable shapes and switching rates of the gate pulses. We further show that writing times in the ns range and retention memory times six orders of magnitude longer, in the ms range, can be achieved on the double quantum dot system using experimentally feasible parameters, thereby demonstrating that the device can operate as a dynamic single electron memory.« less

The scheme machine: A case study in progress in design derivation at system levels

NASA Technical Reports Server (NTRS)

Johnson, Steven D.

1995-01-01

The Scheme Machine is one of several design projects of the Digital Design Derivation group at Indiana University. It differs from the other projects in its focus on issues of system design and its connection to surrounding research in programming language semantics, compiler construction, and programming methodology underway at Indiana and elsewhere. The genesis of the project dates to the early 1980's, when digital design derivation research branched from the surrounding research effort in programming languages. Both branches have continued to develop in parallel, with this particular project serving as a bridge. However, by 1990 there remained little real interaction between the branches and recently we have undertaken to reintegrate them. On the software side, researchers have refined a mathematically rigorous (but not mechanized) treatment starting with the fully abstract semantic definition of Scheme and resulting in an efficient implementation consisting of a compiler and virtual machine model, the latter typically realized with a general purpose microprocessor. The derivation includes a number of sophisticated factorizations and representations and is also deep example of the underlying engineering methodology. The hardware research has created a mechanized algebra supporting the tedious and massive transformations often seen at lower levels of design. This work has progressed to the point that large scale devices, such as processors, can be derived from first-order finite state machine specifications. This is roughly where the language oriented research stops; thus, together, the two efforts establish a thread from the highest levels of abstract specification to detailed digital implementation. The Scheme Machine project challenges hardware derivation research in several ways, although the individual components of the system are of a similar scale to those we have worked with before. The machine has a custom dual-ported memory to support garbage collection

Trends and developments in industrial machine vision: 2013

NASA Astrophysics Data System (ADS)

Niel, Kurt; Heinzl, Christoph

2014-03-01

When following current advancements and implementations in the field of machine vision there seems to be no borders for future developments: Calculating power constantly increases, and new ideas are spreading and previously challenging approaches are introduced in to mass market. Within the past decades these advances have had dramatic impacts on our lives. Consumer electronics, e.g. computers or telephones, which once occupied large volumes, now fit in the palm of a hand. To note just a few examples e.g. face recognition was adopted by the consumer market, 3D capturing became cheap, due to the huge community SW-coding got easier using sophisticated development platforms. However, still there is a remaining gap between consumer and industrial applications. While the first ones have to be entertaining, the second have to be reliable. Recent studies (e.g. VDMA [1], Germany) show a moderately increasing market for machine vision in industry. Asking industry regarding their needs the main challenges for industrial machine vision are simple usage and reliability for the process, quick support, full automation, self/easy adjustment at changing process parameters, "forget it in the line". Furthermore a big challenge is to support quality control: Nowadays the operator has to accurately define the tested features for checking the probes. There is an upcoming development also to let automated machine vision applications find out essential parameters in a more abstract level (top down). In this work we focus on three current and future topics for industrial machine vision: Metrology supporting automation, quality control (inline/atline/offline) as well as visualization and analysis of datasets with steadily growing sizes. Finally the general trend of the pixel orientated towards object orientated evaluation is addressed. We do not directly address the field of robotics taking advances from machine vision. This is actually a fast changing area which is worth an own

Magnet management in electric machines

DOEpatents

Reddy, Patel Bhageerath; El-Refaie, Ayman Mohamed Fawzi; Huh, Kum Kang

2017-03-21

A magnet management method of controlling a ferrite-type permanent magnet electrical machine includes receiving and/or estimating the temperature permanent magnets; determining if that temperature is below a predetermined temperature; and if so, then: selectively heating the magnets in order to prevent demagnetization and/or derating the machine. A similar method provides for controlling magnetization level by analyzing flux or magnetization level. Controllers that employ various methods are disclosed. The present invention has been described in terms of specific embodiment(s), and it is recognized that equivalents, alternatives, and modifications, aside from those expressly stated, are possible and within the scope of the appending claims.

A Comparison of Costs of Searching the Machine-Readable Data Bases ERIC and "Psychological Abstracts" in an Annual Subscription Rate System Against Costs Estimated for the Same Searches Done in the Lockheed DIALOG System and the System Development Corporation for ERIC, and the Lockheed DIALOG System and PASAT for "Psychological Abstracts."

ERIC Educational Resources Information Center

Palmer, Crescentia

A comparison of costs for computer-based searching of Psychological Abstracts and Educational Resources Information Center (ERIC) systems by the New York State Library at Albany was produced by combining data available from search request forms and from bills from the contract subscription service, the State University of New…

Concrete Model Checking with Abstract Matching and Refinement

NASA Technical Reports Server (NTRS)

Pasareanu Corina S.; Peianek Radek; Visser, Willem

2005-01-01

We propose an abstraction-based model checking method which relies on refinement of an under-approximation of the feasible behaviors of the system under analysis. The method preserves errors to safety properties, since all analyzed behaviors are feasible by definition. The method does not require an abstract transition relation to he generated, but instead executes the concrete transitions while storing abstract versions of the concrete states, as specified by a set of abstraction predicates. For each explored transition. the method checks, with the help of a theorem prover, whether there is any loss of precision introduced by abstraction. The results of these checks are used to decide termination or to refine the abstraction, by generating new abstraction predicates. If the (possibly infinite) concrete system under analysis has a finite bisimulation quotient, then the method is guaranteed to eventually explore an equivalent finite bisimilar structure. We illustrate the application of the approach for checking concurrent programs. We also show how a lightweight variant can be used for efficient software testing.

Test Input Generation for Red-Black Trees using Abstraction

NASA Technical Reports Server (NTRS)

Visser, Willem; Pasareanu, Corina S.; Pelanek, Radek

2005-01-01

We consider the problem of test input generation for code that manipulates complex data structures. Test inputs are sequences of method calls from the data structure interface. We describe test input generation techniques that rely on state matching to avoid generation of redundant tests. Exhaustive techniques use explicit state model checking to explore all the possible test sequences up to predefined input sizes. Lossy techniques rely on abstraction mappings to compute and store abstract versions of the concrete states; they explore under-approximations of all the possible test sequences. We have implemented the techniques on top of the Java PathFinder model checker and we evaluate them using a Java implementation of red-black trees.

A Boltzmann machine for the organization of intelligent machines

NASA Technical Reports Server (NTRS)

Moed, Michael C.; Saridis, George N.

1989-01-01

In the present technological society, there is a major need to build machines that would execute intelligent tasks operating in uncertain environments with minimum interaction with a human operator. Although some designers have built smart robots, utilizing heuristic ideas, there is no systematic approach to design such machines in an engineering manner. Recently, cross-disciplinary research from the fields of computers, systems AI and information theory has served to set the foundations of the emerging area of the design of intelligent machines. Since 1977 Saridis has been developing an approach, defined as Hierarchical Intelligent Control, designed to organize, coordinate and execute anthropomorphic tasks by a machine with minimum interaction with a human operator. This approach utilizes analytical (probabilistic) models to describe and control the various functions of the intelligent machine structured by the intuitively defined principle of Increasing Precision with Decreasing Intelligence (IPDI) (Saridis 1979). This principle, even though resembles the managerial structure of organizational systems (Levis 1988), has been derived on an analytic basis by Saridis (1988). The purpose is to derive analytically a Boltzmann machine suitable for optimal connection of nodes in a neural net (Fahlman, Hinton, Sejnowski, 1985). Then this machine will serve to search for the optimal design of the organization level of an intelligent machine. In order to accomplish this, some mathematical theory of the intelligent machines will be first outlined. Then some definitions of the variables associated with the principle, like machine intelligence, machine knowledge, and precision will be made (Saridis, Valavanis 1988). Then a procedure to establish the Boltzmann machine on an analytic basis will be presented and illustrated by an example in designing the organization level of an Intelligent Machine. A new search technique, the Modified Genetic Algorithm, is presented and proved

Determination of the Lowest-Energy States for the Model Distribution of Trained Restricted Boltzmann Machines Using a 1000 Qubit D-Wave 2X Quantum Computer.

PubMed

Koshka, Yaroslav; Perera, Dilina; Hall, Spencer; Novotny, M A

2017-07-01

The possibility of using a quantum computer D-Wave 2X with more than 1000 qubits to determine the global minimum of the energy landscape of trained restricted Boltzmann machines is investigated. In order to overcome the problem of limited interconnectivity in the D-Wave architecture, the proposed RBM embedding combines multiple qubits to represent a particular RBM unit. The results for the lowest-energy (the ground state) and some of the higher-energy states found by the D-Wave 2X were compared with those of the classical simulated annealing (SA) algorithm. In many cases, the D-Wave machine successfully found the same RBM lowest-energy state as that found by SA. In some examples, the D-Wave machine returned a state corresponding to one of the higher-energy local minima found by SA. The inherently nonperfect embedding of the RBM into the Chimera lattice explored in this work (i.e., multiple qubits combined into a single RBM unit were found not to be guaranteed to be all aligned) and the existence of small, persistent biases in the D-Wave hardware may cause a discrepancy between the D-Wave and the SA results. In some of the investigated cases, introduction of a small bias field into the energy function or optimization of the chain-strength parameter in the D-Wave embedding successfully addressed difficulties of the particular RBM embedding. With further development of the D-Wave hardware, the approach will be suitable for much larger numbers of RBM units.

Washing machine related injuries in children: a continuing threat

PubMed Central

Warner, B; Kenney, B; Rice, M

2003-01-01

Objective: To describe washing machine related injuries in children in the United States. Methods: Injury data for 496 washing machine related injuries documented by the Consumer Product Safety Commission's National Electronic Injury Surveillance System and death certificate data files were analyzed. Gender, age, diagnosis, body part injured, disposition, location and mechanism of injury were considered in the analysis of data. Results: The upper extremities were most frequently injured in washing machine related injuries, especially with wringer machines. Fewer than 10% of patients required admission, but automatic washers accounted for most of these and for both of the deaths. Automatic washer injuries involved a wider range of injury mechanism, including 23 children who fell from the machines while in baby seats. Conclusions: Though most injuries associated with washing machines are minor, some are severe and devastating. Many of the injuries could be avoided with improvements in machine design while others suggest a need for increased education of potential dangers and better supervision of children if they are allowed access to areas where washing machines are operating. Furthermore, washing machines should only be used for their intended purpose. Given the limitations of educational efforts to prevent injuries, health professionals should have a major role in public education regarding these seemingly benign household appliances. PMID:14693900

Unorganized machines for seasonal streamflow series forecasting.

PubMed

Siqueira, Hugo; Boccato, Levy; Attux, Romis; Lyra, Christiano

2014-05-01

Modern unorganized machines--extreme learning machines and echo state networks--provide an elegant balance between processing capability and mathematical simplicity, circumventing the difficulties associated with the conventional training approaches of feedforward/recurrent neural networks (FNNs/RNNs). This work performs a detailed investigation of the applicability of unorganized architectures to the problem of seasonal streamflow series forecasting, considering scenarios associated with four Brazilian hydroelectric plants and four distinct prediction horizons. Experimental results indicate the pertinence of these models to the focused task.

Fully automated measuring setup for tactile coordinate measuring machine for three dimensional measurement of freeform eyeglass frames

NASA Astrophysics Data System (ADS)

Rückwardt, M.; Göpfert, A.; Correns, M.; Schellhorn, M.; Linß, G.

2010-07-01

Coordinate measuring machines are high precession all-rounder in three dimensional measuring. Therefore the versatility of parameters and expandability of additionally hardware is very comprehensive. Consequently you need much expert knowledge of the user and mostly a lot of advanced information about the measuring object. In this paper a coordinate measuring machine and a specialized measuring machine are compared at the example of the measuring of eyeglass frames. For this case of three dimensional measuring challenges the main focus is divided into metrological and economical aspects. At first there is shown a fully automated method for tactile measuring of this abstract form. At second there is shown a comparison of the metrological characteristics of a coordinate measuring machine and a tracer for eyeglass frames. The result is in favour to the coordinate measuring machine. It was not surprising in these aspects. At last there is shown a comparison of the machine in front of the economical aspects.

Contrasting State-of-the-Art in the Machine Scoring of Short-Form Constructed Responses

ERIC Educational Resources Information Center

Shermis, Mark D.

2015-01-01

This study compared short-form constructed responses evaluated by both human raters and machine scoring algorithms. The context was a public competition on which both public competitors and commercial vendors vied to develop machine scoring algorithms that would match or exceed the performance of operational human raters in a summative high-stakes…

34 CFR 395.8 - Distribution and use of income from vending machines on Federal property.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 34 Education 2 2012-07-01 2012-07-01 false Distribution and use of income from vending machines on... use of income from vending machines on Federal property. (a) Vending machine income from vending machines on Federal property which has been disbursed to the State licensing agency by a property managing...

34 CFR 395.8 - Distribution and use of income from vending machines on Federal property.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 34 Education 2 2013-07-01 2013-07-01 false Distribution and use of income from vending machines on... use of income from vending machines on Federal property. (a) Vending machine income from vending machines on Federal property which has been disbursed to the State licensing agency by a property managing...

34 CFR 395.8 - Distribution and use of income from vending machines on Federal property.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 34 Education 2 2014-07-01 2013-07-01 true Distribution and use of income from vending machines on... use of income from vending machines on Federal property. (a) Vending machine income from vending machines on Federal property which has been disbursed to the State licensing agency by a property managing...

Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface.

PubMed

Sachs, Nicholas A; Ruiz-Torres, Ricardo; Perreault, Eric J; Miller, Lee E

2016-02-01

It is quite remarkable that brain machine interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed. We have developed a system that uses two independent Wiener filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The decoder combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state. We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor's proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder. We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the individual movement and posture decoders.

Wind energy utilization: A bibliography with abstracts - Cumulative volume 1944/1974

NASA Technical Reports Server (NTRS)

1975-01-01

Bibliography, up to 1974 inclusive, of articles and books on utilization of wind power in energy generation. Worldwide literature is surveyed, and short abstracts are provided in many cases. The citations are grouped by subject: (1) general; (2) utilization; (3) wind power plants; (4) wind power generators (rural, synchronous, remote station); (5) wind machines (motors, pumps, turbines, windmills, home-built); (6) wind data and properties; (7) energy storage; and (8) related topics (control and regulation devices, wind measuring devices, blade design and rotors, wind tunnel simulation, aerodynamics). Gross-referencing is aided by indexes of authors, corporate sources, titles, and keywords.

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

PubMed

Park, Saerom; Lee, Jaewook; Son, Youngdoo

2016-01-01

Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

Ultrashort pulse laser machining of metals and alloys

DOEpatents

Perry, Michael D.; Stuart, Brent C.

2003-09-16

The invention consists of a method for high precision machining (cutting, drilling, sculpting) of metals and alloys. By using pulses of a duration in the range of 10 femtoseconds to 100 picoseconds, extremely precise machining can be achieved with essentially no heat or shock affected zone. Because the pulses are so short, there is negligible thermal conduction beyond the region removed resulting in negligible thermal stress or shock to the material beyond approximately 0.1-1 micron (dependent upon the particular material) from the laser machined surface. Due to the short duration, the high intensity (>10.sup.12 W/cm.sup.2) associated with the interaction converts the material directly from the solid-state into an ionized plasma. Hydrodynamic expansion of the plasma eliminates the need for any ancillary techniques to remove material and produces extremely high quality machined surfaces with negligible redeposition either within the kerf or on the surface. Since there is negligible heating beyond the depth of material removed, the composition of the remaining material is unaffected by the laser machining process. This enables high precision machining of alloys and even pure metals with no change in grain structure.

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Machine learning for neuroimaging with scikit-learn.

PubMed

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

Machine learning for neuroimaging with scikit-learn

PubMed Central

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain. PMID:24600388

Abstract Machines for Polymorphous Computing

DTIC Science & Technology

2007-12-01

s/ /s/ MARK NOVAK WARREN H. DEBANY, Jr. Work Unit Manager Technical Advisor, Information Grid Division Information...models and LLCs have been developed for Raw, MONARCH [18][19], TRIPS [20][21], and Smart Memories [22][23]. These research projects were conducted...used here. In our approach on Raw, two key concepts are used to fully leverage the Raw architecture [34]. First, the tile grid is viewed as a

Temporal abstraction for the analysis of intensive care information

NASA Astrophysics Data System (ADS)

Hadad, Alejandro J.; Evin, Diego A.; Drozdowicz, Bartolomé; Chiotti, Omar

2007-11-01

This paper proposes a scheme for the analysis of time-stamped series data from multiple monitoring devices of intensive care units, using Temporal Abstraction concepts. This scheme is oriented to obtain a description of the patient state evolution in an unsupervised way. The case of study is based on a dataset clinically classified with Pulmonary Edema. For this dataset a trends based Temporal Abstraction mechanism is proposed, by means of a Behaviours Base of time-stamped series and then used in a classification step. Combining this approach with the introduction of expert knowledge, using Fuzzy Logic, and multivariate analysis by means of Self-Organizing Maps, a states characterization model is obtained. This model is feasible of being extended to different patients groups and states. The proposed scheme allows to obtain intermediate states descriptions through which it is passing the patient and that could be used to anticipate alert situations.

A Unified Approach to the Synthesis of Fully Testable Sequential Machines

DTIC Science & Technology

1989-10-01

N A Unified Approach to the Synthesis of Fully Testable Sequential Machines Srinivas Devadas and Kurt Keutzer Abstract • In this paper we attempt to...research was supported in part by the Defense Advanced Research Projects Agency under contract N00014-87-K-0825. Author Information Devadas : Department...Fully Testable Sequential Maine(S P Sritiivas Devadas Departinent of Electrical Engineerinig anid Comivi Sciec Massachusetts Institute of Technology

Exploiting the Dynamics of Soft Materials for Machine Learning

PubMed Central

Hauser, Helmut; Li, Tao; Pfeifer, Rolf

2018-01-01

Abstract Soft materials are increasingly utilized for various purposes in many engineering applications. These materials have been shown to perform a number of functions that were previously difficult to implement using rigid materials. Here, we argue that the diverse dynamics generated by actuating soft materials can be effectively used for machine learning purposes. This is demonstrated using a soft silicone arm through a technique of multiplexing, which enables the rich transient dynamics of the soft materials to be fully exploited as a computational resource. The computational performance of the soft silicone arm is examined through two standard benchmark tasks. Results show that the soft arm compares well to or even outperforms conventional machine learning techniques under multiple conditions. We then demonstrate that this system can be used for the sensory time series prediction problem for the soft arm itself, which suggests its immediate applicability to a real-world machine learning problem. Our approach, on the one hand, represents a radical departure from traditional computational methods, whereas on the other hand, it fits nicely into a more general perspective of computation by way of exploiting the properties of physical materials in the real world. PMID:29708857

Improving the reliability of inverter-based welding machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schiedermayer, M.

1997-02-01

Although inverter-based welding power sources have been available since the late 1980s, many people hesitated to purchase them because of reliability issues. Unfortunately, their hesitancy had a basis, until now. Recent improvements give some inverters a reliability level that approaches that of traditional, transformer-based industrial welding machines, which have a failure rate of about 1%. Acceptance of inverter-based welding machines is important because, for many welding applications, they provide capabilities that solid-state, transformer-based machines cannot deliver. These advantages include enhanced pulsed gas metal arc welding (GMAW-P), lightweight portability, an ultrastable arc, and energy efficiency--all while producing highly aesthetic weld beadsmore » and delivering multiprocess capabilities.« less

Parameterizing Phrase Based Statistical Machine Translation Models: An Analytic Study

ERIC Educational Resources Information Center

Cer, Daniel

2011-01-01

The goal of this dissertation is to determine the best way to train a statistical machine translation system. I first develop a state-of-the-art machine translation system called Phrasal and then use it to examine a wide variety of potential learning algorithms and optimization criteria and arrive at two very surprising results. First, despite the…

Material science and Condensed matter Physics. 8th International Conference. Abstracts.

NASA Astrophysics Data System (ADS)

Kulyuk, L. L.; Paladi, Florentin; Canter, Valeriu; Nikorich, Valentina; Filippova, Irina

2016-08-01

The book includes the abstracts of the communications presented at the 8th International Conference on Materials Science and Condensed Matter Physics (MSCMP 2016), a traditional biennial meeting organized by the Institute of Applied Physics of the Academy of Sciences of Moldova (IAP).A total of 346 abstracts has been included in the book. The Conference programm included plenary lectures, topical keynote lectures, contributed oral and poster presentations distributed into 7 sections: * Condensed Matter Theory; * Advanced Bulk Materials; * Design and Structural Characterization of Materials; * Solid State Nanophysics and Nanotechnology; * Energy Conversion and Storage. Solid State Devices; * Surface Engineering and Applied Electrochemistry; * Digital and Optical holography: Materials and Methods. The abstracts are arranged according to the sections mentioned above. The Abstracts book includes a table of matters at the beginning of the book and an index of authors at the finish of the book.

A comparative analysis of support vector machines and extreme learning machines.

PubMed

Liu, Xueyi; Gao, Chuanhou; Li, Ping

2012-09-01

The theory of extreme learning machines (ELMs) has recently become increasingly popular. As a new learning algorithm for single-hidden-layer feed-forward neural networks, an ELM offers the advantages of low computational cost, good generalization ability, and ease of implementation. Hence the comparison and model selection between ELMs and other kinds of state-of-the-art machine learning approaches has become significant and has attracted many research efforts. This paper performs a comparative analysis of the basic ELMs and support vector machines (SVMs) from two viewpoints that are different from previous works: one is the Vapnik-Chervonenkis (VC) dimension, and the other is their performance under different training sample sizes. It is shown that the VC dimension of an ELM is equal to the number of hidden nodes of the ELM with probability one. Additionally, their generalization ability and computational complexity are exhibited with changing training sample size. ELMs have weaker generalization ability than SVMs for small sample but can generalize as well as SVMs for large sample. Remarkably, great superiority in computational speed especially for large-scale sample problems is found in ELMs. The results obtained can provide insight into the essential relationship between them, and can also serve as complementary knowledge for their past experimental and theoretical comparisons. Copyright © 2012 Elsevier Ltd. All rights reserved.

South Carolina Higher Education Statistical Abstract, 2014. 36th Edition

ERIC Educational Resources Information Center

Armour, Mim, Ed.

2014-01-01

The South Carolina Higher Education Statistical Abstract is a comprehensive, single-source compilation of tables and graphs which report data frequently requested by the Governor, Legislators, college and university staff, other state government officials, and the general public. The 2014 edition of the Statistical Abstract marks the 36th year of…

South Carolina Higher Education Statistical Abstract, 2015. 37th Edition

ERIC Educational Resources Information Center

Armour, Mim, Ed.

2015-01-01

The South Carolina Higher Education Statistical Abstract is a comprehensive, single-source compilation of tables and graphs which report data frequently requested by the Governor, Legislators, college and university staff, other state government officials, and the general public. The 2015 edition of the Statistical Abstract marks the 37th year of…

Active learning machine learns to create new quantum experiments.

PubMed

Melnikov, Alexey A; Poulsen Nautrup, Hendrik; Krenn, Mario; Dunjko, Vedran; Tiersch, Markus; Zeilinger, Anton; Briegel, Hans J

2018-02-06

How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments-a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.

Predicting Market Impact Costs Using Nonparametric Machine Learning Models

PubMed Central

Park, Saerom; Lee, Jaewook; Son, Youngdoo

2016-01-01

Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance. PMID:26926235

16. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

16. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific Railroad Carlin Shops, view to south (90mm lens). Note the large segmental-arched doorway to move locomotives in and out of Machine Shop. - Southern Pacific Railroad, Carlin Shops, Roundhouse Machine Shop Extension, Foot of Sixth Street, Carlin, Elko County, NV

The Effects of Different Electrode Types for Obtaining Surface Machining Shape on Shape Memory Alloy Using Electrochemical Machining

NASA Astrophysics Data System (ADS)

Choi, S. G.; Kim, S. H.; Choi, W. K.; Moon, G. C.; Lee, E. S.

2017-06-01

Shape memory alloy (SMA) is important material used for the medicine and aerospace industry due to its characteristics called the shape memory effect, which involves the recovery of deformed alloy to its original state through the application of temperature or stress. Consumers in modern society demand stability in parts. Electrochemical machining is one of the methods for obtained these stabilities in parts requirements. These parts of shape memory alloy require fine patterns in some applications. In order to machine a fine pattern, the electrochemical machining method is suitable. For precision electrochemical machining using different shape electrodes, the current density should be controlled precisely. And electrode shape is required for precise electrochemical machining. It is possible to obtain precise square holes on the SMA if the insulation layer controlled the unnecessary current between electrode and workpiece. If it is adjusting the unnecessary current to obtain the desired shape, it will be a great contribution to the medical industry and the aerospace industry. It is possible to process a desired shape to the shape memory alloy by micro controlling the unnecessary current. In case of the square electrode without insulation layer, it derives inexact square holes due to the unnecessary current. The results using the insulated electrode in only side show precise square holes. The removal rate improved in case of insulated electrode than others because insulation layer concentrate the applied current to the machining zone.

Human evolution in the age of the intelligent machine

NASA Technical Reports Server (NTRS)

Mclaughlin, W. I.

1983-01-01

A systems analysis of the future evolution of man can be conducted by analyzing the biological material of the galaxy into three subsystems: man, intelligent machines, and intelligent extraterrestrial organisms. A binomial interpretation is applied to this system wherein each of the subsystems is assigned a designation of success or failure. For man the two alternatives are, respectively, 'decline' or 'flourish', for machine they are 'become intelligent' or 'stay dumb', while for extraterrestrial intelligence the dichotomy is that of 'existence' or 'nonexistence'. The choices for each of three subsystems yield a total of eight possible states for the system. The relative lack of integration between brain components makes man a weak evolutionary contestant compared to machines. It is judged that machines should become dominant on earth within 100 years, probably by means of continuing development of existing man-machine systems. Advanced forms of extraterrestrial intelligence may exist but are too difficult to observe. The prospects for communication with extraterrestrial intelligence are reviewed.

Human evolution in the age of the intelligent machine

NASA Astrophysics Data System (ADS)

McLaughlin, W. I.

A systems analysis of the future evolution of man can be conducted by analyzing the biological material of the galaxy into three subsystems: man, intelligent machines, and intelligent extraterrestrial organisms. A binomial interpretation is applied to this system wherein each of the subsystems is assigned a designation of success or failure. For man the two alternatives are, respectively, 'decline' or 'flourish', for machine they are 'become intelligent' or 'stay dumb', while for extraterrestrial intelligence the dichotomy is that of 'existence' or 'nonexistence'. The choices for each of three subsystems yield a total of eight possible states for the system. The relative lack of integration between brain components makes man a weak evolutionary contestant compared to machines. It is judged that machines should become dominant on earth within 100 years, probably by means of continuing development of existing man-machine systems. Advanced forms of extraterrestrial intelligence may exist but are too difficult to observe. The prospects for communication with extraterrestrial intelligence are reviewed.

Abstracts of AF Materials Laboratory Reports

DTIC Science & Technology

1975-09-01

NO: TITLE: AUTHOR(S): CONTRACT NO; CONTRACTOR: AFML-TR-73-307 200,397 IMPROVED AUTOMATED TAPE LAYING MACHINE M. Poullos, W. J. Murray, D.L...AUTOMATED IMPROVED AUTOMATED TAPE LAYING MACHINE AUTOMATION AUTOMATION OF COATING PROCESSES FOR GAS TURBINE DLADcS AND VANES 203222/111 203072...IMP90VE0 TAPE LAYING MACHINE IMPP)VED AUTOMATED TAPE LAYING MACHINE A STUDY O^ THE STRESS-STRAIN TEHAVIOR OF GRAPHITE

Quantifying matrix product state

NASA Astrophysics Data System (ADS)

Bhatia, Amandeep Singh; Kumar, Ajay

2018-03-01

Motivated by the concept of quantum finite-state machines, we have investigated their relation with matrix product state of quantum spin systems. Matrix product states play a crucial role in the context of quantum information processing and are considered as a valuable asset for quantum information and communication purpose. It is an effective way to represent states of entangled systems. In this paper, we have designed quantum finite-state machines of one-dimensional matrix product state representations for quantum spin systems.

Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches.

PubMed

Chen, Xi; Kopsaftopoulos, Fotis; Wu, Qi; Ren, He; Chang, Fu-Kuo

2018-04-29

In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying angles of attack and airspeeds. A large feature pool is created by extracting potential features from the signals covering the time domain, the frequency domain as well as the information domain. Special emphasis is given to feature selection in which a novel filter method is developed based on the combination of a modified distance evaluation algorithm and a variance inflation factor. Machine learning algorithms are then employed to establish the mapping relationship from the feature space to the practical state space. Results from two case studies demonstrate the high identification accuracy and the effectiveness of the model complexity reduction via the proposed method, thus providing new perspectives of self-awareness towards the next generation of intelligent air vehicles.

Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches

PubMed Central

Chen, Xi; Wu, Qi; Ren, He; Chang, Fu-Kuo

2018-01-01

In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying angles of attack and airspeeds. A large feature pool is created by extracting potential features from the signals covering the time domain, the frequency domain as well as the information domain. Special emphasis is given to feature selection in which a novel filter method is developed based on the combination of a modified distance evaluation algorithm and a variance inflation factor. Machine learning algorithms are then employed to establish the mapping relationship from the feature space to the practical state space. Results from two case studies demonstrate the high identification accuracy and the effectiveness of the model complexity reduction via the proposed method, thus providing new perspectives of self-awareness towards the next generation of intelligent air vehicles. PMID:29710832

NASA Patent Abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 21) Abstracts

NASA Technical Reports Server (NTRS)

1982-01-01

Abstracts are cited for 87 patents and applications introduced into the NASA scientific and technical information system during the period of January 1982 through June 1982. Each entry consists of a citation, an abstract, and in mose cases, a key illustration selected from the patent or patent application.

New Abstractions for Mobile Connectivity and Resource Management

DTIC Science & Technology

2016-05-01

networked systems, con- sisting of replicated backend services and mobile , multi-homed clients. We derive a state machine for ECCP supporting migration...makes ECCP useful not only for mobility of client devices, but also for backend services which are increasingly run in VMs or containers on platforms...layers of the network stack, instead of the traditional IP/port, improve mobility for clients and backend services and reduce unnecessary coupling of

Application of Machine Learning to Rotorcraft Health Monitoring

NASA Technical Reports Server (NTRS)

Cody, Tyler; Dempsey, Paula J.

2017-01-01

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

Innovations in Teaching 1973. Abstracts of the Hilroy Fellowship Program.

ERIC Educational Resources Information Center

Canadian Teachers' Federation, Ottawa (Ontario).

This booklet contains abstracts of projects undertaken through the Hilroy Fellowship Program in Canada for the year 1973. The stated aim of the program is to encourage and reward classroom teachers who are developing new ideas for the improvement of teaching practices. The booklet contains 22 abstracts which cover projects dealing with educational…

Grounding Abstractness: Abstract Concepts and the Activation of the Mouth

PubMed Central

Borghi, Anna M.; Zarcone, Edoardo

2016-01-01

One key issue for theories of cognition is how abstract concepts, such as freedom, are represented. According to the WAT (Words As social Tools) proposal, abstract concepts activate both sensorimotor and linguistic/social information, and their acquisition modality involves the linguistic experience more than the acquisition of concrete concepts. We report an experiment in which participants were presented with abstract and concrete definitions followed by concrete and abstract target-words. When the definition and the word matched, participants were required to press a key, either with the hand or with the mouth. Response times and accuracy were recorded. As predicted, we found that abstract definitions and abstract words yielded slower responses and more errors compared to concrete definitions and concrete words. More crucially, there was an interaction between the target-words and the effector used to respond (hand, mouth). While responses with the mouth were overall slower, the advantage of the hand over the mouth responses was more marked with concrete than with abstract concepts. The results are in keeping with grounded and embodied theories of cognition and support the WAT proposal, according to which abstract concepts evoke linguistic-social information, hence activate the mouth. The mechanisms underlying the mouth activation with abstract concepts (re-enactment of acquisition experience, or re-explanation of the word meaning, possibly through inner talk) are discussed. To our knowledge this is the first behavioral study demonstrating with real words that the advantage of the hand over the mouth is more marked with concrete than with abstract concepts, likely because of the activation of linguistic information with abstract concepts. PMID:27777563

Abstraction kinetics of H-atom by OH radical from pinonaldehyde (C10H16O2): ab initio and transition-state theory calculations.

PubMed

Dash, Manas Ranjan; Rajakumar, B

2012-06-21

The kinetics and abstraction rate coefficients of hydroxyl radical (OH) reaction with pinonaldehyde were computed using G3(MP2) theory and transition-state theory (TST) between 200 and 400 K. Structures of the reactants, reaction complexes (RCs), product complexes (PCs), transition states (TSs), and products were optimized at the MP2(FULL)/6-31G* level of theory. Fifteen transition states were identified for the title reaction and confirmed by intrinsic reaction coordinate (IRC) calculations. The contributions of all the individual hydrogens in the substrate molecule to the total reaction are computed. The quantum mechanical tunneling effect was computed using Wigner's and Eckart's methods (both symmetrical and unsymmetrical methods). The reaction exhibits a negative temperature dependent rate coefficient, k(T) = (1.97 ± 0.34) × 10(-13) exp[(1587 ± 48)/T] cm(3) molecule(-1) s(-1), k(T) = (3.02 ± 0.56) × 10(-13) exp[(1534 ± 52/T] cm(3) molecule(-1) s(-1), and k(T) = (4.71 ± 1.85) × 10(-14) exp[(2042 ± 110)/T] cm(3) molecule(-1) s(-1) with Wigner's, Eckart's symmetrical, and Eckart's unsymmetrical tunneling corrections, respectively. Theoretically calculated rate coefficients are found to be in good agreement with the experimentally measured ones and other theoretical results. It is shown that hydrogen abstraction from -CHO position is the major channel, whereas H-abstraction from -COCH(3) is negligible. The atmospheric lifetime of pinonaldehyde is computed to be few hours and found to be in excellent agreement with the experimentally estimated ones.

Exploiting the systematic review protocol for classification of medical abstracts.

PubMed

Frunza, Oana; Inkpen, Diana; Matwin, Stan; Klement, William; O'Blenis, Peter

2011-01-01

To determine whether the automatic classification of documents can be useful in systematic reviews on medical topics, and specifically if the performance of the automatic classification can be enhanced by using the particular protocol of questions employed by the human reviewers to create multiple classifiers. The test collection is the data used in large-scale systematic review on the topic of the dissemination strategy of health care services for elderly people. From a group of 47,274 abstracts marked by human reviewers to be included in or excluded from further screening, we randomly selected 20,000 as a training set, with the remaining 27,274 becoming a separate test set. As a machine learning algorithm we used complement naïve Bayes. We tested both a global classification method, where a single classifier is trained on instances of abstracts and their classification (i.e., included or excluded), and a novel per-question classification method that trains multiple classifiers for each abstract, exploiting the specific protocol (questions) of the systematic review. For the per-question method we tested four ways of combining the results of the classifiers trained for the individual questions. As evaluation measures, we calculated precision and recall for several settings of the two methods. It is most important not to exclude any relevant documents (i.e., to attain high recall for the class of interest) but also desirable to exclude most of the non-relevant documents (i.e., to attain high precision on the class of interest) in order to reduce human workload. For the global method, the highest recall was 67.8% and the highest precision was 37.9%. For the per-question method, the highest recall was 99.2%, and the highest precision was 63%. The human-machine workflow proposed in this paper achieved a recall value of 99.6%, and a precision value of 17.8%. The per-question method that combines classifiers following the specific protocol of the review leads to better

Optimal Control of Induction Machines to Minimize Transient Energy Losses

NASA Astrophysics Data System (ADS)

Plathottam, Siby Jose

Induction machines are electromechanical energy conversion devices comprised of a stator and a rotor. Torque is generated due to the interaction between the rotating magnetic field from the stator, and the current induced in the rotor conductors. Their speed and torque output can be precisely controlled by manipulating the magnitude, frequency, and phase of the three input sinusoidal voltage waveforms. Their ruggedness, low cost, and high efficiency have made them ubiquitous component of nearly every industrial application. Thus, even a small improvement in their energy efficient tend to give a large amount of electrical energy savings over the lifetime of the machine. Hence, increasing energy efficiency (reducing energy losses) in induction machines is a constrained optimization problem that has attracted attention from researchers. The energy conversion efficiency of induction machines depends on both the speed-torque operating point, as well as the input voltage waveform. It also depends on whether the machine is in the transient or steady state. Maximizing energy efficiency during steady state is a Static Optimization problem, that has been extensively studied, with commercial solutions available. On the other hand, improving energy efficiency during transients is a Dynamic Optimization problem that is sparsely studied. This dissertation exclusively focuses on improving energy efficiency during transients. This dissertation treats the transient energy loss minimization problem as an optimal control problem which consists of a dynamic model of the machine, and a cost functional. The rotor field oriented current fed model of the induction machine is selected as the dynamic model. The rotor speed and rotor d-axis flux are the state variables in the dynamic model. The stator currents referred to as d-and q-axis currents are the control inputs. A cost functional is proposed that assigns a cost to both the energy losses in the induction machine, as well as the

Advanced Aircraft Interfaces: The Machine Side of the Man-Machine Interface (Les Interfaces sur les Avions de Pointe: L’Aspect Machine de l’Interface Homme-Machine)

DTIC Science & Technology

1992-10-01

Manager , Advanced Transport Operating Systems Program Office Langley Research Center Mail Stop 265 Hampton, VA 23665-5225 United States Programme Committee...J.H.Lind, and C.G.Burge Advanced Cockpit - Mission and Image Management 4 by J. Struck Aircrew Acceptance of Automation in the Cockpit 5 by M. Hicks and I...DESIGN CONCEPTS AND TOOLS A Systems Approach to the Advanced Aircraft Man-Machine Interface 23 by F. Armogida Management of Avionics Data in the Cockpit

Effect of the crown design and interface lute parameters on the stress-state of a machined crown-tooth system: a finite element analysis.

PubMed

Shahrbaf, Shirin; vanNoort, Richard; Mirzakouchaki, Behnam; Ghassemieh, Elaheh; Martin, Nicolas

2013-08-01

The effect of preparation design and the physical properties of the interface lute on the restored machined ceramic crown-tooth complex are poorly understood. The aim of this work was to determine, by means of three-dimensional finite element analysis (3D FEA) the effect of the tooth preparation design and the elastic modulus of the cement on the stress state of the cemented machined ceramic crown-tooth complex. The three-dimensional structure of human premolar teeth, restored with adhesively cemented machined ceramic crowns, was digitized with a micro-CT scanner. An accurate, high resolution, digital replica model of a restored tooth was created. Two preparation designs, with different occlusal morphologies, were modeled with cements of 3 different elastic moduli. Interactive medical image processing software (mimics and professional CAD modeling software) was used to create sophisticated digital models that included the supporting structures; periodontal ligament and alveolar bone. The generated models were imported into an FEA software program (hypermesh version 10.0, Altair Engineering Inc.) with all degrees of freedom constrained at the outer surface of the supporting cortical bone of the crown-tooth complex. Five different elastic moduli values were given to the adhesive cement interface 1.8GPa, 4GPa, 8GPa, 18.3GPa and 40GPa; the four lower values are representative of currently used cementing lutes and 40GPa is set as an extreme high value. The stress distribution under simulated applied loads was determined. The preparation design demonstrated an effect on the stress state of the restored tooth system. The cement elastic modulus affected the stress state in the cement and dentin structures but not in the crown, the pulp, the periodontal ligament or the cancellous and cortical bone. The results of this study suggest that both the choice of the preparation design and the cement elastic modulus can affect the stress state within the restored crown

The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

PubMed

Librenza-Garcia, Diego; Kotzian, Bruno Jaskulski; Yang, Jessica; Mwangi, Benson; Cao, Bo; Pereira Lima, Luiza Nunes; Bermudez, Mariane Bagatin; Boeira, Manuela Vianna; Kapczinski, Flávio; Passos, Ives Cavalcante

2017-09-01

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Machining and grinding: High rate deformation in practice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Follansbee, P.S.

1993-04-01

Machining and grinding are well-established material-working operations involving highly non-uniform deformation and failure processes. A typical machining operation is characterized by uncertain boundary conditions (e.g.,surface interactions), three-dimensional stress states, large strains, high strain rates, non-uniform temperatures, highly localized deformations, and failure by both nominally ductile and brittle mechanisms. While machining and grinding are thought to be dominated by empiricism, even a cursory inspection leads one to the conclusion that this results more from necessity arising out of the complicated and highly interdisciplinary nature of the processes than from the lack thereof. With these conditions in mind, the purpose of thismore » paper is to outline the current understanding of strain rate effects in metals.« less

Machining and grinding: High rate deformation in practice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Follansbee, P.S.

1993-01-01

Machining and grinding are well-established material-working operations involving highly non-uniform deformation and failure processes. A typical machining operation is characterized by uncertain boundary conditions (e.g.,surface interactions), three-dimensional stress states, large strains, high strain rates, non-uniform temperatures, highly localized deformations, and failure by both nominally ductile and brittle mechanisms. While machining and grinding are thought to be dominated by empiricism, even a cursory inspection leads one to the conclusion that this results more from necessity arising out of the complicated and highly interdisciplinary nature of the processes than from the lack thereof. With these conditions in mind, the purpose of thismore » paper is to outline the current understanding of strain rate effects in metals.« less

Obtaining the Thermal Efficiency of a Steam Railroad Machine Toy According Dale's Cone of Learning

NASA Astrophysics Data System (ADS)

Bautista-Hernandez, Omar Tomas; Ruiz-Chavarria, Gregorio

2011-03-01

Physics is crucial to understanding the world around us, the world inside us, and the world beyond us. It is the most basic and fundamental science, hence, our interest in developing innovative strategies supported by the imagination and knowledge to make the learning process funny, attractive and interesting to people, so, we can help to change the general idea that Physics is an abstract and complicated science. We all know this instinctively, however, turn-of-the-century educationist Edgar Dale illustrated this with research when he developed the Cone of Learning - which states that after two weeks we remember only 10% of what we read, but we remember 90% of what we do. Based on that theory, we obtain the thermal efficiency of a steam railroad machine -this is a toy train that could be bought at any department store-, and show you the great percentage of energy lost when moving this railroad machine, just as the real life is. While doing this practice we don't focus on the results itself, instead, we try to demostrate that physics is funny and it is not difficult to learn. We must stress that this practice was done with pre-universitary and univesitary students, however, can be shown to the community in general.

Industrial Inspection with Open Eyes: Advance with Machine Vision Technology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Zheng; Ukida, H.; Niel, Kurt

Machine vision systems have evolved significantly with the technology advances to tackle the challenges from modern manufacturing industry. A wide range of industrial inspection applications for quality control are benefiting from visual information captured by different types of cameras variously configured in a machine vision system. This chapter screens the state of the art in machine vision technologies in the light of hardware, software tools, and major algorithm advances for industrial inspection. The inspection beyond visual spectrum offers a significant complementary to the visual inspection. The combination with multiple technologies makes it possible for the inspection to achieve a bettermore » performance and efficiency in varied applications. The diversity of the applications demonstrates the great potential of machine vision systems for industry.« less

Fifth international fungus spore conference. [Abstracts]: Final technical report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Timberlake, W.E.

1993-04-01

This folio contains the proceedings of the Fifth International Fungal Spore Conference held August 17-21, 1991 at the Unicoi State Park at Helen, Georgia. The volume contains abstracts of each oral presentation as well as a collection of abstracts describing the poster sessions. Presentations were organized around the themes (1) Induction of Sporulation, (2) Nuclear Division, (3) Spore Formation, (4) Spore Release and Dispersal, and (4) Spore Germination.

"Machine" consciousness and "artificial" thought: an operational architectonics model guided approach.

PubMed

Fingelkurts, Andrew A; Fingelkurts, Alexander A; Neves, Carlos F H

2012-01-05

Instead of using low-level neurophysiology mimicking and exploratory programming methods commonly used in the machine consciousness field, the hierarchical operational architectonics (OA) framework of brain and mind functioning proposes an alternative conceptual-theoretical framework as a new direction in the area of model-driven machine (robot) consciousness engineering. The unified brain-mind theoretical OA model explicitly captures (though in an informal way) the basic essence of brain functional architecture, which indeed constitutes a theory of consciousness. The OA describes the neurophysiological basis of the phenomenal level of brain organization. In this context the problem of producing man-made "machine" consciousness and "artificial" thought is a matter of duplicating all levels of the operational architectonics hierarchy (with its inherent rules and mechanisms) found in the brain electromagnetic field. We hope that the conceptual-theoretical framework described in this paper will stimulate the interest of mathematicians and/or computer scientists to abstract and formalize principles of hierarchy of brain operations which are the building blocks for phenomenal consciousness and thought. Copyright © 2010 Elsevier B.V. All rights reserved.

Landscape epidemiology and machine learning: A geospatial approach to modeling West Nile virus risk in the United States

NASA Astrophysics Data System (ADS)

Young, Sean Gregory

The complex interactions between human health and the physical landscape and environment have been recognized, if not fully understood, since the ancient Greeks. Landscape epidemiology, sometimes called spatial epidemiology, is a sub-discipline of medical geography that uses environmental conditions as explanatory variables in the study of disease or other health phenomena. This theory suggests that pathogenic organisms (whether germs or larger vector and host species) are subject to environmental conditions that can be observed on the landscape, and by identifying where such organisms are likely to exist, areas at greatest risk of the disease can be derived. Machine learning is a sub-discipline of artificial intelligence that can be used to create predictive models from large and complex datasets. West Nile virus (WNV) is a relatively new infectious disease in the United States, and has a fairly well-understood transmission cycle that is believed to be highly dependent on environmental conditions. This study takes a geospatial approach to the study of WNV risk, using both landscape epidemiology and machine learning techniques. A combination of remotely sensed and in situ variables are used to predict WNV incidence with a correlation coefficient as high as 0.86. A novel method of mitigating the small numbers problem is also tested and ultimately discarded. Finally a consistent spatial pattern of model errors is identified, indicating the chosen variables are capable of predicting WNV disease risk across most of the United States, but are inadequate in the northern Great Plains region of the US.

The Physics and Physical Chemistry of Molecular Machines.

PubMed

Astumian, R Dean; Mukherjee, Shayantani; Warshel, Arieh

2016-06-17

The concept of a "power stroke"-a free-energy releasing conformational change-appears in almost every textbook that deals with the molecular details of muscle, the flagellar rotor, and many other biomolecular machines. Here, it is shown by using the constraints of microscopic reversibility that the power stroke model is incorrect as an explanation of how chemical energy is used by a molecular machine to do mechanical work. Instead, chemically driven molecular machines operating under thermodynamic constraints imposed by the reactant and product concentrations in the bulk function as information ratchets in which the directionality and stopping torque or stopping force are controlled entirely by the gating of the chemical reaction that provides the fuel for the machine. The gating of the chemical free energy occurs through chemical state dependent conformational changes of the molecular machine that, in turn, are capable of generating directional mechanical motions. In strong contrast to this general conclusion for molecular machines driven by catalysis of a chemical reaction, a power stroke may be (and often is) an essential component for a molecular machine driven by external modulation of pH or redox potential or by light. This difference between optical and chemical driving properties arises from the fundamental symmetry difference between the physics of optical processes, governed by the Bose-Einstein relations, and the constraints of microscopic reversibility for thermally activated processes. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface

NASA Astrophysics Data System (ADS)

Sachs, Nicholas A.; Ruiz-Torres, Ricardo; Perreault, Eric J.; Miller, Lee E.

2016-02-01

Objective. It is quite remarkable that brain machine interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed. Approach. We have developed a system that uses two independent Wiener filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The decoder combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state. Main results. We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor’s proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder. Significance. We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the

Experimental Investigation of Superconducting Synchronous Machines

DTIC Science & Technology

The report details the design and testing of a synchronous motor with superconducting field and armature windings. Data are furnished on the...as a generator with its armature in LN2 and in the superconducting state are given. Data are given on the machine operated as a synchronous motor. The

Agent Based Computing Machine

DTIC Science & Technology

2005-12-09

decision making logic that respond to the environment (concentration of operands - the state vector), and bias or "mood" as established by its history of...mentioned in the chart, there is no need for file management in a ABC Machine. Information is distributed, no history is maintained. The instruction set... Postgresql ) for collection of cluster samples/snapshots over intervals of time. An prototypical example of an XML file to configure and launch the ABC

14. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

14. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific Railroad Carlin Shops, view to north (90mm lens). - Southern Pacific Railroad, Carlin Shops, Roundhouse Machine Shop Extension, Foot of Sixth Street, Carlin, Elko County, NV

22 CFR 121.10 - Forgings, castings and machined bodies.

Code of Federal Regulations, 2013 CFR

2013-04-01

... STATES MUNITIONS LIST Enumeration of Articles § 121.10 Forgings, castings and machined bodies. Articles on the U.S. Munitions List include articles in a partially completed state (such as forgings... identifiable as defense articles. If the end-item is an article on the U.S. Munitions List (including...

22 CFR 121.10 - Forgings, castings and machined bodies.

Code of Federal Regulations, 2011 CFR

2011-04-01

... STATES MUNITIONS LIST Enumeration of Articles § 121.10 Forgings, castings and machined bodies. Articles on the U.S. Munitions List include articles in a partially completed state (such as forgings... identifiable as defense articles. If the end-item is an article on the U.S. Munitions List (including...

22 CFR 121.10 - Forgings, castings and machined bodies.

Code of Federal Regulations, 2012 CFR

2012-04-01

... STATES MUNITIONS LIST Enumeration of Articles § 121.10 Forgings, castings and machined bodies. Articles on the U.S. Munitions List include articles in a partially completed state (such as forgings... identifiable as defense articles. If the end-item is an article on the U.S. Munitions List (including...

Machine-Learning Algorithms to Code Public Health Spending Accounts

PubMed Central

Leider, Jonathon P.; Resnick, Beth A.; Alfonso, Y. Natalia; Bishai, David

2017-01-01

Objectives: Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. Methods: We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Results: Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Conclusions: Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence

Machine-Learning Algorithms to Code Public Health Spending Accounts.

PubMed

Brady, Eoghan S; Leider, Jonathon P; Resnick, Beth A; Alfonso, Y Natalia; Bishai, David

Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence-based public health resource allocation.

A hierarchical SVG image abstraction layer for medical imaging

NASA Astrophysics Data System (ADS)

Kim, Edward; Huang, Xiaolei; Tan, Gang; Long, L. Rodney; Antani, Sameer

2010-03-01

As medical imaging rapidly expands, there is an increasing need to structure and organize image data for efficient analysis, storage and retrieval. In response, a large fraction of research in the areas of content-based image retrieval (CBIR) and picture archiving and communication systems (PACS) has focused on structuring information to bridge the "semantic gap", a disparity between machine and human image understanding. An additional consideration in medical images is the organization and integration of clinical diagnostic information. As a step towards bridging the semantic gap, we design and implement a hierarchical image abstraction layer using an XML based language, Scalable Vector Graphics (SVG). Our method encodes features from the raw image and clinical information into an extensible "layer" that can be stored in a SVG document and efficiently searched. Any feature extracted from the raw image including, color, texture, orientation, size, neighbor information, etc., can be combined in our abstraction with high level descriptions or classifications. And our representation can natively characterize an image in a hierarchical tree structure to support multiple levels of segmentation. Furthermore, being a world wide web consortium (W3C) standard, SVG is able to be displayed by most web browsers, interacted with by ECMAScript (standardized scripting language, e.g. JavaScript, JScript), and indexed and retrieved by XML databases and XQuery. Using these open source technologies enables straightforward integration into existing systems. From our results, we show that the flexibility and extensibility of our abstraction facilitates effective storage and retrieval of medical images.

RatCar system for estimating locomotion states using neural signals with parameter monitoring: Vehicle-formed brain-machine interfaces for rat.

PubMed

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

2008-01-01

An online brain-machine interface (BMI) in the form of a small vehicle, the 'RatCar,' has been developed. A rat had neural electrodes implanted in its primary motor cortex and basal ganglia regions to continuously record neural signals. Then, a linear state space model represents a correlation between the recorded neural signals and locomotion states (i.e., moving velocity and azimuthal variances) of the rat. The model parameters were set so as to minimize estimation errors, and the locomotion states were estimated from neural firing rates using a Kalman filter algorithm. The results showed a small oscillation to achieve smooth control of the vehicle in spite of fluctuating firing rates with noises applied to the model. Major variation of the model variables converged in a first 30 seconds of the experiments and lasted for the entire one hour session.

37 CFR 1.72 - Title and abstract.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 37 Patents, Trademarks, and Copyrights 1 2014-07-01 2014-07-01 false Title and abstract. 1.72 Section 1.72 Patents, Trademarks, and Copyrights UNITED STATES PATENT AND TRADEMARK OFFICE, DEPARTMENT OF COMMERCE GENERAL RULES OF PRACTICE IN PATENT CASES National Processing Provisions Specification § 1.72...

Surface Characteristics of Machined NiTi Shape Memory Alloy: The Effects of Cryogenic Cooling and Preheating Conditions

NASA Astrophysics Data System (ADS)

Kaynak, Y.; Huang, B.; Karaca, H. E.; Jawahir, I. S.

2017-07-01

This experimental study focuses on the phase state and phase transformation response of the surface and subsurface of machined NiTi alloys. X-ray diffraction (XRD) analysis and differential scanning calorimeter techniques were utilized to measure the phase state and the transformation response of machined specimens, respectively. Specimens were machined under dry machining at ambient temperature, preheated conditions, and cryogenic cooling conditions at various cutting speeds. The findings from this research demonstrate that cryogenic machining substantially alters austenite finish temperature of martensitic NiTi alloy. Austenite finish ( A f) temperature shows more than 25 percent increase resulting from cryogenic machining compared with austenite finish temperature of as-received NiTi. Dry and preheated conditions do not substantially alter austenite finish temperature. XRD analysis shows that distinctive transformation from martensite to austenite occurs during machining process in all three conditions. Complete transformation from martensite to austenite is observed in dry cutting at all selected cutting speeds.

Machine Phase Fullerene Nanotechnology: 1996

NASA Technical Reports Server (NTRS)

Globus, Al; Chancellor, Marisa K. (Technical Monitor)

1997-01-01

NASA has used exotic materials for spacecraft and experimental aircraft to good effect for many decades. In spite of many advances, transportation to space still costs about $10,000 per pound. Drexler has proposed a hypothetical nanotechnology based on diamond and investigated the properties of such molecular systems. These studies and others suggest enormous potential for aerospace systems. Unfortunately, methods to realize diamonoid nanotechnology are at best highly speculative. Recent computational efforts at NASA Ames Research Center and computation and experiment elsewhere suggest that a nanotechnology of machine phase functionalized fullerenes may be synthetically relatively accessible and of great aerospace interest. Machine phase materials are (hypothetical) materials consisting entirely or in large part of microscopic machines. In a sense, most living matter fits this definition. To begin investigation of fullerene nanotechnology, we used molecular dynamics to study the properties of carbon nanotube based gears and gear/shaft configurations. Experiments on C60 and quantum calculations suggest that benzyne may react with carbon nanotubes to form gear teeth. Han has computationally demonstrated that molecular gears fashioned from (14,0) single-walled carbon nanotubes and benzyne teeth should operate well at 50-100 gigahertz. Results suggest that rotation can be converted to rotating or linear motion, and linear motion may be converted into rotation. Preliminary results suggest that these mechanical systems can be cooled by a helium atmosphere. Furthermore, Deepak has successfully simulated using helical electric fields generated by a laser to power fullerene gears once a positive and negative charge have been added to form a dipole. Even with mechanical motion, cooling, and power; creating a viable nanotechnology requires support structures, computer control, a system architecture, a variety of components, and some approach to manufacture. Additional

Multicopy programmable discrimination of general qubit states

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sentis, G.; Bagan, E.; Calsamiglia, J.

2010-10-15

Quantum state discrimination is a fundamental primitive in quantum statistics where one has to correctly identify the state of a system that is in one of two possible known states. A programmable discrimination machine performs this task when the pair of possible states is not a priori known but instead the two possible states are provided through two respective program ports. We study optimal programmable discrimination machines for general qubit states when several copies of states are available in the data or program ports. Two scenarios are considered: One in which the purity of the possible states is a priorimore » known, and the fully universal one where the machine operates over generic mixed states of unknown purity. We find analytical results for both the unambiguous and minimum error discrimination strategies. This allows us to calculate the asymptotic performance of programmable discrimination machines when a large number of copies are provided and to recover the standard state discrimination and state comparison values as different limiting cases.« less

Machine Shop Lathes.

ERIC Educational Resources Information Center

Dunn, James

This guide, the second in a series of five machine shop curriculum manuals, was designed for use in machine shop courses in Oklahoma. The purpose of the manual is to equip students with basic knowledge and skills that will enable them to enter the machine trade at the machine-operator level. The curriculum is designed so that it can be used in…

Patent Abstract Digest. Volume I.

DTIC Science & Technology

1979-04-30

compact. self -contained transducer unit for 151) Field of Stucek............... 73/71 5 US, 67.5 R. electromagnetic generation and detection of...BSTRACT SPONSORED PROGRAMS FROM THE AIR FORCE SYSTEMS COMMAND United States Patent 1191 (111 4,115,616 Heitz et &1. (45 Sep. 19, 1978 154) SELF -SEALING...ioseph E. Rutz; Wiliam J.O’Brien (221 Filed: F . (57 ABSTRACT A self -sealing multi-laminated fuel line composite mate. rial composed of (a) a plastic

Energy Abstracts for Policy Analysis. [DOE abstract journal

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thompson, L.M.

1981-01-01

Energy Abstracts for Policy Analysis (EAPA), while embracing all phases of energy analysis and development, is limited primarily to nontechnological or quasitechnological articles or reports having significant reference value. This selectivity in scope and coverage of EAPA is effected to emphasize the following: programmatic efforts; policy, legislative, and regulatory aspects; social, economic, and environmental impacts; regional and sectoral analyses, institutional factors, etc. Selection for EAPA is based strictly on content (substantive articles) and is made from Congressional committee prints; federal agency and department reports; regional commission and state and local government reports; periodicals, including trade journals and newsweeklies; conference proceedingsmore » and/or conference papers; books, including manuals, directories, encyclopedias, etc.; and documents from industrial firms, private institutes and foundations, educational institutions, societies, associations, etc. EAPA is available on an exchange basis to universities, research institutions, industrial firms, and publishers of energy information. Federal, state, and municipal agencies concerned with energy development, conservation, and usage may obtain EAPA free of charge. Inquiries should be directed to the Technical Information Center, P.O. Box 62, Oak Ridge, Tennessee 37830. EAPA is available to the public on a subscription basis, which includes 12 monthly issues plus the annual index. All items announced in EAPA exist as separate records on the DOE Energy Data Base.« less

[Determination of process variable pH in solid-state fermentation by FT-NIR spectroscopy and extreme learning machine (ELM)].

PubMed

Liu, Guo-hai; Jiang, Hui; Xiao, Xia-hong; Zhang, Dong-juan; Mei, Cong-li; Ding, Yu-han

2012-04-01

Fourier transform near-infrared (FT-NIR) spectroscopy was attempted to determine pH, which is one of the key process parameters in solid-state fermentation of crop straws. First, near infrared spectra of 140 solid-state fermented product samples were obtained by near infrared spectroscopy system in the wavelength range of 10 000-4 000 cm(-1), and then the reference measurement results of pH were achieved by pH meter. Thereafter, the extreme learning machine (ELM) was employed to calibrate model. In the calibration model, the optimal number of PCs and the optimal number of hidden-layer nodes of ELM network were determined by the cross-validation. Experimental results showed that the optimal ELM model was achieved with 1040-1 topology construction as follows: R(p) = 0.961 8 and RMSEP = 0.104 4 in the prediction set. The research achievement could provide technological basis for the on-line measurement of the process parameters in solid-state fermentation.

Machinability of Green Powder Metallurgy Components: Part I. Characterization of the Influence of Tool Wear

NASA Astrophysics Data System (ADS)

Robert-Perron, Etienne; Blais, Carl; Pelletier, Sylvain; Thomas, Yannig

2007-06-01

The green machining process is an interesting approach for solving the mediocre machining behavior of high-performance powder metallurgy (PM) steels. This process appears as a promising method for extending tool life and reducing machining costs. Recent improvements in binder/lubricant technologies have led to high green strength systems that enable green machining. So far, tool wear has been considered negligible when characterizing the machinability of green PM specimens. This inaccurate assumption may lead to the selection of suboptimum cutting conditions. The first part of this study involves the optimization of the machining parameters to minimize the effects of tool wear on the machinability in turning of green PM components. The second part of our work compares the sintered mechanical properties of components machined in green state with other machined after sintering.

Precision machining of advanced materials with waterjets

NASA Astrophysics Data System (ADS)

Liu, H. T.

2017-01-01

Recent advances in abrasive waterjet technology have elevated to the state that it often competes on equal footing with lasers and EDM for precision machining. Under the support of a National Science Foundation SBIR Phase II grant, OMAX has developed and commercialized micro abrasive water technology that is incorporated into a MicroMAX® JetMa- chining® Center. Waterjet technology, combined both abrasive waterjet and micro abrasive waterjet technology, is capable of machining most materials from macro to micro scales for a wide range of part size and thickness. Waterjet technology has technological and manufacturing merits that cannot be matched by most existing tools. As a cold cutting tool that creates no heat-affected zone, for example, waterjet cuts much faster than wire EDM and laser when measures to minimize a heat-affected zone are taken into account. In addition, waterjet is material independent; it cuts materials that cannot be cut or are difficult to cut otherwise. The versatility of waterjet has also demonstrated machining simulated nanomaterials with large gradients of material properties from metal, nonmetal, to anything in between. This paper presents waterjet-machined samples made of a wide range of advanced materials from macro to micro scales.

Finite State Machine with Adaptive Electromyogram (EMG) Feature Extraction to Drive Meal Assistance Robot

NASA Astrophysics Data System (ADS)

Zhang, Xiu; Wang, Xingyu; Wang, Bei; Sugi, Takenao; Nakamura, Masatoshi

Surface electromyogram (EMG) from elbow, wrist and hand has been widely used as an input of multifunction prostheses for many years. However, for patients with high-level limb deficiencies, muscle activities in upper-limbs are not strong enough to be used as control signals. In this paper, EMG from lower-limbs is acquired and applied to drive a meal assistance robot. An onset detection method with adaptive threshold based on EMG power is proposed to recognize different muscle contractions. Predefined control commands are output by finite state machine (FSM), and applied to operate the robot. The performance of EMG control is compared with joystick control by both objective and subjective indices. The results show that FSM provides the user with an easy-performing control strategy, which successfully operates robots with complicated control commands by limited muscle motions. The high accuracy and comfortableness of the EMG-control meal assistance robot make it feasible for users with upper limbs motor disabilities.

Self-replicating machines in continuous space with virtual physics.

PubMed

Smith, Arnold; Turney, Peter; Ewaschuk, Robert

2003-01-01

JohnnyVon is an implementation of self-replicating machines in continuous two-dimensional space. Two types of particles drift about in a virtual liquid. The particles are automata with discrete internal states but continuous external relationships. Their internal states are governed by finite state machines, but their external relationships are governed by a simulated physics that includes Brownian motion, viscosity, and springlike attractive and repulsive forces. The particles can be assembled into patterns that can encode arbitrary strings of bits. We demonstrate that, if an arbitrary seed pattern is put in a soup of separate individual particles, the pattern will replicate by assembling the individual particles into copies of itself. We also show that, given sufficient time, a soup of separate individual particles will eventually spontaneously form self-replicating patterns. We discuss the implications of JohnnyVon for research in nanotechnology, theoretical biology, and artificial life.

Precision Machining Technology. Technical Committee Report.

ERIC Educational Resources Information Center

Idaho State Dept. of Education, Boise. Div. of Vocational Education.

This Technical Committee Report prepared by industry representatives in Idaho lists the skills currently necessary for an employee in that state to obtain a job in precision machining technology, retain a job once hired, and advance in that occupational field. (Task lists are grouped according to duty areas generally used in industry settings, and…

Machine tool locator

DOEpatents

Hanlon, John A.; Gill, Timothy J.

2001-01-01

Machine tools can be accurately measured and positioned on manufacturing machines within very small tolerances by use of an autocollimator on a 3-axis mount on a manufacturing machine and positioned so as to focus on a reference tooling ball or a machine tool, a digital camera connected to the viewing end of the autocollimator, and a marker and measure generator for receiving digital images from the camera, then displaying or measuring distances between the projection reticle and the reference reticle on the monitoring screen, and relating the distances to the actual position of the autocollimator relative to the reference tooling ball. The images and measurements are used to set the position of the machine tool and to measure the size and shape of the machine tool tip, and examine cutting edge wear. patent

Multivariate machine learning distinguishes cross-network dynamic functional connectivity patterns in state and trait neuropathic pain.

PubMed

Cheng, J C; Rogachov, A; Hemington, K S; Kucyi, A; Bosma, R L; Lindquist, M A; Inman, R D; Davis, K D

2018-04-26

Communication within the brain is dynamic. Chronic pain can also be dynamic, with varying intensities experienced over time. Little is known of how brain dynamics are disrupted in chronic pain, or relates to patients' pain assessed at various time-scales (e.g., short-term state versus long-term trait). Patients experience pain "traits" indicative of their general condition, but also pain "states" that vary day to day. Here, we used network-based multivariate machine learning to determine how patterns in dynamic and static brain communication are related to different characteristics and timescales of chronic pain. Our models were based on resting state dynamic and static functional connectivity (dFC, sFC) in patients with chronic neuropathic pain (NP) or non-NP. The most prominent networks in the models were the default mode, salience, and executive control networks. We also found that cross-network measures of dFC rather than sFC were better associated with patients' pain, but only in those with NP features. These associations were also more highly and widely associated with measures of trait rather than state pain. Furthermore, greater dynamic connectivity with executive control networks was associated with milder neuropathic pain, but greater dynamic connectivity with limbic networks was associated greater neuropathic pain. Compared with healthy individuals, the dFC features most highly related to trait neuropathic pain were also more abnormal in patients with greater pain. Our findings indicate that dFC reflects patients' overall pain condition (i.e., trait pain), not just their current state, and is impacted by complexities in pain features beyond intensity.

Application of Machine Learning to Proteomics Data: Classification and Biomarker Identification in Postgenomics Biology

PubMed Central

Swan, Anna Louise; Mobasheri, Ali; Allaway, David; Liddell, Susan

2013-01-01

Abstract Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes. PMID:24116388

Machine learning for epigenetics and future medical applications

PubMed Central

Holder, Lawrence B.; Haque, M. Muksitul; Skinner, Michael K.

2017-01-01

ABSTRACT Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review. PMID:28524769

Machinability of nickel based alloys using electrical discharge machining process

NASA Astrophysics Data System (ADS)

Khan, M. Adam; Gokul, A. K.; Bharani Dharan, M. P.; Jeevakarthikeyan, R. V. S.; Uthayakumar, M.; Thirumalai Kumaran, S.; Duraiselvam, M.

2018-04-01

The high temperature materials such as nickel based alloys and austenitic steel are frequently used for manufacturing critical aero engine turbine components. Literature on conventional and unconventional machining of steel materials is abundant over the past three decades. However the machining studies on superalloy is still a challenging task due to its inherent property and quality. Thus this material is difficult to be cut in conventional processes. Study on unconventional machining process for nickel alloys is focused in this proposed research. Inconel718 and Monel 400 are the two different candidate materials used for electrical discharge machining (EDM) process. Investigation is to prepare a blind hole using copper electrode of 6mm diameter. Electrical parameters are varied to produce plasma spark for diffusion process and machining time is made constant to calculate the experimental results of both the material. Influence of process parameters on tool wear mechanism and material removal are considered from the proposed experimental design. While machining the tool has prone to discharge more materials due to production of high energy plasma spark and eddy current effect. The surface morphology of the machined surface were observed with high resolution FE SEM. Fused electrode found to be a spherical structure over the machined surface as clumps. Surface roughness were also measured with surface profile using profilometer. It is confirmed that there is no deviation and precise roundness of drilling is maintained.

Improving Machining Accuracy of CNC Machines with Innovative Design Methods

NASA Astrophysics Data System (ADS)

Yemelyanov, N. V.; Yemelyanova, I. V.; Zubenko, V. L.

2018-03-01

The article considers achieving the machining accuracy of CNC machines by applying innovative methods in modelling and design of machining systems, drives and machine processes. The topological method of analysis involves visualizing the system as matrices of block graphs with a varying degree of detail between the upper and lower hierarchy levels. This approach combines the advantages of graph theory and the efficiency of decomposition methods, it also has visual clarity, which is inherent in both topological models and structural matrices, as well as the resiliency of linear algebra as part of the matrix-based research. The focus of the study is on the design of automated machine workstations, systems, machines and units, which can be broken into interrelated parts and presented as algebraic, topological and set-theoretical models. Every model can be transformed into a model of another type, and, as a result, can be interpreted as a system of linear and non-linear equations which solutions determine the system parameters. This paper analyses the dynamic parameters of the 1716PF4 machine at the stages of design and exploitation. Having researched the impact of the system dynamics on the component quality, the authors have developed a range of practical recommendations which have enabled one to reduce considerably the amplitude of relative motion, exclude some resonance zones within the spindle speed range of 0...6000 min-1 and improve machining accuracy.

Ultra precision machining

NASA Astrophysics Data System (ADS)

Debra, Daniel B.; Hesselink, Lambertus; Binford, Thomas

1990-05-01

There are a number of fields that require or can use to advantage very high precision in machining. For example, further development of high energy lasers and x ray astronomy depend critically on the manufacture of light weight reflecting metal optical components. To fabricate these optical components with machine tools they will be made of metal with mirror quality surface finish. By mirror quality surface finish, it is meant that the dimensions tolerances on the order of 0.02 microns and surface roughness of 0.07. These accuracy targets fall in the category of ultra precision machining. They cannot be achieved by a simple extension of conventional machining processes and techniques. They require single crystal diamond tools, special attention to vibration isolation, special isolation of machine metrology, and on line correction of imperfection in the motion of the machine carriages on their way.

Quantum machine learning.

PubMed

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-13

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Quantum machine learning

NASA Astrophysics Data System (ADS)

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-01

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Introduction to machine learning for brain imaging.

PubMed

Lemm, Steven; Blankertz, Benjamin; Dickhaus, Thorsten; Müller, Klaus-Robert

2011-05-15

Machine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental for mining vast amounts of neural data of ever increasing measurement precision and detecting minuscule signals from an overwhelming noise floor. They provide the means to decode and characterize task relevant brain states and to distinguish them from non-informative brain signals. While undoubtedly this machinery has helped to gain novel biological insights, it also holds the danger of potential unintentional abuse. Ideally machine learning techniques should be usable for any non-expert, however, unfortunately they are typically not. Overfitting and other pitfalls may occur and lead to spurious and nonsensical interpretation. The goal of this review is therefore to provide an accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences. Copyright © 2010 Elsevier Inc. All rights reserved.

A review of machine learning in obesity.

PubMed

DeGregory, K W; Kuiper, P; DeSilvio, T; Pleuss, J D; Miller, R; Roginski, J W; Fisher, C B; Harness, D; Viswanath, S; Heymsfield, S B; Dungan, I; Thomas, D M

2018-05-01

Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity. © 2018 World Obesity Federation.

Review on the progress of ultra-precision machining technologies

NASA Astrophysics Data System (ADS)

Yuan, Julong; Lyu, Binghai; Hang, Wei; Deng, Qianfa

2017-06-01

Ultra-precision machining technologies are the essential methods, to obtain the highest form accuracy and surface quality. As more research findings are published, such technologies now involve complicated systems engineering and been widely used in the production of components in various aerospace, national defense, optics, mechanics, electronics, and other high-tech applications. The conception, applications and history of ultra-precision machining are introduced in this article, and the developments of ultra-precision machining technologies, especially ultra-precision grinding, ultra-precision cutting and polishing are also reviewed. The current state and problems of this field in China are analyzed. Finally, the development trends of this field and the coping strategies employed in China to keep up with the trends are discussed.

Methods for solving reasoning problems in abstract argumentation – A survey

PubMed Central

Charwat, Günther; Dvořák, Wolfgang; Gaggl, Sarah A.; Wallner, Johannes P.; Woltran, Stefan

2015-01-01

Within the last decade, abstract argumentation has emerged as a central field in Artificial Intelligence. Besides providing a core formalism for many advanced argumentation systems, abstract argumentation has also served to capture several non-monotonic logics and other AI related principles. Although the idea of abstract argumentation is appealingly simple, several reasoning problems in this formalism exhibit high computational complexity. This calls for advanced techniques when it comes to implementation issues, a challenge which has been recently faced from different angles. In this survey, we give an overview on different methods for solving reasoning problems in abstract argumentation and compare their particular features. Moreover, we highlight available state-of-the-art systems for abstract argumentation, which put these methods to practice. PMID:25737590

Stirling machine operating experience

NASA Technical Reports Server (NTRS)

Ross, Brad; Dudenhoefer, James E.

1991-01-01

Numerous Stirling machines have been built and operated, but the operating experience of these machines is not well known. It is important to examine this operating experience in detail, because it largely substantiates the claim that Stirling machines are capable of reliable and lengthy lives. The amount of data that exists is impressive, considering that many of the machines that have been built are developmental machines intended to show proof of concept, and were not expected to operate for any lengthy period of time. Some Stirling machines (typically free-piston machines) achieve long life through non-contact bearings, while other Stirling machines (typically kinematic) have achieved long operating lives through regular seal and bearing replacements. In addition to engine and system testing, life testing of critical components is also considered.

22 CFR 121.10 - Forgings, castings, and machined bodies.

Code of Federal Regulations, 2014 CFR

2014-04-01

... STATES MUNITIONS LIST Enumeration of Articles § 121.10 Forgings, castings, and machined bodies. The U.S. Munitions List controls as defense articles those forgings, castings, and other unfinished products, such as...

3D knitting using large circular knitting machines

NASA Astrophysics Data System (ADS)

Simonis, K.; Gloy, Y.-S.; Gries, T.

2017-10-01

For the first time 3D structures can now be produced on large circular knitting machines. Till date, such structures could only be manufactured on flat knitting machines. Since large circular knitting machines operate much faster, this development increases the overall productivity of 3D knits. It thus opens up a totally new avenue for cost reduction for applications in sportswear, upholstery, aerospace and automotive industry. The following paper presents the state of the art regarding the realisation of three dimensional fabrics. In addition, current knitting technologies regarding three dimensional formations will be explained. Results of the pretrials explaining the change in knitted fabrics´ dimension, executed at the Institut für Textiltechnik of the RWTH Aachen University, will be presented. Finally, the description of the 3D knit prototype developed will be provided as a part of this paper.

NASA Automated Rendezvous and Capture Review. A compilation of the abstracts

NASA Technical Reports Server (NTRS)

1991-01-01

This document presents a compilation of abstracts of papers solicited for presentation at the NASA Automated Rendezvous and Capture Review held in Williamsburg, VA on November 19-21, 1991. Due to limitations on time and other considerations, not all abstracts could be presented during the review. The organizing committee determined however, that all abstracts merited availability to all participants and represented data and information reflecting state-of-the-art of this technology which should be captured in one document for future use and reference. The organizing committee appreciates the interest shown in the review and the response by the authors in submitting these abstracts.

Development of a State Machine Sequencer for the Keck Interferometer: Evolution, Development and Lessons Learned using a CASE Tool Approach

NASA Technical Reports Server (NTRS)

Rede, Leonard J.; Booth, Andrew; Hsieh, Jonathon; Summer, Kellee

2004-01-01

This paper presents a discussion of the evolution of a sequencer from a simple EPICS (Experimental Physics and Industrial Control System) based sequencer into a complex implementation designed utilizing UML (Unified Modeling Language) methodologies and a CASE (Computer Aided Software Engineering) tool approach. The main purpose of the sequencer (called the IF Sequencer) is to provide overall control of the Keck Interferometer to enable science operations be carried out by a single operator (and/or observer). The interferometer links the two 10m telescopes of the W. M. Keck Observatory at Mauna Kea, Hawaii. The IF Sequencer is a high-level, multi-threaded, Hare1 finite state machine, software program designed to orchestrate several lower-level hardware and software hard real time subsystems that must perform their work in a specific and sequential order. The sequencing need not be done in hard real-time. Each state machine thread commands either a high-speed real-time multiple mode embedded controller via CORB A, or slower controllers via EPICS Channel Access interfaces. The overall operation of the system is simplified by the automation. The UML is discussed and our use of it to implement the sequencer is presented. The decision to use the Rhapsody product as our CASE tool is explained and reflected upon. Most importantly, a section on lessons learned is presented and the difficulty of integrating CASE tool automatically generated C++ code into a large control system consisting of multiple infrastructures is presented.

Development of a state machine sequencer for the Keck Interferometer: evolution, development, and lessons learned using a CASE tool approach

NASA Astrophysics Data System (ADS)

Reder, Leonard J.; Booth, Andrew; Hsieh, Jonathan; Summers, Kellee R.

2004-09-01

This paper presents a discussion of the evolution of a sequencer from a simple Experimental Physics and Industrial Control System (EPICS) based sequencer into a complex implementation designed utilizing UML (Unified Modeling Language) methodologies and a Computer Aided Software Engineering (CASE) tool approach. The main purpose of the Interferometer Sequencer (called the IF Sequencer) is to provide overall control of the Keck Interferometer to enable science operations to be carried out by a single operator (and/or observer). The interferometer links the two 10m telescopes of the W. M. Keck Observatory at Mauna Kea, Hawaii. The IF Sequencer is a high-level, multi-threaded, Harel finite state machine software program designed to orchestrate several lower-level hardware and software hard real-time subsystems that must perform their work in a specific and sequential order. The sequencing need not be done in hard real-time. Each state machine thread commands either a high-speed real-time multiple mode embedded controller via CORBA, or slower controllers via EPICS Channel Access interfaces. The overall operation of the system is simplified by the automation. The UML is discussed and our use of it to implement the sequencer is presented. The decision to use the Rhapsody product as our CASE tool is explained and reflected upon. Most importantly, a section on lessons learned is presented and the difficulty of integrating CASE tool automatically generated C++ code into a large control system consisting of multiple infrastructures is presented.

National Machine Guarding Program: Part 1. Machine safeguarding practices in small metal fabrication businesses.

PubMed

Parker, David L; Yamin, Samuel C; Brosseau, Lisa M; Xi, Min; Gordon, Robert; Most, Ivan G; Stanley, Rodney

2015-11-01

Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine-related hazards in 221 business. Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations. The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses. © 2015 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals, Inc.

Automatic microseismic event picking via unsupervised machine learning

NASA Astrophysics Data System (ADS)

Chen, Yangkang

2018-01-01

Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

From Abstract to Concrete Norms in Agent Institutions

NASA Technical Reports Server (NTRS)

Grossi, Davide; Dignum, Frank

2004-01-01

Norms specifying constraints over institutions are stated in such a form that allows them to regulate a wide range of situations over time without need for modification. To guarantee this stability, the formulation of norms need to abstract from a variety of concrete aspects, which are instead relevant for the actual operationalization of institutions. If agent institutions are to be built, which comply with a set of abstract requirements, how can those requirements be translated in more concrete constraints the impact of which can be described directly in the institution? In this work we make use of logical methods in order to provide a formal characterization of the translation rules that operate the connection between abstract and concrete norms. On the basis of this characterization, a comprehensive formalization of the notion of institution is also provided.

Solving a Higgs optimization problem with quantum annealing for machine learning.

PubMed

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-18

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Solving a Higgs optimization problem with quantum annealing for machine learning

NASA Astrophysics Data System (ADS)

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-01

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Abstraction and Consolidation

ERIC Educational Resources Information Center

Monaghan, John; Ozmantar, Mehmet Fatih

2006-01-01

The framework for this paper is a recently developed theory of abstraction in context. The paper reports on data collected from one student working on tasks concerned with absolute value functions. It examines the relationship between mathematical constructions and abstractions. It argues that an abstraction is a consolidated construction that can…

Effect of Extended State Observer and Automatic Voltage Regulator on Synchronous Machine Connected to Infinite Bus Power System

NASA Astrophysics Data System (ADS)

Angu, Rittu; Mehta, R. K.

2018-04-01

This paper presents a robust controller known as Extended State Observer (ESO) in order to improve the stability and voltage regulation of a synchronous machine connected to an infinite bus power system through a transmission line. The ESO-based control scheme is implemented with an automatic voltage regulator in conjunction with an excitation system to enhance the damping of low frequency power system oscillations, as the Power System Stabilizer (PSS) does. The implementation of PSS excitation control techniques however requires reliable information about the entire states, though they are not always directly measureable. To address this issue, the proposed ESO provides the estimate of system states as well as disturbance state together in order to improve not only the damping but also compensates system efficiently in presence of parameter uncertainties and external disturbances. The Closed-Loop Poles (CLPs) of the system have been assigned by the symmetric root locus technique, with the desired level of system damping provided by the dominant CLPs. The performance of the system is analyzed through simulating at different operating conditions. The control method is not only capable of providing zero estimation error in steady-state, but also shows robustness in tracking the reference command under parametric variations and external disturbances. Illustrative examples have been provided to demonstrate the effectiveness of the developed methodology.

Software architecture for time-constrained machine vision applications

NASA Astrophysics Data System (ADS)

Usamentiaga, Rubén; Molleda, Julio; García, Daniel F.; Bulnes, Francisco G.

2013-01-01

Real-time image and video processing applications require skilled architects, and recent trends in the hardware platform make the design and implementation of these applications increasingly complex. Many frameworks and libraries have been proposed or commercialized to simplify the design and tuning of real-time image processing applications. However, they tend to lack flexibility, because they are normally oriented toward particular types of applications, or they impose specific data processing models such as the pipeline. Other issues include large memory footprints, difficulty for reuse, and inefficient execution on multicore processors. We present a novel software architecture for time-constrained machine vision applications that addresses these issues. The architecture is divided into three layers. The platform abstraction layer provides a high-level application programming interface for the rest of the architecture. The messaging layer provides a message-passing interface based on a dynamic publish/subscribe pattern. A topic-based filtering in which messages are published to topics is used to route the messages from the publishers to the subscribers interested in a particular type of message. The application layer provides a repository for reusable application modules designed for machine vision applications. These modules, which include acquisition, visualization, communication, user interface, and data processing, take advantage of the power of well-known libraries such as OpenCV, Intel IPP, or CUDA. Finally, the proposed architecture is applied to a real machine vision application: a jam detector for steel pickling lines.

Electronic gaming machines and gambling disorder: a cross-cultural comparison between Brazil and the United States

PubMed Central

Medeiros, Gustavo Costa; Leppink, Eric W.; Yaemi, Ana; Mariani, Mirella; Tavares, Hermano; Grant, Jon E.

2015-01-01

Aims The objective of this paper is to perform a cross-cultural comparison of gambling disorder (GD) due to electronic gaming machines (EGM), a form of gambling that may have a high addictive potential. Our goal is to investigate two treatment-seeking samples of adults collected in Brazil and the United States, countries with different socio-cultural backgrounds. This comparison may lead to a better understanding of cultural influences on GD. Methods The total studied sample involved 733 treatment-seeking subjects: 353 men and 380 women (average age = 45.80, standard deviation ±10.9). The Brazilian sample had 517 individuals and the American sample 216. Subjects were recruited by analogous strategies. Results We found that the Brazilian sample was younger, predominantly male, less likely to be Caucasian, more likely to be partnered, had a faster progression from recreational gambling to GD, and were more likely to endorse chasing losses. Conclusion This study demonstrated that there are significant differences between treatment-seeking samples of adults presenting GD due to EGM in Brazil and in the United States. These findings suggest that cultural aspects may have a relevant role in GD due to EGM. PMID:26474662

Health-promoting vending machines: evaluation of a pediatric hospital intervention.

PubMed

Van Hulst, Andraea; Barnett, Tracie A; Déry, Véronique; Côté, Geneviève; Colin, Christine

2013-01-01

Taking advantage of a natural experiment made possible by the placement of health-promoting vending machines (HPVMs), we evaluated the impact of the intervention on consumers' attitudes toward and practices with vending machines in a pediatric hospital. Vending machines offering healthy snacks, meals, and beverages were developed to replace four vending machines offering the usual high-energy, low-nutrition fare. A pre- and post-intervention evaluation design was used; data were collected through exit surveys and six-week follow-up telephone surveys among potential vending machine users before (n=293) and after (n=226) placement of HPVMs. Chi-2 statistics were used to compare pre- and post-intervention participants' responses. More than 90% of pre- and post-intervention participants were satisfied with their purchase. Post-intervention participants were more likely to state that nutritional content and appropriateness of portion size were elements that influenced their purchase. Overall, post-intervention participants were more likely than pre-intervention participants to perceive as healthy the options offered by the hospital vending machines. Thirty-three percent of post-intervention participants recalled two or more sources of information integrated in the HPVM concept. No differences were found between pre- and post-intervention participants' readiness to adopt healthy diets. While the HPVM project had challenges as well as strengths, vending machines offering healthy snacks are feasible in hospital settings.

Machine learning in cardiovascular medicine: are we there yet?

PubMed

Shameer, Khader; Johnson, Kipp W; Glicksberg, Benjamin S; Dudley, Joel T; Sengupta, Partho P

2018-01-19

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Modeling Geomagnetic Variations using a Machine Learning Framework

NASA Astrophysics Data System (ADS)

Cheung, C. M. M.; Handmer, C.; Kosar, B.; Gerules, G.; Poduval, B.; Mackintosh, G.; Munoz-Jaramillo, A.; Bobra, M.; Hernandez, T.; McGranaghan, R. M.

2017-12-01

We present a framework for data-driven modeling of Heliophysics time series data. The Solar Terrestrial Interaction Neural net Generator (STING) is an open source python module built on top of state-of-the-art statistical learning frameworks (traditional machine learning methods as well as deep learning). To showcase the capability of STING, we deploy it for the problem of predicting the temporal variation of geomagnetic fields. The data used includes solar wind measurements from the OMNI database and geomagnetic field data taken by magnetometers at US Geological Survey observatories. We examine the predictive capability of different machine learning techniques (recurrent neural networks, support vector machines) for a range of forecasting times (minutes to 12 hours). STING is designed to be extensible to other types of data. We show how STING can be used on large sets of data from different sensors/observatories and adapted to tackle other problems in Heliophysics.

A wide-range programmable frequency synthesizer based on a finite state machine filter

NASA Astrophysics Data System (ADS)

Alser, Mohammed H.; Assaad, Maher M.; Hussin, Fawnizu A.

2013-11-01

In this article, an FPGA-based design and implementation of a fully digital wide-range programmable frequency synthesizer based on a finite state machine filter is presented. The advantages of the proposed architecture are that, it simultaneously generates a high frequency signal from a low frequency reference signal (i.e. synthesising), and synchronising the two signals (signals have the same phase, or a constant difference) without jitter accumulation issue. The architecture is portable and can be easily implemented for various platforms, such as FPGAs and integrated circuits. The frequency synthesizer circuit can be used as a part of SERDES devices in intra/inter chip communication in system-on-chip (SoC). The proposed circuit is designed using Verilog language and synthesized for the Altera DE2-70 development board, with the Cyclone II (EP2C35F672C6) device on board. Simulation and experimental results are included; they prove the synthesizing and tracking features of the proposed architecture. The generated clock signal frequency of a range from 19.8 MHz to 440 MHz is synchronized to the input reference clock with a frequency step of 0.12 MHz.

Your Sewing Machine.

ERIC Educational Resources Information Center

Peacock, Marion E.

The programed instruction manual is designed to aid the student in learning the parts, uses, and operation of the sewing machine. Drawings of sewing machine parts are presented, and space is provided for the student's written responses. Following an introductory section identifying sewing machine parts, the manual deals with each part and its…

SERS internship Fall 1992--Spring 1993: Abstract and research papers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1993-08-05

This report contains the abstracts and research papers by students on a variety of topics in engineering, genetics, solid state physics, thermonuclear energy, astrophysics, and other science related topics.

Machine Learning

DTIC Science & Technology

1990-04-01

DTIC i.LE COPY RADC-TR-90-25 Final Technical Report April 1990 MACHINE LEARNING The MITRE Corporation Melissa P. Chase Cs) CTIC ’- CT E 71 IN 2 11990...S. FUNDING NUMBERS MACHINE LEARNING C - F19628-89-C-0001 PE - 62702F PR - MOlE S. AUTHO(S) TA - 79 Melissa P. Chase WUT - 80 S. PERFORMING...341.280.5500 pm I " Aw Sig rill Ia 2110-01 SECTION 1 INTRODUCTION 1.1 BACKGROUND Research in machine learning has taken two directions in the problem of

EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine

NASA Astrophysics Data System (ADS)

Gao, Lin; Cheng, Wei; Zhang, Jinhua; Wang, Jue

2016-08-01

Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.

Comparative adoption of cone beam computed tomography and panoramic radiography machines across Australia.

PubMed

Zhang, A; Critchley, S; Monsour, P A

2016-12-01

The aim of the present study was to assess the current adoption of cone beam computed tomography (CBCT) and panoramic radiography (PR) machines across Australia. Information regarding registered CBCT and PR machines was obtained from radiation regulators across Australia. The number of X-ray machines was correlated with the population size, the number of dentists, and the gross state product (GSP) per capita, to determine the best fitting regression model(s). In 2014, there were 232 CBCT and 1681 PR machines registered in Australia. Based on absolute counts, Queensland had the largest number of CBCT and PR machines whereas the Northern Territory had the smallest number. However, when based on accessibility in terms of the population size and the number of dentists, the Australian Capital Territory had the most CBCT machines and Western Australia had the most PR machines. The number of X-ray machines correlated strongly with both the population size and the number of dentists, but not with the GSP per capita. In 2014, the ratio of PR to CBCT machines was approximately 7:1. Projected increases in either the population size or the number of dentists could positively impact on the adoption of PR and CBCT machines in Australia. © 2016 Australian Dental Association.

A variable-mode stator consequent pole memory machine

NASA Astrophysics Data System (ADS)

Yang, Hui; Lyu, Shukang; Lin, Heyun; Zhu, Z. Q.

2018-05-01

In this paper, a variable-mode concept is proposed for the speed range extension of a stator-consequent-pole memory machine (SCPMM). An integrated permanent magnet (PM) and electrically excited control scheme is utilized to simplify the flux-weakening control instead of relatively complicated continuous PM magnetization control. Due to the nature of memory machine, the magnetization state of low coercive force (LCF) magnets can be easily changed by applying either a positive or negative current pulse. Therefore, the number of PM poles may be changed to satisfy the specific performance requirement under different speed ranges, i.e. the machine with all PM poles can offer high torque output while that with half PM poles provides wide constant power range. In addition, the SCPMM with non-magnetized PMs can be considered as a dual-three phase electrically excited reluctance machine, which can be fed by an open-winding based dual inverters that provide direct current (DC) bias excitation to further extend the speed range. The effectiveness of the proposed variable-mode operation for extending its operating region and improving the system reliability is verified by both finite element analysis (FEA) and experiments.

Pre-use anesthesia machine check; certified anesthesia technician based quality improvement audit.

PubMed

Al Suhaibani, Mazen; Al Malki, Assaf; Al Dosary, Saad; Al Barmawi, Hanan; Pogoku, Mahdhav

2014-01-01

Quality assurance of providing a work ready machine in multiple theatre operating rooms in a tertiary modern medical center in Riyadh. The aim of the following study is to keep high quality environment for workers and patients in surgical operating rooms. Technicians based audit by using key performance indicators to assure inspection, passing test of machine worthiness for use daily and in between cases and in case of unexpected failure to provide quick replacement by ready to use another anesthetic machine. The anesthetic machines in all operating rooms are daily and continuously inspected and passed as ready by technicians and verified by anesthesiologist consultant or assistant consultant. The daily records of each machines were collected then inspected for data analysis by quality improvement committee department for descriptive analysis and report the degree of staff compliance to daily inspection as "met" items. Replaced machine during use and overall compliance. Distractive statistic using Microsoft Excel 2003 tables and graphs of sums and percentages of item studied in this audit. Audit obtained highest compliance percentage and low rate of replacement of machine which indicate unexpected machine state of use and quick machine switch. The authors are able to conclude that following regular inspection and running self-check recommended by the manufacturers can contribute to abort any possibility of hazard of anesthesia machine failure during operation. Furthermore in case of unexpected reason to replace the anesthesia machine in quick maneuver contributes to high assured operative utilization of man machine inter-phase in modern surgical operating rooms.

A Role for the Motor System in Binding Abstract Emotional Meaning

PubMed Central

Carota, Francesca; Hauk, Olaf; Mohr, Bettina; Pulvermüller, Friedemann

2012-01-01

Sensorimotor areas activate to action- and object-related words, but their role in abstract meaning processing is still debated. Abstract emotion words denoting body internal states are a critical test case because they lack referential links to objects. If actions expressing emotion are crucial for learning correspondences between word forms and emotions, emotion word–evoked activity should emerge in motor brain systems controlling the face and arms, which typically express emotions. To test this hypothesis, we recruited 18 native speakers and used event-related functional magnetic resonance imaging to compare brain activation evoked by abstract emotion words to that by face- and arm-related action words. In addition to limbic regions, emotion words indeed sparked precentral cortex, including body-part–specific areas activated somatotopically by face words or arm words. Control items, including hash mark strings and animal words, failed to activate precentral areas. We conclude that, similar to their role in action word processing, activation of frontocentral motor systems in the dorsal stream reflects the semantic binding of sign and meaning of abstract words denoting emotions and possibly other body internal states. PMID:21914634

National machine guarding program: Part 1. Machine safeguarding practices in small metal fabrication businesses

PubMed Central

Yamin, Samuel C.; Brosseau, Lisa M.; Xi, Min; Gordon, Robert; Most, Ivan G.; Stanley, Rodney

2015-01-01

Background Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. Methods The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine‐related hazards in 221 business. Results Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations. Conclusions The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses. Am. J. Ind. Med. 58:1174–1183, 2015. © 2015 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals, Inc. PMID:26332060

122. BENCH SHOP, SOUTHWEST CORNER SHOWING WOOD BORING MACHINE. DOOR ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

122. BENCH SHOP, SOUTHWEST CORNER SHOWING WOOD BORING MACHINE. DOOR TO WOODSHOP ON RIGHT. - Gruber Wagon Works, Pennsylvania Route 183 & State Hill Road at Red Bridge Park, Bernville, Berks County, PA

6 CFR 37.19 - Machine readable technology on the driver's license or identification card.

Code of Federal Regulations, 2011 CFR

2011-01-01

..., States must use the ISO/IEC 15438:2006(E) Information Technology—Automatic identification and data... 6 Domestic Security 1 2011-01-01 2011-01-01 false Machine readable technology on the driver's..., Verification, and Card Issuance Requirements § 37.19 Machine readable technology on the driver's license or...

6 CFR 37.19 - Machine readable technology on the driver's license or identification card.

Code of Federal Regulations, 2010 CFR

2010-01-01

..., States must use the ISO/IEC 15438:2006(E) Information Technology—Automatic identification and data... 6 Domestic Security 1 2010-01-01 2010-01-01 false Machine readable technology on the driver's..., Verification, and Card Issuance Requirements § 37.19 Machine readable technology on the driver's license or...

A Boltzmann machine for the organization of intelligent machines

NASA Technical Reports Server (NTRS)

Moed, Michael C.; Saridis, George N.

1990-01-01

A three-tier structure consisting of organization, coordination, and execution levels forms the architecture of an intelligent machine using the principle of increasing precision with decreasing intelligence from a hierarchically intelligent control. This system has been formulated as a probabilistic model, where uncertainty and imprecision can be expressed in terms of entropies. The optimal strategy for decision planning and task execution can be found by minimizing the total entropy in the system. The focus is on the design of the organization level as a Boltzmann machine. Since this level is responsible for planning the actions of the machine, the Boltzmann machine is reformulated to use entropy as the cost function to be minimized. Simulated annealing, expanding subinterval random search, and the genetic algorithm are presented as search techniques to efficiently find the desired action sequence and illustrated with numerical examples.

Standardized Curriculum for Machine Tool Operation/Machine Shop.

ERIC Educational Resources Information Center

Mississippi State Dept. of Education, Jackson. Office of Vocational, Technical and Adult Education.

Standardized vocational education course titles and core contents for two courses in Mississippi are provided: machine tool operation/machine shop I and II. The first course contains the following units: (1) orientation; (2) shop safety; (3) shop math; (4) measuring tools and instruments; (5) hand and bench tools; (6) blueprint reading; (7)…

Machine learning of molecular properties: Locality and active learning

NASA Astrophysics Data System (ADS)

Gubaev, Konstantin; Podryabinkin, Evgeny V.; Shapeev, Alexander V.

2018-06-01

In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

Energy-free machine learning force field for aluminum.

PubMed

Kruglov, Ivan; Sergeev, Oleg; Yanilkin, Alexey; Oganov, Artem R

2017-08-17

We used the machine learning technique of Li et al. (PRL 114, 2015) for molecular dynamics simulations. Atomic configurations were described by feature matrix based on internal vectors, and linear regression was used as a learning technique. We implemented this approach in the LAMMPS code. The method was applied to crystalline and liquid aluminum and uranium at different temperatures and densities, and showed the highest accuracy among different published potentials. Phonon density of states, entropy and melting temperature of aluminum were calculated using this machine learning potential. The results are in excellent agreement with experimental data and results of full ab initio calculations.

Analysis of spectrally resolved autofluorescence images by support vector machines

NASA Astrophysics Data System (ADS)

Mateasik, A.; Chorvat, D.; Chorvatova, A.

2013-02-01

Spectral analysis of the autofluorescence images of isolated cardiac cells was performed to evaluate and to classify the metabolic state of the cells in respect to the responses to metabolic modulators. The classification was done using machine learning approach based on support vector machine with the set of the automatically calculated features from recorded spectral profile of spectral autofluorescence images. This classification method was compared with the classical approach where the individual spectral components contributing to cell autofluorescence were estimated by spectral analysis, namely by blind source separation using non-negative matrix factorization. Comparison of both methods showed that machine learning can effectively classify the spectrally resolved autofluorescence images without the need of detailed knowledge about the sources of autofluorescence and their spectral properties.

SPATTER! SPATTER! SPATTER! Workers' health and the spray machine debate.

PubMed

Frounfelker, Rochelle L

2006-02-01

A conflict between industrialization and worker health developed in the painting industry during the early 1900s with the introduction of the spray machine. This technological innovation allowed the application of paint at greater speed and lower cost than hand painting and increased the rate at which painters were exposed to lead and other toxins contained in paint. From roughly 1919 to 1931, the painters' trade union clashed with employers, paint manufacturers, and legislatures over the impact of the spray machine on the health of workers and the need to enact legislation to regulate its use. While painters made gains on local, state, and national levels during the 1920s to prevent the use of the spray machine, their efforts ultimately failed.

Advances in Machine Learning and Data Mining for Astronomy

NASA Astrophysics Data System (ADS)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Classifying smoking urges via machine learning.

PubMed

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-12-01

Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights

Classifying smoking urges via machine learning

PubMed Central

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-01-01

Background and objective Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. Methods To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. Results The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. Conclusions In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms’ performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions

Use of Advanced Machine-Learning Techniques for Non-Invasive Monitoring of Hemorrhage

DTIC Science & Technology

2010-04-01

that state-of-the-art machine learning techniques when integrated with novel non-invasive monitoring technologies could detect subtle, physiological...decompensation. Continuous, non-invasively measured hemodynamic signals (e.g., ECG, blood pressures, stroke volume) were used for the development of machine ... learning algorithms. Accuracy estimates were obtained by building models using 27 subjects and testing on the 28th. This process was repeated 28 times

Machine Learning.

ERIC Educational Resources Information Center

Kirrane, Diane E.

1990-01-01

As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

JTEC panel report on machine translation in Japan

NASA Technical Reports Server (NTRS)

Carbonell, Jaime; Rich, Elaine; Johnson, David; Tomita, Masaru; Vasconcellos, Muriel; Wilks, Yorick

1992-01-01

The goal of this report is to provide an overview of the state of the art of machine translation (MT) in Japan and to provide a comparison between Japanese and Western technology in this area. The term 'machine translation' as used here, includes both the science and technology required for automating the translation of text from one human language to another. Machine translation is viewed in Japan as an important strategic technology that is expected to play a key role in Japan's increasing participation in the world economy. MT is seen in Japan as important both for assimilating information into Japanese as well as for disseminating Japanese information throughout the world. Most of the MT systems now available in Japan are transfer-based systems. The majority of them exploit a case-frame representation of the source text as the basis of the transfer process. There is a gradual movement toward the use of deeper semantic representations, and some groups are beginning to look at interlingua-based systems.

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2011 CFR

2011-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2012 CFR

2012-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2013 CFR

2013-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2014 CFR

2014-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2010 CFR

2010-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

Adiabatic Quantum Anomaly Detection and Machine Learning

NASA Astrophysics Data System (ADS)

Pudenz, Kristen; Lidar, Daniel

2012-02-01

We present methods of anomaly detection and machine learning using adiabatic quantum computing. The machine learning algorithm is a boosting approach which seeks to optimally combine somewhat accurate classification functions to create a unified classifier which is much more accurate than its components. This algorithm then becomes the first part of the larger anomaly detection algorithm. In the anomaly detection routine, we first use adiabatic quantum computing to train two classifiers which detect two sets, the overlap of which forms the anomaly class. We call this the learning phase. Then, in the testing phase, the two learned classification functions are combined to form the final Hamiltonian for an adiabatic quantum computation, the low energy states of which represent the anomalies in a binary vector space.

Integrated human-machine intelligence in space systems

NASA Technical Reports Server (NTRS)

Boy, Guy A.

1992-01-01

The integration of human and machine intelligence in space systems is outlined with respect to the contributions of artificial intelligence. The current state-of-the-art in intelligent assistant systems (IASs) is reviewed, and the requirements of some real-world applications of the technologies are discussed. A concept of integrated human-machine intelligence is examined in the contexts of: (1) interactive systems that tolerate human errors; (2) systems for the relief of workloads; and (3) interactive systems for solving problems in abnormal situations. Key issues in the development of IASs include the compatibility of the systems with astronauts in terms of inputs/outputs, processing, real-time AI, and knowledge-based system validation. Real-world applications are suggested such as the diagnosis, planning, and control of enginnered systems.

Vending Machine Policies and Practices in Delaware

ERIC Educational Resources Information Center

Gemmill, Erin; Cotugna, Nancy

2005-01-01

Overweight has reached alarming proportions among America's youth. Although the cause of the rise in overweight rates in children and adolescents is certainly the result of the interaction of a variety of factors, the presence of vending machines in schools is one issue that has recently come to the forefront. Many states have passed or proposed…

Cooperating reduction machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kluge, W.E.

1983-11-01

This paper presents a concept and a system architecture for the concurrent execution of program expressions of a concrete reduction language based on lamda-expressions. If formulated appropriately, these expressions are well-suited for concurrent execution, following a demand-driven model of computation. In particular, recursive program expressions with nonlinear expansion may, at run time, recursively be partitioned into a hierarchy of independent subexpressions which can be reduced by a corresponding hierarchy of virtual reduction machines. This hierarchy unfolds and collapses dynamically, with virtual machines recursively assuming the role of masters that create and eventually terminate, or synchronize with, slaves. The paper alsomore » proposes a nonhierarchically organized system of reduction machines, each featuring a stack architecture, that effectively supports the allocation of virtual machines to the real machines of the system in compliance with their hierarchical order of creation and termination. 25 references.« less

Leadership Abstracts, Volume 2, Numbers 1-21, 1989.

ERIC Educational Resources Information Center

Doucette, Don, Ed.

This series of abstracts from the League for Innovation in the Community College and the Community College Leadership Program is published approximately bimonthly and distributed to the chief executive officer of every two-year college in the United States and Canada. Addressing a variety of topics of interest to community college administrators,…

Leadership Abstracts, Volume 3, Numbers 1-20, 1990.

ERIC Educational Resources Information Center

Doucette, Don, Ed.

This series of abstracts from the League for Innovation in the Community College and the Community College Leadership Program is published approximately bimonthly and distributed to the chief executive officer of every two-year college in the United States and Canada. Addressing a variety of topics of interest to community college administrators,…

Analysis of acoustic emission signals and monitoring of machining processes

PubMed

Govekar; Gradisek; Grabec

2000-03-01

Monitoring of a machining process on the basis of sensor signals requires a selection of informative inputs in order to reliably characterize and model the process. In this article, a system for selection of informative characteristics from signals of multiple sensors is presented. For signal analysis, methods of spectral analysis and methods of nonlinear time series analysis are used. With the aim of modeling relationships between signal characteristics and the corresponding process state, an adaptive empirical modeler is applied. The application of the system is demonstrated by characterization of different parameters defining the states of a turning machining process, such as: chip form, tool wear, and onset of chatter vibration. The results show that, in spite of the complexity of the turning process, the state of the process can be well characterized by just a few proper characteristics extracted from a representative sensor signal. The process characterization can be further improved by joining characteristics from multiple sensors and by application of chaotic characteristics.

Compensation strategy for machining optical freeform surfaces by the combined on- and off-machine measurement.

PubMed

Zhang, Xiaodong; Zeng, Zhen; Liu, Xianlei; Fang, Fengzhou

2015-09-21

Freeform surface is promising to be the next generation optics, however it needs high form accuracy for excellent performance. The closed-loop of fabrication-measurement-compensation is necessary for the improvement of the form accuracy. It is difficult to do an off-machine measurement during the freeform machining because the remounting inaccuracy can result in significant form deviations. On the other side, on-machine measurement may hides the systematic errors of the machine because the measuring device is placed in situ on the machine. This study proposes a new compensation strategy based on the combination of on-machine and off-machine measurement. The freeform surface is measured in off-machine mode with nanometric accuracy, and the on-machine probe achieves accurate relative position between the workpiece and machine after remounting. The compensation cutting path is generated according to the calculated relative position and shape errors to avoid employing extra manual adjustment or highly accurate reference-feature fixture. Experimental results verified the effectiveness of the proposed method.

Machine Tool Advanced Skills Technology Program (MAST). Overview and Methodology.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

The Machine Tool Advanced Skills Technology Program (MAST) is a geographical partnership of six of the nation's best two-year colleges located in the six states that have about one-third of the density of metals-related industries in the United States. The purpose of the MAST grant is to develop and implement a national training model to overcome…

2018 Congress Poster Abstracts

PubMed

2018-02-21

Each abstract has been indexed according to the first author. Abstracts appear as they were submitted and have not undergone editing or the Oncology Nursing Forum’s review process. Only abstracts that will be presented appear here. Poster numbers are subject to change. For updated poster numbers, visit congress.ons.org or check the Congress guide. Data published in abstracts presented at the ONS 43rd Annual Congress are embargoed until the conclusion of the presentation. Coverage and/or distribution of an abstract, poster, or any of its supplemental material to or by the news media, any commercial entity, or individuals, including the authors of said abstract, is strictly prohibited until the embargo is lifted. Promotion of general topics and speakers is encouraged within these guidelines.

Machine intelligence and robotics: Report of the NASA study group. Executive summary

NASA Technical Reports Server (NTRS)

1979-01-01

A brief overview of applications of machine intelligence and robotics in the space program is given. These space exploration robots, global service robots to collect data for public service use on soil conditions, sea states, global crop conditions, weather, geology, disasters, etc., from Earth orbit, space industrialization and processing technologies, and construction of large structures in space. Program options for research, advanced development, and implementation of machine intelligence and robot technology for use in program planning are discussed. A vigorous and long-range program to incorporate and keep pace with state of the art developments in computer technology, both in spaceborne and ground-based computer systems is recommended.

Scheduling of hybrid types of machines with two-machine flowshop as the first type and a single machine as the second type

NASA Astrophysics Data System (ADS)

Hsiao, Ming-Chih; Su, Ling-Huey

2018-02-01

This research addresses the problem of scheduling hybrid machine types, in which one type is a two-machine flowshop and another type is a single machine. A job is either processed on the two-machine flowshop or on the single machine. The objective is to determine a production schedule for all jobs so as to minimize the makespan. The problem is NP-hard since the two parallel machines problem was proved to be NP-hard. Simulated annealing algorithms are developed to solve the problem optimally. A mixed integer programming (MIP) is developed and used to evaluate the performance for two SAs. Computational experiments demonstrate the efficiency of the simulated annealing algorithms, the quality of the simulated annealing algorithms will also be reported.

14. Machine in north 1922 section of Building 59. Machine ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

14. Machine in north 1922 section of Building 59. Machine is 24' Jointer made by Oliver Machinery Co. Camera pointed E. - Puget Sound Naval Shipyard, Pattern Shop, Farragut Avenue, Bremerton, Kitsap County, WA

Mass Communication: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," July through December 1984 (Vol. 45 Nos. 1 through 6).

ERIC Educational Resources Information Center

ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 37 titles deal with a variety of topics, including the following: (1) the role of television in the lives of older homebound individuals; (2) radio wars between Cuba and the United States; (3) the relationship between…

Mass Communication: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," January through June 1981 (Vol. 41 Nos. 7 through 12).

ERIC Educational Resources Information Center

ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 47 titles deal with a variety of topics, including the following: (1) the effect of source variation upon children's reactions to television commercials; (2) covert observation as a means of measuring the affective states of…

15. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

15. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific Railroad Carlin Shops, view to northeast (90mm lens). The arched cutouts in the bottom chords of the roof trusses were necessary to provide clearance for the smokestacks of steam locomotives, and also mark the location of the former inspection pit in the floor (now filled in and covered by a new concrete floor). - Southern Pacific Railroad, Carlin Shops, Roundhouse Machine Shop Extension, Foot of Sixth Street, Carlin, Elko County, NV

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

PubMed

Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E

2017-06-14

Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

Runtime Verification of C Programs

NASA Technical Reports Server (NTRS)

Havelund, Klaus

2008-01-01

We present in this paper a framework, RMOR, for monitoring the execution of C programs against state machines, expressed in a textual (nongraphical) format in files separate from the program. The state machine language has been inspired by a graphical state machine language RCAT recently developed at the Jet Propulsion Laboratory, as an alternative to using Linear Temporal Logic (LTL) for requirements capture. Transitions between states are labeled with abstract event names and Boolean expressions over such. The abstract events are connected to code fragments using an aspect-oriented pointcut language similar to ASPECTJ's or ASPECTC's pointcut language. The system is implemented in the C analysis and transformation package CIL, and is programmed in OCAML, the implementation language of CIL. The work is closely related to the notion of stateful aspects within aspect-oriented programming, where pointcut languages are extended with temporal assertions over the execution trace.

Optimal design method to minimize users' thinking mapping load in human-machine interactions.

PubMed

Huang, Yanqun; Li, Xu; Zhang, Jie

2015-01-01

The discrepancy between human cognition and machine requirements/behaviors usually results in serious mental thinking mapping loads or even disasters in product operating. It is important to help people avoid human-machine interaction confusions and difficulties in today's mental work mastered society. Improving the usability of a product and minimizing user's thinking mapping and interpreting load in human-machine interactions. An optimal human-machine interface design method is introduced, which is based on the purpose of minimizing the mental load in thinking mapping process between users' intentions and affordance of product interface states. By analyzing the users' thinking mapping problem, an operating action model is constructed. According to human natural instincts and acquired knowledge, an expected ideal design with minimized thinking loads is uniquely determined at first. Then, creative alternatives, in terms of the way human obtains operational information, are provided as digital interface states datasets. In the last, using the cluster analysis method, an optimum solution is picked out from alternatives, by calculating the distances between two datasets. Considering multiple factors to minimize users' thinking mapping loads, a solution nearest to the ideal value is found in the human-car interaction design case. The clustering results show its effectiveness in finding an optimum solution to the mental load minimizing problems in human-machine interaction design.

Energy Abstracts for Policy Analysis: a monthly abstract journal. Abstracts 1-225

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thompson, L.M..

1983-01-01

Energy Abstracts for Policy Analysis (EAPA), while embracing all phases of energy analysis and development, is limited primarily to nontechnological or quasitechnological articles or reports having significant reference value. This selectivity in scope and coverage of EAPA is effected to emphasize the following: programmatic efforts; policy, legislative, and regulatory aspects; social, economic, and environmental impacts; regional and sectoral analyses, institutional factors, etc. Selection for EAPA is based strictly on content (substantive articles) and is made from Congressional committee prints; federal agency and department reports; regional commission and state and local government reports; periodicals, including trade journals and newsweeklies; conference proceedingsmore » and/or conference papers; books, including manuals, directories, encyclopedias, etc.; and documents from industrial firms, private institutes and foundations, educational institutions, societies, associations, etc. EAPA is available to DOE offices and contractors on request, and on an exchange basis to universities, research institutions, industrial firms, and publishers of energy information. Inquiries should be directed to the Technical Information Center, P.O. Box 62, Oak Ridge, Tennessee 37830. EAPA is available to the public on a subscription basis from the Superintendent of Documents, US Government Printing Office, Washington, DC 20402. The subscription rate for the 12 monthly issues plus the annual index is $70.00 for domestic subscribers and $87.50 for subscribers outside the North American continent.« less

Industrial machine systems risk assessment: a critical review of concepts and methods.

PubMed

Etherton, John R

2007-02-01

Reducing the risk of work-related death and injury to machine operators and maintenance personnel poses a continuing occupational safety challenge. The risk of injury from machinery in U.S. workplaces is high. Between 1992 and 2001, there were, on average, 520 fatalities per year involving machines and, on average, 3.8 cases per 10,000 workers of nonfatal caught-in-running-machine injuries involving lost workdays. A U.S. task group recently developed a technical reference guideline, ANSI B11 TR3, "A Guide to Estimate, Evaluate, & Reduce Risks Associated with Machine Tools," that is intended to bring machine tool risk assessment practice in the United States up to or above the level now required by the international standard, ISO 14121. The ANSI guideline emphasizes identifying tasks and hazards not previously considered, particularly those associated with maintenance; and it further emphasizes teamwork among line workers, engineers, and safety professionals. The value of this critical review of concepts and methods resides in (1) its linking current risk theory to machine system risk assessment and (2) its exploration of how various risk estimation tools translate into risk-informed decisions on industrial machine system design and use. The review was undertaken to set the stage for a field evaluation study on machine risk assessment among users of the ANSI B11 TR3 method.

Mass Communication: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," July through December 1981 (Vol. 42 Nos. 1 through 6).

ERIC Educational Resources Information Center

ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 50 titles deal with a variety of topics, including the following: (1) a history of broadcast audience research in the United States from 1920 to 1980; (2) factors contributing to the believability of television newscasters; (3)…

Reporting quality of randomised controlled trial abstracts on age-related macular degeneration health care: a cross-sectional quantification of the adherence to CONSORT abstract reporting recommendations.

PubMed

Baulig, Christine; Krummenauer, Frank; Geis, Berit; Tulka, Sabrina; Knippschild, Stephanie

2018-05-22

To assess the reporting quality of randomised controlled trial (RCT) abstracts on age-related macular degeneration (AMD) healthcare, to evaluate the adherence to the Consolidated Standards of Reporting Trials (CONSORT) statement's recommendations on minimum abstract information and to identify journal characteristics associated with abstract reporting quality. Cross-sectional evaluation of RCT abstracts on AMD healthcare. A PubMed search was implemented to identify RCT abstracts on AMD healthcare published in the English language between January 2004 and December 2013. Data extraction was performed by two parallel readers independently by means of a documentation format in accordance with the 16 items of the CONSORT checklist for abstracts. The total number of criteria fulfilled by an abstract was derived as primary endpoint of the investigation; incidence rate ratios (IRRs) with unadjusted 95% CI were estimated by means of multiple Poisson regression to identify journal and article characteristics (publication year, multicentre design, structured abstract recommendations, effective sample size, effective abstract word counts and journal impact factor) possibly associated with the total number of fulfilled items. 136 of 673 identified abstracts (published in 36 different journals) fulfilled all eligibility criteria. The median number of fulfilled items was 7 (95% CI 7 to 8). No abstract reported all 16 recommended items; the maximum total number was 14, the minimum 3 of 16 items. Multivariate analysis only demonstrated the abstracts' word counts as being significantly associated with a better reporting of abstracts (Poisson regression-based IRR 1.002, 95% CI 1.001 to 1.003). Reporting quality of RCT abstracts on AMD investigations showed a considerable potential for improvement to meet the CONSORT abstract reporting recommendations. Furthermore, word counts of abstracts were identified as significantly associated with the overall abstract reporting quality. �

Diamond machine tool face lapping machine

DOEpatents

Yetter, H.H.

1985-05-06

An apparatus for shaping, sharpening and polishing diamond-tipped single-point machine tools. The isolation of a rotating grinding wheel from its driving apparatus using an air bearing and causing the tool to be shaped, polished or sharpened to be moved across the surface of the grinding wheel so that it does not remain at one radius for more than a single rotation of the grinding wheel has been found to readily result in machine tools of a quality which can only be obtained by the most tedious and costly processing procedures, and previously unattainable by simple lapping techniques.

Atlas/State Data Abstract for the United States, Fiscal Year 1985

DTIC Science & Technology

1985-01-01

SHIPYARD ~STATION JIM CREEK ( PUGET SOUND , NS PUG ET SOUN IPKA NAVAL HOSPITAL SPKAE NAVAL WLJNAVAL STATION * ~SUBMARINE BASE SATL STRATEGIC WEAPONS...14 X X Naval Supply Center, Puget Sound , Bremerton, WA ............. 52 X X Naval Supply Center, San Diego, CA...the Individual Ready Reserve and the Inactive, National Guard, These personnel are distributed by state according to their home of residence . Coast

Irrelevance of the Power Stroke for the Directionality, Stopping Force, and Optimal Efficiency of Chemically Driven Molecular Machines

PubMed Central

Astumian, R. Dean

2015-01-01

A simple model for a chemically driven molecular walker shows that the elastic energy stored by the molecule and released during the conformational change known as the power-stroke (i.e., the free-energy difference between the pre- and post-power-stroke states) is irrelevant for determining the directionality, stopping force, and efficiency of the motor. Further, the apportionment of the dependence on the externally applied force between the forward and reverse rate constants of the power-stroke (or indeed among all rate constants) is irrelevant for determining the directionality, stopping force, and efficiency of the motor. Arguments based on the principle of microscopic reversibility demonstrate that this result is general for all chemically driven molecular machines, and even more broadly that the relative energies of the states of the motor have no role in determining the directionality, stopping force, or optimal efficiency of the machine. Instead, the directionality, stopping force, and optimal efficiency are determined solely by the relative heights of the energy barriers between the states. Molecular recognition—the ability of a molecular machine to discriminate between substrate and product depending on the state of the machine—is far more important for determining the intrinsic directionality and thermodynamics of chemo-mechanical coupling than are the details of the internal mechanical conformational motions of the machine. In contrast to the conclusions for chemical driving, a power-stroke is very important for the directionality and efficiency of light-driven molecular machines and for molecular machines driven by external modulation of thermodynamic parameters. PMID:25606678

Quantification of uncertainty in machining operations for on-machine acceptance.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Claudet, Andre A.; Tran, Hy D.; Su, Jiann-Chemg

2008-09-01

Manufactured parts are designed with acceptance tolerances, i.e. deviations from ideal design conditions, due to unavoidable errors in the manufacturing process. It is necessary to measure and evaluate the manufactured part, compared to the nominal design, to determine whether the part meets design specifications. The scope of this research project is dimensional acceptance of machined parts; specifically, parts machined using numerically controlled (NC, or also CNC for Computer Numerically Controlled) machines. In the design/build/accept cycle, the designer will specify both a nominal value, and an acceptable tolerance. As part of the typical design/build/accept business practice, it is required to verifymore » that the part did meet acceptable values prior to acceptance. Manufacturing cost must include not only raw materials and added labor, but also the cost of ensuring conformance to specifications. Ensuring conformance is a substantial portion of the cost of manufacturing. In this project, the costs of measurements were approximately 50% of the cost of the machined part. In production, cost of measurement would be smaller, but still a substantial proportion of manufacturing cost. The results of this research project will point to a science-based approach to reducing the cost of ensuring conformance to specifications. The approach that we take is to determine, a priori, how well a CNC machine can manufacture a particular geometry from stock. Based on the knowledge of the manufacturing process, we are then able to decide features which need further measurements from features which can be accepted 'as is' from the CNC. By calibration of the machine tool, and establishing a machining accuracy ratio, we can validate the ability of CNC to fabricate to a particular level of tolerance. This will eliminate the costs of checking for conformance for relatively large tolerances.« less

Hydraulic Fatigue-Testing Machine

NASA Technical Reports Server (NTRS)

Hodo, James D.; Moore, Dennis R.; Morris, Thomas F.; Tiller, Newton G.

1987-01-01

Fatigue-testing machine applies fluctuating tension to number of specimens at same time. When sample breaks, machine continues to test remaining specimens. Series of tensile tests needed to determine fatigue properties of materials performed more rapidly than in conventional fatigue-testing machine.

Three-dimensional eddy current solution of a polyphase machine test model (abstract)

NASA Astrophysics Data System (ADS)

Pahner, Uwe; Belmans, Ronnie; Ostovic, Vlado

1994-05-01

This abstract describes a three-dimensional (3D) finite element solution of a test model that has been reported in the literature. The model is a basis for calculating the current redistribution effects in the end windings of turbogenerators. The aim of the study is to see whether the analytical results of the test model can be found using a general purpose finite element package, thus indicating that the finite element model is accurate enough to treat real end winding problems. The real end winding problems cannot be solved analytically, as the geometry is far too complicated. The model consists of a polyphase coil set, containing 44 individual coils. This set generates a two pole mmf distribution on a cylindrical surface. The rotating field causes eddy currents to flow in the inner massive and conducting rotor. In the analytical solution a perfect sinusoidal mmf distribution is put forward. The finite element model contains 85824 tetrahedra and 16451 nodes. A complex single scalar potential representation is used in the nonconducting parts. The computation time required was 3 h and 42 min. The flux plots show that the field distribution is acceptable. Furthermore, the induced currents are calculated and compared with the values found from the analytical solution. The distribution of the eddy currents is very close to the distribution of the analytical solution. The most important results are the losses, both local and global. The value of the overall losses is less than 2% away from those of the analytical solution. Also the local distribution of the losses is at any given point less than 7% away from the analytical solution. The deviations of the results are acceptable and are partially due to the fact that the sinusoidal mmf distribution was not modeled perfectly in the finite element method.

Probability machines: consistent probability estimation using nonparametric learning machines.

PubMed

Malley, J D; Kruppa, J; Dasgupta, A; Malley, K G; Ziegler, A

2012-01-01

Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.

Machine vision based quality inspection of flat glass products

NASA Astrophysics Data System (ADS)

Zauner, G.; Schagerl, M.

2014-03-01

This application paper presents a machine vision solution for the quality inspection of flat glass products. A contact image sensor (CIS) is used to generate digital images of the glass surfaces. The presented machine vision based quality inspection at the end of the production line aims to classify five different glass defect types. The defect images are usually characterized by very little `image structure', i.e. homogeneous regions without distinct image texture. Additionally, these defect images usually consist of only a few pixels. At the same time the appearance of certain defect classes can be very diverse (e.g. water drops). We used simple state-of-the-art image features like histogram-based features (std. deviation, curtosis, skewness), geometric features (form factor/elongation, eccentricity, Hu-moments) and texture features (grey level run length matrix, co-occurrence matrix) to extract defect information. The main contribution of this work now lies in the systematic evaluation of various machine learning algorithms to identify appropriate classification approaches for this specific class of images. In this way, the following machine learning algorithms were compared: decision tree (J48), random forest, JRip rules, naive Bayes, Support Vector Machine (multi class), neural network (multilayer perceptron) and k-Nearest Neighbour. We used a representative image database of 2300 defect images and applied cross validation for evaluation purposes.

Investigations on high speed machining of EN-353 steel alloy under different machining environments

NASA Astrophysics Data System (ADS)

Venkata Vishnu, A.; Jamaleswara Kumar, P.

2018-03-01

The addition of Nano Particles into conventional cutting fluids enhances its cooling capabilities; in the present paper an attempt is made by adding nano sized particles into conventional cutting fluids. Taguchi Robust Design Methodology is employed in order to study the performance characteristics of different turning parameters i.e. cutting speed, feed rate, depth of cut and type of tool under different machining environments i.e. dry machining, machining with lubricant - SAE 40 and machining with mixture of nano sized particles of Boric acid and base fluid SAE 40. A series of turning operations were performed using L27 (3)13 orthogonal array, considering high cutting speeds and the other machining parameters to measure hardness. The results are compared among the different machining environments, and it is concluded that there is considerable improvement in the machining performance using lubricant SAE 40 and mixture of SAE 40 + boric acid compared with dry machining. The ANOVA suggests that the selected parameters and the interactions are significant and cutting speed has most significant effect on hardness.

Asnuntuck Community College's Machine Technology Certificate and Degree Programs.

ERIC Educational Resources Information Center

Irlen, Harvey S.; Gulluni, Frank D.

2002-01-01

States that although manufacturing remains a viable sector in Connecticut, it is experiencing skills shortages in the workforce. Describes the machine technology program's purpose, the development of the Asnuntuck Community College's (Connecticut) partnership with private sector manufacturers, the curriculum, the outcomes, and benefits of…

Identifying saltcedar with hyperspectral data and support vector machines

USDA-ARS?s Scientific Manuscript database

Saltcedar (Tamarix spp.) are a group of dense phreatophytic shrubs and trees that are invasive to riparian areas throughout the United States. This study determined the feasibility of using hyperspectral data and a support vector machine (SVM) classifier to discriminate saltcedar from other cover t...

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 08)

NASA Technical Reports Server (NTRS)

1976-01-01

This bibliography is issued in two sections; abstracts and indexes. The Abstract Section cites 180 patents and applications for patents introduced into the NASA scientific and technical information system during the period of July 1975 through December 1975. Each entry in the Abstract Section consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or application for patent. The index Section contains entries for 2,905 patents and applications for patent citations covering the period May 1969 through December 1975. The Index Section contains five indexes -- subject, inventor, source, number and accession number.

Electric machine

DOEpatents

El-Refaie, Ayman Mohamed Fawzi [Niskayuna, NY; Reddy, Patel Bhageerath [Madison, WI

2012-07-17

An interior permanent magnet electric machine is disclosed. The interior permanent magnet electric machine comprises a rotor comprising a plurality of radially placed magnets each having a proximal end and a distal end, wherein each magnet comprises a plurality of magnetic segments and at least one magnetic segment towards the distal end comprises a high resistivity magnetic material.

ERECTING/MACHINE SHOP, CRANE ACCESS GANGWAY BETWEEN ERECTING (L) AND MACHINE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

ERECTING/MACHINE SHOP, CRANE ACCESS GANGWAY BETWEEN ERECTING (L) AND MACHINE (R) SHOPS, LOOKING NORTH. - Southern Pacific, Sacramento Shops, Erecting Shop, 111 I Street, Sacramento, Sacramento County, CA

Ada Compiler Validation Summary Report: Certificate Number 880318W1. 09042, International Business Machines Corporation, IBM Development System for the Ada Language, Version 2.1.0, IBM 4381 under MVS/XA, Host and Target

DTIC Science & Technology

1988-03-28

International Business Machines Corporation IBM Development System for the Ada Language, Version 2.1.0 IBM 4381 under MVS/XA, host and target Completion...Joint Program Office, AJPO 20. ABSTRACT (Continue on reverse side if necessary and identify by block number) International Business Machines Corporation...in the compiler listed in this declaration. I declare that International Business Machines Corporation is the owner of record of the object code of

Machine Learning and Radiology

PubMed Central

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

Quadrilateral Micro-Hole Array Machining on Invar Thin Film: Wet Etching and Electrochemical Fusion Machining

PubMed Central

Choi, Woong-Kirl; Kim, Seong-Hyun; Choi, Seung-Geon; Lee, Eun-Sang

2018-01-01

Ultra-precision products which contain a micro-hole array have recently shown remarkable demand growth in many fields, especially in the semiconductor and display industries. Photoresist etching and electrochemical machining are widely known as precision methods for machining micro-holes with no residual stress and lower surface roughness on the fabricated products. The Invar shadow masks used for organic light-emitting diodes (OLEDs) contain numerous micro-holes and are currently machined by a photoresist etching method. However, this method has several problems, such as uncontrollable hole machining accuracy, non-etched areas, and overcutting. To solve these problems, a machining method that combines photoresist etching and electrochemical machining can be applied. In this study, negative photoresist with a quadrilateral hole array pattern was dry coated onto 30-µm-thick Invar thin film, and then exposure and development were carried out. After that, photoresist single-side wet etching and a fusion method of wet etching-electrochemical machining were used to machine micro-holes on the Invar. The hole machining geometry, surface quality, and overcutting characteristics of the methods were studied. Wet etching and electrochemical fusion machining can improve the accuracy and surface quality. The overcutting phenomenon can also be controlled by the fusion machining. Experimental results show that the proposed method is promising for the fabrication of Invar film shadow masks. PMID:29351235

Apprentice Machine Theory Outline.

ERIC Educational Resources Information Center

Connecticut State Dept. of Education, Hartford. Div. of Vocational-Technical Schools.

This volume contains outlines for 16 courses in machine theory that are designed for machine tool apprentices. Addressed in the individual course outlines are the following topics: basic concepts; lathes; milling machines; drills, saws, and shapers; heat treatment and metallurgy; grinders; quality control; hydraulics and pneumatics;…

Performance evaluation of the croissant production line with reparable machines

NASA Astrophysics Data System (ADS)

Tsarouhas, Panagiotis H.

2015-03-01

In this study, the analytical probability models for an automated serial production system, bufferless that consists of n-machines in series with common transfer mechanism and control system was developed. Both time to failure and time to repair a failure are assumed to follow exponential distribution. Applying those models, the effect of system parameters on system performance in actual croissant production line was studied. The production line consists of six workstations with different numbers of reparable machines in series. Mathematical models of the croissant production line have been developed using Markov process. The strength of this study is in the classification of the whole system in states, representing failures of different machines. Failure and repair data from the actual production environment have been used to estimate reliability and maintainability for each machine, workstation, and the entire line is based on analytical models. The analysis provides a useful insight into the system's behaviour, helps to find design inherent faults and suggests optimal modifications to upgrade the system and improve its performance.

Data Mining and Machine Learning in Astronomy

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Brunner, Robert J.

We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those in which data mining techniques directly contributed to improving science, and important current and future directions, including probability density functions, parallel algorithms, Peta-Scale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.

Semantic closure demonstrated by the evolution of a universal constructor architecture in an artificial chemistry.

PubMed

Clark, Edward B; Hickinbotham, Simon J; Stepney, Susan

2017-05-01

We present a novel stringmol-based artificial chemistry system modelled on the universal constructor architecture (UCA) first explored by von Neumann. In a UCA, machines interact with an abstract description of themselves to replicate by copying the abstract description and constructing the machines that the abstract description encodes. DNA-based replication follows this architecture, with DNA being the abstract description, the polymerase being the copier, and the ribosome being the principal machine in expressing what is encoded on the DNA. This architecture is semantically closed as the machine that defines what the abstract description means is itself encoded on that abstract description. We present a series of experiments with the stringmol UCA that show the evolution of the meaning of genomic material, allowing the concept of semantic closure and transitions between semantically closed states to be elucidated in the light of concrete examples. We present results where, for the first time in an in silico system, simultaneous evolution of the genomic material, copier and constructor of a UCA, giving rise to viable offspring. © 2017 The Author(s).

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

NASA Astrophysics Data System (ADS)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

Context recognition for a hyperintensional inference machine

NASA Astrophysics Data System (ADS)

Duží, Marie; Fait, Michal; Menšík, Marek

2017-07-01

The goal of this paper is to introduce the algorithm of context recognition in the functional programming language TIL-Script, which is a necessary condition for the implementation of the TIL-Script inference machine. The TIL-Script language is an operationally isomorphic syntactic variant of Tichý's Transparent Intensional Logic (TIL). From the formal point of view, TIL is a hyperintensional, partial, typed λ-calculus with procedural semantics. Hyperintensional, because TIL λ-terms denote procedures (defined as TIL constructions) producing set-theoretic functions rather than the functions themselves; partial, because TIL is a logic of partial functions; and typed, because all the entities of TIL ontology, including constructions, receive a type within a ramified hierarchy of types. These features make it possible to distinguish three levels of abstraction at which TIL constructions operate. At the highest hyperintensional level the object to operate on is a construction (though a higher-order construction is needed to present this lower-order construction as an object of predication). At the middle intensional level the object to operate on is the function presented, or constructed, by a construction, while at the lowest extensional level the object to operate on is the value (if any) of the presented function. Thus a necessary condition for the development of an inference machine for the TIL-Script language is recognizing a context in which a construction occurs, namely extensional, intensional and hyperintensional context, in order to determine the type of an argument at which a given inference rule can be properly applied. As a result, our logic does not flout logical rules of extensional logic, which makes it possible to develop a hyperintensional inference machine for the TIL-Script language.

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 07)

NASA Technical Reports Server (NTRS)

1975-01-01

This bibliography is issued in two sections: Section 1 - Abstracts, and Section 2 - Indexes. This issue of the Abstract Section cites 158 patents and applications for patent introduced into the NASA scientific and technical information system during the period of January 1975 through June 1975. Each entry in the Abstract Section consists of a citation, an abstract, and, in most cases, a key illustration selected from the patent or application for patent. This issue of the Index Section contains entries for 2830 patent and application for patent citations covering the period May 1969 through June 1975. The index section contains five indexes -- subject, inventor, source, number and accession number.

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 09)

NASA Technical Reports Server (NTRS)

1976-01-01

This bibliography is issued in two sections: Section 1 - Abstracts, and Section 2 - Indexes. This issue of the Abstract Section cites 200 patents and applications for patent introduced into the NASA scientific and technical information system during the period of January 1976 through June 1976. Each entry in the Abstract Section consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or application for patent. This issue of the Index Section contains entries for 2994 patent and application for patent citations covering the period May 1969 through June 1976. The Index Section contains five indexes -- subject, inventor, source, number and accession number.

Big Data and machine learning in radiation oncology: State of the art and future prospects.

PubMed

Bibault, Jean-Emmanuel; Giraud, Philippe; Burgun, Anita

2016-11-01

Precision medicine relies on an increasing amount of heterogeneous data. Advances in radiation oncology, through the use of CT Scan, dosimetry and imaging performed before each fraction, have generated a considerable flow of data that needs to be integrated. In the same time, Electronic Health Records now provide phenotypic profiles of large cohorts of patients that could be correlated to this information. In this review, we describe methods that could be used to create integrative predictive models in radiation oncology. Potential uses of machine learning methods such as support vector machine, artificial neural networks, and deep learning are also discussed. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Diverse Effects, Complex Causes: Children Use Information about Machines' Functional Diversity to Infer Internal Complexity

ERIC Educational Resources Information Center

Ahl, Richard E.; Keil, Frank C.

2017-01-01

Four studies explored the abilities of 80 adults and 180 children (4-9 years), from predominantly middle-class families in the Northeastern United States, to use information about machines' observable functional capacities to infer their internal, "hidden" mechanistic complexity. Children as young as 4 and 5 years old used machines'…

Automated Verification of Specifications with Typestates and Access Permissions

NASA Technical Reports Server (NTRS)

Siminiceanu, Radu I.; Catano, Nestor

2011-01-01

We propose an approach to formally verify Plural specifications based on access permissions and typestates, by model-checking automatically generated abstract state-machines. Our exhaustive approach captures all the possible behaviors of abstract concurrent programs implementing the specification. We describe the formal methodology employed by our technique and provide an example as proof of concept for the state-machine construction rules. The implementation of a fully automated algorithm to generate and verify models, currently underway, provides model checking support for the Plural tool, which currently supports only program verification via data flow analysis (DFA).

Executable Architecture of Net Enabled Operations: State Machine of Federated Nodes

DTIC Science & Technology

2009-11-01

verbal descriptions from operators) of the current Command and Control (C2) practices into model form. In theory these should be Standard Operating...faudra une grande quantité de données pour faire en sorte que le modèle reflète les processus véritables, les auteurs recommandent que la machine à...descriptions from operators) of the current C2 practices into model form. In theory these should be SOPs that execute as a thread from start to finish. The

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 13)

NASA Technical Reports Server (NTRS)

1978-01-01

This bibliography is issued in two sections: Section 1 - Abstracts, and Section 2 - Indexes. This issue of the Abstract Section cites 161 patents and applications for patent introduced into the NASA scientific and technical information system during the period January 1978 through June 1978. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or application for patent.

RISMA: A Rule-based Interval State Machine Algorithm for Alerts Generation, Performance Analysis and Monitoring Real-Time Data Processing

NASA Astrophysics Data System (ADS)

Laban, Shaban; El-Desouky, Aly

2013-04-01

The monitoring of real-time systems is a challenging and complicated process. So, there is a continuous need to improve the monitoring process through the use of new intelligent techniques and algorithms for detecting exceptions, anomalous behaviours and generating the necessary alerts during the workflow monitoring of such systems. The interval-based or period-based theorems have been discussed, analysed, and used by many researches in Artificial Intelligence (AI), philosophy, and linguistics. As explained by Allen, there are 13 relations between any two intervals. Also, there have also been many studies of interval-based temporal reasoning and logics over the past decades. Interval-based theorems can be used for monitoring real-time interval-based data processing. However, increasing the number of processed intervals makes the implementation of such theorems a complex and time consuming process as the relationships between such intervals are increasing exponentially. To overcome the previous problem, this paper presents a Rule-based Interval State Machine Algorithm (RISMA) for processing, monitoring, and analysing the behaviour of interval-based data, received from real-time sensors. The proposed intelligent algorithm uses the Interval State Machine (ISM) approach to model any number of interval-based data into well-defined states as well as inferring them. An interval-based state transition model and methodology are presented to identify the relationships between the different states of the proposed algorithm. By using such model, the unlimited number of relationships between similar large numbers of intervals can be reduced to only 18 direct relationships using the proposed well-defined states. For testing the proposed algorithm, necessary inference rules and code have been designed and applied to the continuous data received in near real-time from the stations of International Monitoring System (IMS) by the International Data Centre (IDC) of the Preparatory

Machine Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networksmore » and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.« less

Measurement-induced operation of two-ion quantum heat machines

NASA Astrophysics Data System (ADS)

Chand, Suman; Biswas, Asoka

2017-03-01

We show how one can implement a quantum heat machine by using two interacting trapped ions, in presence of a thermal bath. The electronic states of the ions act like a working substance, while the vibrational mode is modelled as the cold bath. The heat exchange with the cold bath is mimicked by the projective measurement of the electronic states. We show how such measurement in a suitable basis can lead to either a quantum heat engine or a refrigerator, which undergoes a quantum Otto cycle. The local magnetic field is adiabatically changed during the heat cycle. The performance of the heat machine depends upon the interaction strength between the ions, the magnetic fields, and the measurement cost. In our model, the coupling to the hot and the cold baths is never switched off in an alternative fashion during the heat cycle, unlike other existing proposals of quantum heat engines. This makes our proposal experimentally realizable using current tapped-ion technology.

Measurement-induced operation of two-ion quantum heat machines.

PubMed

Chand, Suman; Biswas, Asoka

2017-03-01

We show how one can implement a quantum heat machine by using two interacting trapped ions, in presence of a thermal bath. The electronic states of the ions act like a working substance, while the vibrational mode is modelled as the cold bath. The heat exchange with the cold bath is mimicked by the projective measurement of the electronic states. We show how such measurement in a suitable basis can lead to either a quantum heat engine or a refrigerator, which undergoes a quantum Otto cycle. The local magnetic field is adiabatically changed during the heat cycle. The performance of the heat machine depends upon the interaction strength between the ions, the magnetic fields, and the measurement cost. In our model, the coupling to the hot and the cold baths is never switched off in an alternative fashion during the heat cycle, unlike other existing proposals of quantum heat engines. This makes our proposal experimentally realizable using current tapped-ion technology.

A Comparison of Machine Learning Approaches for Corn Yield Estimation

NASA Astrophysics Data System (ADS)

Kim, N.; Lee, Y. W.

2017-12-01

Machine learning is an efficient empirical method for classification and prediction, and it is another approach to crop yield estimation. The objective of this study is to estimate corn yield in the Midwestern United States by employing the machine learning approaches such as the support vector machine (SVM), random forest (RF), and deep neural networks (DNN), and to perform the comprehensive comparison for their results. We constructed the database using satellite images from MODIS, the climate data of PRISM climate group, and GLDAS soil moisture data. In addition, to examine the seasonal sensitivities of corn yields, two period groups were set up: May to September (MJJAS) and July and August (JA). In overall, the DNN showed the highest accuracies in term of the correlation coefficient for the two period groups. The differences between our predictions and USDA yield statistics were about 10-11 %.

Business and Organizational Communication: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," July through December 1981 (Vol. 42 Nos. 1 through 6).

ERIC Educational Resources Information Center

ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 11 titles deal with the following topics: (1) transnational communication knowledge transfer and utilization, especially between the United States and Asian countries; (2) police personnel evaluation procedures as an aspect of…

Machine vision systems using machine learning for industrial product inspection

NASA Astrophysics Data System (ADS)

Lu, Yi; Chen, Tie Q.; Chen, Jie; Zhang, Jian; Tisler, Anthony

2002-02-01

Machine vision inspection requires efficient processing time and accurate results. In this paper, we present a machine vision inspection architecture, SMV (Smart Machine Vision). SMV decomposes a machine vision inspection problem into two stages, Learning Inspection Features (LIF), and On-Line Inspection (OLI). The LIF is designed to learn visual inspection features from design data and/or from inspection products. During the OLI stage, the inspection system uses the knowledge learnt by the LIF component to inspect the visual features of products. In this paper we will present two machine vision inspection systems developed under the SMV architecture for two different types of products, Printed Circuit Board (PCB) and Vacuum Florescent Displaying (VFD) boards. In the VFD board inspection system, the LIF component learns inspection features from a VFD board and its displaying patterns. In the PCB board inspection system, the LIF learns the inspection features from the CAD file of a PCB board. In both systems, the LIF component also incorporates interactive learning to make the inspection system more powerful and efficient. The VFD system has been deployed successfully in three different manufacturing companies and the PCB inspection system is the process of being deployed in a manufacturing plant.

Walking Machine Control Programming

DTIC Science & Technology

1983-08-31

configuration is useful for two reasons: first, the machine won’t fit through the garage door unless it is in the tuck position, and second, a principal way...machine out of its garage . ’We call the garage a "laboratory" even though the shorter term is more apt.- We regularly run the machine in the parking...comes down from a high push-up. The natural position for the feet as the machine comes out of the garage is the "tuck" in which each knee is bent in as

Specification and Analysis of Parallel Machine Architecture

DTIC Science & Technology

1990-03-17

Parallel Machine Architeture C.V. Ramamoorthy Computer Science Division Dept. of Electrical Engineering and Computer Science University of California...capacity. (4) Adaptive: The overhead in resolution of deadlocks, etc. should be in proportion to their frequency. (5) Avoid rollbacks: Rollbacks can be...snapshots of system state graphically at a rate proportional to simulation time. Some of the examples are as follow: (1) When the simulation clock of

73. INTERIOR VIEW OF MACHINE SHOP LOOKING EAST, NOTE THE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

73. INTERIOR VIEW OF MACHINE SHOP LOOKING EAST, NOTE THE MAIN DRIVE SHAFT ON THE CEILING AND DRIVE BELTS TO THE MACHINERY. MAY 8, 1919. - United States Nitrate Plant No. 2, Reservation Road, Muscle Shoals, Muscle Shoals, Colbert County, AL

Machine learning and radiology.

PubMed

Wang, Shijun; Summers, Ronald M

2012-07-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

Distribution of quantum Fisher information in asymmetric cloning machines

PubMed Central

Xiao, Xing; Yao, Yao; Zhou, Lei-Ming; Wang, Xiaoguang

2014-01-01

An unknown quantum state cannot be copied and broadcast freely due to the no-cloning theorem. Approximate cloning schemes have been proposed to achieve the optimal cloning characterized by the maximal fidelity between the original and its copies. Here, from the perspective of quantum Fisher information (QFI), we investigate the distribution of QFI in asymmetric cloning machines which produce two nonidentical copies. As one might expect, improving the QFI of one copy results in decreasing the QFI of the other copy. It is perhaps also unsurprising that asymmetric phase-covariant cloning outperforms universal cloning in distributing QFI since a priori information of the input state has been utilized. However, interesting results appear when we compare the distributabilities of fidelity (which quantifies the full information of quantum states), and QFI (which only captures the information of relevant parameters) in asymmetric cloning machines. Unlike the results of fidelity, where the distributability of symmetric cloning is always optimal for any d-dimensional cloning, we find that any asymmetric cloning outperforms symmetric cloning on the distribution of QFI for d ≤ 18, whereas some but not all asymmetric cloning strategies could be worse than symmetric ones when d > 18. PMID:25484234

Vane Pump Casing Machining of Dumpling Machine Based on CAD/CAM

NASA Astrophysics Data System (ADS)

Huang, Yusen; Li, Shilong; Li, Chengcheng; Yang, Zhen

Automatic dumpling forming machine is also called dumpling machine, which makes dumplings through mechanical motions. This paper adopts the stuffing delivery mechanism featuring the improved and specially-designed vane pump casing, which can contribute to the formation of dumplings. Its 3D modeling in Pro/E software, machining process planning, milling path optimization, simulation based on UG and compiling post program were introduced and verified. The results indicated that adoption of CAD/CAM offers firms the potential to pursue new innovative strategies.

The Knife Machine. Module 15.

ERIC Educational Resources Information Center

South Carolina State Dept. of Education, Columbia. Office of Vocational Education.

This module on the knife machine, one in a series dealing with industrial sewing machines, their attachments, and operation, covers one topic: performing special operations on the knife machine (a single needle or multi-needle machine which sews and cuts at the same time). These components are provided: an introduction, directions, an objective,…

Paper Abstract Animals

ERIC Educational Resources Information Center

Sutley, Jane

2010-01-01

Abstraction is, in effect, a simplification and reduction of shapes with an absence of detail designed to comprise the essence of the more naturalistic images being depicted. Without even intending to, young children consistently create interesting, and sometimes beautiful, abstract compositions. A child's creations, moreover, will always seem to…

Fiber tractography using machine learning.

PubMed

Neher, Peter F; Côté, Marc-Alexandre; Houde, Jean-Christophe; Descoteaux, Maxime; Maier-Hein, Klaus H

2017-09-01

We present a fiber tractography approach based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw diffusion-weighted signal intensities. For comparison to the state-of-the-art, i.e. tractography pipelines that rely on mathematical modeling, we performed a quantitative and qualitative evaluation with multiple phantom and in vivo experiments, including a comparison to the 96 submissions of the ISMRM tractography challenge 2015. The results demonstrate the vast potential of machine learning for fiber tractography. Copyright © 2017 Elsevier Inc. All rights reserved.

Electrochemical micro/nano-machining: principles and practices.

PubMed

Zhan, Dongping; Han, Lianhuan; Zhang, Jie; He, Quanfeng; Tian, Zhao-Wu; Tian, Zhong-Qun

2017-03-06

Micro/nano-machining (MNM) is becoming the cutting-edge of high-tech manufacturing because of the increasing industrial demand for supersmooth surfaces and functional three-dimensional micro/nano-structures (3D-MNS) in ultra-large scale integrated circuits, microelectromechanical systems, miniaturized total analysis systems, precision optics, and so on. Taking advantage of no tool wear, no surface stress, environmental friendliness, simple operation, and low cost, electrochemical micro/nano-machining (EC-MNM) has an irreplaceable role in MNM. This comprehensive review presents the state-of-art of EC-MNM techniques for direct writing, surface planarization and polishing, and 3D-MNS fabrications. The key point of EC-MNM is to confine electrochemical reactions at the micro/nano-meter scale. This review will bring together various solutions to "confined reaction" ranging from electrochemical principles through technical characteristics to relevant applications.

Machine Learning in the Big Data Era: Are We There Yet?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sukumar, Sreenivas Rangan

In this paper, we discuss the machine learning challenges of the Big Data era. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical machine learning under more scrutiny and evaluation for gleaning insights from the data than ever before. In that context, we pose and debate the question - Are machine learning algorithms scaling with the ability to store and compute? If yes, how? If not, why not? We survey recent developments in the state-of-the-art to discuss emerging and outstandingmore » challenges in the design and implementation of machine learning algorithms at scale. We leverage experience from real-world Big Data knowledge discovery projects across domains of national security and healthcare to suggest our efforts be focused along the following axes: (i) the data science challenge - designing scalable and flexible computational architectures for machine learning (beyond just data-retrieval); (ii) the science of data challenge the ability to understand characteristics of data before applying machine learning algorithms and tools; and (iii) the scalable predictive functions challenge the ability to construct, learn and infer with increasing sample size, dimensionality, and categories of labels. We conclude with a discussion of opportunities and directions for future research.« less

Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

PubMed Central

Karisani, Payam; Qin, Zhaohui S; Agichtein, Eugene

2018-01-01

Abstract The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie PMID:29688379

Understanding of anesthesia machine function is enhanced with a transparent reality simulation.

PubMed

Fischler, Ira S; Kaschub, Cynthia E; Lizdas, David E; Lampotang, Samsun

2008-01-01

Photorealistic simulations may provide efficient transfer of certain skills to the real system, but by being opaque may fail to encourage deeper learning of the structure and function of the system. Schematic simulations that are more abstract, with less visual fidelity but make system structure and function transparent, may enhance deeper learning and optimize retention and transfer of learning. We compared learning effectiveness of these 2 modes of externalizing the output of a common simulation engine (the Virtual Anesthesia Machine, VAM) that models machine function and dynamics and responds in real time to user interventions such as changes in gas flow or ventilation. Undergraduate students (n = 39) and medical students (n = 35) were given a single, 1-hour guided learning session with either a Transparent or an Opaque version of the VAM simulation. The following day, the learners' knowledge of machine components, function, and dynamics was tested. The Transparent-VAM groups scored higher than the Opaque-VAM groups on a set of multiple-choice questions concerning conceptual knowledge about anesthesia machines (P = 0.009), provided better and more complete explanations of component function (P = 0.003), and were more accurate in remembering and inferring cause-and-effect dynamics of the machine and relations among components (P = 0.003). Although the medical students outperformed undergraduates on all measures, a similar pattern of benefits for the Transparent VAM was observed for these 2 groups. Schematic simulations that transparently allow learners to visualize, and explore, underlying system dynamics and relations among components may provide a more effective mental model for certain systems. This may lead to a deeper understanding of how the system works, and therefore, we believe, how to detect and respond to potentially adverse situations.

Investigation of Machine-ability of Inconel 800 in EDM with Coated Electrode

NASA Astrophysics Data System (ADS)

Karunakaran, K.; Chandrasekaran, M.

2017-03-01

The Inconel 800 is a high temperature application alloy which is classified as a nickel based super alloy. It has wide scope in aerospace engineering, gas Turbine etc. The machine-ability studies were found limited on this material. Hence This research focuses on machine-ability studies on EDM of Inconel 800 with Silver Coated Electrolyte Copper Electrode. The purpose of coating on electrode is to reduce tool wear. The factors pulse on Time, Pulse off Time and Peck Current were considered to observe the responses of surface roughness, material removal rate, tool wear rate. Taguchi Full Factorial Design is employed for Design the experiment. Some specific findings were reported and the percentage of contribution of each parameter was furnished

ASTRONAUTICS INFORMATION. Abstracts Vol. III, No. 1. Abstracts 3,082- 3,184

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

1961-01-01

Abstracts are presented on astronautics. The abstracts are generally restricted to spaceflight and to applicable techniques and data. The publication covers the period of January 1961. 102 references. (J.R.D.)

An abstract specification language for Markov reliability models

NASA Technical Reports Server (NTRS)

Butler, R. W.

1985-01-01

Markov models can be used to compute the reliability of virtually any fault tolerant system. However, the process of delineating all of the states and transitions in a model of complex system can be devastatingly tedious and error-prone. An approach to this problem is presented utilizing an abstract model definition language. This high level language is described in a nonformal manner and illustrated by example.

An abstract language for specifying Markov reliability models

NASA Technical Reports Server (NTRS)

Butler, Ricky W.

1986-01-01

Markov models can be used to compute the reliability of virtually any fault tolerant system. However, the process of delineating all of the states and transitions in a model of complex system can be devastatingly tedious and error-prone. An approach to this problem is presented utilizing an abstract model definition language. This high level language is described in a nonformal manner and illustrated by example.

Professional Preparation of Teachers for Rural Schools: Abstracts of Addresses Delivered at a Conference Called by the United States Commissioner of Education, at the Lenox Hotel, Boston, February 25, 1928. Bulletin, 1928, No. 6

ERIC Educational Resources Information Center

Cook, Katherine M.

1928-01-01

This bulletin contains abstracts of the addresses delivered at a conference called by the United States Commissioner of Education to consider problems concerned with the professional preparation of teachers for rural schools. They were prepared from copies of the addresses or abstracts of them furnished by the speakers who prepared or delivered…

[Comparison of machinability of two types of dental machinable ceramic].

PubMed

Fu, Qiang; Zhao, Yunfeng; Li, Yong; Fan, Xinping; Li, Yan; Lin, Xuefeng

2002-11-01

In terms of the problems of now available dental machinable ceramics, a new type of calcium-mica glass-ceramic, PMC-I ceramic, was developed, and its machinability was compared with that of Vita MKII quantitatively. Moreover, the relationship between the strength and the machinability of PMC-I ceramic was studied. Samples of PMC-I ceramic were divided into four groups according to their nucleation procedures. 600-seconds drilling tests were conducted with high-speed steel tools (Phi = 2.3 mm) to measure the drilling depths of Vita MKII ceramic and PMC-I ceramic, while constant drilling speed of 600 rpm and constant axial load of 39.2 N were used. And the 3-point bending strength of the four groups of PMC-I ceramic were recorded. Drilling depth of Vita MKII was 0.71 mm, while the depths of the four groups of PMC-I ceramic were 0.88 mm, 1.40 mm, 0.40 mm and 0.90 mm, respectively. Group B of PMC-I ceramic showed the largest depth of 1.40 mm and was statistically different from other groups and Vita MKII. And the strength of the four groups of PMC-I ceramic were 137.7, 210.2, 118.0 and 106.0 MPa, respectively. The machinability of the new developed dental machinable ceramic of PMC-I could meet the need of the clinic.

Automatic soldering machine

NASA Technical Reports Server (NTRS)

Stein, J. A.

1974-01-01

Fully-automatic tube-joint soldering machine can be used to make leakproof joints in aluminum tubes of 3/16 to 2 in. in diameter. Machine consists of temperature-control unit, heater transformer and heater head, vibrator, and associated circuitry controls, and indicators.

Rhetoric and Public Address: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," January through June 1982 (Vol. 42 Nos. 7 through 12).

ERIC Educational Resources Information Center

ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 14 titles deal with the following topics: (1) the rhetoric of British Prime Minister Margaret Thatcher; (2) the rhetorical strategies employed in the political speaking of George C. Wallace in the 1968 United States presidential…

Significant improvements of electrical discharge machining performance by step-by-step updated adaptive control laws

NASA Astrophysics Data System (ADS)

Zhou, Ming; Wu, Jianyang; Xu, Xiaoyi; Mu, Xin; Dou, Yunping

2018-02-01

In order to obtain improved electrical discharge machining (EDM) performance, we have dedicated more than a decade to correcting one essential EDM defect, the weak stability of the machining, by developing adaptive control systems. The instabilities of machining are mainly caused by complicated disturbances in discharging. To counteract the effects from the disturbances on machining, we theoretically developed three control laws from minimum variance (MV) control law to minimum variance and pole placements coupled (MVPPC) control law and then to a two-step-ahead prediction (TP) control law. Based on real-time estimation of EDM process model parameters and measured ratio of arcing pulses which is also called gap state, electrode discharging cycle was directly and adaptively tuned so that a stable machining could be achieved. To this end, we not only theoretically provide three proved control laws for a developed EDM adaptive control system, but also practically proved the TP control law to be the best in dealing with machining instability and machining efficiency though the MVPPC control law provided much better EDM performance than the MV control law. It was also shown that the TP control law also provided a burn free machining.

The Security of Machine Learning

DTIC Science & Technology

2008-04-24

Machine learning has become a fundamental tool for computer security, since it can rapidly evolve to changing and complex situations. That...adaptability is also a vulnerability: attackers can exploit machine learning systems. We present a taxonomy identifying and analyzing attacks against machine ...We use our framework to survey and analyze the literature of attacks against machine learning systems. We also illustrate our taxonomy by showing

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

PubMed

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

Findings from the National Machine Guarding Program–A Small Business Intervention: Machine Safety

PubMed Central

Yamin, Samuel C.; Xi, Min; Brosseau, Lisa M.; Gordon, Robert; Most, Ivan G.; Stanley, Rodney

2016-01-01

Objectives The purpose of this nationwide intervention was to improve machine safety in small metal fabrication businesses (3 – 150 employees). The failure to implement machine safety programs related to guarding and lockout/tagout (LOTO) are frequent causes of OSHA citations and may result in serious traumatic injury. Methods Insurance safety consultants conducted a standardized evaluation of machine guarding, safety programs, and LOTO. Businesses received a baseline evaluation, two intervention visits and a twelve-month follow-up evaluation. Results The intervention was completed by 160 businesses. Adding a safety committee was associated with a 10-percentage point increase in business-level machine scores (p< 0.0001) and a 33-percentage point increase in LOTO program scores (p <0.0001). Conclusions Insurance safety consultants proved effective at disseminating a machine safety and LOTO intervention via management-employee safety committees. PMID:26716850

Findings From the National Machine Guarding Program-A Small Business Intervention: Machine Safety.

PubMed

Parker, David L; Yamin, Samuel C; Xi, Min; Brosseau, Lisa M; Gordon, Robert; Most, Ivan G; Stanley, Rodney

2016-09-01

The purpose of this nationwide intervention was to improve machine safety in small metal fabrication businesses (3 to 150 employees). The failure to implement machine safety programs related to guarding and lockout/tagout (LOTO) are frequent causes of Occupational Safety and Health Administration (OSHA) citations and may result in serious traumatic injury. Insurance safety consultants conducted a standardized evaluation of machine guarding, safety programs, and LOTO. Businesses received a baseline evaluation, two intervention visits, and a 12-month follow-up evaluation. The intervention was completed by 160 businesses. Adding a safety committee was associated with a 10% point increase in business-level machine scores (P < 0.0001) and a 33% point increase in LOTO program scores (P < 0.0001). Insurance safety consultants proved effective at disseminating a machine safety and LOTO intervention via management-employee safety committees.

Analysis and design of asymmetrical reluctance machine

NASA Astrophysics Data System (ADS)

Harianto, Cahya A.

Over the past few decades the induction machine has been chosen for many applications due to its structural simplicity and low manufacturing cost. However, modest torque density and control challenges have motivated researchers to find alternative machines. The permanent magnet synchronous machine has been viewed as one of the alternatives because it features higher torque density for a given loss than the induction machine. However, the assembly and permanent magnet material cost, along with safety under fault conditions, have been concerns for this class of machine. An alternative machine type, namely the asymmetrical reluctance machine, is proposed in this work. Since the proposed machine is of the reluctance machine type, it possesses desirable feature, such as near absence of rotor losses, low assembly cost, low no-load rotational losses, modest torque ripple, and rather benign fault conditions. Through theoretical analysis performed herein, it is shown that this machine has a higher torque density for a given loss than typical reluctance machines, although not as high as the permanent magnet machines. Thus, the asymmetrical reluctance machine is a viable and advantageous machine alternative where the use of permanent magnet machines are undesirable.

ASTRONAUTICS INFORMATION. ABSTRACTS, VOL. V, NO. 3. Abstracts 5,201- 5,330

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hardgrove, B.J.; Warren, F.L. comps.

1962-03-01

Abstracts of astronautics information covering the period March 1962 are presented. The 129 abstracts cover the subject of spaceflight and applicable data and techniques. Author, subject, and source indexes are included. (M.C.G.)

Mississippi Curriculum Framework for Machine Tool Operation/Machine Shop (Program CIP: 48.0503--Machine Shop Assistant). Secondary Programs.

ERIC Educational Resources Information Center

Mississippi Research and Curriculum Unit for Vocational and Technical Education, State College.

This document, which reflects Mississippi's statutory requirement that instructional programs be based on core curricula and performance-based assessment, contains outlines of the instructional units required in local instructional management plans and daily lesson plans for machine tool operation/machine shop I and II. Presented first are a…

Machine Tool Software

NASA Technical Reports Server (NTRS)

1988-01-01

A NASA-developed software package has played a part in technical education of students who major in Mechanical Engineering Technology at William Rainey Harper College. Professor Hack has been using (APT) Automatically Programmed Tool Software since 1969 in his CAD/CAM Computer Aided Design and Manufacturing curriculum. Professor Hack teaches the use of APT programming languages for control of metal cutting machines. Machine tool instructions are geometry definitions written in APT Language to constitute a "part program." The part program is processed by the machine tool. CAD/CAM students go from writing a program to cutting steel in the course of a semester.

Nanocomposites for Machining Tools

PubMed Central

Loginov, Pavel; Mishnaevsky, Leon; Levashov, Evgeny

2017-01-01

Machining tools are used in many areas of production. To a considerable extent, the performance characteristics of the tools determine the quality and cost of obtained products. The main materials used for producing machining tools are steel, cemented carbides, ceramics and superhard materials. A promising way to improve the performance characteristics of these materials is to design new nanocomposites based on them. The application of micromechanical modeling during the elaboration of composite materials for machining tools can reduce the financial and time costs for development of new tools, with enhanced performance. This article reviews the main groups of nanocomposites for machining tools and their performance. PMID:29027926

Progress in machine consciousness.

PubMed

Gamez, David

2008-09-01

This paper is a review of the work that has been carried out on machine consciousness. A clear overview of this diverse field is achieved by breaking machine consciousness down into four different areas, which are used to understand its aims, discuss its relationship with other subjects and outline the work that has been carried out so far. The criticisms that have been made against machine consciousness are also covered, along with its potential benefits, and the work that has been done on analysing systems for signs of consciousness. Some of the social and ethical issues raised by machine consciousness are examined at the end of the paper.

Arbitrary norm support vector machines.

PubMed

Huang, Kaizhu; Zheng, Danian; King, Irwin; Lyu, Michael R

2009-02-01

Support vector machines (SVM) are state-of-the-art classifiers. Typically L2-norm or L1-norm is adopted as a regularization term in SVMs, while other norm-based SVMs, for example, the L0-norm SVM or even the L(infinity)-norm SVM, are rarely seen in the literature. The major reason is that L0-norm describes a discontinuous and nonconvex term, leading to a combinatorially NP-hard optimization problem. In this letter, motivated by Bayesian learning, we propose a novel framework that can implement arbitrary norm-based SVMs in polynomial time. One significant feature of this framework is that only a sequence of sequential minimal optimization problems needs to be solved, thus making it practical in many real applications. The proposed framework is important in the sense that Bayesian priors can be efficiently plugged into most learning methods without knowing the explicit form. Hence, this builds a connection between Bayesian learning and the kernel machines. We derive the theoretical framework, demonstrate how our approach works on the L0-norm SVM as a typical example, and perform a series of experiments to validate its advantages. Experimental results on nine benchmark data sets are very encouraging. The implemented L0-norm is competitive with or even better than the standard L2-norm SVM in terms of accuracy but with a reduced number of support vectors, -9.46% of the number on average. When compared with another sparse model, the relevance vector machine, our proposed algorithm also demonstrates better sparse properties with a training speed over seven times faster.

Check Sample Abstracts.

PubMed

Alter, David; Grenache, David G; Bosler, David S; Karcher, Raymond E; Nichols, James; Rajadhyaksha, Aparna; Camelo-Piragua, Sandra; Rauch, Carol; Huddleston, Brent J; Frank, Elizabeth L; Sluss, Patrick M; Lewandrowski, Kent; Eichhorn, John H; Hall, Janet E; Rahman, Saud S; McPherson, Richard A; Kiechle, Frederick L; Hammett-Stabler, Catherine; Pierce, Kristin A; Kloehn, Erica A; Thomas, Patricia A; Walts, Ann E; Madan, Rashna; Schlesinger, Kathie; Nawgiri, Ranjana; Bhutani, Manoop; Kanber, Yonca; Abati, Andrea; Atkins, Kristen A; Farrar, Robert; Gopez, Evelyn Valencerina; Jhala, Darshana; Griffin, Sonya; Jhala, Khushboo; Jhala, Nirag; Bentz, Joel S; Emerson, Lyska; Chadwick, Barbara E; Barroeta, Julieta E; Baloch, Zubair W; Collins, Brian T; Middleton, Owen L; Davis, Gregory G; Haden-Pinneri, Kathryn; Chu, Albert Y; Keylock, Joren B; Ramoso, Robert; Thoene, Cynthia A; Stewart, Donna; Pierce, Arand; Barry, Michelle; Aljinovic, Nika; Gardner, David L; Barry, Michelle; Shields, Lisa B E; Arnold, Jack; Stewart, Donna; Martin, Erica L; Rakow, Rex J; Paddock, Christopher; Zaki, Sherif R; Prahlow, Joseph A; Stewart, Donna; Shields, Lisa B E; Rolf, Cristin M; Falzon, Andrew L; Hudacki, Rachel; Mazzella, Fermina M; Bethel, Melissa; Zarrin-Khameh, Neda; Gresik, M Vicky; Gill, Ryan; Karlon, William; Etzell, Joan; Deftos, Michael; Karlon, William J; Etzell, Joan E; Wang, Endi; Lu, Chuanyi M; Manion, Elizabeth; Rosenthal, Nancy; Wang, Endi; Lu, Chuanyi M; Tang, Patrick; Petric, Martin; Schade, Andrew E; Hall, Geraldine S; Oethinger, Margret; Hall, Geraldine; Picton, Avis R; Hoang, Linda; Imperial, Miguel Ranoa; Kibsey, Pamela; Waites, Ken; Duffy, Lynn; Hall, Geraldine S; Salangsang, Jo-Anne M; Bravo, Lulette Tricia C; Oethinger, Margaret D; Veras, Emanuela; Silva, Elvia; Vicens, Jimena; Silva, Elvio; Keylock, Joren; Hempel, James; Rushing, Elizabeth; Posligua, Lorena E; Deavers, Michael T; Nash, Jason W; Basturk, Olca; Perle, Mary Ann; Greco, Alba; Lee, Peng; Maru, Dipen; Weydert, Jamie Allen; Stevens, Todd M; Brownlee, Noel A; Kemper, April E; Williams, H James; Oliverio, Brock J; Al-Agha, Osama M; Eskue, Kyle L; Newlands, Shawn D; Eltorky, Mahmoud A; Puri, Puja K; Royer, Michael C; Rush, Walter L; Tavora, Fabio; Galvin, Jeffrey R; Franks, Teri J; Carter, James Elliot; Kahn, Andrea Graciela; Lozada Muñoz, Luis R; Houghton, Dan; Land, Kevin J; Nester, Theresa; Gildea, Jacob; Lefkowitz, Jerry; Lacount, Rachel A; Thompson, Hannis W; Refaai, Majed A; Quillen, Karen; Lopez, Ana Ortega; Goldfinger, Dennis; Muram, Talia; Thompson, Hannis

2009-02-01

The following abstracts are compiled from Check Sample exercises published in 2008. These peer-reviewed case studies assist laboratory professionals with continuing medical education and are developed in the areas of clinical chemistry, cytopathology, forensic pathology, hematology, microbiology, surgical pathology, and transfusion medicine. Abstracts for all exercises published in the program will appear annually in AJCP.

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.

PubMed

Hansen, Katja; Montavon, Grégoire; Biegler, Franziska; Fazli, Siamac; Rupp, Matthias; Scheffler, Matthias; von Lilienfeld, O Anatole; Tkatchenko, Alexandre; Müller, Klaus-Robert

2013-08-13

The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio calculations have been proposed as an efficient approach for describing the energies of molecules in their given ground-state structure throughout chemical compound space (Rupp et al. Phys. Rev. Lett. 2012, 108, 058301). In this paper we outline a number of established machine learning techniques and investigate the influence of the molecular representation on the methods performance. The best methods achieve prediction errors of 3 kcal/mol for the atomization energies of a wide variety of molecules. Rationales for this performance improvement are given together with pitfalls and challenges when applying machine learning approaches to the prediction of quantum-mechanical observables.

Does providing nutrition information at vending machines reduce calories per item sold?

PubMed

Dingman, Deirdre A; Schulz, Mark R; Wyrick, David L; Bibeau, Daniel L; Gupta, Sat N

2015-02-01

In 2010, the United States (US) enacted a restaurant menu labeling law. The law also applied to vending machine companies selling food. Research suggested that providing nutrition information on menus in restaurants might reduce the number of calories purchased. We tested the effect of providing nutrition information and 'healthy' designations to consumers where vending machines were located in college residence halls. We conducted our study at one university in Southeast US (October-November 2012). We randomly assigned 18 vending machines locations (residence halls) to an intervention or control group. For the intervention we posted nutrition information, interpretive signage, and sent a promotional email to residents of the hall. For the control group we did nothing. We tracked sales over 4 weeks before and 4 weeks after we introduced the intervention. Our intervention did not change what the residents bought. We recommend additional research about providing nutrition information where vending machines are located, including testing formats used to present information.

Preconditioned implicit solvers for the Navier-Stokes equations on distributed-memory machines

NASA Technical Reports Server (NTRS)

Ajmani, Kumud; Liou, Meng-Sing; Dyson, Rodger W.

1994-01-01

The GMRES method is parallelized, and combined with local preconditioning to construct an implicit parallel solver to obtain steady-state solutions for the Navier-Stokes equations of fluid flow on distributed-memory machines. The new implicit parallel solver is designed to preserve the convergence rate of the equivalent 'serial' solver. A static domain-decomposition is used to partition the computational domain amongst the available processing nodes of the parallel machine. The SPMD (Single-Program Multiple-Data) programming model is combined with message-passing tools to develop the parallel code on a 32-node Intel Hypercube and a 512-node Intel Delta machine. The implicit parallel solver is validated for internal and external flow problems, and is found to compare identically with flow solutions obtained on a Cray Y-MP/8. A peak computational speed of 2300 MFlops/sec has been achieved on 512 nodes of the Intel Delta machine,k for a problem size of 1024 K equations (256 K grid points).

Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality.

PubMed

Braithwaite, Scott R; Giraud-Carrier, Christophe; West, Josh; Barnes, Michael D; Hanson, Carl Lee

2016-05-16

One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data.

Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

PubMed Central

2016-01-01

Background One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Objective Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Methods Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Results Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Conclusions Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data. PMID:27185366

Virtual Machine Language

NASA Technical Reports Server (NTRS)

Grasso, Christopher; Page, Dennis; O'Reilly, Taifun; Fteichert, Ralph; Lock, Patricia; Lin, Imin; Naviaux, Keith; Sisino, John

2005-01-01

Virtual Machine Language (VML) is a mission-independent, reusable software system for programming for spacecraft operations. Features of VML include a rich set of data types, named functions, parameters, IF and WHILE control structures, polymorphism, and on-the-fly creation of spacecraft commands from calculated values. Spacecraft functions can be abstracted into named blocks that reside in files aboard the spacecraft. These named blocks accept parameters and execute in a repeatable fashion. The sizes of uplink products are minimized by the ability to call blocks that implement most of the command steps. This block approach also enables some autonomous operations aboard the spacecraft, such as aerobraking, telemetry conditional monitoring, and anomaly response, without developing autonomous flight software. Operators on the ground write blocks and command sequences in a concise, high-level, human-readable programming language (also called VML ). A compiler translates the human-readable blocks and command sequences into binary files (the operations products). The flight portion of VML interprets the uplinked binary files. The ground subsystem of VML also includes an interactive sequence- execution tool hosted on workstations, which runs sequences at several thousand times real-time speed, affords debugging, and generates reports. This tool enables iterative development of blocks and sequences within times of the order of seconds.

Toward Intelligent Machine Learning Algorithms

DTIC Science & Technology

1988-05-01

Machine learning is recognized as a tool for improving the performance of many kinds of systems, yet most machine learning systems themselves are not...directed systems, and with the addition of a knowledge store for organizing and maintaining knowledge to assist learning, a learning machine learning (L...ML) algorithm is possible. The necessary components of L-ML systems are presented along with several case descriptions of existing machine learning systems

Journalism and Journalism Education: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," January through June 1984, (Vol. 44 Nos. 7 through 12).

ERIC Educational Resources Information Center

ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 18 titles deal with the following topics: (1) the meaning of "Cold War" in two York, Pennsylvania, daily newspapers; (2) Tom Paine and the disclosure of secret French aid to the United States; (3) "Schenck V.…

27 CFR 447.22 - Forgings, castings, and machined bodies.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 27 Alcohol, Tobacco Products and Firearms 3 2010-04-01 2010-04-01 false Forgings, castings, and... IMPLEMENTS OF WAR The U.S. Munitions Import List § 447.22 Forgings, castings, and machined bodies. Articles on the U.S. Munitions Import List include articles in a partially completed state (such as forgings...

International Business Machines (IBM) Corporation Interim Agreement EPA Case No. 08-0113-00

EPA Pesticide Factsheets

On March 27, 2008, the United States Environmental Protection Agency (EPA), suspended International Business Machines (IBM) from receiving Federal Contracts, approved subcontracts, assistance, loans and other benefits.

The Hooey Machine.

ERIC Educational Resources Information Center

Scarnati, James T.; Tice, Craig J.

1992-01-01

Describes how students can make and use Hooey Machines to learn how mechanical energy can be transferred from one object to another within a system. The Hooey Machine is made using a pencil, eight thumbtacks, one pushpin, tape, scissors, graph paper, and a plastic lid. (PR)

Abstract Datatypes in PVS

NASA Technical Reports Server (NTRS)

Owre, Sam; Shankar, Natarajan

1997-01-01

PVS (Prototype Verification System) is a general-purpose environment for developing specifications and proofs. This document deals primarily with the abstract datatype mechanism in PVS which generates theories containing axioms and definitions for a class of recursive datatypes. The concepts underlying the abstract datatype mechanism are illustrated using ordered binary trees as an example. Binary trees are described by a PVS abstract datatype that is parametric in its value type. The type of ordered binary trees is then presented as a subtype of binary trees where the ordering relation is also taken as a parameter. We define the operations of inserting an element into, and searching for an element in an ordered binary tree; the bulk of the report is devoted to PVS proofs of some useful properties of these operations. These proofs illustrate various approaches to proving properties of abstract datatype operations. They also describe the built-in capabilities of the PVS proof checker for simplifying abstract datatype expressions.

Effect of the Machined Surfaces of AISI 4337 Steel to Cutting Conditions on Dry Machining Lathe

NASA Astrophysics Data System (ADS)

Rahim, Robbi; Napid, Suhardi; Hasibuan, Abdurrozzaq; Rahmah Sibuea, Siti; Yusmartato, Y.

2018-04-01

The objective of the research is to obtain a cutting condition which has a good chance of realizing dry machining concept on AISI 4337 steel material by studying surface roughness, microstructure and hardness of machining surface. The data generated from the experiment were then processed and analyzed using the standard Taguchi method L9 (34) orthogonal array. Testing of dry and wet machining used surface test and micro hardness test for each of 27 test specimens. The machining results of the experiments showed that average surface roughness (Raavg) was obtained at optimum cutting conditions when VB 0.1 μm, 0.3 μm and 0.6 μm respectively 1.467 μm, 2.133 μm and 2,800 μm fo r dry machining while which was carried out by wet machining the results obtained were 1,833 μm, 2,667 μm and 3,000 μm. It can be concluded that dry machining provides better surface quality of machinery results than wet machining. Therefore, dry machining is a good choice that may be realized in the manufacturing and automotive industries.

Evaluation of machinability and flexural strength of a novel dental machinable glass-ceramic.

PubMed

Qin, Feng; Zheng, Shucan; Luo, Zufeng; Li, Yong; Guo, Ling; Zhao, Yunfeng; Fu, Qiang

2009-10-01

To evaluate the machinability and flexural strength of a novel dental machinable glass-ceramic (named PMC), and to compare the machinability property with that of Vita Mark II and human enamel. The raw batch materials were selected and mixed. Four groups of novel glass-ceramics were formed at different nucleation temperatures, and were assigned to Group 1, Group 2, Group 3 and Group 4. The machinability of the four groups of novel glass-ceramics, Vita Mark II ceramic and freshly extracted human premolars were compared by means of drilling depth measurement. A three-point bending test was used to measure the flexural strength of the novel glass-ceramics. The crystalline phases of the group with the best machinability were identified by X-ray diffraction. In terms of the drilling depth, Group 2 of the novel glass-ceramics proves to have the largest drilling depth. There was no statistical difference among Group 1, Group 4 and the natural teeth. The drilling depth of Vita MK II was statistically less than that of Group 1, Group 4 and the natural teeth. Group 3 had the least drilling depth. In respect of the flexural strength, Group 2 exhibited the maximum flexural strength; Group 1 was statistically weaker than Group 2; there was no statistical difference between Group 3 and Group 4, and they were the weakest materials. XRD of Group 2 ceramic showed that a new type of dental machinable glass-ceramic containing calcium-mica had been developed by the present study and was named PMC. PMC is promising for application as a dental machinable ceramic due to its good machinability and relatively high strength.

Electrical machines with superconducting windings. Part 3: Homopolar dc machines

NASA Astrophysics Data System (ADS)

Kullman, D.; Henninger, P.

1981-01-01

The losses in rotating liquid metal contacts and the problems in including liquid metals were theoretically and experimentally studied. These machines are shown realiable. For electric ship propulsion, they are a more efficient method of power transmission than mechanical gearboxes. However, weight reduction as compared to mechanical gearboxes can hardly be achieved with machines fully shielded by magnetic iron.

Discovering Theorems in Abstract Algebra Using the Software "GAP"

ERIC Educational Resources Information Center

Blyth, Russell D.; Rainbolt, Julianne G.

2010-01-01

A traditional abstract algebra course typically consists of the professor stating and then proving a sequence of theorems. As an alternative to this classical structure, the students could be expected to discover some of the theorems even before they are motivated by classroom examples. This can be done by using a software system to explore a…

Machinability of titanium metal matrix composites (Ti-MMCs)

NASA Astrophysics Data System (ADS)

Aramesh, Maryam

Titanium metal matrix composites (Ti-MMCs), as a new generation of materials, have various potential applications in aerospace and automotive industries. The presence of ceramic particles enhances the physical and mechanical properties of the alloy matrix. However, the hard and abrasive nature of these particles causes various issues in the field of their machinability. Severe tool wear and short tool life are the most important drawbacks of machining this class of materials. There is very limited work in the literature regarding the machinability of this class of materials especially in the area of tool life estimation and tool wear. By far, polycrystalline diamond (PCD) tools appear to be the best choice for machining MMCs from researchers' point of view. However, due to their high cost, economical alternatives are sought. Cubic boron nitride (CBN) inserts, as the second hardest available tools, show superior characteristics such as great wear resistance, high hardness at elevated temperatures, a low coefficient of friction and a high melting point. Yet, so far CBN tools have not been studied during machining of Ti-MMCs. In this study, a comprehensive study has been performed to explore the tool wear mechanisms of CBN inserts during turning of Ti-MMCs. The unique morphology of the worn faces of the tools was investigated for the first time, which led to new insights in the identification of chemical wear mechanisms during machining of Ti-MMCs. Utilizing the full tool life capacity of cutting tools is also very crucial, due to the considerable costs associated with suboptimal replacement of tools. This strongly motivates development of a reliable model for tool life estimation under any cutting conditions. In this study, a novel model based on the survival analysis methodology is developed to estimate the progressive states of tool wear under any cutting conditions during machining of Ti-MMCs. This statistical model takes into account the machining time in

Markovian master equations for quantum thermal machines: local versus global approach

NASA Astrophysics Data System (ADS)

Hofer, Patrick P.; Perarnau-Llobet, Martí; Miranda, L. David M.; Haack, Géraldine; Silva, Ralph; Bohr Brask, Jonatan; Brunner, Nicolas

2017-12-01

The study of quantum thermal machines, and more generally of open quantum systems, often relies on master equations. Two approaches are mainly followed. On the one hand, there is the widely used, but often criticized, local approach, where machine sub-systems locally couple to thermal baths. On the other hand, in the more established global approach, thermal baths couple to global degrees of freedom of the machine. There has been debate as to which of these two conceptually different approaches should be used in situations out of thermal equilibrium. Here we compare the local and global approaches against an exact solution for a particular class of thermal machines. We consider thermodynamically relevant observables, such as heat currents, as well as the quantum state of the machine. Our results show that the use of a local master equation is generally well justified. In particular, for weak inter-system coupling, the local approach agrees with the exact solution, whereas the global approach fails for non-equilibrium situations. For intermediate coupling, the local and the global approach both agree with the exact solution and for strong coupling, the global approach is preferable. These results are backed by detailed derivations of the regimes of validity for the respective approaches.

Modelling abstraction licensing strategies ahead of the UK's water abstraction licensing reform

NASA Astrophysics Data System (ADS)

Klaar, M. J.

2012-12-01

Within England and Wales, river water abstractions are licensed and regulated by the Environment Agency (EA), who uses compliance with the Environmental Flow Indicator (EFI) to ascertain where abstraction may cause undesirable effects on river habitats and species. The EFI is a percentage deviation from natural flow represented using a flow duration curve. The allowable percentage deviation changes with different flows, and also changes depending on an assessment of the sensitivity of the river to changes in flow (Table 1). Within UK abstraction licensing, resource availability is expressed as a surplus or deficit of water resources in relation to the EFI, and utilises the concept of 'hands-off-flows' (HOFs) at the specified flow statistics detailed in Table 1. Use of a HOF system enables abstraction to cease at set flows, but also enables abstraction to occur at periods of time when more water is available. Compliance at low flows (Q95) is used by the EA to determine the hydrological classification and compliance with the Water Framework Directive (WFD) for identifying waterbodies where flow may be causing or contributing to a failure in good ecological status (GES; Table 2). This compliance assessment shows where the scenario flows are below the EFI and by how much, to help target measures for further investigation and assessment. Currently, the EA is reviewing the EFI methodology in order to assess whether or not it can be used within the reformed water abstraction licensing system which is being planned by the Department for Environment, Food and Rural Affairs (DEFRA) to ensure the licensing system is resilient to the challenges of climate change and population growth, while allowing abstractors to meet their water needs efficiently, and better protect the environment. In order to assess the robustness of the EFI, a simple model has been created which allows a number of abstraction, flow and licensing scenarios to be run to determine WFD compliance using the

Study of an Audio Playback Machine Storage, Distribution, and Repair System. Options for Machine Operation. Study II, Part 1, Phase 2, Final Report.

ERIC Educational Resources Information Center

ManTech Technical Services Corp., Fairfax, VA.

This report presents the results of a management study of audio playback equipment operations conducted by the National Library Service, Library of Congress, its associated network of state and local machine lending agencies (MLA), and other parties that play a role in current operations. The objectives were to document current operations,…